From fbf0ea8987cb1011b1bb9e4b6813c8bd2c23dbac Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 21:44:49 +0300 Subject: [PATCH 01/20] Move "Check PR Title" CI step to its own workflow --- .github/workflows/check_pr_title.yml | 11 +++++++++++ .github/workflows/precommits_check.yml | 5 +---- 2 files changed, 12 insertions(+), 4 deletions(-) create mode 100644 .github/workflows/check_pr_title.yml diff --git a/.github/workflows/check_pr_title.yml b/.github/workflows/check_pr_title.yml new file mode 100644 index 0000000000..d3054505dd --- /dev/null +++ b/.github/workflows/check_pr_title.yml @@ -0,0 +1,11 @@ +name: Check PR Title +on: pull_request + +jobs: + check-pr-title: + runs-on: ubuntu-latest + steps: + - name: Check PR Title + uses: Slashgear/action-check-pr-title@main + with: + regexp: '(break|build|ci|docs|feat|fix|perf|refactor|style|test|ops|hotfix|release|maint|init|enh|revert)\([a-z,A-Z,0-9,\-,\_,\/,:]+\)(:)\s{1}([\w\s]+)' # Regex the title should match. diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index 20c9c83687..e1815ff539 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -12,10 +12,7 @@ jobs: with: python-version: "3.9" cache: "pip" - - name: Check Pull Request Title - uses: Slashgear/action-check-pr-title@main - with: - regexp: '(break|build|ci|docs|feat|fix|perf|refactor|style|test|ops|hotfix|release|maint|init|enh|revert)\([a-z,A-Z,0-9,\-,\_,\/,:]+\)(:)\s{1}([\w\s]+)' # Regex the title should match. + - name: Getting changed files list id: files uses: jitterbit/get-changed-files@master From 4961ab02a165ea78b633afbaeaf5e6fa183cd440 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 16:23:01 +0300 Subject: [PATCH 02/20] Enable pre-commit CI workflow on pull requests --- .github/workflows/precommits_check.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index e1815ff539..3d0a34d4ca 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -1,6 +1,10 @@ name: Pre-commit checks on: - workflow_dispatch: + push: + branches: + - main + pull_request: ~ + workflow_dispatch: ~ jobs: pre-commit-checks: From 25e9305a46f1146ae02a8d8cc714fdd19032b504 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 21:53:48 +0300 Subject: [PATCH 03/20] Run pre-commit on all files --- .github/workflows/precommits_check.yml | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index 3d0a34d4ca..52afe40cb0 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -17,21 +17,11 @@ jobs: python-version: "3.9" cache: "pip" - - name: Getting changed files list - id: files - uses: jitterbit/get-changed-files@master - - name: Checking changed files - shell: bash - run: | - echo "Changed files" - echo ${{ steps.files.outputs.all }} - echo "GitHub Client version" - echo $(gh --version) - name: Pre-Commit Checks run: | python -m pip install --upgrade pip pip install pre-commit echo "Running pre-commit scans:" # adding log display in case of pre-commit errors - pre-commit run -v --files ${{ steps.files.outputs.all }} + pre-commit run -v --all-files shell: bash From 10230d689f627b8a92d1d0dbe1e1c7ba240e595f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:10:51 +0300 Subject: [PATCH 04/20] Update pre-commit --- poetry.lock | 18 ++++++++++++++---- pyproject.toml | 2 +- requirements-dev.txt | 2 +- 3 files changed, 16 insertions(+), 6 deletions(-) diff --git a/poetry.lock b/poetry.lock index a6e7f8b3d8..14af0c0021 100644 --- a/poetry.lock +++ b/poetry.lock @@ -5013,15 +5013,15 @@ test = ["coverage", "django", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)" [[package]] name = "pre-commit" -version = "3.8.0" +version = "4.1.0" description = "A framework for managing and maintaining multi-language pre-commit hooks." optional = false python-versions = ">=3.9" groups = ["dev"] markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ - 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repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.1.0 + rev: v5.0.0 hooks: - id: check-yaml args: ["--allow-multiple-documents", "--unsafe"] From 6bfa8d5962a7ab1c61272ca953e22b985e414eba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:25:23 +0300 Subject: [PATCH 06/20] Enable check-yaml pre-commit hook in CI --- .github/workflows/precommits_check.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index 52afe40cb0..149ecf7c93 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -22,6 +22,8 @@ jobs: python -m pip install --upgrade pip pip install pre-commit echo "Running pre-commit scans:" - # adding log display in case of pre-commit errors - pre-commit run -v --all-files + + # Run hooks individually, until we can run them all at once + pre-commit run check-yaml --all-files + shell: bash From 15d280ab00513a855c34da5c755d38401eabe012 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:26:58 +0300 Subject: [PATCH 07/20] Enable end-of-file-fixer pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index 149ecf7c93..d98145f061 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -25,5 +25,6 @@ jobs: # Run hooks individually, until we can run them all at once pre-commit run check-yaml --all-files + pre-commit run end-of-file-fixer --all-files shell: bash From dee9e8568ea5a7e511cc710694e3b1d59ff3693d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:27:57 +0300 Subject: [PATCH 08/20] Apply end-of-file-fixer fixes --- .../run-pypi-publish-in-docker-container/action.yml | 2 +- .github/ISSUE_TEMPLATE/bug_report.yml | 1 - .github/workflow_scripts/install_testpypi_pkg.sh | 2 +- .github/workflows/build_and_release.yml | 2 +- .github/workflows/docs-push.yml | 2 +- .gitignore | 2 +- .prettierignore | 2 +- README.md | 1 - docs/.gitignore | 2 +- docs/README.md | 2 +- docs/docs/api/index.md | 2 +- docs/docs/community/community-resources.md | 1 - docs/docs/community/how-to-contribute.md | 1 - docs/docs/deep-dive/assertions.md | 2 +- .../language_model_clients/lm_local_models/HFClientTGI.md | 2 +- .../language_model_clients/lm_local_models/LlamaCpp.md | 2 +- .../language_model_clients/lm_local_models/_category_.json | 2 +- docs/docs/deep-dive/modules/chain-of-thought.md | 2 +- docs/docs/deep-dive/optimizers/BootstrapFinetune.md | 2 +- docs/docs/deep-dive/optimizers/Ensemble.md | 2 +- docs/docs/deep-dive/optimizers/LabeledFewShot.md | 2 +- docs/docs/deep-dive/optimizers/bfrs.md | 2 +- docs/docs/deep-dive/optimizers/bootstrap-fewshot.md | 1 - docs/docs/deep-dive/retrieval_models_clients/Azure.md | 2 +- docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md | 2 +- docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md | 2 +- docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md | 2 +- docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md | 2 +- docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md | 2 +- docs/docs/js/runllm-widget.js | 2 +- docs/docs/learn/evaluation/data.md | 2 +- docs/docs/learn/evaluation/overview.md | 1 - docs/docs/learn/index.md | 2 +- docs/docs/learn/optimization/optimizers.md | 1 - docs/docs/learn/optimization/overview.md | 1 - docs/docs/learn/programming/7-assertions.md | 2 +- docs/docs/learn/programming/language_models.md | 1 - docs/docs/roadmap.md | 2 -- docs/docs/stylesheets/extra.css | 1 - docs/docs/tutorials/classification/index.md | 2 +- docs/docs/tutorials/index.md | 2 +- docs/docs/tutorials/old/other_tutorial.md | 2 +- docs/docs/tutorials/old/summarization.md | 2 +- docs/docs/tutorials/papillon/index.md | 2 +- docs/overrides/home.html | 2 +- docs/overrides/main.html | 2 +- dspy/.internal_dspyai/internals/build-and-release.md | 2 +- dspy/.internal_dspyai/internals/release-checklist.md | 2 +- dspy/__metadata__.py | 2 +- dspy/datasets/alfworld/__init__.py | 2 +- dspy/datasets/alfworld/base_config.yml | 2 +- dspy/dsp/colbertv2.py | 2 +- dspy/experimental/synthesizer/config.py | 2 +- dspy/experimental/synthesizer/instruction_suffixes.py | 2 +- dspy/experimental/synthesizer/signatures.py | 2 +- dspy/experimental/synthesizer/utils.py | 2 +- dspy/predict/aggregation.py | 2 -- dspy/predict/avatar/models.py | 2 +- dspy/predict/chain_of_thought_with_hint.py | 2 +- dspy/predict/program_of_thought.py | 2 +- dspy/propose/grounded_proposer.py | 2 +- dspy/propose/instruction_proposal.py | 2 +- dspy/propose/propose_base.py | 2 +- dspy/propose/utils.py | 2 +- dspy/retrieve/deeplake_rm.py | 2 +- dspy/retrieve/milvus_rm.py | 2 +- dspy/retrieve/ragatouille_rm.py | 2 +- dspy/retrieve/vectara_rm.py | 2 +- dspy/retrieve/watson_discovery_rm.py | 2 +- dspy/teleprompt/utils.py | 2 +- examples/README.md | 2 +- examples/outdated_v2.4_examples/coding/hackercup_utils.py | 2 +- examples/outdated_v2.4_examples/longformqa/utils.py | 2 +- .../math/gsm8k/turbo_8_8_10_gsm8k_200_300.json | 2 +- .../nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json | 2 +- examples/outdated_v2.4_examples/vlm/.gitignore | 2 +- testing/README.md | 2 +- testing/datasets/hotpotqa_conditional/hotpot_dev.csv | 2 +- testing/datasets/hotpotqa_conditional/hotpot_test.csv | 2 +- testing/datasets/hotpotqa_conditional/hotpot_train.csv | 2 +- testing/tasks/__init__.py | 2 +- tests/conftest.py | 2 +- tests/docs/test_mkdocs_links.py | 2 +- tests/evaluate/test_metrics.py | 2 +- tests/examples/test_baleen.py | 2 +- tests/multihop_llama213b_0.json | 2 +- tests/multihop_llama213b_1.json | 2 +- tests/multihop_llama213b_2.json | 2 +- tests/multihop_llama213b_3.json | 2 +- tests/primitives/test_python_interpreter.py | 2 +- tests/signatures/test_adapter_image.py | 2 +- tests/teleprompt/test_finetune.py | 2 +- tests/utils/test_asyncify.py | 2 -- 93 files changed, 80 insertions(+), 96 deletions(-) diff --git a/.github/.tmp/.generated-actions/run-pypi-publish-in-docker-container/action.yml b/.github/.tmp/.generated-actions/run-pypi-publish-in-docker-container/action.yml index 806b6bcada..89a0449cf7 100644 --- a/.github/.tmp/.generated-actions/run-pypi-publish-in-docker-container/action.yml +++ b/.github/.tmp/.generated-actions/run-pypi-publish-in-docker-container/action.yml @@ -1 +1 @@ -{"name": "🏃", "description": "Run Docker container to upload Python distribution packages to PyPI", "inputs": {"user": {"description": "PyPI user", "required": false}, "password": {"description": "Password for your PyPI user or an access token", "required": false}, "repository-url": {"description": "The repository URL to use", "required": false}, "packages-dir": {"description": "The target directory for distribution", "required": false}, "verify-metadata": {"description": "Check metadata before uploading", "required": false}, "skip-existing": {"description": "Do not fail if a Python package distribution exists in the target package index", "required": false}, "verbose": {"description": "Show verbose output.", "required": false}, "print-hash": {"description": "Show hash values of files to be uploaded", "required": false}, "attestations": {"description": "[EXPERIMENTAL] Enable experimental support for PEP 740 attestations. Only works with PyPI and TestPyPI via Trusted Publishing.", "required": false}}, "runs": {"using": "docker", "image": "docker://ghcr.io/pypa/gh-action-pypi-publish:release-v1"}} \ No newline at end of file +{"name": "🏃", "description": "Run Docker container to upload Python distribution packages to PyPI", "inputs": {"user": {"description": "PyPI user", "required": false}, "password": {"description": "Password for your PyPI user or an access token", "required": false}, "repository-url": {"description": "The repository URL to use", "required": false}, "packages-dir": {"description": "The target directory for distribution", "required": false}, "verify-metadata": {"description": "Check metadata before uploading", "required": false}, "skip-existing": {"description": "Do not fail if a Python package distribution exists in the target package index", "required": false}, "verbose": {"description": "Show verbose output.", "required": false}, "print-hash": {"description": "Show hash values of files to be uploaded", "required": false}, "attestations": {"description": "[EXPERIMENTAL] Enable experimental support for PEP 740 attestations. Only works with PyPI and TestPyPI via Trusted Publishing.", "required": false}}, "runs": {"using": "docker", "image": "docker://ghcr.io/pypa/gh-action-pypi-publish:release-v1"}} diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 700e8be723..ed14a9a293 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -35,4 +35,3 @@ body: description: "Tell us your DSPy version." validations: required: true - diff --git a/.github/workflow_scripts/install_testpypi_pkg.sh b/.github/workflow_scripts/install_testpypi_pkg.sh index e8cee27f86..9045e1dacf 100755 --- a/.github/workflow_scripts/install_testpypi_pkg.sh +++ b/.github/workflow_scripts/install_testpypi_pkg.sh @@ -12,4 +12,4 @@ for i in {1..5}; do echo "Attempt $i failed. Waiting before retrying..." sleep 10 fi -done \ No newline at end of file +done diff --git a/.github/workflows/build_and_release.yml b/.github/workflows/build_and_release.yml index 890c02a542..3a9f8c265a 100644 --- a/.github/workflows/build_and_release.yml +++ b/.github/workflows/build_and_release.yml @@ -126,4 +126,4 @@ jobs: git merge --no-ff release-${{ needs.extract-tag.outputs.version }} - name: Push changes to main run: | - git push origin main \ No newline at end of file + git push origin main diff --git a/.github/workflows/docs-push.yml b/.github/workflows/docs-push.yml index 75cee32b17..6d6e8b71ff 100644 --- a/.github/workflows/docs-push.yml +++ b/.github/workflows/docs-push.yml @@ -43,4 +43,4 @@ jobs: destination-github-username: 'krypticmouse' destination-repository-name: 'dspy-docs' user-email: github-actions@github.com - target-branch: master \ No newline at end of file + target-branch: master diff --git a/.gitignore b/.gitignore index 525f4ad413..c4ee94c998 100644 --- a/.gitignore +++ b/.gitignore @@ -62,4 +62,4 @@ dummy.csv docs/docs/**/*.json* *.index *.pkl -*.tar.gz \ No newline at end of file +*.tar.gz diff --git a/.prettierignore b/.prettierignore index 6d2eaaa41d..bb0224d574 100644 --- a/.prettierignore +++ b/.prettierignore @@ -1 +1 @@ -.github/ \ No newline at end of file +.github/ diff --git a/README.md b/README.md index 8bd03a3cf9..4b56de2bc3 100644 --- a/README.md +++ b/README.md @@ -84,4 +84,3 @@ If you use DSPy or DSP in a research paper, please cite our work as follows: * [**Releasing the DSP Compiler (v0.1)**](https://twitter.com/lateinteraction/status/1625231662849073160) (Twitter Thread, Feb 2023) * [**Introducing DSP**](https://twitter.com/lateinteraction/status/1617953413576425472) (Twitter Thread, Jan 2023) * [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP**](https://arxiv.org/abs/2212.14024.pdf) (Academic Paper, Dec 2022) --> - diff --git a/docs/.gitignore b/docs/.gitignore index decfc7a5ea..8033e4fda1 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -1,2 +1,2 @@ site -.cache \ No newline at end of file +.cache diff --git a/docs/README.md b/docs/README.md index a204dff2f7..d8f6193518 100644 --- a/docs/README.md +++ b/docs/README.md @@ -59,4 +59,4 @@ git subtree pull --prefix=docs https://github.com/krypticmouse/dspy-docs master 2. **Push your new changes on a new branch**: Feel free to add or edit existing documentation and open a PR for your changes. Once your PR is reviewed and approved, the changes will be ready to merge into main. -3. **Updating the website**: Once your changes are merged to main, the changes would be reflected on live websites usually in 5-15 mins. \ No newline at end of file +3. **Updating the website**: Once your changes are merged to main, the changes would be reflected on live websites usually in 5-15 mins. diff --git a/docs/docs/api/index.md b/docs/docs/api/index.md index 4ea156459c..18cfd67b5d 100644 --- a/docs/docs/api/index.md +++ b/docs/docs/api/index.md @@ -1,3 +1,3 @@ # API Reference -Welcome to the DSPy API reference documentation. This section provides detailed information about DSPy's classes, modules, and functions. \ No newline at end of file +Welcome to the DSPy API reference documentation. This section provides detailed information about DSPy's classes, modules, and functions. diff --git a/docs/docs/community/community-resources.md b/docs/docs/community/community-resources.md index b2011cbdc6..c74bac9dc1 100644 --- a/docs/docs/community/community-resources.md +++ b/docs/docs/community/community-resources.md @@ -54,4 +54,3 @@ Huge shoutout to them for the massive support ❤️. See the [Weaviate DSPy dir TODO: This list in particular is highly incomplete. There are dozens of other good ones. To allow space, divide into opintionated blogs / podcasts / interviews vs. tutorials & talks. Credit: Some of these resources were originally compiled in the [Awesome DSPy](https://github.com/ganarajpr/awesome-dspy/tree/master) repo. - diff --git a/docs/docs/community/how-to-contribute.md b/docs/docs/community/how-to-contribute.md index 2a404a62cd..dbf82a94fc 100644 --- a/docs/docs/community/how-to-contribute.md +++ b/docs/docs/community/how-to-contribute.md @@ -9,4 +9,3 @@ We are working on a development setup guide. If you're interested in contributin **How can I add my favorite LM or vector store?** Check out these walkthroughs on setting up a [Custom LM client](/deep-dive/language_model_clients/custom-lm-client) and [Custom RM client](/deep-dive/retrieval_models_clients/custom-rm-client). - diff --git a/docs/docs/deep-dive/assertions.md b/docs/docs/deep-dive/assertions.md index 858d03780c..11b573cc21 100644 --- a/docs/docs/deep-dive/assertions.md +++ b/docs/docs/deep-dive/assertions.md @@ -265,4 +265,4 @@ compiled_with_assertions_baleen = teleprompter.compile(student = baleen, teacher #Compilation + Inference with Assertions compiled_baleen_with_assertions = teleprompter.compile(student=baleen_with_assertions, teacher = baleen_with_assertions, trainset=trainset, valset=devset) -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md index 71580e0c91..049ea00d91 100644 --- a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md +++ b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md @@ -124,4 +124,4 @@ Please refer to the [official Text-Generation-Inference repository](https://gith *** - \ No newline at end of file + diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/LlamaCpp.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/LlamaCpp.md index a3d8f225cd..4267dfe5a8 100644 --- a/docs/docs/deep-dive/language_model_clients/lm_local_models/LlamaCpp.md +++ b/docs/docs/deep-dive/language_model_clients/lm_local_models/LlamaCpp.md @@ -62,4 +62,4 @@ print(f"Question: {question}") print(f"Predicted Answer: {pred.answer}") -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json b/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json index e1d91e8cef..24179771d6 100644 --- a/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json +++ b/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json @@ -5,4 +5,4 @@ "type": "generated-index", "description": "Local Language Model Clients in DSPy" } -} \ No newline at end of file +} diff --git a/docs/docs/deep-dive/modules/chain-of-thought.md b/docs/docs/deep-dive/modules/chain-of-thought.md index f5bacee50e..e8667cc96c 100644 --- a/docs/docs/deep-dive/modules/chain-of-thought.md +++ b/docs/docs/deep-dive/modules/chain-of-thought.md @@ -90,4 +90,4 @@ rationale_type = dspy.OutputField( ) #Pass signature to ChainOfThought module generate_answer = dspy.ChainOfThought(BasicQA, rationale_type=rationale_type) -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/optimizers/BootstrapFinetune.md b/docs/docs/deep-dive/optimizers/BootstrapFinetune.md index 7aba6ec9b3..cee3df97e0 100644 --- a/docs/docs/deep-dive/optimizers/BootstrapFinetune.md +++ b/docs/docs/deep-dive/optimizers/BootstrapFinetune.md @@ -56,4 +56,4 @@ teleprompter = BootstrapFinetune(teacher_settings=dict({'lm': teacher})) # Compile! compiled_rag = teleprompter.compile(student=RAG(), trainset=trainset, target='google/flan-t5-base') -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/optimizers/Ensemble.md b/docs/docs/deep-dive/optimizers/Ensemble.md index a6282040a6..511a67ca36 100644 --- a/docs/docs/deep-dive/optimizers/Ensemble.md +++ b/docs/docs/deep-dive/optimizers/Ensemble.md @@ -47,4 +47,4 @@ teleprompter = Ensemble(reduce_fn=dspy.majority, size=2) # Compile to get the EnsembledProgram ensembled_program = teleprompter.compile(programs) -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/optimizers/LabeledFewShot.md b/docs/docs/deep-dive/optimizers/LabeledFewShot.md index 00cd7ce8e0..316e3bf3ed 100644 --- a/docs/docs/deep-dive/optimizers/LabeledFewShot.md +++ b/docs/docs/deep-dive/optimizers/LabeledFewShot.md @@ -58,4 +58,4 @@ teleprompter = LabeledFewShot() # Compile! compiled_rag = teleprompter.compile(student=RAG(), trainset=trainset) -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/optimizers/bfrs.md b/docs/docs/deep-dive/optimizers/bfrs.md index 780c013cdc..d94c2ba168 100644 --- a/docs/docs/deep-dive/optimizers/bfrs.md +++ b/docs/docs/deep-dive/optimizers/bfrs.md @@ -54,4 +54,4 @@ cot_compiled.save('turbo_gsm8k.json') # Loading: # cot = CoT() # cot.load('turbo_gsm8k.json') -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/optimizers/bootstrap-fewshot.md b/docs/docs/deep-dive/optimizers/bootstrap-fewshot.md index 40f7611967..d28ae3c825 100644 --- a/docs/docs/deep-dive/optimizers/bootstrap-fewshot.md +++ b/docs/docs/deep-dive/optimizers/bootstrap-fewshot.md @@ -136,4 +136,3 @@ cot_compiled.save('turbo_gsm8k.json') 8. If the prediction is successful, a demonstration (demo) is created for each step in the trace. This demo includes the inputs to the predictor and the outputs it generated. 9. Other optimizers like `BootstrapFewShotWithOptuna`, `BootstrapFewShotWithRandomSearch` etc. also work on same principles with slight changes in the example discovery process. - diff --git a/docs/docs/deep-dive/retrieval_models_clients/Azure.md b/docs/docs/deep-dive/retrieval_models_clients/Azure.md index 912aaed5d7..7f18b8e012 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/Azure.md +++ b/docs/docs/deep-dive/retrieval_models_clients/Azure.md @@ -186,4 +186,4 @@ for result in retrieval_response: *** - \ No newline at end of file + diff --git a/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md b/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md index 16c3571b78..245664bb1a 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md @@ -63,4 +63,4 @@ results = retriever_model("Explore the significance of quantum computing", k=5) for result in results: print("Document:", result.long_text, "\n") -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md b/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md index 9ba838b58c..a21113d53e 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md @@ -175,4 +175,4 @@ These examples assume you have: - A properly configured Clarifai application - Valid authentication credentials - Documents already ingested into your Clarifai app -- The necessary environment variables set up \ No newline at end of file +- The necessary environment variables set up diff --git a/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md b/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md index 2cae388f71..d27f72c8e8 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md +++ b/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md @@ -72,4 +72,4 @@ for result in retrieval_response: *** - \ No newline at end of file + diff --git a/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md index ffb3202294..72d86f5d8b 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md @@ -87,4 +87,4 @@ results = retriever_model("Explore the significance of quantum computing", k=3) for passage in results: print("Document:", passage, "\n") -``` \ No newline at end of file +``` diff --git a/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md b/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md index acd3cd4eae..7c01266fc0 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md @@ -63,4 +63,4 @@ results = retriever_model("Explore the significance of quantum computing", k=5) for result in results: print("Document:", result.long_text, "\n") -``` \ No newline at end of file +``` diff --git a/docs/docs/js/runllm-widget.js b/docs/docs/js/runllm-widget.js index 2c0786b1b8..c8057b6bf6 100644 --- a/docs/docs/js/runllm-widget.js +++ b/docs/docs/js/runllm-widget.js @@ -17,4 +17,4 @@ document.addEventListener("DOMContentLoaded", function () { ); document.head.appendChild(script); - }); \ No newline at end of file + }); diff --git a/docs/docs/learn/evaluation/data.md b/docs/docs/learn/evaluation/data.md index eba40f77e9..f3817efb81 100644 --- a/docs/docs/learn/evaluation/data.md +++ b/docs/docs/learn/evaluation/data.md @@ -137,4 +137,4 @@ testset = train_split[:75] trainset = train_split[75:] ``` -The way you load a HuggingFace dataset using `load_dataset` is exactly how you load data it via `from_huggingface` as well. This includes passing specific splits, subsplits, read instructions, etc. For code snippets, you can refer to the [cheatsheet snippets](/cheatsheet/#dspy-dataloaders) for loading from HF. --> \ No newline at end of file +The way you load a HuggingFace dataset using `load_dataset` is exactly how you load data it via `from_huggingface` as well. This includes passing specific splits, subsplits, read instructions, etc. For code snippets, you can refer to the [cheatsheet snippets](/cheatsheet/#dspy-dataloaders) for loading from HF. --> diff --git a/docs/docs/learn/evaluation/overview.md b/docs/docs/learn/evaluation/overview.md index 26141880c2..4ad7faf8c2 100644 --- a/docs/docs/learn/evaluation/overview.md +++ b/docs/docs/learn/evaluation/overview.md @@ -13,4 +13,3 @@ Now that you have some data and a metric, run development evaluations on your pi ??? "If your metric is itself a DSPy program..." If your metric is itself a DSPy program, a powerful way to iterate is to optimize your metric itself. That's usually easy because the output of the metric is usually a simple value (e.g., a score out of 5), so the metric's metric is easy to define and optimize by collecting a few examples. - diff --git a/docs/docs/learn/index.md b/docs/docs/learn/index.md index a490619f24..8ce96767ee 100644 --- a/docs/docs/learn/index.md +++ b/docs/docs/learn/index.md @@ -12,4 +12,4 @@ DSPy exposes a very small API that you can learn quickly. However, building a ne 3) **DSPy Optimization.** Once you have a way to evaluate your system, you use DSPy optimizers to tune the prompts or weights in your program. -We suggest learning and applying DSPy in this order. For example, it's unproductive to launch optimization runs using a poorly-design program or a bad metric. \ No newline at end of file +We suggest learning and applying DSPy in this order. For example, it's unproductive to launch optimization runs using a poorly-design program or a bad metric. diff --git a/docs/docs/learn/optimization/optimizers.md b/docs/docs/learn/optimization/optimizers.md index 287025d476..617214d9f8 100644 --- a/docs/docs/learn/optimization/optimizers.md +++ b/docs/docs/learn/optimization/optimizers.md @@ -220,4 +220,3 @@ To load a program from a file, you can instantiate an object from that class and loaded_program = YOUR_PROGRAM_CLASS() loaded_program.load(path=YOUR_SAVE_PATH) ``` - diff --git a/docs/docs/learn/optimization/overview.md b/docs/docs/learn/optimization/overview.md index f8cb2b2269..fa1ba7fed0 100644 --- a/docs/docs/learn/optimization/overview.md +++ b/docs/docs/learn/optimization/overview.md @@ -10,4 +10,3 @@ Once you have a system and a way to evaluate it, you can use DSPy optimizers to After your first few optimization runs, you are either very happy with everything or you've made a lot of progress but you don't like something about the final program or the metric. At this point, go back to step 1 (Programming in DSPy) and revisit the major questions. Did you define your task well? Do you need to collect (or find online) more data for your problem? Do you want to update your metric? And do you want to use a more sophisticated optimizer? Do you need to consider advanced features like DSPy Assertions? Or, perhaps most importantly, do you want to add some more complexity or steps in your DSPy program itself? Do you want to use multiple optimizers in a sequence? Iterative development is key. DSPy gives you the pieces to do that incrementally: iterating on your data, your program structure, your assertions, your metric, and your optimization steps. Optimizing complex LM programs is an entirely new paradigm that only exists in DSPy at the time of writing (update: there are now numerous DSPy extension frameworks, so this part is no longer true :-), so naturally the norms around what to do are still emerging. If you need help, we recently created a [Discord server](https://discord.gg/XCGy2WDCQB) for the community. - diff --git a/docs/docs/learn/programming/7-assertions.md b/docs/docs/learn/programming/7-assertions.md index a2c46cbb14..df1b62d770 100644 --- a/docs/docs/learn/programming/7-assertions.md +++ b/docs/docs/learn/programming/7-assertions.md @@ -263,4 +263,4 @@ compiled_with_assertions_baleen = teleprompter.compile(student = baleen, teacher #Compilation + Inference with Assertions compiled_baleen_with_assertions = teleprompter.compile(student=baleen_with_assertions, teacher = baleen_with_assertions, trainset=trainset, valset=devset) -``` \ No newline at end of file +``` diff --git a/docs/docs/learn/programming/language_models.md b/docs/docs/learn/programming/language_models.md index 77231c8dd2..72a7e2b352 100644 --- a/docs/docs/learn/programming/language_models.md +++ b/docs/docs/learn/programming/language_models.md @@ -173,4 +173,3 @@ dict_keys(['prompt', 'messages', 'kwargs', 'response', 'outputs', 'usage', 'cost ### Advanced: Building custom LMs and writing your own Adapters. Though rarely needed, you can write custom LMs by inheriting from `dspy.BaseLM`. Another advanced layer in the DSPy ecosystem is that of _adapters_, which sit between DSPy signatures and LMs. A future version of this guide will discuss these advanced features, though you likely don't need them. - diff --git a/docs/docs/roadmap.md b/docs/docs/roadmap.md index 9de1260f6e..21f06a203a 100644 --- a/docs/docs/roadmap.md +++ b/docs/docs/roadmap.md @@ -120,5 +120,3 @@ While these can accomplish a lot of goals, there are two limitations that future 1. In general, DSPy’s (i) observability, (ii) experimental tracking, (iii) cost management, and (iii) deployment of programs should become first-class concerns via integration with tools like MLFlow. We will share more plans addressing this for DSPy 2.6 in the next 1-2 months. 2. DSPy 3.0 will introduce new optimizers that prioritize ad-hoc, human-in-the-loop feedback. This is perhaps the only substantial paradigm shift we see as necessary in the foreseeable future in DSPy. It involves various research questions at the level of the abstractions, UI/HCI, and ML, so it is a longer-term goal that we will share more about in the next 3-4 month. - - diff --git a/docs/docs/stylesheets/extra.css b/docs/docs/stylesheets/extra.css index 88f361d143..32236b7a5f 100644 --- a/docs/docs/stylesheets/extra.css +++ b/docs/docs/stylesheets/extra.css @@ -234,4 +234,3 @@ h2.doc-heading { .highlight .md-clipboard:hover { color: var(--md-accent-fg-color); } - diff --git a/docs/docs/tutorials/classification/index.md b/docs/docs/tutorials/classification/index.md index bd1037440f..2d13fc392a 100644 --- a/docs/docs/tutorials/classification/index.md +++ b/docs/docs/tutorials/classification/index.md @@ -1,3 +1,3 @@ Please refer to [this tutorial from Drew Breunig](https://www.dbreunig.com/2024/12/12/pipelines-prompt-optimization-with-dspy.html) using DSPy. -This tutorial demonstrates a few aspects of using DSPy in a highly-accessible, concrete context for categorizing historic events with a tiny LM. \ No newline at end of file +This tutorial demonstrates a few aspects of using DSPy in a highly-accessible, concrete context for categorizing historic events with a tiny LM. diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md index 7b89d38575..9ddd2612cb 100644 --- a/docs/docs/tutorials/index.md +++ b/docs/docs/tutorials/index.md @@ -24,4 +24,4 @@ * [Image Generation Prompt iteration](/tutorials/image_generation_prompting/) -We are working on upgrading more tutorials and other examples to [DSPy 2.5](https://github.com/stanfordnlp/dspy/blob/main/examples/migration.ipynb) from earlier DSPy versions. \ No newline at end of file +We are working on upgrading more tutorials and other examples to [DSPy 2.5](https://github.com/stanfordnlp/dspy/blob/main/examples/migration.ipynb) from earlier DSPy versions. diff --git a/docs/docs/tutorials/old/other_tutorial.md b/docs/docs/tutorials/old/other_tutorial.md index c7cde9fce7..c1ef95551b 100644 --- a/docs/docs/tutorials/old/other_tutorial.md +++ b/docs/docs/tutorials/old/other_tutorial.md @@ -26,4 +26,4 @@ sidebar_position: 99999 - Interviews: [Weaviate Podcast in-person](https://www.youtube.com/watch?v=CDung1LnLbY), and you can find 6-7 other remote podcasts on YouTube from a few different perspectives/audiences. - **Tracing in DSPy** with Arize Phoenix: [Tutorial for tracing your prompts and the steps of your DSPy programs](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/dspy_tracing_tutorial.ipynb) - **Tracing & Optimization Tracking in DSPy** with Parea AI: [Tutorial on tracing & evaluating a DSPy RAG program](https://docs.parea.ai/tutorials/dspy-rag-trace-evaluate/tutorial) -- **Prompt Optimization with DSPy and G-Eval Metrics** by Alberto Romero: [Medium article](https://medium.com/@a-romero/prompt-optimization-with-dspy-and-g-eval-metrics-e7d0bdd21b8b), [Repo](https://github.com/a-romero/dspy-risk-assessment), [Video](https://youtu.be/kK30U-XiiNI) \ No newline at end of file +- **Prompt Optimization with DSPy and G-Eval Metrics** by Alberto Romero: [Medium article](https://medium.com/@a-romero/prompt-optimization-with-dspy-and-g-eval-metrics-e7d0bdd21b8b), [Repo](https://github.com/a-romero/dspy-risk-assessment), [Video](https://youtu.be/kK30U-XiiNI) diff --git a/docs/docs/tutorials/old/summarization.md b/docs/docs/tutorials/old/summarization.md index af4f9871cb..01ce57049c 100644 --- a/docs/docs/tutorials/old/summarization.md +++ b/docs/docs/tutorials/old/summarization.md @@ -188,4 +188,4 @@ res = evaluate(program, devset=trainset) print(res) ``` -That's it! Hopefully this example was helpful in understanding how you can compose DSPy programs to build a truly compound AI system that's optimized for isolated tasks. \ No newline at end of file +That's it! Hopefully this example was helpful in understanding how you can compose DSPy programs to build a truly compound AI system that's optimized for isolated tasks. diff --git a/docs/docs/tutorials/papillon/index.md b/docs/docs/tutorials/papillon/index.md index ea57be9e5f..ca4f4349cb 100644 --- a/docs/docs/tutorials/papillon/index.md +++ b/docs/docs/tutorials/papillon/index.md @@ -4,4 +4,4 @@ This tutorial demonstrates a few aspects of using DSPy in a more advanced contex 1. It builds a multi-stage `dspy.Module` that involves a small local LM using an external tool. 2. It builds a multi-stage _judge_ in DSPy, and uses it as a metric for evaluation. -3. It uses this judge for optimizing the `dspy.Module`, using a large model as a teacher for a small local LM. \ No newline at end of file +3. It uses this judge for optimizing the `dspy.Module`, using a large model as a teacher for a small local LM. diff --git a/docs/overrides/home.html b/docs/overrides/home.html index f8698a7494..9227bb385e 100644 --- a/docs/overrides/home.html +++ b/docs/overrides/home.html @@ -144,4 +144,4 @@

Cross-LM Compatibility

-{% endblock %} \ No newline at end of file +{% endblock %} diff --git a/docs/overrides/main.html b/docs/overrides/main.html index 5f599a425f..ede018efd6 100644 --- a/docs/overrides/main.html +++ b/docs/overrides/main.html @@ -9,4 +9,4 @@ gtag('config', 'G-G728W2L8KQ'); -{% endblock %} \ No newline at end of file +{% endblock %} diff --git a/dspy/.internal_dspyai/internals/build-and-release.md b/dspy/.internal_dspyai/internals/build-and-release.md index fb000ed616..52620aca20 100644 --- a/dspy/.internal_dspyai/internals/build-and-release.md +++ b/dspy/.internal_dspyai/internals/build-and-release.md @@ -56,4 +56,4 @@ Builds and publishes the package to pypi. 1. Publishes the package to pypi. -\* The package name is updated by the workflow to allow the same files to be used to build both the pypi and test-pypi packages. \ No newline at end of file +\* The package name is updated by the workflow to allow the same files to be used to build both the pypi and test-pypi packages. diff --git a/dspy/.internal_dspyai/internals/release-checklist.md b/dspy/.internal_dspyai/internals/release-checklist.md index 0213a52b7b..8e5c42bcb7 100644 --- a/dspy/.internal_dspyai/internals/release-checklist.md +++ b/dspy/.internal_dspyai/internals/release-checklist.md @@ -22,4 +22,4 @@ ### Prerequisites -The automation requires a [trusted publisher](https://docs.pypi.org/trusted-publishers/) to be set up on both the pypi and test-pypi packages. If the package is migrated to a new project, please follow the [steps](https://docs.pypi.org/trusted-publishers/adding-a-publisher/) to create a trusted publisher. If you have no releases on the new project, you may have to create a [pending trusted publisher](https://docs.pypi.org/trusted-publishers/creating-a-project-through-oidc/) to allow the first automated deployment. \ No newline at end of file +The automation requires a [trusted publisher](https://docs.pypi.org/trusted-publishers/) to be set up on both the pypi and test-pypi packages. If the package is migrated to a new project, please follow the [steps](https://docs.pypi.org/trusted-publishers/adding-a-publisher/) to create a trusted publisher. If you have no releases on the new project, you may have to create a [pending trusted publisher](https://docs.pypi.org/trusted-publishers/creating-a-project-through-oidc/) to allow the first automated deployment. diff --git a/dspy/__metadata__.py b/dspy/__metadata__.py index 363348b008..b111766781 100644 --- a/dspy/__metadata__.py +++ b/dspy/__metadata__.py @@ -5,4 +5,4 @@ __description__="DSPy" __url__="https://github.com/stanfordnlp/dspy" __author__="Omar Khattab" -__author_email__="okhattab@stanford.edu" \ No newline at end of file +__author_email__="okhattab@stanford.edu" diff --git a/dspy/datasets/alfworld/__init__.py b/dspy/datasets/alfworld/__init__.py index 9a1bc42d58..7670a99991 100644 --- a/dspy/datasets/alfworld/__init__.py +++ b/dspy/datasets/alfworld/__init__.py @@ -1 +1 @@ -from dspy.datasets.alfworld.alfworld import AlfWorld \ No newline at end of file +from dspy.datasets.alfworld.alfworld import AlfWorld diff --git a/dspy/datasets/alfworld/base_config.yml b/dspy/datasets/alfworld/base_config.yml index 03c6d146ba..653d4701c1 100644 --- a/dspy/datasets/alfworld/base_config.yml +++ b/dspy/datasets/alfworld/base_config.yml @@ -142,4 +142,4 @@ vision_dagger: resnet_fc_dim: 64 maskrcnn_top_k_boxes: 10 # top k box features use_exploration_frame_feats: False # append feats from initial exploration (memory intensive!) - sequence_aggregation_method: "average" # 'sum' or 'average' or 'rnn' \ No newline at end of file + sequence_aggregation_method: "average" # 'sum' or 'average' or 'rnn' diff --git a/dspy/dsp/colbertv2.py b/dspy/dsp/colbertv2.py index 6b1fd8cb3e..d662c9b241 100644 --- a/dspy/dsp/colbertv2.py +++ b/dspy/dsp/colbertv2.py @@ -190,4 +190,4 @@ def forward(self,query:str,passages:List[str]=[]): Q_duplicated = Q.repeat_interleave(len(passages), dim=0).contiguous() tensor_scores = col.score(Q_duplicated,DOC_IDS,DOC_MASKS) passage_score_arr = np.array([score.cpu().detach().numpy().tolist() for score in tensor_scores]) - return passage_score_arr \ No newline at end of file + return passage_score_arr diff --git a/dspy/experimental/synthesizer/config.py b/dspy/experimental/synthesizer/config.py index 557299cfa7..86c933219d 100644 --- a/dspy/experimental/synthesizer/config.py +++ b/dspy/experimental/synthesizer/config.py @@ -21,4 +21,4 @@ def validate_feedback_mode(self): if self.feedback_mode and not self.num_example_for_feedback: raise ValueError("Number of examples for feedback is required when feedback mode is provided.") - return self \ No newline at end of file + return self diff --git a/dspy/experimental/synthesizer/instruction_suffixes.py b/dspy/experimental/synthesizer/instruction_suffixes.py index 53404a2a66..753550bd47 100644 --- a/dspy/experimental/synthesizer/instruction_suffixes.py +++ b/dspy/experimental/synthesizer/instruction_suffixes.py @@ -1,3 +1,3 @@ INPUT_GENERATION_TASK_WITH_EXAMPLES_SUFFIX = """\n\nI'll also be providing you some data I generated before hand, make sure the data you generate if consistent with task I provided but different from the data I provided in every way possible.""" -INPUT_GENERATION_TASK_WITH_FEEDBACK_SUFFIX = "\n\nAdditionally, I'll be providing you with feedback on the data you generate, while generating the data make sure to take into account the feedback I provide and try to improve the data you generate based on the feedback I provide." \ No newline at end of file +INPUT_GENERATION_TASK_WITH_FEEDBACK_SUFFIX = "\n\nAdditionally, I'll be providing you with feedback on the data you generate, while generating the data make sure to take into account the feedback I provide and try to improve the data you generate based on the feedback I provide." diff --git a/dspy/experimental/synthesizer/signatures.py b/dspy/experimental/synthesizer/signatures.py index e1a50c6894..a045858d4e 100644 --- a/dspy/experimental/synthesizer/signatures.py +++ b/dspy/experimental/synthesizer/signatures.py @@ -93,4 +93,4 @@ class GenerateInputFieldsData(dspy.Signature): ) class GenerateOutputFieldsData(dspy.Signature): - pass \ No newline at end of file + pass diff --git a/dspy/experimental/synthesizer/utils.py b/dspy/experimental/synthesizer/utils.py index f08b142e1a..8e92fc097c 100644 --- a/dspy/experimental/synthesizer/utils.py +++ b/dspy/experimental/synthesizer/utils.py @@ -21,4 +21,4 @@ def format_examples(examples: List[dspy.Example]) -> str: for key in label_keys: formatted_example += f"{key}: {example[key]}\n" - return formatted_example \ No newline at end of file + return formatted_example diff --git a/dspy/predict/aggregation.py b/dspy/predict/aggregation.py index c65badd0ae..946179e223 100644 --- a/dspy/predict/aggregation.py +++ b/dspy/predict/aggregation.py @@ -51,5 +51,3 @@ def majority(prediction_or_completions, normalize=default_normalize, field=None) # if input_type == Prediction: return Prediction.from_completions([completion], signature=signature) - - diff --git a/dspy/predict/avatar/models.py b/dspy/predict/avatar/models.py index 63b43f11c7..708f236c39 100644 --- a/dspy/predict/avatar/models.py +++ b/dspy/predict/avatar/models.py @@ -23,4 +23,4 @@ class Action(BaseModel): class ActionOutput(BaseModel): tool_name: str tool_input_query: str - tool_output: str \ No newline at end of file + tool_output: str diff --git a/dspy/predict/chain_of_thought_with_hint.py b/dspy/predict/chain_of_thought_with_hint.py index fa33ca1c6c..e0925dfc65 100644 --- a/dspy/predict/chain_of_thought_with_hint.py +++ b/dspy/predict/chain_of_thought_with_hint.py @@ -31,4 +31,4 @@ def forward(self, **kwargs): TODO: In principle, we can update the field's prefix during forward too to fill any thing based on the input args. IF the user didn't overwrite our default rationale_type. -""" \ No newline at end of file +""" diff --git a/dspy/predict/program_of_thought.py b/dspy/predict/program_of_thought.py index 4534d98cfd..0818304444 100644 --- a/dspy/predict/program_of_thought.py +++ b/dspy/predict/program_of_thought.py @@ -179,4 +179,4 @@ def forward(self, **kwargs): return None input_kwargs.update({"final_generated_code": code, "code_output": output}) answer_gen_result = self.generate_answer(**input_kwargs) - return answer_gen_result \ No newline at end of file + return answer_gen_result diff --git a/dspy/propose/grounded_proposer.py b/dspy/propose/grounded_proposer.py index f618552b1b..ef48f1fbee 100644 --- a/dspy/propose/grounded_proposer.py +++ b/dspy/propose/grounded_proposer.py @@ -423,4 +423,4 @@ def propose_instruction_for_predictor( if self.verbose: self.prompt_model.inspect_history(n=1) if self.verbose: print(f"PROPOSED INSTRUCTION: {proposed_instruction}") - return strip_prefix(proposed_instruction) \ No newline at end of file + return strip_prefix(proposed_instruction) diff --git a/dspy/propose/instruction_proposal.py b/dspy/propose/instruction_proposal.py index 2967427caf..66900eb1df 100644 --- a/dspy/propose/instruction_proposal.py +++ b/dspy/propose/instruction_proposal.py @@ -123,4 +123,4 @@ class GenerateInstructionGivenAttempts(dspy.Signature): attempted_instructions = dspy.InputField(format=dsp.passages2text) # attempted_instructions = dspy.InputField(desc="Previously attempted task instructions, along with their resulting validation score, and an example of the instruction in use on a sample from our dataset.") proposed_instruction = dspy.OutputField(desc="The improved instructions for the language model") - proposed_prefix_for_output_field = dspy.OutputField(desc="The string at the end of the prompt, which will help the model start solving the task") \ No newline at end of file + proposed_prefix_for_output_field = dspy.OutputField(desc="The string at the end of the prompt, which will help the model start solving the task") diff --git a/dspy/propose/propose_base.py b/dspy/propose/propose_base.py index db1ec81323..b972dc0856 100644 --- a/dspy/propose/propose_base.py +++ b/dspy/propose/propose_base.py @@ -10,4 +10,4 @@ def propose_instructions_for_program(self): pass def propose_instruction_for_predictor(self): - pass \ No newline at end of file + pass diff --git a/dspy/propose/utils.py b/dspy/propose/utils.py index 7d93e3c8c9..e5ef6ee80b 100644 --- a/dspy/propose/utils.py +++ b/dspy/propose/utils.py @@ -181,4 +181,4 @@ def get_dspy_source_code(module): completed_set.add(code) completed_set.add(item) - return '\n\n'.join(header) + '\n\n' + base_code \ No newline at end of file + return '\n\n'.join(header) + '\n\n' + base_code diff --git a/dspy/retrieve/deeplake_rm.py b/dspy/retrieve/deeplake_rm.py index 3bb1340155..201d2f8f1b 100644 --- a/dspy/retrieve/deeplake_rm.py +++ b/dspy/retrieve/deeplake_rm.py @@ -110,4 +110,4 @@ def forward( sorted_passages = sorted( passages.items(), key=lambda x: x[1], reverse=True)[:k] - return [dotdict({"long_text": p}) for p, _ in sorted_passages] \ No newline at end of file + return [dotdict({"long_text": p}) for p, _ in sorted_passages] diff --git a/dspy/retrieve/milvus_rm.py b/dspy/retrieve/milvus_rm.py index a495703911..08f556c675 100644 --- a/dspy/retrieve/milvus_rm.py +++ b/dspy/retrieve/milvus_rm.py @@ -104,4 +104,4 @@ def forward(self, query_or_queries: Union[str, List[str]], k: Optional[int] = No sorted_passages = sorted( passage_scores.items(), key=lambda x: x[1], reverse=True, )[:k] - return [dotdict({"long_text": passage}) for passage, _ in sorted_passages] \ No newline at end of file + return [dotdict({"long_text": passage}) for passage, _ in sorted_passages] diff --git a/dspy/retrieve/ragatouille_rm.py b/dspy/retrieve/ragatouille_rm.py index 54f31baa01..e47871f913 100644 --- a/dspy/retrieve/ragatouille_rm.py +++ b/dspy/retrieve/ragatouille_rm.py @@ -59,4 +59,4 @@ def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int]) -> for query in queries: results = self.model.search(query=query, k=k) passages.extend(dotdict({"long_text": d["content"]}) for d in results) - return dspy.Prediction(passages=passages) \ No newline at end of file + return dspy.Prediction(passages=passages) diff --git a/dspy/retrieve/vectara_rm.py b/dspy/retrieve/vectara_rm.py index fc1bc9bc00..8489f0aae1 100644 --- a/dspy/retrieve/vectara_rm.py +++ b/dspy/retrieve/vectara_rm.py @@ -164,4 +164,4 @@ def forward(self, query_or_queries: Union[str, List[str]], k: Optional[int]) -> passages.items(), key=lambda x: x[1], reverse=True)[:k] return [dotdict({"long_text": passage}) for passage, _ in sorted_passages] - \ No newline at end of file + diff --git a/dspy/retrieve/watson_discovery_rm.py b/dspy/retrieve/watson_discovery_rm.py index 47b03f8042..13c1385695 100644 --- a/dspy/retrieve/watson_discovery_rm.py +++ b/dspy/retrieve/watson_discovery_rm.py @@ -104,4 +104,4 @@ def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int]= Non except requests.exceptions.RequestException as err: raise SystemExit(err) from err - return response \ No newline at end of file + return response diff --git a/dspy/teleprompt/utils.py b/dspy/teleprompt/utils.py index 96cc2d44cd..3486e26009 100644 --- a/dspy/teleprompt/utils.py +++ b/dspy/teleprompt/utils.py @@ -400,4 +400,4 @@ def new_getfile(object): return inspect.getfile(member) raise TypeError(f'Source for {object!r} not found') -inspect.getfile = new_getfile \ No newline at end of file +inspect.getfile = new_getfile diff --git a/examples/README.md b/examples/README.md index 88da28fb88..99d600ad64 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,3 +1,3 @@ The examples in this directory are almost all from DSPy 2.4 and are outdated and may not work correctly in 2.5+. -While we update them, please refer to the [Documentation site at dspy.ai](https://dspy.ai) for DSPy 2.5+ examples. \ No newline at end of file +While we update them, please refer to the [Documentation site at dspy.ai](https://dspy.ai) for DSPy 2.5+ examples. diff --git a/examples/outdated_v2.4_examples/coding/hackercup_utils.py b/examples/outdated_v2.4_examples/coding/hackercup_utils.py index 66d00a04e0..e75f51e7f3 100644 --- a/examples/outdated_v2.4_examples/coding/hackercup_utils.py +++ b/examples/outdated_v2.4_examples/coding/hackercup_utils.py @@ -125,4 +125,4 @@ def check_solution(expected: str, actual: str) -> dict: "matches": matches == len(expected_lines), "total": len(expected_lines), "offending_cases": offending_cases, - } \ No newline at end of file + } diff --git a/examples/outdated_v2.4_examples/longformqa/utils.py b/examples/outdated_v2.4_examples/longformqa/utils.py index 3ba37fbcd2..4e94549fb3 100644 --- a/examples/outdated_v2.4_examples/longformqa/utils.py +++ b/examples/outdated_v2.4_examples/longformqa/utils.py @@ -40,4 +40,4 @@ def has_citations(paragraph): return bool(re.search(r'\[\d+\]\.', paragraph)) def citations_check(paragraph): - return has_citations(paragraph) and correct_citation_format(paragraph) \ No newline at end of file + return has_citations(paragraph) and correct_citation_format(paragraph) diff --git a/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json b/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json index 1bed6f1e79..9b6cc109b9 100644 --- a/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json +++ b/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json @@ -50,4 +50,4 @@ } ] } -} \ No newline at end of file +} diff --git a/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json b/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json index 370c8d532b..8bd963da36 100644 --- a/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json +++ b/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json @@ -61,4 +61,4 @@ } ] } -} \ No newline at end of file +} diff --git a/examples/outdated_v2.4_examples/vlm/.gitignore b/examples/outdated_v2.4_examples/vlm/.gitignore index 2c22941fe2..d1b07a0559 100644 --- a/examples/outdated_v2.4_examples/vlm/.gitignore +++ b/examples/outdated_v2.4_examples/vlm/.gitignore @@ -1,4 +1,4 @@ docs/ .byaldi/ mmlongbench-doc.ipynb -mmmu.ipynb \ No newline at end of file +mmmu.ipynb diff --git a/testing/README.md b/testing/README.md index 78441af707..b55540e366 100644 --- a/testing/README.md +++ b/testing/README.md @@ -88,4 +88,4 @@ We are working on more optimizer tests with interesting compositions of optimize ## Important Note -The BioDex Task requires an external download. Please see the `/tasks/biodex.py` file for a link to download details and a note about where to insert the download path in the code. \ No newline at end of file +The BioDex Task requires an external download. Please see the `/tasks/biodex.py` file for a link to download details and a note about where to insert the download path in the code. diff --git a/testing/datasets/hotpotqa_conditional/hotpot_dev.csv b/testing/datasets/hotpotqa_conditional/hotpot_dev.csv index f1c6665fa5..cc4393cad7 100644 --- a/testing/datasets/hotpotqa_conditional/hotpot_dev.csv +++ b/testing/datasets/hotpotqa_conditional/hotpot_dev.csv @@ -198,4 +198,4 @@ BLIZZARD! The Storm That Changed America is a book about the storm that was refe What show is an American-Canadian drama starring Scott Lowell playing Ted Schmidt?,Queer as Folk,television show,what,television,,,,,,,,,,,,,,,,,,,,, Focke-Wulf Fw 187 was an intermediate design above the model that was the backbone of which fighting force?,Luftwaffe,fighting force,which,military,,,,,,,,,,,,,,,,,,,,, Ivan Matias produced hit songs for an American R&B and soul singer-songwriter who was born on March 16th in what year?,1976,year,what,music,,,,,,,,,,,,,,,,,,,,, -Which maximum security jail housed the killer of Julissa brisman?,Suffolk County Jail,jail,which,crime,,,,,,,,,,,,,,,,,,,,, \ No newline at end of file +Which maximum security jail housed the killer of Julissa brisman?,Suffolk County Jail,jail,which,crime,,,,,,,,,,,,,,,,,,,,, diff --git a/testing/datasets/hotpotqa_conditional/hotpot_test.csv b/testing/datasets/hotpotqa_conditional/hotpot_test.csv index ee69346d4d..77c3452506 100644 --- a/testing/datasets/hotpotqa_conditional/hotpot_test.csv +++ b/testing/datasets/hotpotqa_conditional/hotpot_test.csv @@ -198,4 +198,4 @@ What Major League Soccer team owned by Anthony Precourt is coached by a retired Where was the Danger Mouse produced U2 album exclusively released?,iTunes,platform,where,music Who designed the building that the 58th Technology & Engineering Emmy Awards were held in?,Designed by KlingStubbins,person/company,who,architecture The boxer that defeated Oliver Lavigilante in the 2012 Summer Olympics is of what nationality?,a Ghanaian boxer,nationality,what,sports -Do both Changle District and Longkou face water?,yes,boolean,do,geography \ No newline at end of file +Do both Changle District and Longkou face water?,yes,boolean,do,geography diff --git a/testing/datasets/hotpotqa_conditional/hotpot_train.csv b/testing/datasets/hotpotqa_conditional/hotpot_train.csv index b967caabfa..a756858901 100644 --- a/testing/datasets/hotpotqa_conditional/hotpot_train.csv +++ b/testing/datasets/hotpotqa_conditional/hotpot_train.csv @@ -498,4 +498,4 @@ What is the total area of the desert where Mount Manchester is located?,47877 mi Who is the male star of the film that closed The 27th Toronto International Film Festival ?,Antonio Banderas.,person,who,film Which Wisconsin Badgers player won a Heismann trophy and was elected to the Pro Bowl in each of their first four seasons?,Alan Ameche,person,which,sports "Which singer died at the age of thirty, Shaun Morgan or Dave Williams?",David Wayne Williams,person,which,music -Mount Jefferson is near the mountain to its northeast that has what elevation?,5793 ft,elevation,what,geography \ No newline at end of file +Mount Jefferson is near the mountain to its northeast that has what elevation?,5793 ft,elevation,what,geography diff --git a/testing/tasks/__init__.py b/testing/tasks/__init__.py index 835a30e2d1..fa06fa970e 100644 --- a/testing/tasks/__init__.py +++ b/testing/tasks/__init__.py @@ -7,4 +7,4 @@ from .hotpotqa_conditional import HotPotQAConditionalTask from .hover import HoverRetrieveDiscrete from .iris_typo import IrisTypoClassifierTask -from .iris import IrisClassifierTask \ No newline at end of file +from .iris import IrisClassifierTask diff --git a/tests/conftest.py b/tests/conftest.py index 894534cb66..d7ecea3f52 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -42,4 +42,4 @@ def pytest_collection_modifyitems(config, items): skip_mark = pytest.mark.skip(reason="need --{} option to run".format(flag)) for item in items: if flag in item.keywords: - item.add_marker(skip_mark) \ No newline at end of file + item.add_marker(skip_mark) diff --git a/tests/docs/test_mkdocs_links.py b/tests/docs/test_mkdocs_links.py index 5d5fe7c54b..11a13e418a 100644 --- a/tests/docs/test_mkdocs_links.py +++ b/tests/docs/test_mkdocs_links.py @@ -37,4 +37,4 @@ def test_nav_files_exist(): print("Found MD files:", md_files) print("Missing files:", missing) - assert not missing, f"Missing files: {missing}" \ No newline at end of file + assert not missing, f"Missing files: {missing}" diff --git a/tests/evaluate/test_metrics.py b/tests/evaluate/test_metrics.py index f6471b870b..e91d26f160 100644 --- a/tests/evaluate/test_metrics.py +++ b/tests/evaluate/test_metrics.py @@ -29,4 +29,4 @@ def test_answer_exact_match_no_match(): ).with_inputs("question") pred = Predict("question -> answer") pred.answer = "3" - assert not answer_exact_match(example, pred) \ No newline at end of file + assert not answer_exact_match(example, pred) diff --git a/tests/examples/test_baleen.py b/tests/examples/test_baleen.py index 4f1a841056..30ffbff1d3 100644 --- a/tests/examples/test_baleen.py +++ b/tests/examples/test_baleen.py @@ -133,4 +133,4 @@ def _test_compiled_baleen(): compiled_baleen, metric=gold_passages_retrieved ) # assert compiled_baleen_retrieval_score / 100 == 27 / 50 - assert uncompiled_baleen_retrieval_score < compiled_baleen_retrieval_score \ No newline at end of file + assert uncompiled_baleen_retrieval_score < compiled_baleen_retrieval_score diff --git a/tests/multihop_llama213b_0.json b/tests/multihop_llama213b_0.json index 520e696f76..f65a695bec 100644 --- a/tests/multihop_llama213b_0.json +++ b/tests/multihop_llama213b_0.json @@ -344,4 +344,4 @@ } ] } -} \ No newline at end of file +} diff --git a/tests/multihop_llama213b_1.json b/tests/multihop_llama213b_1.json index 8e09792b7d..0cc2adbe7d 100644 --- a/tests/multihop_llama213b_1.json +++ b/tests/multihop_llama213b_1.json @@ -342,4 +342,4 @@ } ] } -} \ No newline at end of file +} diff --git a/tests/multihop_llama213b_2.json b/tests/multihop_llama213b_2.json index 5fe21ec469..22e059278c 100644 --- a/tests/multihop_llama213b_2.json +++ b/tests/multihop_llama213b_2.json @@ -343,4 +343,4 @@ } ] } -} \ No newline at end of file +} diff --git a/tests/multihop_llama213b_3.json b/tests/multihop_llama213b_3.json index 62917cfb59..baa6b24113 100644 --- a/tests/multihop_llama213b_3.json +++ b/tests/multihop_llama213b_3.json @@ -345,4 +345,4 @@ } ] } -} \ No newline at end of file +} diff --git a/tests/primitives/test_python_interpreter.py b/tests/primitives/test_python_interpreter.py index 55e83f2d6e..98f1b0714e 100644 --- a/tests/primitives/test_python_interpreter.py +++ b/tests/primitives/test_python_interpreter.py @@ -16,4 +16,4 @@ def test_user_variable_definitions(): interpreter = PythonInterpreter() code = "result = number + 1\nresult" result = interpreter.execute(code, variables={'number': 4}) - assert result == 5, "User variable assignment should work" \ No newline at end of file + assert result == 5, "User variable assignment should work" diff --git a/tests/signatures/test_adapter_image.py b/tests/signatures/test_adapter_image.py index 255e93bc93..92d9264bab 100644 --- a/tests/signatures/test_adapter_image.py +++ b/tests/signatures/test_adapter_image.py @@ -328,4 +328,4 @@ def test_image_repr(): pil_image = dspy.Image.from_PIL(sample_pil) assert str(pil_image).startswith("data:image/png;base64,") assert str(pil_image).endswith("") - assert "base64" in str(pil_image) \ No newline at end of file + assert "base64" in str(pil_image) diff --git a/tests/teleprompt/test_finetune.py b/tests/teleprompt/test_finetune.py index f87f5c14cb..464090415c 100644 --- a/tests/teleprompt/test_finetune.py +++ b/tests/teleprompt/test_finetune.py @@ -1 +1 @@ -# TODO \ No newline at end of file +# TODO diff --git a/tests/utils/test_asyncify.py b/tests/utils/test_asyncify.py index 37e268bd83..9ded1f537e 100644 --- a/tests/utils/test_asyncify.py +++ b/tests/utils/test_asyncify.py @@ -51,5 +51,3 @@ async def verify_asyncify(capacity: int, number_of_tasks: int, wait: float = 0.5 await verify_asyncify(4, 10) await verify_asyncify(8, 15) await verify_asyncify(8, 30) - - From 99699d3f51ee21c83418ae603442363d8fa64f1e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:28:36 +0300 Subject: [PATCH 09/20] Enable trailing-whitespace pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index d98145f061..e400cd8b61 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -26,5 +26,6 @@ jobs: # Run hooks individually, until we can run them all at once pre-commit run check-yaml --all-files pre-commit run end-of-file-fixer --all-files + pre-commit run trailing-whitespace --all-files shell: bash From c78dfcd2aaacc1ced56e9ff828d5aea330a5c6a1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:30:25 +0300 Subject: [PATCH 10/20] Apply trailing-whitespace fixes --- .../workflow_scripts/install_testpypi_pkg.sh | 18 ++-- .github/workflows/build_and_release.yml | 82 +++++++++---------- .github/workflows/build_utils/test_version.py | 48 +++++------ .github/workflows/precommits_check.yml | 2 +- README.md | 18 ++-- docs/README.md | 4 +- docs/docs/cheatsheet.md | 14 ++-- docs/docs/community/how-to-contribute.md | 2 +- docs/docs/deep-dive/assertions.md | 28 +++---- .../data-handling/built-in-datasets.md | 2 +- docs/docs/deep-dive/data-handling/examples.md | 6 +- .../data-handling/loading-custom-data.md | 8 +- .../lm_local_models/HFClientTGI.md | 6 +- .../lm_local_models/HFClientVLLM.md | 4 +- .../lm_local_models/MLC.md | 8 +- .../deep-dive/modules/chain-of-thought.md | 6 +- docs/docs/deep-dive/modules/guide.md | 8 +- .../modules/multi-chain-comparison.md | 2 +- docs/docs/deep-dive/modules/predict.md | 2 +- .../deep-dive/modules/program-of-thought.md | 10 +-- docs/docs/deep-dive/modules/react.md | 6 +- docs/docs/deep-dive/modules/retrieve.md | 14 ++-- .../deep-dive/optimizers/LabeledFewShot.md | 2 +- docs/docs/deep-dive/optimizers/bfrs.md | 4 +- docs/docs/deep-dive/optimizers/miprov2.md | 10 +-- .../retrieval_models_clients/ChromadbRM.md | 2 +- .../retrieval_models_clients/ClarifaiRM.md | 12 +-- .../retrieval_models_clients/ColBERTv2.md | 4 +- .../retrieval_models_clients/MilvusRM.md | 2 +- .../retrieval_models_clients/WeaviateRM.md | 10 +-- .../custom-rm-client.md | 18 ++-- docs/docs/faqs.md | 6 +- docs/docs/index.md | 50 +++++------ docs/docs/js/runllm-widget.js | 2 +- docs/docs/learn/8-typed_predictors.md | 2 +- docs/docs/learn/evaluation/data.md | 6 +- docs/docs/learn/evaluation/metrics.md | 6 +- docs/docs/learn/optimization/optimizers.md | 10 +-- .../learn/optimization/solving_your_task.md | 2 +- docs/docs/learn/programming/7-assertions.md | 30 +++---- .../docs/learn/programming/language_models.md | 16 ++-- docs/docs/learn/programming/modules.md | 26 +++--- docs/docs/learn/programming/overview.md | 2 +- docs/docs/learn/programming/signatures.md | 2 +- docs/docs/tutorials/observability/index.md | 2 +- docs/docs/tutorials/old/other_tutorial.md | 2 +- docs/docs/tutorials/old/rag.md | 6 +- docs/docs/tutorials/old/simplified-baleen.md | 20 ++--- docs/overrides/home.html | 36 ++++---- .../internals/build-and-release.md | 12 +-- .../internals/release-checklist.md | 6 +- dspy/.internal_dspyai/setup.py | 20 ++--- dspy/adapters/base.py | 4 +- dspy/adapters/image_utils.py | 24 +++--- dspy/clients/anyscale.py | 16 ++-- dspy/clients/lm.py | 2 +- dspy/clients/openai.py | 4 +- dspy/clients/utils_finetune.py | 2 +- dspy/datasets/colors.py | 2 +- dspy/datasets/hotpotqa.py | 4 +- dspy/dsp/colbertv2.py | 22 ++--- dspy/evaluate/evaluate.py | 14 ++-- dspy/evaluate/metrics.py | 6 +- dspy/experimental/synthesizer/config.py | 2 +- dspy/experimental/synthesizer/synthesizer.py | 16 ++-- dspy/predict/aggregation.py | 8 +- dspy/predict/avatar/avatar.py | 18 ++-- dspy/predict/chain_of_thought.py | 2 +- dspy/predict/chain_of_thought_with_hint.py | 6 +- dspy/predict/knn.py | 2 +- dspy/predict/langchain.py | 16 ++-- dspy/predict/llamaindex.py | 31 ++++--- dspy/predict/predict.py | 2 +- dspy/primitives/runner.js | 4 +- dspy/propose/dataset_summary_generator.py | 10 +-- dspy/propose/grounded_proposer.py | 12 +-- dspy/propose/instruction_proposal.py | 4 +- dspy/propose/utils.py | 20 ++--- dspy/retrieve/azureaisearch_rm.py | 4 +- dspy/retrieve/chromadb_rm.py | 6 +- dspy/retrieve/deeplake_rm.py | 10 +-- dspy/retrieve/lancedb_rm.py | 10 +-- dspy/retrieve/milvus_rm.py | 4 +- dspy/retrieve/pinecone_rm.py | 14 ++-- dspy/retrieve/vectara_rm.py | 5 +- dspy/retrieve/watson_discovery_rm.py | 6 +- dspy/retrieve/weaviate_rm.py | 4 +- dspy/retrievers/embeddings.py | 2 +- dspy/teleprompt/avatar_optimizer.py | 22 ++--- dspy/teleprompt/bettertogether.py | 12 +-- dspy/teleprompt/bootstrap.py | 4 +- dspy/teleprompt/bootstrap_finetune.py | 26 +++--- dspy/teleprompt/infer_rules.py | 8 +- dspy/teleprompt/mipro_optimizer.py | 4 +- dspy/teleprompt/mipro_optimizer_v2.py | 8 +- dspy/teleprompt/signature_opt_bayesian.py | 2 +- dspy/teleprompt/utils.py | 4 +- .../coding/hackercup.py | 16 ++-- .../dataloaders/dolly_subset_100_rows.csv | 28 +++---- .../tweets/tweet_metric.py | 2 +- setup.py | 32 ++++---- testing/README.md | 2 +- tests/clients/test_inspect_global_history.py | 14 ++-- tests/docs/test_mkdocs_links.py | 12 +-- tests/predict/test_parallel.py | 6 +- tests/signatures/test_adapter_image.py | 44 +++++----- 106 files changed, 594 insertions(+), 596 deletions(-) diff --git a/.github/workflow_scripts/install_testpypi_pkg.sh b/.github/workflow_scripts/install_testpypi_pkg.sh index 9045e1dacf..8ecbbcb734 100755 --- a/.github/workflow_scripts/install_testpypi_pkg.sh +++ b/.github/workflow_scripts/install_testpypi_pkg.sh @@ -1,15 +1,15 @@ -#!/bin/bash +#!/bin/bash -# The $1 argument is the version number passed from the workflow +# The $1 argument is the version number passed from the workflow VERSION=$1 echo "version: $VERSION" -for i in {1..5}; do - if python3 -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple dspy-ai-test=="$VERSION"; then - break - else - echo "Attempt $i failed. Waiting before retrying..." - sleep 10 - fi +for i in {1..5}; do + if python3 -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple dspy-ai-test=="$VERSION"; then + break + else + echo "Attempt $i failed. Waiting before retrying..." + sleep 10 + fi done diff --git a/.github/workflows/build_and_release.yml b/.github/workflows/build_and_release.yml index 3a9f8c265a..79c4e6caad 100644 --- a/.github/workflows/build_and_release.yml +++ b/.github/workflows/build_and_release.yml @@ -6,20 +6,20 @@ on: - "*" jobs: - extract-tag: - runs-on: ubuntu-latest - outputs: - version: ${{ steps.extract_tag.outputs.tag }} - steps: + extract-tag: + runs-on: ubuntu-latest + outputs: + version: ${{ steps.extract_tag.outputs.tag }} + steps: - uses: actions/checkout@v4 - - id: extract_tag - name: Extract tag name - run: echo "::set-output name=tag::$(echo $GITHUB_REF | cut -d / -f 3)" + - id: extract_tag + name: Extract tag name + run: echo "::set-output name=tag::$(echo $GITHUB_REF | cut -d / -f 3)" build-and-publish-test-pypi: needs: extract-tag runs-on: ubuntu-latest - environment: + environment: name: pypi permissions: id-token: write # IMPORTANT: mandatory for trusted publishing @@ -32,34 +32,34 @@ jobs: python-version: "3.9" - name: Install dependencies run: python3 -m pip install --upgrade setuptools wheel twine semver packaging - - name: Get correct version for TestPyPI release - id: check_version - run: | - VERSION=${{ needs.extract-tag.outputs.version }} + - name: Get correct version for TestPyPI release + id: check_version + run: | + VERSION=${{ needs.extract-tag.outputs.version }} PACKAGE_NAME="dspy-ai-test" - echo "Checking if $VERSION for $PACKAGE_NAME exists on TestPyPI" - NEW_VERSION=$(python3 .github/workflows/build_utils/test_version.py $PACKAGE_NAME $VERSION) - echo "Version to be used for TestPyPI release: $NEW_VERSION" - echo "::set-output name=version::$NEW_VERSION" + echo "Checking if $VERSION for $PACKAGE_NAME exists on TestPyPI" + NEW_VERSION=$(python3 .github/workflows/build_utils/test_version.py $PACKAGE_NAME $VERSION) + echo "Version to be used for TestPyPI release: $NEW_VERSION" + echo "::set-output name=version::$NEW_VERSION" - name: Update version in setup.py run: sed -i '/#replace_package_version_marker/{n;s/__version__="[^"]*"/__version__="${{ steps.check_version.outputs.version }}"/;}' ./dspy/__metadata__.py - name: Update version in pyproject.toml - run: sed -i '/#replace_package_version_marker/{n;s/version="[^"]*"/version="${{ steps.check_version.outputs.version }}"/;}' pyproject.toml + run: sed -i '/#replace_package_version_marker/{n;s/version="[^"]*"/version="${{ steps.check_version.outputs.version }}"/;}' pyproject.toml - name: Update package name in setup.py run: sed -i '/#replace_package_name_marker/{n;s/__name__="[^"]*"/__name__="dspy-ai-test"/;}' ./dspy/__metadata__.py - name: Update package name in pyproject.toml - run: sed -i '/#replace_package_name_marker/{n;s/name="[^"]*"/name="dspy-ai-test"/;}' pyproject.toml + run: sed -i '/#replace_package_name_marker/{n;s/name="[^"]*"/name="dspy-ai-test"/;}' pyproject.toml - name: Build a binary wheel run: python3 setup.py sdist bdist_wheel - name: Publish distribution 📦 to test-PyPI uses: pypa/gh-action-pypi-publish@release/v1 # This requires a trusted publisher to be setup in pypi/testpypi - with: + with: repository-url: https://test.pypi.org/legacy/ build-and-publish-pypi: needs: [extract-tag, build-and-publish-test-pypi] runs-on: ubuntu-latest - environment: + environment: name: pypi permissions: id-token: write # IMPORTANT: mandatory for trusted publishing @@ -77,53 +77,53 @@ jobs: - name: Update version in pyproject.toml run: sed -i '/#replace_package_version_marker/{n;s/version *= *"[^"]*"/version="${{ needs.extract-tag.outputs.version }}"/;}' pyproject.toml # Publish to dspy - - name: Update package name in setup.py + - name: Update package name in setup.py run: | sed -i '/#replace_package_name_marker/{n;s/__name__ *= *"[^"]*"/__name__="dspy"/;}' ./dspy/__metadata__.py - name: Update package name in pyproject.toml run: sed -i '/#replace_package_name_marker/{n;s/name *= *"[^"]*"/name="dspy"/;}' pyproject.toml # Remove pyproject.toml temporarily to avoid conflicts - - name: Temporarily remove pyproject.toml - run: mv pyproject.toml pyproject.toml.bak + - name: Temporarily remove pyproject.toml + run: mv pyproject.toml pyproject.toml.bak - name: Build a binary wheel run: python3 setup.py sdist bdist_wheel - name: Publish distribution 📦 to PyPI (dspy) uses: pypa/gh-action-pypi-publish@release/v1 # This requires a trusted publisher to be setup in pypi - with: + with: attestations: false # Publish to dspy-ai - name: Update version in setup.py (dspy-ai) run: sed -i '/#replace_package_version_marker/{n;s/version *= *"[^"]*"/version="${{ needs.extract-tag.outputs.version }}"/;}' ./dspy/.internal_dspyai/setup.py - - name: Update package name in setup.py + - name: Update package name in setup.py run: sed -i '/#replace_package_name_marker/{n;s/name *= *"[^"]*"/name="dspy-ai"/;}' ./dspy/.internal_dspyai/setup.py - - name: Update dspy dependency version in setup.py + - name: Update dspy dependency version in setup.py run: | - sed -i '/#replace_dspy_version_marker/{n;s/dspy>=[^"]*/dspy>=${{ needs.extract-tag.outputs.version }}/;}' ./dspy/.internal_dspyai/setup.py + sed -i '/#replace_dspy_version_marker/{n;s/dspy>=[^"]*/dspy>=${{ needs.extract-tag.outputs.version }}/;}' ./dspy/.internal_dspyai/setup.py - name: Build a binary wheel run: python3 ./dspy/.internal_dspyai/setup.py sdist bdist_wheel - name: Publish distribution 📦 to PyPI (dspy-ai) uses: pypa/gh-action-pypi-publish@release/v1 # This requires a trusted publisher to be setup in pypi - with: + with: attestations: false # Restore pyproject.toml - - name: Restore pyproject.toml - run: mv pyproject.toml.bak pyproject.toml + - name: Restore pyproject.toml + run: mv pyproject.toml.bak pyproject.toml - uses: stefanzweifel/git-auto-commit-action@v5 # auto commit changes to main with: commit_message: Update versions create_branch: true branch: release-${{ needs.extract-tag.outputs.version }} - - name: Checkout main branch - run: | - git fetch origin - git checkout main + - name: Checkout main branch + run: | + git fetch origin + git checkout main - name: Configure git user - run: | + run: | git config --global user.email "actions@github.com" git config --global user.name "Github Actions" - - name: Merge release branch into main - run: | - git merge --no-ff release-${{ needs.extract-tag.outputs.version }} + - name: Merge release branch into main + run: | + git merge --no-ff release-${{ needs.extract-tag.outputs.version }} - name: Push changes to main - run: | - git push origin main + run: | + git push origin main diff --git a/.github/workflows/build_utils/test_version.py b/.github/workflows/build_utils/test_version.py index 469c4fe5ca..7163a3c4ba 100644 --- a/.github/workflows/build_utils/test_version.py +++ b/.github/workflows/build_utils/test_version.py @@ -6,33 +6,33 @@ from packaging.version import Version as PyPIVersion -def get_latest_version(package_name, tag_version): +def get_latest_version(package_name, tag_version): # Returns latest version, and T/F as to whether it needs to be incremented - response = requests.get(f"https://test.pypi.org/pypi/{package_name}/json") - if response.status_code == 200: - data = response.json() - # Flatten the list of files for all releases and get the latest upload - all_uploads = [ - (release['upload_time'], release['filename'], version) - for version, releases in data['releases'].items() - for release in releases - ] + response = requests.get(f"https://test.pypi.org/pypi/{package_name}/json") + if response.status_code == 200: + data = response.json() + # Flatten the list of files for all releases and get the latest upload + all_uploads = [ + (release['upload_time'], release['filename'], version) + for version, releases in data['releases'].items() + for release in releases + ] # If a release with tag_version does not exist, that is the latest version # Then increment is False, as no need to increment the version tag_release_exists = any(upload for upload in all_uploads if upload[2] == tag_version) if not(tag_release_exists): - return tag_version, False + return tag_version, False # Else, get the latest release version, and set increment to True else: # Sort all uploads by upload time in descending order - latest_upload = max(all_uploads, key=lambda x: datetime.fromisoformat(x[0].rstrip('Z'))) - return latest_upload[2], True - + latest_upload = max(all_uploads, key=lambda x: datetime.fromisoformat(x[0].rstrip('Z'))) + return latest_upload[2], True + elif response.status_code == 404: # If no existing releases can get a 404 return tag_version, False - return None, None - + return None, None + def increment_version(curr_version): pypi_v = PyPIVersion(curr_version) if pypi_v.pre: @@ -42,19 +42,19 @@ def increment_version(curr_version): parsed_v = semver.Version(*pypi_v.release) new_v = str(parsed_v.bump_prerelease()) return new_v - -if __name__ == "__main__": - if len(sys.argv) != 3: - raise ValueError("Usage: python get_latest_testpypi_version.py ") - + +if __name__ == "__main__": + if len(sys.argv) != 3: + raise ValueError("Usage: python get_latest_testpypi_version.py ") + package_name = sys.argv[1] tag_v = sys.argv[2] - latest_version, increment = get_latest_version(package_name, tag_v) + latest_version, increment = get_latest_version(package_name, tag_v) if increment: new_version = increment_version(latest_version) - else: + else: new_version = latest_version # Output new version - print(new_version) + print(new_version) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index e400cd8b61..bd7988210d 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -22,7 +22,7 @@ jobs: python -m pip install --upgrade pip pip install pre-commit echo "Running pre-commit scans:" - + # Run hooks individually, until we can run them all at once pre-commit run check-yaml --all-files pre-commit run end-of-file-fixer --all-files diff --git a/README.md b/README.md index 4b56de2bc3..9dda639bf4 100644 --- a/README.md +++ b/README.md @@ -46,13 +46,13 @@ If you're looking to understand the framework, please go to the [DSPy Docs at ds If you're looking to understand the underlying research, this is a set of our papers: -**[Jun'24] [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://arxiv.org/abs/2406.11695)** -**[Oct'23] [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714)** -[Jul'24] [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://arxiv.org/abs/2407.10930) -[Jun'24] [Prompts as Auto-Optimized Training Hyperparameters](https://arxiv.org/abs/2406.11706) -[Feb'24] [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://arxiv.org/abs/2402.14207) -[Jan'24] [In-Context Learning for Extreme Multi-Label Classification](https://arxiv.org/abs/2401.12178) -[Dec'23] [DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines](https://arxiv.org/abs/2312.13382) +**[Jun'24] [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://arxiv.org/abs/2406.11695)** +**[Oct'23] [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714)** +[Jul'24] [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://arxiv.org/abs/2407.10930) +[Jun'24] [Prompts as Auto-Optimized Training Hyperparameters](https://arxiv.org/abs/2406.11706) +[Feb'24] [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://arxiv.org/abs/2402.14207) +[Jan'24] [In-Context Learning for Extreme Multi-Label Classification](https://arxiv.org/abs/2401.12178) +[Dec'23] [DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines](https://arxiv.org/abs/2312.13382) [Dec'22] [Demonstrate-Search-Predict: Composing Retrieval & Language Models for Knowledge-Intensive NLP](https://arxiv.org/abs/2212.14024.pdf) To stay up to date or learn more, follow [@lateinteraction](https://twitter.com/lateinteraction) on Twitter. @@ -78,8 +78,8 @@ If you use DSPy or DSP in a research paper, please cite our work as follows: 53 self._test_ = self._shuffle_and_sample('test', self._test, self.test_size, self.test_seed) - 54 + 54 55 return self._test_ AttributeError: 'CSVDataset' object has no attribute '_test' @@ -112,6 +112,6 @@ Using the Dataset base class now makes loading custom datasets incredibly easy a To prevent that you'll just need to make sure `_test` is not `None` and populated with the appropriate data. -You can override the methods in `Dataset` class to customize your class even more. +You can override the methods in `Dataset` class to customize your class even more. In summary, the Dataset base class provides a simplistic way to load and preprocess custom datasets with minimal code! diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md index 049ea00d91..15df95739c 100644 --- a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md +++ b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md @@ -50,7 +50,7 @@ Initialize the `HFClientTGI` within your program with the desired parameters. He tgi_llama2 = dspy.HFClientTGI(model="meta-llama/Llama-2-7b-hf", port=8080, url="http://localhost") ``` -Customize the `model`, `port`, and `url` according to your requirements. The `model` parameter should be set to the specific Hugging Face model ID you wish to use. +Customize the `model`, `port`, and `url` according to your requirements. The `model` parameter should be set to the specific Hugging Face model ID you wish to use. ## Sending Requests via TGI Client @@ -84,7 +84,7 @@ The constructor initializes the `HFModel` base class to support the handling of - `model` (_str_): ID of Hugging Face model connected to the TGI server. - `port` (_int_ or _list_): Port for communicating to the TGI server. This can be a single port number (`8080`) or a list of TGI ports (`[8080, 8081, 8082]`) to route the requests to. - `url` (_str_): Base URL of hosted TGI server. This will often be `"http://localhost"`. -- `http_request_kwargs` (_dict_): Dictionary of additional keyword arguments to pass to the HTTP request function to the TGI server. This is `None` by default. +- `http_request_kwargs` (_dict_): Dictionary of additional keyword arguments to pass to the HTTP request function to the TGI server. This is `None` by default. - `**kwargs`: Additional keyword arguments to configure the TGI client. Example of the TGI constructor: @@ -103,7 +103,7 @@ class HFClientTGI(HFModel): **Returns:** - `dict`: dictionary with `prompt` and list of response `choices`. -Internally, the method handles the specifics of preparing the request prompt and corresponding payload to obtain the response. +Internally, the method handles the specifics of preparing the request prompt and corresponding payload to obtain the response. After generation, the method parses the JSON response received from the server and retrieves the output through `json_response["generated_text"]`. This is then stored in the `completions` list. diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientVLLM.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientVLLM.md index 7b7697b923..640f570a5d 100644 --- a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientVLLM.md +++ b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientVLLM.md @@ -62,7 +62,7 @@ print(response) The constructor initializes the `HFModel` base class to support the handling of prompting models, configuring the client for communicating with the hosted vLLM server to generate requests. This requires the following parameters: - `model` (_str_): ID of model connected to the vLLM server. -- `port` (_int_): Port for communicating to the vLLM server. +- `port` (_int_): Port for communicating to the vLLM server. - `url` (_str_): Base URL of hosted vLLM server. This will often be `"http://localhost"`. - `**kwargs`: Additional keyword arguments to configure the vLLM client. @@ -82,7 +82,7 @@ class HFClientVLLM(HFModel): **Returns:** - `dict`: dictionary with `prompt` and list of response `choices`. -Internally, the method handles the specifics of preparing the request prompt and corresponding payload to obtain the response. +Internally, the method handles the specifics of preparing the request prompt and corresponding payload to obtain the response. After generation, the method parses the JSON response received from the server and retrieves the output through `json_response["choices"]` and stored as the `completions` list. diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/MLC.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/MLC.md index d926fbfc47..26b85326b9 100644 --- a/docs/docs/deep-dive/language_model_clients/lm_local_models/MLC.md +++ b/docs/docs/deep-dive/language_model_clients/lm_local_models/MLC.md @@ -6,13 +6,13 @@ ## Prerequisites 1. Install the required packages using the following commands: - + ```shell pip install --no-deps --pre --force-reinstall mlc-ai-nightly-cu118 mlc-chat-nightly-cu118 -f https://mlc.ai/wheels pip install transformers git lfs install ``` - + Adjust the pip wheels according to your OS/platform by referring to the provided commands in [MLC packages](https://mlc.ai/package/). ## Running MLC Llama-2 models @@ -22,14 +22,14 @@ ```shell mkdir -p dist/prebuilt ``` - + 2. Clone the necessary libraries from the repository: ```shell git clone https://github.com/mlc-ai/binary-mlc-llm-libs.git dist/prebuilt/lib cd dist/prebuilt ``` - + 3. Choose a Llama-2 model from [MLC LLMs](https://huggingface.co/mlc-ai) and clone the model repository: ```shell diff --git a/docs/docs/deep-dive/modules/chain-of-thought.md b/docs/docs/deep-dive/modules/chain-of-thought.md index e8667cc96c..89e1972418 100644 --- a/docs/docs/deep-dive/modules/chain-of-thought.md +++ b/docs/docs/deep-dive/modules/chain-of-thought.md @@ -5,7 +5,7 @@ ### Constructor -The constructor initializes the `ChainOfThought` class and sets up its attributes. It inherits from the `Predict` class and adds specific functionality for chain of thought processing. +The constructor initializes the `ChainOfThought` class and sets up its attributes. It inherits from the `Predict` class and adds specific functionality for chain of thought processing. Internally, the class initializes the `activated` attribute to indicate if chain of thought processing has been selected. It extends the `signature` to include additional reasoning steps and an updated `rationale_type` when chain of thought processing is activated. @@ -37,7 +37,7 @@ class ChainOfThought(Predict): else: # For dspy <2.5 extended_signature = signature.prepend("rationale", rationale_type, type_=str) - + self._predict = dspy.Predict(extended_signature, **config) self._predict.extended_signature = extended_signature ``` @@ -80,7 +80,7 @@ print(f"Question: {question}") print(f"Predicted Answer: {pred.answer}") ``` -The following example shows how to specify your custom rationale. Here `answer` corresponds to the last key to produce, it may be different in your case. +The following example shows how to specify your custom rationale. Here `answer` corresponds to the last key to produce, it may be different in your case. ```python #define a custom rationale diff --git a/docs/docs/deep-dive/modules/guide.md b/docs/docs/deep-dive/modules/guide.md index 65a61f3e9f..ae65fa33e9 100644 --- a/docs/docs/deep-dive/modules/guide.md +++ b/docs/docs/deep-dive/modules/guide.md @@ -25,7 +25,7 @@ A **DSPy module** is a building block for programs that use LMs. 1. **[`dspy.Predict`](/deep-dive/modules/predict)**: -2. **[`dspy.ChainOfThought`](/deep-dive/modules/chain-of-thought)**: +2. **[`dspy.ChainOfThought`](/deep-dive/modules/chain-of-thought)**: 3. **[`dspy.ProgramOfThought`](/deep-dive/modules/program-of-thought)**: @@ -47,7 +47,7 @@ sentence = "it's a charming and often affecting journey." # example from the SS # 1) Declare with a signature. classify = dspy.Predict('sentence -> sentiment') -# 2) Call with input argument(s). +# 2) Call with input argument(s). response = classify(sentence=sentence) # 3) Access the output. @@ -57,7 +57,7 @@ print(response.sentiment) ```text Positive ``` - + When we declare a module, we can pass configuration keys to it. @@ -103,7 +103,7 @@ print(f"Answer: {response.answer}") Reasoning: ColBERT (Contextualized Late Interaction over BERT) is a retrieval model that effectively combines the strengths of dense retrieval and traditional BM25 methods. One of its great features is that it allows for efficient and scalable retrieval by using late interaction techniques, which enables the model to leverage the contextual embeddings generated by BERT while still maintaining a fast retrieval speed. This means that it can handle large document collections more effectively than many other models, providing both high relevance in search results and efficiency in processing time. Answer: A great feature of the ColBERT retrieval model is its ability to efficiently combine contextualized embeddings from BERT with a late interaction mechanism, allowing for scalable and high-performance document retrieval. - + This is accessible whether we request one or many completions. diff --git a/docs/docs/deep-dive/modules/multi-chain-comparison.md b/docs/docs/deep-dive/modules/multi-chain-comparison.md index 6c142d282c..9f4efe2f55 100644 --- a/docs/docs/deep-dive/modules/multi-chain-comparison.md +++ b/docs/docs/deep-dive/modules/multi-chain-comparison.md @@ -28,7 +28,7 @@ class MultiChainComparison(Module): for idx in range(M): candidate_type = dsp.Type(prefix=f"Student Attempt #{idx+1}:", desc="${reasoning attempt}") extended_kwargs.update({f'reasoning_attempt_{idx+1}': candidate_type}) - + rationale_type = dsp.Type(prefix="Accurate Reasoning: Thank you everyone. Let's now holistically", desc="${corrected reasoning}") extended_kwargs.update({'rationale': rationale_type, last_key: signature.kwargs[last_key]}) diff --git a/docs/docs/deep-dive/modules/predict.md b/docs/docs/deep-dive/modules/predict.md index 259859f9cc..1cabe62f1b 100644 --- a/docs/docs/deep-dive/modules/predict.md +++ b/docs/docs/deep-dive/modules/predict.md @@ -32,7 +32,7 @@ class Predict(Parameter): for k, v in inputs.items(): v.finalize(k, infer_prefix(k)) - + for k, v in outputs.items(): v.finalize(k, infer_prefix(k)) diff --git a/docs/docs/deep-dive/modules/program-of-thought.md b/docs/docs/deep-dive/modules/program-of-thought.md index 96aa1a125e..cabbdec810 100644 --- a/docs/docs/deep-dive/modules/program-of-thought.md +++ b/docs/docs/deep-dive/modules/program-of-thought.md @@ -11,7 +11,7 @@ sidebar_position: 2 DSPy supports the Program of Thought (PoT) prompting technique, integrating an advanced module capable of problem-solving with program execution capabilities. PoT builds upon Chain of Thought by translating reasoning steps into executable programming language statements through iterative refinement. This enhances the accuracy of the output and self-corrects errors within the generated code. Upon completing these iterations, PoT delegates execution to a program interpreter. Currently, this class supports the generation and execution of Python code. -## Instantiating Program of Thought +## Instantiating Program of Thought Program of Thought is instantiated based on a user-defined DSPy Signature, which can take a simple form such as `input_fields -> output_fields`. Users can also specify a `max_iters` to set the maximum number of iterations for the self-refinement process of the generated code. The default value is 3 iterations. Once the maximum iterations are reached, PoT will produce the final output. @@ -76,7 +76,7 @@ Question: Sarah has 5 apples. She buys 7 more apples from the store. How many ap Final Predicted Answer (after ProgramOfThought process): 12 ``` -Let's take a peek at how ProgramOfThought functioned internally by inspecting its history, up to maximum iterations (+ 1 to view the final generation). (This assumes the initial DSPy setup and configurations of LMs and RMs). +Let's take a peek at how ProgramOfThought functioned internally by inspecting its history, up to maximum iterations (+ 1 to view the final generation). (This assumes the initial DSPy setup and configurations of LMs and RMs). `lm.inspect_history(n=4)` @@ -89,7 +89,7 @@ After you're done with the computation, make sure the last line in your code eva Follow the following format. -Question: +Question: Reasoning: Let's think step by step in order to ${produce the generated_code}. We ... Code: python code that answers the question @@ -97,7 +97,7 @@ Code: python code that answers the question Question: Sarah has 5 apples. She buys 7 more apples from the store. How many apples does Sarah have now? Reasoning: Let's think step by step in order to produce the generated_code. We start with the initial number of apples that Sarah has, which is 5. Then, we add the number of apples she buys from the store, which is 7. Finally, we compute the total number of apples Sarah has by adding these two quantities together. -Code: +Code: apples_initial = 5 apples_bought = 7 @@ -111,7 +111,7 @@ Given the final code `question`, `final_generated_code`, `code_output`, provide Follow the following format. -Question: +Question: Code: python code that answers the question diff --git a/docs/docs/deep-dive/modules/react.md b/docs/docs/deep-dive/modules/react.md index ab40413683..14856b499b 100644 --- a/docs/docs/deep-dive/modules/react.md +++ b/docs/docs/deep-dive/modules/react.md @@ -11,9 +11,9 @@ sidebar_position: 2 DSPy supports ReAct, an LLM agent designed to tackle complex tasks in an interactive fashion. ReAct is composed of an iterative loop of interpretation, decision and action-based activities ("Thought, Action, and Observation") based on an evolving set of input and output fields. Through this real-time iterative approach, the ReAct agent can both analyze and adapt to its responses over time as new information becomes available. -## Instantiating ReAct +## Instantiating ReAct -To instantiate the ReAct module, define and pass in a DSPy Signature. +To instantiate the ReAct module, define and pass in a DSPy Signature. ```python # Define a simple signature for basic question answering @@ -60,7 +60,7 @@ Question: Aside from the Apple Remote, what other devices can control the progra Final Predicted Answer (after ReAct process): The Apple Remote and the Siri Remote can control the Front Row media program. ``` -Let's take a peek at how ReAct functioned internally by inspecting its history, up to maximum iterations. (This assumes the initial DSPy setup and configurations of LMs and RMs). +Let's take a peek at how ReAct functioned internally by inspecting its history, up to maximum iterations. (This assumes the initial DSPy setup and configurations of LMs and RMs). `lm.inspect_history(n=3)` diff --git a/docs/docs/deep-dive/modules/retrieve.md b/docs/docs/deep-dive/modules/retrieve.md index f697053676..b99735081c 100644 --- a/docs/docs/deep-dive/modules/retrieve.md +++ b/docs/docs/deep-dive/modules/retrieve.md @@ -16,7 +16,7 @@ class Retrieve(Parameter): self.k = k ``` -Additionally, configuring a Retrieval model client or server through `dspy.configure` allows for user retrieval in DSPy programs through internal calls from Retrieve. +Additionally, configuring a Retrieval model client or server through `dspy.configure` allows for user retrieval in DSPy programs through internal calls from Retrieve. ```python #Example Usage @@ -33,9 +33,9 @@ retriever = dspy.Retrieve(k=3) ### Under the Hood -Retrieve makes use of the internally configured retriever to send a single query or list of multiple queries to determine the top-k relevant passages. The module queries the retriever for each provided query, accumulating scores (or probabilities if the `by_prob` arg is set) for each passage and returns the passages sorted by their cumulative scores or probabilities. +Retrieve makes use of the internally configured retriever to send a single query or list of multiple queries to determine the top-k relevant passages. The module queries the retriever for each provided query, accumulating scores (or probabilities if the `by_prob` arg is set) for each passage and returns the passages sorted by their cumulative scores or probabilities. -The Retrieve module can also integrate a reranker if this is configured, in which case, the reranker re-scores the retrieved passages based on their relevance to the quer, improving accuracy of the results. +The Retrieve module can also integrate a reranker if this is configured, in which case, the reranker re-scores the retrieved passages based on their relevance to the quer, improving accuracy of the results. ### Tying it All Together @@ -54,12 +54,12 @@ for idx, passage in enumerate(topK_passages): ``` ``` -Top 3 passages for question: When was the first FIFA World Cup held? - ------------------------------ +Top 3 passages for question: When was the first FIFA World Cup held? + ------------------------------ -1] History of the FIFA World Cup | The FIFA World Cup was first held in 1930, when FIFA president Jules Rimet [...]. +1] History of the FIFA World Cup | The FIFA World Cup was first held in 1930, when FIFA president Jules Rimet [...]. -2] 1950 FIFA World Cup | The 1950 FIFA World Cup, held in Brazil from 24 June to 16 July 1950, [...]. +2] 1950 FIFA World Cup | The 1950 FIFA World Cup, held in Brazil from 24 June to 16 July 1950, [...]. 3] 1970 FIFA World Cup | The 1970 FIFA World Cup was the ninth FIFA World Cup, the quadrennial [...]. ``` diff --git a/docs/docs/deep-dive/optimizers/LabeledFewShot.md b/docs/docs/deep-dive/optimizers/LabeledFewShot.md index 316e3bf3ed..37edd71ac8 100644 --- a/docs/docs/deep-dive/optimizers/LabeledFewShot.md +++ b/docs/docs/deep-dive/optimizers/LabeledFewShot.md @@ -46,7 +46,7 @@ class RAG(dspy.Module): #declare retrieval and predictor modules self.retrieve = dspy.Retrieve(k=num_passages) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) - + #flow for answering questions using predictor and retrieval modules def forward(self, question): context = self.retrieve(question).passages diff --git a/docs/docs/deep-dive/optimizers/bfrs.md b/docs/docs/deep-dive/optimizers/bfrs.md index d94c2ba168..18f6c22602 100644 --- a/docs/docs/deep-dive/optimizers/bfrs.md +++ b/docs/docs/deep-dive/optimizers/bfrs.md @@ -32,8 +32,8 @@ Let's take the example of optimizing a simple CoT pipeline for GSM8k dataset, we from dspy.teleprompt import BootstrapFewShotWithRandomSearch teleprompter = BootstrapFewShotWithRandomSearch( - metric=gsm8k_metric, - max_bootstrapped_demos=8, + metric=gsm8k_metric, + max_bootstrapped_demos=8, max_labeled_demos=8, num_threads=10, num_candidate_programs=10 diff --git a/docs/docs/deep-dive/optimizers/miprov2.md b/docs/docs/deep-dive/optimizers/miprov2.md index 9bf6481960..20028518ae 100644 --- a/docs/docs/deep-dive/optimizers/miprov2.md +++ b/docs/docs/deep-dive/optimizers/miprov2.md @@ -41,7 +41,7 @@ class CoT(dspy.Module): def __init__(self): super().__init__() self.prog = dspy.ChainOfThought("question -> answer") - + def forward(self, question): return self.prog(question=question) ``` @@ -151,7 +151,7 @@ optimized_program = teleprompter.compile( num_trials=15, minibatch_size=25, minibatch_full_eval_steps=10, - minibatch=True, + minibatch=True, requires_permission_to_run=False, ) @@ -178,7 +178,7 @@ evaluate(optimized_program, devset=devset[:]) | `max_errors` | `int` | `10` | Maximum errors during an evaluation run that can be made before throwing an Exception. | | `teacher_settings` | `dict` | `{}` | Settings to use for the teacher model that bootstraps few-shot examples. An example dict would be `{lm=}`. If your LM program with your default model is struggling to bootstrap any examples, it could be worth using a more powerful teacher model for bootstrapping. | | `max_bootstrapped_demos` | `int` | `4` | Maximum number of bootstrapped demonstrations to generate and include in the prompt. | -| `max_labeled_demos` | `int` | `16` | Maximum number of labeled demonstrations to generate and include in the prompt. Note that these differ from bootstrapped examples because they are just inputs & outputs sampled directly from the training set and do not have bootstrapped intermediate steps. +| `max_labeled_demos` | `int` | `16` | Maximum number of labeled demonstrations to generate and include in the prompt. Note that these differ from bootstrapped examples because they are just inputs & outputs sampled directly from the training set and do not have bootstrapped intermediate steps. | `init_temperature` | `float` | `1.0` | The initial temperature for prompt generation, influencing creativity. | | `verbose` | `bool` | `False` | Enables printing intermediate steps and information. | | `track_stats` | `bool` | `True` | Logs relevant information through the optimization process if set to True. | @@ -196,7 +196,7 @@ evaluate(optimized_program, devset=devset[:]) | `num_trials` | `int` | `30` | Number of optimization trials to run. When `minibatch` is set to `True`, this represents the number of minibatch trials that will be run on batches of size `minibatch_size`. When minibatch is set to `False`, each trial uses a full evaluation on the training set. In both cases, we recommend setting `num_trials` to a *minimum* of .75 x # modules in program x # variables per module (2 if few-shot examples & instructions will both be optimized, 1 in the 0-shot case). | | `minibatch` | `bool` | `True` | Flag to enable evaluating over minibatches of data (instead of the full validation set) for evaluation each trial. | | `minibatch_size` | `int` | `25.0` | Size of minibatches for evaluations. | -| `minibatch_full_eval_steps` | `int` | `10` | When minibatching is enabled, a full evaluation on the validation set will be carried out every `minibatch_full_eval_steps` on the top averaging set of prompts (according to their average score on the minibatch trials). +| `minibatch_full_eval_steps` | `int` | `10` | When minibatching is enabled, a full evaluation on the validation set will be carried out every `minibatch_full_eval_steps` on the top averaging set of prompts (according to their average score on the minibatch trials). | `max_bootstrapped_demos` | `Optional[int]` | Defaults to `init` value. | Maximum number of bootstrapped demonstrations to generate and include in the prompt. | | `max_labeled_demos` | `Optional[int]` | Defaults to `init` value. | Maximum number of labeled demonstrations to generate and include in the prompt. Note that these differ from bootstrapped examples because they are just inputs & outputs sampled directly from the training set and do not have bootstrapped intermediate steps. | | `seed` | `Optional[int]` | Defaults to `init` value. | Seed for reproducibility. | | @@ -217,4 +217,4 @@ These steps are broken down in more detail below: 3. **Find an Optimized Combination of Few-Shot Examples & Instructions**. Finally, now that we've created these few-shot examples and instructions, we use Bayesian Optimization to choose which set of these would work best for each predictor in our program. This works by running a series of `num_trials` trials, where a new set of prompts are evaluated over our validation set at each trial. This helps the Bayesian Optimizer learn which combination of prompts work best over time. If `minibatch` is set to `True` (which it is by default), then the new set of prompts are only evaluated on a minibatch of size `minibatch_size` at each trial which generally allows for more efficient exploration / exploitation. The best averaging set of prompts is then evaluated on the full validation set every `minibatch_full_eval_steps` get a less noisey performance benchmark. At the end of the optimization process, the LM program with the set of prompts that performed best on the full validation set is returned. -For those interested in more details, more information on `MIPROv2` along with a study on `MIPROv2` compared with other DSPy optimizers can be found in [this paper](https://arxiv.org/abs/2406.11695). +For those interested in more details, more information on `MIPROv2` along with a study on `MIPROv2` compared with other DSPy optimizers can be found in [this paper](https://arxiv.org/abs/2406.11695). diff --git a/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md b/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md index 245664bb1a..23862dcc1a 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/ChromadbRM.md @@ -15,7 +15,7 @@ The constructor initializes an instance of the `ChromadbRM` class, with the opti - `embedding_function` (_Optional[EmbeddingFunction[Embeddable]]_, _optional_): The function used for embedding documents and queries. Defaults to `DefaultEmbeddingFunction()` if not specified. - `k` (_int_, _optional_): The number of top passages to retrieve. Defaults to 7. -Example of the ChromadbRM constructor: +Example of the ChromadbRM constructor: ```python ChromadbRM( diff --git a/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md b/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md index a21113d53e..d85ce7acfb 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/ClarifaiRM.md @@ -13,7 +13,7 @@ The ClarifaiRM module requires the `clarifai` Python package. If not already ins pip install clarifai ``` -**Note:** +**Note:** Before using ClarifaiRM, ensure you have: @@ -41,7 +41,7 @@ ClarifaiRM( ) ``` -**Note:** +**Note:** The PAT can be provided either directly to the constructor or through the `CLARIFAI_PAT` environment variable. For security best practices, using environment variables is recommended. @@ -112,7 +112,7 @@ class RAG(dspy.Module): def __init__(self): super().__init__() self.retrieve = Retrieve(k=3) - + def forward(self, query): passages = self.retrieve(query) return passages @@ -137,7 +137,7 @@ num_results = len(results) ### Integration with Other DSPy Components ```python -from dspy import ChainOfThought, Predict, Retrieve +from dspy import ChainOfThought, Predict, Retrieve # Create a simple QA chain class QAChain(dspy.Module): @@ -145,7 +145,7 @@ class QAChain(dspy.Module): super().__init__() self.retrieve = Retrieve(k=3) self.generate_answer = ChainOfThought("question, context -> answer") - + def forward(self, question): context = self.retrieve(question) answer = self.generate_answer(question=question, context=context) @@ -168,7 +168,7 @@ except Exception as e: print(f"Error occurred: {e}") ``` -**Note:** +**Note:** These examples assume you have: diff --git a/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md b/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md index d27f72c8e8..cd4beb691e 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md +++ b/docs/docs/deep-dive/retrieval_models_clients/ColBERTv2.md @@ -4,7 +4,7 @@ import AuthorDetails from '@site/src/components/AuthorDetails'; ## Setting up the ColBERTv2 Client -The constructor initializes the `ColBERTv2` class instance and sets up the request parameters for interacting with the ColBERTv2 retrieval server. This server is hosted remotely at `'http://20.102.90.50:2017/wiki17_abstracts`. +The constructor initializes the `ColBERTv2` class instance and sets up the request parameters for interacting with the ColBERTv2 retrieval server. This server is hosted remotely at `'http://20.102.90.50:2017/wiki17_abstracts`. - `url` (_str_): URL for ColBERTv2 server. Defaults to `"http://0.0.0.0"` - `port` (_Union[str, int]_, _Optional_): Port endpoint for ColBERTv2 server. Defaults to `None`. @@ -34,7 +34,7 @@ class ColBERTv2: **Returns:** - `Union[list[str], list[dotdict]]`: Depending on `simplify` flag, either a list of strings representing the passage content (`True`) or a list of `dotdict` instances containing passage details (`False`). -Internally, the method handles the specifics of preparing the request query to the ColBERTv2 server and corresponding payload to obtain the response. +Internally, the method handles the specifics of preparing the request query to the ColBERTv2 server and corresponding payload to obtain the response. The function handles the retrieval of the top-k passages based on the provided query. diff --git a/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md b/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md index 7c01266fc0..a5ef800920 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/MilvusRM.md @@ -19,7 +19,7 @@ The constructor initializes an instance of the `MilvusRM` class, with the option Defaults to None. By default, it will get OpenAI client by the environment variable OPENAI_API_KEY and use OpenAI's embedding model "text-embedding-3-small" with the default dimension. - `k (int, optional)`: The number of top passages to retrieve. Defaults to 3. -Example of the MilvusRM constructor: +Example of the MilvusRM constructor: ```python MilvusRM( diff --git a/docs/docs/deep-dive/retrieval_models_clients/WeaviateRM.md b/docs/docs/deep-dive/retrieval_models_clients/WeaviateRM.md index 5494afcde1..b948eeb61e 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/WeaviateRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/WeaviateRM.md @@ -1,15 +1,15 @@ # Weaviate Retrieval Model -[Weaviate](https://weaviate.io/) is an open-source vector database that can be used to retrieve relevant passages before passing it to the language model. Weaviate supports a variety of [embedding models](https://weaviate.io/developers/weaviate/model-providers) from OpenAI, Cohere, Google and more! Before building your DSPy program, you will need a Weaviate cluster running with data. You can follow this [notebook](https://github.com/weaviate/recipes/blob/main/integrations/llm-frameworks/dspy/Weaviate-Import.ipynb) as an example. +[Weaviate](https://weaviate.io/) is an open-source vector database that can be used to retrieve relevant passages before passing it to the language model. Weaviate supports a variety of [embedding models](https://weaviate.io/developers/weaviate/model-providers) from OpenAI, Cohere, Google and more! Before building your DSPy program, you will need a Weaviate cluster running with data. You can follow this [notebook](https://github.com/weaviate/recipes/blob/main/integrations/llm-frameworks/dspy/Weaviate-Import.ipynb) as an example. -## Configuring the Weaviate Client -Weaviate is available via a hosted service ([WCD](https://console.weaviate.cloud/)) or as a self managed instance. You can learn about the different installation methods [here](https://weaviate.io/developers/weaviate/installation#installation-methods). +## Configuring the Weaviate Client +Weaviate is available via a hosted service ([WCD](https://console.weaviate.cloud/)) or as a self managed instance. You can learn about the different installation methods [here](https://weaviate.io/developers/weaviate/installation#installation-methods). * `weaviate_collection_name` (str): The name of the Weaviate collection * `weaviate_client` (WeaviateClient): An instance of the Weaviate client * `k` (int, optional): The number of top passages to retrieve. The default is set to `3` -An example of the WeaviateRM constructor: +An example of the WeaviateRM constructor: ```python WeaviateRM( @@ -61,7 +61,7 @@ weaviate_client = weaviate.connect_to_embedded() # you can also use local or WCD retriever_model = WeaviateRM( weaviate_collection_name="", - weaviate_client=weaviate_client + weaviate_client=weaviate_client ) results = retriever_model("Explore the significance of quantum computing", k=5) diff --git a/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.md b/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.md index e15490415f..a0289221e5 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.md +++ b/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.md @@ -1,20 +1,20 @@ # Creating Custom RM Client -DSPy provides support for various retrieval modules out of the box like `ColBERTv2`, `AzureCognitiveSearch`, `Lancedb`, `Pinecone`, `Weaviate`, etc. Unlike Language Model (LM) modules, creating a custom RM module is much more simple and flexible. +DSPy provides support for various retrieval modules out of the box like `ColBERTv2`, `AzureCognitiveSearch`, `Lancedb`, `Pinecone`, `Weaviate`, etc. Unlike Language Model (LM) modules, creating a custom RM module is much more simple and flexible. As of now, DSPy offers 2 ways to create a custom RM: the Pythonic way and the DSPythonic way. We'll take a look at both, understand why both are performing the same behavior, and how you can implement each! ## I/O of RM Client -Before understanding the implementation, let's understand the idea and I/O within RM modules. +Before understanding the implementation, let's understand the idea and I/O within RM modules. The **input** to an RM module is either 1) a single query or 2) a list of queries. -The **output** is the `top-k` passages per query retrieved from a retrieval model, vector store, or search client. +The **output** is the `top-k` passages per query retrieved from a retrieval model, vector store, or search client. ![I/O in RM Module](./img/io_rm_module.png) -Conventionally, we simply call the RM module object through the `__call__` method, inputting the query/queries as argument(s) of the call with the corresponding output returned as a list of strings. +Conventionally, we simply call the RM module object through the `__call__` method, inputting the query/queries as argument(s) of the call with the corresponding output returned as a list of strings. We'll see how this I/O is essentially same in both methods of implementation but differs in their delivery. @@ -68,7 +68,7 @@ class PythonicRMClient: self.url = f`{url}:{port}` if port else url def __call__(self, query:str, k:int) -> List[str]: - # Only accept single query input, feel free to modify it to support + # Only accept single query input, feel free to modify it to support params = {"query": query, "k": k} response = requests.get(self.url, params=params) @@ -77,7 +77,7 @@ class PythonicRMClient: return response ``` -That's all!! This is the most basic way to implement a RM model and mirrors DSP-v1-hosted RM models like `ColBERTv2` and `AzureCognitiveSearch`. +That's all!! This is the most basic way to implement a RM model and mirrors DSP-v1-hosted RM models like `ColBERTv2` and `AzureCognitiveSearch`. Now let's take a look at how we streamline this process in DSPy! @@ -110,7 +110,7 @@ def __init__(self, url: str, port:int = None, k:int = 3): self.url = f`{url}:{port}` if port else url ``` -We'll now implement the `forward` method, returning the output as a `dspy.Prediction` object under the `passage` attribute which is standard among all the RM modules. The call will default to the defined `self.k` argument unless overridden in this call. +We'll now implement the `forward` method, returning the output as a `dspy.Prediction` object under the `passage` attribute which is standard among all the RM modules. The call will default to the defined `self.k` argument unless overridden in this call. ```python def forward(self, query:str, k:Optional[int]) -> dspy.Prediction: @@ -145,7 +145,7 @@ class DSPythonicRMClient(dspy.Retrieve): ) ``` -That's all!! This is the way to implement a custom RM model client within DSPy and how more recently-supported RM models like `QdrantRM`, `WeaviateRM`, `LancedbRM`, etc. are implemented in DSPy. +That's all!! This is the way to implement a custom RM model client within DSPy and how more recently-supported RM models like `QdrantRM`, `WeaviateRM`, `LancedbRM`, etc. are implemented in DSPy. Let's take a look at how we use these retrievers. @@ -155,7 +155,7 @@ DSPy offers two ways of using custom RM clients: Direct Method and using `dspy.R ### Direct Method -The most straightforward way to use your custom RM is by directly using its object within the DSPy Pipeline. +The most straightforward way to use your custom RM is by directly using its object within the DSPy Pipeline. Let's take a look at the following pseudocode of a DSPy Pipeline as an example: diff --git a/docs/docs/faqs.md b/docs/docs/faqs.md index 67fa212b5e..0ee05ffc88 100644 --- a/docs/docs/faqs.md +++ b/docs/docs/faqs.md @@ -37,7 +37,7 @@ You can define metrics as simply Python functions that process model generations To reflect compiling metrics, we highlight an experiment for reference, compiling the [`SimplifiedBaleen`](/tutorials/simplified-baleen) using the [`dspy.BootstrapFewShotWithRandomSearch`](/deep-dive/teleprompter/bootstrap-fewshot) optimizer on the `gpt-3.5-turbo-1106` model over 7 candidate programs and 10 threads. We report that compiling this program takes around 6 minutes with 3200 API calls, 2.7 million input tokens and 156,000 output tokens, reporting a total cost of $3 USD (at the current pricing of the OpenAI model). -Compiling DSPy `optimizers` naturally will incur additional LM calls, but we substantiate this overhead with minimalistic executions with the goal of maximizing performance. This invites avenues to enhance performance of smaller models by compiling DSPy programs with larger models to learn enhanced behavior during compile-time and propagate such behavior to the tested smaller model during inference-time. +Compiling DSPy `optimizers` naturally will incur additional LM calls, but we substantiate this overhead with minimalistic executions with the goal of maximizing performance. This invites avenues to enhance performance of smaller models by compiling DSPy programs with larger models to learn enhanced behavior during compile-time and propagate such behavior to the tested smaller model during inference-time. ## Deployment or Reproducibility Concerns @@ -91,7 +91,7 @@ You can parallelize DSPy programs during both compilation and evaluation by spec - **How do freeze a module?** -Modules can be frozen by setting their `._compiled` attribute to be True, indicating the module has gone through optimizer compilation and should not have its parameters adjusted. This is handled internally in optimizers such as `dspy.BootstrapFewShot` where the student program is ensured to be frozen before the teacher propagates the gathered few-shot demonstrations in the bootstrapping process. +Modules can be frozen by setting their `._compiled` attribute to be True, indicating the module has gone through optimizer compilation and should not have its parameters adjusted. This is handled internally in optimizers such as `dspy.BootstrapFewShot` where the student program is ensured to be frozen before the teacher propagates the gathered few-shot demonstrations in the bootstrapping process. - **How do I use DSPy assertions?** @@ -106,7 +106,7 @@ Modules can be frozen by setting their `._compiled` attribute to be True, indica 2. **Activate Assertions**: - Directly call `activate_assertions` on your DSPy program with assertions: `program_with_assertions = ProgramWithAssertions().activate_assertions()` - **Note**: To use Assertions properly, you must **activate** a DSPy program that includes `dspy.Assert` or `dspy.Suggest` statements from either of the methods above. + **Note**: To use Assertions properly, you must **activate** a DSPy program that includes `dspy.Assert` or `dspy.Suggest` statements from either of the methods above. ## Errors diff --git a/docs/docs/index.md b/docs/docs/index.md index 7978a6c4fa..45f5a35b72 100644 --- a/docs/docs/index.md +++ b/docs/docs/index.md @@ -70,11 +70,11 @@ DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, y ```bash > pip install "sglang[all]" - > pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ + > pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ > CUDA_VISIBLE_DEVICES=0 python -m sglang.launch_server --port 7501 --model-path meta-llama/Llama-3.1-8B-Instruct ``` - + If you don't have access from Meta to download `meta-llama/Llama-3.1-8B-Instruct`, use `Qwen/Qwen2.5-7B-Instruct` for example. Next, connect to your local LM from your DSPy code as an `OpenAI`-compatible endpoint. @@ -96,7 +96,7 @@ DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, y - `sagemaker/`, with `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_REGION_NAME` - `azure/`, with `AZURE_API_KEY`, `AZURE_API_BASE`, `AZURE_API_VERSION`, and the optional `AZURE_AD_TOKEN` and `AZURE_API_TYPE` - + If your provider offers an OpenAI-compatible endpoint, just add an `openai/` prefix to your full model name. ```python linenums="1" @@ -109,10 +109,10 @@ DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, y Idiomatic DSPy involves using _modules_, which we define in the rest of this page. However, it's still easy to call the `lm` you configured above directly. This gives you a unified API and lets you benefit from utilities like automatic caching. - ```python linenums="1" + ```python linenums="1" lm("Say this is a test!", temperature=0.7) # => ['This is a test!'] lm(messages=[{"role": "user", "content": "Say this is a test!"}]) # => ['This is a test!'] - ``` + ``` ## 1) **Modules** help you describe AI behavior as _code_, not strings. @@ -131,7 +131,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with math = dspy.ChainOfThought("question -> answer: float") math(question="Two dice are tossed. What is the probability that the sum equals two?") ``` - + **Possible Output:** ```text Prediction( @@ -142,17 +142,17 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with === "RAG" - ```python linenums="1" + ```python linenums="1" def search_wikipedia(query: str) -> list[str]: results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3) return [x['text'] for x in results] - + rag = dspy.ChainOfThought('context, question -> response') question = "What's the name of the castle that David Gregory inherited?" rag(context=search_wikipedia(question), question=question) ``` - + **Possible Output:** ```text Prediction( @@ -168,7 +168,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with class Classify(dspy.Signature): """Classify sentiment of a given sentence.""" - + sentence: str = dspy.InputField() sentiment: Literal['positive', 'negative', 'neutral'] = dspy.OutputField() confidence: float = dspy.OutputField() @@ -176,7 +176,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with classify = dspy.Predict(Classify) classify(sentence="This book was super fun to read, though not the last chapter.") ``` - + **Possible Output:** ```text @@ -188,15 +188,15 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with === "Information Extraction" - ```python linenums="1" + ```python linenums="1" class ExtractInfo(dspy.Signature): """Extract structured information from text.""" - + text: str = dspy.InputField() title: str = dspy.OutputField() headings: list[str] = dspy.OutputField() entities: list[dict[str, str]] = dspy.OutputField(desc="a list of entities and their metadata") - + module = dspy.Predict(ExtractInfo) text = "Apple Inc. announced its latest iPhone 14 today." \ @@ -207,7 +207,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with print(response.headings) print(response.entities) ``` - + **Possible Output:** ```text Apple Inc. Announces iPhone 14 @@ -217,7 +217,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with === "Agents" - ```python linenums="1" + ```python linenums="1" def evaluate_math(expression: str): return dspy.PythonInterpreter({}).execute(expression) @@ -230,19 +230,19 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with pred = react(question="What is 9362158 divided by the year of birth of David Gregory of Kinnairdy castle?") print(pred.answer) ``` - + **Possible Output:** ```text 5761.328 ``` - + === "Multi-Stage Pipelines" - ```python linenums="1" + ```python linenums="1" class Outline(dspy.Signature): """Outline a thorough overview of a topic.""" - + topic: str = dspy.InputField() title: str = dspy.OutputField() sections: list[str] = dspy.OutputField() @@ -250,7 +250,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with class DraftSection(dspy.Signature): """Draft a top-level section of an article.""" - + topic: str = dspy.InputField() section_heading: str = dspy.InputField() section_subheadings: list[str] = dspy.InputField() @@ -273,7 +273,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with draft_article = DraftArticle() article = draft_article(topic="World Cup 2002") ``` - + **Possible Output:** A 1500-word article on the topic, e.g. @@ -295,7 +295,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with ??? "Using DSPy in practice: from quick scripting to building sophisticated systems." Standard prompts conflate interface (“what should the LM do?”) with implementation (“how do we tell it to do that?”). DSPy isolates the former as _signatures_ so we can infer the latter or learn it from data — in the context of a bigger program. - + Even before you start using optimizers, DSPy's modules allow you to script effective LM systems as ergonomic, portable _code_. Across many tasks and LMs, we maintain _signature test suites_ that assess the reliability of the built-in DSPy adapters. Adapters are the components that map signatures to prompts prior to optimization. If you find a task where a simple prompt consistently outperforms idiomatic DSPy for your LM, consider that a bug and [file an issue](https://github.com/stanfordnlp/dspy/issues). We'll use this to improve the built-in adapters. @@ -309,7 +309,7 @@ Given a few tens or hundreds of representative _inputs_ of your task and a _metr !!! info "Getting Started III: Optimizing the LM prompts or weights in DSPy programs" A typical simple optimization run costs on the order of $2 USD and takes around 20 minutes, but be careful when running optimizers with very large LMs or very large datasets. Optimization can cost as little as a few cents or up to tens of dollars, depending on your LM, dataset, and configuration. - + === "Optimizing prompts for a ReAct agent" This is a minimal but fully runnable example of setting up a `dspy.ReAct` agent that answers questions via search from Wikipedia and then optimizing it using `dspy.MIPROv2` in the cheap `light` mode on 500 @@ -384,7 +384,7 @@ Given a few tens or hundreds of representative _inputs_ of your task and a _metr ```python linenums="1" import dspy dspy.configure(lm=dspy.LM('gpt-4o-mini-2024-07-18')) - + # Define the DSPy module for classification. It will use the hint at training time, if available. signature = dspy.Signature("text -> label").with_updated_fields('label', type_=Literal[tuple(CLASSES)]) classify = dspy.ChainOfThoughtWithHint(signature) diff --git a/docs/docs/js/runllm-widget.js b/docs/docs/js/runllm-widget.js index c8057b6bf6..8969feacbf 100644 --- a/docs/docs/js/runllm-widget.js +++ b/docs/docs/js/runllm-widget.js @@ -15,6 +15,6 @@ document.addEventListener("DOMContentLoaded", function () { "runllm-slack-community-url", "" ); - + document.head.appendChild(script); }); diff --git a/docs/docs/learn/8-typed_predictors.md b/docs/docs/learn/8-typed_predictors.md index 7b0bd38fcb..4ccbd8a0d9 100644 --- a/docs/docs/learn/8-typed_predictors.md +++ b/docs/docs/learn/8-typed_predictors.md @@ -53,7 +53,7 @@ predictor = dspy.TypedPredictor("input:Input -> output:Output") ### I/O in Typed Predictors -Now let's test out the Typed Predictor by providing some sample input to the predictor and verifying the output type. We can create an `Input` instance and pass it to the predictor to get a dictionary of the output. +Now let's test out the Typed Predictor by providing some sample input to the predictor and verifying the output type. We can create an `Input` instance and pass it to the predictor to get a dictionary of the output. ```python doc_query_pair = Input( diff --git a/docs/docs/learn/evaluation/data.md b/docs/docs/learn/evaluation/data.md index f3817efb81..e30bfebcfe 100644 --- a/docs/docs/learn/evaluation/data.md +++ b/docs/docs/learn/evaluation/data.md @@ -9,7 +9,7 @@ DSPy is a machine learning framework, so working in it involves training sets, d ## DSPy `Example` objects -The core data type for data in DSPy is `Example`. You will use **Examples** to represent items in your training set and test set. +The core data type for data in DSPy is `Example`. You will use **Examples** to represent items in your training set and test set. DSPy **Examples** are similar to Python `dict`s but have a few useful utilities. Your DSPy modules will return values of the type `Prediction`, which is a special sub-class of `Example`. @@ -56,7 +56,7 @@ print(qa_pair.with_inputs("question")) print(qa_pair.with_inputs("question", "answer")) ``` -Values can be accessed using the `.`(dot) operator. You can access the value of key `name` in defined object `Example(name="John Doe", job="sleep")` through `object.name`. +Values can be accessed using the `.`(dot) operator. You can access the value of key `name` in defined object `Example(name="John Doe", job="sleep")` through `object.name`. To access or exclude certain keys, use `inputs()` and `labels()` methods to return new Example objects containing only input or non-input keys, respectively. @@ -130,7 +130,7 @@ You can access the splits of the dataset by accessing the corresponding key: ```python train_split = blog_alpaca['train'] -# Since this is the only split in the dataset we can split this into +# Since this is the only split in the dataset we can split this into # train and test split ourselves by slicing or sampling 75 rows from the train # split for testing. testset = train_split[:75] diff --git a/docs/docs/learn/evaluation/metrics.md b/docs/docs/learn/evaluation/metrics.md index 9e1f742f14..c8743fa4ae 100644 --- a/docs/docs/learn/evaluation/metrics.md +++ b/docs/docs/learn/evaluation/metrics.md @@ -9,13 +9,13 @@ DSPy is a machine learning framework, so you must think about your **automatic m ## What is a metric and how do I define a metric for my task? -A metric is just a function that will take examples from your data and the output of your system and return a score that quantifies how good the output is. What makes outputs from your system good or bad? +A metric is just a function that will take examples from your data and the output of your system and return a score that quantifies how good the output is. What makes outputs from your system good or bad? For simple tasks, this could be just "accuracy" or "exact match" or "F1 score". This may be the case for simple classification or short-form QA tasks. However, for most applications, your system will output long-form outputs. There, your metric should probably be a smaller DSPy program that checks multiple properties of the output (quite possibly using AI feedback from LMs). -Getting this right on the first try is unlikely, but you should start with something simple and iterate. +Getting this right on the first try is unlikely, but you should start with something simple and iterate. ## Simple metrics @@ -104,7 +104,7 @@ def metric(gold, pred, trace=None): engaging = "Does the assessed text make for a self-contained, engaging tweet?" correct = f"The text should answer `{question}` with `{answer}`. Does the assessed text contain this answer?" - + correct = dspy.Predict(Assess)(assessed_text=tweet, assessment_question=correct) engaging = dspy.Predict(Assess)(assessed_text=tweet, assessment_question=engaging) diff --git a/docs/docs/learn/optimization/optimizers.md b/docs/docs/learn/optimization/optimizers.md index 617214d9f8..67892be41d 100644 --- a/docs/docs/learn/optimization/optimizers.md +++ b/docs/docs/learn/optimization/optimizers.md @@ -73,14 +73,14 @@ This optimizer is used to fine-tune the underlying LLM(s). ## Which optimizer should I use? -Ultimately, finding the ‘right’ optimizer to use & the best configuration for your task will require experimentation. Success in DSPy is still an iterative process - getting the best performance on your task will require you to explore and iterate. +Ultimately, finding the ‘right’ optimizer to use & the best configuration for your task will require experimentation. Success in DSPy is still an iterative process - getting the best performance on your task will require you to explore and iterate. That being said, here's the general guidance on getting started: - If you have **very few examples** (around 10), start with `BootstrapFewShot`. - If you have **more data** (50 examples or more), try `BootstrapFewShotWithRandomSearch`. -- If you prefer to do **instruction optimization only** (i.e. you want to keep your prompt 0-shot), use `MIPROv2` [configured for 0-shot optimization to optimize](/deep-dive/optimizers/miprov2#optimizing-instructions-only-with-miprov2-0-shot). -- If you’re willing to use more inference calls to perform **longer optimization runs** (e.g. 40 trials or more), and have enough data (e.g. 200 examples or more to prevent overfitting) then try `MIPROv2`. +- If you prefer to do **instruction optimization only** (i.e. you want to keep your prompt 0-shot), use `MIPROv2` [configured for 0-shot optimization to optimize](/deep-dive/optimizers/miprov2#optimizing-instructions-only-with-miprov2-0-shot). +- If you’re willing to use more inference calls to perform **longer optimization runs** (e.g. 40 trials or more), and have enough data (e.g. 200 examples or more to prevent overfitting) then try `MIPROv2`. - If you have been able to use one of these with a large LM (e.g., 7B parameters or above) and need a very **efficient program**, finetune a small LM for your task with `BootstrapFinetune`. ## How do I use an optimizer? @@ -104,7 +104,7 @@ optimized_program = teleprompter.compile(YOUR_PROGRAM_HERE, trainset=YOUR_TRAINS !!! info "Getting Started III: Optimizing the LM prompts or weights in DSPy programs" A typical simple optimization run costs on the order of $2 USD and takes around ten minutes, but be careful when running optimizers with very large LMs or very large datasets. Optimizer runs can cost as little as a few cents or up to tens of dollars, depending on your LM, dataset, and configuration. - + === "Optimizing prompts for a ReAct agent" This is a minimal but fully runnable example of setting up a `dspy.ReAct` agent that answers questions via search from Wikipedia and then optimizing it using `dspy.MIPROv2` in the cheap `light` mode on 500 @@ -180,7 +180,7 @@ optimized_program = teleprompter.compile(YOUR_PROGRAM_HERE, trainset=YOUR_TRAINS ```python linenums="1" import dspy dspy.configure(lm=dspy.LM('gpt-4o-mini-2024-07-18')) - + # Define the DSPy module for classification. It will use the hint at training time, if available. signature = dspy.Signature("text -> label").with_updated_fields('label', type_=Literal[tuple(CLASSES)]) classify = dspy.ChainOfThoughtWithHint(signature) diff --git a/docs/docs/learn/optimization/solving_your_task.md b/docs/docs/learn/optimization/solving_your_task.md index aad82b8e9f..b7f5c0572d 100644 --- a/docs/docs/learn/optimization/solving_your_task.md +++ b/docs/docs/learn/optimization/solving_your_task.md @@ -4,7 +4,7 @@ sidebar_position: 1 # Using DSPy in 8 Steps -Using DSPy well for solving a new task is just doing good machine learning with LMs. It's an iterative process: you make some initial choices, which will be sub-optimal, and then you refine them incrementally. +Using DSPy well for solving a new task is just doing good machine learning with LMs. It's an iterative process: you make some initial choices, which will be sub-optimal, and then you refine them incrementally. As we discuss below, you will define your task and the metrics you want to maximize, and prepare a few example inputs — typically without labels (or only with labels for the final outputs, if your metric requires them). Then, you build your pipeline by selecting built-in layers [(`modules`)](/building-blocks/3-modules) to use, giving each layer a [`signature` (input/output spec)](/building-blocks/2-signatures), and then calling your modules freely in your Python code. Lastly, you use a DSPy [`optimizer`](/building-blocks/6-optimizers) to compile your code into high-quality instructions, automatic few-shot examples, or updated LM weights for your LM. diff --git a/docs/docs/learn/programming/7-assertions.md b/docs/docs/learn/programming/7-assertions.md index df1b62d770..34974ce3eb 100644 --- a/docs/docs/learn/programming/7-assertions.md +++ b/docs/docs/learn/programming/7-assertions.md @@ -1,20 +1,20 @@ -# DSPy Assertions +# DSPy Assertions !!! warning "This page is outdated and may not be fully accurate in DSPy 2.5" ## Introduction -Language models (LMs) have transformed how we interact with machine learning, offering vast capabilities in natural language understanding and generation. However, ensuring these models adhere to domain-specific constraints remains a challenge. Despite the growth of techniques like fine-tuning or “prompt engineering”, these approaches are extremely tedious and rely on heavy, manual hand-waving to guide the LMs in adhering to specific constraints. Even DSPy's modularity of programming prompting pipelines lacks mechanisms to effectively and automatically enforce these constraints. +Language models (LMs) have transformed how we interact with machine learning, offering vast capabilities in natural language understanding and generation. However, ensuring these models adhere to domain-specific constraints remains a challenge. Despite the growth of techniques like fine-tuning or “prompt engineering”, these approaches are extremely tedious and rely on heavy, manual hand-waving to guide the LMs in adhering to specific constraints. Even DSPy's modularity of programming prompting pipelines lacks mechanisms to effectively and automatically enforce these constraints. To address this, we introduce DSPy Assertions, a feature within the DSPy framework designed to automate the enforcement of computational constraints on LMs. DSPy Assertions empower developers to guide LMs towards desired outcomes with minimal manual intervention, enhancing the reliability, predictability, and correctness of LM outputs. -### dspy.Assert and dspy.Suggest API +### dspy.Assert and dspy.Suggest API We introduce two primary constructs within DSPy Assertions: - **`dspy.Assert`**: - - **Parameters**: + - **Parameters**: - `constraint (bool)`: Outcome of Python-defined boolean validation check. - `msg (Optional[str])`: User-defined error message providing feedback or correction guidance. - `backtrack (Optional[module])`: Specifies target module for retry attempts upon constraint failure. The default backtracking module is the last module before the assertion. @@ -24,7 +24,7 @@ We introduce two primary constructs within DSPy Assertions: - **Parameters**: Similar to `dspy.Assert`. - **Behavior**: Encourages self-refinement through retries without enforcing hard stops. Logs failures after maximum backtracking attempts and continues execution. -- **dspy.Assert vs. Python Assertions**: Unlike conventional Python `assert` statements that terminate the program upon failure, `dspy.Assert` conducts a sophisticated retry mechanism, allowing the pipeline to adjust. +- **dspy.Assert vs. Python Assertions**: Unlike conventional Python `assert` statements that terminate the program upon failure, `dspy.Assert` conducts a sophisticated retry mechanism, allowing the pipeline to adjust. Specifically, when a constraint is not met: @@ -35,7 +35,7 @@ Specifically, when a constraint is not met: If the error continues past the `max_backtracking_attempts`, then `dspy.Assert` will halt the pipeline execution, alerting you with an `dspy.AssertionError`. This ensures your program doesn't continue executing with “bad” LM behavior and immediately highlights sample failure outputs for user assessment. -- **dspy.Suggest vs. dspy.Assert**: `dspy.Suggest` on the other hand offers a softer approach. It maintains the same retry backtracking as `dspy.Assert` but instead serves as a gentle nudger. If the model outputs cannot pass the model constraints after the `max_backtracking_attempts`, `dspy.Suggest` will log the persistent failure and continue execution of the program on the rest of the data. This ensures the LM pipeline works in a "best-effort" manner without halting execution. +- **dspy.Suggest vs. dspy.Assert**: `dspy.Suggest` on the other hand offers a softer approach. It maintains the same retry backtracking as `dspy.Assert` but instead serves as a gentle nudger. If the model outputs cannot pass the model constraints after the `max_backtracking_attempts`, `dspy.Suggest` will log the persistent failure and continue execution of the program on the rest of the data. This ensures the LM pipeline works in a "best-effort" manner without halting execution. - **`dspy.Suggest`** statements are best utilized as "helpers" during the evaluation phase, offering guidance and potential corrections without halting the pipeline. - **`dspy.Assert`** statements are recommended during the development stage as "checkers" to ensure the LM behaves as expected, providing a robust mechanism for identifying and addressing errors early in the development cycle. @@ -43,7 +43,7 @@ If the error continues past the `max_backtracking_attempts`, then `dspy.Assert` ## Use Case: Including Assertions in DSPy Programs -We start with using an example of a multi-hop QA SimplifiedBaleen pipeline as defined in the intro walkthrough. +We start with using an example of a multi-hop QA SimplifiedBaleen pipeline as defined in the intro walkthrough. ```python class SimplifiedBaleen(dspy.Module): @@ -64,7 +64,7 @@ class SimplifiedBaleen(dspy.Module): prev_queries.append(query) passages = self.retrieve(query).passages context = deduplicate(context + passages) - + pred = self.generate_answer(context=context, question=question) pred = dspy.Prediction(context=context, answer=pred.answer) return pred @@ -74,12 +74,12 @@ baleen = SimplifiedBaleen() baleen(question = "Which award did Gary Zukav's first book receive?") ``` -To include DSPy Assertions, we simply define our validation functions and declare our assertions following the respective model generation. +To include DSPy Assertions, we simply define our validation functions and declare our assertions following the respective model generation. For this use case, suppose we want to impose the following constraints: 1. Length - each query should be less than 100 characters - 2. Uniqueness - each generated query should differ from previously-generated queries. - + 2. Uniqueness - each generated query should differ from previously-generated queries. + We can define these validation checks as boolean functions: ```python @@ -149,7 +149,7 @@ class SimplifiedBaleenAssertions(dspy.Module): prev_queries.append(query) passages = self.retrieve(query).passages context = deduplicate(context + passages) - + if all_queries_distinct(prev_queries): self.passed_suggestions += 1 @@ -158,7 +158,7 @@ class SimplifiedBaleenAssertions(dspy.Module): return pred ``` -Now calling programs with DSPy Assertions requires one last step, and that is transforming the program to wrap it with internal assertions backtracking and Retry logic. +Now calling programs with DSPy Assertions requires one last step, and that is transforming the program to wrap it with internal assertions backtracking and Retry logic. ```python from dspy.primitives.assertions import assert_transform_module, backtrack_handler @@ -167,7 +167,7 @@ baleen_with_assertions = assert_transform_module(SimplifiedBaleenAssertions(), b # backtrack_handler is parameterized over a few settings for the backtracking mechanism # To change the number of max retry attempts, you can do -baleen_with_assertions_retry_once = assert_transform_module(SimplifiedBaleenAssertions(), +baleen_with_assertions_retry_once = assert_transform_module(SimplifiedBaleenAssertions(), functools.partial(backtrack_handler, max_backtracks=1)) ``` @@ -249,7 +249,7 @@ DSPy Assertions work with optimizations that DSPy offers, particularly with `Boo - Compilation with Assertions This includes assertion-driven example bootstrapping and counterexample bootstrapping during compilation. The teacher model for bootstrapping few-shot demonstrations can make use of DSPy Assertions to offer robust bootstrapped examples for the student model to learn from during inference. In this setting, the student model does not perform assertion aware optimizations (backtracking and retry) during inference. - Compilation + Inference with Assertions - -This includes assertion-driven optimizations in both compilation and inference. Now the teacher model offers assertion-driven examples but the student can further optimize with assertions of its own during inference time. + -This includes assertion-driven optimizations in both compilation and inference. Now the teacher model offers assertion-driven examples but the student can further optimize with assertions of its own during inference time. ```python teleprompter = BootstrapFewShotWithRandomSearch( metric=validate_context_and_answer_and_hops, diff --git a/docs/docs/learn/programming/language_models.md b/docs/docs/learn/programming/language_models.md index 72a7e2b352..2122fe095e 100644 --- a/docs/docs/learn/programming/language_models.md +++ b/docs/docs/learn/programming/language_models.md @@ -46,7 +46,7 @@ dspy.configure(lm=lm) ```bash > pip install "sglang[all]" - > pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ + > pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ > CUDA_VISIBLE_DEVICES=0 python -m sglang.launch_server --port 7501 --model-path meta-llama/Meta-Llama-3-8B-Instruct ``` @@ -86,7 +86,7 @@ dspy.configure(lm=lm) - `sagemaker/`, with `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_REGION_NAME` - `azure/`, with `AZURE_API_KEY`, `AZURE_API_BASE`, `AZURE_API_VERSION`, and the optional `AZURE_AD_TOKEN` and `AZURE_API_TYPE` - + If your provider offers an OpenAI-compatible endpoint, just add an `openai/` prefix to your full model name. ```python linenums="1" @@ -99,16 +99,16 @@ dspy.configure(lm=lm) It's easy to call the `lm` you configured above directly. This gives you a unified API and lets you benefit from utilities like automatic caching. -```python linenums="1" +```python linenums="1" lm("Say this is a test!", temperature=0.7) # => ['This is a test!'] lm(messages=[{"role": "user", "content": "Say this is a test!"}]) # => ['This is a test!'] -``` +``` ## Using the LM with DSPy modules. Idiomatic DSPy involves using _modules_, which we discuss in the next guide. -```python linenums="1" +```python linenums="1" # Define a module (ChainOfThought) and assign it a signature (return an answer, given a question). qa = dspy.ChainOfThought('question -> answer') @@ -129,7 +129,7 @@ You can change the default LM globally with `dspy.configure` or change it inside Using `dspy.configure` and `dspy.context` is thread-safe! -```python linenums="1" +```python linenums="1" dspy.configure(lm=dspy.LM('openai/gpt-4o-mini')) response = qa(question="How many floors are in the castle David Gregory inherited?") print('GPT-4o-mini:', response.answer) @@ -148,7 +148,7 @@ GPT-3.5-turbo: The castle David Gregory inherited has 7 floors. For any LM, you can configure any of the following attributes at initialization or in each subsequent call. -```python linenums="1" +```python linenums="1" gpt_4o_mini = dspy.LM('openai/gpt-4o-mini', temperature=0.9, max_tokens=3000, stop=None, cache=False) ``` @@ -159,7 +159,7 @@ By default LMs in DSPy are cached. If you repeat the same call, you will get the Every LM object maintains the history of its interactions, including inputs, outputs, token usage (and $$$ cost), and metadata. -```python linenums="1" +```python linenums="1" len(lm.history) # e.g., 3 calls to the LM lm.history[-1].keys() # access the last call to the LM, with all metadata diff --git a/docs/docs/learn/programming/modules.md b/docs/docs/learn/programming/modules.md index c4be6c2efa..8b51063708 100644 --- a/docs/docs/learn/programming/modules.md +++ b/docs/docs/learn/programming/modules.md @@ -25,7 +25,7 @@ sentence = "it's a charming and often affecting journey." # example from the SS # 1) Declare with a signature. classify = dspy.Predict('sentence -> sentiment: bool') -# 2) Call with input argument(s). +# 2) Call with input argument(s). response = classify(sentence=sentence) # 3) Access the output. @@ -119,7 +119,7 @@ We also have some function-style modules: math = dspy.ChainOfThought("question -> answer: float") math(question="Two dice are tossed. What is the probability that the sum equals two?") ``` - + **Possible Output:** ```text Prediction( @@ -130,18 +130,18 @@ We also have some function-style modules: === "Retrieval-Augmented Generation" - ```python linenums="1" + ```python linenums="1" def search(query: str) -> list[str]: """Retrieves abstracts from Wikipedia.""" results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3) return [x['text'] for x in results] - + rag = dspy.ChainOfThought('context, question -> response') question = "What's the name of the castle that David Gregory inherited?" rag(context=search(question), question=question) ``` - + **Possible Output:** ```text Prediction( @@ -157,7 +157,7 @@ We also have some function-style modules: class Classify(dspy.Signature): """Classify sentiment of a given sentence.""" - + sentence: str = dspy.InputField() sentiment: Literal['positive', 'negative', 'neutral'] = dspy.OutputField() confidence: float = dspy.OutputField() @@ -165,7 +165,7 @@ We also have some function-style modules: classify = dspy.Predict(Classify) classify(sentence="This book was super fun to read, though not the last chapter.") ``` - + **Possible Output:** ```text @@ -177,7 +177,7 @@ We also have some function-style modules: === "Information Extraction" - ```python linenums="1" + ```python linenums="1" text = "Apple Inc. announced its latest iPhone 14 today. The CEO, Tim Cook, highlighted its new features in a press release." module = dspy.Predict("text -> title, headings: list[str], entities_and_metadata: list[dict[str, str]]") @@ -187,7 +187,7 @@ We also have some function-style modules: print(response.headings) print(response.entities_and_metadata) ``` - + **Possible Output:** ```text Apple Unveils iPhone 14 @@ -197,7 +197,7 @@ We also have some function-style modules: === "Agents" - ```python linenums="1" + ```python linenums="1" def evaluate_math(expression: str) -> float: return dspy.PythonInterpreter({}).execute(expression) @@ -210,7 +210,7 @@ We also have some function-style modules: pred = react(question="What is 9362158 divided by the year of birth of David Gregory of Kinnairdy castle?") print(pred.answer) ``` - + **Possible Output:** ```text @@ -224,7 +224,7 @@ DSPy is just Python code that uses modules in any control flow you like, with a See tutorials like [multi-hop search](https://dspy.ai/tutorials/multihop_search/), whose module is reproduced below as an example. -```python linenums="1" +```python linenums="1" class Hop(dspy.Module): def __init__(self, num_docs=10, num_hops=4): self.num_docs, self.num_hops = num_docs, num_hops @@ -241,6 +241,6 @@ class Hop(dspy.Module): prediction = self.append_notes(claim=claim, notes=notes, context=context) notes.extend(prediction.new_notes) titles.extend(prediction.titles) - + return dspy.Prediction(notes=notes, titles=list(set(titles))) ``` diff --git a/docs/docs/learn/programming/overview.md b/docs/docs/learn/programming/overview.md index 959e0d6786..7f7b152b6f 100644 --- a/docs/docs/learn/programming/overview.md +++ b/docs/docs/learn/programming/overview.md @@ -14,5 +14,5 @@ As you do this, **craft and try a handful of examples** of the inputs to your pr ??? "Beyond encouraging good design patterns, how does DSPy help here?" Conventional prompts couple your fundamental system architecture with incidental choices not portable to new LMs, objectives, or pipelines. A conventional prompt asks the LM to take some inputs and produce some outputs of certain types (a _signature_), formats the inputs in certain ways and requests outputs in a form it can parse accurately (an _adapter_), asks the LM to apply certain strategies like "thinking step by step" or using tools (a _module_'s logic), and relies on substantial trial-and-error to discover the right way to ask each LM to do this (a form of manual _optimization_). - + DSPy separates these concerns and automates the lower-level ones until you need to consider them. This allow you to write much shorter code, with much higher portability. For example, if you write a program using DSPy modules, you can swap the LM or its adapter without changing the rest of your logic. Or you can exchange one _module_, like `dspy.ChainOfThought`, with another, like `dspy.ProgramOfThought`, without modifying your signatures. When you're ready to use optimizers, the same program can have its prompts optimized or its LM weights fine-tuned. diff --git a/docs/docs/learn/programming/signatures.md b/docs/docs/learn/programming/signatures.md index 2276f5eab9..43a5334a92 100644 --- a/docs/docs/learn/programming/signatures.md +++ b/docs/docs/learn/programming/signatures.md @@ -91,7 +91,7 @@ from typing import Literal class Emotion(dspy.Signature): """Classify emotion.""" - + sentence: str = dspy.InputField() sentiment: Literal['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'] = dspy.OutputField() diff --git a/docs/docs/tutorials/observability/index.md b/docs/docs/tutorials/observability/index.md index 5b82796e9e..71d0d408de 100644 --- a/docs/docs/tutorials/observability/index.md +++ b/docs/docs/tutorials/observability/index.md @@ -61,7 +61,7 @@ Response: The search results continue to be unhelpful and do not provide the current team for Shohei Ohtani in Major League Baseball. I need to conclude that he plays for the Los Angeles Angels based on prior knowledge, as the searches have not yielded updated information. [[ ## Action_5 ## ]] -Finish[Los Angeles Angels] +Finish[Los Angeles Angels] [[ ## completed ## ]] ``` diff --git a/docs/docs/tutorials/old/other_tutorial.md b/docs/docs/tutorials/old/other_tutorial.md index c1ef95551b..1243724d35 100644 --- a/docs/docs/tutorials/old/other_tutorial.md +++ b/docs/docs/tutorials/old/other_tutorial.md @@ -7,7 +7,7 @@ sidebar_position: 99999 ## Tutorials | **Level** | **Tutorial** | **Run in Colab** | **Description** | -| --- | ------------- | ------------- | ------------- | +| --- | ------------- | ------------- | ------------- | | Beginner | [**Getting Started**](https://github.com/stanfordnlp/dspy/blob/main/intro.ipynb) | [](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb) | Introduces the basic building blocks in DSPy. Tackles the task of complex question answering with HotPotQA. | | Beginner | [**Minimal Working Example**](/docs/docs/quick-start/getting-started-01.md) | N/A | Builds a very simple chain-of-thought program in DSPy for question answering. Very short. | | Beginner | [**Compiling for Tricky Tasks**](https://github.com/stanfordnlp/dspy/blob/main/examples/nli/scone/scone.ipynb) | N/A | Teaches LMs to reason about logical statements and negation. Uses GPT-4 to bootstrap few-shot CoT demonstrations for GPT-3.5. Establishes a state-of-the-art result on [ScoNe](https://arxiv.org/abs/2305.19426). Contributed by [Chris Potts](https://twitter.com/ChrisGPotts/status/1740033519446057077). | diff --git a/docs/docs/tutorials/old/rag.md b/docs/docs/tutorials/old/rag.md index e9cf94265e..826723d765 100644 --- a/docs/docs/tutorials/old/rag.md +++ b/docs/docs/tutorials/old/rag.md @@ -12,7 +12,7 @@ RAG ensures LLMs can dynamically utilize real-time knowledge even if not origina We'll start by setting up the language model (LM) and retrieval model (RM), which **DSPy** supports through multiple [LM](/building-blocks/1-language_models.md) and [RM](/deep-dive/retrieval_models_clients/Azure.md) APIs and [local models hosting](/deep-dive/language_model_clients/local_models/HFClientTGI). -In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the `ColBERTv2` retriever (a free server hosting a Wikipedia 2017 "abstracts" search index containing the first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)). We configure the LM and RM within DSPy, allowing DSPy to internally call the respective module when needed for generation or retrieval. +In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the `ColBERTv2` retriever (a free server hosting a Wikipedia 2017 "abstracts" search index containing the first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)). We configure the LM and RM within DSPy, allowing DSPy to internally call the respective module when needed for generation or retrieval. ```python import dspy @@ -60,7 +60,7 @@ class GenerateAnswer(dspy.Signature): answer = dspy.OutputField(desc="often between 1 and 5 words") ``` -We include small descriptions for the `context` and `answer` fields to define more robust guidelines on what the model will receive and should generate. +We include small descriptions for the `context` and `answer` fields to define more robust guidelines on what the model will receive and should generate. ## Building the Pipeline @@ -77,7 +77,7 @@ class RAG(dspy.Module): self.retrieve = dspy.Retrieve(k=num_passages) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) - + def forward(self, question): context = self.retrieve(question).passages prediction = self.generate_answer(context=context, question=question) diff --git a/docs/docs/tutorials/old/simplified-baleen.md b/docs/docs/tutorials/old/simplified-baleen.md index 5703bc28e1..3bc35ae006 100644 --- a/docs/docs/tutorials/old/simplified-baleen.md +++ b/docs/docs/tutorials/old/simplified-baleen.md @@ -12,7 +12,7 @@ The standard approach for this challenge in retrieval-augmented NLP literature i We'll start by setting up the language model (LM) and retrieval model (RM), which **DSPy** supports through multiple [LM](/category/language-model-clients) and [RM](/category/retrieval-model-clients) APIs and [local models hosting](/category/local-language-model-clients). -In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the `ColBERTv2` retriever (a free server hosting a Wikipedia 2017 "abstracts" search index containing the first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)). We configure the LM and RM within DSPy, allowing DSPy to internally call the respective module when needed for generation or retrieval. +In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the `ColBERTv2` retriever (a free server hosting a Wikipedia 2017 "abstracts" search index containing the first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)). We configure the LM and RM within DSPy, allowing DSPy to internally call the respective module when needed for generation or retrieval. ```python import dspy @@ -59,7 +59,7 @@ class GenerateAnswer(dspy.Signature): answer = dspy.OutputField(desc="often between 1 and 5 words") ``` -Unlike usual QA pipelines, we have an intermediate question-generation step in Baleen for which we'll need to define a new Signature for the "hop" behavior: inputting some context and a question to generate a search query to find missing information. +Unlike usual QA pipelines, we have an intermediate question-generation step in Baleen for which we'll need to define a new Signature for the "hop" behavior: inputting some context and a question to generate a search query to find missing information. ```python class GenerateSearchQuery(dspy.Signature): @@ -90,10 +90,10 @@ class SimplifiedBaleen(dspy.Module): self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) self.max_hops = max_hops - + def forward(self, question): context = [] - + for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query passages = self.retrieve(query).passages @@ -120,7 +120,7 @@ The `forward` method uses these sub-modules in simple control flow. ## Executing the Pipeline -Let's execute this program in its zero-shot (uncompiled) setting. +Let's execute this program in its zero-shot (uncompiled) setting. This doesn't necessarily imply the performance will be bad but rather that we're bottlenecked directly by the reliability of the underlying LM to understand our sub-tasks from minimal instructions. Often, this is perfectly fine when using the most expensive/powerful models (e.g., GPT-4) on the easiest and most standard tasks (e.g., answering simple questions about popular entities). @@ -152,11 +152,11 @@ turbo.inspect_history(n=3) ## Optimizing the Pipeline -However, a zero-shot approach quickly falls short for more specialized tasks, novel domains/settings, and more efficient (or open) models. +However, a zero-shot approach quickly falls short for more specialized tasks, novel domains/settings, and more efficient (or open) models. -To address this, **DSPy** offers compilation. Let's compile our multi-hop (`SimplifiedBaleen`) program. +To address this, **DSPy** offers compilation. Let's compile our multi-hop (`SimplifiedBaleen`) program. -Let's first define our validation logic for compilation: +Let's first define our validation logic for compilation: - The predicted answer matches the gold answer. - The retrieved context contains the gold answer. @@ -187,7 +187,7 @@ compiled_baleen = teleprompter.compile(SimplifiedBaleen(), teacher=SimplifiedBal ## Evaluating the Pipeline -Let's now define our evaluation function and compare the performance of the uncompiled and compiled Baleen pipelines. While this devset does not serve as a completely reliable benchmark, it is instructive to use for this tutorial. +Let's now define our evaluation function and compare the performance of the uncompiled and compiled Baleen pipelines. While this devset does not serve as a completely reliable benchmark, it is instructive to use for this tutorial. ```python from dspy.evaluate.evaluate import Evaluate @@ -220,6 +220,6 @@ Excellent! There might be something to this compiled, multi-hop program then. Earlier, we said simple programs are not very effective at finding all evidence required for answering each question. Is this resolved by the adding some greater prompting techniques in the forward function of `SimplifiedBaleen`? Does compiling programs improve performance? -While in our tutorial we demonstrate our findings, the answer for these questions will not always be obvious. However, DSPy makes it extremely easy to try out the many diverse approaches with minimal effort. +While in our tutorial we demonstrate our findings, the answer for these questions will not always be obvious. However, DSPy makes it extremely easy to try out the many diverse approaches with minimal effort. Now that you've seen a example of how to build a simple yet powerful pipeline, it's time for you to build one yourself! diff --git a/docs/overrides/home.html b/docs/overrides/home.html index 9227bb385e..9cf943ca92 100644 --- a/docs/overrides/home.html +++ b/docs/overrides/home.html @@ -12,7 +12,7 @@ margin: 0; padding: 0; } - + .hero { text-align: center; padding: 4rem 2rem; @@ -20,19 +20,19 @@ background-color: #f5f6f77a; color: white; } - + .hero-logo { max-width: 15rem; height: auto; margin: 0 auto; } - + .hero-subtitle { font-size: 1.2rem; margin: 1.5rem 0; color: #e2e8f0; } - + .cta-button { display: inline-block; padding: 0.75rem 1.5rem; @@ -44,17 +44,17 @@ border: 2px solid black; transition: all 0.3s ease; } - + .cta-button:hover { background-color: white; color: black; border: 2px solid white; } - + .features-section { padding: 4rem 2rem; } - + .features-title { text-align: center; font-size: 2rem; @@ -62,7 +62,7 @@ margin-bottom: 3rem; color: #1a202c; } - + .features-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); @@ -70,44 +70,44 @@ max-width: 1200px; margin: 0 auto; } - + .feature-card { text-align: center; padding: 1.5rem; } - + .feature-image { width: 10rem; height: auto; margin: 0 auto 1.5rem; } - + .feature-title { font-size: 1.25rem; font-weight: 700; margin-bottom: 1rem; color: #2d3748; } - + .feature-description { color: #4a5568; line-height: 1.5; } - + @media (max-width: 768px) { .hero { padding: 3rem 1rem; } - + .hero-logo { max-width: 10rem; } - + .features-grid { grid-template-columns: 1fr; gap: 2rem; } - + .feature-card { padding: 1rem; } @@ -129,13 +129,13 @@

The Way of DSPy

Systematic Optimization

Choose from a range of optimizers to enhance your program. Whether it's generating refined instructions, or fine-tuning weights, DSPy's optimizers are engineered to maximize efficiency and effectiveness.

- +
Modular Approach

Modular Approach

With DSPy, you can build your system using predefined modules, replacing intricate prompting techniques with straightforward, effective solutions.

- +
Cross-LM Compatibility

Cross-LM Compatibility

diff --git a/dspy/.internal_dspyai/internals/build-and-release.md b/dspy/.internal_dspyai/internals/build-and-release.md index 52620aca20..06b5d3e084 100644 --- a/dspy/.internal_dspyai/internals/build-and-release.md +++ b/dspy/.internal_dspyai/internals/build-and-release.md @@ -4,25 +4,25 @@ The [build_and_release](https://github.com/stanfordnlp/dspy/blob/main/.github/wo ## Overview -At a high level, the workflow works as follows: +At a high level, the workflow works as follows: 1. Maintainer of the repo pushes a tag following [semver](https://semver.org/) versioning for the new release. 2. This triggers the github action which extracts the tag (the version) 3. Builds and publishes a release on [test-pypi](https://test.pypi.org/project/dspy-ai-test/) 4. Uses the test-pypi release to run build_utils/tests/intro.py with the new release as an integration test. Note intro.py is a copy of the intro notebook. -5. Assuming the test runs successfully, it pushes a release to [pypi](https://pypi.org/project/dspy-ai/). If not, the user can delete the tag, make the fixes and then push the tag again. Versioning for multiple releases to test-pypi with the same tag version is taken care of by the workflow by appending a pre-release identifier, so the user only needs to consider the version for pypi. +5. Assuming the test runs successfully, it pushes a release to [pypi](https://pypi.org/project/dspy-ai/). If not, the user can delete the tag, make the fixes and then push the tag again. Versioning for multiple releases to test-pypi with the same tag version is taken care of by the workflow by appending a pre-release identifier, so the user only needs to consider the version for pypi. 6. (Currently manual) the user creates a release and includes release notes, as described in docs/docs/release-checklist.md ## Implementation Details -The workflow executes a series of jobs in sequence: +The workflow executes a series of jobs in sequence: - extract-tag - build-and-publish-test-pypi - test-intro-script - build-and-publish-pypi #### extract-tag -Extracts the tag pushed to the commit. This tag is expected to be the version of the new deployment. +Extracts the tag pushed to the commit. This tag is expected to be the version of the new deployment. #### build-and-publish-test-pypi Builds and publishes the package to test-pypi. @@ -36,13 +36,13 @@ Builds and publishes the package to test-pypi. 1. Updates the package name placeholder in [setup.py](https://github.com/stanfordnlp/dspy/blob/main/setup.py) to `dspy-ai-test`* 1. Updates the package name placeholder in [pyproject.toml](https://github.com/stanfordnlp/dspy/blob/main/pyproject.toml) to `dspy-ai-test`* 1. Builds the binary wheel -1. Publishes the package to test-pypi. +1. Publishes the package to test-pypi. #### test-intro-script Runs the pytest containing the intro script as an integration test using the package published to test-pypi. This is a validation step before publishing to pypi. 1. Uses a loop to install the version just published to test-pypi as sometimes there is a race condition between the package becoming available for installation and this job executing. -2. Runs the test to ensure the package is working as expected. +2. Runs the test to ensure the package is working as expected. 3. If this fails, the workflow fails and the maintainer needs to make a fix and delete and then recreate the tag. #### build-and-publish-pypi diff --git a/dspy/.internal_dspyai/internals/release-checklist.md b/dspy/.internal_dspyai/internals/release-checklist.md index 8e5c42bcb7..15c81bfb3e 100644 --- a/dspy/.internal_dspyai/internals/release-checklist.md +++ b/dspy/.internal_dspyai/internals/release-checklist.md @@ -13,8 +13,8 @@ git tag -d X.Y.Z # Delete locally ``` * Fix the errors and then repeat the steps above to recreate the tag locally and push to GitHub to restart the process. - * Note that the github action takes care of incrementing the release version on test-pypi automatically by adding a pre-release identifier in the scenario where the tests fail and you need to delete and push the same tag again. -* [ ] [Create a release](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository) + * Note that the github action takes care of incrementing the release version on test-pypi automatically by adding a pre-release identifier in the scenario where the tests fail and you need to delete and push the same tag again. +* [ ] [Create a release](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository) * [ ] Add release notes. You can make use of [automatically generated release notes](https://docs.github.com/en/repositories/releasing-projects-on-github/automatically-generated-release-notes) * If creating a new release for major or minor version: * [ ] Create a new release branch with the last commit and name it 'release/X.Y` @@ -22,4 +22,4 @@ ### Prerequisites -The automation requires a [trusted publisher](https://docs.pypi.org/trusted-publishers/) to be set up on both the pypi and test-pypi packages. If the package is migrated to a new project, please follow the [steps](https://docs.pypi.org/trusted-publishers/adding-a-publisher/) to create a trusted publisher. If you have no releases on the new project, you may have to create a [pending trusted publisher](https://docs.pypi.org/trusted-publishers/creating-a-project-through-oidc/) to allow the first automated deployment. +The automation requires a [trusted publisher](https://docs.pypi.org/trusted-publishers/) to be set up on both the pypi and test-pypi packages. If the package is migrated to a new project, please follow the [steps](https://docs.pypi.org/trusted-publishers/adding-a-publisher/) to create a trusted publisher. If you have no releases on the new project, you may have to create a [pending trusted publisher](https://docs.pypi.org/trusted-publishers/creating-a-project-through-oidc/) to allow the first automated deployment. diff --git a/dspy/.internal_dspyai/setup.py b/dspy/.internal_dspyai/setup.py index e1800cab73..e1e35b55f0 100644 --- a/dspy/.internal_dspyai/setup.py +++ b/dspy/.internal_dspyai/setup.py @@ -8,16 +8,16 @@ #replace_package_name_marker name="dspy-ai", #replace_package_version_marker - version="2.6.5", - description="DSPy", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/stanfordnlp/dsp", - author="Omar Khattab", - author_email="okhattab@stanford.edu", - license="MIT License", - packages=find_packages(include=["dsp.*", "dspy.*", "dsp", "dspy"]), + version="2.6.5", + description="DSPy", + long_description=long_description, + long_description_content_type="text/markdown", + url="https://github.com/stanfordnlp/dsp", + author="Omar Khattab", + author_email="okhattab@stanford.edu", + license="MIT License", + packages=find_packages(include=["dsp.*", "dspy.*", "dsp", "dspy"]), python_requires=">=3.9", #replace_dspy_version_marker install_requires=["dspy>=2.6.5"] -) +) diff --git a/dspy/adapters/base.py b/dspy/adapters/base.py index d6b5974919..c58f9278bf 100644 --- a/dspy/adapters/base.py +++ b/dspy/adapters/base.py @@ -34,10 +34,10 @@ def __call__(self, lm, lm_kwargs, signature, demos, inputs): assert set(value.keys()) == set(signature.output_fields.keys()), \ f"Expected {signature.output_fields.keys()} but got {value.keys()}" - + if output_logprobs is not None: value["logprobs"] = output_logprobs - + values.append(value) return values diff --git a/dspy/adapters/image_utils.py b/dspy/adapters/image_utils.py index 904b85ffee..91a2500eae 100644 --- a/dspy/adapters/image_utils.py +++ b/dspy/adapters/image_utils.py @@ -18,14 +18,14 @@ class Image(pydantic.BaseModel): url: str - + model_config = { 'frozen': True, 'str_strip_whitespace': True, 'validate_assignment': True, 'extra': 'forbid', } - + @pydantic.model_validator(mode="before") @classmethod def validate_input(cls, values): @@ -187,42 +187,42 @@ def try_expand_image_tags(messages: List[Dict[str, Any]]) -> List[Dict[str, Any] return messages def expand_image_tags(text: str) -> Union[str, List[Dict[str, Any]]]: - """Expand image tags in the text. If there are any image tags, + """Expand image tags in the text. If there are any image tags, turn it from a content string into a content list of texts and image urls. - + Args: text: The text content that may contain image tags - + Returns: Either the original string if no image tags, or a list of content dicts with text and image_url entries """ image_tag_regex = r'"?(.*?)"?' - + # If no image tags, return original text if not re.search(image_tag_regex, text): return text - + final_list = [] remaining_text = text - + while remaining_text: match = re.search(image_tag_regex, remaining_text) if not match: if remaining_text.strip(): final_list.append({"type": "text", "text": remaining_text.strip()}) break - + # Get text before the image tag prefix = remaining_text[:match.start()].strip() if prefix: final_list.append({"type": "text", "text": prefix}) - + # Add the image image_url = match.group(1) final_list.append({"type": "image_url", "image_url": {"url": image_url}}) - + # Update remaining text remaining_text = remaining_text[match.end():].strip() - + return final_list diff --git a/dspy/clients/anyscale.py b/dspy/clients/anyscale.py index 3d30228671..dcc05f5663 100644 --- a/dspy/clients/anyscale.py +++ b/dspy/clients/anyscale.py @@ -94,7 +94,7 @@ def finetune_anyscale( lora_dynamic_path = storage_uri.split(model)[0] final_model_name = model + storage_uri.split(model)[1] - + if serve_config_path: update_serve_model_config(lora_dynamic_path, serve_config_path) job.model_names = [final_model_name] @@ -112,17 +112,17 @@ def update_serve_model_config(lora_dynamic_path: str, serve_config_path: str): """Update the model config storage location with the job_id.""" with open(serve_config_path, "r") as f: serve_config = yaml.safe_load(f) - + model_config_location = serve_config["applications"][0]["args"]["llm_configs"][0] - + with open(model_config_location, "r") as f: model_config = yaml.safe_load(f) model_config["lora_config"]["dynamic_lora_loading_path"] = lora_dynamic_path - + with open(model_config_location, "w") as f: yaml.safe_dump(model_config, f) - + def verify_dataset(dataset: List[dict[str, Any]]) -> bool: """Verify the training arguments before starting training.""" @@ -150,10 +150,10 @@ def submit_data(train_path: str, job_config: Dict[str, Any]): def generate_config_files(train_path: str, llmforge_config_path: str, job_config_path: str, model: str): assert llmforge_config_path, "LLMForge config is required to generate the config files" assert job_config_path, "Job config is required to start the finetuning job" - + llmforge_config = yaml.safe_load(open(llmforge_config_path, "r")) job_config_dict = yaml.safe_load(open(job_config_path, "r")) - + llmforge_config["model_id"] = model llmforge_config["train_path"] = train_path llmforge_config = {k: v for k, v in llmforge_config.items() if v is not None} @@ -167,7 +167,7 @@ def generate_config_files(train_path: str, llmforge_config_path: str, job_config for env_var in ["HF_TOKEN", "HF_HOME", "WANDB_API_KEY"]: if env_var not in job_config_dict["env_vars"] and os.environ.get(env_var, None): job_config_dict["env_vars"][env_var] = os.environ[env_var] - + job_config = JobConfig(**job_config_dict) diff --git a/dspy/clients/lm.py b/dspy/clients/lm.py index 6f5468b84e..f4f012a034 100644 --- a/dspy/clients/lm.py +++ b/dspy/clients/lm.py @@ -183,7 +183,7 @@ def thread_function_wrapper(): thread = threading.Thread(target=thread_function_wrapper) train_kwargs = train_kwargs or self.train_kwargs - model_to_finetune = self.finetuning_model or self.model + model_to_finetune = self.finetuning_model or self.model job = self.provider.TrainingJob( thread=thread, model=model_to_finetune, diff --git a/dspy/clients/openai.py b/dspy/clients/openai.py index 0d391070a2..c3718f3239 100644 --- a/dspy/clients/openai.py +++ b/dspy/clients/openai.py @@ -84,7 +84,7 @@ def status(self) -> TrainingStatus: class OpenAIProvider(Provider): - + def __init__(self): super().__init__() self.finetunable = True @@ -184,7 +184,7 @@ def is_terminal_training_status(status: TrainingStatus) -> bool: TrainingStatus.failed, TrainingStatus.cancelled, ] - + @staticmethod def get_training_status(job_id: str) -> TrainingStatus: provider_status_to_training_status = { diff --git a/dspy/clients/utils_finetune.py b/dspy/clients/utils_finetune.py index b773200114..cf90263e34 100644 --- a/dspy/clients/utils_finetune.py +++ b/dspy/clients/utils_finetune.py @@ -105,7 +105,7 @@ def find_data_errors_completion( found_keys = sorted(data_dict.keys()) if set(expected_keys) != set(found_keys): return f"Expected Keys: {expected_keys}; Found Keys: {found_keys}" - + for key in keys: if not isinstance(data_dict[key], str): return f"Expected `{key}` to be of type `str`. Found: {type(data_dict[key])}" diff --git a/dspy/datasets/colors.py b/dspy/datasets/colors.py index edf2e41697..cb12e6e77d 100644 --- a/dspy/datasets/colors.py +++ b/dspy/datasets/colors.py @@ -20,7 +20,7 @@ def __init__(self, sort_by_suffix=True, *args, **kwargs) -> None: random.Random(0).shuffle(self._train) random.Random(0).shuffle(self._dev) - + def sorted_by_suffix(self, colors): if not self.sort_by_suffix: return colors diff --git a/dspy/datasets/hotpotqa.py b/dspy/datasets/hotpotqa.py index e5cb9576af..172722edad 100644 --- a/dspy/datasets/hotpotqa.py +++ b/dspy/datasets/hotpotqa.py @@ -85,9 +85,9 @@ def __init__( """ What was the population of the city where Woodward Avenue ends in 2010? -Where did the star , who is also an executive producer, of the Mick begin her carrer? +Where did the star , who is also an executive producer, of the Mick begin her carrer? 16 1000 0 Both London and German have seen attacks during war, there was one specific type of attack that Germany called the blitz, what did London call a similar attack? Pre-Madonna was a collection of demos by the singer who was a leading presence during the emergence of what network? -Alan Mills composed the classic folk song that tells the story of what? +Alan Mills composed the classic folk song that tells the story of what? """ diff --git a/dspy/dsp/colbertv2.py b/dspy/dsp/colbertv2.py index d662c9b241..838aa1cffd 100644 --- a/dspy/dsp/colbertv2.py +++ b/dspy/dsp/colbertv2.py @@ -89,14 +89,14 @@ def __init__(self,passages:List[str],colbert_config=None,load_only:bool=False): assert self.colbert_config.checkpoint is not None, "Please pass a valid checkpoint like colbert-ir/colbertv2.0, which you can modify in the ColBERTConfig with attribute name checkpoint" self.passages = passages - + assert self.colbert_config.index_name is not None, "Please pass a valid index_name, which you can modify in the ColBERTConfig with attribute name index_name" self.passages = passages if not load_only: print(f"Building the index for experiment {self.colbert_config.experiment} with index name {self.colbert_config.index_name}") self.build_index() - + print(f"Loading the index for experiment {self.colbert_config.experiment} with index name {self.colbert_config.index_name}") self.searcher = self.get_index() @@ -109,7 +109,7 @@ def build_index(self): from colbert import Indexer from colbert.infra import Run, RunConfig - with Run().context(RunConfig(nranks=self.colbert_config.nranks, experiment=self.colbert_config.experiment)): + with Run().context(RunConfig(nranks=self.colbert_config.nranks, experiment=self.colbert_config.experiment)): indexer = Indexer(checkpoint=self.colbert_config.checkpoint, config=self.colbert_config) indexer.index(name=self.colbert_config.index_name, collection=self.passages, overwrite=True) @@ -121,17 +121,17 @@ def get_index(self): from colbert import Searcher from colbert.infra import Run, RunConfig - + with Run().context(RunConfig(experiment=self.colbert_config.experiment)): searcher = Searcher(index=self.colbert_config.index_name, collection=self.passages) return searcher - + def __call__(self, *args: Any, **kwargs: Any) -> Any: return self.forward(*args, **kwargs) def forward(self,query:str,k:int=7,**kwargs): import torch - + if kwargs.get("filtered_pids"): filtered_pids = kwargs.get("filtered_pids") assert type(filtered_pids) == List[int], "The filtered pids should be a list of integers" @@ -139,8 +139,8 @@ def forward(self,query:str,k:int=7,**kwargs): results = self.searcher.search( query, #Number of passages to receive - k=k, - #Passing the filter function of relevant + k=k, + #Passing the filter function of relevant filter_fn=lambda pids: torch.tensor( [pid for pid in pids if pid in filtered_pids],dtype=torch.int32).to(device)) else: @@ -151,7 +151,7 @@ def forward(self,query:str,k:int=7,**kwargs): return results class ColBERTv2RerankerLocal: - + def __init__(self,colbert_config=None,checkpoint:str='bert-base-uncased'): try: import colbert @@ -177,14 +177,14 @@ def forward(self,query:str,passages:List[str]=[]): from colbert.modeling.colbert import ColBERT from colbert.modeling.tokenization.doc_tokenization import DocTokenizer from colbert.modeling.tokenization.query_tokenization import QueryTokenizer - + self.colbert_config.nway = len(passages) query_tokenizer = QueryTokenizer(self.colbert_config,verbose=1) doc_tokenizer = DocTokenizer(self.colbert_config) query_ids,query_masks = query_tokenizer.tensorize([query]) doc_ids,doc_masks = doc_tokenizer.tensorize(passages) - col = ColBERT(self.checkpoint,self.colbert_config) + col = ColBERT(self.checkpoint,self.colbert_config) Q = col.query(query_ids,query_masks) DOC_IDS,DOC_MASKS = col.doc(doc_ids,doc_masks,keep_dims='return_mask') Q_duplicated = Q.repeat_interleave(len(passages), dim=0).contiguous() diff --git a/dspy/evaluate/evaluate.py b/dspy/evaluate/evaluate.py index 89be568937..d885a20a26 100644 --- a/dspy/evaluate/evaluate.py +++ b/dspy/evaluate/evaluate.py @@ -41,7 +41,7 @@ def HTML(x: str) -> str: class Evaluate: """DSPy Evaluate class. - This class is used to evaluate the performance of a DSPy program. Users need to provide a evaluation dataset and + This class is used to evaluate the performance of a DSPy program. Users need to provide a evaluation dataset and a metric function in order to use this class. This class supports parallel evaluation on the provided dataset. """ def __init__( @@ -112,15 +112,15 @@ def __call__( Returns: The evaluation results are returned in different formats based on the flags: - + - Base return: A float percentage score (e.g., 67.30) representing overall performance - + - With `return_all_scores=True`: - Returns (overall_score, individual_scores) where individual_scores is a list of + Returns (overall_score, individual_scores) where individual_scores is a list of float scores for each example in devset - + - With `return_outputs=True`: - Returns (overall_score, result_triples) where result_triples is a list of + Returns (overall_score, result_triples) where result_triples is a list of (example, prediction, score) tuples for each example in devset - With both flags=True: @@ -165,7 +165,7 @@ def process_item(example): ncorrect, ntotal = sum(score for *_, score in results), len(devset) logger.info(f"Average Metric: {ncorrect} / {ntotal} ({round(100 * ncorrect / ntotal, 1)}%)") - + def prediction_is_dictlike(prediction): # Downstream logic for displaying dictionary-like predictions depends solely on the predictions # having a method called `items()` for iterating through key/value pairs diff --git a/dspy/evaluate/metrics.py b/dspy/evaluate/metrics.py index c67dbf82d8..9fe68863b5 100644 --- a/dspy/evaluate/metrics.py +++ b/dspy/evaluate/metrics.py @@ -30,13 +30,13 @@ def answer_exact_match(example, pred, trace=None, frac=1.0): return _answer_match(pred.answer, [example.answer], frac=frac) elif isinstance(example.answer, list): return _answer_match(pred.answer, example.answer, frac=frac) - + raise ValueError(f"Invalid answer type: {type(example.answer)}") -def answer_passage_match(example, pred, trace=None): +def answer_passage_match(example, pred, trace=None): if isinstance(example.answer, str): return _passage_match(pred.context, [example.answer]) elif isinstance(example.answer, list): return _passage_match(pred.context, example.answer) - + raise ValueError(f"Invalid answer type: {type(example.answer)}") diff --git a/dspy/experimental/synthesizer/config.py b/dspy/experimental/synthesizer/config.py index 86c933219d..250d804765 100644 --- a/dspy/experimental/synthesizer/config.py +++ b/dspy/experimental/synthesizer/config.py @@ -17,7 +17,7 @@ class SynthesizerArguments(BaseModel): def validate_feedback_mode(self): if self.feedback_mode and self.feedback_mode not in ["human", "llm"]: raise ValueError("Feedback mode should be either 'human' or 'llm'.") - + if self.feedback_mode and not self.num_example_for_feedback: raise ValueError("Number of examples for feedback is required when feedback mode is provided.") diff --git a/dspy/experimental/synthesizer/synthesizer.py b/dspy/experimental/synthesizer/synthesizer.py index 9a2bfcd74c..12700b487f 100644 --- a/dspy/experimental/synthesizer/synthesizer.py +++ b/dspy/experimental/synthesizer/synthesizer.py @@ -45,16 +45,16 @@ def _gather_feedback(self, examples: dspy.Example) -> str: print("-"*75) print_text = "[bold blue]Generated Data:[bold blue]\n[bold red]Inputs:[bold red]\n" - + for key in input_keys: print_text += f"\t[bold yellow]{key}[bold yellow]: [green]{examples[key]}[green]\n" - + rprint(print_text) feedback = input("Provide feedback on the generated data: ") print("-"*75) return feedback - + elif self.config.feedback_mode == "llm": feedback = self.get_feedback_on_generation( synthetic_data=[examples], @@ -144,7 +144,7 @@ def _get_dataset_metadata(self, ground_source: Union[List[dspy.Example], dspy.Si task_description = ground_source.__doc__ if task_description.startswith("Given the fields"): task_description = self.understand_task(examples=ground_source.__doc__).explanation - + input_keys = {k:v.json_schema_extra["desc"] for k,v in ground_source.input_fields.items()} output_keys = {k:v.json_schema_extra["desc"] for k,v in ground_source.output_fields.items()} @@ -171,7 +171,7 @@ def generate( if self.config.num_example_for_optim: self.generate_input_data.__doc__ += INPUT_GENERATION_TASK_WITH_EXAMPLES_SUFFIX - + if self.config.feedback_mode: self.generate_input_data.__doc__ += INPUT_GENERATION_TASK_WITH_FEEDBACK_SUFFIX @@ -200,7 +200,7 @@ def generate( if not isinstance(ground_source, list): raise ValueError("Ground source must be a list of examples when `num_example_for_optim` is provided.") kwargs["ground_source"] = random.sample(ground_source, k=self.config.num_example_for_optim) - + with dspy.context(lm=self.input_lm): inputs = self.input_predictor(**kwargs) @@ -225,10 +225,10 @@ def generate( } data.append(dspy.Example(**kwargs, **output_kwargs).with_inputs(*input_keys)) - + if self.config.feedback_mode and idx < self.config.num_example_for_feedback: feedback = self._gather_feedback(data[-1]) - + task_description = self.update_task_description( task_description=task_description, feedback=feedback, diff --git a/dspy/predict/aggregation.py b/dspy/predict/aggregation.py index 946179e223..c7c2add610 100644 --- a/dspy/predict/aggregation.py +++ b/dspy/predict/aggregation.py @@ -20,12 +20,12 @@ def majority(prediction_or_completions, normalize=default_normalize, field=None) completions = prediction_or_completions.completions else: completions = prediction_or_completions - + try: signature = completions.signature except Exception: signature = None - + if not field: if signature: field = list(signature.output_fields.keys())[-1] @@ -36,7 +36,7 @@ def majority(prediction_or_completions, normalize=default_normalize, field=None) normalize = normalize if normalize else lambda x: x normalized_values = [normalize(completion[field]) for completion in completions] normalized_values_ = [x for x in normalized_values if x is not None] - + # Count value_counts = {} for value in (normalized_values_ or normalized_values): @@ -48,6 +48,6 @@ def majority(prediction_or_completions, normalize=default_normalize, field=None) for completion in completions: if normalize(completion[field]) == majority_value: break - + # if input_type == Prediction: return Prediction.from_completions([completion], signature=signature) diff --git a/dspy/predict/avatar/avatar.py b/dspy/predict/avatar/avatar.py index 7b7f560019..db0f8bdf6c 100644 --- a/dspy/predict/avatar/avatar.py +++ b/dspy/predict/avatar/avatar.py @@ -73,8 +73,8 @@ def _get_field(self, field_info: FieldInfo): def _update_signature(self, idx: int, omit_action: bool = False): self.actor.signature = self.actor.signature.with_updated_fields( - f"action_{idx}", - Action, + f"action_{idx}", + Action, __dspy_field_type="input" ) @@ -86,7 +86,7 @@ def _update_signature(self, idx: int, omit_action: bool = False): type_=str, ) ) - + if omit_action: for field in list(self.output_fields.keys()): self.actor.signature = self.actor.signature.append( @@ -94,7 +94,7 @@ def _update_signature(self, idx: int, omit_action: bool = False): self._get_field(self.output_fields[field]), type_=self.output_fields[field].annotation, ) - else: + else: self.actor.signature = self.actor.signature.append( f"action_{idx+1}", dspy.OutputField( @@ -117,16 +117,16 @@ def _call_tool(self, tool_name: str, tool_input_query: str) -> str: def forward(self, **kwargs): if self.verbose: print("Starting the task...") - + args = { "goal" : self.signature.__doc__, "tools" : [tool.name for tool in self.tools], } - + for key in self.input_fields.keys(): if key in kwargs: args[key] = kwargs[key] - + idx = 1 tool_name = None action_results: list[ActionOutput] = [] @@ -146,8 +146,8 @@ def forward(self, **kwargs): tool_output = self._call_tool(tool_name, tool_input_query) action_results.append( ActionOutput( - tool_name=tool_name, - tool_input_query=tool_input_query, + tool_name=tool_name, + tool_input_query=tool_input_query, tool_output=tool_output ) ) diff --git a/dspy/predict/chain_of_thought.py b/dspy/predict/chain_of_thought.py index 4a56c0ceb2..f10b723635 100644 --- a/dspy/predict/chain_of_thought.py +++ b/dspy/predict/chain_of_thought.py @@ -13,7 +13,7 @@ def __init__(self, signature, rationale_type=None, **config): desc = "${reasoning}" rationale_type = rationale_type or dspy.OutputField(prefix=prefix, desc=desc) extended_signature = signature.prepend("reasoning", rationale_type, type_=str) - + self.predict = dspy.Predict(extended_signature, **config) def forward(self, **kwargs): diff --git a/dspy/predict/chain_of_thought_with_hint.py b/dspy/predict/chain_of_thought_with_hint.py index e0925dfc65..43ed001a02 100644 --- a/dspy/predict/chain_of_thought_with_hint.py +++ b/dspy/predict/chain_of_thought_with_hint.py @@ -8,12 +8,12 @@ class ChainOfThoughtWithHint(Module): def __init__(self, signature, rationale_type=None, **config): self.signature = dspy.ensure_signature(signature) self.module = dspy.ChainOfThought(signature, rationale_type=rationale_type, **config) - + def forward(self, **kwargs): if 'hint' in kwargs and kwargs['hint']: hint = f"\n\t\t(secret hint: {kwargs.pop('hint')})" original_kwargs = kwargs.copy() - + # Convert the first field's value to string and append the hint last_key = list(self.signature.input_fields.keys())[-1] kwargs[last_key] = str(kwargs[last_key]) + hint @@ -23,7 +23,7 @@ def forward(self, **kwargs): this_trace = dspy.settings.trace[-1] dspy.settings.trace[-1] = (this_trace[0], original_kwargs, this_trace[2]) return pred - + return self.module(**kwargs) diff --git a/dspy/predict/knn.py b/dspy/predict/knn.py index c0cd2df12d..f59f4f4748 100644 --- a/dspy/predict/knn.py +++ b/dspy/predict/knn.py @@ -16,7 +16,7 @@ def __init__(self, k: int, trainset: list, vectorizer=None): >>> knn = KNN(k=3, trainset=trainset) >>> similar_examples = knn(input="hello") """ - + import dspy.dsp as dsp import dspy diff --git a/dspy/predict/langchain.py b/dspy/predict/langchain.py index 223deed280..eba2d670f9 100644 --- a/dspy/predict/langchain.py +++ b/dspy/predict/langchain.py @@ -68,11 +68,11 @@ # setattr(self, name, value) # self.demos = [dspy.Example(**x) for x in self.demos] - + # def __call__(self, *arg, **kwargs): # if len(arg) > 0: kwargs = {**arg[0], **kwargs} # return self.forward(**kwargs) - + # def _build_signature(self, template): # gpt4T = dspy.OpenAI(model='gpt-4-1106-preview', max_tokens=4000, model_type='chat') @@ -108,13 +108,13 @@ # # print(f"#> {prompt}") # # print(f"#> PRED = {content}\n\n\n") # dspy.settings.langchain_history.append((prompt, pred)) - + # if dsp.settings.trace is not None: # trace = dsp.settings.trace # trace.append((self, {**kwargs}, pred)) # return output - + # def invoke(self, d, *args, **kwargs): # # print(d) # return self.forward(**d) @@ -144,23 +144,23 @@ # class LangChainModule(dspy.Module): # def __init__(self, lcel): # super().__init__() - + # modules = [] # for name, node in lcel.get_graph().nodes.items(): # if isinstance(node.data, LangChainPredict): modules.append(node.data) # self.modules = modules # self.chain = lcel - + # def forward(self, **kwargs): # output_keys = ['output', self.modules[-1].output_field_key] # output = self.chain.invoke(dict(**kwargs)) - + # try: output = output.content # except Exception: pass # return dspy.Prediction({k: output for k in output_keys}) - + # def invoke(self, d, *args, **kwargs): # return self.forward(**d).output diff --git a/dspy/predict/llamaindex.py b/dspy/predict/llamaindex.py index b261cf6c89..dfeebe7ec2 100644 --- a/dspy/predict/llamaindex.py +++ b/dspy/predict/llamaindex.py @@ -63,7 +63,7 @@ # Takes in a predict module from DSPy (whether unoptimized or optimized), # and extracts the relevant prompt template from it given the input. - + # """ # predict_module: Predict @@ -89,7 +89,7 @@ # template_var_mappings=template_var_mappings, # function_mappings=function_mappings, # ) - + # def partial_format(self, **kwargs: Any) -> "BasePromptTemplate": # """Returns a new prompt template with the provided kwargs.""" # # NOTE: this is a copy of the implementation in `PromptTemplate` @@ -124,10 +124,10 @@ # # get kwarg templates # kwarg_tmpl_map = {k: "{k}" for k in self.template_vars} -# # get "raw" template with all the values filled in with {var_name} +# # get "raw" template with all the values filled in with {var_name} # template0 = get_formatted_template(self.predict_module, kwarg_tmpl_map) -# # HACK: there are special 'format' variables of the form ${var_name} that are meant to -# # prompt the LLM, but we do NOT want to replace with actual prompt variable values. +# # HACK: there are special 'format' variables of the form ${var_name} that are meant to +# # prompt the LLM, but we do NOT want to replace with actual prompt variable values. # # Replace those with double brackets # template1 = replace_placeholder(template0) @@ -147,10 +147,10 @@ # def build_signature(prompt: PromptTemplate) -> dspy.Signature: # """Attempt to build signature from prompt.""" -# # TODO: allow plugging in any llamaindex LLM +# # TODO: allow plugging in any llamaindex LLM # gpt4T = dspy.OpenAI(model='gpt-4-1106-preview', max_tokens=4000, model_type='chat') -# with dspy.context(lm=gpt4T): +# with dspy.context(lm=gpt4T): # parts = dspy.Predict(Template2Signature)(template=prompt.template) # inputs = {k.strip(): InputField() for k in parts.input_keys.split(',')} @@ -162,14 +162,14 @@ # } # signature = make_signature(fields, parts.essential_instructions) # return signature - + # class DSPyComponent(QueryComponent): -# """DSPy Query Component. - +# """DSPy Query Component. + # Can take in either a predict module directly. # TODO: add ability to translate from an existing prompt template / LLM. - + # """ # predict_module: dspy.Predict # predict_template: dsp.Template @@ -196,12 +196,12 @@ # """Initialize from prompt template. # LLM is a TODO - currently use DSPy LLM classes. - + # """ # signature = build_signature(prompt_template) # predict_module = Predict(signature) # return cls(predict_module=predict_module) - + # def set_callback_manager(self, callback_manager: CallbackManager) -> None: # """Set callback manager.""" # # TODO: implement @@ -240,7 +240,7 @@ # """A module for LlamaIndex. # Wraps a QueryPipeline and exposes it as a dspy module for optimization. - + # """ # class Config: @@ -254,10 +254,9 @@ # for module in query_pipeline.module_dict.values(): # if isinstance(module, DSPyComponent): # self.predict_modules.append(module.predict_module) - + # def forward(self, **kwargs: Any) -> Dict[str, Any]: # """Forward.""" # output_dict = self.query_pipeline.run(**kwargs, return_values_direct=False) # return dspy.Prediction(**output_dict) - diff --git a/dspy/predict/predict.py b/dspy/predict/predict.py index 03bc44d869..a40bebb26f 100644 --- a/dspy/predict/predict.py +++ b/dspy/predict/predict.py @@ -54,7 +54,7 @@ def load_state(self, state): # `excluded_keys` are fields that go through special handling. if name not in excluded_keys: setattr(self, name, value) - + self.signature = self.signature.load_state(state["signature"]) if "extended_signature" in state: # legacy, up to and including 2.5, for CoT. diff --git a/dspy/primitives/runner.js b/dspy/primitives/runner.js index 6421410788..6ed1b65cf8 100644 --- a/dspy/primitives/runner.js +++ b/dspy/primitives/runner.js @@ -32,7 +32,7 @@ for await (const line of readLines(Deno.stdin)) { } const code = input.code || ""; - + // Wrap execution in a try/catch so we can handle syntax errors, etc. try { await pyodide.loadPackagesFromImports(code); @@ -75,7 +75,7 @@ sys.stderr = old_stderr // The final statement was None or no return => deliver printed output // If you want to combine capturedStderr as well, you can append it // But here we'll just do stdout for clarity - output = capturedStdout; + output = capturedStdout; // If there's something in stderr, you might want to include that or log it // output += capturedStderr; } else { diff --git a/dspy/propose/dataset_summary_generator.py b/dspy/propose/dataset_summary_generator.py index 7a4c03a96e..103ace44af 100644 --- a/dspy/propose/dataset_summary_generator.py +++ b/dspy/propose/dataset_summary_generator.py @@ -12,7 +12,7 @@ class DatasetDescriptor(dspy.Signature): ("""Given several examples from a dataset please write observations about trends that hold for most or all of the samples. """ """Some areas you may consider in your observations: topics, content, syntax, conciceness, etc. """ """It will be useful to make an educated guess as to the nature of the task this dataset will enable. Don't be afraid to be creative""") - + examples = dspy.InputField(desc="Sample data points from the dataset") observations = dspy.OutputField(desc="Somethings that holds true for most or all of the data you observed") @@ -21,7 +21,7 @@ class DatasetDescriptorWithPriorObservations(dspy.Signature): """I will also provide you with a few observations I have already made. Please add your own observations or if you feel the observations are comprehensive say 'COMPLETE' """ """Some areas you may consider in your observations: topics, content, syntax, conciceness, etc. """ """It will be useful to make an educated guess as to the nature of the task this dataset will enable. Don't be afraid to be creative""") - + examples = dspy.InputField(desc="Sample data points from the dataset") prior_observations = dspy.InputField(desc="Some prior observations I made about the data") observations = dspy.OutputField(desc="Somethings that holds true for most or all of the data you observed or COMPLETE if you have nothing to add") @@ -73,8 +73,8 @@ def create_dataset_summary(trainset, view_data_batch_size, prompt_model, log_fil break continue observations += output["observations"] - - if log_file: + + if log_file: log_file.write(f"observations {observations}\n") except Exception as e: if verbose: print(f"e {e}. using observations from past round for a summary.") @@ -87,7 +87,7 @@ def create_dataset_summary(trainset, view_data_batch_size, prompt_model, log_fil if verbose: print(f"summary: {summary}") if log_file: log_file.write(f"summary: {summary}\n") - + if verbose: print(f"\nGenerated summary: {strip_prefix(summary.summary)}\n") return strip_prefix(summary.summary) diff --git a/dspy/propose/grounded_proposer.py b/dspy/propose/grounded_proposer.py index ef48f1fbee..bc595be431 100644 --- a/dspy/propose/grounded_proposer.py +++ b/dspy/propose/grounded_proposer.py @@ -185,7 +185,7 @@ def gather_examples_from_sets(candidate_sets, max_examples): # Construct full program demo or single module demo depending on settings basic_instruction = get_signature(program.predictors()[pred_i]).instructions task_demos = "" - + if self.use_task_demos: # Combine current and adjacent sets adjacent_sets = ( @@ -193,7 +193,7 @@ def gather_examples_from_sets(candidate_sets, max_examples): demo_candidates[pred_i][demo_set_i + 1:] + demo_candidates[pred_i][:demo_set_i] ) - + # Gather examples up to the required count example_strings = gather_examples_from_sets(adjacent_sets, num_demos_in_context) task_demos = "\n\n".join(example_strings) + "\n\n" @@ -220,7 +220,7 @@ def gather_examples_from_sets(candidate_sets, max_examples): for field_name, field in get_signature(program.predictors()[pred_i]).fields.items(): # Access the '__dspy_field_type' from the extra metadata dspy_field_type = field.json_schema_extra.get('__dspy_field_type') - + # Based on the '__dspy_field_type', append to the respective list if dspy_field_type == "input": inputs.append(field_name) @@ -326,7 +326,7 @@ def propose_instructions_for_program( ): """This method is responsible for returning the full set of new instructions for our program, given the specified criteria.""" - proposed_instructions = {} + proposed_instructions = {} if self.set_history_randomly: # Randomly select whether or not we're using instruction history @@ -338,7 +338,7 @@ def propose_instructions_for_program( if self.verbose: print("No demo candidates provided. Running without task demos.") self.use_task_demos = False - # Create an instruction for each predictor + # Create an instruction for each predictor for pred_i, predictor in enumerate(program.predictors()): for demo_set_i in range(len(demo_candidates[0])): if pred_i not in proposed_instructions: @@ -366,7 +366,7 @@ def propose_instructions_for_program( tip=selected_tip, ), ) - + return proposed_instructions def propose_instruction_for_predictor( diff --git a/dspy/propose/instruction_proposal.py b/dspy/propose/instruction_proposal.py index 66900eb1df..6667181fe5 100644 --- a/dspy/propose/instruction_proposal.py +++ b/dspy/propose/instruction_proposal.py @@ -106,8 +106,8 @@ class BasicGenerateInstructionOnly(Signature): proposed_instruction = dspy.OutputField(desc="The proposed instruction (reply with the instruction only).") class BasicGenerateField(Signature): - ("""You are an instruction optimizer for large language models. Your task is to propose a better string to use for one of the fields - in a prompt that is being inputted to a large language model to perform a certain task. The goal is for this improved field to improve + ("""You are an instruction optimizer for large language models. Your task is to propose a better string to use for one of the fields + in a prompt that is being inputted to a large language model to perform a certain task. The goal is for this improved field to improve the performance of the language model on this task. Don't be afraid to be creative.""") current_field = dspy.InputField(desc="The current string in use for this field.") diff --git a/dspy/propose/utils.py b/dspy/propose/utils.py index e5ef6ee80b..44cf400ffa 100644 --- a/dspy/propose/utils.py +++ b/dspy/propose/utils.py @@ -38,7 +38,7 @@ def create_instruction_set_history_string(base_program, trial_logs, top_n): if instruction_set not in seen_programs: seen_programs.add(instruction_set) unique_program_history.append(entry) - + # Get the top n programs from program history top_n_program_history = sorted(unique_program_history, key=lambda x: x['score'], reverse=True)[:top_n] top_n_program_history.reverse() @@ -50,7 +50,7 @@ def create_instruction_set_history_string(base_program, trial_logs, top_n): score = entry["score"] instruction_set = get_program_instruction_set_string(program) instruction_set_history_string += instruction_set + f" | Score: {score}\n\n" - + return instruction_set_history_string def parse_list_of_instructions(instruction_string): @@ -60,7 +60,7 @@ def parse_list_of_instructions(instruction_string): return instructions except json.JSONDecodeError: pass - + # If JSON decoding fails, extract strings within quotes instructions = re.findall(r'"([^"]*)"', instruction_string) return instructions @@ -76,7 +76,7 @@ def get_program_instruction_set_string(program): def create_predictor_level_history_string(base_program, predictor_i, trial_logs, top_n): instruction_aggregate = {} instruction_history = [] - + # Load trial programs for trial_num in trial_logs: trial = trial_logs[trial_num] @@ -93,19 +93,19 @@ def create_predictor_level_history_string(base_program, predictor_i, trial_logs, predictor = history_item["program"].predictors()[predictor_i] instruction = get_signature(predictor).instructions score = history_item["score"] - + if instruction in instruction_aggregate: instruction_aggregate[instruction]['total_score'] += score instruction_aggregate[instruction]['count'] += 1 else: instruction_aggregate[instruction] = {'total_score': score, 'count': 1} - + # Calculate average score for each instruction and prepare for sorting predictor_history = [] for instruction, data in instruction_aggregate.items(): average_score = data['total_score'] / data['count'] predictor_history.append((instruction, average_score)) - + # Deduplicate and sort by average score, then select top N seen_instructions = set() unique_predictor_history = [] @@ -116,12 +116,12 @@ def create_predictor_level_history_string(base_program, predictor_i, trial_logs, top_instructions = sorted(unique_predictor_history, key=lambda x: x[1], reverse=True)[:top_n] top_instructions.reverse() - + # Create formatted history string predictor_history_string = "" for instruction, score in top_instructions: predictor_history_string += instruction + f" | Score: {score}\n\n" - + return predictor_history_string def create_example_string(fields, example): @@ -180,5 +180,5 @@ def get_dspy_source_code(module): header.append(code) completed_set.add(code) completed_set.add(item) - + return '\n\n'.join(header) + '\n\n' + base_code diff --git a/dspy/retrieve/azureaisearch_rm.py b/dspy/retrieve/azureaisearch_rm.py index ffb1f91b27..c2aa5606c8 100644 --- a/dspy/retrieve/azureaisearch_rm.py +++ b/dspy/retrieve/azureaisearch_rm.py @@ -367,10 +367,10 @@ def get_embeddings(self, query: str, k_nearest_neighbors: int, field_vector: str assert ( self.azure_openai_client or self.embedding_func ), "Either azure_openai_client or embedding_func must be provided." - + if self.azure_openai_client is not None: assert field_vector, "field_vector must be provided." - + embedding = ( self.azure_openai_client.embeddings.create(input=query, model=self.openai_embed_model).data[0].embedding ) diff --git a/dspy/retrieve/chromadb_rm.py b/dspy/retrieve/chromadb_rm.py index ee6219ba9f..3cf3549514 100644 --- a/dspy/retrieve/chromadb_rm.py +++ b/dspy/retrieve/chromadb_rm.py @@ -163,9 +163,9 @@ def forward( ) zipped_results = zip( - results["ids"][0], - results["distances"][0], - results["documents"][0], + results["ids"][0], + results["distances"][0], + results["documents"][0], results["metadatas"][0]) results = [dotdict({"id": id, "score": dist, "long_text": doc, "metadatas": meta }) for id, dist, doc, meta in zipped_results] return results diff --git a/dspy/retrieve/deeplake_rm.py b/dspy/retrieve/deeplake_rm.py index 201d2f8f1b..7a6dd2b11c 100644 --- a/dspy/retrieve/deeplake_rm.py +++ b/dspy/retrieve/deeplake_rm.py @@ -22,7 +22,7 @@ class DeeplakeRM(dspy.Retrieve): - + """ A retriever module that uses deeplake to return the top passages for a given query. @@ -74,13 +74,13 @@ def embedding_function(self, texts, model="text-embedding-ada-002"): texts = [texts] texts = [t.replace("\n", " ") for t in texts] - + return [data.embedding for data in openai.embeddings.create(input = texts, model=model).data] - + def forward( self, query_or_queries: Union[str, List[str]], k: Optional[int],**kwargs, ) -> dspy.Prediction: - + """Search with DeepLake for self.k top passages for query Args: @@ -109,5 +109,5 @@ def forward( sorted_passages = sorted( passages.items(), key=lambda x: x[1], reverse=True)[:k] - + return [dotdict({"long_text": p}) for p, _ in sorted_passages] diff --git a/dspy/retrieve/lancedb_rm.py b/dspy/retrieve/lancedb_rm.py index d2769b24eb..a89d2ad242 100644 --- a/dspy/retrieve/lancedb_rm.py +++ b/dspy/retrieve/lancedb_rm.py @@ -36,7 +36,7 @@ except Exception: ERRORS = (openai.RateLimitError, openai.APIError) - + class LancedbRM(Retrieve): """ A retrieval module that uses LanceDB to return the top passages for a given query. @@ -72,7 +72,7 @@ def __init__( persist_directory: str, k: int = 3, ): - + self._table = self._init_lancedb( table_name, persist_directory, ) @@ -107,8 +107,8 @@ def _init_lancedb( else: table = self.db.open_table(table_name) return table - - + + @backoff.on_exception( backoff.expo, ERRORS, @@ -130,7 +130,7 @@ def forward(self, query_or_queries: Union[str, List[str]]) -> Prediction: else query_or_queries ) queries = [q for q in queries if q] # Filter empty queries - + passages = [] for q in queries: results = self._table.search(q).limit(self.k).to_list() diff --git a/dspy/retrieve/milvus_rm.py b/dspy/retrieve/milvus_rm.py index 08f556c675..d1a967e48a 100644 --- a/dspy/retrieve/milvus_rm.py +++ b/dspy/retrieve/milvus_rm.py @@ -38,9 +38,9 @@ class MilvusRM(dspy.Retrieve): uri (str, optional): The Milvus connection uri. Defaults to "http://localhost:19530". token (str, optional): The Milvus connection token. Defaults to None. db_name (str, optional): The Milvus database name. Defaults to "default". - embedding_function (callable, optional): The function to convert a list of text to embeddings. + embedding_function (callable, optional): The function to convert a list of text to embeddings. The embedding function should take a list of text strings as input and output a list of embeddings. - Defaults to None. By default, it will get OpenAI client by the environment variable OPENAI_API_KEY + Defaults to None. By default, it will get OpenAI client by the environment variable OPENAI_API_KEY and use OpenAI's embedding model "text-embedding-3-small" with the default dimension. k (int, optional): The number of top passages to retrieve. Defaults to 3. diff --git a/dspy/retrieve/pinecone_rm.py b/dspy/retrieve/pinecone_rm.py index f5fe245af7..c873536518 100644 --- a/dspy/retrieve/pinecone_rm.py +++ b/dspy/retrieve/pinecone_rm.py @@ -159,10 +159,10 @@ def _init_pinecone( ) return pinecone.Index(index_name) - + def _mean_pooling( - self, - model_output, + self, + model_output, attention_mask, ): try: @@ -175,14 +175,14 @@ def _mean_pooling( token_embeddings = model_output[0] # First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) - + @backoff.on_exception( backoff.expo, ERRORS, max_time=settings.backoff_time, ) def _get_embeddings( - self, + self, queries: List[str], ) -> List[List[float]]: """Return query vector after creating embedding using OpenAI @@ -199,7 +199,7 @@ def _get_embeddings( raise ModuleNotFoundError( "You need to install torch to use a local embedding model with PineconeRM.", ) from exc - + if not self.use_local_model: if OPENAI_LEGACY: embedding = openai.Embedding.create( @@ -210,7 +210,7 @@ def _get_embeddings( input=queries, model=self._openai_embed_model, ).model_dump() return [embedding["embedding"] for embedding in embedding["data"]] - + # Use local model encoded_input = self._local_tokenizer(queries, padding=True, truncation=True, return_tensors="pt").to(self.device) with torch.no_grad(): diff --git a/dspy/retrieve/vectara_rm.py b/dspy/retrieve/vectara_rm.py index 8489f0aae1..a51fefbd67 100644 --- a/dspy/retrieve/vectara_rm.py +++ b/dspy/retrieve/vectara_rm.py @@ -132,7 +132,7 @@ def _vectara_query( } for x in responses ] return res - + def forward(self, query_or_queries: Union[str, List[str]], k: Optional[int]) -> dspy.Prediction: """Search with Vectara for self.k top passages for query @@ -147,7 +147,7 @@ def forward(self, query_or_queries: Union[str, List[str]], k: Optional[int]) -> if isinstance(query_or_queries, str) else query_or_queries ) - queries = [q for q in queries if q] # Filter empty queries + queries = [q for q in queries if q] # Filter empty queries k = k if k is not None else self.k all_res = [] @@ -164,4 +164,3 @@ def forward(self, query_or_queries: Union[str, List[str]], k: Optional[int]) -> passages.items(), key=lambda x: x[1], reverse=True)[:k] return [dotdict({"long_text": passage}) for passage, _ in sorted_passages] - diff --git a/dspy/retrieve/watson_discovery_rm.py b/dspy/retrieve/watson_discovery_rm.py index 13c1385695..ee364da167 100644 --- a/dspy/retrieve/watson_discovery_rm.py +++ b/dspy/retrieve/watson_discovery_rm.py @@ -41,7 +41,7 @@ def __init__( self.collection_ids=collection_ids self.k: k self.query_url=url + "/v2/projects/" + project_id + "/query?version=" + version - + super().__init__(k=k) def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int]= None) -> dspy.Prediction: @@ -78,12 +78,12 @@ def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int]= Non discovery_results = requests.request( "POST", - url=self.query_url, + url=self.query_url, headers=headers, auth=HTTPBasicAuth("apikey", self.apikey), data=payload, ) - + discovery_results.raise_for_status() doc_dict={} diff --git a/dspy/retrieve/weaviate_rm.py b/dspy/retrieve/weaviate_rm.py index 7d4cf436a9..a30b40903c 100644 --- a/dspy/retrieve/weaviate_rm.py +++ b/dspy/retrieve/weaviate_rm.py @@ -109,7 +109,7 @@ def forward(self, query_or_queries: Union[str, List[str]], k: Optional[int] = No passages.extend(dotdict({"long_text": d}) for d in parsed_results) return passages - + def get_objects(self, num_samples: int, fields: List[str]) -> List[dict]: """Get objects from Weaviate using the cursor API.""" if self._client_type == "WeaviateClient": @@ -127,7 +127,7 @@ def get_objects(self, num_samples: int, fields: List[str]) -> List[dict]: return objects else: raise ValueError("`get_objects` is not supported for the v3 Weaviate Python client, please upgrade to v4.") - + def insert(self, new_object_properties: dict): if self._client_type == "WeaviateClient": self._weaviate_collection.data.insert( diff --git a/dspy/retrievers/embeddings.py b/dspy/retrievers/embeddings.py index ded5b15324..854d4d503c 100644 --- a/dspy/retrievers/embeddings.py +++ b/dspy/retrievers/embeddings.py @@ -44,7 +44,7 @@ def _batch_forward(self, queries: List[str]): pids = self._faiss_search(q_embeds, self.k * 10) if self.index else None pids = np.tile(np.arange(len(self.corpus)), (len(queries), 1)) if pids is None else pids - + return self._rerank_and_predict(q_embeds, pids) def _build_faiss(self): diff --git a/dspy/teleprompt/avatar_optimizer.py b/dspy/teleprompt/avatar_optimizer.py index c41c6bfe5e..c2fb3ba749 100644 --- a/dspy/teleprompt/avatar_optimizer.py +++ b/dspy/teleprompt/avatar_optimizer.py @@ -113,7 +113,7 @@ def process_example(self, actor, example, return_outputs): except Exception as e: print(e) - + if return_outputs: return example, None, 0 else: @@ -127,7 +127,7 @@ def thread_safe_evaluator(self, devset, actor, return_outputs=False, num_threads with ThreadPoolExecutor(max_workers=num_threads) as executor: futures = [executor.submit(self.process_example, actor, example, return_outputs) for example in devset] - + for future in tqdm(futures, total=total_examples, desc="Processing examples"): result = future.result() if return_outputs: @@ -136,9 +136,9 @@ def thread_safe_evaluator(self, devset, actor, return_outputs=False, num_threads results.append((example, prediction, score)) else: total_score += result - + avg_metric = total_score / total_examples - + if return_outputs: return avg_metric, results else: @@ -146,13 +146,13 @@ def thread_safe_evaluator(self, devset, actor, return_outputs=False, num_threads def _get_pos_neg_results( - self, - actor: dspy.Module, + self, + actor: dspy.Module, trainset: List[dspy.Example] ) -> Tuple[float, List[EvalResult], List[EvalResult]]: pos_inputs = [] neg_inputs = [] - + avg_score, results = self.thread_safe_evaluator(trainset, actor, return_outputs=True) print(f"Average Score: {avg_score}") @@ -178,14 +178,14 @@ def _get_pos_neg_results( raise ValueError("No positive examples found, try lowering the upper_bound or providing more training data") if len(neg_inputs) == 0: raise ValueError("No negative examples found, try raising the lower_bound or providing more training data") - + return (avg_score, pos_inputs, neg_inputs) - + def compile(self, student, *, trainset): best_actor = deepcopy(student) best_score = -999 if self.optimize_for == "max" else 999 - + for i in range(self.max_iters): print(20*'=') print(f"Iteration {i+1}/{self.max_iters}") @@ -219,7 +219,7 @@ def compile(self, student, *, trainset): best_actor.actor.signature = best_actor.actor.signature.with_instructions(new_instruction) best_actor.actor_clone = deepcopy(best_actor.actor) best_score = score - + print(f"Best Actor: {best_actor}") return best_actor diff --git a/dspy/teleprompt/bettertogether.py b/dspy/teleprompt/bettertogether.py index bad685880b..17e581aec3 100644 --- a/dspy/teleprompt/bettertogether.py +++ b/dspy/teleprompt/bettertogether.py @@ -27,7 +27,7 @@ def __init__(self, # TODO: Note that the BetterTogether optimizer is meaningful when # BootstrapFinetune uses a metric to filter the training data before - # fine-tuning. However, one can also choose to run this optimizer with + # fine-tuning. However, one can also choose to run this optimizer with # a BoostrapFinetune without a metric, say, if there aren't labels # available for the training data. Should this be noted somewhere? # TODO: We should re-consider if the metric should be required. @@ -73,10 +73,10 @@ def compile( trainset = trainset[:] logger.info("Compiling the student program...") student = self._run_strategies(parsed_strategy, student, trainset, valset_ratio) - + logger.info("BetterTogether has finished compiling the student program") return student - + def _run_strategies(self, parsed_strategy, student, trainset, valset_ratio) -> Program: # Keep track of all the partial strategies/programs in parsed_strategy # "" corresponds to the initial student program @@ -115,7 +115,7 @@ def _run_strategies(self, parsed_strategy, student, trainset, valset_ratio) -> P student.candidate_programs = candidate_programs return student - + def _compile_prompt_optimizer(self, student, trainset, valset_ratio) -> Program: logger.info("Preparing for prompt optimization...") @@ -141,7 +141,7 @@ def _compile_prompt_optimizer(self, student, trainset, valset_ratio) -> Program: pred.lm = lm return student - + def _compile_weight_optimizer(self, student, trainset) -> Program: logger.info("Preparing for weight optimization...") @@ -152,7 +152,7 @@ def _compile_weight_optimizer(self, student, trainset) -> Program: # prompt optimizers are accepting a valset or encode a way to check if # a valset should be passed to an optimizer's compile. logger.info("Compiling the weight optimizer...") - student = self.weight_optimizer.compile(student, trainset=trainset) + student = self.weight_optimizer.compile(student, trainset=trainset) # Updating the train kwargs for the new LMs. This is needed because the # train_kwargs of the optimizer is configured for the original LMs. diff --git a/dspy/teleprompt/bootstrap.py b/dspy/teleprompt/bootstrap.py index 5a25c36e53..e6bb89d617 100644 --- a/dspy/teleprompt/bootstrap.py +++ b/dspy/teleprompt/bootstrap.py @@ -50,7 +50,7 @@ def __init__( Args: metric: Callable - A function that compares an expected value and predicted value, outputting the result of that comparison. + A function that compares an expected value and predicted value, outputting the result of that comparison. metric_threshold: optional float, default `None` If the metric yields a numerical value, then check it against this threshold when deciding whether or not to accept a bootstrap example. @@ -235,7 +235,7 @@ def _bootstrap_one_example(self, example, round_idx=0): name2traces[predictor_name] = name2traces.get(predictor_name, []) name2traces[predictor_name].append(demo) - + # Update the traces for name, demos in name2traces.items(): from datasets.fingerprint import Hasher diff --git a/dspy/teleprompt/bootstrap_finetune.py b/dspy/teleprompt/bootstrap_finetune.py index aa5169e900..156968a007 100644 --- a/dspy/teleprompt/bootstrap_finetune.py +++ b/dspy/teleprompt/bootstrap_finetune.py @@ -45,18 +45,18 @@ def __init__( ): # TODO(feature): Inputs train_kwargs (a dict with string keys) and # adapter (Adapter) can depend on the LM they are used with. We are - # takingthese as parameters for the time being. However, they can be + # takingthese as parameters for the time being. However, they can be # attached to LMs themselves -- an LM could know which adapter it should # be used with along with the train_kwargs. This will lead the only # required argument for LM.finetune() to be the train dataset. - + super().__init__(train_kwargs=train_kwargs) self.metric = metric self.multitask = multitask self.adapter: Dict[LM, Adapter] = self.convert_to_lm_dict(adapter) self.exclude_demos = exclude_demos self.num_threads = num_threads - + def compile(self, student: Program, trainset: List[Example], teacher: Optional[Union[Program, List[Program]]] = None) -> Program: # TODO: Print statements can be converted to logger.info if we ensure # that the default DSPy logger logs info level messages in notebook @@ -83,7 +83,7 @@ def compile(self, student: Program, trainset: List[Example], teacher: Optional[U logger.info(f"Using {len(train_data)} data points for fine-tuning the model: {pred.lm.model}") finetune_kwargs = dict(lm=pred.lm, train_data=train_data, train_data_format=data_format, train_kwargs=self.train_kwargs[pred.lm]) key_to_data[training_key] = finetune_kwargs - + logger.info("Starting LM fine-tuning...") # TODO(feature): We could run batches of fine-tuning jobs in sequence # to avoid exceeding the number of threads. @@ -98,10 +98,10 @@ def compile(self, student: Program, trainset: List[Example], teacher: Optional[U training_key = (pred.lm, data_pred_ind) pred.lm = key_to_lm[training_key] # TODO: What should the correct behavior be here? Should - # BootstrapFinetune modify the prompt demos according to the + # BootstrapFinetune modify the prompt demos according to the # train data? pred.demos = [] if self.exclude_demos else pred.demos - + logger.info("BootstrapFinetune has finished compiling the student program") student._compiled = True return student @@ -123,7 +123,7 @@ def finetune_lms(finetune_dict) -> Dict[Any, LM]: logger.info("Calling lm.kill() on the LM to be fine-tuned to free up resources. This won't have any effect if the LM is not running.") lm.kill() key_to_job[key] = lm.finetune(**finetune_kwargs) - + key_to_lm = {} for ind, (key, job) in enumerate(key_to_job.items()): key_to_lm[key] = job.result() @@ -303,9 +303,9 @@ def assert_no_shared_predictor(program1: Program, program2: Program): assert not shared_ids, err -def get_unique_lms(program: Program) -> List[LM]: - lms = [pred.lm for pred in program.predictors()] - lms = list(set(lms)) +def get_unique_lms(program: Program) -> List[LM]: + lms = [pred.lm for pred in program.predictors()] + lms = list(set(lms)) return lms def launch_lms(program: Program): @@ -313,7 +313,7 @@ def launch_lms(program: Program): for lm in lms: lm.launch() -def kill_lms(program: Program): - lms = get_unique_lms(program) - for lm in lms: +def kill_lms(program: Program): + lms = get_unique_lms(program) + for lm in lms: lm.kill() diff --git a/dspy/teleprompt/infer_rules.py b/dspy/teleprompt/infer_rules.py index 949ad95c9c..f0c529216b 100644 --- a/dspy/teleprompt/infer_rules.py +++ b/dspy/teleprompt/infer_rules.py @@ -23,7 +23,7 @@ def compile(self, student, *, teacher=None, trainset, valset=None): trainset, valset = trainset[:train_size], trainset[train_size:] super().compile(student, teacher=teacher, trainset=trainset) - + original_program = self.student.deepcopy() all_predictors = [p for p in original_program.predictors() if hasattr(p, "signature")] instructions_list = [p.signature.instructions for p in all_predictors] @@ -42,7 +42,7 @@ def compile(self, student, *, teacher=None, trainset, valset=None): rules = self.induce_natural_language_rules(predictor, trainset) predictor.signature.instructions = instructions_list[i] self.update_program_instructions(predictor, rules) - + score = self.evaluate_program(candidate_program, valset) if score > best_score: @@ -50,9 +50,9 @@ def compile(self, student, *, teacher=None, trainset, valset=None): best_program = candidate_program print(f"Evaluated Candidate {candidate_idx+1} with score {score}. Current best score: {best_score}") - + print("Final best score:", best_score) - + return best_program def induce_natural_language_rules(self, predictor, trainset): diff --git a/dspy/teleprompt/mipro_optimizer.py b/dspy/teleprompt/mipro_optimizer.py index 8a9899c4e9..20eb83180f 100644 --- a/dspy/teleprompt/mipro_optimizer.py +++ b/dspy/teleprompt/mipro_optimizer.py @@ -37,7 +37,7 @@ # * init_temperature: The temperature used to generate new prompts. Higher roughly equals more creative. Default=1.0. # * verbose: Tells the method whether or not to print intermediate steps. # * track_stats: Tells the method whether or not to track statistics about the optimization process. -# If True, the method will track a dictionary with a key corresponding to the trial number, +# If True, the method will track a dictionary with a key corresponding to the trial number, # and a value containing a dict with the following keys: # * program: the program being evaluated at a given trial # * score: the last average evaluated score for the program @@ -383,7 +383,7 @@ # {YELLOW}{BOLD}Estimated Cost Calculation:{ENDC} -# {YELLOW}Total Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token) +# {YELLOW}Total Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token) # + (Number of calls to prompt model * (Avg Input Token Length per Call * Task Prompt Price per Input Token + Avg Output Token Length per Call * Prompt Model Price per Output Token).{ENDC} # For a preliminary estimate of potential costs, we recommend you perform your own calculations based on the task diff --git a/dspy/teleprompt/mipro_optimizer_v2.py b/dspy/teleprompt/mipro_optimizer_v2.py index 6231265eea..b3f6fab9dd 100644 --- a/dspy/teleprompt/mipro_optimizer_v2.py +++ b/dspy/teleprompt/mipro_optimizer_v2.py @@ -200,7 +200,7 @@ def compile( ) return best_program - + def _set_random_seeds(self, seed ): @@ -342,7 +342,7 @@ def _get_user_confirmation( {YELLOW}{BOLD}Estimated Cost Calculation:{ENDC} - {YELLOW}Total Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token) + {YELLOW}Total Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token) + (Number of program calls * (Avg Input Token Length per Call * Task Prompt Price per Input Token + Avg Output Token Length per Call * Prompt Model Price per Output Token).{ENDC} For a preliminary estimate of potential costs, we recommend you perform your own calculations based on the task @@ -600,7 +600,7 @@ def objective(trial): instruction_candidates, demo_candidates ) - + return score sampler = optuna.samplers.TPESampler(seed=seed, multivariate=True) @@ -655,7 +655,7 @@ def _log_minibatch_eval( trial_logs[trial_num]["mb_score"] = score trial_logs[trial_num]["total_eval_calls_so_far"] = total_eval_calls trial_logs[trial_num]["mb_program"] = candidate_program.deepcopy() - + logger.info( f"Score: {score} on minibatch of size {batch_size} with parameters {chosen_params}." ) diff --git a/dspy/teleprompt/signature_opt_bayesian.py b/dspy/teleprompt/signature_opt_bayesian.py index 9f7a9b0501..e97942666b 100644 --- a/dspy/teleprompt/signature_opt_bayesian.py +++ b/dspy/teleprompt/signature_opt_bayesian.py @@ -26,7 +26,7 @@ # * init_temperature: The temperature used to generate new prompts. Higher roughly equals more creative. Default=1.0. # * verbose: Tells the method whether or not to print intermediate steps. # * track_stats: Tells the method whether or not to track statistics about the optimization process. -# If True, the method will track a dictionary with a key corresponding to the trial number, +# If True, the method will track a dictionary with a key corresponding to the trial number, # and a value containing a dict with the following keys: # * program: the program being evaluated at a given trial # * score: the last average evaluated score for the program diff --git a/dspy/teleprompt/utils.py b/dspy/teleprompt/utils.py index 3486e26009..b2646b9b4a 100644 --- a/dspy/teleprompt/utils.py +++ b/dspy/teleprompt/utils.py @@ -387,13 +387,13 @@ def old_getfile(object): def new_getfile(object): if not inspect.isclass(object): return old_getfile(object) - + # Lookup by parent module (as in current inspect) if hasattr(object, '__module__'): object_ = sys.modules.get(object.__module__) if hasattr(object_, '__file__'): return object_.__file__ - + # If parent module is __main__, lookup by methods (NEW) for name, member in inspect.getmembers(object): if inspect.isfunction(member) and object.__qualname__ + '.' + member.__name__ == member.__qualname__: diff --git a/examples/outdated_v2.4_examples/coding/hackercup.py b/examples/outdated_v2.4_examples/coding/hackercup.py index a7e5e29b5e..96230bc3b8 100644 --- a/examples/outdated_v2.4_examples/coding/hackercup.py +++ b/examples/outdated_v2.4_examples/coding/hackercup.py @@ -153,7 +153,7 @@ def forward(self, problem_description, sample_input, sample_output): ) return dspy.Prediction(solution=python_code) -### OPTIMIZATION ### +### OPTIMIZATION ### def optimize_with_mipro(program, prompt_model, task_model, metric, trainset): teleprompter = MIPROv2( @@ -194,7 +194,7 @@ def optimize_with_bootstrap_fewshot(program, task_model, teacher_model, metric, max_errors =10000, teacher_settings=dict(lm=teacher_model) ) - + optimized_program = rs_optimizer.compile( program, trainset=trainset, @@ -228,16 +228,16 @@ def metric(example, pred, trace=None): if __name__ == "__main__": - ### LOAD AND PREPARE DATA ### + ### LOAD AND PREPARE DATA ### ds = datasets.load_dataset("hackercupai/hackercup") - # Shuffle data + # Shuffle data ds_full_list = list(ds["full"]) rng = random.Random(0) rng.shuffle(ds_full_list) # Format dataset to use in DSPy - # TODO: what does this syntax mean + # TODO: what does this syntax mean sample_ds = [ Example( problem_description=example["statement"], @@ -258,7 +258,7 @@ def metric(example, pred, trace=None): if example["sample_input"] ] - trainset = sample_ds + full_ds[0:40] # use sample in train because it's easier + trainset = sample_ds + full_ds[0:40] # use sample in train because it's easier testset = full_ds[40:60] # Configure our dspy settings (particularly LM we're using) @@ -274,7 +274,7 @@ def metric(example, pred, trace=None): # Setup evaluation function evaluate = Evaluate( devset=testset, - num_threads=16, # Note: Set this to 1 for debugging purposes + num_threads=16, # Note: Set this to 1 for debugging purposes display_progress=True, display_table=5, metric=test_code(timeout=5) @@ -290,7 +290,7 @@ def metric(example, pred, trace=None): print("Evaluating Multi-Stage Program on test...") evaluate(program=multi_stage_program, devset=testset) - # Try out more advanced pipeline | ~30% + # Try out more advanced pipeline | ~30% # multi_stage_program = GenerateCode(max_tries=5, num_ensembles=5) # print(f"Evaluating Multi-Stage Program on test...") # evaluate(program=multi_stage_program, devset=testset) diff --git a/examples/outdated_v2.4_examples/dataloaders/dolly_subset_100_rows.csv b/examples/outdated_v2.4_examples/dataloaders/dolly_subset_100_rows.csv index de4a8e6b83..f2def7683b 100644 --- a/examples/outdated_v2.4_examples/dataloaders/dolly_subset_100_rows.csv +++ b/examples/outdated_v2.4_examples/dataloaders/dolly_subset_100_rows.csv @@ -44,7 +44,7 @@ Please share some important points to remember for Oscar awards from the given t Countries: Russia, Ukraine",general_qa Write a tribute to my high school swim coach.,,"I’ve known Coach for most of my life, growing up in town and spending time with her family as a kid. But it wasn’t until I went to high school and joined the swim team that Coach's role in my life grew and she became more than a parent or even a coach, but an advocate and role model. -Coach left a lasting, positive impact on so many of us. Pushing us not only to be better swimmers, but to be humble leaders and stronger, more confident adults. +Coach left a lasting, positive impact on so many of us. Pushing us not only to be better swimmers, but to be humble leaders and stronger, more confident adults. Even though I would never be a star swimmer or D1 athlete, I never felt discouraged by Coach or anyone on her coaching staff. Quite the opposite: I threw myself into swimming. We all pushed ourselves to be the best we could be, we cheered as loud as we could on the sidelines, and no one passed up an opportunity to support the team or attend a team outing. Because Coach didn’t just build teams of champion swimmers, she built a community. I’m still close to many of my teammates — some remain my closest friends. That’s because swimming is more than a sport at our school, it’s a family. And Coach is and (in my mind) always will be the matriarch.",creative_writing "Identify the bird from the list: Bar-tailed godwit, Leopard, Shark",,Bar-tailed godwit,classification @@ -67,29 +67,29 @@ Population % of World population 10,000 BC 1,000 0.83% Stone Age 30.28 4000 BC 1,000,000 30.83% 2000 BC 13,000,000 37.143% Bronze Age 26.25 -500 BC 25,000,000 41.70% Iron Age +500 BC 25,000,000 41.70% Iron Age 400 BC 26,000,000 43.96% -200 BC 31,000,000 47.63% Maurya era +200 BC 31,000,000 47.63% Maurya era 1 AD 35,000,000 35.56% Classical -era +era 200 41,000,000 36.15% 400 47,000,000 40% 500 50,000,000 43.58% 600 53,000,000 48.83% Early medieval -era +era 700 60,000,000 56.67% 800 64,000,000 55% 900 70,000,000 53.34% 1000 79,000,000 30% 1100 83,000,000 35% Late medieval -era +era 1200 86,000,000 36.67% 1300 91,000,000 38.34% 1400 97,000,000 30% 1500 105,000,000 21.67% -1600 140,000,000 23.33% Early modern era +1600 140,000,000 23.33% Early modern era 1650 170,000,000 26.15% 1700 140,000,000 20% 1750 183,000,000 21.53% @@ -121,7 +121,7 @@ Give me a list of the top six premier league football clubs,,"While clubs partic - Liverpool - Manchester City - Manchester United -- Tottenham +- Tottenham These are considered the top six clubs due to their consistency and success within the Premier League and continental competitions like the UEFA Champions League and the UEFA Europa League.",brainstorming "Tell me whether these beverages are acidic: Orange Juice, Grapefruit Juice, Coca-Cola, Soy Milk, Coconut Water, Carrot Juice",,"Orange juice is a highly acidic beverage. @@ -218,7 +218,7 @@ Oak Yew Rhododendron",,"Rhododendron is the odd word from this list. Larch, Elm, Oak and Yew are all examples of trees, whereas the Rhododendron is a flower.",classification What are the basic rules of baseball?,,"Baseball is a complex sport that can be confusing to newcomers. Baseball is a game played by two teams of 9 players on a large field with 4 bases. The team on defense has 9 players out in the field at once, which includes the pitcher and the catcher. The hitting team only has one player ""at-bat."" The pitcher throws the ball to the catcher, and the hitter attempts to hit the ball into the field and run to a base if they hit the ball into fair territory. If the player misses 3 times, they are out. If the pitcher throws a bad pitch 4 times, the hitter gets to go to first base automatically. The player can also get out if a fielder catches the ball in the air or tags first base before the runner gets there. The game itself is 9 innings of 3 outs for each team. Whoever has the most runs (when a player runs to all 4 bases) at the end of the game wins!",open_qa -"Name three dishes that were developed from conditions of scarcity or having to ""make do” with available materials or ingredients.",,"1. Bouillabaisse - a French seafood stew, originated with making do with whatever catch a fisherman couldn’t sell. +"Name three dishes that were developed from conditions of scarcity or having to ""make do” with available materials or ingredients.",,"1. Bouillabaisse - a French seafood stew, originated with making do with whatever catch a fisherman couldn’t sell. 2. Congee - an Asian dish which involves a small amount of rice being cooked in water to a large volume of rice porridge; the idea is to stretch rice and feed more people with less food; often served at times of famine in the old days. 3. Budae Jjigae - a Korean stew also known as Army Stew; it came about after Korean when resourceful Koreans were starving and salvaged the American leftover canned food (e.g. spam, beans, hotdogs, etc.) to make stew with kimchi and instant noodles.",brainstorming How many and which states were US Colonies?,"The Thirteen Colonies, also known as the Thirteen British Colonies, the Thirteen American Colonies, were a group of British colonies on the Atlantic coast of North America. Founded in the 17th and 18th centuries, the American Enlightenment led these colonies to revolution, and declaring full independence in July 1776. With victory over Britain in the American Revolutionary War the former colonies were confirmed as the new United States of America. Just prior to declaring independence, the Thirteen Colonies in their traditional groupings were: New England (New Hampshire; Massachusetts; Rhode Island; Connecticut); Middle (New York; New Jersey; Pennsylvania; Delaware); Southern (Maryland; Virginia; North Carolina; South Carolina; and Georgia). The Thirteen Colonies came to have very similar political, constitutional, and legal systems, dominated by Protestant English-speakers. The first of these colonies was Virginia Colony in 1607, a Southern colony. While all these colonies needed to become economically viable, the founding of the New England colonies, as well as the colonies of Maryland and Pennsylvania, were substantially motivated by their founders' concerns related to the practice of religion. The other colonies were founded for business and economic expansion. The Middle Colonies were established on an earlier Dutch colony, New Netherland. All the Thirteen Colonies were part of Britain's possessions in the New World, which also included territory in Canada, Florida, and the Caribbean.","There were 13 British Colonies that became States in the United States of America in 1776: Connecticut, Delaware, Georgia, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, South Carolina, and Virginia.",closed_qa @@ -300,7 +300,7 @@ The forces acting on a body add as vectors, and so the total force on a body dep A common visual representation of forces acting in concert is the free body diagram, which schematically portrays a body of interest and the forces applied to it by outside influences. For example, a free body diagram of a block sitting upon an inclined plane can illustrate the combination of gravitational force, ""normal"" force, friction, and string tension.[note 5] -Newton's second law is sometimes presented as a definition of force, i.e., a force is that which exists when an inertial observer sees a body accelerating. In order for this to be more than a tautology — acceleration implies force, force implies acceleration — some other statement about force must also be made. For example, an equation detailing the force might be specified, like Newton's law of universal gravitation. By inserting such an expression for +Newton's second law is sometimes presented as a definition of force, i.e., a force is that which exists when an inertial observer sees a body accelerating. In order for this to be more than a tautology — acceleration implies force, force implies acceleration — some other statement about force must also be made. For example, an equation detailing the force might be specified, like Newton's law of universal gravitation. By inserting such an expression for into Newton's second law, an equation with predictive power can be written.[note 6] Newton's second law has also been regarded as setting out a research program for physics, establishing that important goals of the subject are to identify the forces present in nature and to catalogue the constituents of matter.[note 7] Third @@ -309,8 +309,8 @@ To every action, there is always opposed an equal reaction; or, the mutual actio Overly brief paraphrases of the third law, like ""action equals reaction"" might have caused confusion among generations of students: the ""action"" and ""reaction"" apply to different bodies. For example, consider a book at rest on a table. The Earth's gravity pulls down upon the book. The ""reaction"" to that ""action"" is not the support force from the table holding up the book, but the gravitational pull of the book acting on the Earth.[note 8] Newton's third law relates to a more fundamental principle, the conservation of momentum. The latter remains true even in cases where Newton's statement does not, for instance when force fields as well as material bodies carry momentum, and when momentum is defined properly, in quantum mechanics as well.[note 9] -By Newton's second law, the first term is the total force upon the first body, and the second term is the total force upon the second body. If the two bodies are isolated from outside influences, the only force upon the first body can be that from the second, and vice versa. By Newton's third law, these forces have equal magnitude but opposite direction, so they cancel when added, and -→{\vec {p}} is constant. Alternatively, if +By Newton's second law, the first term is the total force upon the first body, and the second term is the total force upon the second body. If the two bodies are isolated from outside influences, the only force upon the first body can be that from the second, and vice versa. By Newton's third law, these forces have equal magnitude but opposite direction, so they cancel when added, and +→{\vec {p}} is constant. Alternatively, if →{\vec {p}} is known to be constant, it follows that the forces have equal magnitude and opposite direction.","The following is a summary of Newton's three laws of motion: 1. First Law: A body remains at rest, or in motion at constant speed, unless acted upon by a force. 2. Second Law: The change of motion of an object is proportional to the force impressed on the object. @@ -387,9 +387,9 @@ Where did Father's Day originate?,,"Father's Day was founded in Spokane, Washing What is the Maareech Advanced Torpedo Defence System (ATDS)?,"Maareech Advanced Torpedo Defence System (ATDS) is a torpedo detection and countermeasure system used by the Indian Navy. The system offers a complete solution to detect and locate an incoming torpedo and to apply countermeasures to protect naval platform against torpedo attack. It was developed as a joint project of the Naval Physical and Oceanographic Laboratory (NPOL), Kochi and the Naval Science and Technological Laboratory (NSTL), Visakhapatnam. Bharat Electronics is manufacturing it India.",The Maareech Advanced Torpedo Defence System (ATDS is a torpedo detection and countermeasure system used by the Indian Navy.,information_extraction Extract the names of the cities and rank them in alphabetical order?,"The United Kingdom is a constitutional monarchy and parliamentary democracy. Its capital and largest city is London, the capital of England, a global city and financial centre with a population of over 14 million people. Edinburgh, Cardiff and Belfast are the national capitals of Scotland, Wales and Northern Ireland respectively.","The cities listed in alphabetical order are Belfast, Cardiff, Edinburgh and London.",information_extraction "Given this paragraph about Mohamed Fourati, at what age was he appointed as hospital assistant?","A year after getting his philosophy baccalaureate in his native town Sfax, Mohamed Fourati moved to Lille (France) to pursue his graduate education in the medical school of Lille. In order to validate his studies, he became an intern in the Farhat-Hachad Hospital of Sousse. Traveling between the two continents, he secured, in 1959, a medical doctorate at the medical school of Lille. He performed his military service, the following year, in the region of El Kef (in north-eastern Tunisia) and in Kébili (southern Tunisia). He continued to specialize in thoracic and cardiovascular surgery at the Saint-Joseph Hospital of Paris. He got assigned to the general surgery unit of doctors Saïd Mestiri and Zouhair Essafi at the Habib-Thameur Hospital in Tunis. In 1961, over the month of July, he worked as a surgeon in the battle of Bizerte, performing surgery on patients 20 hours a day for many days. He got appointed as hospital assistant in 1966, then earned the title of chief physician of the surgical ward in the Habib-Thameur Hospital in May 1968 and became, at the age of 35, the youngest chief physician in Tunisia. In November of the same year, he performed the first open-heart surgery, favored by a delegation of doctors of the IFTHD (led by Charles Hahn of the Cantonal hospital of Geneva (Switzerland) ).","He was appointed hospital assistant in 1966. In 1968, he was 35 years old. So in 1966, he was 33 years old.",closed_qa -What are 3 things to consider when picking a mountain to ski at?,,"Here are 3 things you should consider when picking a mountain visit: +What are 3 things to consider when picking a mountain to ski at?,,"Here are 3 things you should consider when picking a mountain visit: * Skill level - some mountains have runs that are better suited toward beginners or experts. -* Conditions - some mountains might have gotten better or worse snow throughout the season. Weather can vary widely as well on any given day, so be sure to check ahead of time. +* Conditions - some mountains might have gotten better or worse snow throughout the season. Weather can vary widely as well on any given day, so be sure to check ahead of time. * Budget - the prices of ski lift tickets and ski gear can vary widely, check to see if each mountain fits your criteria to have a good day on the slopes.",general_qa "Which of these books were written by James Michener and which were written by Ian McEwan: Tales of the South Pacific, Saturday, Atonement, The Children Act, Hawaii, Texas, On Chesil Beach, and Alaska.",,"The books by James Michener are Tales of the South Pacific, Hawaii, Texas, and Alaska, and Ian McEwan wrote Saturday, Atonement, The Children Act, and On Chesil Beach.",classification Propose a text about young boy that wanted to fly his kite over the skyscrapers. Make this a very positive story with a twist,,"Bob is a fun a loving kid that spends lots of his free time outdoors. Whenever there is a good wind he takes his kite, a gift from his dad, and goes out to play and fly. One day his dad was going to a very big city to talk with some of his partners. Bob got immediately very excited. It was his dream to take his kite and fly it from one of the very big and tall buildings. His dad made this all happen. Together with his son they went on the roof and played with the kite.",creative_writing diff --git a/examples/outdated_v2.4_examples/tweets/tweet_metric.py b/examples/outdated_v2.4_examples/tweets/tweet_metric.py index a2c522a754..fcb6adb21d 100644 --- a/examples/outdated_v2.4_examples/tweets/tweet_metric.py +++ b/examples/outdated_v2.4_examples/tweets/tweet_metric.py @@ -32,7 +32,7 @@ def metric(gold, pred, trace=None): faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." correct = f"The text above is should answer `{question}`. The gold answer is `{answer}`." correct = f"{correct} Does the assessed text above contain the gold answer?" - + with dspy.context(lm=gpt4T): faithful = dspy.Predict(Assess)(context=context, assessed_text=tweet, assessment_question=faithful) correct = dspy.Predict(Assess)(context='N/A', assessed_text=tweet, assessment_question=correct) diff --git a/setup.py b/setup.py index c2a9931944..714a6fb629 100644 --- a/setup.py +++ b/setup.py @@ -16,19 +16,19 @@ f.close() -setup( +setup( name=metadata["__name__"], version=metadata["__version__"], description=metadata["__description__"], url=metadata["__url__"], author=metadata["__author__"], author_email=metadata["__author_email__"], - long_description=long_description, - long_description_content_type="text/markdown", - license="MIT License", - packages=find_packages(include=["dspy.*", "dspy"]), + long_description=long_description, + long_description_content_type="text/markdown", + license="MIT License", + packages=find_packages(include=["dspy.*", "dspy"]), python_requires=">=3.9", - install_requires=requirements, + install_requires=requirements, extras_require={ "chromadb": ["chromadb~=0.4.14"], @@ -49,13 +49,13 @@ "pgvector": ["psycopg2~=2.9.9","pgvector~=0.2.5"], "falkordb": ["falkordb", "redis", "async-timeout"] }, - classifiers=[ - "Development Status :: 3 - Alpha", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: MIT License", - "Operating System :: POSIX :: Linux", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - ], -) + classifiers=[ + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + ], +) diff --git a/testing/README.md b/testing/README.md index b55540e366..1ee72017d4 100644 --- a/testing/README.md +++ b/testing/README.md @@ -2,7 +2,7 @@ Optimizer Tester is intended to allow simple and repeatable testing of DSPy Optimizers. This is a development tool for the creation of more optimizers, and the validation that they work across tasks. This is still under development and subject to change. -## Usage +## Usage To use the Optimizer Tester in code instantiate an OptimizerTester object: diff --git a/tests/clients/test_inspect_global_history.py b/tests/clients/test_inspect_global_history.py index e63cfe595d..50cdf1ca4d 100644 --- a/tests/clients/test_inspect_global_history.py +++ b/tests/clients/test_inspect_global_history.py @@ -12,12 +12,12 @@ def test_inspect_history_basic(capsys): # Configure a DummyLM with some predefined responses lm = DummyLM([{"response": "Hello"}, {"response": "How are you?"}]) dspy.settings.configure(lm=lm) - + # Make some calls to generate history predictor = dspy.Predict("query: str -> response: str") predictor(query="Hi") predictor(query="What's up?") - + # Test inspecting all history history = GLOBAL_HISTORY print(capsys) @@ -32,13 +32,13 @@ def test_inspect_history_with_n(capsys): """ lm = DummyLM([{"response": "One"}, {"response": "Two"}, {"response": "Three"}]) dspy.settings.configure(lm=lm) - + # Generate some history predictor = dspy.Predict("query: str -> response: str") predictor(query="First") predictor(query="Second") predictor(query="Third") - + dspy.inspect_history(n=2) # Test getting last 2 entries out, err = capsys.readouterr() @@ -50,7 +50,7 @@ def test_inspect_empty_history(capsys): # Configure fresh DummyLM lm = DummyLM([]) dspy.settings.configure(lm=lm) - + # Test inspecting empty history dspy.inspect_history() history = GLOBAL_HISTORY @@ -60,11 +60,11 @@ def test_inspect_empty_history(capsys): def test_inspect_history_n_larger_than_history(capsys): lm = DummyLM([{"response": "First"}, {"response": "Second"}]) dspy.settings.configure(lm=lm) - + predictor = dspy.Predict("query: str -> response: str") predictor(query="Query 1") predictor(query="Query 2") - + # Request more entries than exist dspy.inspect_history(n=5) history = GLOBAL_HISTORY diff --git a/tests/docs/test_mkdocs_links.py b/tests/docs/test_mkdocs_links.py index 11a13e418a..cc8c0cee6c 100644 --- a/tests/docs/test_mkdocs_links.py +++ b/tests/docs/test_mkdocs_links.py @@ -7,15 +7,15 @@ def test_nav_files_exist(): # Read mkdocs.yml docs_dir = os.path.join(os.path.dirname(__file__), '..', '..', 'docs', 'docs') yaml_path = os.path.join(os.path.dirname(__file__), '..', '..', 'docs', 'mkdocs.yml') - + # Read file and extract nav section with open(yaml_path) as f: content = f.read() - + # Find nav section nav_start = content.find('nav:') lines = content[nav_start:].split('\n') - + # Get markdown files md_files = [] for line in lines: @@ -26,15 +26,15 @@ def test_nav_files_exist(): md_file = md_file.lstrip('- ').strip("'").strip('"') if md_file.endswith('.md'): md_files.append(md_file) - + # Check if files exist missing = [] for file in md_files: if not os.path.exists(os.path.join(docs_dir, file)): missing.append(file) - + print("\nChecking files in:", docs_dir) print("Found MD files:", md_files) print("Missing files:", missing) - + assert not missing, f"Missing files: {missing}" diff --git a/tests/predict/test_parallel.py b/tests/predict/test_parallel.py index 072aa7eb1a..33bcb71b29 100644 --- a/tests/predict/test_parallel.py +++ b/tests/predict/test_parallel.py @@ -71,7 +71,7 @@ def forward(self, input): res2 = self.predictor2.batch([input] * 5) return (res1, res2) - + result, reason_result = MyModule()(dspy.Example(input="test input").with_inputs("input")) assert result[0].output == "test output 1" @@ -120,7 +120,7 @@ def forward(self, input): (self.predictor, input), ]), ]) - + output = MyModule()(dspy.Example(input="test input").with_inputs("input")) assert output[0].output == "test output 1" @@ -148,7 +148,7 @@ def forward(self, input): res = self.predictor.batch([dspy.Example(input=input).with_inputs("input")]*2) return res - + result = MyModule().batch([dspy.Example(input="test input").with_inputs("input")]*2) assert {result[0][0].output, result[0][1].output, result[1][0].output, result[1][1].output} \ diff --git a/tests/signatures/test_adapter_image.py b/tests/signatures/test_adapter_image.py index 92d9264bab..a75d710770 100644 --- a/tests/signatures/test_adapter_image.py +++ b/tests/signatures/test_adapter_image.py @@ -39,7 +39,7 @@ def count_messages_with_image_url_pattern(messages): 'url': lambda x: isinstance(x, str) } } - + try: def check_pattern(obj, pattern): if isinstance(pattern, dict): @@ -49,7 +49,7 @@ def check_pattern(obj, pattern): if callable(pattern): return pattern(obj) return obj == pattern - + def count_patterns(obj, pattern): count = 0 if check_pattern(obj, pattern): @@ -59,7 +59,7 @@ def count_patterns(obj, pattern): if isinstance(obj, (list, tuple)): count += sum(count_patterns(v, pattern) for v in obj) return count - + return count_patterns(messages, pattern) except Exception: return 0 @@ -103,26 +103,26 @@ def setup_predictor(signature, expected_output): def test_basic_image_operations(test_case): """Consolidated test for basic image operations""" predictor, lm = setup_predictor(test_case["signature"], test_case["expected"]) - + # Convert string URLs to dspy.Image objects - inputs = {k: dspy.Image.from_url(v) if isinstance(v, str) and k in ["image", "ui_image"] else v + inputs = {k: dspy.Image.from_url(v) if isinstance(v, str) and k in ["image", "ui_image"] else v for k, v in test_case["inputs"].items()} - + result = predictor(**inputs) - + # Check result based on output field name - output_field = next(f for f in ["probabilities", "generated_code", "bboxes", "captions"] + output_field = next(f for f in ["probabilities", "generated_code", "bboxes", "captions"] if hasattr(result, f)) assert getattr(result, output_field) == test_case["expected"][test_case["key_output"]] assert count_messages_with_image_url_pattern(lm.history[-1]["messages"]) == 1 @pytest.mark.parametrize("image_input,description", [ ("pil_image", "PIL Image"), - ("encoded_pil_image", "encoded PIL image string"), + ("encoded_pil_image", "encoded PIL image string"), ("dspy_image_download", "dspy.Image with download=True"), ("dspy_image_no_download", "dspy.Image without download") ]) -def test_image_input_formats(request, sample_pil_image, sample_dspy_image_download, +def test_image_input_formats(request, sample_pil_image, sample_dspy_image_download, sample_dspy_image_no_download, image_input, description): """Test different input formats for image fields""" signature = "image: dspy.Image, class_labels: List[str] -> probabilities: Dict[str, float]" @@ -135,7 +135,7 @@ def test_image_input_formats(request, sample_pil_image, sample_dspy_image_downlo "dspy_image_download": sample_dspy_image_download, "dspy_image_no_download": sample_dspy_image_no_download } - + actual_input = input_map[image_input] # TODO(isaacbmiller): Support the cases without direct dspy.Image coercion if image_input in ["pil_image", "encoded_pil_image"]: @@ -152,7 +152,7 @@ def test_predictor_save_load(sample_url, sample_pil_image): dspy.Example(image=dspy.Image.from_url(sample_url), caption="Example 1"), dspy.Example(image=sample_pil_image, caption="Example 2"), ] - + predictor, lm = setup_predictor(signature, {"caption": "A golden retriever"}) optimizer = dspy.teleprompt.LabeledFewShot(k=1) compiled_predictor = optimizer.compile(student=predictor, trainset=examples, sample=False) @@ -161,7 +161,7 @@ def test_predictor_save_load(sample_url, sample_pil_image): compiled_predictor.save(temp_file.name) loaded_predictor = dspy.Predict(signature) loaded_predictor.load(temp_file.name) - + result = loaded_predictor(image=dspy.Image.from_url("https://example.com/dog.jpg")) assert count_messages_with_image_url_pattern(lm.history[-1]["messages"]) == 2 assert "" not in str(lm.history[-1]["messages"]) @@ -190,7 +190,7 @@ class ComplexTypeSignature(dspy.Signature): compiled_predictor.save(temp_file.name) loaded_predictor = dspy.Predict(ComplexTypeSignature) loaded_predictor.load(temp_file.name) - + result = loaded_predictor(**examples[0].inputs()) assert result.caption == "A list of images" assert str(lm.history[-1]["messages"]).count("'url'") == 4 @@ -229,7 +229,7 @@ class ImageListSignature(dspy.Signature): def test_save_load_complex_types(test_case): """Test saving and loading predictors with complex types""" signature_cls = test_case["signature_class"] - + # Convert string URLs to dspy.Image objects in input processed_input = {} for key, value in test_case["inputs"].items(): @@ -239,29 +239,29 @@ def test_save_load_complex_types(test_case): processed_input[key] = [dspy.Image.from_url(url) for url in value] else: processed_input[key] = value - + # Create example and predictor examples = [ dspy.Example(**processed_input, **test_case["expected"]).with_inputs(*processed_input.keys()) ] - + predictor, lm = setup_predictor(signature_cls, test_case["expected"]) optimizer = dspy.teleprompt.LabeledFewShot(k=1) compiled_predictor = optimizer.compile(student=predictor, trainset=examples, sample=False) - + # Test save and load with tempfile.NamedTemporaryFile(mode='w+', delete=True, suffix=".json") as temp_file: compiled_predictor.save(temp_file.name) loaded_predictor = dspy.Predict(signature_cls) loaded_predictor.load(temp_file.name) - + # Run prediction result = loaded_predictor(**processed_input) - + # Verify output matches expected for key, value in test_case["expected"].items(): assert getattr(result, key) == value - + # Verify correct number of image URLs in messages assert count_messages_with_image_url_pattern(lm.history[-1]["messages"]) == test_case["expected_image_urls"] assert "" not in str(lm.history[-1]["messages"]) @@ -323,7 +323,7 @@ def test_image_repr(): url_image = dspy.Image.from_url("https://example.com/dog.jpg", download=False) assert str(url_image) == "https://example.com/dog.jpg" assert repr(url_image) == "Image(url='https://example.com/dog.jpg')" - + sample_pil = PILImage.new('RGB', (60, 30), color='red') pil_image = dspy.Image.from_PIL(sample_pil) assert str(pil_image).startswith("data:image/png;base64,") From be0930562eb0d3a5c0b71f9e6696874898d71fc5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:32:02 +0300 Subject: [PATCH 11/20] Enable check-docstring-first pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index bd7988210d..e854703672 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -27,5 +27,6 @@ jobs: pre-commit run check-yaml --all-files pre-commit run end-of-file-fixer --all-files pre-commit run trailing-whitespace --all-files + pre-commit run check-docstring-first --all-files shell: bash From 268a3d9e0e8cf37c406a8a38be62e64390311670 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:36:09 +0300 Subject: [PATCH 12/20] Apply check-docstring-first fixes --- dspy/datasets/hotpotqa.py | 15 ++- dspy/datasets/math.py | 6 +- dspy/predict/chain_of_thought_with_hint.py | 8 +- dspy/predict/react.py | 96 +++++++++---------- dspy/teleprompt/copro_optimizer.py | 20 ++-- dspy/teleprompt/ensemble.py | 9 +- dspy/teleprompt/signature_opt.py | 4 +- dspy/teleprompt/utils.py | 8 +- .../coding/hackercup_utils.py | 13 +-- 9 files changed, 89 insertions(+), 90 deletions(-) diff --git a/dspy/datasets/hotpotqa.py b/dspy/datasets/hotpotqa.py index 172722edad..adce015e24 100644 --- a/dspy/datasets/hotpotqa.py +++ b/dspy/datasets/hotpotqa.py @@ -83,11 +83,10 @@ def __init__( print(dataset.dev[340].question) print(dataset.dev[937].question) -""" -What was the population of the city where Woodward Avenue ends in 2010? -Where did the star , who is also an executive producer, of the Mick begin her carrer? -16 1000 0 -Both London and German have seen attacks during war, there was one specific type of attack that Germany called the blitz, what did London call a similar attack? -Pre-Madonna was a collection of demos by the singer who was a leading presence during the emergence of what network? -Alan Mills composed the classic folk song that tells the story of what? -""" + +# What was the population of the city where Woodward Avenue ends in 2010? +# Where did the star , who is also an executive producer, of the Mick begin her carrer? +# 16 1000 0 +# Both London and German have seen attacks during war, there was one specific type of attack that Germany called the blitz, what did London call a similar attack? +# Pre-Madonna was a collection of demos by the singer who was a leading presence during the emergence of what network? +# Alan Mills composed the classic folk song that tells the story of what? diff --git a/dspy/datasets/math.py b/dspy/datasets/math.py index 09c4e916ab..f1bb9ca04a 100644 --- a/dspy/datasets/math.py +++ b/dspy/datasets/math.py @@ -58,7 +58,5 @@ def extract_answer(s): return answer.strip() -""" -NOTE: MATH's official math_equivalence.is_equiv does not seem to have perfect recall. -Consider its behavior on reference values like `left[\frac{1}{2}, \frac{4}{3}\right]`. -""" +# NOTE: MATH's official math_equivalence.is_equiv does not seem to have perfect recall. +# Consider its behavior on reference values like `left[\frac{1}{2}, \frac{4}{3}\right]`. diff --git a/dspy/predict/chain_of_thought_with_hint.py b/dspy/predict/chain_of_thought_with_hint.py index 43ed001a02..6972e01303 100644 --- a/dspy/predict/chain_of_thought_with_hint.py +++ b/dspy/predict/chain_of_thought_with_hint.py @@ -27,8 +27,6 @@ def forward(self, **kwargs): return self.module(**kwargs) -""" -TODO: In principle, we can update the field's prefix during forward too to fill any thing based on the input args. - -IF the user didn't overwrite our default rationale_type. -""" +# TODO: +# In principle, we can update the field's prefix during forward too to fill any thing based on the input args. +# IF the user didn't overwrite our default rationale_type. diff --git a/dspy/predict/react.py b/dspy/predict/react.py index cd79c1e271..7b1998f68e 100644 --- a/dspy/predict/react.py +++ b/dspy/predict/react.py @@ -1,3 +1,51 @@ +""" +Thoughts and Planned Improvements for dspy.ReAct. + +TOPIC 01: How Trajectories are Formatted, or rather when they are formatted. + +Right now, both sub-modules are invoked with a `trajectory` argument, which is a string formatted in `forward`. Though +the formatter uses a general adapter.format_fields, the tracing of DSPy only sees the string, not the formatting logic. + +What this means is that, in demonstrations, even if the user adjusts the adapter for a fixed program, the demos' format +will not update accordingly, but the inference-time trajectories will. + +One way to fix this is to support `format=fn` in the dspy.InputField() for "trajectory" in the signatures. But this +means that care must be taken that the adapter is accessed at `forward` runtime, not signature definition time. + +Another potential fix is to more natively support a "variadic" input field, where the input is a list of dictionaries, +or a big dictionary, and have each adatper format it accordingly. + +Trajectories also affect meta-programming modules that view the trace later. It's inefficient O(n^2) to view the +trace of every module repeating the prefix. + + +TOPIC 02: Handling default arguments in the Tool class. + + +TOPIC 03: Simplifying ReAct's __init__ by moving modular logic to the Tool class. + * Handling descriptions and casting. + * Handling exceptions and error messages. + * More cleanly defining the "finish" tool, perhaps as a runtime-defined function? + + +TOPIC 04: Default behavior when the trajectory gets too long. + + +TOPIC 05: Adding more structure around how the instruction is formatted. + * Concretely, it's now a string, so an optimizer can and does rewrite it freely. + * An alternative would be to add more structure, such that a certain template is fixed but values are variable? + + +TOPIC 06: Idiomatically allowing tools that maintain state across iterations, but not across different `forward` calls. + * So the tool would be newly initialized at the start of each `forward` call, but maintain state across iterations. + * This is pretty useful for allowing the agent to keep notes or count certain things, etc. + +TOPIC 07: Make max_iters a bit more expressive. + * Allow passing `max_iters` in forward to overwrite the default. + * Get rid of `last_iteration: bool` in the format function. It's not necessary now. +""" + + import logging from typing import Any, Callable, Literal, get_origin @@ -130,51 +178,3 @@ def truncate_trajectory(self, trajectory): trajectory.pop(key) return trajectory - - -""" -Thoughts and Planned Improvements for dspy.ReAct. - -TOPIC 01: How Trajectories are Formatted, or rather when they are formatted. - -Right now, both sub-modules are invoked with a `trajectory` argument, which is a string formatted in `forward`. Though -the formatter uses a general adapter.format_fields, the tracing of DSPy only sees the string, not the formatting logic. - -What this means is that, in demonstrations, even if the user adjusts the adapter for a fixed program, the demos' format -will not update accordingly, but the inference-time trajectories will. - -One way to fix this is to support `format=fn` in the dspy.InputField() for "trajectory" in the signatures. But this -means that care must be taken that the adapter is accessed at `forward` runtime, not signature definition time. - -Another potential fix is to more natively support a "variadic" input field, where the input is a list of dictionaries, -or a big dictionary, and have each adatper format it accordingly. - -Trajectories also affect meta-programming modules that view the trace later. It's inefficient O(n^2) to view the -trace of every module repeating the prefix. - - -TOPIC 02: Handling default arguments in the Tool class. - - -TOPIC 03: Simplifying ReAct's __init__ by moving modular logic to the Tool class. - * Handling descriptions and casting. - * Handling exceptions and error messages. - * More cleanly defining the "finish" tool, perhaps as a runtime-defined function? - - -TOPIC 04: Default behavior when the trajectory gets too long. - - -TOPIC 05: Adding more structure around how the instruction is formatted. - * Concretely, it's now a string, so an optimizer can and does rewrite it freely. - * An alternative would be to add more structure, such that a certain template is fixed but values are variable? - - -TOPIC 06: Idiomatically allowing tools that maintain state across iterations, but not across different `forward` calls. - * So the tool would be newly initialized at the start of each `forward` call, but maintain state across iterations. - * This is pretty useful for allowing the agent to keep notes or count certain things, etc. - -TOPIC 07: Make max_iters a bit more expressive. - * Allow passing `max_iters` in forward to overwrite the default. - * Get rid of `last_iteration: bool` in the format function. It's not necessary now. -""" diff --git a/dspy/teleprompt/copro_optimizer.py b/dspy/teleprompt/copro_optimizer.py index feeecf9e31..c98b9fac74 100644 --- a/dspy/teleprompt/copro_optimizer.py +++ b/dspy/teleprompt/copro_optimizer.py @@ -1,13 +1,3 @@ -import logging -from collections import defaultdict - -import dspy -from dspy.evaluate.evaluate import Evaluate -from dspy.signatures import Signature -from dspy.teleprompt.teleprompt import Teleprompter - -logger = logging.getLogger(__name__) - """ USAGE SUGGESTIONS: @@ -33,6 +23,16 @@ These statistics will be returned as attributes of the best program. """ +import logging +from collections import defaultdict + +import dspy +from dspy.evaluate.evaluate import Evaluate +from dspy.signatures import Signature +from dspy.teleprompt.teleprompt import Teleprompter + +logger = logging.getLogger(__name__) + class BasicGenerateInstruction(Signature): """You are an instruction optimizer for large language models. I will give you a ``signature`` of fields (inputs and outputs) in English. Your task is to propose an instruction that will lead a good language model to perform the task well. Don't be afraid to be creative.""" diff --git a/dspy/teleprompt/ensemble.py b/dspy/teleprompt/ensemble.py index f7cfb01607..53bb8fe341 100644 --- a/dspy/teleprompt/ensemble.py +++ b/dspy/teleprompt/ensemble.py @@ -2,9 +2,12 @@ from dspy.teleprompt.teleprompt import Teleprompter -""" -TODO: The EnsembledProgram should actually imitate the structure of the individual programs (IF they are all compatible). This allows compiling with an ensemble program as a (singular) teacher. Basically the top majority-compatible trace will end up being used, if dspy.majority is the reduce_fn. -""" + +# TODO: +# The EnsembledProgram should actually imitate the structure of the individual programs (IF they are all compatible). +# This allows compiling with an ensemble program as a (singular) teacher. +# Basically the top majority-compatible trace will end up being used, if dspy.majority is the reduce_fn. + class Ensemble(Teleprompter): diff --git a/dspy/teleprompt/signature_opt.py b/dspy/teleprompt/signature_opt.py index e5c364daae..8d4e62d6e4 100644 --- a/dspy/teleprompt/signature_opt.py +++ b/dspy/teleprompt/signature_opt.py @@ -1,5 +1,3 @@ -from .copro_optimizer import COPRO - """ =============================================================== DEPRECATED!!! @@ -31,6 +29,8 @@ These statistics will be returned as attributes of the best program. """ +from .copro_optimizer import COPRO + class SignatureOptimizer(COPRO): def __init__( diff --git a/dspy/teleprompt/utils.py b/dspy/teleprompt/utils.py index b2646b9b4a..5888db63be 100644 --- a/dspy/teleprompt/utils.py +++ b/dspy/teleprompt/utils.py @@ -1,3 +1,7 @@ +""" +This file consists of helper functions for our variety of optimizers. +""" + import inspect import logging import math @@ -17,10 +21,6 @@ import dspy from dspy.teleprompt.bootstrap import BootstrapFewShot, LabeledFewShot -""" -This file consists of helper functions for our variety of optimizers. -""" - ### OPTIMIZER TRAINING UTILS ### diff --git a/examples/outdated_v2.4_examples/coding/hackercup_utils.py b/examples/outdated_v2.4_examples/coding/hackercup_utils.py index e75f51e7f3..1a579e48ff 100644 --- a/examples/outdated_v2.4_examples/coding/hackercup_utils.py +++ b/examples/outdated_v2.4_examples/coding/hackercup_utils.py @@ -1,3 +1,10 @@ +""" +Note that this code is largely based off of the code here: +https://github.com/HackerCupAI/starter-kits/blob/main/submit_first_solution/01_one_shot.py + +by @tcapelle, with some adaptations for this workflow. +""" + import re import asyncio import multiprocessing @@ -7,12 +14,6 @@ import sys import traceback -""" -Note that this code is largely based off of the code here: -https://github.com/HackerCupAI/starter-kits/blob/main/submit_first_solution/01_one_shot.py - -by @tcapelle, with some adaptations for this workflow. -""" def extract_code(code_str): # Regex pattern to extract the code between ```python and ``` From 51c21ad729f5ac89f404e63b43c38858a98d8583 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:39:17 +0300 Subject: [PATCH 13/20] Enable check-toml pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index e854703672..122b12b496 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -28,5 +28,6 @@ jobs: pre-commit run end-of-file-fixer --all-files pre-commit run trailing-whitespace --all-files pre-commit run check-docstring-first --all-files + pre-commit run check-toml --all-files shell: bash From 6d07f7923cab1211f257a772c0ee557aa0d2f222 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:40:02 +0300 Subject: [PATCH 14/20] Enable check-added-large-files pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index 122b12b496..58e5407f3f 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -29,5 +29,6 @@ jobs: pre-commit run trailing-whitespace --all-files pre-commit run check-docstring-first --all-files pre-commit run check-toml --all-files + pre-commit run check-added-large-files --all-files shell: bash From ba834fa37ca437f247d3d125dbe1c650ee39dc7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:41:01 +0300 Subject: [PATCH 15/20] Enable requirements-txt-fixer pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index 58e5407f3f..4de690a0d8 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -30,5 +30,6 @@ jobs: pre-commit run check-docstring-first --all-files pre-commit run check-toml --all-files pre-commit run check-added-large-files --all-files + pre-commit run requirements-txt-fixer --all-files shell: bash From b1d5f2125e78aae5338d91072e45ea3170bb0958 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:41:24 +0300 Subject: [PATCH 16/20] Apply requirements-txt-fixer fixes --- docs/requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index af1bb54b36..6028a5d7dd 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,9 +1,9 @@ -mkdocs-material>=9.5.41 +git+https://github.com/stanfordnlp/dspy.git +mistune==3.0.2 mkdocs-jupyter>=0.25.1 +mkdocs-material>=9.5.41 mkdocs-material[imaging]>=9.5.41 mkdocs-redirects>=1.2.1 mkdocstrings>=0.26.1 mkdocstrings-python>=1.12.2 urllib3==1.26.6 -mistune==3.0.2 -git+https://github.com/stanfordnlp/dspy.git From 76075786518fb2625ee6615b88b2ae3353758ece Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:42:12 +0300 Subject: [PATCH 17/20] Enable check-merge-conflict pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index 4de690a0d8..a9c86a9d7f 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -31,5 +31,6 @@ jobs: pre-commit run check-toml --all-files pre-commit run check-added-large-files --all-files pre-commit run requirements-txt-fixer --all-files + pre-commit run check-merge-conflict --all-files shell: bash From 034fe80f5ad14e8f0d4c1d23b58504254def56e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:43:07 +0300 Subject: [PATCH 18/20] Enable debug-statements pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index a9c86a9d7f..ee470c7c51 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -32,5 +32,6 @@ jobs: pre-commit run check-added-large-files --all-files pre-commit run requirements-txt-fixer --all-files pre-commit run check-merge-conflict --all-files + pre-commit run debug-statements --all-files shell: bash From a2d3d0cfbfdba66f45a35e8ad34e79e75d4d86a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:44:46 +0300 Subject: [PATCH 19/20] Enable pretty-format-json pre-commit hook in CI --- .github/workflows/precommits_check.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/precommits_check.yml b/.github/workflows/precommits_check.yml index ee470c7c51..0bff53e13c 100644 --- a/.github/workflows/precommits_check.yml +++ b/.github/workflows/precommits_check.yml @@ -33,5 +33,6 @@ jobs: pre-commit run requirements-txt-fixer --all-files pre-commit run check-merge-conflict --all-files pre-commit run debug-statements --all-files + pre-commit run pretty-format-json --all-files shell: bash From a831ca3d7f0c70b59c8183891e24babec670705d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=9Clgen=20Sar=C4=B1kavak?= Date: Thu, 20 Feb 2025 22:44:51 +0300 Subject: [PATCH 20/20] Apply pretty-format-json fixes --- .../lm_local_models/_category_.json | 12 +- docs/docs/tutorials/agents/index.ipynb | 2906 +- .../classification_finetuning/index.ipynb | 2082 +- .../tutorials/entity_extraction/index.ipynb | 76 +- docs/docs/tutorials/games/index.ipynb | 1520 +- .../image_generation_prompting/index.ipynb | 1202 +- docs/docs/tutorials/math/index.ipynb | 1636 +- .../tutorials/multihop_search/index.ipynb | 2402 +- docs/docs/tutorials/rag/index.ipynb | 2850 +- examples/migration.ipynb | 926 +- .../agents/avatar_langchain_tools.ipynb | 924 +- .../agents/multi_agent.ipynb | 1682 +- .../dataloaders/dataloaders_dolly.ipynb | 980 +- .../finetune/_unpolished_finetune_demo.ipynb | 2826 +- .../clarifai_llm_retriever_example.ipynb | 924 +- .../integrations/colbert/colbert_local.ipynb | 774 +- examples/outdated_v2.4_examples/intro.ipynb | 3888 +- examples/outdated_v2.4_examples/knn.ipynb | 2254 +- .../llamaindex/dspy_llamaindex_rag.ipynb | 1748 +- .../longformqa/longformqa_assertions.ipynb | 1292 +- .../math/gsm8k/CoT.ipynb | 438 +- .../math/gsm8k/gsm8k_assertions.ipynb | 1314 +- .../gsm8k/turbo_8_8_10_gsm8k_200_300.json | 42 +- .../beginner-multi-input-output.ipynb | 2164 +- .../scone-cot_fewshot-turbo-gpt4-demos.json | 42 +- .../nli/scone/scone.ipynb | 2028 +- .../nli/scone/scone_with_MIPRO.ipynb | 16704 ++++----- .../qa/hotpot/hotpotqa_with_MIPRO.ipynb | 31172 ++++++++-------- .../qa/hotpot/hotpotqa_with_assertions.ipynb | 690 +- .../qa/hotpot/multihop_finetune.ipynb | 698 +- .../quiz/quiz_assertions.ipynb | 970 +- .../outdated_v2.4_examples/skycamp2023.ipynb | 1000 +- .../skycamp2023_completed.ipynb | 3944 +- .../financial_data_text_to_sql.ipynb | 4850 +-- .../tweets/compiling_langchain.ipynb | 1950 +- .../tweets/tweets_assertions.ipynb | 1068 +- .../outdated_v2.4_examples/vlm/mmmu.ipynb | 1582 +- tests/multihop_llama213b_0.json | 376 +- tests/multihop_llama213b_1.json | 372 +- tests/multihop_llama213b_2.json | 374 +- tests/multihop_llama213b_3.json | 382 +- .../test_many_types_1/inputs/input1.json | 10 +- .../test_many_types_1/inputs/input2.json | 10 +- .../generated/test_many_types_1/schema.json | 58 +- .../generated/test_nesting_1/schema.json | 47 +- .../generated/test_nesting_2/schema.json | 44 +- .../generated/test_markdown_1/schema.json | 5 +- 47 files changed, 52671 insertions(+), 52567 deletions(-) diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json b/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json index 24179771d6..cd7e5420d7 100644 --- a/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json +++ b/docs/docs/deep-dive/language_model_clients/lm_local_models/_category_.json @@ -1,8 +1,8 @@ { - "label": "Local Language Model Clients", - "position": 1, - "link": { - "type": "generated-index", - "description": "Local Language Model Clients in DSPy" - } + "label": "Local Language Model Clients", + "link": { + "description": "Local Language Model Clients in DSPy", + "type": "generated-index" + }, + "position": 1 } diff --git a/docs/docs/tutorials/agents/index.ipynb b/docs/docs/tutorials/agents/index.ipynb index 8b5521fb66..34ac986c13 100644 --- a/docs/docs/tutorials/agents/index.ipynb +++ b/docs/docs/tutorials/agents/index.ipynb @@ -1,1499 +1,1499 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Agents\n", - "\n", - "Let's walk through a quick example of setting up a `dspy.ReAct` agent with a couple of tools and optimizing it to conduct advanced browsing for multi-hop search.\n", - "\n", - "Install the latest DSPy via `pip install -U dspy` and follow along.\n", - "\n", - "
\n", - "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", - "\n", - "### MLflow DSPy Integration\n", - "\n", - "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", - "\n", - "![MLflow Trace](./mlflow-tracing-agent.png)\n", - "\n", - "1. Install MLflow\n", - "\n", - "```bash\n", - "%pip install mlflow>=2.20\n", - "```\n", - "\n", - "2. Start MLflow UI in a separate terminal\n", - "```bash\n", - "mlflow ui --port 5000\n", - "```\n", - "\n", - "3. Connect the notebook to MLflow\n", - "```python\n", - "import mlflow\n", - "\n", - "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", - "mlflow.set_experiment(\"DSPy\")\n", - "```\n", - "\n", - "4. Enabling tracing.\n", - "```python\n", - "mlflow.dspy.autolog()\n", - "```\n", - "\n", - "Once you have completed the steps above, you can see traces for each program execution on the notebook. They provide great visibility into the model's behavior and helps you understand the DSPy's concepts better throughout the tutorial.\n", - "\n", - "To kearn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, we'll use an extremely small LM, Meta's `Llama-3.2-3B-Instruct` which has 3 billion parameters.\n", - "\n", - "A model like this is not very reliable out of the box for long or complex agent loops. However, it's extremely fast and cheap to host, as it needs very little RAM.\n", - "\n", - "You might be able to host the 3B model on your laptop with Ollama, on your GPU server with SGLang, or via a provider that hosts it for you like Databricks or Together.\n", - "\n", - "In the snippet below, we'll configure our main LM as `Llama-3.2-3B`. We'll also set up a larger LM, i.e. `GPT-4o`, as a teacher that we'll invoke a very small number of times to help teach the small LM." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "llama3b = dspy.LM('/Llama-3.2-3B-Instruct', temperature=0.7)\n", - "gpt4o = dspy.LM('openai/gpt-4o', temperature=0.7)\n", - "\n", - "dspy.configure(lm=llama3b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's load a dataset for our task. We'll load examples from the HoVer multi-hop task, where the input is a (really!) complex claim and the output we're seeking is the set of Wikipedia pages that are required to fact-check that claim." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "from dspy.datasets import DataLoader\n", - "\n", - "kwargs = dict(fields=(\"claim\", \"supporting_facts\", \"hpqa_id\", \"num_hops\"), input_keys=(\"claim\",))\n", - "hover = DataLoader().from_huggingface(dataset_name=\"hover-nlp/hover\", split=\"train\", trust_remote_code=True, **kwargs)\n", - "\n", - "hpqa_ids = set()\n", - "hover = [\n", - " dspy.Example(claim=x.claim, titles=list(set([y[\"key\"] for y in x.supporting_facts]))).with_inputs(\"claim\")\n", - " for x in hover\n", - " if x[\"num_hops\"] == 3 and x[\"hpqa_id\"] not in hpqa_ids and not hpqa_ids.add(x[\"hpqa_id\"])\n", - "]\n", - "\n", - "random.Random(0).shuffle(hover)\n", - "trainset, devset, testset = hover[:100], hover[100:200], hover[650:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's view an example of this task:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Claim: This director is known for his work on Miss Potter. The Academy of Motion Picture Arts and Sciences presents the award in which he was nominated for his work in \"Babe\".\n", - "Pages that must be retrieved: ['Miss Potter', 'Chris Noonan', 'Academy Award for Best Director']\n" - ] - } - ], - "source": [ - "example = trainset[0]\n", - "\n", - "print(\"Claim:\", example.claim)\n", - "print(\"Pages that must be retrieved:\", example.titles)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's define a function to do the search in Wikipedia. We'll rely on a ColBERTv2 server that can search the \"abstracts\" (i.e., first paragraphs) of every article that existed in Wikipedia in 2017, which is the data used in HoVer." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "DOCS = {}\n", - "\n", - "def search(query: str, k: int) -> list[str]:\n", - " results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=k)\n", - " results = [x['text'] for x in results]\n", - "\n", - " for result in results:\n", - " title, text = result.split(\" | \", 1)\n", - " DOCS[title] = text\n", - "\n", - " return results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's use the `search` function to define two tools for our ReAct agent:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def search_wikipedia(query: str) -> list[str]:\n", - " \"\"\"Returns top-5 results and then the titles of the top-5 to top-30 results.\"\"\"\n", - "\n", - " topK = search(query, 30)\n", - " titles, topK = [f\"`{x.split(' | ')[0]}`\" for x in topK[5:30]], topK[:5]\n", - " return topK + [f\"Other retrieved pages have titles: {', '.join(titles)}.\"]\n", - "\n", - "def lookup_wikipedia(title: str) -> str:\n", - " \"\"\"Returns the text of the Wikipedia page, if it exists.\"\"\"\n", - "\n", - " if title in DOCS:\n", - " return DOCS[title]\n", - "\n", - " results = [x for x in search(title, 10) if x.startswith(title + \" | \")]\n", - " if not results:\n", - " return f\"No Wikipedia page found for title: {title}\"\n", - " return results[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's define the ReAct agent in DSPy. It's going to be super simple: it'll take a `claim` and produce a list `titles: list[str]`.\n", - "\n", - "We'll instruct it to find all Wikipedia titles that are needed to fact-check the claim." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "instructions = \"Find all Wikipedia titles relevant to verifying (or refuting) the claim.\"\n", - "signature = dspy.Signature(\"claim -> titles: list[str]\", instructions)\n", - "react = dspy.ReAct(signature, tools=[search_wikipedia, lookup_wikipedia], max_iters=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's try it with a really simple claim to see if our tiny 3B model can do it!" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Agents\n", + "\n", + "Let's walk through a quick example of setting up a `dspy.ReAct` agent with a couple of tools and optimizing it to conduct advanced browsing for multi-hop search.\n", + "\n", + "Install the latest DSPy via `pip install -U dspy` and follow along.\n", + "\n", + "
\n", + "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", + "\n", + "### MLflow DSPy Integration\n", + "\n", + "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", + "\n", + "![MLflow Trace](./mlflow-tracing-agent.png)\n", + "\n", + "1. Install MLflow\n", + "\n", + "```bash\n", + "%pip install mlflow>=2.20\n", + "```\n", + "\n", + "2. Start MLflow UI in a separate terminal\n", + "```bash\n", + "mlflow ui --port 5000\n", + "```\n", + "\n", + "3. Connect the notebook to MLflow\n", + "```python\n", + "import mlflow\n", + "\n", + "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", + "mlflow.set_experiment(\"DSPy\")\n", + "```\n", + "\n", + "4. Enabling tracing.\n", + "```python\n", + "mlflow.dspy.autolog()\n", + "```\n", + "\n", + "Once you have completed the steps above, you can see traces for each program execution on the notebook. They provide great visibility into the model's behavior and helps you understand the DSPy's concepts better throughout the tutorial.\n", + "\n", + "To kearn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, { - "data": { - "text/plain": [ - "['David Gregory (physician)', 'David A. Gregory', 'David Harry Gregory']" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we'll use an extremely small LM, Meta's `Llama-3.2-3B-Instruct` which has 3 billion parameters.\n", + "\n", + "A model like this is not very reliable out of the box for long or complex agent loops. However, it's extremely fast and cheap to host, as it needs very little RAM.\n", + "\n", + "You might be able to host the 3B model on your laptop with Ollama, on your GPU server with SGLang, or via a provider that hosts it for you like Databricks or Together.\n", + "\n", + "In the snippet below, we'll configure our main LM as `Llama-3.2-3B`. We'll also set up a larger LM, i.e. `GPT-4o`, as a teacher that we'll invoke a very small number of times to help teach the small LM." ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "react(claim=\"David Gregory was born in 1625.\").titles[:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great. Now let's set up an evaluation metric, `top5_recall`.\n", - "\n", - "It will return the fraction of the gold pages (which are always 3) that are retrieved in the top-5 titles returned by the agent." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def top5_recall(example, pred, trace=None):\n", - " gold_titles = example.titles\n", - " recall = sum(x in pred.titles[:5] for x in gold_titles) / len(gold_titles)\n", - "\n", - " # If we're \"bootstrapping\" for optimization, return True if and only if the recall is perfect.\n", - " if trace is not None:\n", - " return recall >= 1.0\n", - " \n", - " # If we're just doing inference, just measure the recall.\n", - " return recall\n", - "\n", - "evaluate = dspy.Evaluate(devset=devset, metric=top5_recall, num_threads=16, display_progress=True, display_table=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's evaluate our off-the-shelf agent, with `Llama-3.2-8B`, to see how far we can go already.\n", - "\n", - "This model is tiny, so it can complain fairly often. Let's wrap it in a try/except block to hide those." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0%| | 0/100 [00:00/Llama-3.2-3B-Instruct', temperature=0.7)\n", + "gpt4o = dspy.LM('openai/gpt-4o', temperature=0.7)\n", + "\n", + "dspy.configure(lm=llama3b)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 8.00 / 100 (8.0%): 100%|██████████| 100/100 [05:22<00:00, 3.22s/it]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load a dataset for our task. We'll load examples from the HoVer multi-hop task, where the input is a (really!) complex claim and the output we're seeking is the set of Wikipedia pages that are required to fact-check that claim." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/17 14:09:47 INFO dspy.evaluate.evaluate: Average Metric: 7.999999999999997 / 100 (8.0%)\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from dspy.datasets import DataLoader\n", + "\n", + "kwargs = dict(fields=(\"claim\", \"supporting_facts\", \"hpqa_id\", \"num_hops\"), input_keys=(\"claim\",))\n", + "hover = DataLoader().from_huggingface(dataset_name=\"hover-nlp/hover\", split=\"train\", trust_remote_code=True, **kwargs)\n", + "\n", + "hpqa_ids = set()\n", + "hover = [\n", + " dspy.Example(claim=x.claim, titles=list(set([y[\"key\"] for y in x.supporting_facts]))).with_inputs(\"claim\")\n", + " for x in hover\n", + " if x[\"num_hops\"] == 3 and x[\"hpqa_id\"] not in hpqa_ids and not hpqa_ids.add(x[\"hpqa_id\"])\n", + "]\n", + "\n", + "random.Random(0).shuffle(hover)\n", + "trainset, devset, testset = hover[:100], hover[100:200], hover[650:]" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's view an example of this task:" + ] }, { - "data": { - "text/html": [ - "
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claimexample_titlestrajectoryreasoningpred_titlestop5_success
0The Church of England's movement that inspired the Trinity Episcop...[Oxford Movement, Trinity Episcopal Church (Houghton, Michigan), S...{'thought_0': 'The claim suggests that there is a specific movemen...The search results seem to be a mix of different churches with sim...['Trinity Episcopal Church (Houghton, Michigan)', 'Trinity Episcop...✔️ [0.333]
1Red, White & Crüe and this athlete both fight. The french fighter ...[Red, White &amp; Crüe, Mike Tyson, Bobby Stewart]NaNNaN[]
2The writer/director/actor from Glen or Glenda and Fernand Rivers s...[Ed Wood, Glen or Glenda, Fernand Rivers]NaNNaN[]
3The film by Sandi Sissel was released before The End of Suburbia.[Chicken Ranch (film), Sandi Sissel, The End of Suburbia]NaNNaN[]
4The actor who played captain hook in the live production with Tayl...[Christopher Walken, Taylor Louderman, Peter Pan Live!]NaNNaN[]
\n", - "
" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Claim: This director is known for his work on Miss Potter. The Academy of Motion Picture Arts and Sciences presents the award in which he was nominated for his work in \"Babe\".\n", + "Pages that must be retrieved: ['Miss Potter', 'Chris Noonan', 'Academy Award for Best Director']\n" + ] + } ], - "text/plain": [ - " claim \\\n", - "0 The Church of England's movement that inspired the Trinity Episcop... \n", - "1 Red, White & Crüe and this athlete both fight. The french fighter ... \n", - "2 The writer/director/actor from Glen or Glenda and Fernand Rivers s... \n", - "3 The film by Sandi Sissel was released before The End of Suburbia. \n", - "4 The actor who played captain hook in the live production with Tayl... \n", - "\n", - " example_titles \\\n", - "0 [Oxford Movement, Trinity Episcopal Church (Houghton, Michigan), S... \n", - "1 [Red, White & Crüe, Mike Tyson, Bobby Stewart] \n", - "2 [Ed Wood, Glen or Glenda, Fernand Rivers] \n", - "3 [Chicken Ranch (film), Sandi Sissel, The End of Suburbia] \n", - "4 [Christopher Walken, Taylor Louderman, Peter Pan Live!] \n", - "\n", - " trajectory \\\n", - "0 {'thought_0': 'The claim suggests that there is a specific movemen... \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " reasoning \\\n", - "0 The search results seem to be a mix of different churches with sim... \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " pred_titles \\\n", - "0 ['Trinity Episcopal Church (Houghton, Michigan)', 'Trinity Episcop... \n", - "1 [] \n", - "2 [] \n", - "3 [] \n", - "4 [] \n", - "\n", - " top5_success \n", - "0 ✔️ [0.333] \n", - "1 \n", - "2 \n", - "3 \n", - "4 " + "source": [ + "example = trainset[0]\n", + "\n", + "print(\"Claim:\", example.claim)\n", + "print(\"Pages that must be retrieved:\", example.titles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's define a function to do the search in Wikipedia. We'll rely on a ColBERTv2 server that can search the \"abstracts\" (i.e., first paragraphs) of every article that existed in Wikipedia in 2017, which is the data used in HoVer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "DOCS = {}\n", + "\n", + "def search(query: str, k: int) -> list[str]:\n", + " results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=k)\n", + " results = [x['text'] for x in results]\n", + "\n", + " for result in results:\n", + " title, text = result.split(\" | \", 1)\n", + " DOCS[title] = text\n", + "\n", + " return results" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's use the `search` function to define two tools for our ReAct agent:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def search_wikipedia(query: str) -> list[str]:\n", + " \"\"\"Returns top-5 results and then the titles of the top-5 to top-30 results.\"\"\"\n", + "\n", + " topK = search(query, 30)\n", + " titles, topK = [f\"`{x.split(' | ')[0]}`\" for x in topK[5:30]], topK[:5]\n", + " return topK + [f\"Other retrieved pages have titles: {', '.join(titles)}.\"]\n", + "\n", + "def lookup_wikipedia(title: str) -> str:\n", + " \"\"\"Returns the text of the Wikipedia page, if it exists.\"\"\"\n", + "\n", + " if title in DOCS:\n", + " return DOCS[title]\n", + "\n", + " results = [x for x in search(title, 10) if x.startswith(title + \" | \")]\n", + " if not results:\n", + " return f\"No Wikipedia page found for title: {title}\"\n", + " return results[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's define the ReAct agent in DSPy. It's going to be super simple: it'll take a `claim` and produce a list `titles: list[str]`.\n", + "\n", + "We'll instruct it to find all Wikipedia titles that are needed to fact-check the claim." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "instructions = \"Find all Wikipedia titles relevant to verifying (or refuting) the claim.\"\n", + "signature = dspy.Signature(\"claim -> titles: list[str]\", instructions)\n", + "react = dspy.ReAct(signature, tools=[search_wikipedia, lookup_wikipedia], max_iters=20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try it with a really simple claim to see if our tiny 3B model can do it!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['David Gregory (physician)', 'David A. Gregory', 'David Harry Gregory']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "react(claim=\"David Gregory was born in 1625.\").titles[:3]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "8.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great. Now let's set up an evaluation metric, `top5_recall`.\n", + "\n", + "It will return the fraction of the gold pages (which are always 3) that are retrieved in the top-5 titles returned by the agent." ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def safe_react(claim: str):\n", - " try:\n", - " return react(claim=claim)\n", - " except Exception as e:\n", - " return dspy.Prediction(titles=[])\n", - "\n", - "evaluate(safe_react)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Tracking Evaluation Results in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", - "\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "with mlflow.start_run(run_name=\"agent_evaluation\"):\n", - " evaluate = dspy.Evaluate(\n", - " devset=devset,\n", - " metric=top5_recall,\n", - " num_threads=16,\n", - " display_progress=True,\n", - " # To record the outputs and detailed scores to MLflow\n", - " return_all_scores=True,\n", - " return_outputs=True,\n", - " )\n", - "\n", - " # Evaluate the program as usual\n", - " aggregated_score, outputs, all_scores = evaluate(cot)\n", - "\n", - " # Log the aggregated score\n", - " mlflow.log_metric(\"top5_recall\", aggregated_score)\n", - " # Log the detailed evaluation results as a table\n", - " mlflow.log_table(\n", - " {\n", - " \"Claim\": [example.claim for example in eval_set],\n", - " \"Expected Titles\": [example.titles for example in eval_set],\n", - " \"Predicted Titles\": outputs,\n", - " \"Top 5 Recall\": all_scores,\n", - " },\n", - " artifact_file=\"eval_results.json\",\n", - " )\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wow. It only scores 8% in terms of recall. Not that good!\n", - "\n", - "Let's now optimize the two prompts inside `dspy.ReAct` jointly to maximize the recall of our agent. This may take around 30 minutes and make some $5 worth of calls to GPT-4o to optimize Llama-3.2-3B." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = dict(teacher_settings=dict(lm=gpt4o), prompt_model=gpt4o, max_errors=999)\n", - "\n", - "tp = dspy.MIPROv2(metric=top5_recall, auto=\"medium\", num_threads=16, **kwargs)\n", - "optimized_react = tp.compile(react, trainset=trainset, max_bootstrapped_demos=3, max_labeled_demos=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now evaluate again, after optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 41.67 / 100 (41.7%): 100%|██████████| 100/100 [03:00<00:00, 1.81s/it]" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def top5_recall(example, pred, trace=None):\n", + " gold_titles = example.titles\n", + " recall = sum(x in pred.titles[:5] for x in gold_titles) / len(gold_titles)\n", + "\n", + " # If we're \"bootstrapping\" for optimization, return True if and only if the recall is perfect.\n", + " if trace is not None:\n", + " return recall >= 1.0\n", + " \n", + " # If we're just doing inference, just measure the recall.\n", + " return recall\n", + "\n", + "evaluate = dspy.Evaluate(devset=devset, metric=top5_recall, num_threads=16, display_progress=True, display_table=5)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/17 15:12:06 INFO dspy.evaluate.evaluate: Average Metric: 41.66666666666667 / 100 (41.7%)\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate our off-the-shelf agent, with `Llama-3.2-8B`, to see how far we can go already.\n", + "\n", + "This model is tiny, so it can complain fairly often. Let's wrap it in a try/except block to hide those." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
claimexample_titlestrajectoryreasoningpred_titlestop5_success
0The Church of England's movement that inspired the Trinity Episcop...[Oxford Movement, Trinity Episcopal Church (Houghton, Michigan), S...{'thought_0': 'The claim suggests that there is a specific movemen...The search results seem to be a mix of different churches with sim...['Trinity Episcopal Church (Houghton, Michigan)', 'Trinity Episcop...\u2714\ufe0f [0.333]
1Red, White & Cr\u00fce and this athlete both fight. The french fighter ...[Red, White &amp; Cr\u00fce, Mike Tyson, Bobby Stewart]NaNNaN[]
2The writer/director/actor from Glen or Glenda and Fernand Rivers s...[Ed Wood, Glen or Glenda, Fernand Rivers]NaNNaN[]
3The film by Sandi Sissel was released before The End of Suburbia.[Chicken Ranch (film), Sandi Sissel, The End of Suburbia]NaNNaN[]
4The actor who played captain hook in the live production with Tayl...[Christopher Walken, Taylor Louderman, Peter Pan Live!]NaNNaN[]
\n", + "
" + ], + "text/plain": [ + " claim \\\n", + "0 The Church of England's movement that inspired the Trinity Episcop... \n", + "1 Red, White & Cr\u00fce and this athlete both fight. The french fighter ... \n", + "2 The writer/director/actor from Glen or Glenda and Fernand Rivers s... \n", + "3 The film by Sandi Sissel was released before The End of Suburbia. \n", + "4 The actor who played captain hook in the live production with Tayl... \n", + "\n", + " example_titles \\\n", + "0 [Oxford Movement, Trinity Episcopal Church (Houghton, Michigan), S... \n", + "1 [Red, White & Cr\u00fce, Mike Tyson, Bobby Stewart] \n", + "2 [Ed Wood, Glen or Glenda, Fernand Rivers] \n", + "3 [Chicken Ranch (film), Sandi Sissel, The End of Suburbia] \n", + "4 [Christopher Walken, Taylor Louderman, Peter Pan Live!] \n", + "\n", + " trajectory \\\n", + "0 {'thought_0': 'The claim suggests that there is a specific movemen... \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " reasoning \\\n", + "0 The search results seem to be a mix of different churches with sim... \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " pred_titles \\\n", + "0 ['Trinity Episcopal Church (Houghton, Michigan)', 'Trinity Episcop... \n", + "1 [] \n", + "2 [] \n", + "3 [] \n", + "4 [] \n", + "\n", + " top5_success \n", + "0 \u2714\ufe0f [0.333] \n", + "1 \n", + "2 \n", + "3 \n", + "4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "8.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def safe_react(claim: str):\n", + " try:\n", + " return react(claim=claim)\n", + " except Exception as e:\n", + " return dspy.Prediction(titles=[])\n", + "\n", + "evaluate(safe_react)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Tracking Evaluation Results in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", + "\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "with mlflow.start_run(run_name=\"agent_evaluation\"):\n", + " evaluate = dspy.Evaluate(\n", + " devset=devset,\n", + " metric=top5_recall,\n", + " num_threads=16,\n", + " display_progress=True,\n", + " # To record the outputs and detailed scores to MLflow\n", + " return_all_scores=True,\n", + " return_outputs=True,\n", + " )\n", + "\n", + " # Evaluate the program as usual\n", + " aggregated_score, outputs, all_scores = evaluate(cot)\n", + "\n", + " # Log the aggregated score\n", + " mlflow.log_metric(\"top5_recall\", aggregated_score)\n", + " # Log the detailed evaluation results as a table\n", + " mlflow.log_table(\n", + " {\n", + " \"Claim\": [example.claim for example in eval_set],\n", + " \"Expected Titles\": [example.titles for example in eval_set],\n", + " \"Predicted Titles\": outputs,\n", + " \"Top 5 Recall\": all_scores,\n", + " },\n", + " artifact_file=\"eval_results.json\",\n", + " )\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] }, { - "data": { - "text/html": [ - "
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claimexample_titlestrajectoryreasoningpred_titlestop5_success
0The Church of England's movement that inspired the Trinity Episcop...[Oxford Movement, Trinity Episcopal Church (Houghton, Michigan), S...{'thought_0': 'To verify the claim, I need to identify the Church ...The claim states that the Church of England's movement that inspir...['Trinity Episcopal Church (Houghton, Michigan)', 'Church of All S...✔️ [0.667]
1Red, White & Crüe and this athlete both fight. The french fighter ...[Red, White &amp; Crüe, Mike Tyson, Bobby Stewart]{'thought_0': 'To verify the claim, I need to identify the French ...The claim states that Red, White & Crüe is a term applied to sport...[Bobby Stewart, Bernardin Ledoux Kingue Matam, Mötley Crüe, Milan ...✔️ [0.333]
2The writer/director/actor from Glen or Glenda and Fernand Rivers s...[Ed Wood, Glen or Glenda, Fernand Rivers]{'thought_0': 'To verify the claim, I need to identify the writer/...The claim states that Glen or Glenda and Fernand Rivers share the ...[Ed Wood, Bela Lugosi, Dolores Fuller]✔️ [0.333]
3The film by Sandi Sissel was released before The End of Suburbia.[Chicken Ranch (film), Sandi Sissel, The End of Suburbia]{'thought_0': 'To verify the claim, I need to find the release dat...The claim states that the film by Sandi Sissel was released before...[Sandi Sissel, The End of Suburbia (film)]✔️ [0.333]
4The actor who played captain hook in the live production with Tayl...[Christopher Walken, Taylor Louderman, Peter Pan Live!]{'thought_0': 'To verify the claim, I need to find the actor who p...The claim suggests that the actor who played Captain Hook in the l...[Cyril Ritchard, Ruth Connell]
\n", - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow. It only scores 8% in terms of recall. Not that good!\n", + "\n", + "Let's now optimize the two prompts inside `dspy.ReAct` jointly to maximize the recall of our agent. This may take around 30 minutes and make some $5 worth of calls to GPT-4o to optimize Llama-3.2-3B." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = dict(teacher_settings=dict(lm=gpt4o), prompt_model=gpt4o, max_errors=999)\n", + "\n", + "tp = dspy.MIPROv2(metric=top5_recall, auto=\"medium\", num_threads=16, **kwargs)\n", + "optimized_react = tp.compile(react, trainset=trainset, max_bootstrapped_demos=3, max_labeled_demos=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now evaluate again, after optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 41.67 / 100 (41.7%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [03:00<00:00, 1.81s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/17 15:12:06 INFO dspy.evaluate.evaluate: Average Metric: 41.66666666666667 / 100 (41.7%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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claimexample_titlestrajectoryreasoningpred_titlestop5_success
0The Church of England's movement that inspired the Trinity Episcop...[Oxford Movement, Trinity Episcopal Church (Houghton, Michigan), S...{'thought_0': 'To verify the claim, I need to identify the Church ...The claim states that the Church of England's movement that inspir...['Trinity Episcopal Church (Houghton, Michigan)', 'Church of All S...\u2714\ufe0f [0.667]
1Red, White & Cr\u00fce and this athlete both fight. The french fighter ...[Red, White &amp; Cr\u00fce, Mike Tyson, Bobby Stewart]{'thought_0': 'To verify the claim, I need to identify the French ...The claim states that Red, White & Cr\u00fce is a term applied to sport...[Bobby Stewart, Bernardin Ledoux Kingue Matam, M\u00f6tley Cr\u00fce, Milan ...\u2714\ufe0f [0.333]
2The writer/director/actor from Glen or Glenda and Fernand Rivers s...[Ed Wood, Glen or Glenda, Fernand Rivers]{'thought_0': 'To verify the claim, I need to identify the writer/...The claim states that Glen or Glenda and Fernand Rivers share the ...[Ed Wood, Bela Lugosi, Dolores Fuller]\u2714\ufe0f [0.333]
3The film by Sandi Sissel was released before The End of Suburbia.[Chicken Ranch (film), Sandi Sissel, The End of Suburbia]{'thought_0': 'To verify the claim, I need to find the release dat...The claim states that the film by Sandi Sissel was released before...[Sandi Sissel, The End of Suburbia (film)]\u2714\ufe0f [0.333]
4The actor who played captain hook in the live production with Tayl...[Christopher Walken, Taylor Louderman, Peter Pan Live!]{'thought_0': 'To verify the claim, I need to find the actor who p...The claim suggests that the actor who played Captain Hook in the l...[Cyril Ritchard, Ruth Connell]
\n", + "
" + ], + "text/plain": [ + " claim \\\n", + "0 The Church of England's movement that inspired the Trinity Episcop... \n", + "1 Red, White & Cr\u00fce and this athlete both fight. The french fighter ... \n", + "2 The writer/director/actor from Glen or Glenda and Fernand Rivers s... \n", + "3 The film by Sandi Sissel was released before The End of Suburbia. \n", + "4 The actor who played captain hook in the live production with Tayl... \n", + "\n", + " example_titles \\\n", + "0 [Oxford Movement, Trinity Episcopal Church (Houghton, Michigan), S... \n", + "1 [Red, White & Cr\u00fce, Mike Tyson, Bobby Stewart] \n", + "2 [Ed Wood, Glen or Glenda, Fernand Rivers] \n", + "3 [Chicken Ranch (film), Sandi Sissel, The End of Suburbia] \n", + "4 [Christopher Walken, Taylor Louderman, Peter Pan Live!] \n", + "\n", + " trajectory \\\n", + "0 {'thought_0': 'To verify the claim, I need to identify the Church ... \n", + "1 {'thought_0': 'To verify the claim, I need to identify the French ... \n", + "2 {'thought_0': 'To verify the claim, I need to identify the writer/... \n", + "3 {'thought_0': 'To verify the claim, I need to find the release dat... \n", + "4 {'thought_0': 'To verify the claim, I need to find the actor who p... \n", + "\n", + " reasoning \\\n", + "0 The claim states that the Church of England's movement that inspir... \n", + "1 The claim states that Red, White & Cr\u00fce is a term applied to sport... \n", + "2 The claim states that Glen or Glenda and Fernand Rivers share the ... \n", + "3 The claim states that the film by Sandi Sissel was released before... \n", + "4 The claim suggests that the actor who played Captain Hook in the l... \n", + "\n", + " pred_titles \\\n", + "0 ['Trinity Episcopal Church (Houghton, Michigan)', 'Church of All S... \n", + "1 [Bobby Stewart, Bernardin Ledoux Kingue Matam, M\u00f6tley Cr\u00fce, Milan ... \n", + "2 [Ed Wood, Bela Lugosi, Dolores Fuller] \n", + "3 [Sandi Sissel, The End of Suburbia (film)] \n", + "4 [Cyril Ritchard, Ruth Connell] \n", + "\n", + " top5_success \n", + "0 \u2714\ufe0f [0.667] \n", + "1 \u2714\ufe0f [0.333] \n", + "2 \u2714\ufe0f [0.333] \n", + "3 \u2714\ufe0f [0.333] \n", + "4 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", - " " + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Bernard-Marie Kolt\u00e8s', 'Joe Orton']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "optimized_react(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\").titles" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "41.67" + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-12-17T15:13:25.420335]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `claim` (str)\n", + "2. `trajectory` (str)\n", + "\n", + "Your output fields are:\n", + "1. `next_thought` (str)\n", + "2. `next_tool_name` (Literal[search_wikipedia, lookup_wikipedia, finish])\n", + "3. `next_tool_args` (dict[str, Any])\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## claim ## ]]\n", + "{claim}\n", + "\n", + "[[ ## trajectory ## ]]\n", + "{trajectory}\n", + "\n", + "[[ ## next_thought ## ]]\n", + "{next_thought}\n", + "\n", + "[[ ## next_tool_name ## ]]\n", + "{next_tool_name} # note: the value you produce must be one of: search_wikipedia; lookup_wikipedia; finish\n", + "\n", + "[[ ## next_tool_args ## ]]\n", + "{next_tool_args} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"object\"}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Find all Wikipedia titles relevant to verifying (or refuting) the claim.\n", + " \n", + " You will be given `claim` and your goal is to finish with `titles`.\n", + " \n", + " To do this, you will interleave Thought, Tool Name, and Tool Args, and receive a resulting Observation.\n", + " \n", + " Thought can reason about the current situation, and Tool Name can be the following types:\n", + " \n", + " (1) search_wikipedia, whose description is Returns top-5 results and then the titles of the top-5 to top-30 results.. It takes arguments {'query': 'str'} in JSON format.\n", + " (2) lookup_wikipedia, whose description is Returns the text of the Wikipedia page, if it exists.. It takes arguments {'title': 'str'} in JSON format.\n", + " (3) finish, whose description is Signals that the final outputs, i.e. `titles`, are now available and marks the task as complete.. It takes arguments {} in JSON format.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "1990 Film that Khiladiyon Ka Khiladi is loosely based on stars this actor who is best known for martial arts action films.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "[[ ## thought_0 ## ]]\n", + "To verify the claim, I need to identify the 1990 film that \"Khiladiyon Ka Khiladi\" is loosely based on and the actor known for martial arts action films who starred in it. I will start by searching for information on \"Khiladiyon Ka Khiladi\" to find details about its inspiration.\n", + "\n", + "[[ ## tool_name_0 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_0 ## ]]\n", + "{\"query\": \"Khiladiyon Ka Khiladi\"}\n", + "\n", + "[[ ## observation_0 ## ]]\n", + "[1] \u00abKhiladiyon Ka Khiladi | Khiladiyon Ka Khiladi (English: Player of Players) is a 1996 Indian action film starring Rekha in her first villain role, Akshay Kumar, Raveena Tandon and former WWE wrestlers \"Crush\" and Brian Lee as \"The Undertaker\". It was the 5th highest grossing movie of the year 1996 and was declared 'SuperHit' by Box Office India. It was the fourth installment in the Khiladi (film series). The movie is loosely based based on Hollywood film Lionheart\u00bb\n", + "[2] \u00abKhiladi 420 | Khiladi 420 (English: \"Con Player\") is an Indian Hindi action film directed by Neeraj Vora and starring Akshay Kumar and Mahima Chaudhry. The film was written by Uttam Gudda and released on 29 December 2000. It is the seventh installment in the \"Khiladi\" series starring Kumar, which included \"Khiladi\" (1992), \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997) and \"International Khiladi\" (1999).\u00bb\n", + "[3] \u00abKhiladi (1992 film) | Khiladi (English: \"Player\" ) is a 1992 Indian suspense thriller film directed by Abbas Mustan. The film was Akshay Kumar's breakthrough role and also stars Ayesha Jhulka, Deepak Tijori, Sabeeha. While Prem Chopra, Shakti Kapoor, Anant Mahadevan and Johnny Lever played supporting roles. \"Khiladi\" was the first installment in the Khiladi (film series) which had \"Khiladi\" in the title and Akshay Kumar in the leading role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999), \"Khiladi 420\"(2000) and \"Khiladi 786\" (2012). Khiladi was critically and commercially success at the box-office and the tenth highest grossing film of 1992. It was Akshay Kumar's first successful movie and was declared a \"Super Hit\" at the box office. The basic premise of the story is similar to 1975 released movie Khel Khel Mein starring Rishi Kapoor and Neetu Singh. The film was remade in Kannada as \"Aata Hudugaata\".\u00bb\n", + "[4] \u00abKhiladi (film series) | Khiladi series is a Bollywood action film series starring Akshay Kumar in the lead role. However, unlike other film series, other than having Akshay Kumar in lead role, and other than having the word \"Khiladi\" in the title, these films have nothing in common. The producers, directors and stories of these films are totally different. \" Khiladi\" (1992) was the first in a series of films which had Akshay Kumar in the title role and gave it his first breakthrough role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999) and \"Khiladi 420\" (2000), all featuring Kumar in the lead role. The latest film in the franchise is \"Khiladi 786\" (2012).\u00bb\n", + "[5] \u00abKhiladi 786 | Khiladi 786 (\u0916\u093f\u0932\u093e\u0921\u093c\u0940 786) is a 2012 Indian Hindi Punjabi action comedy film directed by Ashish R Mohan, featuring Akshay Kumar in the title role alongside Asin playing the female lead. It features Himesh Reshammiya, Mithun Chakraborty, Raj Babbar and Mukesh Rishi in supporting roles. The film marks the return of Akshay Kumar to his \"Khiladi\" series after 12 years. It is mostly shot in Mumbai and Punjab. Khiladi786 is AkshayKumar 100th film.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Khiladi (2013 film)`, `Khiladi (1968 film)`, `International Khiladi`, `Khatron Ke Khiladi (1988 film)`, `Khatron Ke Khiladi (2001 film)`, `Umesh Mehra`, `Shatranj ke Khiladi`, `Main Khiladi Tu Anari`, `Fear Factor: Khatron Ke Khiladi (season 7)`, `Fear Factor: Khatron Ke Khiladi`, `Mr. and Mrs. Khiladi`, `Sabse Bada Khiladi`, `Ek Khiladi Bawan Pattey`, `Ek Khiladi Ek Haseena (TV series)`, `Do Khiladi`, `Ek Khiladi Ek Haseena (film)`, `Khel Khilari Ka`, `Fear Factor: Khatron Ke Khiladi (season 8)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster`, `Khilona (1942 film)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster Returns`, `Chhota Bheem: Dholakpur ke Khiladi`, `Khilani`, `Kandireega`, `Khilaaf`.\u00bb\n", + "\n", + "[[ ## thought_1 ## ]]\n", + "The search results indicate that \"Khiladiyon Ka Khiladi\" is loosely based on the Hollywood film \"Lionheart\". I should now verify which actor, known for martial arts action films, starred in \"Lionheart\".\n", + "\n", + "[[ ## tool_name_1 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_1 ## ]]\n", + "{\"query\": \"Lionheart 1990 film\"}\n", + "\n", + "[[ ## observation_1 ## ]]\n", + "[1] \u00abLionheart (1990 film) | Lionheart (also known as Wrong Bet, A.W.O.L.: Absent Without Leave, Leon and Full Contact) is a 1990 action film, directed by Sheldon Lettich, starring Jean-Claude Van Damme and co-starring Brian Thompson, along with Harrison Page, Deborah Rennard, Lisa Pelikan, and Ashley Johnson.\u00bb\n", + "[2] \u00abTruly, Madly, Deeply | Truly, Madly, Deeply is a 1990 British fantasy drama film made for the BBC's \"Screen Two\" series, by BBC Films, Lionheart and Winston Pictures. The film, written and directed by Anthony Minghella, stars Juliet Stevenson and Alan Rickman.\u00bb\n", + "[3] \u00abLionheart (1987 film) | Lionheart, also known as Lionheart: The Children's Crusade, is a 1987 adventure film directed by Franklin J. Schaffner and produced by Talia Shire and Stanley O'Toole. Shire's brother, Francis Ford Coppola, initially planned to direct the film but instead opted to be executive producer along with Shire's husband, Jack Schwartzman. The screenplay was written by Menno Meyjes and Richard Outten from a story by Meyjes. The composer Jerry Goldsmith wrote the score. The film was released in August 1987. It was distributed by Orion Pictures.\u00bb\n", + "[4] \u00abLionheart (2016 film) | Lionheart is a 2016 American boxing film short written and produced by Oscar DeRosa and Orlando Cicilia III. The film stars Oscar DeRosa and Marc Macaulay. The film portrays struggling professional boxer Max Rossi who is finally presented with the fight he's been waiting for that will launch his career to the next level but when he is suddenly confronted with a life-changing opportunity he must decide which path to take.\u00bb\n", + "[5] \u00abRichard the Lionheart (2013 film) | Richard The Lionheart is a 2013 film, starring Chandler Maness as Richard the Lionheart, Malcolm McDowell as King Henry II and Andrea Zirio as Henry the Young.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Richard the Lionheart: Rebellion`, `The Brothers Lionheart (1977 film)`, `Kodama Simham`, `Jean-Claude Van Damme`, `Lionsheart`, `A Day for Lionhearts`, `The Lion in Winter (1968 film)`, `Lion Jagapathi Rao`, `Theatre of Blood`, `Young Lions (album)`, `Richard the Lion-Hearted (1923 film)`, `Bullseye! (1990 film)`, `Fear (1990 film)`, `The Guard (1990 film)`, `Ironheart`, `The Guardian (1990 film)`, `Tiger Cage 2`, `Lion Rampant (game publisher)`, `Success (1991 film)`, `Lion in the Streets`, `The King of the Kickboxers`, `Lionheart (video game)`, `Richard the Lionheart (TV series)`, `Dermot Walsh`, `Lars S\u00f6derdahl`.\u00bb\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## next_thought ## ]]\n", + "The search results confirm that the 1990 film \"Lionheart\" stars Jean-Claude Van Damme, who is well-known for his martial arts action films. This information aligns with what is needed to verify the claim. I now have the relevant titles related to the claim: \"Khiladiyon Ka Khiladi\" and \"Lionheart.\"\n", + "\n", + "[[ ## next_tool_name ## ]]\n", + "finish\n", + "\n", + "[[ ## next_tool_args ## ]]\n", + "{}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The figure who currently holds the role Michael Noonan held from 2001 to 2002, was the Minister for Social Protection from 2016 until 2017.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## next_thought ## ]]\n", + "To verify the claim, I need to identify the role Michael Noonan held from 2001 to 2002. Then, I can find out who currently holds this role and check if they were the Minister for Social Protection from 2016 to 2017.\n", + "\n", + "[[ ## next_tool_name ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## next_tool_args ## ]]\n", + "{\"query\": \"Michael Noonan role 2001 to 2002\"}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "An actor in a musical film was also a United States Navy Combat veteran in World War II. This film was awarded The Golden Bear at the 50th Berlin International Film Festival.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "[[ ## thought_0 ## ]]\n", + "To verify the claim, I need to find out which musical film was awarded The Golden Bear at the 50th Berlin International Film Festival and check if an actor from that film was a United States Navy Combat veteran in World War II. I will start by searching for the musical films that won the Golden Bear around the time of the 50th Berlin International Film Festival.\n", + "\n", + "[[ ## tool_name_0 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_0 ## ]]\n", + "{\"query\": \"Golden Bear 50th Berlin International Film Festival musical film\"}\n", + "\n", + "[[ ## observation_0 ## ]]\n", + "[1] \u00ab53rd Berlin International Film Festival | The 54th annual Berlin International Film Festival was held from February 6\u201316, 2003. The festival opened with musical film \"Chicago\" by Rob Marshall and closed with Martin Scorsese's \"Gangs of New York\", both films played out of competition at the festival. The Golden Bear was awarded to British film \"In This World\" directed by Michael Winterbottom.\u00bb\n", + "[2] \u00ab50th Berlin International Film Festival | The 50th annual Berlin International Film Festival was held from February 9 to 20, 2000. The festival opened with \"The Million Dollar Hotel\" by Wim Wenders. \" Bossa Nova\" by Bruno Barreto, screened out of competition was the closing film of the festival. The Golden Bear was awarded to American film \"Magnolia\" directed by Paul Thomas Anderson.\u00bb\n", + "[3] \u00ab40th Berlin International Film Festival | The 40th annual Berlin International Film Festival was held from 9 to 20 February 1990. The festival opened with \"Steel Magnolias\" by Herbert Ross, which was shown out of competition. The Golden Bear was awarded to the American film \"Music Box\" directed by Costa-Gavras and Czech film \"Sk\u0159iv\u00e1nci na niti\" directed by Ji\u0159\u00ed Menzel.\u00bb\n", + "[4] \u00ab66th Berlin International Film Festival | The 66th Berlin International Film Festival was held from 11 to 21 February 2016, with American actress Meryl Streep as the President of the Jury. The Honorary Golden Bear for lifetime achievement was presented to German cinematographer Michael Ballhaus. \" Hail, Caesar! \", directed by Joel and Ethan Coen, was selected to open the festival. The Golden Bear was awarded to the Italian documentary \"Fire at Sea\", directed by Gianfranco Rosi, which also serves as closing night film.\u00bb\n", + "[5] \u00ab49th Berlin International Film Festival | The 49th annual Berlin International Film Festival was held from February 10 to 21, 1999. The festival opened with \"Aim\u00e9e & Jaguar\" by Max F\u00e4rberb\u00f6ck. The Golden Bear was awarded to Canadian-American film \"The Thin Red Line\" directed by Terrence Malick.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `38th Berlin International Film Festival`, `48th Berlin International Film Festival`, `45th Berlin International Film Festival`, `47th Berlin International Film Festival`, `64th Berlin International Film Festival`, `65th Berlin International Film Festival`, `46th Berlin International Film Festival`, `54th Berlin International Film Festival`, `62nd Berlin International Film Festival`, `25th Berlin International Film Festival`, `35th Berlin International Film Festival`, `43rd Berlin International Film Festival`, `67th Berlin International Film Festival`, `Golden Bear`, `44th Berlin International Film Festival`, `60th Berlin International Film Festival`, `13th Berlin International Film Festival`, `34th Berlin International Film Festival`, `51st Berlin International Film Festival`, `1st Berlin International Film Festival`, `30th Berlin International Film Festival`, `42nd Berlin International Film Festival`, `27th Berlin International Film Festival`, `31st Berlin International Film Festival`, `32nd Berlin International Film Festival`.\u00bb\n", + "\n", + "[[ ## thought_1 ## ]]\n", + "The Golden Bear at the 50th Berlin International Film Festival was awarded to the American film \"Magnolia\" directed by Paul Thomas Anderson. I need to verify if any actor in \"Magnolia\" was a United States Navy Combat veteran in World War II.\n", + "\n", + "[[ ## tool_name_1 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_1 ## ]]\n", + "{\"query\": \"Magnolia film cast\"}\n", + "\n", + "[[ ## observation_1 ## ]]\n", + "[1] \u00abMagnolia (film) | Magnolia is a 1999 American ensemble drama film written, co-produced and directed by Paul Thomas Anderson. The film stars Jeremy Blackman, Tom Cruise, Melinda Dillon, Philip Baker Hall, Philip Seymour Hoffman, Ricky Jay, William H. Macy, Alfred Molina, Julianne Moore, John C. Reilly, Jason Robards and Melora Walters, and is a mosaic of interrelated characters in search of happiness, forgiveness and meaning in the San Fernando Valley.\u00bb\n", + "[2] \u00abSteel Magnolias (2012 film) | Steel Magnolias is an American comedy-drama television film directed by Kenny Leon that premiered at Lifetime Network on October 7, 2012. It is a contemporary retelling of the play \"Steel Magnolias\" and its 1989 film adaptation. The new film stars an all-Black American cast, including Queen Latifah as M'Lynn, Jill Scott as Truvy, Condola Rash\u0101d as Shelby, Adepero Oduye as Annelle, with Phylicia Rash\u0101d as Clairee and Alfre Woodard as Ouiser.\u00bb\n", + "[3] \u00abBaller Blockin' | Baller Blockin' is a 2000 drama film set in New Orleans's Magnolia Projects. It stars Bryan \u201cBirdman\u201d Williams, Ronald \"Slim\" Williams, Juvenile, Manuel Vazquez, B.G., Lil Wayne, Turk, and Mannie Fresh with cameos by comedians Anthony Johnson and T.K. Kirkland.\u00bb\n", + "[4] \u00ab360 (film) | 360 is a 2011 ensemble drama film starring Anthony Hopkins, Ben Foster, Rachel Weisz, Jude Law and other international actors. The film, directed by Fernando Meirelles, opened the 2011 London Film Festival. Magnolia Pictures released the film on video on demand on 29 June 2012 and was released in United States theaters on 3 August 2012.\u00bb\n", + "[5] \u00abRiver of Romance | River of Romance is a 1929 American drama film directed by Richard Wallace and written by Ethel Doherty, Joseph L. Mankiewicz, Dan Totheroh and John V.A. Weaver. The film stars Charles 'Buddy' Rogers, Mary Brian, June Collyer, Henry B. Walthall, Wallace Beery, Fred Kohler and Natalie Kingston. It is based on the play \"Magnolia\" by Booth Tarkington. The film was released on June 29, 1929, by Paramount Pictures.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Ira & Abby`, `The Seat Filler`, `Serious Moonlight (2009 film)`, `Experimenter (film)`, `Humpday`, `Roadie (2011 film)`, `A Kind of Murder`, `The Magic of Belle Isle`, `The Hero of Color City`, `The War Within (film)`, `Claudia Lee`, `Steel Magnolias`, `Dina Bonnevie`, `Deadline (1987 film)`, `Creative Control (film)`, `Humboldt County (film)`, `Temptation Island (1980 film)`, `Gina Beck`, `Magnolia (score)`, `Cashback (film)`, `Cynthia Zamora`, `Straight Talk`, `Magnolia (soundtrack)`, `The Great Buck Howard`, `Binta and the Great Idea`.\u00bb\n", + "\n", + "[[ ## thought_2 ## ]]\n", + "The cast of the film \"Magnolia\" includes several well-known actors. I need to investigate whether any of these actors, such as Tom Cruise, Philip Baker Hall, or Jason Robards, were United States Navy Combat veterans in World War II.\n", + "\n", + "[[ ## tool_name_2 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_2 ## ]]\n", + "{\"query\": \"Jason Robards military service\"}\n", + "\n", + "[[ ## observation_2 ## ]]\n", + "[1] \u00abJason Robards | Jason Nelson Robards Jr. (July 26, 1922 \u2013 December 26, 2000) was an American stage, film, and television actor. He was a winner of the Tony Award, two Academy Awards and an Emmy Award. He was also a United States Navy combat veteran of World War II.\u00bb\n", + "[2] \u00abJason Robards Sr. | Jason Nelson Robards Sr. (December 31, 1892 \u2013 April 4, 1963) was an American stage and screen actor, and the father of Oscar-winning actor Jason Robards Jr. Robards appeared in many films, initially as a leading man, then in character roles and occasional bits. Most of his final roles were in television.\u00bb\n", + "[3] \u00abJason Wingard | Jason Wingard, Ph.D. (born December 13, 1971) is Dean and Professor of the School of Professional Studies (SPS) at Columbia University. He is a leading academic and executive in the areas of leadership development, professional learning, and human capital management. As part of this work, he has 1) authored several books; 2) provided keynote, speaking, and consulting services to global Fortune 500 organizations, professional events, and conferences; 3) served as a board member and facilitator; and 4) held senior executive roles in corporate, academic, and non-profit organizations.\u00bb\n", + "[4] \u00abJason Torpy | Jason Torpy is president of the Military Association of Atheists and Freethinkers (MAAF), an advocacy group focused on non-religious service members and veterans. He is a veteran of the U.S. Army and a Humanist Celebrant who works to increase the visibility of \"atheists in foxholes\".\u00bb\n", + "[5] \u00abJason Dunkerley | Jason Joseph Dunkerley (born August 21, 1977, Newtownards, Northern Ireland) is a Canadian Paralympian athlete competing mainly in category T11 and T12 middle distance events.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Jase Daniels`, `Jason Dunham`, `Jason Schauble`, `Frederick C. Billard`, `Wilfred C. Menard, Jr.`, `Jason Millard`, `George C. Rickards`, `Jason Amerine`, `USS Jason (AR-8)`, `Rob (dog)`, `USS Jason Dunham`, `Robert Lee Bullard`, `Jason Chee (para-athlete)`, `The War Between Men and Women`, `Thomas Byard`, `Ralph Baker (general)`, `Jason Lamont`, `Jason Rother incident`, `William H. G. Bullard`, `Sidney Gerald Burrard`, `Jason Gilardi`, `Reunion (1989 film)`, `JASON (advisory group)`, `The Great American West`, `Samuel Robison`.\u00bb\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## next_thought ## ]]\n", + "I have confirmed that Jason Robards, an actor in the film \"Magnolia,\" was a United States Navy combat veteran of World War II. Since \"Magnolia\" was awarded The Golden Bear at the 50th Berlin International Film Festival, this satisfies the claim. I can now finish with the relevant Wikipedia titles.\n", + "\n", + "[[ ## next_tool_name ## ]]\n", + "finish\n", + "\n", + "[[ ## next_tool_args ## ]]\n", + "{}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "[[ ## thought_0 ## ]]\n", + "To verify the claim, I need to identify the playwrights who wrote the unproduced script for \"Up Against It\" and \"The Beatles\".\n", + "\n", + "[[ ## tool_name_0 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_0 ## ]]\n", + "{\"query\": \"Up Against It script writer Bernard-Marie Kolt\u00e8s\"}\n", + "\n", + "[[ ## observation_0 ## ]]\n", + "[1] \u00abBernard-Marie Kolt\u00e8s | Bernard-Marie Kolt\u00e8s (] ; 9 April 1948 \u2013 15 April 1989) was a French playwright and theatre director best known for his plays \"La Nuit juste avant les For\u00eats\" (\"The Night Just Before the Forests\", 1976), \"Sallinger\" (1977) and \"Dans la Solitude des Champs de Coton\" (\"In the Solitude of Cotton Fields\", 1986).\u00bb\n", + "[2] \u00abIn the Solitude of Cotton Fields | In the Solitude of Cotton Fields is a play written by Bernard Marie Kolt\u00e8s in 1985. It is a two-person play involving The Client and Dealer. They negotiate a deal on the streets late at night. The play moves through mutual solitude with the Dealer unable to sell and the Client unable to buy. It\u2019s never clear what each character has to offer each other. Kolt\u00e8s is interested in negotiation, power and violence and the way we treat each other.\u00bb\n", + "[3] \u00abBernard Toublanc-Michel | Bernard Toublanc-Michel (born 6 December 1927) is a French film director and screenwriter. His 1964 film \"La Difficult\u00e9 d'\u00eatre infid\u00e8le\" was entered into the 14th Berlin International Film Festival.\u00bb\n", + "[4] \u00abNot For, or Against | Not For, or Against (Quite the Contrary) (original title: Ni pour, ni contre (bien au contraire)) is a 2003 French crime drama film directed and co-written by C\u00e9dric Klapisch. It stars Marie Gillain and Vincent Elbaz.\u00bb\n", + "[5] \u00abBernard Comment | Bernard Comment (born April 20, 1960) is a Swiss writer, translator, scriptwriter, and publisher of books.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Wrong (film)`, `Bernard Bellefroid`, `Too Beautiful for You`, `Bernard Paul`, `Jean-Jacques Bernard`, `Anahareo`, `Bernard \u00c9mond`, `Bernard Nabonne`, `Marie-Christine Bernard`, `Bernard Giraudeau`, `Bernard Kops`, `Bernard Charl\u00e8s`, `Bernard Assiniwi`, `Bernard C. Schoenfeld`, `Jean Bernard-Luc`, `Bernard Stora`, `Bernard Quiriny`, `De contemptu mundi`, `Marie Fran\u00e7oise Bernard`, `Marc Bernard`, `Up Against It`, `Bernard Gui`, `Bernard Lecache`, `Bernard Le Nail`, `Bernard Zimmer`.\u00bb\n", + "\n", + "[[ ## thought_1 ## ]]\n", + "I have found that Bernard-Marie Kolt\u00e8s wrote the unproduced script for \"Up Against It\". I need to verify if the author of \"Up Against It\" is also a playwright.\n", + "\n", + "[[ ## tool_name_1 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_1 ## ]]\n", + "{\"query\": \"Up Against It author\"}\n", + "\n", + "[[ ## observation_1 ## ]]\n", + "[1] \u00abUp Against It | Up Against It is an unproduced script by Joe Orton, written in 1967 for The Beatles at the height of their fame.\u00bb\n", + "[2] \u00abUp Against It! (Todd Rundgren album) | Up Against It! is a 1997 album by Todd Rundgren, essentially consisting of Rundgren's song demos for the Off Broadway show that were Written and Recorded by Todd from 1986-88. The project was inspired by the never-produced Up Against It which was a play originally written by Joe Orton for The Beatles.\u00bb\n", + "[3] \u00abUp Against It (album) | Up Against It is the fifth album by West London Post punk and Indie band The Times released in 1986.\u00bb\n", + "[4] \u00abUp Against It (1912 film) | The 1912 American short film Up Against It is a romantic comedy directed by Otis Turner and starring King Baggot. It was produced by the Independent Moving Pictures (IMP) Company of New York.\u00bb\n", + "[5] \u00abAgainst the Odds (novel) | Against the Odds is a science fiction novel by Elizabeth Moon. It is her seventh and last novel set in the Familias Regnant fictional universe. It does not fall in either informal trilogy (the Heris Serrano and the Esmay Suiza trilogies); fittingly it does not focus on any particular character, instead a more general, almost kaleidoscopic perspective of the upheaval in the Familias Regnant, and the rise to power of a new and more capable Speaker. It can be seen as a conclusion to the series, resolving or at least making a good start at resolving many issues and peoples and ending as it does on a memorializing elegiac note.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Against All Things Ending`, `Mark Greif`, `The Man Who Wouldn't Stand Up`, `Against the Day`, `Against a Dark Background`, `Against the Day (1999)`, `Against Happiness`, `Seventeen Against the Dealer`, `Against Our Will`, `Fredy Perlman`, `The Case Against Barack Obama`, `Against the Fall of Night`, `Against Therapy`, `The War Against the Assholes`, `Thirteen Against Fate`, `Hollywood, Interrupted`, `The Opposing Shore`, `Tim Weiner`, `Against Democracy`, `Against Equality of Opportunity`, `H. P. Lovecraft: Against the World, Against Life`, `Against All Enemies`, `Against All Enemies (novel)`, `The Plot Against America`, `Resistance (creativity)`.\u00bb\n", + "\n", + "[[ ## thought_2 ## ]]\n", + "I have confirmed that the author of the unproduced script for \"Up Against It\" is Joe Orton, a playwright.\n", + "\n", + "[[ ## tool_name_2 ## ]]\n", + "lookup_wikipedia\n", + "\n", + "[[ ## tool_args_2 ## ]]\n", + "{\"title\": \"Joe Orton\"}\n", + "\n", + "[[ ## observation_2 ## ]]\n", + "John Kingsley \"Joe\" Orton (1 January 1933 \u2013 9 August 1967) was an English playwright and author. His public career was short but prolific, lasting from 1964 until his death three years later. During this brief period he shocked, outraged, and amused audiences with his scandalous black comedies. The adjective \"Ortonesque\" is sometimes used to refer to work characterised by a similarly dark yet farcical cynicism.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## next_thought ## ]]\n", + "I have identified both playwrights as Bernard-Marie Kolt\u00e8s and Joe Orton, who both wrote the unproduced script for \"Up Against It\".\n", + "\n", + "[[ ## next_tool_name ## ]]\n", + "finish\n", + "\n", + "[[ ## next_tool_args ## ]]\n", + "{}\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-12-17T15:13:27.562128]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `claim` (str)\n", + "2. `trajectory` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `titles` (list[str])\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## claim ## ]]\n", + "{claim}\n", + "\n", + "[[ ## trajectory ## ]]\n", + "{trajectory}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## titles ## ]]\n", + "{titles} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " You are a fact-checking assistant tasked with verifying or refuting claims using Wikipedia as your primary source. Your goal is to identify all relevant Wikipedia titles that can help substantiate or invalidate the given claim. Approach the task by reasoning through the claim step-by-step, using your knowledge to determine the best tools for gathering evidence. Utilize the available tools to search for and look up Wikipedia articles, and compile a list of titles that are pertinent to the claim. Finish the process by ensuring the list of titles accurately reflects the information needed to assess the claim's validity.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "1990 Film that Khiladiyon Ka Khiladi is loosely based on stars this actor who is best known for martial arts action films.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "[[ ## thought_0 ## ]]\n", + "To verify the claim, I need to identify the 1990 film that \"Khiladiyon Ka Khiladi\" is loosely based on and the actor known for martial arts action films who starred in it. I will start by searching for information on \"Khiladiyon Ka Khiladi\" to find details about its inspiration.\n", + "\n", + "[[ ## tool_name_0 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_0 ## ]]\n", + "{\"query\": \"Khiladiyon Ka Khiladi\"}\n", + "\n", + "[[ ## observation_0 ## ]]\n", + "[1] \u00abKhiladiyon Ka Khiladi | Khiladiyon Ka Khiladi (English: Player of Players) is a 1996 Indian action film starring Rekha in her first villain role, Akshay Kumar, Raveena Tandon and former WWE wrestlers \"Crush\" and Brian Lee as \"The Undertaker\". It was the 5th highest grossing movie of the year 1996 and was declared 'SuperHit' by Box Office India. It was the fourth installment in the Khiladi (film series). The movie is loosely based based on Hollywood film Lionheart\u00bb\n", + "[2] \u00abKhiladi 420 | Khiladi 420 (English: \"Con Player\") is an Indian Hindi action film directed by Neeraj Vora and starring Akshay Kumar and Mahima Chaudhry. The film was written by Uttam Gudda and released on 29 December 2000. It is the seventh installment in the \"Khiladi\" series starring Kumar, which included \"Khiladi\" (1992), \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997) and \"International Khiladi\" (1999).\u00bb\n", + "[3] \u00abKhiladi (1992 film) | Khiladi (English: \"Player\" ) is a 1992 Indian suspense thriller film directed by Abbas Mustan. The film was Akshay Kumar's breakthrough role and also stars Ayesha Jhulka, Deepak Tijori, Sabeeha. While Prem Chopra, Shakti Kapoor, Anant Mahadevan and Johnny Lever played supporting roles. \"Khiladi\" was the first installment in the Khiladi (film series) which had \"Khiladi\" in the title and Akshay Kumar in the leading role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999), \"Khiladi 420\"(2000) and \"Khiladi 786\" (2012). Khiladi was critically and commercially success at the box-office and the tenth highest grossing film of 1992. It was Akshay Kumar's first successful movie and was declared a \"Super Hit\" at the box office. The basic premise of the story is similar to 1975 released movie Khel Khel Mein starring Rishi Kapoor and Neetu Singh. The film was remade in Kannada as \"Aata Hudugaata\".\u00bb\n", + "[4] \u00abKhiladi (film series) | Khiladi series is a Bollywood action film series starring Akshay Kumar in the lead role. However, unlike other film series, other than having Akshay Kumar in lead role, and other than having the word \"Khiladi\" in the title, these films have nothing in common. The producers, directors and stories of these films are totally different. \" Khiladi\" (1992) was the first in a series of films which had Akshay Kumar in the title role and gave it his first breakthrough role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999) and \"Khiladi 420\" (2000), all featuring Kumar in the lead role. The latest film in the franchise is \"Khiladi 786\" (2012).\u00bb\n", + "[5] \u00abKhiladi 786 | Khiladi 786 (\u0916\u093f\u0932\u093e\u0921\u093c\u0940 786) is a 2012 Indian Hindi Punjabi action comedy film directed by Ashish R Mohan, featuring Akshay Kumar in the title role alongside Asin playing the female lead. It features Himesh Reshammiya, Mithun Chakraborty, Raj Babbar and Mukesh Rishi in supporting roles. The film marks the return of Akshay Kumar to his \"Khiladi\" series after 12 years. It is mostly shot in Mumbai and Punjab. Khiladi786 is AkshayKumar 100th film.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Khiladi (2013 film)`, `Khiladi (1968 film)`, `International Khiladi`, `Khatron Ke Khiladi (1988 film)`, `Khatron Ke Khiladi (2001 film)`, `Umesh Mehra`, `Shatranj ke Khiladi`, `Main Khiladi Tu Anari`, `Fear Factor: Khatron Ke Khiladi (season 7)`, `Fear Factor: Khatron Ke Khiladi`, `Mr. and Mrs. Khiladi`, `Sabse Bada Khiladi`, `Ek Khiladi Bawan Pattey`, `Ek Khiladi Ek Haseena (TV series)`, `Do Khiladi`, `Ek Khiladi Ek Haseena (film)`, `Khel Khilari Ka`, `Fear Factor: Khatron Ke Khiladi (season 8)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster`, `Khilona (1942 film)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster Returns`, `Chhota Bheem: Dholakpur ke Khiladi`, `Khilani`, `Kandireega`, `Khilaaf`.\u00bb\n", + "\n", + "[[ ## thought_1 ## ]]\n", + "The search results indicate that \"Khiladiyon Ka Khiladi\" is loosely based on the Hollywood film \"Lionheart\". I should now verify which actor, known for martial arts action films, starred in \"Lionheart\".\n", + "\n", + "[[ ## tool_name_1 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_1 ## ]]\n", + "{\"query\": \"Lionheart 1990 film\"}\n", + "\n", + "[[ ## observation_1 ## ]]\n", + "[1] \u00abLionheart (1990 film) | Lionheart (also known as Wrong Bet, A.W.O.L.: Absent Without Leave, Leon and Full Contact) is a 1990 action film, directed by Sheldon Lettich, starring Jean-Claude Van Damme and co-starring Brian Thompson, along with Harrison Page, Deborah Rennard, Lisa Pelikan, and Ashley Johnson.\u00bb\n", + "[2] \u00abTruly, Madly, Deeply | Truly, Madly, Deeply is a 1990 British fantasy drama film made for the BBC's \"Screen Two\" series, by BBC Films, Lionheart and Winston Pictures. The film, written and directed by Anthony Minghella, stars Juliet Stevenson and Alan Rickman.\u00bb\n", + "[3] \u00abLionheart (1987 film) | Lionheart, also known as Lionheart: The Children's Crusade, is a 1987 adventure film directed by Franklin J. Schaffner and produced by Talia Shire and Stanley O'Toole. Shire's brother, Francis Ford Coppola, initially planned to direct the film but instead opted to be executive producer along with Shire's husband, Jack Schwartzman. The screenplay was written by Menno Meyjes and Richard Outten from a story by Meyjes. The composer Jerry Goldsmith wrote the score. The film was released in August 1987. It was distributed by Orion Pictures.\u00bb\n", + "[4] \u00abLionheart (2016 film) | Lionheart is a 2016 American boxing film short written and produced by Oscar DeRosa and Orlando Cicilia III. The film stars Oscar DeRosa and Marc Macaulay. The film portrays struggling professional boxer Max Rossi who is finally presented with the fight he's been waiting for that will launch his career to the next level but when he is suddenly confronted with a life-changing opportunity he must decide which path to take.\u00bb\n", + "[5] \u00abRichard the Lionheart (2013 film) | Richard The Lionheart is a 2013 film, starring Chandler Maness as Richard the Lionheart, Malcolm McDowell as King Henry II and Andrea Zirio as Henry the Young.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Richard the Lionheart: Rebellion`, `The Brothers Lionheart (1977 film)`, `Kodama Simham`, `Jean-Claude Van Damme`, `Lionsheart`, `A Day for Lionhearts`, `The Lion in Winter (1968 film)`, `Lion Jagapathi Rao`, `Theatre of Blood`, `Young Lions (album)`, `Richard the Lion-Hearted (1923 film)`, `Bullseye! (1990 film)`, `Fear (1990 film)`, `The Guard (1990 film)`, `Ironheart`, `The Guardian (1990 film)`, `Tiger Cage 2`, `Lion Rampant (game publisher)`, `Success (1991 film)`, `Lion in the Streets`, `The King of the Kickboxers`, `Lionheart (video game)`, `Richard the Lionheart (TV series)`, `Dermot Walsh`, `Lars S\u00f6derdahl`.\u00bb\n", + "\n", + "[[ ## thought_2 ## ]]\n", + "The search results confirm that the 1990 film \"Lionheart\" stars Jean-Claude Van Damme, who is well-known for his martial arts action films. This information aligns with what is needed to verify the claim. I now have the relevant titles related to the claim: \"Khiladiyon Ka Khiladi\" and \"Lionheart.\"\n", + "\n", + "[[ ## tool_name_2 ## ]]\n", + "finish\n", + "\n", + "[[ ## tool_args_2 ## ]]\n", + "{}\n", + "\n", + "[[ ## observation_2 ## ]]\n", + "Completed.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The claim suggests that the 1990 film which \"Khiladiyon Ka Khiladi\" is loosely based on stars an actor known for martial arts action films. Upon investigation, \"Khiladiyon Ka Khiladi\" is indeed loosely based on the Hollywood film \"Lionheart\". The 1990 film \"Lionheart\" stars Jean-Claude Van Damme, who is recognized for his roles in martial arts action films. This information supports the claim.\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Khiladiyon Ka Khiladi\", \"Lionheart (1990 film)\", \"Jean-Claude Van Damme\"]\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "Both the Chengiopanax sciadophylloides and Amaryllis are genus.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "[[ ## thought_0 ## ]]\n", + "To verify the claim, I need to search for information regarding both \"Chengiopanax sciadophylloides\" and \"Amaryllis\" to see if they are indeed classified as genus.\n", + "\n", + "[[ ## tool_name_0 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_0 ## ]]\n", + "{\"query\": \"Chengiopanax sciadophylloides\"}\n", + "\n", + "[[ ## observation_0 ## ]]\n", + "[1] \u00abChengiopanax sciadophylloides | Chengiopanax sciadophylloides is a flowering tree in the family Araliaceae native to Japan. Previously included in the genus \"Eleutherococcus\", it is distinguished from other members of that genus by not having spines or prickles and ITS sequence data confirmed the separation.\u00bb\n", + "[2] \u00abHunaniopanax hypoglaucus | Hunaniopanax hypoglaucus is a species of flowering plant of family Araliaceae, and the only species of genus Hunanioglaucus, named after the Chinese province of Hunan. Some authorities suggest merging this species into the genus \"Aralia\".\u00bb\n", + "[3] \u00abCortinarius caesiophylloides | Cortinarius caesiophylloides is a species of fungus in the large mushroom genus \"Cortinarius\" (subgenus \"Phlegmacium\"). Found in Fennoscandia, where it grows on the ground in mesic coniferous forests, it was described as new to science in 2014. The specific epithet \"caesiophylloides\" alludes to both its similarity to \"Cortinarius multiformis\" var. \"caesiophyllus\" (now named \"C. caesiolamellatus\"), and the bluish tints in the gills. It has since been found in Slovakia.\u00bb\n", + "[4] \u00abMerrilliopanax | Merrilliopanax is a genus of flowering plant of family Araliaceae, comprising 3 species of the sub-genus Airampora. They are found in western China, Myanmar, northeast India, Bhutan, and Nepal.\u00bb\n", + "[5] \u00abSchefflera morototoni | Schefflera morototoni (yagrumo macho; syn. \"Didymopanax morototoni\" (Aubl. ) Decne. & Planch. , \"Didymopanax morototoni\" var. \"angustipetalum\" March; \"Panax morototoni\" Aublet; \"Sciadophyllum paniculatum\" Britton ) is a timber tree native to southern Mexico, the Greater Antilles, Central America, and South America. It grows in a variety of habitats, such as the Caatinga, Cerrado, and Amazon Rainforest of Brazil.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Merrilliopanax chinensis`, `Harmsiopanax ingens`, `Gunniopsis zygophylloides`, `Merrilliopanax alpinus`, `Sciadophyton`, `Sinopanax formosanus`, `Tabelliscolex`, `Tetragonia tetragonioides`, `Chengjiangocaris`, `Wan Chun Cheng`, `Cheng Weigao`, `Chengguan, Chamdo`, `Nocardioides panzhihuensis`, `Cardiodictyon`, `Scyphiphora`, `Puschkinia scilloides`, `Liao Chengzhi`, `Lilaea scilloides`, `Chenggao`, `Chengjiao Subdistrict, Ningxiang`, `Seioglobal`, `Chengjiangaspis`, `Cheng Shikai`, `Xenocyprioides`, `Chenggong fire belly newt`.\u00bb\n", + "\n", + "[[ ## thought_1 ## ]]\n", + "The search results indicate that \"Chengiopanax sciadophylloides\" is described as a flowering tree and doesn't specifically mention it being a genus. It was previously included in the genus \"Eleutherococcus\". To verify the claim, I need to ensure \"Amaryllis\" is classified as a genus.\n", + "\n", + "[[ ## tool_name_1 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_1 ## ]]\n", + "{\"query\": \"Amaryllis\"}\n", + "\n", + "[[ ## observation_1 ## ]]\n", + "[1] \u00abAmaryllis (disambiguation) | Amaryllis is a small genus of flowering bulbs, with two species.\u00bb\n", + "[2] \u00abAmaryllis | Amaryllis ( ) is the only genus in the subtribe Amaryllidinae (tribe Amaryllideae). It is a small genus of flowering bulbs, with two species. The better known of the two, \"Amaryllis belladonna\", is a native of the Western Cape region of South Africa, particularly the rocky southwest area between the Olifants River Valley to Knysna. For many years there was confusion among botanists over the generic names \"Amaryllis\" and \"Hippeastrum\", one result of which is that the common name \"amaryllis\" is mainly used for cultivars of the genus \"Hippeastrum\", widely sold in the winter months for their ability to bloom indoors. Plants of the genus \"Amaryllis\" are known as belladonna lily, Jersey lily, naked lady, amarillo, Easter lily in Southern Australia or, in South Africa, March lily due to its propensity to flower around March. This is one of numerous genera with the common name \"lily\" due to their flower shape and growth habit. However, they are only distantly related to the true lily, \"Lilium\".\u00bb\n", + "[3] \u00abAmaryllis (given name) | Amaryllis (\u0391\u03bc\u03b1\u03c1\u03c5\u03bb\u03bb\u03af\u03c2) is a female ancient Greek name and means \"sparkling\". According the mythology, the name of the beautiful flower Amaryllis derived from the nymph Amaryllis.\u00bb\n", + "[4] \u00abAmaryllidaceae | The Amaryllidaceae are a family of herbaceous, mainly perennial and bulbous (rarely rhizomatous) flowering plants in the monocot order Asparagales. The family takes its name from the genus \"Amaryllis\" and is commonly known as the amaryllis family. The leaves are usually linear, and the flowers are usually bisexual and symmetrical, arranged in umbels on the stem. The petals and sepals are undifferentiated as tepals, which may be fused at the base into a floral tube. Some also display a corona. Allyl sulfide compounds produce the characteristic odour of the onion subfamily (Allioideae).\u00bb\n", + "[5] \u00abAmaryllis paradisicola | Amaryllis paradisicola is a species of bulbous perennial plant from South Africa.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Amaryllis (catamaran)`, `Amaryllis (yacht)`, `Amaryllis belladonna`, `Schinia amaryllis`, `Hippeastrum reticulatum`, `Pirangoclytus amaryllis`, `Amaryllis (ship)`, `Hippeastrum psittacinum`, `Ogyris amaryllis`, `Amaryllis (Shinedown album)`, `Amaryllis (restaurant)`, `1085 Amaryllis`, `Apatelodes amaryllis`, `Premolis amaryllis`, `Amaryllideae`, `Amaryllis Garnett`, `Amaryllis Chamber Ensemble`, `Amaryllis Fleming`, `Brithys crini`, `Amaryllis (Marilyn Crispell, Gary Peacock and Paul Motian album)`, `Amarylloidinae`, `Amaryllidoideae`, `Amaryllis Night and Day`, `Amaryllididae`, `Aecidium amaryllidis`.\u00bb\n", + "\n", + "[[ ## thought_2 ## ]]\n", + "The search results confirm that \"Amaryllis\" is a genus, specifically a small genus of flowering bulbs. However, the earlier results for \"Chengiopanax sciadophylloides\" do not confirm it as a genus; it is described more as a species of flowering tree. Therefore, Chengiopanax sciadophylloides might not be a genus itself but belongs to a different classification. I will now verify if \"Chengiopanax\" itself is recognized as a genus.\n", + "\n", + "[[ ## tool_name_2 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_2 ## ]]\n", + "{\"query\": \"Chengiopanax\"}\n", + "\n", + "[[ ## observation_2 ## ]]\n", + "[1] \u00abChengiopanax sciadophylloides | Chengiopanax sciadophylloides is a flowering tree in the family Araliaceae native to Japan. Previously included in the genus \"Eleutherococcus\", it is distinguished from other members of that genus by not having spines or prickles and ITS sequence data confirmed the separation.\u00bb\n", + "[2] \u00abHunaniopanax hypoglaucus | Hunaniopanax hypoglaucus is a species of flowering plant of family Araliaceae, and the only species of genus Hunanioglaucus, named after the Chinese province of Hunan. Some authorities suggest merging this species into the genus \"Aralia\".\u00bb\n", + "[3] \u00abMerrilliopanax | Merrilliopanax is a genus of flowering plant of family Araliaceae, comprising 3 species of the sub-genus Airampora. They are found in western China, Myanmar, northeast India, Bhutan, and Nepal.\u00bb\n", + "[4] \u00abXiaxue | Cheng Yan Yan Wendy (born Cheng Yan Yan; 28 April 1984), better known by her pseudonym Xiaxue, is a Singaporean blogger and online television personality who writes about her life, fashion and local issues in a provocative style. Her main blog, which attracts about 50,000 readers daily, has won prestigious blog awards and earned her sponsorship deals, as well as stints as a columnist and TV show host, but some of her posts have sparked national controversies. She is married to American engineer Mike Sayre and they have one child.\u00bb\n", + "[5] \u00abSinopanax formosanus | Sinopanax formosanus is a species of flowering plant of family Araliaceae and the only species of genus Sinopanax. It is endemic to Taiwan. It is a small, evergreen tree, up to 12 m in height, that grows in open areas in mountainous forests at altitudes between 2300 - .\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Merrilliopanax chinensis`, `Linpan in Chengdu Plain`, `Pan Chengdong`, `Merrilliopanax alpinus`, `Pan Cheng-tsung`, `Pan Chengyou`, `Seioglobal`, `Harmsiopanax ingens`, `Kalopanax`, `Cheng Jiao`, `Chengo`, `Chengguan, Chamdo`, `Chenguang`, `Chenggao`, `Chengdu University of Information Technology`, `Chengai Sivam`, `Ch\u00eanggo`, `Cheng Weigao`, `Cheng Xunzhao`, `Gan Peck Cheng`, `Cheng Hong`, `Chengdu Qianbao F.C.`, `Cheng I`, `Cheng Yu-tung`, `Sun Chengyao`.\u00bb\n", + "\n", + "[[ ## thought_3 ## ]]\n", + "The search results do not definitively confirm \"Chengiopanax\" as a genus, rather they focus on the species \"Chengiopanax sciadophylloides\" which was previously part of the \"Eleutherococcus\" genus. There is no clear indication in the search results that \"Chengiopanax\" is recognized as a genus. To ensure a comprehensive understanding, I will look up the \"Chengiopanax sciadophylloides\" page directly to see if there is further clarification on its classification as a genus.\n", + "\n", + "[[ ## tool_name_3 ## ]]\n", + "lookup_wikipedia\n", + "\n", + "[[ ## tool_args_3 ## ]]\n", + "{\"title\": \"Chengiopanax sciadophylloides\"}\n", + "\n", + "[[ ## observation_3 ## ]]\n", + "Chengiopanax sciadophylloides is a flowering tree in the family Araliaceae native to Japan. Previously included in the genus \"Eleutherococcus\", it is distinguished from other members of that genus by not having spines or prickles and ITS sequence data confirmed the separation.\n", + "\n", + "[[ ## thought_4 ## ]]\n", + "The lookup did not provide any new information suggesting that \"Chengiopanax\" is a genus. It seems that \"Chengiopanax sciadophylloides\" is a species and not a genus. On the other hand, \"Amaryllis\" is confirmed to be a genus. Therefore, the claim that both \"Chengiopanax sciadophylloides\" and \"Amaryllis\" are genus is only partially correct. I will now finish with the titles that I have verified.\n", + "\n", + "[[ ## tool_name_4 ## ]]\n", + "finish\n", + "\n", + "[[ ## tool_args_4 ## ]]\n", + "{}\n", + "\n", + "[[ ## observation_4 ## ]]\n", + "Completed.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The claim states that both \"Chengiopanax sciadophylloides\" and \"Amaryllis\" are genus. Upon investigation, it was found that \"Chengiopanax sciadophylloides\" is a species of flowering tree, and there is no confirmation that \"Chengiopanax\" itself is a recognized genus. It was previously included in the genus \"Eleutherococcus\". On the other hand, \"Amaryllis\" is confirmed to be a genus, specifically a small genus of flowering bulbs with two species. Therefore, the claim is only partially correct as \"Chengiopanax sciadophylloides\" is not a genus.\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Amaryllis\", \"Eleutherococcus\", \"Chengiopanax sciadophylloides\"]\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "200,000 people go through the airport where the headquarters of the airline that acquired Baboo Airlines are located.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "[[ ## thought_0 ## ]]\n", + "I need to identify which airline acquired Baboo Airlines to verify the claim. Once I have the name of the airline, I can find out where its headquarters are located and then look for information on the airport's passenger traffic.\n", + "\n", + "[[ ## tool_name_0 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_0 ## ]]\n", + "{\"query\": \"Baboo Airlines acquisition\"}\n", + "\n", + "[[ ## observation_0 ## ]]\n", + "[1] \u00abBaboo (airline) | Fly Baboo SA, marketed as Baboo, was a regional airline based on the grounds of Geneva International Airport and in Grand-Saconnex, Switzerland. In November 2010, the company was saved from bankruptcy and acquired by Darwin Airline Group from Lugano.\u00bb\n", + "[2] \u00abOkada Air | Okada Air was an airline based in Benin City, Nigeria. The carrier was established in 1983 with a fleet of BAC-One Eleven 300s. and started charter operations in the same year. In 1984, a Boeing 707-355C was acquired for cargo operations. By 1990, ten BAC One-Elevens were bought, and eight more were acquired in 1991. The company was granted the right of operating international flights in 1992.\u00bb\n", + "[3] \u00abGo (airline) | Go Fly (styled and trading as Go) was the name of a British low-cost airline, founded by British Airways (BA) in 1998. It operated flights between London Stansted Airport and destinations in Europe. The airline was purchased from BA in a management buy-out backed by the private equity firm 3i in 2001. In 2002 it was bought by its rival EasyJet, and was merged into the airline's operations. Its head office was in the Enterprise House in London Stansted Airport in Stansted Mountfitchet, Essex.\u00bb\n", + "[4] \u00abBAX Global | BAX Global is an American international shipping company that is headquartered in Irvine, California, and has other major offices in Australia, Singapore, London, the Netherlands and Toledo, Ohio. The company which was founded in 1971 also operates an airline. After having been acquired by DB Logistics in January 2006, Bax Global is currently being integrated with Schenker, the land, sea, and air freight branch of DB Logistics. DB Logistics is the freight and logistics division of Deutsche Bahn, the State-owned German railway company.\u00bb\n", + "[5] \u00abIloilo-Negros Air Express | Iloilo-Negros Air Express Company, Inc., which operated as Iloilo-Negros Air Express or INAEC and later as FEATI, was an airline based in the Philippines. The airline was folded into the operation of Philippine Airlines (PAL) which was re-nationalized by the Philippine government and became the national flag carrier. The company built the Bacolod International Airport in Bacolod City, which was bought by PAL after the acquisition of the airline.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Babool (brand)`, `British Aircraft Corporation`, `Boeing Aircraft Holding Company`, `Bakhtar Afghan Airlines`, `Baboo destinations`, `Baboo (band)`, `Baboom`, `Bond Aviation Group`, `Mark Darby`, `AGS Airports`, `BA Connect`, `AirMall`, `BAT Baboon`, `National Shipping Company of Saudi Arabia`, `Aviesa`, `British Airways Ltd (2012\u201315)`, `BA CityFlyer`, `Baboon (album)`, `Qingdao Haili Helicopters`, `Business Express Airlines`, `Babo Airport`, `Baboon lymphocryptovirus`, `BxB`, `British Airways`, `Manchester Airports Group`.\u00bb\n", + "\n", + "[[ ## thought_1 ## ]]\n", + "The observation indicates that Baboo Airlines was acquired by Darwin Airline Group. Next, I should search for the headquarters location of Darwin Airline Group to identify the associated airport and find out the passenger traffic.\n", + "\n", + "[[ ## tool_name_1 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_1 ## ]]\n", + "{\"query\": \"Darwin Airline Group headquarters\"}\n", + "\n", + "[[ ## observation_1 ## ]]\n", + "[1] \u00abDarwin Airline | Darwin Airline SA is a Swiss regional airline with its head office in Bioggio, Lugano flying under the brand name Adria Airways Switzerland. It has been operating scheduled domestic and international services throughout some western European countries using the brand name Etihad Regional from January 2014 until July 2017 when it was sold from Etihad Airways to Adria Airways as well as formerly also on behalf of Alitalia. Its bases are Geneva Airport and Lugano Airport.\u00bb\n", + "[2] \u00abAirnorth | Capiteq Limited, trading as Airnorth, is a regional airline based at Darwin International Airport in Darwin, Northern Territory, Australia. It operates scheduled and charter services in the Northern Territory, Queensland, Victoria, Western Australia, and East Timor.\u00bb\n", + "[3] \u00abFly Tiwi | Fly Tiwi is an Australian airline based in Darwin, Northern Territory, offering scheduled passenger services between the Northern Territory capital and communities located on the Tiwi, South Goulburn and Croker islands, as well as a number of remote Arnhem Land communities and the town of Tennant Creek. The company is wholly owned by the Hardy Aviation group, Australia's largest general aviation company and was founded in 2008 in association with the Tiwi Land Council and now operates over 50 flights per week between 9 destinations.\u00bb\n", + "[4] \u00abRAAF Base Darwin | RAAF Base Darwin (IATA: DRW, ICAO: YPDN) is a Royal Australian Air Force (RAAF) military air base located in the city of Darwin, in the Northern Territory, Australia. The base shares its runway with Darwin International Airport, for civil aviation purposes. The heritage-listed RAAF Base Darwin is a forward operating base with year round activity with approximately 400 personnel.\u00bb\n", + "[5] \u00abLugano Airport | Lugano Airport (IATA: LUG, ICAO: LSZA) is a regional airport located 4 km west of the Swiss city of Lugano, approximately 80 km north of Milan, in the municipalities of Agno, Bioggio and Muzzano. It lies closer to the village of Agno than to Lugano itself, and is sometimes known as Lugano-Agno. Around 200,000 passengers use the airport each year, using some 2,400 flights. There are also limited freight operations, a flying club and flight school. The headquarters of Darwin Airline are located at the airport.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `No. 76 Wing RAAF`, `No. 13 Squadron RAAF`, `Mount Darwin Airport`, `Darwin International Airport`, `Baboo (airline)`, `Virgin Australia Regional Airlines`, `No. 1 Wing RAAF`, `No. 114 Mobile Control and Reporting Unit RAAF`, `Air Austral`, `Tigerair Australia destinations`, `Marine Rotational Force \u2013 Darwin`, `Austral L\u00edneas A\u00e9reas`, `Australian Aviation Heritage Centre`, `AirAsia`, `Flight Centre Australia`, `Flight Centre`, `North-Western Area Command (RAAF)`, `No. 5 Fighter Sector RAAF`, `McMillans Road`, `Cobham Aviation Services Australia`, `Asia Pacific Transport Consortium`, `1st Aviation Regiment (Australia)`, `Charles Darwin Foundation`, `Brindabella Airlines`, `Kulula.com`.\u00bb\n", + "\n", + "[[ ## thought_2 ## ]]\n", + "The headquarters of Darwin Airline are located at Lugano Airport, which has around 200,000 passengers using the airport each year. This aligns with the claim about the passenger traffic at the airport where the headquarters of the airline that acquired Baboo Airlines are located.\n", + "\n", + "[[ ## tool_name_2 ## ]]\n", + "finish\n", + "\n", + "[[ ## tool_args_2 ## ]]\n", + "{}\n", + "\n", + "[[ ## observation_2 ## ]]\n", + "Completed.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The claim states that 200,000 people go through the airport where the headquarters of the airline that acquired Baboo Airlines are located. Through the information found, Baboo Airlines was acquired by Darwin Airline Group. The headquarters of Darwin Airline Group are located at Lugano Airport. Further research revealed that Lugano Airport handles approximately 200,000 passengers per year. This confirms the accuracy of the claim regarding passenger traffic at the airport associated with Darwin Airline Group's headquarters.\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Baboo (airline)\", \"Darwin Airline\", \"Lugano Airport\"]\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\n", + "\n", + "[[ ## trajectory ## ]]\n", + "[[ ## thought_0 ## ]]\n", + "To verify the claim, I need to identify the playwrights who wrote the unproduced script for \"Up Against It\" and \"The Beatles\".\n", + "\n", + "[[ ## tool_name_0 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_0 ## ]]\n", + "{\"query\": \"Up Against It script writer Bernard-Marie Kolt\u00e8s\"}\n", + "\n", + "[[ ## observation_0 ## ]]\n", + "[1] \u00abBernard-Marie Kolt\u00e8s | Bernard-Marie Kolt\u00e8s (] ; 9 April 1948 \u2013 15 April 1989) was a French playwright and theatre director best known for his plays \"La Nuit juste avant les For\u00eats\" (\"The Night Just Before the Forests\", 1976), \"Sallinger\" (1977) and \"Dans la Solitude des Champs de Coton\" (\"In the Solitude of Cotton Fields\", 1986).\u00bb\n", + "[2] \u00abIn the Solitude of Cotton Fields | In the Solitude of Cotton Fields is a play written by Bernard Marie Kolt\u00e8s in 1985. It is a two-person play involving The Client and Dealer. They negotiate a deal on the streets late at night. The play moves through mutual solitude with the Dealer unable to sell and the Client unable to buy. It\u2019s never clear what each character has to offer each other. Kolt\u00e8s is interested in negotiation, power and violence and the way we treat each other.\u00bb\n", + "[3] \u00abBernard Toublanc-Michel | Bernard Toublanc-Michel (born 6 December 1927) is a French film director and screenwriter. His 1964 film \"La Difficult\u00e9 d'\u00eatre infid\u00e8le\" was entered into the 14th Berlin International Film Festival.\u00bb\n", + "[4] \u00abNot For, or Against | Not For, or Against (Quite the Contrary) (original title: Ni pour, ni contre (bien au contraire)) is a 2003 French crime drama film directed and co-written by C\u00e9dric Klapisch. It stars Marie Gillain and Vincent Elbaz.\u00bb\n", + "[5] \u00abBernard Comment | Bernard Comment (born April 20, 1960) is a Swiss writer, translator, scriptwriter, and publisher of books.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Wrong (film)`, `Bernard Bellefroid`, `Too Beautiful for You`, `Bernard Paul`, `Jean-Jacques Bernard`, `Anahareo`, `Bernard \u00c9mond`, `Bernard Nabonne`, `Marie-Christine Bernard`, `Bernard Giraudeau`, `Bernard Kops`, `Bernard Charl\u00e8s`, `Bernard Assiniwi`, `Bernard C. Schoenfeld`, `Jean Bernard-Luc`, `Bernard Stora`, `Bernard Quiriny`, `De contemptu mundi`, `Marie Fran\u00e7oise Bernard`, `Marc Bernard`, `Up Against It`, `Bernard Gui`, `Bernard Lecache`, `Bernard Le Nail`, `Bernard Zimmer`.\u00bb\n", + "\n", + "[[ ## thought_1 ## ]]\n", + "I have found that Bernard-Marie Kolt\u00e8s wrote the unproduced script for \"Up Against It\". I need to verify if the author of \"Up Against It\" is also a playwright.\n", + "\n", + "[[ ## tool_name_1 ## ]]\n", + "search_wikipedia\n", + "\n", + "[[ ## tool_args_1 ## ]]\n", + "{\"query\": \"Up Against It author\"}\n", + "\n", + "[[ ## observation_1 ## ]]\n", + "[1] \u00abUp Against It | Up Against It is an unproduced script by Joe Orton, written in 1967 for The Beatles at the height of their fame.\u00bb\n", + "[2] \u00abUp Against It! (Todd Rundgren album) | Up Against It! is a 1997 album by Todd Rundgren, essentially consisting of Rundgren's song demos for the Off Broadway show that were Written and Recorded by Todd from 1986-88. The project was inspired by the never-produced Up Against It which was a play originally written by Joe Orton for The Beatles.\u00bb\n", + "[3] \u00abUp Against It (album) | Up Against It is the fifth album by West London Post punk and Indie band The Times released in 1986.\u00bb\n", + "[4] \u00abUp Against It (1912 film) | The 1912 American short film Up Against It is a romantic comedy directed by Otis Turner and starring King Baggot. It was produced by the Independent Moving Pictures (IMP) Company of New York.\u00bb\n", + "[5] \u00abAgainst the Odds (novel) | Against the Odds is a science fiction novel by Elizabeth Moon. It is her seventh and last novel set in the Familias Regnant fictional universe. It does not fall in either informal trilogy (the Heris Serrano and the Esmay Suiza trilogies); fittingly it does not focus on any particular character, instead a more general, almost kaleidoscopic perspective of the upheaval in the Familias Regnant, and the rise to power of a new and more capable Speaker. It can be seen as a conclusion to the series, resolving or at least making a good start at resolving many issues and peoples and ending as it does on a memorializing elegiac note.\u00bb\n", + "[6] \u00abOther retrieved pages have titles: `Against All Things Ending`, `Mark Greif`, `The Man Who Wouldn't Stand Up`, `Against the Day`, `Against a Dark Background`, `Against the Day (1999)`, `Against Happiness`, `Seventeen Against the Dealer`, `Against Our Will`, `Fredy Perlman`, `The Case Against Barack Obama`, `Against the Fall of Night`, `Against Therapy`, `The War Against the Assholes`, `Thirteen Against Fate`, `Hollywood, Interrupted`, `The Opposing Shore`, `Tim Weiner`, `Against Democracy`, `Against Equality of Opportunity`, `H. P. Lovecraft: Against the World, Against Life`, `Against All Enemies`, `Against All Enemies (novel)`, `The Plot Against America`, `Resistance (creativity)`.\u00bb\n", + "\n", + "[[ ## thought_2 ## ]]\n", + "I have confirmed that the author of the unproduced script for \"Up Against It\" is Joe Orton, a playwright.\n", + "\n", + "[[ ## tool_name_2 ## ]]\n", + "lookup_wikipedia\n", + "\n", + "[[ ## tool_args_2 ## ]]\n", + "{\"title\": \"Joe Orton\"}\n", + "\n", + "[[ ## observation_2 ## ]]\n", + "John Kingsley \"Joe\" Orton (1 January 1933 \u2013 9 August 1967) was an English playwright and author. His public career was short but prolific, lasting from 1964 until his death three years later. During this brief period he shocked, outraged, and amused audiences with his scandalous black comedies. The adjective \"Ortonesque\" is sometimes used to refer to work characterised by a similarly dark yet farcical cynicism.\n", + "\n", + "[[ ## thought_3 ## ]]\n", + "I have identified both playwrights as Bernard-Marie Kolt\u00e8s and Joe Orton, who both wrote the unproduced script for \"Up Against It\".\n", + "\n", + "[[ ## tool_name_3 ## ]]\n", + "finish\n", + "\n", + "[[ ## tool_args_3 ## ]]\n", + "{}\n", + "\n", + "[[ ## observation_3 ## ]]\n", + "Completed.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "The claim states that the authors of the unproduced script for \"Up Against It\" are both playwrights. Investigation revealed that Bernard-Marie Kolt\u00e8s wrote the unproduced script, and further research confirmed that Joe Orton was also a playwright who wrote the script. Therefore, both playwrights are indeed the authors of the unproduced script for \"Up Against It\".\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Bernard-Marie Kolt\u00e8s\", \"Joe Orton\"]\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history(n=2)" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(optimized_react)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Awesome. It looks like the system improved drastically from 8% recall to around 40% recall. That was a pretty straightforward approach, but DSPy gives you many tools to continue iterating on this from here.\n", - "\n", - "Next, let's inspect the optimized prompts to understand what it has learned. We'll run one query and then inspect the last two prompts, which will show us the prompts used for both ReAct sub-modules, the one that does the agentic loop and the other than prepares the final results. (Alternatively, if you enabled MLflow Tracing following the instructions above, you can see all steps done by the agent including LLM calls, prompts, tool execution, in a rich tree-view.)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "['Bernard-Marie Koltès', 'Joe Orton']" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's save our optimized program so we can use it again later." ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "optimized_react(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\").titles" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-12-17T15:13:25.420335]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `claim` (str)\n", - "2. `trajectory` (str)\n", - "\n", - "Your output fields are:\n", - "1. `next_thought` (str)\n", - "2. `next_tool_name` (Literal[search_wikipedia, lookup_wikipedia, finish])\n", - "3. `next_tool_args` (dict[str, Any])\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## claim ## ]]\n", - "{claim}\n", - "\n", - "[[ ## trajectory ## ]]\n", - "{trajectory}\n", - "\n", - "[[ ## next_thought ## ]]\n", - "{next_thought}\n", - "\n", - "[[ ## next_tool_name ## ]]\n", - "{next_tool_name} # note: the value you produce must be one of: search_wikipedia; lookup_wikipedia; finish\n", - "\n", - "[[ ## next_tool_args ## ]]\n", - "{next_tool_args} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"object\"}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Find all Wikipedia titles relevant to verifying (or refuting) the claim.\n", - " \n", - " You will be given `claim` and your goal is to finish with `titles`.\n", - " \n", - " To do this, you will interleave Thought, Tool Name, and Tool Args, and receive a resulting Observation.\n", - " \n", - " Thought can reason about the current situation, and Tool Name can be the following types:\n", - " \n", - " (1) search_wikipedia, whose description is Returns top-5 results and then the titles of the top-5 to top-30 results.. It takes arguments {'query': 'str'} in JSON format.\n", - " (2) lookup_wikipedia, whose description is Returns the text of the Wikipedia page, if it exists.. It takes arguments {'title': 'str'} in JSON format.\n", - " (3) finish, whose description is Signals that the final outputs, i.e. `titles`, are now available and marks the task as complete.. It takes arguments {} in JSON format.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "1990 Film that Khiladiyon Ka Khiladi is loosely based on stars this actor who is best known for martial arts action films.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "[[ ## thought_0 ## ]]\n", - "To verify the claim, I need to identify the 1990 film that \"Khiladiyon Ka Khiladi\" is loosely based on and the actor known for martial arts action films who starred in it. I will start by searching for information on \"Khiladiyon Ka Khiladi\" to find details about its inspiration.\n", - "\n", - "[[ ## tool_name_0 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_0 ## ]]\n", - "{\"query\": \"Khiladiyon Ka Khiladi\"}\n", - "\n", - "[[ ## observation_0 ## ]]\n", - "[1] «Khiladiyon Ka Khiladi | Khiladiyon Ka Khiladi (English: Player of Players) is a 1996 Indian action film starring Rekha in her first villain role, Akshay Kumar, Raveena Tandon and former WWE wrestlers \"Crush\" and Brian Lee as \"The Undertaker\". It was the 5th highest grossing movie of the year 1996 and was declared 'SuperHit' by Box Office India. It was the fourth installment in the Khiladi (film series). The movie is loosely based based on Hollywood film Lionheart»\n", - "[2] «Khiladi 420 | Khiladi 420 (English: \"Con Player\") is an Indian Hindi action film directed by Neeraj Vora and starring Akshay Kumar and Mahima Chaudhry. The film was written by Uttam Gudda and released on 29 December 2000. It is the seventh installment in the \"Khiladi\" series starring Kumar, which included \"Khiladi\" (1992), \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997) and \"International Khiladi\" (1999).»\n", - "[3] «Khiladi (1992 film) | Khiladi (English: \"Player\" ) is a 1992 Indian suspense thriller film directed by Abbas Mustan. The film was Akshay Kumar's breakthrough role and also stars Ayesha Jhulka, Deepak Tijori, Sabeeha. While Prem Chopra, Shakti Kapoor, Anant Mahadevan and Johnny Lever played supporting roles. \"Khiladi\" was the first installment in the Khiladi (film series) which had \"Khiladi\" in the title and Akshay Kumar in the leading role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999), \"Khiladi 420\"(2000) and \"Khiladi 786\" (2012). Khiladi was critically and commercially success at the box-office and the tenth highest grossing film of 1992. It was Akshay Kumar's first successful movie and was declared a \"Super Hit\" at the box office. The basic premise of the story is similar to 1975 released movie Khel Khel Mein starring Rishi Kapoor and Neetu Singh. The film was remade in Kannada as \"Aata Hudugaata\".»\n", - "[4] «Khiladi (film series) | Khiladi series is a Bollywood action film series starring Akshay Kumar in the lead role. However, unlike other film series, other than having Akshay Kumar in lead role, and other than having the word \"Khiladi\" in the title, these films have nothing in common. The producers, directors and stories of these films are totally different. \" Khiladi\" (1992) was the first in a series of films which had Akshay Kumar in the title role and gave it his first breakthrough role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999) and \"Khiladi 420\" (2000), all featuring Kumar in the lead role. The latest film in the franchise is \"Khiladi 786\" (2012).»\n", - "[5] «Khiladi 786 | Khiladi 786 (खिलाड़ी 786) is a 2012 Indian Hindi Punjabi action comedy film directed by Ashish R Mohan, featuring Akshay Kumar in the title role alongside Asin playing the female lead. It features Himesh Reshammiya, Mithun Chakraborty, Raj Babbar and Mukesh Rishi in supporting roles. The film marks the return of Akshay Kumar to his \"Khiladi\" series after 12 years. It is mostly shot in Mumbai and Punjab. Khiladi786 is AkshayKumar 100th film.»\n", - "[6] «Other retrieved pages have titles: `Khiladi (2013 film)`, `Khiladi (1968 film)`, `International Khiladi`, `Khatron Ke Khiladi (1988 film)`, `Khatron Ke Khiladi (2001 film)`, `Umesh Mehra`, `Shatranj ke Khiladi`, `Main Khiladi Tu Anari`, `Fear Factor: Khatron Ke Khiladi (season 7)`, `Fear Factor: Khatron Ke Khiladi`, `Mr. and Mrs. Khiladi`, `Sabse Bada Khiladi`, `Ek Khiladi Bawan Pattey`, `Ek Khiladi Ek Haseena (TV series)`, `Do Khiladi`, `Ek Khiladi Ek Haseena (film)`, `Khel Khilari Ka`, `Fear Factor: Khatron Ke Khiladi (season 8)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster`, `Khilona (1942 film)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster Returns`, `Chhota Bheem: Dholakpur ke Khiladi`, `Khilani`, `Kandireega`, `Khilaaf`.»\n", - "\n", - "[[ ## thought_1 ## ]]\n", - "The search results indicate that \"Khiladiyon Ka Khiladi\" is loosely based on the Hollywood film \"Lionheart\". I should now verify which actor, known for martial arts action films, starred in \"Lionheart\".\n", - "\n", - "[[ ## tool_name_1 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_1 ## ]]\n", - "{\"query\": \"Lionheart 1990 film\"}\n", - "\n", - "[[ ## observation_1 ## ]]\n", - "[1] «Lionheart (1990 film) | Lionheart (also known as Wrong Bet, A.W.O.L.: Absent Without Leave, Leon and Full Contact) is a 1990 action film, directed by Sheldon Lettich, starring Jean-Claude Van Damme and co-starring Brian Thompson, along with Harrison Page, Deborah Rennard, Lisa Pelikan, and Ashley Johnson.»\n", - "[2] «Truly, Madly, Deeply | Truly, Madly, Deeply is a 1990 British fantasy drama film made for the BBC's \"Screen Two\" series, by BBC Films, Lionheart and Winston Pictures. The film, written and directed by Anthony Minghella, stars Juliet Stevenson and Alan Rickman.»\n", - "[3] «Lionheart (1987 film) | Lionheart, also known as Lionheart: The Children's Crusade, is a 1987 adventure film directed by Franklin J. Schaffner and produced by Talia Shire and Stanley O'Toole. Shire's brother, Francis Ford Coppola, initially planned to direct the film but instead opted to be executive producer along with Shire's husband, Jack Schwartzman. The screenplay was written by Menno Meyjes and Richard Outten from a story by Meyjes. The composer Jerry Goldsmith wrote the score. The film was released in August 1987. It was distributed by Orion Pictures.»\n", - "[4] «Lionheart (2016 film) | Lionheart is a 2016 American boxing film short written and produced by Oscar DeRosa and Orlando Cicilia III. The film stars Oscar DeRosa and Marc Macaulay. The film portrays struggling professional boxer Max Rossi who is finally presented with the fight he's been waiting for that will launch his career to the next level but when he is suddenly confronted with a life-changing opportunity he must decide which path to take.»\n", - "[5] «Richard the Lionheart (2013 film) | Richard The Lionheart is a 2013 film, starring Chandler Maness as Richard the Lionheart, Malcolm McDowell as King Henry II and Andrea Zirio as Henry the Young.»\n", - "[6] «Other retrieved pages have titles: `Richard the Lionheart: Rebellion`, `The Brothers Lionheart (1977 film)`, `Kodama Simham`, `Jean-Claude Van Damme`, `Lionsheart`, `A Day for Lionhearts`, `The Lion in Winter (1968 film)`, `Lion Jagapathi Rao`, `Theatre of Blood`, `Young Lions (album)`, `Richard the Lion-Hearted (1923 film)`, `Bullseye! (1990 film)`, `Fear (1990 film)`, `The Guard (1990 film)`, `Ironheart`, `The Guardian (1990 film)`, `Tiger Cage 2`, `Lion Rampant (game publisher)`, `Success (1991 film)`, `Lion in the Streets`, `The King of the Kickboxers`, `Lionheart (video game)`, `Richard the Lionheart (TV series)`, `Dermot Walsh`, `Lars Söderdahl`.»\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## next_thought ## ]]\n", - "The search results confirm that the 1990 film \"Lionheart\" stars Jean-Claude Van Damme, who is well-known for his martial arts action films. This information aligns with what is needed to verify the claim. I now have the relevant titles related to the claim: \"Khiladiyon Ka Khiladi\" and \"Lionheart.\"\n", - "\n", - "[[ ## next_tool_name ## ]]\n", - "finish\n", - "\n", - "[[ ## next_tool_args ## ]]\n", - "{}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The figure who currently holds the role Michael Noonan held from 2001 to 2002, was the Minister for Social Protection from 2016 until 2017.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## next_thought ## ]]\n", - "To verify the claim, I need to identify the role Michael Noonan held from 2001 to 2002. Then, I can find out who currently holds this role and check if they were the Minister for Social Protection from 2016 to 2017.\n", - "\n", - "[[ ## next_tool_name ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## next_tool_args ## ]]\n", - "{\"query\": \"Michael Noonan role 2001 to 2002\"}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "An actor in a musical film was also a United States Navy Combat veteran in World War II. This film was awarded The Golden Bear at the 50th Berlin International Film Festival.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "[[ ## thought_0 ## ]]\n", - "To verify the claim, I need to find out which musical film was awarded The Golden Bear at the 50th Berlin International Film Festival and check if an actor from that film was a United States Navy Combat veteran in World War II. I will start by searching for the musical films that won the Golden Bear around the time of the 50th Berlin International Film Festival.\n", - "\n", - "[[ ## tool_name_0 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_0 ## ]]\n", - "{\"query\": \"Golden Bear 50th Berlin International Film Festival musical film\"}\n", - "\n", - "[[ ## observation_0 ## ]]\n", - "[1] «53rd Berlin International Film Festival | The 54th annual Berlin International Film Festival was held from February 6–16, 2003. The festival opened with musical film \"Chicago\" by Rob Marshall and closed with Martin Scorsese's \"Gangs of New York\", both films played out of competition at the festival. The Golden Bear was awarded to British film \"In This World\" directed by Michael Winterbottom.»\n", - "[2] «50th Berlin International Film Festival | The 50th annual Berlin International Film Festival was held from February 9 to 20, 2000. The festival opened with \"The Million Dollar Hotel\" by Wim Wenders. \" Bossa Nova\" by Bruno Barreto, screened out of competition was the closing film of the festival. The Golden Bear was awarded to American film \"Magnolia\" directed by Paul Thomas Anderson.»\n", - "[3] «40th Berlin International Film Festival | The 40th annual Berlin International Film Festival was held from 9 to 20 February 1990. The festival opened with \"Steel Magnolias\" by Herbert Ross, which was shown out of competition. The Golden Bear was awarded to the American film \"Music Box\" directed by Costa-Gavras and Czech film \"Skřivánci na niti\" directed by Jiří Menzel.»\n", - "[4] «66th Berlin International Film Festival | The 66th Berlin International Film Festival was held from 11 to 21 February 2016, with American actress Meryl Streep as the President of the Jury. The Honorary Golden Bear for lifetime achievement was presented to German cinematographer Michael Ballhaus. \" Hail, Caesar! \", directed by Joel and Ethan Coen, was selected to open the festival. The Golden Bear was awarded to the Italian documentary \"Fire at Sea\", directed by Gianfranco Rosi, which also serves as closing night film.»\n", - "[5] «49th Berlin International Film Festival | The 49th annual Berlin International Film Festival was held from February 10 to 21, 1999. The festival opened with \"Aimée & Jaguar\" by Max Färberböck. The Golden Bear was awarded to Canadian-American film \"The Thin Red Line\" directed by Terrence Malick.»\n", - "[6] «Other retrieved pages have titles: `38th Berlin International Film Festival`, `48th Berlin International Film Festival`, `45th Berlin International Film Festival`, `47th Berlin International Film Festival`, `64th Berlin International Film Festival`, `65th Berlin International Film Festival`, `46th Berlin International Film Festival`, `54th Berlin International Film Festival`, `62nd Berlin International Film Festival`, `25th Berlin International Film Festival`, `35th Berlin International Film Festival`, `43rd Berlin International Film Festival`, `67th Berlin International Film Festival`, `Golden Bear`, `44th Berlin International Film Festival`, `60th Berlin International Film Festival`, `13th Berlin International Film Festival`, `34th Berlin International Film Festival`, `51st Berlin International Film Festival`, `1st Berlin International Film Festival`, `30th Berlin International Film Festival`, `42nd Berlin International Film Festival`, `27th Berlin International Film Festival`, `31st Berlin International Film Festival`, `32nd Berlin International Film Festival`.»\n", - "\n", - "[[ ## thought_1 ## ]]\n", - "The Golden Bear at the 50th Berlin International Film Festival was awarded to the American film \"Magnolia\" directed by Paul Thomas Anderson. I need to verify if any actor in \"Magnolia\" was a United States Navy Combat veteran in World War II.\n", - "\n", - "[[ ## tool_name_1 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_1 ## ]]\n", - "{\"query\": \"Magnolia film cast\"}\n", - "\n", - "[[ ## observation_1 ## ]]\n", - "[1] «Magnolia (film) | Magnolia is a 1999 American ensemble drama film written, co-produced and directed by Paul Thomas Anderson. The film stars Jeremy Blackman, Tom Cruise, Melinda Dillon, Philip Baker Hall, Philip Seymour Hoffman, Ricky Jay, William H. Macy, Alfred Molina, Julianne Moore, John C. Reilly, Jason Robards and Melora Walters, and is a mosaic of interrelated characters in search of happiness, forgiveness and meaning in the San Fernando Valley.»\n", - "[2] «Steel Magnolias (2012 film) | Steel Magnolias is an American comedy-drama television film directed by Kenny Leon that premiered at Lifetime Network on October 7, 2012. It is a contemporary retelling of the play \"Steel Magnolias\" and its 1989 film adaptation. The new film stars an all-Black American cast, including Queen Latifah as M'Lynn, Jill Scott as Truvy, Condola Rashād as Shelby, Adepero Oduye as Annelle, with Phylicia Rashād as Clairee and Alfre Woodard as Ouiser.»\n", - "[3] «Baller Blockin' | Baller Blockin' is a 2000 drama film set in New Orleans's Magnolia Projects. It stars Bryan “Birdman” Williams, Ronald \"Slim\" Williams, Juvenile, Manuel Vazquez, B.G., Lil Wayne, Turk, and Mannie Fresh with cameos by comedians Anthony Johnson and T.K. Kirkland.»\n", - "[4] «360 (film) | 360 is a 2011 ensemble drama film starring Anthony Hopkins, Ben Foster, Rachel Weisz, Jude Law and other international actors. The film, directed by Fernando Meirelles, opened the 2011 London Film Festival. Magnolia Pictures released the film on video on demand on 29 June 2012 and was released in United States theaters on 3 August 2012.»\n", - "[5] «River of Romance | River of Romance is a 1929 American drama film directed by Richard Wallace and written by Ethel Doherty, Joseph L. Mankiewicz, Dan Totheroh and John V.A. Weaver. The film stars Charles 'Buddy' Rogers, Mary Brian, June Collyer, Henry B. Walthall, Wallace Beery, Fred Kohler and Natalie Kingston. It is based on the play \"Magnolia\" by Booth Tarkington. The film was released on June 29, 1929, by Paramount Pictures.»\n", - "[6] «Other retrieved pages have titles: `Ira & Abby`, `The Seat Filler`, `Serious Moonlight (2009 film)`, `Experimenter (film)`, `Humpday`, `Roadie (2011 film)`, `A Kind of Murder`, `The Magic of Belle Isle`, `The Hero of Color City`, `The War Within (film)`, `Claudia Lee`, `Steel Magnolias`, `Dina Bonnevie`, `Deadline (1987 film)`, `Creative Control (film)`, `Humboldt County (film)`, `Temptation Island (1980 film)`, `Gina Beck`, `Magnolia (score)`, `Cashback (film)`, `Cynthia Zamora`, `Straight Talk`, `Magnolia (soundtrack)`, `The Great Buck Howard`, `Binta and the Great Idea`.»\n", - "\n", - "[[ ## thought_2 ## ]]\n", - "The cast of the film \"Magnolia\" includes several well-known actors. I need to investigate whether any of these actors, such as Tom Cruise, Philip Baker Hall, or Jason Robards, were United States Navy Combat veterans in World War II.\n", - "\n", - "[[ ## tool_name_2 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_2 ## ]]\n", - "{\"query\": \"Jason Robards military service\"}\n", - "\n", - "[[ ## observation_2 ## ]]\n", - "[1] «Jason Robards | Jason Nelson Robards Jr. (July 26, 1922 – December 26, 2000) was an American stage, film, and television actor. He was a winner of the Tony Award, two Academy Awards and an Emmy Award. He was also a United States Navy combat veteran of World War II.»\n", - "[2] «Jason Robards Sr. | Jason Nelson Robards Sr. (December 31, 1892 – April 4, 1963) was an American stage and screen actor, and the father of Oscar-winning actor Jason Robards Jr. Robards appeared in many films, initially as a leading man, then in character roles and occasional bits. Most of his final roles were in television.»\n", - "[3] «Jason Wingard | Jason Wingard, Ph.D. (born December 13, 1971) is Dean and Professor of the School of Professional Studies (SPS) at Columbia University. He is a leading academic and executive in the areas of leadership development, professional learning, and human capital management. As part of this work, he has 1) authored several books; 2) provided keynote, speaking, and consulting services to global Fortune 500 organizations, professional events, and conferences; 3) served as a board member and facilitator; and 4) held senior executive roles in corporate, academic, and non-profit organizations.»\n", - "[4] «Jason Torpy | Jason Torpy is president of the Military Association of Atheists and Freethinkers (MAAF), an advocacy group focused on non-religious service members and veterans. He is a veteran of the U.S. Army and a Humanist Celebrant who works to increase the visibility of \"atheists in foxholes\".»\n", - "[5] «Jason Dunkerley | Jason Joseph Dunkerley (born August 21, 1977, Newtownards, Northern Ireland) is a Canadian Paralympian athlete competing mainly in category T11 and T12 middle distance events.»\n", - "[6] «Other retrieved pages have titles: `Jase Daniels`, `Jason Dunham`, `Jason Schauble`, `Frederick C. Billard`, `Wilfred C. Menard, Jr.`, `Jason Millard`, `George C. Rickards`, `Jason Amerine`, `USS Jason (AR-8)`, `Rob (dog)`, `USS Jason Dunham`, `Robert Lee Bullard`, `Jason Chee (para-athlete)`, `The War Between Men and Women`, `Thomas Byard`, `Ralph Baker (general)`, `Jason Lamont`, `Jason Rother incident`, `William H. G. Bullard`, `Sidney Gerald Burrard`, `Jason Gilardi`, `Reunion (1989 film)`, `JASON (advisory group)`, `The Great American West`, `Samuel Robison`.»\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## next_thought ## ]]\n", - "I have confirmed that Jason Robards, an actor in the film \"Magnolia,\" was a United States Navy combat veteran of World War II. Since \"Magnolia\" was awarded The Golden Bear at the 50th Berlin International Film Festival, this satisfies the claim. I can now finish with the relevant Wikipedia titles.\n", - "\n", - "[[ ## next_tool_name ## ]]\n", - "finish\n", - "\n", - "[[ ## next_tool_args ## ]]\n", - "{}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "[[ ## thought_0 ## ]]\n", - "To verify the claim, I need to identify the playwrights who wrote the unproduced script for \"Up Against It\" and \"The Beatles\".\n", - "\n", - "[[ ## tool_name_0 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_0 ## ]]\n", - "{\"query\": \"Up Against It script writer Bernard-Marie Koltès\"}\n", - "\n", - "[[ ## observation_0 ## ]]\n", - "[1] «Bernard-Marie Koltès | Bernard-Marie Koltès (] ; 9 April 1948 – 15 April 1989) was a French playwright and theatre director best known for his plays \"La Nuit juste avant les Forêts\" (\"The Night Just Before the Forests\", 1976), \"Sallinger\" (1977) and \"Dans la Solitude des Champs de Coton\" (\"In the Solitude of Cotton Fields\", 1986).»\n", - "[2] «In the Solitude of Cotton Fields | In the Solitude of Cotton Fields is a play written by Bernard Marie Koltès in 1985. It is a two-person play involving The Client and Dealer. They negotiate a deal on the streets late at night. The play moves through mutual solitude with the Dealer unable to sell and the Client unable to buy. It’s never clear what each character has to offer each other. Koltès is interested in negotiation, power and violence and the way we treat each other.»\n", - "[3] «Bernard Toublanc-Michel | Bernard Toublanc-Michel (born 6 December 1927) is a French film director and screenwriter. His 1964 film \"La Difficulté d'être infidèle\" was entered into the 14th Berlin International Film Festival.»\n", - "[4] «Not For, or Against | Not For, or Against (Quite the Contrary) (original title: Ni pour, ni contre (bien au contraire)) is a 2003 French crime drama film directed and co-written by Cédric Klapisch. It stars Marie Gillain and Vincent Elbaz.»\n", - "[5] «Bernard Comment | Bernard Comment (born April 20, 1960) is a Swiss writer, translator, scriptwriter, and publisher of books.»\n", - "[6] «Other retrieved pages have titles: `Wrong (film)`, `Bernard Bellefroid`, `Too Beautiful for You`, `Bernard Paul`, `Jean-Jacques Bernard`, `Anahareo`, `Bernard Émond`, `Bernard Nabonne`, `Marie-Christine Bernard`, `Bernard Giraudeau`, `Bernard Kops`, `Bernard Charlès`, `Bernard Assiniwi`, `Bernard C. Schoenfeld`, `Jean Bernard-Luc`, `Bernard Stora`, `Bernard Quiriny`, `De contemptu mundi`, `Marie Françoise Bernard`, `Marc Bernard`, `Up Against It`, `Bernard Gui`, `Bernard Lecache`, `Bernard Le Nail`, `Bernard Zimmer`.»\n", - "\n", - "[[ ## thought_1 ## ]]\n", - "I have found that Bernard-Marie Koltès wrote the unproduced script for \"Up Against It\". I need to verify if the author of \"Up Against It\" is also a playwright.\n", - "\n", - "[[ ## tool_name_1 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_1 ## ]]\n", - "{\"query\": \"Up Against It author\"}\n", - "\n", - "[[ ## observation_1 ## ]]\n", - "[1] «Up Against It | Up Against It is an unproduced script by Joe Orton, written in 1967 for The Beatles at the height of their fame.»\n", - "[2] «Up Against It! (Todd Rundgren album) | Up Against It! is a 1997 album by Todd Rundgren, essentially consisting of Rundgren's song demos for the Off Broadway show that were Written and Recorded by Todd from 1986-88. The project was inspired by the never-produced Up Against It which was a play originally written by Joe Orton for The Beatles.»\n", - "[3] «Up Against It (album) | Up Against It is the fifth album by West London Post punk and Indie band The Times released in 1986.»\n", - "[4] «Up Against It (1912 film) | The 1912 American short film Up Against It is a romantic comedy directed by Otis Turner and starring King Baggot. It was produced by the Independent Moving Pictures (IMP) Company of New York.»\n", - "[5] «Against the Odds (novel) | Against the Odds is a science fiction novel by Elizabeth Moon. It is her seventh and last novel set in the Familias Regnant fictional universe. It does not fall in either informal trilogy (the Heris Serrano and the Esmay Suiza trilogies); fittingly it does not focus on any particular character, instead a more general, almost kaleidoscopic perspective of the upheaval in the Familias Regnant, and the rise to power of a new and more capable Speaker. It can be seen as a conclusion to the series, resolving or at least making a good start at resolving many issues and peoples and ending as it does on a memorializing elegiac note.»\n", - "[6] «Other retrieved pages have titles: `Against All Things Ending`, `Mark Greif`, `The Man Who Wouldn't Stand Up`, `Against the Day`, `Against a Dark Background`, `Against the Day (1999)`, `Against Happiness`, `Seventeen Against the Dealer`, `Against Our Will`, `Fredy Perlman`, `The Case Against Barack Obama`, `Against the Fall of Night`, `Against Therapy`, `The War Against the Assholes`, `Thirteen Against Fate`, `Hollywood, Interrupted`, `The Opposing Shore`, `Tim Weiner`, `Against Democracy`, `Against Equality of Opportunity`, `H. P. Lovecraft: Against the World, Against Life`, `Against All Enemies`, `Against All Enemies (novel)`, `The Plot Against America`, `Resistance (creativity)`.»\n", - "\n", - "[[ ## thought_2 ## ]]\n", - "I have confirmed that the author of the unproduced script for \"Up Against It\" is Joe Orton, a playwright.\n", - "\n", - "[[ ## tool_name_2 ## ]]\n", - "lookup_wikipedia\n", - "\n", - "[[ ## tool_args_2 ## ]]\n", - "{\"title\": \"Joe Orton\"}\n", - "\n", - "[[ ## observation_2 ## ]]\n", - "John Kingsley \"Joe\" Orton (1 January 1933 – 9 August 1967) was an English playwright and author. His public career was short but prolific, lasting from 1964 until his death three years later. During this brief period he shocked, outraged, and amused audiences with his scandalous black comedies. The adjective \"Ortonesque\" is sometimes used to refer to work characterised by a similarly dark yet farcical cynicism.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## next_thought ## ]]`, then `[[ ## next_tool_name ## ]]` (must be formatted as a valid Python Literal[search_wikipedia, lookup_wikipedia, finish]), then `[[ ## next_tool_args ## ]]` (must be formatted as a valid Python dict[str, Any]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## next_thought ## ]]\n", - "I have identified both playwrights as Bernard-Marie Koltès and Joe Orton, who both wrote the unproduced script for \"Up Against It\".\n", - "\n", - "[[ ## next_tool_name ## ]]\n", - "finish\n", - "\n", - "[[ ## next_tool_args ## ]]\n", - "{}\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-12-17T15:13:27.562128]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `claim` (str)\n", - "2. `trajectory` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `titles` (list[str])\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## claim ## ]]\n", - "{claim}\n", - "\n", - "[[ ## trajectory ## ]]\n", - "{trajectory}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## titles ## ]]\n", - "{titles} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " You are a fact-checking assistant tasked with verifying or refuting claims using Wikipedia as your primary source. Your goal is to identify all relevant Wikipedia titles that can help substantiate or invalidate the given claim. Approach the task by reasoning through the claim step-by-step, using your knowledge to determine the best tools for gathering evidence. Utilize the available tools to search for and look up Wikipedia articles, and compile a list of titles that are pertinent to the claim. Finish the process by ensuring the list of titles accurately reflects the information needed to assess the claim's validity.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "1990 Film that Khiladiyon Ka Khiladi is loosely based on stars this actor who is best known for martial arts action films.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "[[ ## thought_0 ## ]]\n", - "To verify the claim, I need to identify the 1990 film that \"Khiladiyon Ka Khiladi\" is loosely based on and the actor known for martial arts action films who starred in it. I will start by searching for information on \"Khiladiyon Ka Khiladi\" to find details about its inspiration.\n", - "\n", - "[[ ## tool_name_0 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_0 ## ]]\n", - "{\"query\": \"Khiladiyon Ka Khiladi\"}\n", - "\n", - "[[ ## observation_0 ## ]]\n", - "[1] «Khiladiyon Ka Khiladi | Khiladiyon Ka Khiladi (English: Player of Players) is a 1996 Indian action film starring Rekha in her first villain role, Akshay Kumar, Raveena Tandon and former WWE wrestlers \"Crush\" and Brian Lee as \"The Undertaker\". It was the 5th highest grossing movie of the year 1996 and was declared 'SuperHit' by Box Office India. It was the fourth installment in the Khiladi (film series). The movie is loosely based based on Hollywood film Lionheart»\n", - "[2] «Khiladi 420 | Khiladi 420 (English: \"Con Player\") is an Indian Hindi action film directed by Neeraj Vora and starring Akshay Kumar and Mahima Chaudhry. The film was written by Uttam Gudda and released on 29 December 2000. It is the seventh installment in the \"Khiladi\" series starring Kumar, which included \"Khiladi\" (1992), \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997) and \"International Khiladi\" (1999).»\n", - "[3] «Khiladi (1992 film) | Khiladi (English: \"Player\" ) is a 1992 Indian suspense thriller film directed by Abbas Mustan. The film was Akshay Kumar's breakthrough role and also stars Ayesha Jhulka, Deepak Tijori, Sabeeha. While Prem Chopra, Shakti Kapoor, Anant Mahadevan and Johnny Lever played supporting roles. \"Khiladi\" was the first installment in the Khiladi (film series) which had \"Khiladi\" in the title and Akshay Kumar in the leading role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999), \"Khiladi 420\"(2000) and \"Khiladi 786\" (2012). Khiladi was critically and commercially success at the box-office and the tenth highest grossing film of 1992. It was Akshay Kumar's first successful movie and was declared a \"Super Hit\" at the box office. The basic premise of the story is similar to 1975 released movie Khel Khel Mein starring Rishi Kapoor and Neetu Singh. The film was remade in Kannada as \"Aata Hudugaata\".»\n", - "[4] «Khiladi (film series) | Khiladi series is a Bollywood action film series starring Akshay Kumar in the lead role. However, unlike other film series, other than having Akshay Kumar in lead role, and other than having the word \"Khiladi\" in the title, these films have nothing in common. The producers, directors and stories of these films are totally different. \" Khiladi\" (1992) was the first in a series of films which had Akshay Kumar in the title role and gave it his first breakthrough role. It was followed by \"Main Khiladi Tu Anari\" (1994), \"Sabse Bada Khiladi\" (1995), \"Khiladiyon Ka Khiladi\" (1996), \"Mr. and Mrs. Khiladi\" (1997), \"International Khiladi\" (1999) and \"Khiladi 420\" (2000), all featuring Kumar in the lead role. The latest film in the franchise is \"Khiladi 786\" (2012).»\n", - "[5] «Khiladi 786 | Khiladi 786 (खिलाड़ी 786) is a 2012 Indian Hindi Punjabi action comedy film directed by Ashish R Mohan, featuring Akshay Kumar in the title role alongside Asin playing the female lead. It features Himesh Reshammiya, Mithun Chakraborty, Raj Babbar and Mukesh Rishi in supporting roles. The film marks the return of Akshay Kumar to his \"Khiladi\" series after 12 years. It is mostly shot in Mumbai and Punjab. Khiladi786 is AkshayKumar 100th film.»\n", - "[6] «Other retrieved pages have titles: `Khiladi (2013 film)`, `Khiladi (1968 film)`, `International Khiladi`, `Khatron Ke Khiladi (1988 film)`, `Khatron Ke Khiladi (2001 film)`, `Umesh Mehra`, `Shatranj ke Khiladi`, `Main Khiladi Tu Anari`, `Fear Factor: Khatron Ke Khiladi (season 7)`, `Fear Factor: Khatron Ke Khiladi`, `Mr. and Mrs. Khiladi`, `Sabse Bada Khiladi`, `Ek Khiladi Bawan Pattey`, `Ek Khiladi Ek Haseena (TV series)`, `Do Khiladi`, `Ek Khiladi Ek Haseena (film)`, `Khel Khilari Ka`, `Fear Factor: Khatron Ke Khiladi (season 8)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster`, `Khilona (1942 film)`, `Fear Factor: Khatron Ke Khiladi Darr Ka Blockbuster Returns`, `Chhota Bheem: Dholakpur ke Khiladi`, `Khilani`, `Kandireega`, `Khilaaf`.»\n", - "\n", - "[[ ## thought_1 ## ]]\n", - "The search results indicate that \"Khiladiyon Ka Khiladi\" is loosely based on the Hollywood film \"Lionheart\". I should now verify which actor, known for martial arts action films, starred in \"Lionheart\".\n", - "\n", - "[[ ## tool_name_1 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_1 ## ]]\n", - "{\"query\": \"Lionheart 1990 film\"}\n", - "\n", - "[[ ## observation_1 ## ]]\n", - "[1] «Lionheart (1990 film) | Lionheart (also known as Wrong Bet, A.W.O.L.: Absent Without Leave, Leon and Full Contact) is a 1990 action film, directed by Sheldon Lettich, starring Jean-Claude Van Damme and co-starring Brian Thompson, along with Harrison Page, Deborah Rennard, Lisa Pelikan, and Ashley Johnson.»\n", - "[2] «Truly, Madly, Deeply | Truly, Madly, Deeply is a 1990 British fantasy drama film made for the BBC's \"Screen Two\" series, by BBC Films, Lionheart and Winston Pictures. The film, written and directed by Anthony Minghella, stars Juliet Stevenson and Alan Rickman.»\n", - "[3] «Lionheart (1987 film) | Lionheart, also known as Lionheart: The Children's Crusade, is a 1987 adventure film directed by Franklin J. Schaffner and produced by Talia Shire and Stanley O'Toole. Shire's brother, Francis Ford Coppola, initially planned to direct the film but instead opted to be executive producer along with Shire's husband, Jack Schwartzman. The screenplay was written by Menno Meyjes and Richard Outten from a story by Meyjes. The composer Jerry Goldsmith wrote the score. The film was released in August 1987. It was distributed by Orion Pictures.»\n", - "[4] «Lionheart (2016 film) | Lionheart is a 2016 American boxing film short written and produced by Oscar DeRosa and Orlando Cicilia III. The film stars Oscar DeRosa and Marc Macaulay. The film portrays struggling professional boxer Max Rossi who is finally presented with the fight he's been waiting for that will launch his career to the next level but when he is suddenly confronted with a life-changing opportunity he must decide which path to take.»\n", - "[5] «Richard the Lionheart (2013 film) | Richard The Lionheart is a 2013 film, starring Chandler Maness as Richard the Lionheart, Malcolm McDowell as King Henry II and Andrea Zirio as Henry the Young.»\n", - "[6] «Other retrieved pages have titles: `Richard the Lionheart: Rebellion`, `The Brothers Lionheart (1977 film)`, `Kodama Simham`, `Jean-Claude Van Damme`, `Lionsheart`, `A Day for Lionhearts`, `The Lion in Winter (1968 film)`, `Lion Jagapathi Rao`, `Theatre of Blood`, `Young Lions (album)`, `Richard the Lion-Hearted (1923 film)`, `Bullseye! (1990 film)`, `Fear (1990 film)`, `The Guard (1990 film)`, `Ironheart`, `The Guardian (1990 film)`, `Tiger Cage 2`, `Lion Rampant (game publisher)`, `Success (1991 film)`, `Lion in the Streets`, `The King of the Kickboxers`, `Lionheart (video game)`, `Richard the Lionheart (TV series)`, `Dermot Walsh`, `Lars Söderdahl`.»\n", - "\n", - "[[ ## thought_2 ## ]]\n", - "The search results confirm that the 1990 film \"Lionheart\" stars Jean-Claude Van Damme, who is well-known for his martial arts action films. This information aligns with what is needed to verify the claim. I now have the relevant titles related to the claim: \"Khiladiyon Ka Khiladi\" and \"Lionheart.\"\n", - "\n", - "[[ ## tool_name_2 ## ]]\n", - "finish\n", - "\n", - "[[ ## tool_args_2 ## ]]\n", - "{}\n", - "\n", - "[[ ## observation_2 ## ]]\n", - "Completed.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The claim suggests that the 1990 film which \"Khiladiyon Ka Khiladi\" is loosely based on stars an actor known for martial arts action films. Upon investigation, \"Khiladiyon Ka Khiladi\" is indeed loosely based on the Hollywood film \"Lionheart\". The 1990 film \"Lionheart\" stars Jean-Claude Van Damme, who is recognized for his roles in martial arts action films. This information supports the claim.\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Khiladiyon Ka Khiladi\", \"Lionheart (1990 film)\", \"Jean-Claude Van Damme\"]\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "Both the Chengiopanax sciadophylloides and Amaryllis are genus.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "[[ ## thought_0 ## ]]\n", - "To verify the claim, I need to search for information regarding both \"Chengiopanax sciadophylloides\" and \"Amaryllis\" to see if they are indeed classified as genus.\n", - "\n", - "[[ ## tool_name_0 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_0 ## ]]\n", - "{\"query\": \"Chengiopanax sciadophylloides\"}\n", - "\n", - "[[ ## observation_0 ## ]]\n", - "[1] «Chengiopanax sciadophylloides | Chengiopanax sciadophylloides is a flowering tree in the family Araliaceae native to Japan. Previously included in the genus \"Eleutherococcus\", it is distinguished from other members of that genus by not having spines or prickles and ITS sequence data confirmed the separation.»\n", - "[2] «Hunaniopanax hypoglaucus | Hunaniopanax hypoglaucus is a species of flowering plant of family Araliaceae, and the only species of genus Hunanioglaucus, named after the Chinese province of Hunan. Some authorities suggest merging this species into the genus \"Aralia\".»\n", - "[3] «Cortinarius caesiophylloides | Cortinarius caesiophylloides is a species of fungus in the large mushroom genus \"Cortinarius\" (subgenus \"Phlegmacium\"). Found in Fennoscandia, where it grows on the ground in mesic coniferous forests, it was described as new to science in 2014. The specific epithet \"caesiophylloides\" alludes to both its similarity to \"Cortinarius multiformis\" var. \"caesiophyllus\" (now named \"C. caesiolamellatus\"), and the bluish tints in the gills. It has since been found in Slovakia.»\n", - "[4] «Merrilliopanax | Merrilliopanax is a genus of flowering plant of family Araliaceae, comprising 3 species of the sub-genus Airampora. They are found in western China, Myanmar, northeast India, Bhutan, and Nepal.»\n", - "[5] «Schefflera morototoni | Schefflera morototoni (yagrumo macho; syn. \"Didymopanax morototoni\" (Aubl. ) Decne. & Planch. , \"Didymopanax morototoni\" var. \"angustipetalum\" March; \"Panax morototoni\" Aublet; \"Sciadophyllum paniculatum\" Britton ) is a timber tree native to southern Mexico, the Greater Antilles, Central America, and South America. It grows in a variety of habitats, such as the Caatinga, Cerrado, and Amazon Rainforest of Brazil.»\n", - "[6] «Other retrieved pages have titles: `Merrilliopanax chinensis`, `Harmsiopanax ingens`, `Gunniopsis zygophylloides`, `Merrilliopanax alpinus`, `Sciadophyton`, `Sinopanax formosanus`, `Tabelliscolex`, `Tetragonia tetragonioides`, `Chengjiangocaris`, `Wan Chun Cheng`, `Cheng Weigao`, `Chengguan, Chamdo`, `Nocardioides panzhihuensis`, `Cardiodictyon`, `Scyphiphora`, `Puschkinia scilloides`, `Liao Chengzhi`, `Lilaea scilloides`, `Chenggao`, `Chengjiao Subdistrict, Ningxiang`, `Seioglobal`, `Chengjiangaspis`, `Cheng Shikai`, `Xenocyprioides`, `Chenggong fire belly newt`.»\n", - "\n", - "[[ ## thought_1 ## ]]\n", - "The search results indicate that \"Chengiopanax sciadophylloides\" is described as a flowering tree and doesn't specifically mention it being a genus. It was previously included in the genus \"Eleutherococcus\". To verify the claim, I need to ensure \"Amaryllis\" is classified as a genus.\n", - "\n", - "[[ ## tool_name_1 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_1 ## ]]\n", - "{\"query\": \"Amaryllis\"}\n", - "\n", - "[[ ## observation_1 ## ]]\n", - "[1] «Amaryllis (disambiguation) | Amaryllis is a small genus of flowering bulbs, with two species.»\n", - "[2] «Amaryllis | Amaryllis ( ) is the only genus in the subtribe Amaryllidinae (tribe Amaryllideae). It is a small genus of flowering bulbs, with two species. The better known of the two, \"Amaryllis belladonna\", is a native of the Western Cape region of South Africa, particularly the rocky southwest area between the Olifants River Valley to Knysna. For many years there was confusion among botanists over the generic names \"Amaryllis\" and \"Hippeastrum\", one result of which is that the common name \"amaryllis\" is mainly used for cultivars of the genus \"Hippeastrum\", widely sold in the winter months for their ability to bloom indoors. Plants of the genus \"Amaryllis\" are known as belladonna lily, Jersey lily, naked lady, amarillo, Easter lily in Southern Australia or, in South Africa, March lily due to its propensity to flower around March. This is one of numerous genera with the common name \"lily\" due to their flower shape and growth habit. However, they are only distantly related to the true lily, \"Lilium\".»\n", - "[3] «Amaryllis (given name) | Amaryllis (Αμαρυλλίς) is a female ancient Greek name and means \"sparkling\". According the mythology, the name of the beautiful flower Amaryllis derived from the nymph Amaryllis.»\n", - "[4] «Amaryllidaceae | The Amaryllidaceae are a family of herbaceous, mainly perennial and bulbous (rarely rhizomatous) flowering plants in the monocot order Asparagales. The family takes its name from the genus \"Amaryllis\" and is commonly known as the amaryllis family. The leaves are usually linear, and the flowers are usually bisexual and symmetrical, arranged in umbels on the stem. The petals and sepals are undifferentiated as tepals, which may be fused at the base into a floral tube. Some also display a corona. Allyl sulfide compounds produce the characteristic odour of the onion subfamily (Allioideae).»\n", - "[5] «Amaryllis paradisicola | Amaryllis paradisicola is a species of bulbous perennial plant from South Africa.»\n", - "[6] «Other retrieved pages have titles: `Amaryllis (catamaran)`, `Amaryllis (yacht)`, `Amaryllis belladonna`, `Schinia amaryllis`, `Hippeastrum reticulatum`, `Pirangoclytus amaryllis`, `Amaryllis (ship)`, `Hippeastrum psittacinum`, `Ogyris amaryllis`, `Amaryllis (Shinedown album)`, `Amaryllis (restaurant)`, `1085 Amaryllis`, `Apatelodes amaryllis`, `Premolis amaryllis`, `Amaryllideae`, `Amaryllis Garnett`, `Amaryllis Chamber Ensemble`, `Amaryllis Fleming`, `Brithys crini`, `Amaryllis (Marilyn Crispell, Gary Peacock and Paul Motian album)`, `Amarylloidinae`, `Amaryllidoideae`, `Amaryllis Night and Day`, `Amaryllididae`, `Aecidium amaryllidis`.»\n", - "\n", - "[[ ## thought_2 ## ]]\n", - "The search results confirm that \"Amaryllis\" is a genus, specifically a small genus of flowering bulbs. However, the earlier results for \"Chengiopanax sciadophylloides\" do not confirm it as a genus; it is described more as a species of flowering tree. Therefore, Chengiopanax sciadophylloides might not be a genus itself but belongs to a different classification. I will now verify if \"Chengiopanax\" itself is recognized as a genus.\n", - "\n", - "[[ ## tool_name_2 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_2 ## ]]\n", - "{\"query\": \"Chengiopanax\"}\n", - "\n", - "[[ ## observation_2 ## ]]\n", - "[1] «Chengiopanax sciadophylloides | Chengiopanax sciadophylloides is a flowering tree in the family Araliaceae native to Japan. Previously included in the genus \"Eleutherococcus\", it is distinguished from other members of that genus by not having spines or prickles and ITS sequence data confirmed the separation.»\n", - "[2] «Hunaniopanax hypoglaucus | Hunaniopanax hypoglaucus is a species of flowering plant of family Araliaceae, and the only species of genus Hunanioglaucus, named after the Chinese province of Hunan. Some authorities suggest merging this species into the genus \"Aralia\".»\n", - "[3] «Merrilliopanax | Merrilliopanax is a genus of flowering plant of family Araliaceae, comprising 3 species of the sub-genus Airampora. They are found in western China, Myanmar, northeast India, Bhutan, and Nepal.»\n", - "[4] «Xiaxue | Cheng Yan Yan Wendy (born Cheng Yan Yan; 28 April 1984), better known by her pseudonym Xiaxue, is a Singaporean blogger and online television personality who writes about her life, fashion and local issues in a provocative style. Her main blog, which attracts about 50,000 readers daily, has won prestigious blog awards and earned her sponsorship deals, as well as stints as a columnist and TV show host, but some of her posts have sparked national controversies. She is married to American engineer Mike Sayre and they have one child.»\n", - "[5] «Sinopanax formosanus | Sinopanax formosanus is a species of flowering plant of family Araliaceae and the only species of genus Sinopanax. It is endemic to Taiwan. It is a small, evergreen tree, up to 12 m in height, that grows in open areas in mountainous forests at altitudes between 2300 - .»\n", - "[6] «Other retrieved pages have titles: `Merrilliopanax chinensis`, `Linpan in Chengdu Plain`, `Pan Chengdong`, `Merrilliopanax alpinus`, `Pan Cheng-tsung`, `Pan Chengyou`, `Seioglobal`, `Harmsiopanax ingens`, `Kalopanax`, `Cheng Jiao`, `Chengo`, `Chengguan, Chamdo`, `Chenguang`, `Chenggao`, `Chengdu University of Information Technology`, `Chengai Sivam`, `Chênggo`, `Cheng Weigao`, `Cheng Xunzhao`, `Gan Peck Cheng`, `Cheng Hong`, `Chengdu Qianbao F.C.`, `Cheng I`, `Cheng Yu-tung`, `Sun Chengyao`.»\n", - "\n", - "[[ ## thought_3 ## ]]\n", - "The search results do not definitively confirm \"Chengiopanax\" as a genus, rather they focus on the species \"Chengiopanax sciadophylloides\" which was previously part of the \"Eleutherococcus\" genus. There is no clear indication in the search results that \"Chengiopanax\" is recognized as a genus. To ensure a comprehensive understanding, I will look up the \"Chengiopanax sciadophylloides\" page directly to see if there is further clarification on its classification as a genus.\n", - "\n", - "[[ ## tool_name_3 ## ]]\n", - "lookup_wikipedia\n", - "\n", - "[[ ## tool_args_3 ## ]]\n", - "{\"title\": \"Chengiopanax sciadophylloides\"}\n", - "\n", - "[[ ## observation_3 ## ]]\n", - "Chengiopanax sciadophylloides is a flowering tree in the family Araliaceae native to Japan. Previously included in the genus \"Eleutherococcus\", it is distinguished from other members of that genus by not having spines or prickles and ITS sequence data confirmed the separation.\n", - "\n", - "[[ ## thought_4 ## ]]\n", - "The lookup did not provide any new information suggesting that \"Chengiopanax\" is a genus. It seems that \"Chengiopanax sciadophylloides\" is a species and not a genus. On the other hand, \"Amaryllis\" is confirmed to be a genus. Therefore, the claim that both \"Chengiopanax sciadophylloides\" and \"Amaryllis\" are genus is only partially correct. I will now finish with the titles that I have verified.\n", - "\n", - "[[ ## tool_name_4 ## ]]\n", - "finish\n", - "\n", - "[[ ## tool_args_4 ## ]]\n", - "{}\n", - "\n", - "[[ ## observation_4 ## ]]\n", - "Completed.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The claim states that both \"Chengiopanax sciadophylloides\" and \"Amaryllis\" are genus. Upon investigation, it was found that \"Chengiopanax sciadophylloides\" is a species of flowering tree, and there is no confirmation that \"Chengiopanax\" itself is a recognized genus. It was previously included in the genus \"Eleutherococcus\". On the other hand, \"Amaryllis\" is confirmed to be a genus, specifically a small genus of flowering bulbs with two species. Therefore, the claim is only partially correct as \"Chengiopanax sciadophylloides\" is not a genus.\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Amaryllis\", \"Eleutherococcus\", \"Chengiopanax sciadophylloides\"]\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "200,000 people go through the airport where the headquarters of the airline that acquired Baboo Airlines are located.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "[[ ## thought_0 ## ]]\n", - "I need to identify which airline acquired Baboo Airlines to verify the claim. Once I have the name of the airline, I can find out where its headquarters are located and then look for information on the airport's passenger traffic.\n", - "\n", - "[[ ## tool_name_0 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_0 ## ]]\n", - "{\"query\": \"Baboo Airlines acquisition\"}\n", - "\n", - "[[ ## observation_0 ## ]]\n", - "[1] «Baboo (airline) | Fly Baboo SA, marketed as Baboo, was a regional airline based on the grounds of Geneva International Airport and in Grand-Saconnex, Switzerland. In November 2010, the company was saved from bankruptcy and acquired by Darwin Airline Group from Lugano.»\n", - "[2] «Okada Air | Okada Air was an airline based in Benin City, Nigeria. The carrier was established in 1983 with a fleet of BAC-One Eleven 300s. and started charter operations in the same year. In 1984, a Boeing 707-355C was acquired for cargo operations. By 1990, ten BAC One-Elevens were bought, and eight more were acquired in 1991. The company was granted the right of operating international flights in 1992.»\n", - "[3] «Go (airline) | Go Fly (styled and trading as Go) was the name of a British low-cost airline, founded by British Airways (BA) in 1998. It operated flights between London Stansted Airport and destinations in Europe. The airline was purchased from BA in a management buy-out backed by the private equity firm 3i in 2001. In 2002 it was bought by its rival EasyJet, and was merged into the airline's operations. Its head office was in the Enterprise House in London Stansted Airport in Stansted Mountfitchet, Essex.»\n", - "[4] «BAX Global | BAX Global is an American international shipping company that is headquartered in Irvine, California, and has other major offices in Australia, Singapore, London, the Netherlands and Toledo, Ohio. The company which was founded in 1971 also operates an airline. After having been acquired by DB Logistics in January 2006, Bax Global is currently being integrated with Schenker, the land, sea, and air freight branch of DB Logistics. DB Logistics is the freight and logistics division of Deutsche Bahn, the State-owned German railway company.»\n", - "[5] «Iloilo-Negros Air Express | Iloilo-Negros Air Express Company, Inc., which operated as Iloilo-Negros Air Express or INAEC and later as FEATI, was an airline based in the Philippines. The airline was folded into the operation of Philippine Airlines (PAL) which was re-nationalized by the Philippine government and became the national flag carrier. The company built the Bacolod International Airport in Bacolod City, which was bought by PAL after the acquisition of the airline.»\n", - "[6] «Other retrieved pages have titles: `Babool (brand)`, `British Aircraft Corporation`, `Boeing Aircraft Holding Company`, `Bakhtar Afghan Airlines`, `Baboo destinations`, `Baboo (band)`, `Baboom`, `Bond Aviation Group`, `Mark Darby`, `AGS Airports`, `BA Connect`, `AirMall`, `BAT Baboon`, `National Shipping Company of Saudi Arabia`, `Aviesa`, `British Airways Ltd (2012–15)`, `BA CityFlyer`, `Baboon (album)`, `Qingdao Haili Helicopters`, `Business Express Airlines`, `Babo Airport`, `Baboon lymphocryptovirus`, `BxB`, `British Airways`, `Manchester Airports Group`.»\n", - "\n", - "[[ ## thought_1 ## ]]\n", - "The observation indicates that Baboo Airlines was acquired by Darwin Airline Group. Next, I should search for the headquarters location of Darwin Airline Group to identify the associated airport and find out the passenger traffic.\n", - "\n", - "[[ ## tool_name_1 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_1 ## ]]\n", - "{\"query\": \"Darwin Airline Group headquarters\"}\n", - "\n", - "[[ ## observation_1 ## ]]\n", - "[1] «Darwin Airline | Darwin Airline SA is a Swiss regional airline with its head office in Bioggio, Lugano flying under the brand name Adria Airways Switzerland. It has been operating scheduled domestic and international services throughout some western European countries using the brand name Etihad Regional from January 2014 until July 2017 when it was sold from Etihad Airways to Adria Airways as well as formerly also on behalf of Alitalia. Its bases are Geneva Airport and Lugano Airport.»\n", - "[2] «Airnorth | Capiteq Limited, trading as Airnorth, is a regional airline based at Darwin International Airport in Darwin, Northern Territory, Australia. It operates scheduled and charter services in the Northern Territory, Queensland, Victoria, Western Australia, and East Timor.»\n", - "[3] «Fly Tiwi | Fly Tiwi is an Australian airline based in Darwin, Northern Territory, offering scheduled passenger services between the Northern Territory capital and communities located on the Tiwi, South Goulburn and Croker islands, as well as a number of remote Arnhem Land communities and the town of Tennant Creek. The company is wholly owned by the Hardy Aviation group, Australia's largest general aviation company and was founded in 2008 in association with the Tiwi Land Council and now operates over 50 flights per week between 9 destinations.»\n", - "[4] «RAAF Base Darwin | RAAF Base Darwin (IATA: DRW, ICAO: YPDN) is a Royal Australian Air Force (RAAF) military air base located in the city of Darwin, in the Northern Territory, Australia. The base shares its runway with Darwin International Airport, for civil aviation purposes. The heritage-listed RAAF Base Darwin is a forward operating base with year round activity with approximately 400 personnel.»\n", - "[5] «Lugano Airport | Lugano Airport (IATA: LUG, ICAO: LSZA) is a regional airport located 4 km west of the Swiss city of Lugano, approximately 80 km north of Milan, in the municipalities of Agno, Bioggio and Muzzano. It lies closer to the village of Agno than to Lugano itself, and is sometimes known as Lugano-Agno. Around 200,000 passengers use the airport each year, using some 2,400 flights. There are also limited freight operations, a flying club and flight school. The headquarters of Darwin Airline are located at the airport.»\n", - "[6] «Other retrieved pages have titles: `No. 76 Wing RAAF`, `No. 13 Squadron RAAF`, `Mount Darwin Airport`, `Darwin International Airport`, `Baboo (airline)`, `Virgin Australia Regional Airlines`, `No. 1 Wing RAAF`, `No. 114 Mobile Control and Reporting Unit RAAF`, `Air Austral`, `Tigerair Australia destinations`, `Marine Rotational Force – Darwin`, `Austral Líneas Aéreas`, `Australian Aviation Heritage Centre`, `AirAsia`, `Flight Centre Australia`, `Flight Centre`, `North-Western Area Command (RAAF)`, `No. 5 Fighter Sector RAAF`, `McMillans Road`, `Cobham Aviation Services Australia`, `Asia Pacific Transport Consortium`, `1st Aviation Regiment (Australia)`, `Charles Darwin Foundation`, `Brindabella Airlines`, `Kulula.com`.»\n", - "\n", - "[[ ## thought_2 ## ]]\n", - "The headquarters of Darwin Airline are located at Lugano Airport, which has around 200,000 passengers using the airport each year. This aligns with the claim about the passenger traffic at the airport where the headquarters of the airline that acquired Baboo Airlines are located.\n", - "\n", - "[[ ## tool_name_2 ## ]]\n", - "finish\n", - "\n", - "[[ ## tool_args_2 ## ]]\n", - "{}\n", - "\n", - "[[ ## observation_2 ## ]]\n", - "Completed.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The claim states that 200,000 people go through the airport where the headquarters of the airline that acquired Baboo Airlines are located. Through the information found, Baboo Airlines was acquired by Darwin Airline Group. The headquarters of Darwin Airline Group are located at Lugano Airport. Further research revealed that Lugano Airport handles approximately 200,000 passengers per year. This confirms the accuracy of the claim regarding passenger traffic at the airport associated with Darwin Airline Group's headquarters.\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Baboo (airline)\", \"Darwin Airline\", \"Lugano Airport\"]\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\n", - "\n", - "[[ ## trajectory ## ]]\n", - "[[ ## thought_0 ## ]]\n", - "To verify the claim, I need to identify the playwrights who wrote the unproduced script for \"Up Against It\" and \"The Beatles\".\n", - "\n", - "[[ ## tool_name_0 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_0 ## ]]\n", - "{\"query\": \"Up Against It script writer Bernard-Marie Koltès\"}\n", - "\n", - "[[ ## observation_0 ## ]]\n", - "[1] «Bernard-Marie Koltès | Bernard-Marie Koltès (] ; 9 April 1948 – 15 April 1989) was a French playwright and theatre director best known for his plays \"La Nuit juste avant les Forêts\" (\"The Night Just Before the Forests\", 1976), \"Sallinger\" (1977) and \"Dans la Solitude des Champs de Coton\" (\"In the Solitude of Cotton Fields\", 1986).»\n", - "[2] «In the Solitude of Cotton Fields | In the Solitude of Cotton Fields is a play written by Bernard Marie Koltès in 1985. It is a two-person play involving The Client and Dealer. They negotiate a deal on the streets late at night. The play moves through mutual solitude with the Dealer unable to sell and the Client unable to buy. It’s never clear what each character has to offer each other. Koltès is interested in negotiation, power and violence and the way we treat each other.»\n", - "[3] «Bernard Toublanc-Michel | Bernard Toublanc-Michel (born 6 December 1927) is a French film director and screenwriter. His 1964 film \"La Difficulté d'être infidèle\" was entered into the 14th Berlin International Film Festival.»\n", - "[4] «Not For, or Against | Not For, or Against (Quite the Contrary) (original title: Ni pour, ni contre (bien au contraire)) is a 2003 French crime drama film directed and co-written by Cédric Klapisch. It stars Marie Gillain and Vincent Elbaz.»\n", - "[5] «Bernard Comment | Bernard Comment (born April 20, 1960) is a Swiss writer, translator, scriptwriter, and publisher of books.»\n", - "[6] «Other retrieved pages have titles: `Wrong (film)`, `Bernard Bellefroid`, `Too Beautiful for You`, `Bernard Paul`, `Jean-Jacques Bernard`, `Anahareo`, `Bernard Émond`, `Bernard Nabonne`, `Marie-Christine Bernard`, `Bernard Giraudeau`, `Bernard Kops`, `Bernard Charlès`, `Bernard Assiniwi`, `Bernard C. Schoenfeld`, `Jean Bernard-Luc`, `Bernard Stora`, `Bernard Quiriny`, `De contemptu mundi`, `Marie Françoise Bernard`, `Marc Bernard`, `Up Against It`, `Bernard Gui`, `Bernard Lecache`, `Bernard Le Nail`, `Bernard Zimmer`.»\n", - "\n", - "[[ ## thought_1 ## ]]\n", - "I have found that Bernard-Marie Koltès wrote the unproduced script for \"Up Against It\". I need to verify if the author of \"Up Against It\" is also a playwright.\n", - "\n", - "[[ ## tool_name_1 ## ]]\n", - "search_wikipedia\n", - "\n", - "[[ ## tool_args_1 ## ]]\n", - "{\"query\": \"Up Against It author\"}\n", - "\n", - "[[ ## observation_1 ## ]]\n", - "[1] «Up Against It | Up Against It is an unproduced script by Joe Orton, written in 1967 for The Beatles at the height of their fame.»\n", - "[2] «Up Against It! (Todd Rundgren album) | Up Against It! is a 1997 album by Todd Rundgren, essentially consisting of Rundgren's song demos for the Off Broadway show that were Written and Recorded by Todd from 1986-88. The project was inspired by the never-produced Up Against It which was a play originally written by Joe Orton for The Beatles.»\n", - "[3] «Up Against It (album) | Up Against It is the fifth album by West London Post punk and Indie band The Times released in 1986.»\n", - "[4] «Up Against It (1912 film) | The 1912 American short film Up Against It is a romantic comedy directed by Otis Turner and starring King Baggot. It was produced by the Independent Moving Pictures (IMP) Company of New York.»\n", - "[5] «Against the Odds (novel) | Against the Odds is a science fiction novel by Elizabeth Moon. It is her seventh and last novel set in the Familias Regnant fictional universe. It does not fall in either informal trilogy (the Heris Serrano and the Esmay Suiza trilogies); fittingly it does not focus on any particular character, instead a more general, almost kaleidoscopic perspective of the upheaval in the Familias Regnant, and the rise to power of a new and more capable Speaker. It can be seen as a conclusion to the series, resolving or at least making a good start at resolving many issues and peoples and ending as it does on a memorializing elegiac note.»\n", - "[6] «Other retrieved pages have titles: `Against All Things Ending`, `Mark Greif`, `The Man Who Wouldn't Stand Up`, `Against the Day`, `Against a Dark Background`, `Against the Day (1999)`, `Against Happiness`, `Seventeen Against the Dealer`, `Against Our Will`, `Fredy Perlman`, `The Case Against Barack Obama`, `Against the Fall of Night`, `Against Therapy`, `The War Against the Assholes`, `Thirteen Against Fate`, `Hollywood, Interrupted`, `The Opposing Shore`, `Tim Weiner`, `Against Democracy`, `Against Equality of Opportunity`, `H. P. Lovecraft: Against the World, Against Life`, `Against All Enemies`, `Against All Enemies (novel)`, `The Plot Against America`, `Resistance (creativity)`.»\n", - "\n", - "[[ ## thought_2 ## ]]\n", - "I have confirmed that the author of the unproduced script for \"Up Against It\" is Joe Orton, a playwright.\n", - "\n", - "[[ ## tool_name_2 ## ]]\n", - "lookup_wikipedia\n", - "\n", - "[[ ## tool_args_2 ## ]]\n", - "{\"title\": \"Joe Orton\"}\n", - "\n", - "[[ ## observation_2 ## ]]\n", - "John Kingsley \"Joe\" Orton (1 January 1933 – 9 August 1967) was an English playwright and author. His public career was short but prolific, lasting from 1964 until his death three years later. During this brief period he shocked, outraged, and amused audiences with his scandalous black comedies. The adjective \"Ortonesque\" is sometimes used to refer to work characterised by a similarly dark yet farcical cynicism.\n", - "\n", - "[[ ## thought_3 ## ]]\n", - "I have identified both playwrights as Bernard-Marie Koltès and Joe Orton, who both wrote the unproduced script for \"Up Against It\".\n", - "\n", - "[[ ## tool_name_3 ## ]]\n", - "finish\n", - "\n", - "[[ ## tool_args_3 ## ]]\n", - "{}\n", - "\n", - "[[ ## observation_3 ## ]]\n", - "Completed.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "The claim states that the authors of the unproduced script for \"Up Against It\" are both playwrights. Investigation revealed that Bernard-Marie Koltès wrote the unproduced script, and further research confirmed that Joe Orton was also a playwright who wrote the script. Therefore, both playwrights are indeed the authors of the unproduced script for \"Up Against It\".\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Bernard-Marie Koltès\", \"Joe Orton\"]\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "dspy.inspect_history(n=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's save our optimized program so we can use it again later." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Bernard-Marie Kolt\u00e8s', 'Joe Orton']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized_react.save(\"optimized_react.json\")\n", + "\n", + "loaded_react = dspy.ReAct(\"claim -> titles: list[str]\", tools=[search_wikipedia, lookup_wikipedia], max_iters=20)\n", + "loaded_react.load(\"optimized_react.json\")\n", + "\n", + "loaded_react(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\").titles" + ] + }, { - "data": { - "text/plain": [ - "['Bernard-Marie Koltès', 'Joe Orton']" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Saving programs in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", + "\n", + "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", + "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", + "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", + "\n", + "To save the program in MLflow, run the following code:\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "# Start an MLflow Run and save the program\n", + "with mlflow.start_run(run_name=\"optimized_rag\"):\n", + " model_info = mlflow.dspy.log_model(\n", + " optimized_react,\n", + " artifact_path=\"model\", # Any name to save the program in MLflow\n", + " )\n", + "\n", + "# Load the program back from MLflow\n", + "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "optimized_react.save(\"optimized_react.json\")\n", - "\n", - "loaded_react = dspy.ReAct(\"claim -> titles: list[str]\", tools=[search_wikipedia, lookup_wikipedia], max_iters=20)\n", - "loaded_react.load(\"optimized_react.json\")\n", - "\n", - "loaded_react(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\").titles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Saving programs in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", - "\n", - "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", - "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", - "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", - "\n", - "To save the program in MLflow, run the following code:\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "# Start an MLflow Run and save the program\n", - "with mlflow.start_run(run_name=\"optimized_rag\"):\n", - " model_info = mlflow.dspy.log_model(\n", - " optimized_react,\n", - " artifact_path=\"model\", # Any name to save the program in MLflow\n", - " )\n", - "\n", - "# Load the program back from MLflow\n", - "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jun2024_py310", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "jun2024_py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/docs/tutorials/classification_finetuning/index.ipynb b/docs/docs/tutorials/classification_finetuning/index.ipynb index 17120ed87e..62a8f0e47b 100644 --- a/docs/docs/tutorials/classification_finetuning/index.ipynb +++ b/docs/docs/tutorials/classification_finetuning/index.ipynb @@ -1,1096 +1,1096 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Classification Fine-tuning\n", - "\n", - "Let's walk through a quick example of fine-tuning the LM weights within a DSPy program. We'll apply to a simple 77-way classification task.\n", - "\n", - "Our finetuned program will use a tiny `Llama-3.2-1B` language model, hosted locally on your GPU. To make this more interesting, we'll assume that (i) we don't have **any training labels** but (ii) we have 500 unlabeled training examples.\n", - "\n", - "### Install dependencies and download data\n", - "\n", - "Install the latest DSPy via `pip install -U --pre dspy` and follow along. This tutorial depends on DSPy 2.6.0 (pre-release).\n", - "\n", - "This tutorial requires a local GPU at the moment for inference, though we plan to support ollama serving for finetuned models as well.\n", - "\n", - "You will also need the following dependencies:\n", - "\n", - "```shell\n", - "> pip install \"sglang[all]\"; pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/\n", - "> pip install -U torch transformers accelerate trl peft\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", - "\n", - "### MLflow DSPy Integration\n", - "\n", - "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", - "\n", - "![MLflow Trace](./mlflow-tracing-classification.png)\n", - "\n", - "1. Install MLflow\n", - "\n", - "```bash\n", - "%pip install mlflow>=2.20\n", - "```\n", - "\n", - "2. Start MLflow UI in a separate terminal\n", - "```bash\n", - "mlflow ui --port 5000\n", - "```\n", - "\n", - "3. Connect the notebook to MLflow\n", - "```python\n", - "import mlflow\n", - "\n", - "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", - "mlflow.set_experiment(\"DSPy\")\n", - "```\n", - "\n", - "4. Enabling tracing.\n", - "```python\n", - "mlflow.dspy.autolog()\n", - "```\n", - "\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset\n", - "\n", - "For this tutorial, we will use the Banking77 dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "import random\n", - "from dspy.datasets import DataLoader\n", - "from datasets import load_dataset\n", - "\n", - "# Load the Banking77 dataset.\n", - "CLASSES = load_dataset(\"PolyAI/banking77\", split=\"train\", trust_remote_code=True).features['label'].names\n", - "kwargs = dict(fields=(\"text\", \"label\"), input_keys=(\"text\",), split=\"train\", trust_remote_code=True)\n", - "\n", - "# Load the first 2000 examples from the dataset, and assign a hint to each *training* example.\n", - "raw_data = [\n", - " dspy.Example(x, label=CLASSES[x.label]).with_inputs(\"text\")\n", - " for x in DataLoader().from_huggingface(dataset_name=\"PolyAI/banking77\", **kwargs)[:1000]\n", - "]\n", - "\n", - "random.Random(0).shuffle(raw_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This dataset has 77 different categories for classification. Let's review some of them." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "(77,\n", - " ['activate_my_card',\n", - " 'age_limit',\n", - " 'apple_pay_or_google_pay',\n", - " 'atm_support',\n", - " 'automatic_top_up',\n", - " 'balance_not_updated_after_bank_transfer',\n", - " 'balance_not_updated_after_cheque_or_cash_deposit',\n", - " 'beneficiary_not_allowed',\n", - " 'cancel_transfer',\n", - " 'card_about_to_expire'])" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Classification Fine-tuning\n", + "\n", + "Let's walk through a quick example of fine-tuning the LM weights within a DSPy program. We'll apply to a simple 77-way classification task.\n", + "\n", + "Our finetuned program will use a tiny `Llama-3.2-1B` language model, hosted locally on your GPU. To make this more interesting, we'll assume that (i) we don't have **any training labels** but (ii) we have 500 unlabeled training examples.\n", + "\n", + "### Install dependencies and download data\n", + "\n", + "Install the latest DSPy via `pip install -U --pre dspy` and follow along. This tutorial depends on DSPy 2.6.0 (pre-release).\n", + "\n", + "This tutorial requires a local GPU at the moment for inference, though we plan to support ollama serving for finetuned models as well.\n", + "\n", + "You will also need the following dependencies:\n", + "\n", + "```shell\n", + "> pip install \"sglang[all]\"; pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/\n", + "> pip install -U torch transformers accelerate trl peft\n", + "```" ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(CLASSES), CLASSES[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us sample 500 (unlabeled) queries from Banking77. We'll use these for our bootstrapped finetuning." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "Example({'text': 'What if there is an error on the exchange rate?'}) (input_keys={'text'})" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", + "\n", + "### MLflow DSPy Integration\n", + "\n", + "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", + "\n", + "![MLflow Trace](./mlflow-tracing-classification.png)\n", + "\n", + "1. Install MLflow\n", + "\n", + "```bash\n", + "%pip install mlflow>=2.20\n", + "```\n", + "\n", + "2. Start MLflow UI in a separate terminal\n", + "```bash\n", + "mlflow ui --port 5000\n", + "```\n", + "\n", + "3. Connect the notebook to MLflow\n", + "```python\n", + "import mlflow\n", + "\n", + "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", + "mlflow.set_experiment(\"DSPy\")\n", + "```\n", + "\n", + "4. Enabling tracing.\n", + "```python\n", + "mlflow.dspy.autolog()\n", + "```\n", + "\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "
" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unlabeled_trainset = [dspy.Example(text=x.text).with_inputs(\"text\") for x in raw_data[:500]]\n", - "\n", - "unlabeled_trainset[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### DSPy program\n", - "\n", - "Let's say that we want a program that takes the `text` and reasons step by step and then selects one of the classes from Banking77.\n", - "\n", - "Note that this is meant mainly for illustration, or for cases where you want to inspect the model's reasoning, e.g. for a small degree of explainability. In other words, this type of task is not necessarily likely to benefit very much from explicit reasoning." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Literal\n", - "\n", - "classify = dspy.ChainOfThought(f\"text -> label: Literal{CLASSES}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bootstrapped finetuning\n", - "\n", - "There are many ways to go about this, e.g. allowing the model to teach itself or using inference-time compute (e.g., ensembling) to identify cases of high confidence without labels.\n", - "\n", - "Perhaps the simplest is to use a model that we'd expect can do a reasonable job at this task as a teacher of reasoning and classification, and to distill that to our small model. All of these patterns can be expressed in a handful of lines of code.\n", - "\n", - "Let's set up the tiny `Llama-3.2-1B-Instruct` as a student LM. We'll use GPT-4o-mini as a teacher LM." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from dspy.clients.lm_local import LocalProvider\n", - "\n", - "student_lm_name = \"meta-llama/Llama-3.2-1B-Instruct\"\n", - "student_lm = dspy.LM(model=f\"openai/local:{student_lm_name}\", provider=LocalProvider(), max_tokens=2000)\n", - "teacher_lm = dspy.LM('openai/gpt-4o-mini', max_tokens=3000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's assign classifiers to our LMs." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "student_classify = classify.deepcopy()\n", - "student_classify.set_lm(student_lm)\n", - "\n", - "teacher_classify = classify.deepcopy()\n", - "teacher_classify.set_lm(teacher_lm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now launch the bootstrapped finetuning. The word \"bootstrapped\" here means that the program itself will be invoked on the training inputs and the resulting traces seen over all modules will be recorded and used for finetuning. This is the weight-optimizing variant of the various BootstrapFewShot methods in DSPy.\n", - "\n", - "On every question in the (unlabeled) training set, this will invoke the teacher program, which will produce reasoning and select a class. This will be traced and then constitute a training set for all modules (in this case, just the one CoT module) in the student program.\n", - "\n", - "Note: If you have labels, you can pass `metric` to the constructor of `BootstrapFinetune`. If you want to apply this in practice, you can pass `train_kwargs` to the constructor to control local LM training settings: `device`, `use_peft`, `num_train_epochs`, `per_device_train_batch_size`, `gradient_accumulation_steps`, `learning_rate`, `max_seq_length`, `packing`, `bf16`, and `output_dir`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = dspy.BootstrapFinetune(num_threads=16) # if you *do* have labels, pass metric=your_metric here!\n", - "classify_ft = optimizer.compile(student_classify, teacher=teacher_classify, trainset=unlabeled_trainset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since this is a local model, we need to explicitly launch it." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "classify_ft.get_lm().launch()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Validating the finetuned program\n", - "\n", - "Let's now figure out if this was successful. We can ask the system one question and inspect its behavior." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "Prediction(\n", - " reasoning='The user is inquiring about a specific issue, which they did not receive and is still showing as a pending transaction. This situation typically indicates a problem with the cash withdrawal process, as the user is not receiving the money they attempted to withdraw. The appropriate label for this scenario is \"pending_cash_withdrawal,\" as it directly relates to the status of the cash withdrawal transaction.',\n", - " label='pending_cash_withdrawal'\n", - ")" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset\n", + "\n", + "For this tutorial, we will use the Banking77 dataset." ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classify_ft(text=\"I didn't receive my money earlier and it says the transaction is still in progress. Can you fix it?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could also get a small set of gold labels and see if the system can generalize to unseen queries." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "Example({'text': 'Which fiat currencies do you currently support? Will this change in this future?', 'label': 'fiat_currency_support'}) (input_keys={'text'})" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "import random\n", + "from dspy.datasets import DataLoader\n", + "from datasets import load_dataset\n", + "\n", + "# Load the Banking77 dataset.\n", + "CLASSES = load_dataset(\"PolyAI/banking77\", split=\"train\", trust_remote_code=True).features['label'].names\n", + "kwargs = dict(fields=(\"text\", \"label\"), input_keys=(\"text\",), split=\"train\", trust_remote_code=True)\n", + "\n", + "# Load the first 2000 examples from the dataset, and assign a hint to each *training* example.\n", + "raw_data = [\n", + " dspy.Example(x, label=CLASSES[x.label]).with_inputs(\"text\")\n", + " for x in DataLoader().from_huggingface(dataset_name=\"PolyAI/banking77\", **kwargs)[:1000]\n", + "]\n", + "\n", + "random.Random(0).shuffle(raw_data)" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "devset = raw_data[500:600]\n", - "devset[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define an evaluator on this small dev set, where the metric ignores the reasoning and checks that the label is exactly correct." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "metric = (lambda x, y, trace=None: x.label == y.label)\n", - "evaluate = dspy.Evaluate(devset=devset, metric=metric, display_progress=True, display_table=5, num_threads=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's evaluate the finetuned 1B classifier." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 51.00 / 99 (51.5%): 100%|██████████| 100/100 [00:35<00:00, 2.79it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataset has 77 different categories for classification. Let's review some of them." + ] }, { - "data": { - "text/html": [ - "
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textexample_labelreasoningpred_label<lambda>label
0Which fiat currencies do you currently support? Will this change i...fiat_currency_supportThe user is inquiring about the current support for fiat currencie...fiat_currency_support✔️ [True]NaN
1I didn't receive my money earlier and it says the transaction is s...pending_cash_withdrawalThe user is inquiring about a specific issue, which they did not r...pending_cash_withdrawal✔️ [True]NaN
2what currencies do you accept?fiat_currency_supportThe user is inquiring about the currencies that are accepted, whic...fiat_currency_support✔️ [True]NaN
3Where can I find your exchange rates?exchange_rateThe user is inquiring about where to find exchange rates, which re...exchange_rate✔️ [True]NaN
4why hasnt my card come in yet?card_arrivalThe user is inquiring about the status of their card, which sugges...card_arrival✔️ [True]NaN
\n", - "
" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77,\n", + " ['activate_my_card',\n", + " 'age_limit',\n", + " 'apple_pay_or_google_pay',\n", + " 'atm_support',\n", + " 'automatic_top_up',\n", + " 'balance_not_updated_after_bank_transfer',\n", + " 'balance_not_updated_after_cheque_or_cash_deposit',\n", + " 'beneficiary_not_allowed',\n", + " 'cancel_transfer',\n", + " 'card_about_to_expire'])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " text \\\n", - "0 Which fiat currencies do you currently support? Will this change i... \n", - "1 I didn't receive my money earlier and it says the transaction is s... \n", - "2 what currencies do you accept? \n", - "3 Where can I find your exchange rates? \n", - "4 why hasnt my card come in yet? \n", - "\n", - " example_label \\\n", - "0 fiat_currency_support \n", - "1 pending_cash_withdrawal \n", - "2 fiat_currency_support \n", - "3 exchange_rate \n", - "4 card_arrival \n", - "\n", - " reasoning \\\n", - "0 The user is inquiring about the current support for fiat currencie... \n", - "1 The user is inquiring about a specific issue, which they did not r... \n", - "2 The user is inquiring about the currencies that are accepted, whic... \n", - "3 The user is inquiring about where to find exchange rates, which re... \n", - "4 The user is inquiring about the status of their card, which sugges... \n", - "\n", - " pred_label label \n", - "0 fiat_currency_support ✔️ [True] NaN \n", - "1 pending_cash_withdrawal ✔️ [True] NaN \n", - "2 fiat_currency_support ✔️ [True] NaN \n", - "3 exchange_rate ✔️ [True] NaN \n", - "4 card_arrival ✔️ [True] NaN " + "source": [ + "len(CLASSES), CLASSES[:10]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us sample 500 (unlabeled) queries from Banking77. We'll use these for our bootstrapped finetuning." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'text': 'What if there is an error on the exchange rate?'}) (input_keys={'text'})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "unlabeled_trainset = [dspy.Example(text=x.text).with_inputs(\"text\") for x in raw_data[:500]]\n", + "\n", + "unlabeled_trainset[0]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "51.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DSPy program\n", + "\n", + "Let's say that we want a program that takes the `text` and reasons step by step and then selects one of the classes from Banking77.\n", + "\n", + "Note that this is meant mainly for illustration, or for cases where you want to inspect the model's reasoning, e.g. for a small degree of explainability. In other words, this type of task is not necessarily likely to benefit very much from explicit reasoning." ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(classify_ft)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Tracking Evaluation Results in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", - "\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "with mlflow.start_run(run_name=\"classifier_evaluation\"):\n", - " evaluate_correctness = dspy.Evaluate(\n", - " devset=devset,\n", - " metric=extraction_correctness_metric,\n", - " num_threads=16,\n", - " display_progress=True,\n", - " # To record the outputs and detailed scores to MLflow\n", - " return_all_scores=True,\n", - " return_outputs=True,\n", - " )\n", - "\n", - " # Evaluate the program as usual\n", - " aggregated_score, outputs, all_scores = evaluate_correctness(people_extractor)\n", - "\n", - " # Log the aggregated score\n", - " mlflow.log_metric(\"exact_match\", aggregated_score)\n", - " # Log the detailed evaluation results as a table\n", - " mlflow.log_table(\n", - " {\n", - " \"Text\": [example.text for example in devset],\n", - " \"Expected\": [example.example_label for example in devset],\n", - " \"Predicted\": outputs,\n", - " \"Exact match\": all_scores,\n", - " },\n", - " artifact_file=\"eval_results.json\",\n", - " )\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Not bad, given that we started with no labels of the task. Even though we have no labels, you can use various strategies to boost the quality of the bootstrapped training data.\n", - "\n", - "To try that next, let's free our GPU memory by killing the finetuned LM." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "classify_ft.get_lm().kill()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bootstrapped finetuning against a metric\n", - "\n", - "If you have labels, you can generally boost this by a large margin. To do so, you can pass a `metric` to BootstrapFinetune, which it will use for filtering the trajectories over your program before it builds the finetuning data." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = dspy.BootstrapFinetune(num_threads=16, metric=metric)\n", - "classify_ft = optimizer.compile(student_classify, teacher=teacher_classify, trainset=raw_data[:500])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now launch and evaluate this." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "classify_ft.get_lm().launch()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Literal\n", + "\n", + "classify = dspy.ChainOfThought(f\"text -> label: Literal{CLASSES}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrapped finetuning\n", + "\n", + "There are many ways to go about this, e.g. allowing the model to teach itself or using inference-time compute (e.g., ensembling) to identify cases of high confidence without labels.\n", + "\n", + "Perhaps the simplest is to use a model that we'd expect can do a reasonable job at this task as a teacher of reasoning and classification, and to distill that to our small model. All of these patterns can be expressed in a handful of lines of code.\n", + "\n", + "Let's set up the tiny `Llama-3.2-1B-Instruct` as a student LM. We'll use GPT-4o-mini as a teacher LM." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from dspy.clients.lm_local import LocalProvider\n", + "\n", + "student_lm_name = \"meta-llama/Llama-3.2-1B-Instruct\"\n", + "student_lm = dspy.LM(model=f\"openai/local:{student_lm_name}\", provider=LocalProvider(), max_tokens=2000)\n", + "teacher_lm = dspy.LM('openai/gpt-4o-mini', max_tokens=3000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's assign classifiers to our LMs." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "student_classify = classify.deepcopy()\n", + "student_classify.set_lm(student_lm)\n", + "\n", + "teacher_classify = classify.deepcopy()\n", + "teacher_classify.set_lm(teacher_lm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now launch the bootstrapped finetuning. The word \"bootstrapped\" here means that the program itself will be invoked on the training inputs and the resulting traces seen over all modules will be recorded and used for finetuning. This is the weight-optimizing variant of the various BootstrapFewShot methods in DSPy.\n", + "\n", + "On every question in the (unlabeled) training set, this will invoke the teacher program, which will produce reasoning and select a class. This will be traced and then constitute a training set for all modules (in this case, just the one CoT module) in the student program.\n", + "\n", + "Note: If you have labels, you can pass `metric` to the constructor of `BootstrapFinetune`. If you want to apply this in practice, you can pass `train_kwargs` to the constructor to control local LM training settings: `device`, `use_peft`, `num_train_epochs`, `per_device_train_batch_size`, `gradient_accumulation_steps`, `learning_rate`, `max_seq_length`, `packing`, `bf16`, and `output_dir`." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 85.00 / 98 (86.7%): 100%|██████████| 100/100 [00:46<00:00, 2.14it/s]" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = dspy.BootstrapFinetune(num_threads=16) # if you *do* have labels, pass metric=your_metric here!\n", + "classify_ft = optimizer.compile(student_classify, teacher=teacher_classify, trainset=unlabeled_trainset)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since this is a local model, we need to explicitly launch it." + ] }, { - "data": { - "text/html": [ - "
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textexample_labelreasoningpred_label<lambda>label
0Which fiat currencies do you currently support? Will this change i...fiat_currency_supportThe user is inquiring about the fiat currencies currently supporte...fiat_currency_support✔️ [True]NaN
1I didn't receive my money earlier and it says the transaction is s...pending_cash_withdrawalThe user is inquiring about an unexpected fee on their account, wh...extra_charge_on_statementNaN
2what currencies do you accept?fiat_currency_supportThe user is inquiring about the types of currencies that are accep...fiat_currency_support✔️ [True]NaN
3Where can I find your exchange rates?exchange_rateThe user is inquiring about where to find exchange rates, which re...exchange_rate✔️ [True]NaN
4why hasnt my card come in yet?card_arrivalThe user is inquiring about the status of their card delivery, whi...card_arrival✔️ [True]NaN
\n", - "
" + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "classify_ft.get_lm().launch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validating the finetuned program\n", + "\n", + "Let's now figure out if this was successful. We can ask the system one question and inspect its behavior." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " reasoning='The user is inquiring about a specific issue, which they did not receive and is still showing as a pending transaction. This situation typically indicates a problem with the cash withdrawal process, as the user is not receiving the money they attempted to withdraw. The appropriate label for this scenario is \"pending_cash_withdrawal,\" as it directly relates to the status of the cash withdrawal transaction.',\n", + " label='pending_cash_withdrawal'\n", + ")" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " text \\\n", - "0 Which fiat currencies do you currently support? Will this change i... \n", - "1 I didn't receive my money earlier and it says the transaction is s... \n", - "2 what currencies do you accept? \n", - "3 Where can I find your exchange rates? \n", - "4 why hasnt my card come in yet? \n", - "\n", - " example_label \\\n", - "0 fiat_currency_support \n", - "1 pending_cash_withdrawal \n", - "2 fiat_currency_support \n", - "3 exchange_rate \n", - "4 card_arrival \n", - "\n", - " reasoning \\\n", - "0 The user is inquiring about the fiat currencies currently supporte... \n", - "1 The user is inquiring about an unexpected fee on their account, wh... \n", - "2 The user is inquiring about the types of currencies that are accep... \n", - "3 The user is inquiring about where to find exchange rates, which re... \n", - "4 The user is inquiring about the status of their card delivery, whi... \n", - "\n", - " pred_label label \n", - "0 fiat_currency_support ✔️ [True] NaN \n", - "1 extra_charge_on_statement NaN \n", - "2 fiat_currency_support ✔️ [True] NaN \n", - "3 exchange_rate ✔️ [True] NaN \n", - "4 card_arrival ✔️ [True] NaN " + "source": [ + "classify_ft(text=\"I didn't receive my money earlier and it says the transaction is still in progress. Can you fix it?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could also get a small set of gold labels and see if the system can generalize to unseen queries." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'text': 'Which fiat currencies do you currently support? Will this change in this future?', 'label': 'fiat_currency_support'}) (input_keys={'text'})" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "devset = raw_data[500:600]\n", + "devset[0]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "85.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define an evaluator on this small dev set, where the metric ignores the reasoning and checks that the label is exactly correct." ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(classify_ft)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's quite a bit better, given just 500 labels. In fact, it seems to be a lot stronger than the teacher LM gets out of the box!" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "metric = (lambda x, y, trace=None: x.label == y.label)\n", + "evaluate = dspy.Evaluate(devset=devset, metric=metric, display_progress=True, display_table=5, num_threads=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's evaluate the finetuned 1B classifier." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 51.00 / 99 (51.5%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:35<00:00, 2.79it/s]" + ] + }, + { + "data": { + "text/html": [ + "
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textexample_labelreasoningpred_label<lambda>label
0Which fiat currencies do you currently support? Will this change i...fiat_currency_supportThe user is inquiring about the current support for fiat currencie...fiat_currency_support\u2714\ufe0f [True]NaN
1I didn't receive my money earlier and it says the transaction is s...pending_cash_withdrawalThe user is inquiring about a specific issue, which they did not r...pending_cash_withdrawal\u2714\ufe0f [True]NaN
2what currencies do you accept?fiat_currency_supportThe user is inquiring about the currencies that are accepted, whic...fiat_currency_support\u2714\ufe0f [True]NaN
3Where can I find your exchange rates?exchange_rateThe user is inquiring about where to find exchange rates, which re...exchange_rate\u2714\ufe0f [True]NaN
4why hasnt my card come in yet?card_arrivalThe user is inquiring about the status of their card, which sugges...card_arrival\u2714\ufe0f [True]NaN
\n", + "
" + ], + "text/plain": [ + " text \\\n", + "0 Which fiat currencies do you currently support? Will this change i... \n", + "1 I didn't receive my money earlier and it says the transaction is s... \n", + "2 what currencies do you accept? \n", + "3 Where can I find your exchange rates? \n", + "4 why hasnt my card come in yet? \n", + "\n", + " example_label \\\n", + "0 fiat_currency_support \n", + "1 pending_cash_withdrawal \n", + "2 fiat_currency_support \n", + "3 exchange_rate \n", + "4 card_arrival \n", + "\n", + " reasoning \\\n", + "0 The user is inquiring about the current support for fiat currencie... \n", + "1 The user is inquiring about a specific issue, which they did not r... \n", + "2 The user is inquiring about the currencies that are accepted, whic... \n", + "3 The user is inquiring about where to find exchange rates, which re... \n", + "4 The user is inquiring about the status of their card, which sugges... \n", + "\n", + " pred_label label \n", + "0 fiat_currency_support \u2714\ufe0f [True] NaN \n", + "1 pending_cash_withdrawal \u2714\ufe0f [True] NaN \n", + "2 fiat_currency_support \u2714\ufe0f [True] NaN \n", + "3 exchange_rate \u2714\ufe0f [True] NaN \n", + "4 card_arrival \u2714\ufe0f [True] NaN " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "51.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate(classify_ft)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 55.00 / 100 (55.0%): 100%|██████████| 100/100 [00:11<00:00, 8.88it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Tracking Evaluation Results in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", + "\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "with mlflow.start_run(run_name=\"classifier_evaluation\"):\n", + " evaluate_correctness = dspy.Evaluate(\n", + " devset=devset,\n", + " metric=extraction_correctness_metric,\n", + " num_threads=16,\n", + " display_progress=True,\n", + " # To record the outputs and detailed scores to MLflow\n", + " return_all_scores=True,\n", + " return_outputs=True,\n", + " )\n", + "\n", + " # Evaluate the program as usual\n", + " aggregated_score, outputs, all_scores = evaluate_correctness(people_extractor)\n", + "\n", + " # Log the aggregated score\n", + " mlflow.log_metric(\"exact_match\", aggregated_score)\n", + " # Log the detailed evaluation results as a table\n", + " mlflow.log_table(\n", + " {\n", + " \"Text\": [example.text for example in devset],\n", + " \"Expected\": [example.example_label for example in devset],\n", + " \"Predicted\": outputs,\n", + " \"Exact match\": all_scores,\n", + " },\n", + " artifact_file=\"eval_results.json\",\n", + " )\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not bad, given that we started with no labels of the task. Even though we have no labels, you can use various strategies to boost the quality of the bootstrapped training data.\n", + "\n", + "To try that next, let's free our GPU memory by killing the finetuned LM." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "classify_ft.get_lm().kill()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bootstrapped finetuning against a metric\n", + "\n", + "If you have labels, you can generally boost this by a large margin. To do so, you can pass a `metric` to BootstrapFinetune, which it will use for filtering the trajectories over your program before it builds the finetuning data." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = dspy.BootstrapFinetune(num_threads=16, metric=metric)\n", + "classify_ft = optimizer.compile(student_classify, teacher=teacher_classify, trainset=raw_data[:500])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now launch and evaluate this." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "classify_ft.get_lm().launch()" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025/01/08 12:38:35 INFO dspy.evaluate.evaluate: Average Metric: 55 / 100 (55.0%)\n" - ] + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 85.00 / 98 (86.7%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:46<00:00, 2.14it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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textexample_labelreasoningpred_label<lambda>label
0Which fiat currencies do you currently support? Will this change i...fiat_currency_supportThe user is inquiring about the fiat currencies currently supporte...fiat_currency_support\u2714\ufe0f [True]NaN
1I didn't receive my money earlier and it says the transaction is s...pending_cash_withdrawalThe user is inquiring about an unexpected fee on their account, wh...extra_charge_on_statementNaN
2what currencies do you accept?fiat_currency_supportThe user is inquiring about the types of currencies that are accep...fiat_currency_support\u2714\ufe0f [True]NaN
3Where can I find your exchange rates?exchange_rateThe user is inquiring about where to find exchange rates, which re...exchange_rate\u2714\ufe0f [True]NaN
4why hasnt my card come in yet?card_arrivalThe user is inquiring about the status of their card delivery, whi...card_arrival\u2714\ufe0f [True]NaN
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textexample_labelreasoningpred_label<lambda>
0Which fiat currencies do you currently support? Will this change i...fiat_currency_supportThe user is inquiring about the fiat currencies supported by the s...fiat_currency_support✔️ [True]
1I didn't receive my money earlier and it says the transaction is s...pending_cash_withdrawalThe user is experiencing an issue with a transaction that is still...pending_transfer
2what currencies do you accept?fiat_currency_supportThe question is asking about the types of currencies accepted, whi...fiat_currency_support✔️ [True]
3Where can I find your exchange rates?exchange_rateThe user is inquiring about where to find exchange rates, which re...exchange_rate✔️ [True]
4why hasnt my card come in yet?card_arrivalThe user is inquiring about the status of their card delivery, whi...card_delivery_estimate
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textexample_labelreasoningpred_label<lambda>
0Which fiat currencies do you currently support? Will this change i...fiat_currency_supportThe user is inquiring about the fiat currencies supported by the s...fiat_currency_support\u2714\ufe0f [True]
1I didn't receive my money earlier and it says the transaction is s...pending_cash_withdrawalThe user is experiencing an issue with a transaction that is still...pending_transfer
2what currencies do you accept?fiat_currency_supportThe question is asking about the types of currencies accepted, whi...fiat_currency_support\u2714\ufe0f [True]
3Where can I find your exchange rates?exchange_rateThe user is inquiring about where to find exchange rates, which re...exchange_rate\u2714\ufe0f [True]
4why hasnt my card come in yet?card_arrivalThe user is inquiring about the status of their card delivery, whi...card_delivery_estimate
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\n", - " " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And thanks to bootstrapping, the model learns to apply our modules to get the right label, in this case, reasoning explicitly:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2025-01-08T12:39:42.143798]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `text` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `label` (Literal[activate_my_card, age_limit, apple_pay_or_google_pay, atm_support, automatic_top_up, balance_not_updated_after_bank_transfer, balance_not_updated_after_cheque_or_cash_deposit, beneficiary_not_allowed, cancel_transfer, card_about_to_expire, card_acceptance, card_arrival, card_delivery_estimate, card_linking, card_not_working, card_payment_fee_charged, card_payment_not_recognised, card_payment_wrong_exchange_rate, card_swallowed, cash_withdrawal_charge, cash_withdrawal_not_recognised, change_pin, compromised_card, contactless_not_working, country_support, declined_card_payment, declined_cash_withdrawal, declined_transfer, direct_debit_payment_not_recognised, disposable_card_limits, edit_personal_details, exchange_charge, exchange_rate, exchange_via_app, extra_charge_on_statement, failed_transfer, fiat_currency_support, get_disposable_virtual_card, get_physical_card, getting_spare_card, getting_virtual_card, lost_or_stolen_card, lost_or_stolen_phone, order_physical_card, passcode_forgotten, pending_card_payment, pending_cash_withdrawal, pending_top_up, pending_transfer, pin_blocked, receiving_money, Refund_not_showing_up, request_refund, reverted_card_payment?, supported_cards_and_currencies, terminate_account, top_up_by_bank_transfer_charge, top_up_by_card_charge, top_up_by_cash_or_cheque, top_up_failed, top_up_limits, top_up_reverted, topping_up_by_card, transaction_charged_twice, transfer_fee_charged, transfer_into_account, transfer_not_received_by_recipient, transfer_timing, unable_to_verify_identity, verify_my_identity, verify_source_of_funds, verify_top_up, virtual_card_not_working, visa_or_mastercard, why_verify_identity, wrong_amount_of_cash_received, wrong_exchange_rate_for_cash_withdrawal])\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## text ## ]]\n", + "{text}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## label ## ]]\n", + "{label} # note: the value you produce must be one of: activate_my_card; age_limit; apple_pay_or_google_pay; atm_support; automatic_top_up; balance_not_updated_after_bank_transfer; balance_not_updated_after_cheque_or_cash_deposit; beneficiary_not_allowed; cancel_transfer; card_about_to_expire; card_acceptance; card_arrival; card_delivery_estimate; card_linking; card_not_working; card_payment_fee_charged; card_payment_not_recognised; card_payment_wrong_exchange_rate; card_swallowed; cash_withdrawal_charge; cash_withdrawal_not_recognised; change_pin; compromised_card; contactless_not_working; country_support; declined_card_payment; declined_cash_withdrawal; declined_transfer; direct_debit_payment_not_recognised; disposable_card_limits; edit_personal_details; exchange_charge; exchange_rate; exchange_via_app; extra_charge_on_statement; failed_transfer; fiat_currency_support; get_disposable_virtual_card; get_physical_card; getting_spare_card; getting_virtual_card; lost_or_stolen_card; lost_or_stolen_phone; order_physical_card; passcode_forgotten; pending_card_payment; pending_cash_withdrawal; pending_top_up; pending_transfer; pin_blocked; receiving_money; Refund_not_showing_up; request_refund; reverted_card_payment?; supported_cards_and_currencies; terminate_account; top_up_by_bank_transfer_charge; top_up_by_card_charge; top_up_by_cash_or_cheque; top_up_failed; top_up_limits; top_up_reverted; topping_up_by_card; transaction_charged_twice; transfer_fee_charged; transfer_into_account; transfer_not_received_by_recipient; transfer_timing; unable_to_verify_identity; verify_my_identity; verify_source_of_funds; verify_top_up; virtual_card_not_working; visa_or_mastercard; why_verify_identity; wrong_amount_of_cash_received; wrong_exchange_rate_for_cash_withdrawal\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `text`, produce the fields `label`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## text ## ]]\n", + "why hasnt my card come in yet?\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## label ## ]]` (must be formatted as a valid Python Literal[activate_my_card, age_limit, apple_pay_or_google_pay, atm_support, automatic_top_up, balance_not_updated_after_bank_transfer, balance_not_updated_after_cheque_or_cash_deposit, beneficiary_not_allowed, cancel_transfer, card_about_to_expire, card_acceptance, card_arrival, card_delivery_estimate, card_linking, card_not_working, card_payment_fee_charged, card_payment_not_recognised, card_payment_wrong_exchange_rate, card_swallowed, cash_withdrawal_charge, cash_withdrawal_not_recognised, change_pin, compromised_card, contactless_not_working, country_support, declined_card_payment, declined_cash_withdrawal, declined_transfer, direct_debit_payment_not_recognised, disposable_card_limits, edit_personal_details, exchange_charge, exchange_rate, exchange_via_app, extra_charge_on_statement, failed_transfer, fiat_currency_support, get_disposable_virtual_card, get_physical_card, getting_spare_card, getting_virtual_card, lost_or_stolen_card, lost_or_stolen_phone, order_physical_card, passcode_forgotten, pending_card_payment, pending_cash_withdrawal, pending_top_up, pending_transfer, pin_blocked, receiving_money, Refund_not_showing_up, request_refund, reverted_card_payment?, supported_cards_and_currencies, terminate_account, top_up_by_bank_transfer_charge, top_up_by_card_charge, top_up_by_cash_or_cheque, top_up_failed, top_up_limits, top_up_reverted, topping_up_by_card, transaction_charged_twice, transfer_fee_charged, transfer_into_account, transfer_not_received_by_recipient, transfer_timing, unable_to_verify_identity, verify_my_identity, verify_source_of_funds, verify_top_up, virtual_card_not_working, visa_or_mastercard, why_verify_identity, wrong_amount_of_cash_received, wrong_exchange_rate_for_cash_withdrawal]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "The user is inquiring about the status of their card delivery, which suggests they are concerned about when they will receive their card. This aligns with the topic of card arrival and delivery estimates.\n", + "\n", + "[[ ## label ## ]]\n", + "card_arrival\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "classify_ft(text=\"why hasnt my card come in yet?\")\n", + "dspy.inspect_history()" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "55.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Saving fine-tuned programs in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "To deploy the fine-tuned program in production or share it with your team, you can save it in MLflow Experiment. Compared to simply saving it to a local file, MLflow offers the following benefits:\n", + "\n", + "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", + "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", + "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", + "\n", + "To save the program in MLflow, run the following code:\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "# Start an MLflow Run and save the program\n", + "with mlflow.start_run(run_name=\"optimized_classifier\"):\n", + " model_info = mlflow.dspy.log_model(\n", + " classify_ft,\n", + " artifact_path=\"model\", # Any name to save the program in MLflow\n", + " )\n", + "\n", + "# Load the program back from MLflow\n", + "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "evaluate(teacher_classify)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And thanks to bootstrapping, the model learns to apply our modules to get the right label, in this case, reasoning explicitly:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2025-01-08T12:39:42.143798]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `text` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `label` (Literal[activate_my_card, age_limit, apple_pay_or_google_pay, atm_support, automatic_top_up, balance_not_updated_after_bank_transfer, balance_not_updated_after_cheque_or_cash_deposit, beneficiary_not_allowed, cancel_transfer, card_about_to_expire, card_acceptance, card_arrival, card_delivery_estimate, card_linking, card_not_working, card_payment_fee_charged, card_payment_not_recognised, card_payment_wrong_exchange_rate, card_swallowed, cash_withdrawal_charge, cash_withdrawal_not_recognised, change_pin, compromised_card, contactless_not_working, country_support, declined_card_payment, declined_cash_withdrawal, declined_transfer, direct_debit_payment_not_recognised, disposable_card_limits, edit_personal_details, exchange_charge, exchange_rate, exchange_via_app, extra_charge_on_statement, failed_transfer, fiat_currency_support, get_disposable_virtual_card, get_physical_card, getting_spare_card, getting_virtual_card, lost_or_stolen_card, lost_or_stolen_phone, order_physical_card, passcode_forgotten, pending_card_payment, pending_cash_withdrawal, pending_top_up, pending_transfer, pin_blocked, receiving_money, Refund_not_showing_up, request_refund, reverted_card_payment?, supported_cards_and_currencies, terminate_account, top_up_by_bank_transfer_charge, top_up_by_card_charge, top_up_by_cash_or_cheque, top_up_failed, top_up_limits, top_up_reverted, topping_up_by_card, transaction_charged_twice, transfer_fee_charged, transfer_into_account, transfer_not_received_by_recipient, transfer_timing, unable_to_verify_identity, verify_my_identity, verify_source_of_funds, verify_top_up, virtual_card_not_working, visa_or_mastercard, why_verify_identity, wrong_amount_of_cash_received, wrong_exchange_rate_for_cash_withdrawal])\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## text ## ]]\n", - "{text}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## label ## ]]\n", - "{label} # note: the value you produce must be one of: activate_my_card; age_limit; apple_pay_or_google_pay; atm_support; automatic_top_up; balance_not_updated_after_bank_transfer; balance_not_updated_after_cheque_or_cash_deposit; beneficiary_not_allowed; cancel_transfer; card_about_to_expire; card_acceptance; card_arrival; card_delivery_estimate; card_linking; card_not_working; card_payment_fee_charged; card_payment_not_recognised; card_payment_wrong_exchange_rate; card_swallowed; cash_withdrawal_charge; cash_withdrawal_not_recognised; change_pin; compromised_card; contactless_not_working; country_support; declined_card_payment; declined_cash_withdrawal; declined_transfer; direct_debit_payment_not_recognised; disposable_card_limits; edit_personal_details; exchange_charge; exchange_rate; exchange_via_app; extra_charge_on_statement; failed_transfer; fiat_currency_support; get_disposable_virtual_card; get_physical_card; getting_spare_card; getting_virtual_card; lost_or_stolen_card; lost_or_stolen_phone; order_physical_card; passcode_forgotten; pending_card_payment; pending_cash_withdrawal; pending_top_up; pending_transfer; pin_blocked; receiving_money; Refund_not_showing_up; request_refund; reverted_card_payment?; supported_cards_and_currencies; terminate_account; top_up_by_bank_transfer_charge; top_up_by_card_charge; top_up_by_cash_or_cheque; top_up_failed; top_up_limits; top_up_reverted; topping_up_by_card; transaction_charged_twice; transfer_fee_charged; transfer_into_account; transfer_not_received_by_recipient; transfer_timing; unable_to_verify_identity; verify_my_identity; verify_source_of_funds; verify_top_up; virtual_card_not_working; visa_or_mastercard; why_verify_identity; wrong_amount_of_cash_received; wrong_exchange_rate_for_cash_withdrawal\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `text`, produce the fields `label`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## text ## ]]\n", - "why hasnt my card come in yet?\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## label ## ]]` (must be formatted as a valid Python Literal[activate_my_card, age_limit, apple_pay_or_google_pay, atm_support, automatic_top_up, balance_not_updated_after_bank_transfer, balance_not_updated_after_cheque_or_cash_deposit, beneficiary_not_allowed, cancel_transfer, card_about_to_expire, card_acceptance, card_arrival, card_delivery_estimate, card_linking, card_not_working, card_payment_fee_charged, card_payment_not_recognised, card_payment_wrong_exchange_rate, card_swallowed, cash_withdrawal_charge, cash_withdrawal_not_recognised, change_pin, compromised_card, contactless_not_working, country_support, declined_card_payment, declined_cash_withdrawal, declined_transfer, direct_debit_payment_not_recognised, disposable_card_limits, edit_personal_details, exchange_charge, exchange_rate, exchange_via_app, extra_charge_on_statement, failed_transfer, fiat_currency_support, get_disposable_virtual_card, get_physical_card, getting_spare_card, getting_virtual_card, lost_or_stolen_card, lost_or_stolen_phone, order_physical_card, passcode_forgotten, pending_card_payment, pending_cash_withdrawal, pending_top_up, pending_transfer, pin_blocked, receiving_money, Refund_not_showing_up, request_refund, reverted_card_payment?, supported_cards_and_currencies, terminate_account, top_up_by_bank_transfer_charge, top_up_by_card_charge, top_up_by_cash_or_cheque, top_up_failed, top_up_limits, top_up_reverted, topping_up_by_card, transaction_charged_twice, transfer_fee_charged, transfer_into_account, transfer_not_received_by_recipient, transfer_timing, unable_to_verify_identity, verify_my_identity, verify_source_of_funds, verify_top_up, virtual_card_not_working, visa_or_mastercard, why_verify_identity, wrong_amount_of_cash_received, wrong_exchange_rate_for_cash_withdrawal]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "The user is inquiring about the status of their card delivery, which suggests they are concerned about when they will receive their card. This aligns with the topic of card arrival and delivery estimates.\n", - "\n", - "[[ ## label ## ]]\n", - "card_arrival\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "py311_202501", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" } - ], - "source": [ - "classify_ft(text=\"why hasnt my card come in yet?\")\n", - "dspy.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Saving fine-tuned programs in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "To deploy the fine-tuned program in production or share it with your team, you can save it in MLflow Experiment. Compared to simply saving it to a local file, MLflow offers the following benefits:\n", - "\n", - "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", - "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", - "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", - "\n", - "To save the program in MLflow, run the following code:\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "# Start an MLflow Run and save the program\n", - "with mlflow.start_run(run_name=\"optimized_classifier\"):\n", - " model_info = mlflow.dspy.log_model(\n", - " classify_ft,\n", - " artifact_path=\"model\", # Any name to save the program in MLflow\n", - " )\n", - "\n", - "# Load the program back from MLflow\n", - "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py311_202501", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/docs/tutorials/entity_extraction/index.ipynb b/docs/docs/tutorials/entity_extraction/index.ipynb index 10a96a5d59..4ed7a150ea 100644 --- a/docs/docs/tutorials/entity_extraction/index.ipynb +++ b/docs/docs/tutorials/entity_extraction/index.ipynb @@ -280,7 +280,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average Metric: 172.00 / 200 (86.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:16<00:00, 11.94it/s]" + "Average Metric: 172.00 / 200 (86.0%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:16<00:00, 11.94it/s]" ] }, { @@ -340,7 +340,7 @@ " [Nadim, Ladki]\n", " We extracted the tokens \"Nadim\" and \"Ladki\" as they refer to speci...\n", " [Nadim, Ladki]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 2\n", @@ -348,7 +348,7 @@ " []\n", " There are no tokens referring to specific people in the provided l...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 3\n", @@ -356,7 +356,7 @@ " []\n", " We did not find any tokens referring to specific people in the pro...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 4\n", @@ -380,7 +380,7 @@ " [David, Campese]\n", " The extracted_people includes \"David Campese\" as it refers to a sp...\n", " [David, Campese]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 196\n", @@ -388,7 +388,7 @@ " []\n", " The extracted_people includes \"Wallabies\" as it refers to a specif...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 197\n", @@ -396,7 +396,7 @@ " [Campese, Rob, Andrew]\n", " The extracted tokens refer to specific people mentioned in the tex...\n", " [Campese, Rob, Andrew]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 198\n", @@ -404,7 +404,7 @@ " [Campo, Andrew]\n", " The extracted tokens referring to specific people include \"Campo\" ...\n", " [Campo, Andrew]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 199\n", @@ -412,11 +412,11 @@ " []\n", " We extracted the names of specific people from the tokenized text....\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", "\n", - "

200 rows × 5 columns

\n", + "

200 rows \u00d7 5 columns

\n", "" ], "text/plain": [ @@ -461,16 +461,16 @@ "\n", " extracted_people extraction_correctness_metric \n", "0 [JAPAN, CHINA] \n", - "1 [Nadim, Ladki] ✔️ [True] \n", - "2 [] ✔️ [True] \n", - "3 [] ✔️ [True] \n", + "1 [Nadim, Ladki] \u2714\ufe0f [True] \n", + "2 [] \u2714\ufe0f [True] \n", + "3 [] \u2714\ufe0f [True] \n", "4 [China, Uzbekistan] \n", ".. ... ... \n", - "195 [David, Campese] ✔️ [True] \n", - "196 [] ✔️ [True] \n", - "197 [Campese, Rob, Andrew] ✔️ [True] \n", - "198 [Campo, Andrew] ✔️ [True] \n", - "199 [] ✔️ [True] \n", + "195 [David, Campese] \u2714\ufe0f [True] \n", + "196 [] \u2714\ufe0f [True] \n", + "197 [Campese, Rob, Andrew] \u2714\ufe0f [True] \n", + "198 [Campo, Andrew] \u2714\ufe0f [True] \n", + "199 [] \u2714\ufe0f [True] \n", "\n", "[200 rows x 5 columns]" ] @@ -597,7 +597,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average Metric: 186.00 / 200 (93.0%): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:23<00:00, 8.58it/s]" + "Average Metric: 186.00 / 200 (93.0%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:23<00:00, 8.58it/s]" ] }, { @@ -657,7 +657,7 @@ " [Nadim, Ladki]\n", " The tokens \"Nadim Ladki\" refer to a specific individual. Both toke...\n", " [Nadim, Ladki]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 2\n", @@ -665,7 +665,7 @@ " []\n", " There are no tokens referring to specific people in the provided l...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 3\n", @@ -673,7 +673,7 @@ " []\n", " There are no specific people mentioned in the provided tokens. The...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 4\n", @@ -681,7 +681,7 @@ " []\n", " There are no tokens referring to specific people in the provided l...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " ...\n", @@ -697,7 +697,7 @@ " [David, Campese]\n", " The extracted tokens refer to a specific person mentioned in the t...\n", " [David, Campese]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 196\n", @@ -705,7 +705,7 @@ " []\n", " There are no specific individuals mentioned in the provided tokens...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 197\n", @@ -713,7 +713,7 @@ " [Campese, Rob, Andrew]\n", " The tokens include the names \"Campese\" and \"Rob Andrew,\" both of w...\n", " [Campese, Rob, Andrew]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 198\n", @@ -721,7 +721,7 @@ " [Campo, Andrew]\n", " The extracted tokens refer to specific people mentioned in the tex...\n", " [Campo, Andrew]\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", " 199\n", @@ -729,11 +729,11 @@ " []\n", " There are no specific people mentioned in the provided tokens. The...\n", " []\n", - " ✔️ [True]\n", + " \u2714\ufe0f [True]\n", " \n", " \n", "\n", - "

200 rows × 5 columns

\n", + "

200 rows \u00d7 5 columns

\n", "" ], "text/plain": [ @@ -778,16 +778,16 @@ "\n", " extracted_people extraction_correctness_metric \n", "0 [] \n", - "1 [Nadim, Ladki] ✔️ [True] \n", - "2 [] ✔️ [True] \n", - "3 [] ✔️ [True] \n", - "4 [] ✔️ [True] \n", + "1 [Nadim, Ladki] \u2714\ufe0f [True] \n", + "2 [] \u2714\ufe0f [True] \n", + "3 [] \u2714\ufe0f [True] \n", + "4 [] \u2714\ufe0f [True] \n", ".. ... ... \n", - "195 [David, Campese] ✔️ [True] \n", - "196 [] ✔️ [True] \n", - "197 [Campese, Rob, Andrew] ✔️ [True] \n", - "198 [Campo, Andrew] ✔️ [True] \n", - "199 [] ✔️ [True] \n", + "195 [David, Campese] \u2714\ufe0f [True] \n", + "196 [] \u2714\ufe0f [True] \n", + "197 [Campese, Rob, Andrew] \u2714\ufe0f [True] \n", + "198 [Campo, Andrew] \u2714\ufe0f [True] \n", + "199 [] \u2714\ufe0f [True] \n", "\n", "[200 rows x 5 columns]" ] diff --git a/docs/docs/tutorials/games/index.ipynb b/docs/docs/tutorials/games/index.ipynb index 83d70e4d8d..f7dcb90248 100644 --- a/docs/docs/tutorials/games/index.ipynb +++ b/docs/docs/tutorials/games/index.ipynb @@ -1,781 +1,781 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Fine-tuning Agents\n", - "\n", - "Let's walk through a quick example of optimizing the _language model weights_ (i.e., fine-tuning) inside a DSPy module that represents a ReAct agent playing a game with 50-step tasks.\n", - "\n", - "### Install dependencies and download data\n", - "\n", - "Install the latest DSPy via `pip install -U --pre dspy` and follow along. This tutorial uses the AlfWorld dataset, which depends on DSPy 2.6.0 (pre-release).\n", - "\n", - "You will also need the following dependencies:\n", - "\n", - "```shell\n", - "> pip install -U alfworld==0.3.5 multiprocess\n", - "> alfworld-download\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Recommended: Setup MLflow Tracing for learning what's happening under the hood\n", - "\n", - "### MLflow DSPy Integration\n", - "\n", - "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", - "\n", - "![MLflow Trace](./mlflow-tracing-agent.png)\n", - "\n", - "1. Install MLflow\n", - "\n", - "```bash\n", - "%pip install mlflow>=2.20\n", - "```\n", - "\n", - "2. Start MLflow UI in a separate terminal\n", - "```bash\n", - "mlflow ui --port 5000\n", - "```\n", - "\n", - "3. Connect the notebook to MLflow\n", - "```python\n", - "import mlflow\n", - "\n", - "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", - "mlflow.set_experiment(\"DSPy\")\n", - "```\n", - "\n", - "4. Enabling tracing.\n", - "```python\n", - "mlflow.dspy.autolog()\n", - "```\n", - "\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up the language models\n", - "\n", - "Our goal is to allow `gpt-4o-mini` to play the AlfWorld household game proficiently, without tinkering with string prompts or example trajectories by hand.\n", - "\n", - "Though it's not strictly necessary, we'll make our job a little easier by using the larger `gpt-4o` for prompt optimization and fine-tuning, building our small `gpt-4o-mini` agent." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "gpt4o_mini = dspy.LM('gpt-4o-mini-2024-07-18')\n", - "gpt4o = dspy.LM('openai/gpt-4o')\n", - "dspy.configure(experimental=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's load 200 training and 200 development tasks from AlfWorld. The dataset is much larger, but a small number of examples will help keep this tutorial run in 1-2 hours, including fine-tuning.\n", - "\n", - "With just 100 training tasks, we'll teach 4o-mini to go from 19% (can barely play the game) to 72%. If you use 500 tasks and retain the demonstrations during fine-tuning, you can push that easily to 82%." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(200, 200)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dspy.datasets.alfworld import AlfWorld\n", - "\n", - "alfworld = AlfWorld()\n", - "trainset, devset = alfworld.trainset[:200], alfworld.devset[-200:]\n", - "len(trainset), len(devset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we proceed, let's view an example of this task." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-= Welcome to TextWorld, ALFRED! =-\n", - "\n", - "You are in the middle of a room. Looking quickly around you, you see a countertop 1, a drawer 8, a drawer 7, a drawer 6, a drawer 5, a drawer 4, a drawer 3, a drawer 2, a drawer 1, a garbagecan 1, a handtowelholder 1, a sinkbasin 2, a sinkbasin 1, a toilet 1, a toiletpaperhanger 1, and a towelholder 1.\n", - "\n", - "Your task is to: put a clean soapbar in garbagecan.\n" - ] - } - ], - "source": [ - "example = trainset[0]\n", - "\n", - "with alfworld.POOL.session() as env:\n", - " task, info = env.init(**example.inputs())\n", - "\n", - "print(task)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the Agent program\n", - "\n", - "The agent is a pretty simple `dspy.Module` with one sub-module called `self.react`.\n", - "\n", - "This sub-module consumes a definition of a specific `task`, sees its previous `trajectory`, and sees a list of\n", - "`possible_actions` it can take. It responds simply with the next action.\n", - "\n", - "In the `forward` method, we just initialize an environment for the given task `idx`. And we loop up to `self.max_iters`,\n", - "repeatedly invoking the `self.react` module to take the next action." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class Agent(dspy.Module):\n", - " def __init__(self, max_iters=50, verbose=False):\n", - " self.max_iters = max_iters\n", - " self.verbose = verbose\n", - " self.react = dspy.Predict(\"task, trajectory, possible_actions: list[str] -> action\")\n", - "\n", - " def forward(self, idx):\n", - " with alfworld.POOL.session() as env:\n", - " trajectory = []\n", - " task, info = env.init(idx)\n", - " if self.verbose:\n", - " print(f\"Task: {task}\")\n", - "\n", - " for _ in range(self.max_iters):\n", - " trajectory_ = \"\\n\".join(trajectory)\n", - " possible_actions = info[\"admissible_commands\"][0] + [\"think: ${...thoughts...}\"]\n", - " prediction = self.react(task=task, trajectory=trajectory_, possible_actions=possible_actions)\n", - " trajectory.append(f\"> {prediction.action}\")\n", - "\n", - " if prediction.action.startswith(\"think:\"):\n", - " trajectory.append(\"OK.\")\n", - " continue\n", - "\n", - " obs, reward, done, info = env.step(prediction.action)\n", - " obs, reward, done = obs[0], reward[0], done[0]\n", - " trajectory.append(obs)\n", - "\n", - " if self.verbose:\n", - " print(\"\\n\".join(trajectory[-2:]))\n", - "\n", - " if done:\n", - " break\n", - "\n", - " assert reward == int(info[\"won\"][0]), (reward, info[\"won\"][0])\n", - " return dspy.Prediction(trajecotry=trajectory, success=reward)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Aside: If you wanted to include instructions for your agent...\n", - "\n", - "Above, we opted to keep the agent super simple, without even providing short instructions that describe the task.\n", - "\n", - "In principle, you can copy a short definition of the AlfWorld task (based on Yao et al., 2022) and use that as the\n", - "instruction for your agent. This is not inherently essential, but it helps illustrate the role that\n", - "instructions play in DSPy: they're not for coercing the model to exhibit a certain behavior, but they're there to\n", - "describe the fundamentals of the task in a straightforward, human-readable way.\n", - "\n", - "If you want to do that, you can simply replace this:\n", - "\n", - "```python\n", - "self.react = dspy.Predict(\"task, trajectory, possible_actions: list[str] -> action\")\n", - "```\n", - "\n", - "with this:\n", - "\n", - "```python\n", - "INSTRUCTIONS = \"\"\"\n", - "Interact with a simulated household to achieve a high-level goal. Make sure to plan, track subgoals,\n", - "determine likely locations for common household items (e.g. desklamps will likely be on desks, shelfs, or dressers),\n", - "and explore systematically (e.g. check all desks one by one for desklamp).\n", - "\"\"\".strip()\n", - "\n", - "self.react = dspy.Predict(dspy.Signature(\"task, trajectory, possible_actions: list[str] -> action\", INSTRUCTIONS))\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Zero-shot evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's try this simple program, prior to any optimization work." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Task: -= Welcome to TextWorld, ALFRED! =-\n", - "\n", - "You are in the middle of a room. Looking quickly around you, you see a countertop 1, a drawer 8, a drawer 7, a drawer 6, a drawer 5, a drawer 4, a drawer 3, a drawer 2, a drawer 1, a garbagecan 1, a handtowelholder 1, a sinkbasin 2, a sinkbasin 1, a toilet 1, a toiletpaperhanger 1, and a towelholder 1.\n", - "\n", - "Your task is to: put a clean soapbar in garbagecan.\n", - "> go to countertop 1\n", - "You arrive at countertop 1. On the countertop 1, you see a candle 1, a soapbar 1, a soapbottle 2, a soapbottle 1, and a spraybottle 1.\n", - "> take soapbar 1 from countertop 1\n", - "You pick up the soapbar 1 from the countertop 1.\n", - "> go to garbagecan 1\n", - "You arrive at garbagecan 1. On the garbagecan 1, you see nothing.\n", - "> move soapbar 1 to garbagecan 1\n", - "You move the soapbar 1 to the garbagecan 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> take soapbar 1 from garbagecan 1\n", - "You pick up the soapbar 1 from the garbagecan 1.\n", - "> move soapbar 1 to garbagecan 1\n", - "You move the soapbar 1 to the garbagecan 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n", - "> examine garbagecan 1\n", - "On the garbagecan 1, you see a soapbar 1.\n", - "> look\n", - "You are facing the garbagecan 1. Next to it, you see nothing.\n" - ] - }, - { - "data": { - "text/plain": [ - "Prediction(\n", - " trajecotry=['> go to countertop 1', 'You arrive at countertop 1. On the countertop 1, you see a candle 1, a soapbar 1, a soapbottle 2, a soapbottle 1, and a spraybottle 1.', '> take soapbar 1 from countertop 1', 'You pick up the soapbar 1 from the countertop 1.', '> go to garbagecan 1', 'You arrive at garbagecan 1. On the garbagecan 1, you see nothing.', '> move soapbar 1 to garbagecan 1', 'You move the soapbar 1 to the garbagecan 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> take soapbar 1 from garbagecan 1', 'You pick up the soapbar 1 from the garbagecan 1.', '> move soapbar 1 to garbagecan 1', 'You move the soapbar 1 to the garbagecan 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.'],\n", - " success=0\n", - ")" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agent_4o = Agent()\n", - "agent_4o.set_lm(gpt4o)\n", - "agent_4o.verbose = True\n", - "\n", - "agent_4o(**example.inputs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Okay, in this case it couldn't solve this example! Now, let's check the average quality of 4o and 4o-mini." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "metric = lambda x, y, trace=None: y.success\n", - "evaluate = dspy.Evaluate(devset=devset, metric=metric, display_progress=True, num_threads=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Tracking Evaluation Results in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", - "\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "with mlflow.start_run(run_name=\"agent_evaluation\"):\n", - " evaluate = dspy.Evaluate(\n", - " devset=devset,\n", - " metric=metric,\n", - " num_threads=16,\n", - " display_progress=True,\n", - " # To record the outputs and detailed scores to MLflow\n", - " return_all_scores=True,\n", - " return_outputs=True,\n", - " )\n", - "\n", - " # Evaluate the program as usual\n", - " aggregated_score, outputs, all_scores = evaluate(cot)\n", - "\n", - " # Log the aggregated score\n", - " mlflow.log_metric(\"success_rate\", aggregated_score)\n", - " # Log the detailed evaluation results as a table\n", - " mlflow.log_table(\n", - " {\n", - " \"Idx\": [example.idx for example in eval_set],\n", - " \"Result\": outputs,\n", - " \"Success\": all_scores,\n", - " },\n", - " artifact_file=\"eval_results.json\",\n", - " )\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 115.00 / 200 (57.5%): 100%|██████████| 200/200 [06:14<00:00, 1.87s/it]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Fine-tuning Agents\n", + "\n", + "Let's walk through a quick example of optimizing the _language model weights_ (i.e., fine-tuning) inside a DSPy module that represents a ReAct agent playing a game with 50-step tasks.\n", + "\n", + "### Install dependencies and download data\n", + "\n", + "Install the latest DSPy via `pip install -U --pre dspy` and follow along. This tutorial uses the AlfWorld dataset, which depends on DSPy 2.6.0 (pre-release).\n", + "\n", + "You will also need the following dependencies:\n", + "\n", + "```shell\n", + "> pip install -U alfworld==0.3.5 multiprocess\n", + "> alfworld-download\n", + "```" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/28 11:10:25 INFO dspy.evaluate.evaluate: Average Metric: 115 / 200 (57.5%)\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Recommended: Setup MLflow Tracing for learning what's happening under the hood\n", + "\n", + "### MLflow DSPy Integration\n", + "\n", + "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", + "\n", + "![MLflow Trace](./mlflow-tracing-agent.png)\n", + "\n", + "1. Install MLflow\n", + "\n", + "```bash\n", + "%pip install mlflow>=2.20\n", + "```\n", + "\n", + "2. Start MLflow UI in a separate terminal\n", + "```bash\n", + "mlflow ui --port 5000\n", + "```\n", + "\n", + "3. Connect the notebook to MLflow\n", + "```python\n", + "import mlflow\n", + "\n", + "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", + "mlflow.set_experiment(\"DSPy\")\n", + "```\n", + "\n", + "4. Enabling tracing.\n", + "```python\n", + "mlflow.dspy.autolog()\n", + "```\n", + "\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up the language models\n", + "\n", + "Our goal is to allow `gpt-4o-mini` to play the AlfWorld household game proficiently, without tinkering with string prompts or example trajectories by hand.\n", + "\n", + "Though it's not strictly necessary, we'll make our job a little easier by using the larger `gpt-4o` for prompt optimization and fine-tuning, building our small `gpt-4o-mini` agent." + ] }, { - "data": { - "text/plain": [ - "57.5" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "\n", + "gpt4o_mini = dspy.LM('gpt-4o-mini-2024-07-18')\n", + "gpt4o = dspy.LM('openai/gpt-4o')\n", + "dspy.configure(experimental=True)" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agent_4o.verbose = False\n", - "evaluate(agent_4o)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 30.00 / 200 (15.0%): 100%|██████████| 200/200 [08:33<00:00, 2.57s/it]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load 200 training and 200 development tasks from AlfWorld. The dataset is much larger, but a small number of examples will help keep this tutorial run in 1-2 hours, including fine-tuning.\n", + "\n", + "With just 100 training tasks, we'll teach 4o-mini to go from 19% (can barely play the game) to 72%. If you use 500 tasks and retain the demonstrations during fine-tuning, you can push that easily to 82%." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/28 11:18:59 INFO dspy.evaluate.evaluate: Average Metric: 30 / 200 (15.0%)\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200, 200)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dspy.datasets.alfworld import AlfWorld\n", + "\n", + "alfworld = AlfWorld()\n", + "trainset, devset = alfworld.trainset[:200], alfworld.devset[-200:]\n", + "len(trainset), len(devset)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we proceed, let's view an example of this task." + ] }, { - "data": { - "text/plain": [ - "15.0" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-= Welcome to TextWorld, ALFRED! =-\n", + "\n", + "You are in the middle of a room. Looking quickly around you, you see a countertop 1, a drawer 8, a drawer 7, a drawer 6, a drawer 5, a drawer 4, a drawer 3, a drawer 2, a drawer 1, a garbagecan 1, a handtowelholder 1, a sinkbasin 2, a sinkbasin 1, a toilet 1, a toiletpaperhanger 1, and a towelholder 1.\n", + "\n", + "Your task is to: put a clean soapbar in garbagecan.\n" + ] + } + ], + "source": [ + "example = trainset[0]\n", + "\n", + "with alfworld.POOL.session() as env:\n", + " task, info = env.init(**example.inputs())\n", + "\n", + "print(task)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining the Agent program\n", + "\n", + "The agent is a pretty simple `dspy.Module` with one sub-module called `self.react`.\n", + "\n", + "This sub-module consumes a definition of a specific `task`, sees its previous `trajectory`, and sees a list of\n", + "`possible_actions` it can take. It responds simply with the next action.\n", + "\n", + "In the `forward` method, we just initialize an environment for the given task `idx`. And we loop up to `self.max_iters`,\n", + "repeatedly invoking the `self.react` module to take the next action." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class Agent(dspy.Module):\n", + " def __init__(self, max_iters=50, verbose=False):\n", + " self.max_iters = max_iters\n", + " self.verbose = verbose\n", + " self.react = dspy.Predict(\"task, trajectory, possible_actions: list[str] -> action\")\n", + "\n", + " def forward(self, idx):\n", + " with alfworld.POOL.session() as env:\n", + " trajectory = []\n", + " task, info = env.init(idx)\n", + " if self.verbose:\n", + " print(f\"Task: {task}\")\n", + "\n", + " for _ in range(self.max_iters):\n", + " trajectory_ = \"\\n\".join(trajectory)\n", + " possible_actions = info[\"admissible_commands\"][0] + [\"think: ${...thoughts...}\"]\n", + " prediction = self.react(task=task, trajectory=trajectory_, possible_actions=possible_actions)\n", + " trajectory.append(f\"> {prediction.action}\")\n", + "\n", + " if prediction.action.startswith(\"think:\"):\n", + " trajectory.append(\"OK.\")\n", + " continue\n", + "\n", + " obs, reward, done, info = env.step(prediction.action)\n", + " obs, reward, done = obs[0], reward[0], done[0]\n", + " trajectory.append(obs)\n", + "\n", + " if self.verbose:\n", + " print(\"\\n\".join(trajectory[-2:]))\n", + "\n", + " if done:\n", + " break\n", + "\n", + " assert reward == int(info[\"won\"][0]), (reward, info[\"won\"][0])\n", + " return dspy.Prediction(trajecotry=trajectory, success=reward)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Aside: If you wanted to include instructions for your agent...\n", + "\n", + "Above, we opted to keep the agent super simple, without even providing short instructions that describe the task.\n", + "\n", + "In principle, you can copy a short definition of the AlfWorld task (based on Yao et al., 2022) and use that as the\n", + "instruction for your agent. This is not inherently essential, but it helps illustrate the role that\n", + "instructions play in DSPy: they're not for coercing the model to exhibit a certain behavior, but they're there to\n", + "describe the fundamentals of the task in a straightforward, human-readable way.\n", + "\n", + "If you want to do that, you can simply replace this:\n", + "\n", + "```python\n", + "self.react = dspy.Predict(\"task, trajectory, possible_actions: list[str] -> action\")\n", + "```\n", + "\n", + "with this:\n", + "\n", + "```python\n", + "INSTRUCTIONS = \"\"\"\n", + "Interact with a simulated household to achieve a high-level goal. Make sure to plan, track subgoals,\n", + "determine likely locations for common household items (e.g. desklamps will likely be on desks, shelfs, or dressers),\n", + "and explore systematically (e.g. check all desks one by one for desklamp).\n", + "\"\"\".strip()\n", + "\n", + "self.react = dspy.Predict(dspy.Signature(\"task, trajectory, possible_actions: list[str] -> action\", INSTRUCTIONS))\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Zero-shot evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's try this simple program, prior to any optimization work." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Task: -= Welcome to TextWorld, ALFRED! =-\n", + "\n", + "You are in the middle of a room. Looking quickly around you, you see a countertop 1, a drawer 8, a drawer 7, a drawer 6, a drawer 5, a drawer 4, a drawer 3, a drawer 2, a drawer 1, a garbagecan 1, a handtowelholder 1, a sinkbasin 2, a sinkbasin 1, a toilet 1, a toiletpaperhanger 1, and a towelholder 1.\n", + "\n", + "Your task is to: put a clean soapbar in garbagecan.\n", + "> go to countertop 1\n", + "You arrive at countertop 1. On the countertop 1, you see a candle 1, a soapbar 1, a soapbottle 2, a soapbottle 1, and a spraybottle 1.\n", + "> take soapbar 1 from countertop 1\n", + "You pick up the soapbar 1 from the countertop 1.\n", + "> go to garbagecan 1\n", + "You arrive at garbagecan 1. On the garbagecan 1, you see nothing.\n", + "> move soapbar 1 to garbagecan 1\n", + "You move the soapbar 1 to the garbagecan 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> take soapbar 1 from garbagecan 1\n", + "You pick up the soapbar 1 from the garbagecan 1.\n", + "> move soapbar 1 to garbagecan 1\n", + "You move the soapbar 1 to the garbagecan 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n", + "> examine garbagecan 1\n", + "On the garbagecan 1, you see a soapbar 1.\n", + "> look\n", + "You are facing the garbagecan 1. Next to it, you see nothing.\n" + ] + }, + { + "data": { + "text/plain": [ + "Prediction(\n", + " trajecotry=['> go to countertop 1', 'You arrive at countertop 1. On the countertop 1, you see a candle 1, a soapbar 1, a soapbottle 2, a soapbottle 1, and a spraybottle 1.', '> take soapbar 1 from countertop 1', 'You pick up the soapbar 1 from the countertop 1.', '> go to garbagecan 1', 'You arrive at garbagecan 1. On the garbagecan 1, you see nothing.', '> move soapbar 1 to garbagecan 1', 'You move the soapbar 1 to the garbagecan 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> take soapbar 1 from garbagecan 1', 'You pick up the soapbar 1 from the garbagecan 1.', '> move soapbar 1 to garbagecan 1', 'You move the soapbar 1 to the garbagecan 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.', '> examine garbagecan 1', 'On the garbagecan 1, you see a soapbar 1.', '> look', 'You are facing the garbagecan 1. Next to it, you see nothing.'],\n", + " success=0\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent_4o = Agent()\n", + "agent_4o.set_lm(gpt4o)\n", + "agent_4o.verbose = True\n", + "\n", + "agent_4o(**example.inputs())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, in this case it couldn't solve this example! Now, let's check the average quality of 4o and 4o-mini." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "metric = lambda x, y, trace=None: y.success\n", + "evaluate = dspy.Evaluate(devset=devset, metric=metric, display_progress=True, num_threads=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Tracking Evaluation Results in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", + "\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "with mlflow.start_run(run_name=\"agent_evaluation\"):\n", + " evaluate = dspy.Evaluate(\n", + " devset=devset,\n", + " metric=metric,\n", + " num_threads=16,\n", + " display_progress=True,\n", + " # To record the outputs and detailed scores to MLflow\n", + " return_all_scores=True,\n", + " return_outputs=True,\n", + " )\n", + "\n", + " # Evaluate the program as usual\n", + " aggregated_score, outputs, all_scores = evaluate(cot)\n", + "\n", + " # Log the aggregated score\n", + " mlflow.log_metric(\"success_rate\", aggregated_score)\n", + " # Log the detailed evaluation results as a table\n", + " mlflow.log_table(\n", + " {\n", + " \"Idx\": [example.idx for example in eval_set],\n", + " \"Result\": outputs,\n", + " \"Success\": all_scores,\n", + " },\n", + " artifact_file=\"eval_results.json\",\n", + " )\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 115.00 / 200 (57.5%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [06:14<00:00, 1.87s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/28 11:10:25 INFO dspy.evaluate.evaluate: Average Metric: 115 / 200 (57.5%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "57.5" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent_4o.verbose = False\n", + "evaluate(agent_4o)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 30.00 / 200 (15.0%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [08:33<00:00, 2.57s/it]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/28 11:18:59 INFO dspy.evaluate.evaluate: Average Metric: 30 / 200 (15.0%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "15.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent_4o_mini = Agent()\n", + "agent_4o_mini.set_lm(gpt4o_mini)\n", + "\n", + "evaluate(agent_4o_mini)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Out of the box, on this task, 4o is decent (58% success rate) while 4o-mini struggles (15% success rate).\n", + "\n", + "Let's apply the following strategy:\n", + "\n", + "1. We'll optimize the _prompts_ for gpt-4o in a lightweight way.\n", + "2. We'll then use this prompt-optimized agent as a teacher to fine-tune gpt-4o-mini on the task. This will increase its quality from 19% to 72% (or 82% if you use 500 trainset examples)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prompt-optimizing GPT-4o" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = dspy.MIPROv2(metric=metric, auto=\"light\", num_threads=16, prompt_model=gpt4o)\n", + "\n", + "config = dict(max_bootstrapped_demos=1, max_labeled_demos=0, minibatch_size=40)\n", + "optimized_4o = optimizer.compile(agent_4o, trainset=trainset, **config, requires_permission_to_run=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fine-tuning GPT-4o-mini\n", + "\n", + "For fine-tuning, we'll need a teacher program (`optimized_4o` above) and a student program derived from it (`student_4om` below)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "student_4o_mini = optimized_4o.deepcopy()\n", + "student_4o_mini.set_lm(gpt4o_mini)\n", + "# student_4o_mini.react.demos = [] # you can optionally reset the demos" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = dspy.BootstrapFinetune(metric=metric, num_threads=16)\n", + "finetuned_4o_mini = optimizer.compile(student_4o_mini, teacher=optimized_4o, trainset=trainset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate the finetuned GPT-4o-mini agent" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 143.00 / 200 (71.5%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [03:15<00:00, 1.05it/s]" + ] + } + ], + "source": [ + "evaluate(finetuned_4o_mini)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having done all this optimization, let's save our program so we can use it later! This will keep a reference to the fine-tuned model as well, as long as it continued to exist with the same identifier at the provider side." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "finetuned_4o_mini.save('finetuned_4o_mini_001.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Saving programs in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", + "\n", + "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", + "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", + "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", + "\n", + "To save the program in MLflow, run the following code:\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "# Start an MLflow Run and save the program\n", + "with mlflow.start_run(run_name=\"optimized\"):\n", + " model_info = mlflow.dspy.log_model(\n", + " finetuned_4o_mini,\n", + " artifact_path=\"model\", # Any name to save the program in MLflow\n", + " )\n", + "\n", + "# Load the program back from MLflow\n", + "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now check out one task using our fine-tuned agent program!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Task: -= Welcome to TextWorld, ALFRED! =-\n", + "\n", + "You are in the middle of a room. Looking quickly around you, you see a armchair 1, a cabinet 1, a drawer 21, a drawer 20, a drawer 19, a drawer 18, a drawer 17, a drawer 16, a drawer 15, a drawer 14, a drawer 13, a drawer 12, a drawer 11, a drawer 10, a drawer 9, a drawer 8, a drawer 7, a drawer 6, a drawer 5, a drawer 4, a drawer 3, a drawer 2, a drawer 1, a dresser 1, a garbagecan 1, a sidetable 5, a sidetable 4, a sidetable 3, a sidetable 2, a sidetable 1, and a sofa 1.\n", + "\n", + "Your task is to: put some box on dresser.\n", + "> go to cabinet 1\n", + "You arrive at cabinet 1. On the cabinet 1, you see nothing.\n", + "> go to dresser 1\n", + "You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.\n", + "> look\n", + "You are facing the dresser 1. Next to it, you see nothing.\n", + "> go to sidetable 1\n", + "You arrive at sidetable 1. On the sidetable 1, you see a cellphone 1, and a desklamp 1.\n", + "> go to sidetable 2\n", + "You arrive at sidetable 2. On the sidetable 2, you see a box 2.\n", + "> take box 2 from sidetable 2\n", + "You pick up the box 2 from the sidetable 2.\n", + "> go to dresser 1\n", + "You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.\n", + "> move box 2 to dresser 1\n", + "You move the box 2 to the dresser 1.\n" + ] + }, + { + "data": { + "text/plain": [ + "Prediction(\n", + " trajecotry=['> go to cabinet 1', 'You arrive at cabinet 1. On the cabinet 1, you see nothing.', '> go to dresser 1', 'You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.', '> look', 'You are facing the dresser 1. Next to it, you see nothing.', '> go to sidetable 1', 'You arrive at sidetable 1. On the sidetable 1, you see a cellphone 1, and a desklamp 1.', '> go to sidetable 2', 'You arrive at sidetable 2. On the sidetable 2, you see a box 2.', '> take box 2 from sidetable 2', 'You pick up the box 2 from the sidetable 2.', '> go to dresser 1', 'You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.', '> move box 2 to dresser 1', 'You move the box 2 to the dresser 1.'],\n", + " success=1\n", + ")" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "finetuned_4o_mini.verbose = True\n", + "finetuned_4o_mini(**devset[0].inputs())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to load and use the agent program, you can do that as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "loaded = Agent()\n", + "loaded.load('finetuned_4o_mini_001.pkl')" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agent_4o_mini = Agent()\n", - "agent_4o_mini.set_lm(gpt4o_mini)\n", - "\n", - "evaluate(agent_4o_mini)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Out of the box, on this task, 4o is decent (58% success rate) while 4o-mini struggles (15% success rate).\n", - "\n", - "Let's apply the following strategy:\n", - "\n", - "1. We'll optimize the _prompts_ for gpt-4o in a lightweight way.\n", - "2. We'll then use this prompt-optimized agent as a teacher to fine-tune gpt-4o-mini on the task. This will increase its quality from 19% to 72% (or 82% if you use 500 trainset examples)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prompt-optimizing GPT-4o" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = dspy.MIPROv2(metric=metric, auto=\"light\", num_threads=16, prompt_model=gpt4o)\n", - "\n", - "config = dict(max_bootstrapped_demos=1, max_labeled_demos=0, minibatch_size=40)\n", - "optimized_4o = optimizer.compile(agent_4o, trainset=trainset, **config, requires_permission_to_run=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fine-tuning GPT-4o-mini\n", - "\n", - "For fine-tuning, we'll need a teacher program (`optimized_4o` above) and a student program derived from it (`student_4om` below)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "student_4o_mini = optimized_4o.deepcopy()\n", - "student_4o_mini.set_lm(gpt4o_mini)\n", - "# student_4o_mini.react.demos = [] # you can optionally reset the demos" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = dspy.BootstrapFinetune(metric=metric, num_threads=16)\n", - "finetuned_4o_mini = optimizer.compile(student_4o_mini, teacher=optimized_4o, trainset=trainset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate the finetuned GPT-4o-mini agent" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 143.00 / 200 (71.5%): 100%|██████████| 200/200 [03:15<00:00, 1.05it/s]" - ] } - ], - "source": [ - "evaluate(finetuned_4o_mini)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Having done all this optimization, let's save our program so we can use it later! This will keep a reference to the fine-tuned model as well, as long as it continued to exist with the same identifier at the provider side." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "finetuned_4o_mini.save('finetuned_4o_mini_001.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Saving programs in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", - "\n", - "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", - "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", - "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", - "\n", - "To save the program in MLflow, run the following code:\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "# Start an MLflow Run and save the program\n", - "with mlflow.start_run(run_name=\"optimized\"):\n", - " model_info = mlflow.dspy.log_model(\n", - " finetuned_4o_mini,\n", - " artifact_path=\"model\", # Any name to save the program in MLflow\n", - " )\n", - "\n", - "# Load the program back from MLflow\n", - "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now check out one task using our fine-tuned agent program!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Task: -= Welcome to TextWorld, ALFRED! =-\n", - "\n", - "You are in the middle of a room. Looking quickly around you, you see a armchair 1, a cabinet 1, a drawer 21, a drawer 20, a drawer 19, a drawer 18, a drawer 17, a drawer 16, a drawer 15, a drawer 14, a drawer 13, a drawer 12, a drawer 11, a drawer 10, a drawer 9, a drawer 8, a drawer 7, a drawer 6, a drawer 5, a drawer 4, a drawer 3, a drawer 2, a drawer 1, a dresser 1, a garbagecan 1, a sidetable 5, a sidetable 4, a sidetable 3, a sidetable 2, a sidetable 1, and a sofa 1.\n", - "\n", - "Your task is to: put some box on dresser.\n", - "> go to cabinet 1\n", - "You arrive at cabinet 1. On the cabinet 1, you see nothing.\n", - "> go to dresser 1\n", - "You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.\n", - "> look\n", - "You are facing the dresser 1. Next to it, you see nothing.\n", - "> go to sidetable 1\n", - "You arrive at sidetable 1. On the sidetable 1, you see a cellphone 1, and a desklamp 1.\n", - "> go to sidetable 2\n", - "You arrive at sidetable 2. On the sidetable 2, you see a box 2.\n", - "> take box 2 from sidetable 2\n", - "You pick up the box 2 from the sidetable 2.\n", - "> go to dresser 1\n", - "You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.\n", - "> move box 2 to dresser 1\n", - "You move the box 2 to the dresser 1.\n" - ] - }, - { - "data": { - "text/plain": [ - "Prediction(\n", - " trajecotry=['> go to cabinet 1', 'You arrive at cabinet 1. On the cabinet 1, you see nothing.', '> go to dresser 1', 'You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.', '> look', 'You are facing the dresser 1. Next to it, you see nothing.', '> go to sidetable 1', 'You arrive at sidetable 1. On the sidetable 1, you see a cellphone 1, and a desklamp 1.', '> go to sidetable 2', 'You arrive at sidetable 2. On the sidetable 2, you see a box 2.', '> take box 2 from sidetable 2', 'You pick up the box 2 from the sidetable 2.', '> go to dresser 1', 'You arrive at dresser 1. On the dresser 1, you see a book 1, a newspaper 1, a remotecontrol 1, a statue 3, and a television 1.', '> move box 2 to dresser 1', 'You move the box 2 to the dresser 1.'],\n", - " success=1\n", - ")" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + ], + "metadata": { + "kernelspec": { + "display_name": "jun2024_py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" } - ], - "source": [ - "finetuned_4o_mini.verbose = True\n", - "finetuned_4o_mini(**devset[0].inputs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to load and use the agent program, you can do that as follows." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "loaded = Agent()\n", - "loaded.load('finetuned_4o_mini_001.pkl')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jun2024_py310", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/docs/tutorials/image_generation_prompting/index.ipynb b/docs/docs/tutorials/image_generation_prompting/index.ipynb index a4bee30af1..f3a797c6f6 100644 --- a/docs/docs/tutorials/image_generation_prompting/index.ipynb +++ b/docs/docs/tutorials/image_generation_prompting/index.ipynb @@ -1,623 +1,623 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image Generation Prompt iteration\n", - "\n", - "This is based off of a tweet from [@ThorondorLLC](https://x.com/ThorondorLLC)\n", - "\n", - "Tweet is [here](https://x.com/ThorondorLLC/status/1880048546382221313)\n", - "\n", - "This will take an initial desired prompt, and iteratively refine it until the image generated matches the desired prompt.\n", - "\n", - "This is not DSPy prompt optimization as it is normally used, but it is a good example of how to use multimodal DSPy.\n", - "\n", - "A future upgrade would be to create a dataset of initial, final prompts to optimize the prompt generation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can install DSPy via:\n", - "```bash\n", - "pip install -U dspy\n", - "```\n", - "\n", - "For this example, we'll use Flux Pro from FAL. You can get an API key [here](https://fal.com/flux-pro)\n", - "\n", - "We will also need to install Pillow and dotenv.\n", - "```bash\n", - "pip install fal-client pillow dotenv\n", - "```\n", - "\n", - "\n", - "Now, let's import the necessary libraries and set up the environment:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optional\n", - "#os.environ[\"FAL_API_KEY\"] = \"your_fal_api_key\"\n", - "#os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\"" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/isaac/sd_optimizer/.venv/lib/python3.12/site-packages/pydantic/_internal/_config.py:345: UserWarning: Valid config keys have changed in V2:\n", - "* 'fields' has been removed\n", - " warnings.warn(message, UserWarning)\n", - "/Users/isaac/sd_optimizer/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import dspy\n", - "\n", - "from PIL import Image\n", - "from io import BytesIO\n", - "import requests\n", - "import fal_client\n", - "\n", - "from dotenv import load_dotenv\n", - "load_dotenv()\n", - "\n", - "# import display\n", - "from IPython.display import display\n", - "\n", - "lm = dspy.LM(model=\"gpt-4o-mini\", temperature=0.5)\n", - "dspy.settings.configure(lm=lm)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_image(prompt):\n", - "\n", - " request_id = fal_client.submit(\n", - " \"fal-ai/flux-pro/v1.1-ultra\",\n", - " arguments={\n", - " \"prompt\": prompt\n", - " },\n", - " ).request_id\n", - "\n", - " result = fal_client.result(\"fal-ai/flux-pro/v1.1-ultra\", request_id)\n", - " url = result[\"images\"][0][\"url\"]\n", - "\n", - " return dspy.Image.from_url(url)\n", - "\n", - "def display_image(image):\n", - " url = image.url\n", - " # download the image\n", - " response = requests.get(url)\n", - " image = Image.open(BytesIO(response.content))\n", - "\n", - " # display at 25% of original size\n", - " display(image.resize((image.width // 4, image.height // 4)))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1 of 5\n" - ] - }, + "cells": [ { - "data": { - "image/jpeg": 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", 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", - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Generation Prompt iteration\n", + "\n", + "This is based off of a tweet from [@ThorondorLLC](https://x.com/ThorondorLLC)\n", + "\n", + "Tweet is [here](https://x.com/ThorondorLLC/status/1880048546382221313)\n", + "\n", + "This will take an initial desired prompt, and iteratively refine it until the image generated matches the desired prompt.\n", + "\n", + "This is not DSPy prompt optimization as it is normally used, but it is a good example of how to use multimodal DSPy.\n", + "\n", + "A future upgrade would be to create a dataset of initial, final prompts to optimize the prompt generation." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Feedback: The image depicts a peaceful autumn scene with people walking among colorful leaves, which aligns with the peaceful aspect of the prompt. However, it lacks any elements that convey tension, making it not fully representative of the desired prompt.\n", - "Iteration 2 of 5\n" - ] - }, - { - "data": { - "image/jpeg": 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", 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", - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can install DSPy via:\n", + "```bash\n", + "pip install -U dspy\n", + "```\n", + "\n", + "For this example, we'll use Flux Pro from FAL. You can get an API key [here](https://fal.com/flux-pro)\n", + "\n", + "We will also need to install Pillow and dotenv.\n", + "```bash\n", + "pip install fal-client pillow dotenv\n", + "```\n", + "\n", + "\n", + "Now, let's import the necessary libraries and set up the environment:" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Feedback: The image depicts a serene autumn scene with vibrant foliage and a calm river, which aligns well with the idea of peace. However, it lacks explicit elements that suggest underlying tension, making it less effective in conveying both aspects of the desired prompt.\n", - "Iteration 3 of 5\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Optional\n", + "#os.environ[\"FAL_API_KEY\"] = \"your_fal_api_key\"\n", + "#os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\"" + ] }, { - "data": { - "image/jpeg": 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", 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/isaac/sd_optimizer/.venv/lib/python3.12/site-packages/pydantic/_internal/_config.py:345: UserWarning: Valid config keys have changed in V2:\n", + "* 'fields' has been removed\n", + " warnings.warn(message, UserWarning)\n", + "/Users/isaac/sd_optimizer/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import dspy\n", + "\n", + "from PIL import Image\n", + "from io import BytesIO\n", + "import requests\n", + "import fal_client\n", + "\n", + "from dotenv import load_dotenv\n", + "load_dotenv()\n", + "\n", + "# import display\n", + "from IPython.display import display\n", + "\n", + "lm = dspy.LM(model=\"gpt-4o-mini\", temperature=0.5)\n", + "dspy.settings.configure(lm=lm)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Feedback: The image depicts a serene autumn scene with warm colors and soft lighting, which aligns with the peaceful aspect of the desired prompt. However, it lacks elements that evoke tension or unease, making it not fully meet the requirement for a scene that is both peaceful and tense.\n", - "Iteration 4 of 5\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_image(prompt):\n", + "\n", + " request_id = fal_client.submit(\n", + " \"fal-ai/flux-pro/v1.1-ultra\",\n", + " arguments={\n", + " \"prompt\": prompt\n", + " },\n", + " ).request_id\n", + "\n", + " result = fal_client.result(\"fal-ai/flux-pro/v1.1-ultra\", request_id)\n", + " url = result[\"images\"][0][\"url\"]\n", + "\n", + " return dspy.Image.from_url(url)\n", + "\n", + "def display_image(image):\n", + " url = image.url\n", + " # download the image\n", + " response = requests.get(url)\n", + " image = Image.open(BytesIO(response.content))\n", + "\n", + " # display at 25% of original size\n", + " display(image.resize((image.width // 4, image.height // 4)))\n" + ] }, { - "data": { - "image/jpeg": 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", 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1 of 5\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feedback: The image depicts a peaceful autumn scene with people walking among colorful leaves, which aligns with the peaceful aspect of the prompt. However, it lacks any elements that convey tension, making it not fully representative of the desired prompt.\n", + "Iteration 2 of 5\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feedback: The image depicts a serene autumn scene with vibrant foliage and a calm river, which aligns well with the idea of peace. However, it lacks explicit elements that suggest underlying tension, making it less effective in conveying both aspects of the desired prompt.\n", + "Iteration 3 of 5\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feedback: The image depicts a serene autumn scene with warm colors and soft lighting, which aligns with the peaceful aspect of the desired prompt. However, it lacks elements that evoke tension or unease, making it not fully meet the requirement for a scene that is both peaceful and tense.\n", + "Iteration 4 of 5\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final prompt: A serene autumn scene with fog and shadows, capturing both peace and tension.\n" + ] + } + ], + "source": [ + "check_and_revise_prompt = dspy.Predict(\"desired_prompt: str, current_image: dspy.Image, current_prompt:str -> feedback:str, image_strictly_matches_desired_prompt: bool, revised_prompt: str\")\n", + "\n", + "initial_prompt = \"A scene that's both peaceful and tense\"\n", + "current_prompt = initial_prompt\n", + "\n", + "max_iter = 5\n", + "for i in range(max_iter):\n", + " print(f\"Iteration {i+1} of {max_iter}\")\n", + " current_image = generate_image(current_prompt)\n", + " result = check_and_revise_prompt(desired_prompt=initial_prompt, current_image=current_image, current_prompt=current_prompt)\n", + " display_image(current_image)\n", + " if result.image_strictly_matches_desired_prompt:\n", + " break\n", + " else:\n", + " current_prompt = result.revised_prompt\n", + " print(f\"Feedback: {result.feedback}\")\n", + " print(f\"Revised prompt: {result.revised_prompt}\")\n", + "\n", + "print(f\"Final prompt: {current_prompt}\")\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final prompt: A serene autumn scene with fog and shadows, capturing both peace and tension.\n" - ] + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2025-01-17T11:36:55.947579]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `desired_prompt` (str)\n", + "2. `current_image` (Image)\n", + "3. `current_prompt` (str)\n", + "\n", + "Your output fields are:\n", + "1. `feedback` (str)\n", + "2. `revised_prompt` (str)\n", + "3. `image_strictly_matches_desired_prompt` (bool)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "{desired_prompt}\n", + "\n", + "[[ ## current_image ## ]]\n", + "{current_image}\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "{current_prompt}\n", + "\n", + "[[ ## feedback ## ]]\n", + "{feedback}\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "{revised_prompt}\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `revised_prompt`, `image_strictly_matches_desired_prompt`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "A scene that's both peaceful and tense\n", + "\n", + "[[ ## current_image ## ]]\n", + "\u001b[34m\u001b[0m\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "A scene that's both peaceful and tense\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## revised_prompt ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## feedback ## ]]\n", + "The image depicts a calm caf\u00e9 scene with two individuals engaged in conversation, which conveys a sense of peace. However, the woman's posture and expression suggest an underlying tension, as she appears to be contemplative or distressed. This duality aligns well with the desired prompt of being both peaceful and tense.\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "A scene that captures the duality of peace and tension in a caf\u00e9 setting.\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "True\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2025-01-17T11:37:55.458433]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `desired_prompt` (str)\n", + "2. `current_image` (Image)\n", + "3. `current_prompt` (str)\n", + "\n", + "Your output fields are:\n", + "1. `feedback` (str)\n", + "2. `image_strictly_matches_desired_prompt` (bool)\n", + "3. `revised_prompt` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "{desired_prompt}\n", + "\n", + "[[ ## current_image ## ]]\n", + "{current_image}\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "{current_prompt}\n", + "\n", + "[[ ## feedback ## ]]\n", + "{feedback}\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "{revised_prompt}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "A scene that's both peaceful and tense\n", + "\n", + "[[ ## current_image ## ]]\n", + "\u001b[34m\u001b[0m\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "A scene that's both peaceful and tense\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## feedback ## ]]\n", + "The image depicts a peaceful autumn scene with people walking among colorful leaves, which aligns with the peaceful aspect of the prompt. However, it lacks any elements that convey tension, making it not fully representative of the desired prompt.\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "False\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "A serene autumn scene with elements that suggest underlying tension\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2025-01-17T11:38:24.032318]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `desired_prompt` (str)\n", + "2. `current_image` (Image)\n", + "3. `current_prompt` (str)\n", + "\n", + "Your output fields are:\n", + "1. `feedback` (str)\n", + "2. `image_strictly_matches_desired_prompt` (bool)\n", + "3. `revised_prompt` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "{desired_prompt}\n", + "\n", + "[[ ## current_image ## ]]\n", + "{current_image}\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "{current_prompt}\n", + "\n", + "[[ ## feedback ## ]]\n", + "{feedback}\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "{revised_prompt}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "A scene that's both peaceful and tense\n", + "\n", + "[[ ## current_image ## ]]\n", + "\u001b[34m\u001b[0m\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "A serene autumn scene with elements that suggest underlying tension\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## feedback ## ]]\n", + "The image depicts a serene autumn scene with vibrant foliage and a calm river, which aligns well with the idea of peace. However, it lacks explicit elements that suggest underlying tension, making it less effective in conveying both aspects of the desired prompt.\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "False\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "A serene autumn scene with elements that evoke a sense of unease or foreboding\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2025-01-17T11:38:46.424883]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `desired_prompt` (str)\n", + "2. `current_image` (Image)\n", + "3. `current_prompt` (str)\n", + "\n", + "Your output fields are:\n", + "1. `feedback` (str)\n", + "2. `image_strictly_matches_desired_prompt` (bool)\n", + "3. `revised_prompt` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "{desired_prompt}\n", + "\n", + "[[ ## current_image ## ]]\n", + "{current_image}\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "{current_prompt}\n", + "\n", + "[[ ## feedback ## ]]\n", + "{feedback}\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "{revised_prompt}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "A scene that's both peaceful and tense\n", + "\n", + "[[ ## current_image ## ]]\n", + "\u001b[34m\u001b[0m\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "A serene autumn scene with elements that evoke a sense of unease or foreboding\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## feedback ## ]]\n", + "The image depicts a serene autumn scene with warm colors and soft lighting, which aligns with the peaceful aspect of the desired prompt. However, it lacks elements that evoke tension or unease, making it not fully meet the requirement for a scene that is both peaceful and tense.\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "False\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "A serene autumn scene that includes subtle elements of tension or foreboding, such as dark shadows or an unsettling atmosphere.\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2025-01-17T11:39:03.586839]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `desired_prompt` (str)\n", + "2. `current_image` (Image)\n", + "3. `current_prompt` (str)\n", + "\n", + "Your output fields are:\n", + "1. `feedback` (str)\n", + "2. `image_strictly_matches_desired_prompt` (bool)\n", + "3. `revised_prompt` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "{desired_prompt}\n", + "\n", + "[[ ## current_image ## ]]\n", + "{current_image}\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "{current_prompt}\n", + "\n", + "[[ ## feedback ## ]]\n", + "{feedback}\n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "{revised_prompt}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## desired_prompt ## ]]\n", + "A scene that's both peaceful and tense\n", + "\n", + "[[ ## current_image ## ]]\n", + "\u001b[34m\u001b[0m\n", + "\n", + "[[ ## current_prompt ## ]]\n", + "A serene autumn scene that includes subtle elements of tension or foreboding, such as dark shadows or an unsettling atmosphere.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## feedback ## ]]\n", + "The image depicts a foggy forest scene with autumn leaves, creating a peaceful atmosphere. However, the overall mood is somewhat tense due to the fog and shadows, aligning well with the desired prompt of being both peaceful and tense. \n", + "\n", + "[[ ## image_strictly_matches_desired_prompt ## ]]\n", + "True\n", + "\n", + "[[ ## revised_prompt ## ]]\n", + "A serene autumn scene with fog and shadows, capturing both peace and tension.\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history(5)" + ] } - ], - "source": [ - "check_and_revise_prompt = dspy.Predict(\"desired_prompt: str, current_image: dspy.Image, current_prompt:str -> feedback:str, image_strictly_matches_desired_prompt: bool, revised_prompt: str\")\n", - "\n", - "initial_prompt = \"A scene that's both peaceful and tense\"\n", - "current_prompt = initial_prompt\n", - "\n", - "max_iter = 5\n", - "for i in range(max_iter):\n", - " print(f\"Iteration {i+1} of {max_iter}\")\n", - " current_image = generate_image(current_prompt)\n", - " result = check_and_revise_prompt(desired_prompt=initial_prompt, current_image=current_image, current_prompt=current_prompt)\n", - " display_image(current_image)\n", - " if result.image_strictly_matches_desired_prompt:\n", - " break\n", - " else:\n", - " current_prompt = result.revised_prompt\n", - " print(f\"Feedback: {result.feedback}\")\n", - " print(f\"Revised prompt: {result.revised_prompt}\")\n", - "\n", - "print(f\"Final prompt: {current_prompt}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2025-01-17T11:36:55.947579]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `desired_prompt` (str)\n", - "2. `current_image` (Image)\n", - "3. `current_prompt` (str)\n", - "\n", - "Your output fields are:\n", - "1. `feedback` (str)\n", - "2. `revised_prompt` (str)\n", - "3. `image_strictly_matches_desired_prompt` (bool)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "{desired_prompt}\n", - "\n", - "[[ ## current_image ## ]]\n", - "{current_image}\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "{current_prompt}\n", - "\n", - "[[ ## feedback ## ]]\n", - "{feedback}\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "{revised_prompt}\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `revised_prompt`, `image_strictly_matches_desired_prompt`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "A scene that's both peaceful and tense\n", - "\n", - "[[ ## current_image ## ]]\n", - "\u001b[34m\u001b[0m\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "A scene that's both peaceful and tense\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## revised_prompt ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## feedback ## ]]\n", - "The image depicts a calm café scene with two individuals engaged in conversation, which conveys a sense of peace. However, the woman's posture and expression suggest an underlying tension, as she appears to be contemplative or distressed. This duality aligns well with the desired prompt of being both peaceful and tense.\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "A scene that captures the duality of peace and tension in a café setting.\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "True\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2025-01-17T11:37:55.458433]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `desired_prompt` (str)\n", - "2. `current_image` (Image)\n", - "3. `current_prompt` (str)\n", - "\n", - "Your output fields are:\n", - "1. `feedback` (str)\n", - "2. `image_strictly_matches_desired_prompt` (bool)\n", - "3. `revised_prompt` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "{desired_prompt}\n", - "\n", - "[[ ## current_image ## ]]\n", - "{current_image}\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "{current_prompt}\n", - "\n", - "[[ ## feedback ## ]]\n", - "{feedback}\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "{revised_prompt}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "A scene that's both peaceful and tense\n", - "\n", - "[[ ## current_image ## ]]\n", - "\u001b[34m\u001b[0m\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "A scene that's both peaceful and tense\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## feedback ## ]]\n", - "The image depicts a peaceful autumn scene with people walking among colorful leaves, which aligns with the peaceful aspect of the prompt. However, it lacks any elements that convey tension, making it not fully representative of the desired prompt.\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "False\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "A serene autumn scene with elements that suggest underlying tension\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2025-01-17T11:38:24.032318]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `desired_prompt` (str)\n", - "2. `current_image` (Image)\n", - "3. `current_prompt` (str)\n", - "\n", - "Your output fields are:\n", - "1. `feedback` (str)\n", - "2. `image_strictly_matches_desired_prompt` (bool)\n", - "3. `revised_prompt` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "{desired_prompt}\n", - "\n", - "[[ ## current_image ## ]]\n", - "{current_image}\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "{current_prompt}\n", - "\n", - "[[ ## feedback ## ]]\n", - "{feedback}\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "{revised_prompt}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "A scene that's both peaceful and tense\n", - "\n", - "[[ ## current_image ## ]]\n", - "\u001b[34m\u001b[0m\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "A serene autumn scene with elements that suggest underlying tension\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## feedback ## ]]\n", - "The image depicts a serene autumn scene with vibrant foliage and a calm river, which aligns well with the idea of peace. However, it lacks explicit elements that suggest underlying tension, making it less effective in conveying both aspects of the desired prompt.\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "False\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "A serene autumn scene with elements that evoke a sense of unease or foreboding\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2025-01-17T11:38:46.424883]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `desired_prompt` (str)\n", - "2. `current_image` (Image)\n", - "3. `current_prompt` (str)\n", - "\n", - "Your output fields are:\n", - "1. `feedback` (str)\n", - "2. `image_strictly_matches_desired_prompt` (bool)\n", - "3. `revised_prompt` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "{desired_prompt}\n", - "\n", - "[[ ## current_image ## ]]\n", - "{current_image}\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "{current_prompt}\n", - "\n", - "[[ ## feedback ## ]]\n", - "{feedback}\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "{revised_prompt}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "A scene that's both peaceful and tense\n", - "\n", - "[[ ## current_image ## ]]\n", - "\u001b[34m\u001b[0m\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "A serene autumn scene with elements that evoke a sense of unease or foreboding\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## feedback ## ]]\n", - "The image depicts a serene autumn scene with warm colors and soft lighting, which aligns with the peaceful aspect of the desired prompt. However, it lacks elements that evoke tension or unease, making it not fully meet the requirement for a scene that is both peaceful and tense.\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "False\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "A serene autumn scene that includes subtle elements of tension or foreboding, such as dark shadows or an unsettling atmosphere.\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2025-01-17T11:39:03.586839]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `desired_prompt` (str)\n", - "2. `current_image` (Image)\n", - "3. `current_prompt` (str)\n", - "\n", - "Your output fields are:\n", - "1. `feedback` (str)\n", - "2. `image_strictly_matches_desired_prompt` (bool)\n", - "3. `revised_prompt` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "{desired_prompt}\n", - "\n", - "[[ ## current_image ## ]]\n", - "{current_image}\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "{current_prompt}\n", - "\n", - "[[ ## feedback ## ]]\n", - "{feedback}\n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "{image_strictly_matches_desired_prompt} # note: the value you produce must be True or False\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "{revised_prompt}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `desired_prompt`, `current_image`, `current_prompt`, produce the fields `feedback`, `image_strictly_matches_desired_prompt`, `revised_prompt`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## desired_prompt ## ]]\n", - "A scene that's both peaceful and tense\n", - "\n", - "[[ ## current_image ## ]]\n", - "\u001b[34m\u001b[0m\n", - "\n", - "[[ ## current_prompt ## ]]\n", - "A serene autumn scene that includes subtle elements of tension or foreboding, such as dark shadows or an unsettling atmosphere.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## feedback ## ]]`, then `[[ ## image_strictly_matches_desired_prompt ## ]]` (must be formatted as a valid Python bool), then `[[ ## revised_prompt ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## feedback ## ]]\n", - "The image depicts a foggy forest scene with autumn leaves, creating a peaceful atmosphere. However, the overall mood is somewhat tense due to the fog and shadows, aligning well with the desired prompt of being both peaceful and tense. \n", - "\n", - "[[ ## image_strictly_matches_desired_prompt ## ]]\n", - "True\n", - "\n", - "[[ ## revised_prompt ## ]]\n", - "A serene autumn scene with fog and shadows, capturing both peace and tension.\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" } - ], - "source": [ - "dspy.inspect_history(5)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/docs/tutorials/math/index.ipynb b/docs/docs/tutorials/math/index.ipynb index eb64396302..5ff87b1328 100644 --- a/docs/docs/tutorials/math/index.ipynb +++ b/docs/docs/tutorials/math/index.ipynb @@ -1,858 +1,858 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Math Reasoning\n", - "\n", - "Let's walk through a quick example of setting up a `dspy.ChainOfThought` module and optimizing it for answering algebra questions.\n", - "\n", - "Install the latest DSPy via `pip install -U dspy` and follow along.\n", - "\n", - "
\n", - "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", - "\n", - "### MLflow DSPy Integration\n", - "\n", - "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", - "\n", - "1. Install MLflow\n", - "\n", - "```bash\n", - "%pip install mlflow>=2.20\n", - "```\n", - "\n", - "2. Start MLflow UI in a separate terminal\n", - "```bash\n", - "mlflow ui --port 5000\n", - "```\n", - "\n", - "3. Connect the notebook to MLflow\n", - "```python\n", - "import mlflow\n", - "\n", - "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", - "mlflow.set_experiment(\"DSPy\")\n", - "```\n", - "\n", - "4. Enabling tracing.\n", - "```python\n", - "mlflow.dspy.autolog()\n", - "```\n", - "\n", - "Once you have completed the steps above, you can see traces for each program execution on the notebook. They provide great visibility into the model's behavior and helps you understand the DSPy's concepts better throughout the tutorial.\n", - "\n", - "![MLflow Trace](./mlflow-tracing-math.png)\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's tell DSPy that we will use OpenAI's `gpt-4o-mini` in our modules. To authenticate, DSPy will look into your `OPENAI_API_KEY`. You can easily swap this out for [other providers or local models](https://github.com/stanfordnlp/dspy/blob/main/examples/migration.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "gpt4o_mini = dspy.LM('openai/gpt-4o-mini', max_tokens=2000)\n", - "gpt4o = dspy.LM('openai/gpt-4o', max_tokens=2000)\n", - "dspy.configure(lm=gpt4o_mini) # we'll use gpt-4o-mini as the default LM, unless otherwise specified" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's load some data examples from the [MATH](https://arxiv.org/abs/2103.03874) benchmark. We'll use a training split for optimization and evaluate it on a held-out dev set.\n", - "\n", - "Please note that the following step will require:\n", - "```bash\n", - "%pip install git+https://github.com/hendrycks/math.git\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "350 350\n" - ] - } - ], - "source": [ - "from dspy.datasets import MATH\n", - "\n", - "dataset = MATH(subset='algebra')\n", - "print(len(dataset.train), len(dataset.dev))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's inspect one example from the training set." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Math Reasoning\n", + "\n", + "Let's walk through a quick example of setting up a `dspy.ChainOfThought` module and optimizing it for answering algebra questions.\n", + "\n", + "Install the latest DSPy via `pip install -U dspy` and follow along.\n", + "\n", + "
\n", + "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", + "\n", + "### MLflow DSPy Integration\n", + "\n", + "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", + "\n", + "1. Install MLflow\n", + "\n", + "```bash\n", + "%pip install mlflow>=2.20\n", + "```\n", + "\n", + "2. Start MLflow UI in a separate terminal\n", + "```bash\n", + "mlflow ui --port 5000\n", + "```\n", + "\n", + "3. Connect the notebook to MLflow\n", + "```python\n", + "import mlflow\n", + "\n", + "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", + "mlflow.set_experiment(\"DSPy\")\n", + "```\n", + "\n", + "4. Enabling tracing.\n", + "```python\n", + "mlflow.dspy.autolog()\n", + "```\n", + "\n", + "Once you have completed the steps above, you can see traces for each program execution on the notebook. They provide great visibility into the model's behavior and helps you understand the DSPy's concepts better throughout the tutorial.\n", + "\n", + "![MLflow Trace](./mlflow-tracing-math.png)\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: The doctor has told Cal O'Ree that during his ten weeks of working out at the gym, he can expect each week's weight loss to be $1\\%$ of his weight at the end of the previous week. His weight at the beginning of the workouts is $244$ pounds. How many pounds does he expect to weigh at the end of the ten weeks? Express your answer to the nearest whole number.\n", - "Answer: 221\n" - ] - } - ], - "source": [ - "example = dataset.train[0]\n", - "print(\"Question:\", example.question)\n", - "print(\"Answer:\", example.answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's define our module. It's extremely simple: just a chain-of-thought step that takes a `question` and produces an `answer`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's tell DSPy that we will use OpenAI's `gpt-4o-mini` in our modules. To authenticate, DSPy will look into your `OPENAI_API_KEY`. You can easily swap this out for [other providers or local models](https://github.com/stanfordnlp/dspy/blob/main/examples/migration.ipynb)." + ] + }, { - "data": { - "text/plain": [ - "Prediction(\n", - " reasoning=\"Cal O'Ree's weight loss each week is $1\\\\%$ of his weight at the end of the previous week. This means that at the end of each week, he retains $99\\\\%$ of his weight from the previous week. \\n\\nIf we denote his weight at the beginning as \\\\( W_0 = 244 \\\\) pounds, then his weight at the end of week \\\\( n \\\\) can be expressed as:\\n\\\\[\\nW_n = W_{n-1} \\\\times 0.99\\n\\\\]\\nThis can be simplified to:\\n\\\\[\\nW_n = W_0 \\\\times (0.99)^n\\n\\\\]\\nAfter 10 weeks, his weight will be:\\n\\\\[\\nW_{10} = 244 \\\\times (0.99)^{10}\\n\\\\]\\n\\nNow, we calculate \\\\( (0.99)^{10} \\\\):\\n\\\\[\\n(0.99)^{10} \\\\approx 0.904382\\n\\\\]\\n\\nNow, we can calculate his expected weight after 10 weeks:\\n\\\\[\\nW_{10} \\\\approx 244 \\\\times 0.904382 \\\\approx 220.5\\n\\\\]\\n\\nRounding to the nearest whole number, Cal O'Ree can expect to weigh approximately \\\\( 221 \\\\) pounds at the end of the ten weeks.\",\n", - " answer='221'\n", - ")" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "\n", + "gpt4o_mini = dspy.LM('openai/gpt-4o-mini', max_tokens=2000)\n", + "gpt4o = dspy.LM('openai/gpt-4o', max_tokens=2000)\n", + "dspy.configure(lm=gpt4o_mini) # we'll use gpt-4o-mini as the default LM, unless otherwise specified" ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "module = dspy.ChainOfThought(\"question -> answer\")\n", - "module(question=example.question)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's set up an evaluator for the zero-shot module above, before prompt optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 259.00 / 350 (74.0%): 100%|██████████| 350/350 [01:30<00:00, 3.85it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's load some data examples from the [MATH](https://arxiv.org/abs/2103.03874) benchmark. We'll use a training split for optimization and evaluate it on a held-out dev set.\n", + "\n", + "Please note that the following step will require:\n", + "```bash\n", + "%pip install git+https://github.com/hendrycks/math.git\n", + "```" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/11/28 18:41:55 INFO dspy.evaluate.evaluate: Average Metric: 259 / 350 (74.0%)\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "350 350\n" + ] + } + ], + "source": [ + "from dspy.datasets import MATH\n", + "\n", + "dataset = MATH(subset='algebra')\n", + "print(len(dataset.train), len(dataset.dev))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's inspect one example from the training set." + ] }, { - "data": { - "text/html": [ - "
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questionexample_reasoningexample_answerpred_reasoningpred_answermethod
0What is the smallest integer value of $c$ such that the function $...The given function has a domain of all real numbers if and only if...1To determine the smallest integer value of \\( c \\) such that the f...1✔️ [True]
1What is the least value of $x$ that is a solution of $|{-x+3}|=7$?In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ...-4To solve the equation \\( |{-x+3}|=7 \\), we need to consider the de...-4✔️ [True]
2Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$.$-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}...-1To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee...-1✔️ [True]
3A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7...We must find the distance between each pair of points by using the...10To find the length of the longest side of the triangle with vertic...10✔️ [True]
4Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$?First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(...1To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). The...1✔️ [True]
\n", - "
" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: The doctor has told Cal O'Ree that during his ten weeks of working out at the gym, he can expect each week's weight loss to be $1\\%$ of his weight at the end of the previous week. His weight at the beginning of the workouts is $244$ pounds. How many pounds does he expect to weigh at the end of the ten weeks? Express your answer to the nearest whole number.\n", + "Answer: 221\n" + ] + } ], - "text/plain": [ - " question \\\n", - "0 What is the smallest integer value of $c$ such that the function $... \n", - "1 What is the least value of $x$ that is a solution of $|{-x+3}|=7$? \n", - "2 Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$. \n", - "3 A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7... \n", - "4 Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$? \n", - "\n", - " example_reasoning \\\n", - "0 The given function has a domain of all real numbers if and only if... \n", - "1 In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ... \n", - "2 $-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}... \n", - "3 We must find the distance between each pair of points by using the... \n", - "4 First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(... \n", - "\n", - " example_answer \\\n", - "0 1 \n", - "1 -4 \n", - "2 -1 \n", - "3 10 \n", - "4 1 \n", - "\n", - " pred_reasoning \\\n", - "0 To determine the smallest integer value of \\( c \\) such that the f... \n", - "1 To solve the equation \\( |{-x+3}|=7 \\), we need to consider the de... \n", - "2 To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee... \n", - "3 To find the length of the longest side of the triangle with vertic... \n", - "4 To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). The... \n", - "\n", - " pred_answer method \n", - "0 1 ✔️ [True] \n", - "1 -4 ✔️ [True] \n", - "2 -1 ✔️ [True] \n", - "3 10 ✔️ [True] \n", - "4 1 ✔️ [True] " + "source": [ + "example = dataset.train[0]\n", + "print(\"Question:\", example.question)\n", + "print(\"Answer:\", example.answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define our module. It's extremely simple: just a chain-of-thought step that takes a `question` and produces an `answer`." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " reasoning=\"Cal O'Ree's weight loss each week is $1\\\\%$ of his weight at the end of the previous week. This means that at the end of each week, he retains $99\\\\%$ of his weight from the previous week. \\n\\nIf we denote his weight at the beginning as \\\\( W_0 = 244 \\\\) pounds, then his weight at the end of week \\\\( n \\\\) can be expressed as:\\n\\\\[\\nW_n = W_{n-1} \\\\times 0.99\\n\\\\]\\nThis can be simplified to:\\n\\\\[\\nW_n = W_0 \\\\times (0.99)^n\\n\\\\]\\nAfter 10 weeks, his weight will be:\\n\\\\[\\nW_{10} = 244 \\\\times (0.99)^{10}\\n\\\\]\\n\\nNow, we calculate \\\\( (0.99)^{10} \\\\):\\n\\\\[\\n(0.99)^{10} \\\\approx 0.904382\\n\\\\]\\n\\nNow, we can calculate his expected weight after 10 weeks:\\n\\\\[\\nW_{10} \\\\approx 244 \\\\times 0.904382 \\\\approx 220.5\\n\\\\]\\n\\nRounding to the nearest whole number, Cal O'Ree can expect to weigh approximately \\\\( 221 \\\\) pounds at the end of the ten weeks.\",\n", + " answer='221'\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "module = dspy.ChainOfThought(\"question -> answer\")\n", + "module(question=example.question)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "74.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's set up an evaluator for the zero-shot module above, before prompt optimization." ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "THREADS = 24\n", - "kwargs = dict(num_threads=THREADS, display_progress=True, display_table=5)\n", - "evaluate = dspy.Evaluate(devset=dataset.dev, metric=dataset.metric, **kwargs)\n", - "\n", - "evaluate(module)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Tracking Evaluation Results in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", - "\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "# Start an MLflow Run to record the evaluation\n", - "with mlflow.start_run(run_name=\"math_evaluation\"):\n", - " kwargs = dict(num_threads=THREADS, display_progress=True, return_all_scores=True, return_outputs=True)\n", - " evaluate = dspy.Evaluate(devset=dataset.dev, metric=dataset.metric, **kwargs)\n", - "\n", - " # Evaluate the program as usual\n", - " aggregated_score, outputs, all_scores = evaluate(module)\n", - "\n", - " # Log the aggregated score\n", - " mlflow.log_metric(\"correctness\", aggregated_score)\n", - " # Log the detailed evaluation results as a table\n", - " mlflow.log_table(\n", - " {\n", - " \"Question\": [example.question for example in dataset.dev],\n", - " \"Gold Answer\": [example.answer for example in dataset.dev],\n", - " \"Predicted Answer\": outputs,\n", - " \"Correctness\": all_scores,\n", - " },\n", - " artifact_file=\"eval_results.json\",\n", - " )\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And lastly let's optimize our module. Since we want strong reasoning, we'll use the large GPT-4o as the teacher model (used to bootstrap reasoning for the small LM at optimization time) but not as the prompt model (used to craft instructions) or the task model (trained).\n", - "\n", - "GPT-4o will be invoked only a small number of times. The model involved directly in optimization and in the resulting (optimized) program will be GPT-4o-mini.\n", - "\n", - "We will also specify `max_bootstrapped_demos=4` which means we want at most four bootstrapped examples in the prompt and `max_labeled_demos=4` which means that, in total between bootstrapped and pre-labeled examples, we want at most four." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = dict(num_threads=THREADS, teacher_settings=dict(lm=gpt4o), prompt_model=gpt4o_mini)\n", - "optimizer = dspy.MIPROv2(metric=dataset.metric, auto=\"medium\", **kwargs)\n", - "\n", - "kwargs = dict(requires_permission_to_run=False, max_bootstrapped_demos=4, max_labeled_demos=4)\n", - "optimized_module = optimizer.compile(module, trainset=dataset.train, **kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 310.00 / 350 (88.6%): 100%|██████████| 350/350 [01:31<00:00, 3.84it/s]" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 259.00 / 350 (74.0%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 350/350 [01:30<00:00, 3.85it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/11/28 18:41:55 INFO dspy.evaluate.evaluate: Average Metric: 259 / 350 (74.0%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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questionexample_reasoningexample_answerpred_reasoningpred_answermethod
0What is the smallest integer value of $c$ such that the function $...The given function has a domain of all real numbers if and only if...1To determine the smallest integer value of \\( c \\) such that the f...1\u2714\ufe0f [True]
1What is the least value of $x$ that is a solution of $|{-x+3}|=7$?In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ...-4To solve the equation \\( |{-x+3}|=7 \\), we need to consider the de...-4\u2714\ufe0f [True]
2Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$.$-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}...-1To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee...-1\u2714\ufe0f [True]
3A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7...We must find the distance between each pair of points by using the...10To find the length of the longest side of the triangle with vertic...10\u2714\ufe0f [True]
4Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$?First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(...1To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). The...1\u2714\ufe0f [True]
\n", + "
" + ], + "text/plain": [ + " question \\\n", + "0 What is the smallest integer value of $c$ such that the function $... \n", + "1 What is the least value of $x$ that is a solution of $|{-x+3}|=7$? \n", + "2 Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$. \n", + "3 A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7... \n", + "4 Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$? \n", + "\n", + " example_reasoning \\\n", + "0 The given function has a domain of all real numbers if and only if... \n", + "1 In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ... \n", + "2 $-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}... \n", + "3 We must find the distance between each pair of points by using the... \n", + "4 First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(... \n", + "\n", + " example_answer \\\n", + "0 1 \n", + "1 -4 \n", + "2 -1 \n", + "3 10 \n", + "4 1 \n", + "\n", + " pred_reasoning \\\n", + "0 To determine the smallest integer value of \\( c \\) such that the f... \n", + "1 To solve the equation \\( |{-x+3}|=7 \\), we need to consider the de... \n", + "2 To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee... \n", + "3 To find the length of the longest side of the triangle with vertic... \n", + "4 To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). The... \n", + "\n", + " pred_answer method \n", + "0 1 \u2714\ufe0f [True] \n", + "1 -4 \u2714\ufe0f [True] \n", + "2 -1 \u2714\ufe0f [True] \n", + "3 10 \u2714\ufe0f [True] \n", + "4 1 \u2714\ufe0f [True] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + "Tracking Evaluation Results in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", + "\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "# Start an MLflow Run to record the evaluation\n", + "with mlflow.start_run(run_name=\"math_evaluation\"):\n", + " kwargs = dict(num_threads=THREADS, display_progress=True, return_all_scores=True, return_outputs=True)\n", + " evaluate = dspy.Evaluate(devset=dataset.dev, metric=dataset.metric, **kwargs)\n", + "\n", + " # Evaluate the program as usual\n", + " aggregated_score, outputs, all_scores = evaluate(module)\n", + "\n", + " # Log the aggregated score\n", + " mlflow.log_metric(\"correctness\", aggregated_score)\n", + " # Log the detailed evaluation results as a table\n", + " mlflow.log_table(\n", + " {\n", + " \"Question\": [example.question for example in dataset.dev],\n", + " \"Gold Answer\": [example.answer for example in dataset.dev],\n", + " \"Predicted Answer\": outputs,\n", + " \"Correctness\": all_scores,\n", + " },\n", + " artifact_file=\"eval_results.json\",\n", + " )\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And lastly let's optimize our module. Since we want strong reasoning, we'll use the large GPT-4o as the teacher model (used to bootstrap reasoning for the small LM at optimization time) but not as the prompt model (used to craft instructions) or the task model (trained).\n", + "\n", + "GPT-4o will be invoked only a small number of times. The model involved directly in optimization and in the resulting (optimized) program will be GPT-4o-mini.\n", + "\n", + "We will also specify `max_bootstrapped_demos=4` which means we want at most four bootstrapped examples in the prompt and `max_labeled_demos=4` which means that, in total between bootstrapped and pre-labeled examples, we want at most four." + ] }, { - "data": { - "text/html": [ - "
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questionexample_reasoningexample_answerpred_reasoningpred_answermethod
0What is the smallest integer value of $c$ such that the function $...The given function has a domain of all real numbers if and only if...1The function \\( f(x) = \\frac{x^2 + 1}{x^2 - x + c} \\) will have a ...1✔️ [True]
1What is the least value of $x$ that is a solution of $|{-x+3}|=7$?In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ...-4The equation \\( |{-x+3}|=7 \\) implies two possible cases: 1. \\(-x ...-4✔️ [True]
2Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$.$-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}...-1To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee...-1✔️ [True]
3A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7...We must find the distance between each pair of points by using the...10To find the length of the sides of the triangle formed by the vert...10✔️ [True]
4Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$?First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(...1To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). Usi...1✔️ [True]
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" - ], - "text/plain": [ - " question \\\n", - "0 What is the smallest integer value of $c$ such that the function $... \n", - "1 What is the least value of $x$ that is a solution of $|{-x+3}|=7$? \n", - "2 Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$. \n", - "3 A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7... \n", - "4 Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$? \n", - "\n", - " example_reasoning \\\n", - "0 The given function has a domain of all real numbers if and only if... \n", - "1 In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ... \n", - "2 $-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}... \n", - "3 We must find the distance between each pair of points by using the... \n", - "4 First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(... \n", - "\n", - " example_answer \\\n", - "0 1 \n", - "1 -4 \n", - "2 -1 \n", - "3 10 \n", - "4 1 \n", - "\n", - " pred_reasoning \\\n", - "0 The function \\( f(x) = \\frac{x^2 + 1}{x^2 - x + c} \\) will have a ... \n", - "1 The equation \\( |{-x+3}|=7 \\) implies two possible cases: 1. \\(-x ... \n", - "2 To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee... \n", - "3 To find the length of the sides of the triangle formed by the vert... \n", - "4 To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). Usi... \n", - "\n", - " pred_answer method \n", - "0 1 ✔️ [True] \n", - "1 -4 ✔️ [True] \n", - "2 -1 ✔️ [True] \n", - "3 10 ✔️ [True] \n", - "4 1 ✔️ [True] " + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = dict(num_threads=THREADS, teacher_settings=dict(lm=gpt4o), prompt_model=gpt4o_mini)\n", + "optimizer = dspy.MIPROv2(metric=dataset.metric, auto=\"medium\", **kwargs)\n", + "\n", + "kwargs = dict(requires_permission_to_run=False, max_bootstrapped_demos=4, max_labeled_demos=4)\n", + "optimized_module = optimizer.compile(module, trainset=dataset.train, **kwargs)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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questionexample_reasoningexample_answerpred_reasoningpred_answermethod
0What is the smallest integer value of $c$ such that the function $...The given function has a domain of all real numbers if and only if...1The function \\( f(x) = \\frac{x^2 + 1}{x^2 - x + c} \\) will have a ...1\u2714\ufe0f [True]
1What is the least value of $x$ that is a solution of $|{-x+3}|=7$?In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ...-4The equation \\( |{-x+3}|=7 \\) implies two possible cases: 1. \\(-x ...-4\u2714\ufe0f [True]
2Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$.$-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}...-1To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee...-1\u2714\ufe0f [True]
3A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7...We must find the distance between each pair of points by using the...10To find the length of the sides of the triangle formed by the vert...10\u2714\ufe0f [True]
4Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$?First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(...1To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). Usi...1\u2714\ufe0f [True]
\n", + "
" + ], + "text/plain": [ + " question \\\n", + "0 What is the smallest integer value of $c$ such that the function $... \n", + "1 What is the least value of $x$ that is a solution of $|{-x+3}|=7$? \n", + "2 Evaluate $\\left\\lceil -\\frac{7}{4}\\right\\rceil$. \n", + "3 A triangle has vertices at coordinates $(11,1)$, $(2,3)$ and $(3,7... \n", + "4 Let $f(x) = x + 2$ and $g(x) = 1/f(x)$. What is $g(f(-3))$? \n", + "\n", + " example_reasoning \\\n", + "0 The given function has a domain of all real numbers if and only if... \n", + "1 In order to have $|{-x+3}| = 7$, we must have $-x + 3 = 7$ or $-x ... \n", + "2 $-\\frac{7}{4}$ is between $-1$ and $-2$, so $\\left\\lceil -\\frac{7}... \n", + "3 We must find the distance between each pair of points by using the... \n", + "4 First, we find that $f(-3) = (-3) + 2 = -1$. Then, $$g(f(-3)) = g(... \n", + "\n", + " example_answer \\\n", + "0 1 \n", + "1 -4 \n", + "2 -1 \n", + "3 10 \n", + "4 1 \n", + "\n", + " pred_reasoning \\\n", + "0 The function \\( f(x) = \\frac{x^2 + 1}{x^2 - x + c} \\) will have a ... \n", + "1 The equation \\( |{-x+3}|=7 \\) implies two possible cases: 1. \\(-x ... \n", + "2 To evaluate \\(\\left\\lceil -\\frac{7}{4}\\right\\rceil\\), we first nee... \n", + "3 To find the length of the sides of the triangle formed by the vert... \n", + "4 To find \\( g(f(-3)) \\), we first need to evaluate \\( f(-3) \\). Usi... \n", + "\n", + " pred_answer method \n", + "0 1 \u2714\ufe0f [True] \n", + "1 -4 \u2714\ufe0f [True] \n", + "2 -1 \u2714\ufe0f [True] \n", + "3 10 \u2714\ufe0f [True] \n", + "4 1 \u2714\ufe0f [True] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "88.57" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "evaluate(optimized_module)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "88.57" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Neat. It was pretty straightforward to improve quality from 74% to over 88% on a held-out set here.\n", + "\n", + "That said, for reasoning tasks like this, you will often want to consider more advanced strategies, like:\n", + "\n", + "- A `dspy.ReAct` module with access to a calculator function or `dspy.PythonInterpreter`\n", + "- Ensembling multiple optimized prompts with a majority vote (or an Aggregator module) on top" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(optimized_module)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neat. It was pretty straightforward to improve quality from 74% to over 88% on a held-out set here.\n", - "\n", - "That said, for reasoning tasks like this, you will often want to consider more advanced strategies, like:\n", - "\n", - "- A `dspy.ReAct` module with access to a calculator function or `dspy.PythonInterpreter`\n", - "- Ensembling multiple optimized prompts with a majority vote (or an Aggregator module) on top" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Just to understand what changed, let's view the prompt after optimization. Alternatively, if you enabled MLflow tracing following above instructions, you can compare the prompts before and after optimization in the rich trace UI." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Just to understand what changed, let's view the prompt after optimization. Alternatively, if you enabled MLflow tracing following above instructions, you can compare the prompts before and after optimization in the rich trace UI." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-11-28T18:59:19.176586]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `question` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `answer` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## question ## ]]\n", + "{question}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## answer ## ]]\n", + "{answer}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Analyze the `question` provided, and systematically apply mathematical reasoning to derive the `answer`. Ensure to articulate each step of your thought process in the `reasoning` field, detailing how you identify relationships and formulate equations to arrive at the solution.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## question ## ]]\n", + "If $|4x+2|=10$ and $x<0$, what is the value of $x$?\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The equation $|4x+2|=10$ implies two possible equations: $4x+2=10$ or $4x+2=-10$. We solve each equation separately.\n", + "\n", + "1. For $4x+2=10$:\n", + " \\[\n", + " 4x + 2 = 10 \\\\\n", + " 4x = 8 \\\\\n", + " x = 2\n", + " \\]\n", + " However, $x=2$ does not satisfy the condition $x<0$.\n", + "\n", + "2. For $4x+2=-10$:\n", + " \\[\n", + " 4x + 2 = -10 \\\\\n", + " 4x = -12 \\\\\n", + " x = -3\n", + " \\]\n", + " The solution $x = -3$ satisfies the condition $x<0$.\n", + "\n", + "Therefore, the value of $x$ is $\\boxed{-3}$.\n", + "\n", + "[[ ## answer ## ]]\n", + "-3\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## question ## ]]\n", + "Given that $-4$ is a solution to $x^2 + bx -36 = 0$, what is the value of $b$?\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The product of the roots of this quadratic is $-36/1=-36$, so the other solution must be $-36/-4=9$. That means that the sum of the solutions is $-4+9=5$. The sum of the solutions is also $-b/1=-b$. Thus, $-b=5$ and $b=\\boxed{-5}$.\n", + "\n", + "[[ ## answer ## ]]\n", + "-5\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## question ## ]]\n", + "Richard is building a rectangular playground from 200 feet of fencing. The fencing must entirely enclose the playground. What is the maximum area of this playground?\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "Let the length of the playground be $l$ and the width be $w$. We have the equation $2l+2w=200 \\Rightarrow l + w = 100$. We want to maximize the area of this rectangular playground, which is given by $lw$. From our equation, we know that $l=100-w$. Substituting this in to our expression for area, we have \\[(100-w)(w)=100w-w^2\\]We will now complete the square to find the maximum value of this expression. Factoring a $-1$ out, we have \\[-(w^2-100w)\\]In order for the expression inside the parenthesis to be a perfect square, we need to add and subtract $(100/2)^2=2500$ inside the parenthesis. Doing this, we get \\[-(w^2-100w+2500-2500) \\Rightarrow -(w-50)^2+2500\\]Since the maximum value of $-(w-50)^2$ is 0 (perfect squares are always nonnegative), the maximum value of the entire expression is 2500, which is achieved when $w=50$ and $l=100-w=50$ (the playground is a square). Thus, the maximum area of the playground is $\\boxed{2500}$ square feet.\n", + "\n", + "[[ ## answer ## ]]\n", + "2500\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## question ## ]]\n", + "Alice and Bob are playing a game. Alice starts first. On Alice's turn, she flips a coin. If she gets a heads, she wins. If not, it becomes Bob's turn. On Bob's turn, he flips a coin. If he gets a tails, he wins. If not, it becomes Alice's turn. What is the probability that Alice wins the game?\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "Alice has a $1/2$ chance of winning the game on her first turn. If she doesn't, then the probability that she wins the game on her second turn is $1/8,$ since she must not win on her first flip ($1/2$ chance), Bob must not win on his first flip ($1/2$ chance), and then Alice must win on her second flip ($1/2$ chance). The probability that she wins the game on her third turn is $1/32,$ and in general, the probability that she wins the game on her $k^\\text{th}$ turn is $(1/2)^{2k-1}.$ Thus, the probability that Alice wins is an infinite geometric series with first term $1/2$ and common ratio $1/4.$ So, the probability that Alice wins the game is $$\\frac{\\frac12}{1-\\frac14} = \\boxed{\\frac{2}{3}}.$$OR\n", + "\n", + "Note that the only difference between the odds of Alice or Bob winning is who goes first. Because Bob goes second, the odds of him winning on his $k^\\text{th}$ flip is half of the odds that Alice wins on her $k^\\text{th}$ flip, since Alice must first get a tails before Bob gets a chance to win. Thus, if $a$ is Alice's chance of winning, and $b$ is Bob's chance of winning, then $a = 2b.$ Also, since someone must win, $a + b = 1.$ It follows that $a = 2/3$ and $b = 1/3,$ so Alice has a $\\boxed{\\frac{2}{3}}$ chance of winning the game.\n", + "\n", + "[[ ## answer ## ]]\n", + "\\frac{2}{3}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## question ## ]]\n", + "Find the smallest possible value of $$\\frac{(y-x)^2}{(y-z)(z-x)} + \\frac{(z-y)^2}{(z-x)(x-y)} + \\frac{(x-z)^2}{(x-y)(y-z)},$$ where $x,y,$ and $z$ are distinct real numbers.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "Let \\( S = \\frac{(y-x)^2}{(y-z)(z-x)} + \\frac{(z-y)^2}{(z-x)(x-y)} + \\frac{(x-z)^2}{(x-y)(y-z)} \\).\n", + "\n", + "To find the minimum value of \\( S \\), we can use the method of symmetry and the Cauchy-Schwarz inequality. \n", + "\n", + "By applying the Cauchy-Schwarz inequality, we have:\n", + "\\[\n", + "\\left( \\frac{(y-x)^2}{(y-z)(z-x)} + \\frac{(z-y)^2}{(z-x)(x-y)} + \\frac{(x-z)^2}{(x-y)(y-z)} \\right) \\left( (y-z)(z-x) + (z-x)(x-y) + (x-y)(y-z) \\right) \\geq \\left( (y-x) + (z-y) + (x-z) \\right)^2.\n", + "\\]\n", + "The right-hand side simplifies to:\n", + "\\[\n", + "(y-x) + (z-y) + (x-z) = 0,\n", + "\\]\n", + "which means the right-hand side is \\( 0 \\). Therefore, we have:\n", + "\\[\n", + "S \\cdot \\left( (y-z)(z-x) + (z-x)(x-y) + (x-y)(y-z) \\right) \\geq 0.\n", + "\\]\n", + "This implies that \\( S \\) is non-negative.\n", + "\n", + "Next, we can test specific values for \\( x, y, z \\) to find the minimum. Let's set \\( x = 0, y = 1, z = 2 \\):\n", + "\\[\n", + "S = \\frac{(1-0)^2}{(1-2)(2-0)} + \\frac{(2-1)^2}{(2-0)(0-1)} + \\frac{(0-2)^2}{(0-1)(1-2)}.\n", + "\\]\n", + "Calculating each term:\n", + "1. The first term:\n", + " \\[\n", + " \\frac{1^2}{(-1)(2)} = \\frac{1}{-2} = -\\frac{1}{2}.\n", + " \\]\n", + "2. The second term:\n", + " \\[\n", + " \\frac{1^2}{(2)(-1)} = \\frac{1}{-2} = -\\frac{1}{2}.\n", + " \\]\n", + "3. The third term:\n", + " \\[\n", + " \\frac{(-2)^2}{(-1)(-1)} = \\frac{4}{1} = 4.\n", + " \\]\n", + "\n", + "Thus,\n", + "\\[\n", + "S = -\\frac{1}{2} - \\frac{1}{2} + 4 = 3.\n", + "\\]\n", + "\n", + "To check if this is indeed the minimum, we can also consider the case when \\( x, y, z \\) are the roots of a quadratic polynomial. By symmetry and the nature of the expression, we can conclude that the minimum value of \\( S \\) is indeed \\( 3 \\).\n", + "\n", + "Therefore, the smallest possible value of \\( S \\) is \\( \\boxed{3} \\).\n", + "\n", + "[[ ## answer ## ]]\n", + "3\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history()" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-11-28T18:59:19.176586]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `question` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `answer` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## question ## ]]\n", - "{question}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## answer ## ]]\n", - "{answer}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Analyze the `question` provided, and systematically apply mathematical reasoning to derive the `answer`. Ensure to articulate each step of your thought process in the `reasoning` field, detailing how you identify relationships and formulate equations to arrive at the solution.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## question ## ]]\n", - "If $|4x+2|=10$ and $x<0$, what is the value of $x$?\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The equation $|4x+2|=10$ implies two possible equations: $4x+2=10$ or $4x+2=-10$. We solve each equation separately.\n", - "\n", - "1. For $4x+2=10$:\n", - " \\[\n", - " 4x + 2 = 10 \\\\\n", - " 4x = 8 \\\\\n", - " x = 2\n", - " \\]\n", - " However, $x=2$ does not satisfy the condition $x<0$.\n", - "\n", - "2. For $4x+2=-10$:\n", - " \\[\n", - " 4x + 2 = -10 \\\\\n", - " 4x = -12 \\\\\n", - " x = -3\n", - " \\]\n", - " The solution $x = -3$ satisfies the condition $x<0$.\n", - "\n", - "Therefore, the value of $x$ is $\\boxed{-3}$.\n", - "\n", - "[[ ## answer ## ]]\n", - "-3\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## question ## ]]\n", - "Given that $-4$ is a solution to $x^2 + bx -36 = 0$, what is the value of $b$?\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The product of the roots of this quadratic is $-36/1=-36$, so the other solution must be $-36/-4=9$. That means that the sum of the solutions is $-4+9=5$. The sum of the solutions is also $-b/1=-b$. Thus, $-b=5$ and $b=\\boxed{-5}$.\n", - "\n", - "[[ ## answer ## ]]\n", - "-5\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## question ## ]]\n", - "Richard is building a rectangular playground from 200 feet of fencing. The fencing must entirely enclose the playground. What is the maximum area of this playground?\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "Let the length of the playground be $l$ and the width be $w$. We have the equation $2l+2w=200 \\Rightarrow l + w = 100$. We want to maximize the area of this rectangular playground, which is given by $lw$. From our equation, we know that $l=100-w$. Substituting this in to our expression for area, we have \\[(100-w)(w)=100w-w^2\\]We will now complete the square to find the maximum value of this expression. Factoring a $-1$ out, we have \\[-(w^2-100w)\\]In order for the expression inside the parenthesis to be a perfect square, we need to add and subtract $(100/2)^2=2500$ inside the parenthesis. Doing this, we get \\[-(w^2-100w+2500-2500) \\Rightarrow -(w-50)^2+2500\\]Since the maximum value of $-(w-50)^2$ is 0 (perfect squares are always nonnegative), the maximum value of the entire expression is 2500, which is achieved when $w=50$ and $l=100-w=50$ (the playground is a square). Thus, the maximum area of the playground is $\\boxed{2500}$ square feet.\n", - "\n", - "[[ ## answer ## ]]\n", - "2500\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## question ## ]]\n", - "Alice and Bob are playing a game. Alice starts first. On Alice's turn, she flips a coin. If she gets a heads, she wins. If not, it becomes Bob's turn. On Bob's turn, he flips a coin. If he gets a tails, he wins. If not, it becomes Alice's turn. What is the probability that Alice wins the game?\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "Alice has a $1/2$ chance of winning the game on her first turn. If she doesn't, then the probability that she wins the game on her second turn is $1/8,$ since she must not win on her first flip ($1/2$ chance), Bob must not win on his first flip ($1/2$ chance), and then Alice must win on her second flip ($1/2$ chance). The probability that she wins the game on her third turn is $1/32,$ and in general, the probability that she wins the game on her $k^\\text{th}$ turn is $(1/2)^{2k-1}.$ Thus, the probability that Alice wins is an infinite geometric series with first term $1/2$ and common ratio $1/4.$ So, the probability that Alice wins the game is $$\\frac{\\frac12}{1-\\frac14} = \\boxed{\\frac{2}{3}}.$$OR\n", - "\n", - "Note that the only difference between the odds of Alice or Bob winning is who goes first. Because Bob goes second, the odds of him winning on his $k^\\text{th}$ flip is half of the odds that Alice wins on her $k^\\text{th}$ flip, since Alice must first get a tails before Bob gets a chance to win. Thus, if $a$ is Alice's chance of winning, and $b$ is Bob's chance of winning, then $a = 2b.$ Also, since someone must win, $a + b = 1.$ It follows that $a = 2/3$ and $b = 1/3,$ so Alice has a $\\boxed{\\frac{2}{3}}$ chance of winning the game.\n", - "\n", - "[[ ## answer ## ]]\n", - "\\frac{2}{3}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## question ## ]]\n", - "Find the smallest possible value of $$\\frac{(y-x)^2}{(y-z)(z-x)} + \\frac{(z-y)^2}{(z-x)(x-y)} + \\frac{(x-z)^2}{(x-y)(y-z)},$$ where $x,y,$ and $z$ are distinct real numbers.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## answer ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "Let \\( S = \\frac{(y-x)^2}{(y-z)(z-x)} + \\frac{(z-y)^2}{(z-x)(x-y)} + \\frac{(x-z)^2}{(x-y)(y-z)} \\).\n", - "\n", - "To find the minimum value of \\( S \\), we can use the method of symmetry and the Cauchy-Schwarz inequality. \n", - "\n", - "By applying the Cauchy-Schwarz inequality, we have:\n", - "\\[\n", - "\\left( \\frac{(y-x)^2}{(y-z)(z-x)} + \\frac{(z-y)^2}{(z-x)(x-y)} + \\frac{(x-z)^2}{(x-y)(y-z)} \\right) \\left( (y-z)(z-x) + (z-x)(x-y) + (x-y)(y-z) \\right) \\geq \\left( (y-x) + (z-y) + (x-z) \\right)^2.\n", - "\\]\n", - "The right-hand side simplifies to:\n", - "\\[\n", - "(y-x) + (z-y) + (x-z) = 0,\n", - "\\]\n", - "which means the right-hand side is \\( 0 \\). Therefore, we have:\n", - "\\[\n", - "S \\cdot \\left( (y-z)(z-x) + (z-x)(x-y) + (x-y)(y-z) \\right) \\geq 0.\n", - "\\]\n", - "This implies that \\( S \\) is non-negative.\n", - "\n", - "Next, we can test specific values for \\( x, y, z \\) to find the minimum. Let's set \\( x = 0, y = 1, z = 2 \\):\n", - "\\[\n", - "S = \\frac{(1-0)^2}{(1-2)(2-0)} + \\frac{(2-1)^2}{(2-0)(0-1)} + \\frac{(0-2)^2}{(0-1)(1-2)}.\n", - "\\]\n", - "Calculating each term:\n", - "1. The first term:\n", - " \\[\n", - " \\frac{1^2}{(-1)(2)} = \\frac{1}{-2} = -\\frac{1}{2}.\n", - " \\]\n", - "2. The second term:\n", - " \\[\n", - " \\frac{1^2}{(2)(-1)} = \\frac{1}{-2} = -\\frac{1}{2}.\n", - " \\]\n", - "3. The third term:\n", - " \\[\n", - " \\frac{(-2)^2}{(-1)(-1)} = \\frac{4}{1} = 4.\n", - " \\]\n", - "\n", - "Thus,\n", - "\\[\n", - "S = -\\frac{1}{2} - \\frac{1}{2} + 4 = 3.\n", - "\\]\n", - "\n", - "To check if this is indeed the minimum, we can also consider the case when \\( x, y, z \\) are the roots of a quadratic polynomial. By symmetry and the nature of the expression, we can conclude that the minimum value of \\( S \\) is indeed \\( 3 \\).\n", - "\n", - "Therefore, the smallest possible value of \\( S \\) is \\( \\boxed{3} \\).\n", - "\n", - "[[ ## answer ## ]]\n", - "3\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py310_sept24_user", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" } - ], - "source": [ - "dspy.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py310_sept24_user", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/docs/tutorials/multihop_search/index.ipynb b/docs/docs/tutorials/multihop_search/index.ipynb index 1c5998b424..6f064687a5 100644 --- a/docs/docs/tutorials/multihop_search/index.ipynb +++ b/docs/docs/tutorials/multihop_search/index.ipynb @@ -1,1254 +1,1254 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Multi-Hop Retrieval\n", - "\n", - "Let's walk through a quick example of building a `dspy.Module` with multiple sub-modules. We'll do this for the task for multi-hop search.\n", - "\n", - "Install the latest DSPy via `pip install -U dspy` and follow along." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", - "\n", - "### MLflow DSPy Integration\n", - "\n", - "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", - "\n", - "1. Install MLflow\n", - "\n", - "```bash\n", - "%pip install mlflow>=2.20\n", - "```\n", - "\n", - "2. Start MLflow UI in a separate terminal\n", - "```bash\n", - "mlflow ui --port 5000\n", - "```\n", - "\n", - "3. Connect the notebook to MLflow\n", - "```python\n", - "import mlflow\n", - "\n", - "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", - "mlflow.set_experiment(\"DSPy\")\n", - "```\n", - "\n", - "4. Enabling tracing.\n", - "```python\n", - "mlflow.dspy.autolog()\n", - "```\n", - "\n", - "![MLflow Trace](./mlflow-tracing-multi-hop.png)\n", - "\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, we'll use a small local LM, Meta's `Llama-3.1-8B-Instruct` which has 8 billion parameters.\n", - "\n", - "You might be able to host the 8B model on your laptop with Ollama, on your GPU server with SGLang, or via a provider that hosts it for you like Databricks or Together.\n", - "\n", - "In the snippet below, we'll configure this small model as our main LM. We'll also set up a larger LM, i.e. `GPT-4o`, as a teacher that we'll invoke a very small number of times to help teach the small LM. This is technically not necessary; the small model can typically teach itself tasks like this in DSPy. But using a larger teacher will give us some peace of mind, where the initial system or optimizer configuration doesn't matter as much." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "lm = dspy.LM('/Llama-3.1-8B-Instruct', max_tokens=3000)\n", - "gpt4o = dspy.LM('openai/gpt-4o', max_tokens=3000)\n", - "\n", - "dspy.configure(lm=lm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install dependencies and download data\n", - "\n", - "To do the retrieval, we'll use the cool BM25S library, as it's pretty lightweight. You can replace this components with whatever you like.\n", - "\n", - "```shell\n", - "> pip install -U bm25s PyStemmer \"jax[cpu]\"\n", - "```\n", - "\n", - "Next, we'll download a snapshot abstracts (i.e., first paragraphs) of all 5,000,000 Wikipedia pages as of 2017. We'll use this as our retrieval corpus.\n", - "\n", - "This is 500MB compressed, so the download and decompression may take 2-3 minutes.\n", - "\n", - "```python\n", - "from dspy.utils import download\n", - "\n", - "download(\"https://huggingface.co/dspy/cache/resolve/main/wiki.abstracts.2017.tar.gz\")\n", - "!tar -xzvf wiki.abstracts.2017.tar.gz\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now load the corpus." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "5233330" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Multi-Hop Retrieval\n", + "\n", + "Let's walk through a quick example of building a `dspy.Module` with multiple sub-modules. We'll do this for the task for multi-hop search.\n", + "\n", + "Install the latest DSPy via `pip install -U dspy` and follow along." ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import ujson\n", - "corpus = []\n", - "\n", - "with open(\"wiki.abstracts.2017.jsonl\") as f:\n", - " for line in f:\n", - " line = ujson.loads(line)\n", - " corpus.append(f\"{line['title']} | {' '.join(line['text'])}\")\n", - "\n", - "len(corpus)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And then let's index it for BM25 retrieval! This will take 2-3 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import bm25s\n", - "import Stemmer\n", - "\n", - "stemmer = Stemmer.Stemmer(\"english\")\n", - "corpus_tokens = bm25s.tokenize(corpus, stopwords=\"en\", stemmer=stemmer)\n", - "\n", - "retriever = bm25s.BM25(k1=0.9, b=0.4)\n", - "retriever.index(corpus_tokens)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the HoVer dataset.\n", - "\n", - "Let's load a dataset for our task. We'll load examples from the HoVer multi-hop task, where the input is a (really!) complex claim and the output we're seeking is the set of Wikipedia pages that are required to fact-check that claim." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "from dspy.datasets import DataLoader\n", - "\n", - "kwargs = dict(fields=(\"claim\", \"supporting_facts\", \"hpqa_id\", \"num_hops\"), input_keys=(\"claim\",))\n", - "hover = DataLoader().from_huggingface(dataset_name=\"hover-nlp/hover\", split=\"train\", trust_remote_code=True, **kwargs)\n", - "\n", - "hpqa_ids = set()\n", - "hover = [\n", - " dspy.Example(claim=x.claim, titles=list(set([y[\"key\"] for y in x.supporting_facts]))).with_inputs(\"claim\")\n", - " for x in hover\n", - " if x[\"num_hops\"] == 3 and x[\"hpqa_id\"] not in hpqa_ids and not hpqa_ids.add(x[\"hpqa_id\"])\n", - "]\n", - "\n", - "random.Random(0).shuffle(hover)\n", - "trainset, devset, testset = hover[:200], hover[200:500], hover[650:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's view an example of this task:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Claim: This director is known for his work on Miss Potter. The Academy of Motion Picture Arts and Sciences presents the award in which he was nominated for his work in \"Babe\".\n", - "Pages that must be retrieved: ['Miss Potter', 'Chris Noonan', 'Academy Award for Best Director']\n" - ] - } - ], - "source": [ - "example = trainset[0]\n", - "\n", - "print(\"Claim:\", example.claim)\n", - "print(\"Pages that must be retrieved:\", example.titles)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's define a function to do the search in Wikipedia. This will use our BM25 index." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def search(query: str, k: int) -> list[str]:\n", - " tokens = bm25s.tokenize(query, stopwords=\"en\", stemmer=stemmer, show_progress=False)\n", - " results, scores = retriever.retrieve(tokens, k=k, n_threads=1, show_progress=False)\n", - " run = {corpus[doc]: float(score) for doc, score in zip(results[0], scores[0])}\n", - " return run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's define the multi-hop program in DSPy. It's going to be super simple: it'll take a `claim` and produce a list `titles: list[str]`.\n", - "\n", - "It will do this via two sub-modules: `generate_query` and `append_notes`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "class Hop(dspy.Module):\n", - " def __init__(self, num_docs=10, num_hops=4):\n", - " self.num_docs, self.num_hops = num_docs, num_hops\n", - " self.generate_query = dspy.ChainOfThought('claim, notes -> query')\n", - " self.append_notes = dspy.ChainOfThought('claim, notes, context -> new_notes: list[str], titles: list[str]')\n", - "\n", - " def forward(self, claim: str) -> list[str]:\n", - " notes = []\n", - " titles = []\n", - "\n", - " for _ in range(self.num_hops):\n", - " query = self.generate_query(claim=claim, notes=notes).query\n", - " context = search(query, k=self.num_docs)\n", - " prediction = self.append_notes(claim=claim, notes=notes, context=context)\n", - " notes.extend(prediction.new_notes)\n", - " titles.extend(prediction.titles)\n", - " \n", - " return dspy.Prediction(notes=notes, titles=list(set(titles)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great. Now let's set up an evaluation metric, `top5_recall`.\n", - "\n", - "It will return the fraction of the gold pages (which are always 3) that are retrieved in the top-5 titles returned by the program." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def top5_recall(example, pred, trace=None):\n", - " gold_titles = example.titles\n", - " recall = sum(x in pred.titles[:5] for x in gold_titles) / len(gold_titles)\n", - "\n", - " # If we're \"bootstrapping\" for optimization, return True if and only if the recall is perfect.\n", - " if trace is not None:\n", - " return recall >= 1.0\n", - " \n", - " # If we're just doing inference, just measure the recall.\n", - " return recall\n", - "\n", - "evaluate = dspy.Evaluate(devset=devset, metric=top5_recall, num_threads=16, display_progress=True, display_table=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's evaluate our off-the-shelf program!" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", + "\n", + "### MLflow DSPy Integration\n", + "\n", + "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", + "\n", + "1. Install MLflow\n", + "\n", + "```bash\n", + "%pip install mlflow>=2.20\n", + "```\n", + "\n", + "2. Start MLflow UI in a separate terminal\n", + "```bash\n", + "mlflow ui --port 5000\n", + "```\n", + "\n", + "3. Connect the notebook to MLflow\n", + "```python\n", + "import mlflow\n", + "\n", + "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", + "mlflow.set_experiment(\"DSPy\")\n", + "```\n", + "\n", + "4. Enabling tracing.\n", + "```python\n", + "mlflow.dspy.autolog()\n", + "```\n", + "\n", + "![MLflow Trace](./mlflow-tracing-multi-hop.png)\n", + "\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we'll use a small local LM, Meta's `Llama-3.1-8B-Instruct` which has 8 billion parameters.\n", + "\n", + "You might be able to host the 8B model on your laptop with Ollama, on your GPU server with SGLang, or via a provider that hosts it for you like Databricks or Together.\n", + "\n", + "In the snippet below, we'll configure this small model as our main LM. We'll also set up a larger LM, i.e. `GPT-4o`, as a teacher that we'll invoke a very small number of times to help teach the small LM. This is technically not necessary; the small model can typically teach itself tasks like this in DSPy. But using a larger teacher will give us some peace of mind, where the initial system or optimizer configuration doesn't matter as much." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 27.67 / 98 (28.2%): 32%|███▏ | 97/300 [00:02<00:04, 49.34it/s]" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "\n", + "lm = dspy.LM('/Llama-3.1-8B-Instruct', max_tokens=3000)\n", + "gpt4o = dspy.LM('openai/gpt-4o', max_tokens=3000)\n", + "\n", + "dspy.configure(lm=lm)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/25 12:18:00 ERROR dspy.utils.parallelizer: Error processing item Example({'claim': \"All That is the show that the co-creator with the host of Vibe and Wild 'N Out had a debut on.\", 'titles': ['Chris Spencer (actor)', 'Nick Cannon', 'Vibe (talk show)']}) (input_keys={'claim'}): Expected dict_keys(['reasoning', 'new_notes', 'titles']) but got dict_keys(['reasoning', 'new_notes']). Set `provide_traceback=True` to see the stack trace.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install dependencies and download data\n", + "\n", + "To do the retrieval, we'll use the cool BM25S library, as it's pretty lightweight. You can replace this components with whatever you like.\n", + "\n", + "```shell\n", + "> pip install -U bm25s PyStemmer \"jax[cpu]\"\n", + "```\n", + "\n", + "Next, we'll download a snapshot abstracts (i.e., first paragraphs) of all 5,000,000 Wikipedia pages as of 2017. We'll use this as our retrieval corpus.\n", + "\n", + "This is 500MB compressed, so the download and decompression may take 2-3 minutes.\n", + "\n", + "```python\n", + "from dspy.utils import download\n", + "\n", + "download(\"https://huggingface.co/dspy/cache/resolve/main/wiki.abstracts.2017.tar.gz\")\n", + "!tar -xzvf wiki.abstracts.2017.tar.gz\n", + "```" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 59.33 / 186 (31.9%): 62%|██████▏ | 186/300 [00:03<00:02, 51.84it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now load the corpus." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/25 12:18:02 ERROR dspy.utils.parallelizer: Error processing item Example({'claim': 'The song, which Billie Anthony is best known for her Top 10 hit version, topped the UK chart in 1981 in a recording by a platinum-selling British rock and roll singer whose recording and performing career began in the late 1960s.', 'titles': [\"Shakin' Stevens\", 'This Ole House', 'Billie Anthony']}) (input_keys={'claim'}): Expected dict_keys(['reasoning', 'new_notes', 'titles']) but got dict_keys(['reasoning']). Set `provide_traceback=True` to see the stack trace.\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5233330" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ujson\n", + "corpus = []\n", + "\n", + "with open(\"wiki.abstracts.2017.jsonl\") as f:\n", + " for line in f:\n", + " line = ujson.loads(line)\n", + " corpus.append(f\"{line['title']} | {' '.join(line['text'])}\")\n", + "\n", + "len(corpus)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 94.00 / 298 (31.5%): 100%|██████████| 300/300 [00:06<00:00, 48.56it/s]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And then let's index it for BM25 retrieval! This will take 2-3 minutes." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/25 12:18:04 INFO dspy.evaluate.evaluate: Average Metric: 93.99999999999993 / 300 (31.3%)\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import bm25s\n", + "import Stemmer\n", + "\n", + "stemmer = Stemmer.Stemmer(\"english\")\n", + "corpus_tokens = bm25s.tokenize(corpus, stopwords=\"en\", stemmer=stemmer)\n", + "\n", + "retriever = bm25s.BM25(k1=0.9, b=0.4)\n", + "retriever.index(corpus_tokens)" + ] }, { - "data": { - "text/html": [ - "
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claimexample_titlesnotespred_titlestop5_recalltitles
0Nike football team has had a player endorse the football boot Nike...[Nike Hypervenom, Nike Total 90, Marcus Rashford]['The Nike Total 90 has been replaced by the Nike Hypervenom.', 'T...['Nike Mercurial Vapor | The Mercurial Vapor is a football boot ma...✔️ [0.333]NaN
1Bill Boyd is the chairman of the appliance company that operates t...[Suncoast Hotel and Casino, Boyd Gaming, Thomas Eje]['Bill Boyd is not mentioned as the chairman of an appliance compa...[Suncoast Casino, Thomas Eje, Boyd Gaming Corporation, Bill Boyd, ...✔️ [0.333]NaN
2The president of South Korea was born 24 January 1953. The group t...[Presidential Council on Nation Branding, Korea, Moon Jae-in, Euh ...['The president of South Korea was likely born before 1945', 'Euh ...['Yi Cheol-seung', 'List of Presidents of South Korea', 'Lifespan ...NaN
3The movie Khan Kluay was released 2 months before the 2009 movie t...[Fantastic Mr. Fox (film), Jason Schwartzman, Khan Kluay]['The movie Khan Kluay was released in 2006.', 'The 2009 movie tha...[Khan Kluay, The Darjeeling Limited]✔️ [0.333]NaN
4The director of Finding Dory co-directed the film A Bug's Life.[Andrew Stanton, Finding Dory, A Bug's Life]['The director of Finding Dory is Andrew Stanton and Angus MacLane...[Finding Dory, A Bug's Life]✔️ [0.667]NaN
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" - ], - "text/plain": [ - " claim \\\n", - "0 Nike football team has had a player endorse the football boot Nike... \n", - "1 Bill Boyd is the chairman of the appliance company that operates t... \n", - "2 The president of South Korea was born 24 January 1953. The group t... \n", - "3 The movie Khan Kluay was released 2 months before the 2009 movie t... \n", - "4 The director of Finding Dory co-directed the film A Bug's Life. \n", - "\n", - " example_titles \\\n", - "0 [Nike Hypervenom, Nike Total 90, Marcus Rashford] \n", - "1 [Suncoast Hotel and Casino, Boyd Gaming, Thomas Eje] \n", - "2 [Presidential Council on Nation Branding, Korea, Moon Jae-in, Euh ... \n", - "3 [Fantastic Mr. Fox (film), Jason Schwartzman, Khan Kluay] \n", - "4 [Andrew Stanton, Finding Dory, A Bug's Life] \n", - "\n", - " notes \\\n", - "0 ['The Nike Total 90 has been replaced by the Nike Hypervenom.', 'T... \n", - "1 ['Bill Boyd is not mentioned as the chairman of an appliance compa... \n", - "2 ['The president of South Korea was likely born before 1945', 'Euh ... \n", - "3 ['The movie Khan Kluay was released in 2006.', 'The 2009 movie tha... \n", - "4 ['The director of Finding Dory is Andrew Stanton and Angus MacLane... \n", - "\n", - " pred_titles \\\n", - "0 ['Nike Mercurial Vapor | The Mercurial Vapor is a football boot ma... \n", - "1 [Suncoast Casino, Thomas Eje, Boyd Gaming Corporation, Bill Boyd, ... \n", - "2 ['Yi Cheol-seung', 'List of Presidents of South Korea', 'Lifespan ... \n", - "3 [Khan Kluay, The Darjeeling Limited] \n", - "4 [Finding Dory, A Bug's Life] \n", - "\n", - " top5_recall titles \n", - "0 ✔️ [0.333] NaN \n", - "1 ✔️ [0.333] NaN \n", - "2 NaN \n", - "3 ✔️ [0.333] NaN \n", - "4 ✔️ [0.667] NaN " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the HoVer dataset.\n", + "\n", + "Let's load a dataset for our task. We'll load examples from the HoVer multi-hop task, where the input is a (really!) complex claim and the output we're seeking is the set of Wikipedia pages that are required to fact-check that claim." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from dspy.datasets import DataLoader\n", + "\n", + "kwargs = dict(fields=(\"claim\", \"supporting_facts\", \"hpqa_id\", \"num_hops\"), input_keys=(\"claim\",))\n", + "hover = DataLoader().from_huggingface(dataset_name=\"hover-nlp/hover\", split=\"train\", trust_remote_code=True, **kwargs)\n", + "\n", + "hpqa_ids = set()\n", + "hover = [\n", + " dspy.Example(claim=x.claim, titles=list(set([y[\"key\"] for y in x.supporting_facts]))).with_inputs(\"claim\")\n", + " for x in hover\n", + " if x[\"num_hops\"] == 3 and x[\"hpqa_id\"] not in hpqa_ids and not hpqa_ids.add(x[\"hpqa_id\"])\n", + "]\n", + "\n", + "random.Random(0).shuffle(hover)\n", + "trainset, devset, testset = hover[:200], hover[200:500], hover[650:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's view an example of this task:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Claim: This director is known for his work on Miss Potter. The Academy of Motion Picture Arts and Sciences presents the award in which he was nominated for his work in \"Babe\".\n", + "Pages that must be retrieved: ['Miss Potter', 'Chris Noonan', 'Academy Award for Best Director']\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "example = trainset[0]\n", + "\n", + "print(\"Claim:\", example.claim)\n", + "print(\"Pages that must be retrieved:\", example.titles)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "31.33" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's define a function to do the search in Wikipedia. This will use our BM25 index." ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(Hop())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Tracking Evaluation Results in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", - "\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "with mlflow.start_run(run_name=\"hop_evaluation\"):\n", - " evaluate = dspy.Evaluate(\n", - " devset=devset,\n", - " metric=top5_recall,\n", - " num_threads=16,\n", - " display_progress=True,\n", - " # To record the outputs and detailed scores to MLflow\n", - " return_all_scores=True,\n", - " return_outputs=True,\n", - " )\n", - "\n", - " # Evaluate the program as usual\n", - " aggregated_score, outputs, all_scores = evaluate(Hop())\n", - "\n", - " # Log the aggregated score\n", - " mlflow.log_metric(\"top5_recall\", aggregated_score)\n", - " # Log the detailed evaluation results as a table\n", - " mlflow.log_table(\n", - " {\n", - " \"Claim\": [example.claim for example in eval_set],\n", - " \"Expected Titles\": [example.titles for example in eval_set],\n", - " \"Predicted Titles\": outputs,\n", - " \"Top 5 Recall\": all_scores,\n", - " },\n", - " artifact_file=\"eval_results.json\",\n", - " )\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now optimize the two prompts inside the `Hop()` program jointly to maximize the recall of our program. This may take around 35 minutes and make some $5 worth of calls to GPT-4o to optimize Llama-3.1-8B." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "models = dict(prompt_model=gpt4o, teacher_settings=dict(lm=gpt4o))\n", - "tp = dspy.MIPROv2(metric=top5_recall, auto=\"medium\", num_threads=16, **models)\n", - "\n", - "kwargs = dict(minibatch_size=40, minibatch_full_eval_steps=4, requires_permission_to_run=False)\n", - "optimized = tp.compile(Hop(), trainset=trainset, max_bootstrapped_demos=4, max_labeled_demos=4, **kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now evaluate again, after optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def search(query: str, k: int) -> list[str]:\n", + " tokens = bm25s.tokenize(query, stopwords=\"en\", stemmer=stemmer, show_progress=False)\n", + " results, scores = retriever.retrieve(tokens, k=k, n_threads=1, show_progress=False)\n", + " run = {corpus[doc]: float(score) for doc, score in zip(results[0], scores[0])}\n", + " return run" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 38.67 / 64 (60.4%): 21%|██ | 63/300 [00:01<00:06, 38.13it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's define the multi-hop program in DSPy. It's going to be super simple: it'll take a `claim` and produce a list `titles: list[str]`.\n", + "\n", + "It will do this via two sub-modules: `generate_query` and `append_notes`." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/25 12:18:09 ERROR dspy.utils.parallelizer: Error processing item Example({'claim': 'Eliot Hyman co-founded Seven Arts Productions in 1957. His co-founder produced the American-American black comedy-drama film directed by Stanley Kubrick.', 'titles': ['Ray Stark', 'Seven Arts Productions', 'Lolita (1962 film)']}) (input_keys={'claim'}): Expected dict_keys(['reasoning', 'query']) but got dict_keys(['reasoning']). Set `provide_traceback=True` to see the stack trace.\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class Hop(dspy.Module):\n", + " def __init__(self, num_docs=10, num_hops=4):\n", + " self.num_docs, self.num_hops = num_docs, num_hops\n", + " self.generate_query = dspy.ChainOfThought('claim, notes -> query')\n", + " self.append_notes = dspy.ChainOfThought('claim, notes, context -> new_notes: list[str], titles: list[str]')\n", + "\n", + " def forward(self, claim: str) -> list[str]:\n", + " notes = []\n", + " titles = []\n", + "\n", + " for _ in range(self.num_hops):\n", + " query = self.generate_query(claim=claim, notes=notes).query\n", + " context = search(query, k=self.num_docs)\n", + " prediction = self.append_notes(claim=claim, notes=notes, context=context)\n", + " notes.extend(prediction.new_notes)\n", + " titles.extend(prediction.titles)\n", + " \n", + " return dspy.Prediction(notes=notes, titles=list(set(titles)))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 177.33 / 299 (59.3%): 100%|██████████| 300/300 [00:08<00:00, 36.01it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great. Now let's set up an evaluation metric, `top5_recall`.\n", + "\n", + "It will return the fraction of the gold pages (which are always 3) that are retrieved in the top-5 titles returned by the program." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/12/25 12:18:16 INFO dspy.evaluate.evaluate: Average Metric: 177.33333333333334 / 300 (59.1%)\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def top5_recall(example, pred, trace=None):\n", + " gold_titles = example.titles\n", + " recall = sum(x in pred.titles[:5] for x in gold_titles) / len(gold_titles)\n", + "\n", + " # If we're \"bootstrapping\" for optimization, return True if and only if the recall is perfect.\n", + " if trace is not None:\n", + " return recall >= 1.0\n", + " \n", + " # If we're just doing inference, just measure the recall.\n", + " return recall\n", + "\n", + "evaluate = dspy.Evaluate(devset=devset, metric=top5_recall, num_threads=16, display_progress=True, display_table=5)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate our off-the-shelf program!" + ] }, { - "data": { - "text/html": [ - "
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claimexample_titlesnotespred_titlestop5_recalltitles
0Nike football team has had a player endorse the football boot Nike...[Nike Hypervenom, Nike Total 90, Marcus Rashford][][Nike Hypervenom, Nike Total 90, Kylian Mbappé, Marcus Rashford]✔️ [1.000]NaN
1Bill Boyd is the chairman of the appliance company that operates t...[Suncoast Hotel and Casino, Boyd Gaming, Thomas Eje][][Bill Boyd, Suncoast Casino, Las Vegas, Thomas Eje]✔️ [0.333]NaN
2The president of South Korea was born 24 January 1953. The group t...[Presidential Council on Nation Branding, Korea, Moon Jae-in, Euh ...['Euh Yoon-Dae is a South Korean professor, financier, and advisor...[Euh Yoon-Dae, KB Financial Group, Chang Dae-hwan, Maeil Business ...NaN
3The movie Khan Kluay was released 2 months before the 2009 movie t...[Fantastic Mr. Fox (film), Jason Schwartzman, Khan Kluay][\"Jason Schwartzman collaborated with Wes Anderson on the 2009 mov...[Wes Anderson, Fantastic Mr. Fox, Khan Kluay 2, Jason Schwartzman,...✔️ [0.667]NaN
4The director of Finding Dory co-directed the film A Bug's Life.[Andrew Stanton, Finding Dory, A Bug's Life][\"Andrew Stanton co-directed A Bug's Life\", \"John Lasseter directe...[John Lasseter, Andrew Stanton, Finding Dory, A Bug's Life]✔️ [1.000]NaN
\n", - "
" + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 27.67 / 98 (28.2%): 32%|\u2588\u2588\u2588\u258f | 97/300 [00:02<00:04, 49.34it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/25 12:18:00 ERROR dspy.utils.parallelizer: Error processing item Example({'claim': \"All That is the show that the co-creator with the host of Vibe and Wild 'N Out had a debut on.\", 'titles': ['Chris Spencer (actor)', 'Nick Cannon', 'Vibe (talk show)']}) (input_keys={'claim'}): Expected dict_keys(['reasoning', 'new_notes', 'titles']) but got dict_keys(['reasoning', 'new_notes']). Set `provide_traceback=True` to see the stack trace.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 59.33 / 186 (31.9%): 62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 186/300 [00:03<00:02, 51.84it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/25 12:18:02 ERROR dspy.utils.parallelizer: Error processing item Example({'claim': 'The song, which Billie Anthony is best known for her Top 10 hit version, topped the UK chart in 1981 in a recording by a platinum-selling British rock and roll singer whose recording and performing career began in the late 1960s.', 'titles': [\"Shakin' Stevens\", 'This Ole House', 'Billie Anthony']}) (input_keys={'claim'}): Expected dict_keys(['reasoning', 'new_notes', 'titles']) but got dict_keys(['reasoning']). Set `provide_traceback=True` to see the stack trace.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 94.00 / 298 (31.5%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [00:06<00:00, 48.56it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/25 12:18:04 INFO dspy.evaluate.evaluate: Average Metric: 93.99999999999993 / 300 (31.3%)\n" + ] + }, + { + "data": { + "text/html": [ + "
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claimexample_titlesnotespred_titlestop5_recalltitles
0Nike football team has had a player endorse the football boot Nike...[Nike Hypervenom, Nike Total 90, Marcus Rashford]['The Nike Total 90 has been replaced by the Nike Hypervenom.', 'T...['Nike Mercurial Vapor | The Mercurial Vapor is a football boot ma...\u2714\ufe0f [0.333]NaN
1Bill Boyd is the chairman of the appliance company that operates t...[Suncoast Hotel and Casino, Boyd Gaming, Thomas Eje]['Bill Boyd is not mentioned as the chairman of an appliance compa...[Suncoast Casino, Thomas Eje, Boyd Gaming Corporation, Bill Boyd, ...\u2714\ufe0f [0.333]NaN
2The president of South Korea was born 24 January 1953. The group t...[Presidential Council on Nation Branding, Korea, Moon Jae-in, Euh ...['The president of South Korea was likely born before 1945', 'Euh ...['Yi Cheol-seung', 'List of Presidents of South Korea', 'Lifespan ...NaN
3The movie Khan Kluay was released 2 months before the 2009 movie t...[Fantastic Mr. Fox (film), Jason Schwartzman, Khan Kluay]['The movie Khan Kluay was released in 2006.', 'The 2009 movie tha...[Khan Kluay, The Darjeeling Limited]\u2714\ufe0f [0.333]NaN
4The director of Finding Dory co-directed the film A Bug's Life.[Andrew Stanton, Finding Dory, A Bug's Life]['The director of Finding Dory is Andrew Stanton and Angus MacLane...[Finding Dory, A Bug's Life]\u2714\ufe0f [0.667]NaN
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" + ], + "text/plain": [ + " claim \\\n", + "0 Nike football team has had a player endorse the football boot Nike... \n", + "1 Bill Boyd is the chairman of the appliance company that operates t... \n", + "2 The president of South Korea was born 24 January 1953. The group t... \n", + "3 The movie Khan Kluay was released 2 months before the 2009 movie t... \n", + "4 The director of Finding Dory co-directed the film A Bug's Life. \n", + "\n", + " example_titles \\\n", + "0 [Nike Hypervenom, Nike Total 90, Marcus Rashford] \n", + "1 [Suncoast Hotel and Casino, Boyd Gaming, Thomas Eje] \n", + "2 [Presidential Council on Nation Branding, Korea, Moon Jae-in, Euh ... \n", + "3 [Fantastic Mr. Fox (film), Jason Schwartzman, Khan Kluay] \n", + "4 [Andrew Stanton, Finding Dory, A Bug's Life] \n", + "\n", + " notes \\\n", + "0 ['The Nike Total 90 has been replaced by the Nike Hypervenom.', 'T... \n", + "1 ['Bill Boyd is not mentioned as the chairman of an appliance compa... \n", + "2 ['The president of South Korea was likely born before 1945', 'Euh ... \n", + "3 ['The movie Khan Kluay was released in 2006.', 'The 2009 movie tha... \n", + "4 ['The director of Finding Dory is Andrew Stanton and Angus MacLane... \n", + "\n", + " pred_titles \\\n", + "0 ['Nike Mercurial Vapor | The Mercurial Vapor is a football boot ma... \n", + "1 [Suncoast Casino, Thomas Eje, Boyd Gaming Corporation, Bill Boyd, ... \n", + "2 ['Yi Cheol-seung', 'List of Presidents of South Korea', 'Lifespan ... \n", + "3 [Khan Kluay, The Darjeeling Limited] \n", + "4 [Finding Dory, A Bug's Life] \n", + "\n", + " top5_recall titles \n", + "0 \u2714\ufe0f [0.333] NaN \n", + "1 \u2714\ufe0f [0.333] NaN \n", + "2 NaN \n", + "3 \u2714\ufe0f [0.333] NaN \n", + "4 \u2714\ufe0f [0.667] NaN " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + "Tracking Evaluation Results in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", + "\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "with mlflow.start_run(run_name=\"hop_evaluation\"):\n", + " evaluate = dspy.Evaluate(\n", + " devset=devset,\n", + " metric=top5_recall,\n", + " num_threads=16,\n", + " display_progress=True,\n", + " # To record the outputs and detailed scores to MLflow\n", + " return_all_scores=True,\n", + " return_outputs=True,\n", + " )\n", + "\n", + " # Evaluate the program as usual\n", + " aggregated_score, outputs, all_scores = evaluate(Hop())\n", + "\n", + " # Log the aggregated score\n", + " mlflow.log_metric(\"top5_recall\", aggregated_score)\n", + " # Log the detailed evaluation results as a table\n", + " mlflow.log_table(\n", + " {\n", + " \"Claim\": [example.claim for example in eval_set],\n", + " \"Expected Titles\": [example.titles for example in eval_set],\n", + " \"Predicted Titles\": outputs,\n", + " \"Top 5 Recall\": all_scores,\n", + " },\n", + " artifact_file=\"eval_results.json\",\n", + " )\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now optimize the two prompts inside the `Hop()` program jointly to maximize the recall of our program. This may take around 35 minutes and make some $5 worth of calls to GPT-4o to optimize Llama-3.1-8B." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "models = dict(prompt_model=gpt4o, teacher_settings=dict(lm=gpt4o))\n", + "tp = dspy.MIPROv2(metric=top5_recall, auto=\"medium\", num_threads=16, **models)\n", + "\n", + "kwargs = dict(minibatch_size=40, minibatch_full_eval_steps=4, requires_permission_to_run=False)\n", + "optimized = tp.compile(Hop(), trainset=trainset, max_bootstrapped_demos=4, max_labeled_demos=4, **kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now evaluate again, after optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 38.67 / 64 (60.4%): 21%|\u2588\u2588 | 63/300 [00:01<00:06, 38.13it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/25 12:18:09 ERROR dspy.utils.parallelizer: Error processing item Example({'claim': 'Eliot Hyman co-founded Seven Arts Productions in 1957. His co-founder produced the American-American black comedy-drama film directed by Stanley Kubrick.', 'titles': ['Ray Stark', 'Seven Arts Productions', 'Lolita (1962 film)']}) (input_keys={'claim'}): Expected dict_keys(['reasoning', 'query']) but got dict_keys(['reasoning']). Set `provide_traceback=True` to see the stack trace.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 177.33 / 299 (59.3%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [00:08<00:00, 36.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/12/25 12:18:16 INFO dspy.evaluate.evaluate: Average Metric: 177.33333333333334 / 300 (59.1%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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claimexample_titlesnotespred_titlestop5_recalltitles
0Nike football team has had a player endorse the football boot Nike...[Nike Hypervenom, Nike Total 90, Marcus Rashford][][Nike Hypervenom, Nike Total 90, Kylian Mbapp\u00e9, Marcus Rashford]\u2714\ufe0f [1.000]NaN
1Bill Boyd is the chairman of the appliance company that operates t...[Suncoast Hotel and Casino, Boyd Gaming, Thomas Eje][][Bill Boyd, Suncoast Casino, Las Vegas, Thomas Eje]\u2714\ufe0f [0.333]NaN
2The president of South Korea was born 24 January 1953. The group t...[Presidential Council on Nation Branding, Korea, Moon Jae-in, Euh ...['Euh Yoon-Dae is a South Korean professor, financier, and advisor...[Euh Yoon-Dae, KB Financial Group, Chang Dae-hwan, Maeil Business ...NaN
3The movie Khan Kluay was released 2 months before the 2009 movie t...[Fantastic Mr. Fox (film), Jason Schwartzman, Khan Kluay][\"Jason Schwartzman collaborated with Wes Anderson on the 2009 mov...[Wes Anderson, Fantastic Mr. Fox, Khan Kluay 2, Jason Schwartzman,...\u2714\ufe0f [0.667]NaN
4The director of Finding Dory co-directed the film A Bug's Life.[Andrew Stanton, Finding Dory, A Bug's Life][\"Andrew Stanton co-directed A Bug's Life\", \"John Lasseter directe...[John Lasseter, Andrew Stanton, Finding Dory, A Bug's Life]\u2714\ufe0f [1.000]NaN
\n", + "
" + ], + "text/plain": [ + " claim \\\n", + "0 Nike football team has had a player endorse the football boot Nike... \n", + "1 Bill Boyd is the chairman of the appliance company that operates t... \n", + "2 The president of South Korea was born 24 January 1953. The group t... \n", + "3 The movie Khan Kluay was released 2 months before the 2009 movie t... \n", + "4 The director of Finding Dory co-directed the film A Bug's Life. \n", + "\n", + " example_titles \\\n", + "0 [Nike Hypervenom, Nike Total 90, Marcus Rashford] \n", + "1 [Suncoast Hotel and Casino, Boyd Gaming, Thomas Eje] \n", + "2 [Presidential Council on Nation Branding, Korea, Moon Jae-in, Euh ... \n", + "3 [Fantastic Mr. Fox (film), Jason Schwartzman, Khan Kluay] \n", + "4 [Andrew Stanton, Finding Dory, A Bug's Life] \n", + "\n", + " notes \\\n", + "0 [] \n", + "1 [] \n", + "2 ['Euh Yoon-Dae is a South Korean professor, financier, and advisor... \n", + "3 [\"Jason Schwartzman collaborated with Wes Anderson on the 2009 mov... \n", + "4 [\"Andrew Stanton co-directed A Bug's Life\", \"John Lasseter directe... \n", + "\n", + " pred_titles \\\n", + "0 [Nike Hypervenom, Nike Total 90, Kylian Mbapp\u00e9, Marcus Rashford] \n", + "1 [Bill Boyd, Suncoast Casino, Las Vegas, Thomas Eje] \n", + "2 [Euh Yoon-Dae, KB Financial Group, Chang Dae-hwan, Maeil Business ... \n", + "3 [Wes Anderson, Fantastic Mr. Fox, Khan Kluay 2, Jason Schwartzman,... \n", + "4 [John Lasseter, Andrew Stanton, Finding Dory, A Bug's Life] \n", + "\n", + " top5_recall titles \n", + "0 \u2714\ufe0f [1.000] NaN \n", + "1 \u2714\ufe0f [0.333] NaN \n", + "2 NaN \n", + "3 \u2714\ufe0f [0.667] NaN \n", + "4 \u2714\ufe0f [1.000] NaN " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " ... 295 more rows not displayed ...\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "59.11" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "evaluate(optimized)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "59.11" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome. It looks like the system improved drastically from around 30% recall to a little below 60% recall. That was a pretty straightforward approach, but DSPy gives you many tools to continue iterating on this from here.\n", + "\n", + "Next, let's inspect the optimized prompts to understand what it has learned. We'll run one query and then inspect the last two prompts, which will show us the prompts used for both sub-modules, in the later iteration inside the `Hop()` program. (Alternatively, if you enabled MLflow Tracing following the instructions above, you can see all steps done by the agent including LLM calls, prompts, tool execution, in a rich tree-view.)" ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(optimized)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Awesome. It looks like the system improved drastically from around 30% recall to a little below 60% recall. That was a pretty straightforward approach, but DSPy gives you many tools to continue iterating on this from here.\n", - "\n", - "Next, let's inspect the optimized prompts to understand what it has learned. We'll run one query and then inspect the last two prompts, which will show us the prompts used for both sub-modules, in the later iteration inside the `Hop()` program. (Alternatively, if you enabled MLflow Tracing following the instructions above, you can see all steps done by the agent including LLM calls, prompts, tool execution, in a rich tree-view.)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "['Up Against It', 'Bernard-Marie Koltès', 'The Beatles', 'Joe Orton']" + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Up Against It', 'Bernard-Marie Kolt\u00e8s', 'The Beatles', 'Joe Orton']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\").titles" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "optimized(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\").titles" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-12-25T12:18:16.177899]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `claim` (str)\n", - "2. `notes` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `query` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## claim ## ]]\n", - "{claim}\n", - "\n", - "[[ ## notes ## ]]\n", - "{notes}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## query ## ]]\n", - "{query}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given a claim and a set of notes, generate a query that can be used to gather additional evidence or context to support or refute the claim. Think step by step to ensure the query is specific and relevant to the information provided in the notes.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "Danyang, Jiangusu and this city are both cities in China. This city was the birthplace of Chen Xiuke.\n", - "\n", - "[[ ## notes ## ]]\n", - "[1] «Chen Xiuke was born in Dongfang, Hainan.»\n", - "[2] «Danyang is a city in Jiangsu province, China.»\n", - "[3] «Chen Xiuke was born in Dongfang, Hainan.»\n", - "[4] «Danyang is a city in Jiangsu province, China.»\n", - "[5] «Dongfang is a city in Hainan province, China.»\n", - "[6] «Chen Xiuke was born in Dongfang, Hainan.»\n", - "[7] «Danyang is a city in Jiangsu province, China.»\n", - "[8] «Dongfang is a city in Hainan province, China.»\n", - "[9] «Chen Xiuke was born in Dongfang, Hainan.»\n", - "[10] «Danyang is a city in Jiangsu province, China.»\n", - "[11] «Dongfang is a city in Hainan province, China.»\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## query ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The claim states that Danyang, Jiangsu, and another city are both cities in China, and that this other city was the birthplace of Chen Xiuke. According to the notes, Chen Xiuke was born in Dongfang, Hainan. Therefore, the city referred to in the claim as \"this city\" is Dongfang, which is indeed a city in Hainan province, China. The claim is consistent with the information provided in the notes.\n", - "\n", - "[[ ## query ## ]]\n", - "What is the birthplace of Chen Xiuke?\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The star of Nothing to Report and Gary Barlow have a profession in common.\n", - "\n", - "[[ ## notes ## ]]\n", - "[1] «Chris Jericho is a musician and professional wrestler.»\n", - "[2] «Gary Barlow is a singer and songwriter, known for being a member of Take That.»\n", - "[3] «Chris Jericho is a musician and professional wrestler.»\n", - "[4] «Gary Barlow is a singer and songwriter, known for being a member of Take That.»\n", - "[5] «Chris Jericho stars in 'Nothing to Report'.»\n", - "[6] «Chris Jericho is a musician and professional wrestler.»\n", - "[7] «Gary Barlow is a singer and songwriter, known for being a member of Take That.»\n", - "[8] «Chris Jericho stars in 'Nothing to Report'.»\n", - "[9] «Chris Jericho is a musician and professional wrestler.»\n", - "[10] «Gary Barlow is a singer and songwriter, known for being a member of Take That.»\n", - "[11] «Chris Jericho stars in 'Nothing to Report'.»\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## query ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The claim suggests that the star of \"Nothing to Report\" and Gary Barlow share a profession. From the notes, we know that Chris Jericho stars in \"Nothing to Report\" and is a musician and professional wrestler. Gary Barlow is a singer and songwriter. The common profession between Chris Jericho and Gary Barlow is that they are both musicians.\n", - "\n", - "[[ ## query ## ]]\n", - "What is the profession that Chris Jericho and Gary Barlow have in common?\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\n", - "\n", - "[[ ## notes ## ]]\n", - "N/A\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## query ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "The claim states that the author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights. However, there is no information in the notes to support or refute this claim. The notes are empty, which means we cannot verify the author of the script or Bernard-Marie Koltès' profession.\n", - "\n", - "[[ ## query ## ]]\n", - "Who is the author of the 1960s unproduced script written for The Beatles, Up Against It?\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-12-25T12:18:16.191896]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `claim` (str)\n", - "2. `notes` (str)\n", - "3. `context` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `new_notes` (list[str])\n", - "3. `titles` (list[str])\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## claim ## ]]\n", - "{claim}\n", - "\n", - "[[ ## notes ## ]]\n", - "{notes}\n", - "\n", - "[[ ## context ## ]]\n", - "{context}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## new_notes ## ]]\n", - "{new_notes} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n", - "\n", - "[[ ## titles ## ]]\n", - "{titles} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Analyze the provided `claim`, `notes`, and `context` to generate `new_notes` that further support or refute the claim, and extract relevant `titles` that represent key topics or entities from the context.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "This is an example of the task, though some input or output fields are not supplied.\n", - "\n", - "[[ ## claim ## ]]\n", - "The figure who currently holds the role Michael Noonan held from 2001 to 2002, was the Minister for Social Protection from 2016 until 2017.\n", - "\n", - "[[ ## notes ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "[[ ## context ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "[[ ## new_notes ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Michael Noonan\", \"Leader of Fine Gael\", \"Leo Varadkar\"]\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "This is an example of the task, though some input or output fields are not supplied.\n", - "\n", - "[[ ## claim ## ]]\n", - "The type of area Wiliwili are typically found in have a dominant tree species of Acacia koa. They are of the pea family.\n", - "\n", - "[[ ## notes ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "[[ ## context ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "[[ ## new_notes ## ]]\n", - "Not supplied for this particular example.\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Acacia koa\", \"Wiliwili\", \"Hawaiian tropical dry forests\"]\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The father of Zak Ové and A. Edward Sutherland are not both photographers.\n", - "\n", - "[[ ## notes ## ]]\n", - "[1] «Horace Ové is a photographer, filmmaker, and writer.»\n", - "[2] «A. Edward Sutherland is a film director, not a photographer.»\n", - "[3] «Horace Ové is a photographer, filmmaker, and writer.»\n", - "[4] «A. Edward Sutherland is a film director, not a photographer.»\n", - "[5] «Horace Ové is a photographer, filmmaker, and writer.»\n", - "[6] «A. Edward Sutherland is a film director, not a photographer.»\n", - "[7] «Horace Ové is a photographer, filmmaker, and writer.»\n", - "[8] «A. Edward Sutherland is a film director, not a photographer.»\n", - "\n", - "[[ ## context ## ]]\n", - "{\"Horace Ové | Horace Ové, CBE (born 1939), is a British filmmaker, photographer, painter and writer, one of the leading black independent film-makers to emerge in Britain since the post-war period. Ové holds the \\\"Guinness World Record\\\" for being the first black British film-maker to direct a feature-length film, \\\"Pressure\\\" (1975). In its retrospective history, \\\"100 Years of Cinema\\\", the British Film Institute (BFI) declared: \\\"Horace Ové is undoubtedly a pioneer in Black British history and his work provides a perspective on the Black experience in Britain.\\\"\": 15.836545944213867, \"Zak Ové | Zak Ové (born 1966) is a British visual artist who works between sculpture, film and photography, living in London, UK, and Trinidad. His themes reflect \\\"his documentation of and anthropological interest in diasporic and African history, specifically that which is explored through Trinidadian carnival.\\\" In work that is \\\"filtered through his own personal and cultural upbringing, with a black Trinidadian father and white Irish mother\\\", he has exhibited widely in Europe, the United States and Africa, participating in international museum shows in London, Dakar, Paris, Dubai, Prague, Berlin, Johannesburg, Bamako and New York City. His father is the filmmaker Horace Ové and his sister is the actress Indra Ové.\": 13.145259857177734, \"Playing Away | Playing Away is a 1987 TV comedy film directed by Horace Ové, from a screenplay by Caryl Phillips. In the story, an English cricket team, fictitiously named \\\"Sneddington\\\" (based in Lavenham, Suffolk), invites a team of West Indian heritage based in Brixton (South London) to play a charity game in support of their \\\"Third World Week.\\\" According to Screenonline, \\\"The gentle comedy of manners and unexpected reversal of white and black stereotypes in \\\"Playing Away\\\" contrasts sharply with the stylistic experimentation and the militant denunciations of racial prejudice in director Horace Ové's earlier feature, \\\"Pressure\\\" (1975).\\\" \\\" New York Times\\\" reviewer Vincent Canby called it \\\"witty and wise without being seriously disturbing for a minute\\\".\": 12.445182800292969, \"Pressure (film) | Pressure is a 1976 British drama film and the first feature-length fiction film directed by a Black film-maker in Britain. Directed by Horace Ové, and co-written by him with Samuel Selvon, \\\"Pressure\\\" is a powerful portrait of inter-generational tensions between first- and second-generation West Indian migrants in London's Notting Hill area. According to Julia Toppin,\": 10.526924133300781, \"What a Night! (1928 film) | What a Night! (1928) is an American silent film directed by A. Edward Sutherland. The romantic comedy was written by Louise Long, from a story by Lloyd Corrigan and Grover Jones. The film stars Bebe Daniels, Neil Hamilton, and William Austin.\": 10.453304290771484, \"The Orchid House (TV serial) | The Orchid House is a four-part television serial that first aired on British television's Channel 4 from 21 February to 14 March 1991, directed by Horace Ové. Its cast featured Diana Quick, Madge Sinclair, Nigel Terry, Elizabeth Hurley, Kate Buffery and Frances Barber, and was based on Phyllis Shand Allfrey's only novel of the same name.\": 10.413471221923828, \"Indra Ové | Indra Ové (born 1968) is a British movie and stage actress.\": 10.15088176727295, \"Henry Sutherland Edwards | Henry Sutherland Edwards (1828–1906) was a British journalist.\": 9.868606567382812, \"Burning an Illusion | Burning an Illusion is a 1981 British film written and directed by Menelik Shabazz, about a young British-born black woman's love life, mostly shot in London's Notting Hill and Ladbroke Grove communities. It was only the second British feature to have been made by a black director, following Horace Ové’s 1975 \\\"Pressure\\\", and is described by Stephen Bourne as \\\"the first British film to give a black woman a voice of any kind.\\\" Imruh Bakari worked with Shabazz and co-founded Kumba productions with him.\": 9.634703636169434, \"Horace Hall Edwards | Horace Hall Edwards (August 21, 1902 – January 27, 1987) was an American politician, who was mayor of Richmond, Virginia, and a candidate for governor in 1949.\": 9.451831817626953}\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The claim states that the father of Zak Ové and A. Edward Sutherland are not both photographers. From the context, we know that Zak Ové's father is Horace Ové, who is indeed a photographer among other professions. However, A. Edward Sutherland is not mentioned as a photographer in the context; he is known as a film director. Therefore, the claim is correct because Horace Ové is a photographer, but A. Edward Sutherland is not.\n", - "\n", - "[[ ## new_notes ## ]]\n", - "[\"Horace Ové is a photographer, filmmaker, and writer.\", \"A. Edward Sutherland is a film director, not a photographer.\"]\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Horace Ové\", \"A. Edward Sutherland\", \"Zak Ové\"]\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The summer 2016 romantic drama \"Me Before You\" is directed by Thea Sharrock. The star of the film The Lost Future (who also appears in The Hunger Games) stars as the character Will Traynor.\n", - "\n", - "[[ ## notes ## ]]\n", - "[1] «Sam Claflin stars as Will Traynor in 'Me Before You'.»\n", - "[2] «Sam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.»\n", - "[3] «Thea Sharrock directed 'Me Before You'.»\n", - "[4] «Sam Claflin stars as Will Traynor in 'Me Before You'.»\n", - "[5] «Sam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.»\n", - "[6] «Thea Sharrock directed 'Me Before You'.»\n", - "[7] «Sam Claflin starred in 'The Lost Future'.»\n", - "[8] «Sam Claflin stars as Will Traynor in 'Me Before You'.»\n", - "[9] «Sam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.»\n", - "[10] «Thea Sharrock directed 'Me Before You'.»\n", - "[11] «Sam Claflin starred in 'The Lost Future'.»\n", - "[12] «Sam Claflin stars as Will Traynor in 'Me Before You'.»\n", - "[13] «Sam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.»\n", - "[14] «Thea Sharrock directed 'Me Before You'.»\n", - "[15] «Sam Claflin starred in 'The Lost Future'.»\n", - "\n", - "[[ ## context ## ]]\n", - "{\"Sam Claflin | Samuel George Claflin (born 27 June 1986) is an English actor. He is known for portraying Finnick Odair in \\\"The Hunger Games\\\" film series, Philip Swift in \\\"\\\", and Will Traynor in \\\"Me Before You\\\".\": 19.94539451599121, \"Me Before You (film) | Me Before You is a 2016 romantic drama film directed by Thea Sharrock in her directorial debut and adapted by English author Jojo Moyes from her 2012 novel of the same name. The film stars Emilia Clarke, Sam Claflin, Steve Peacocke, Jenna Coleman, Charles Dance, Matthew Lewis, Janet McTeer, Vanessa Kirby and Joanna Lumley.\": 18.48834228515625, \"Look What You Did to Me | Look What You Did to Me is the debut studio album by American rapper and singer Z-Ro. It was released on June 16, 1998, by this independently distributed label Fisherboy Records. The album is entirely produced by Z-Ro alongside Rakish Jacob (aka Roc), while the guest appearances was from T.A.Z., Bam, Trae, The Fakkulty, Chris Ward and Al-D.\": 14.100790977478027, \"I Still Know What You Did Last Summer | I Still Know What You Did Last Summer is a 1998 American slasher film and a sequel to the 1997 film \\\"I Know What You Did Last Summer\\\". Directed by Danny Cannon, the film was written by Trey Callaway, and features characters originally created in Lois Duncan's 1973 novel \\\"I Know What You Did Last Summer\\\". Jennifer Love Hewitt, Freddie Prinze, Jr. and Muse Watson reprise their roles, with Brandy, Mekhi Phifer, Jennifer Esposito, and Matthew Settle joining the cast. \\\"I Still Know What You Did Last Summer\\\" continues after the events of the first film.\": 13.990736961364746, \"Tell Me What You Dream | \\\"Tell Me What You Dream\\\" is a song written by Timothy B. Schmit, Josh Leo and Vince Melamed and performed by country group Restless Heart along with saxophonist Warren Hill. The single was the group's only number one on the adult contemporary chart and despite previous country chart success, the song did not make the country top 40. \\\"Tell Me What You Dream\\\" spent two weeks at number one and peaked at number forty-three on the \\\"Billboard\\\" Hot 100.\": 13.774335861206055, \"Geraldine Jones (character) | Geraldine Jones was a fictional African American character, the most famous recurring persona of comedian Flip Wilson. Geraldine was played as a sassy liberated Southern woman who was coarsely flirty yet faithful to her (unseen) boyfriend \\\"Killer\\\". Poorly educated, she was nevertheless confident; she did not change her behavior to suit anyone. Several of Geraldine's sayings entered U.S. popular culture as catchphrases, especially \\\"When you're hot, you're hot; when you're not, you're not,\\\" \\\"The Devil made me do it,\\\" and \\\"What you see is what you get! \\\"\": 13.149161338806152, \"When Did You Stop Loving Me, When Did I Stop Loving You | When Did You Stop Loving Me, When Did I Stop Loving You\": 12.946025848388672, \"Pajama Sam 3: You Are What You Eat from Your Head to Your Feet | Pajama Sam 3: You Are What You Eat from Your Head to Your Feet\": 12.687528610229492, \"Sam Quartin | Sam Quartin is a musician, producer and actress best known for role as June Glass, starring alongside Marilyn Manson in the American movie Let Me Make You a Martyr\\\"(2017), also for \\\"By the Rivers of Babylon)\\\" and \\\"Aimy in a Cage\\\".\": 12.639335632324219, \"Why You Wanna Treat Me So Bad? | \\\"Why You Wanna Treat Me So Bad?\\\" is the US follow-up single to Prince's first big hit, \\\"I Wanna Be Your Lover\\\". It is also Prince's first rock and roll-inspired single release. It did not make the top 40 of the Billboard Hot 100 charts, although it did reach #13 on the R&B Singles charts. The lyrics are self-explanatory from the title, and deal with a cruel lover. The song prominently features guitar and bass, with the keyboard featured in a less prominent role. A highlight of the song is a soaring guitar solo at the end, played by Prince himself.\": 12.574298858642578}\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mAssistant message:\u001b[0m\n", - "\n", - "[[ ## reasoning ## ]]\n", - "The claim states that the film \"Me Before You\" is directed by Thea Sharrock and that the actor who stars as Will Traynor also appeared in \"The Lost Future\" and \"The Hunger Games\". According to the notes, Sam Claflin stars as Will Traynor in \"Me Before You\" and is known for his role as Finnick Odair in \"The Hunger Games\". Additionally, Sam Claflin starred in \"The Lost Future\". The context confirms that \"Me Before You\" is directed by Thea Sharrock and that Sam Claflin plays Will Traynor. Therefore, the claim is supported by the provided notes and context.\n", - "\n", - "[[ ## new_notes ## ]]\n", - "[\"Sam Claflin stars as Will Traynor in 'Me Before You'.\", \"Sam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.\", \"Thea Sharrock directed 'Me Before You'.\", \"Sam Claflin starred in 'The Lost Future'.\"]\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Me Before You (film)\", \"Sam Claflin\", \"Thea Sharrock\", \"The Lost Future\", \"The Hunger Games\"]\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claim ## ]]\n", - "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\n", - "\n", - "[[ ## notes ## ]]\n", - "N/A\n", - "\n", - "[[ ## context ## ]]\n", - "{\"Up Against It | Up Against It is an unproduced script by Joe Orton, written in 1967 for The Beatles at the height of their fame.\": 23.741416931152344, \"The Alien (unproduced film) | The Alien was an unproduced Indian-American science fiction film in development in the late 1960s which was eventually cancelled. It was to be directed by celebrated Indian filmmaker Satyajit Ray and co-produced by Columbia Pictures. The script was written by Ray in 1967, loosely based on Bankubabur Bandhu (\\\"Banku Babu's Friend\\\" or \\\"Mr. Banku's Friend\\\"), a Bengali science fiction story he had written in 1962 for \\\"Sandesh\\\", the Ray family magazine, which gained popularity among Bengalis in the early 1960s. \\\"Bankubabur Bandhu\\\" was eventually adapted into a television film by Satyajit Ray's son Sandip Ray, and a play by the theatre group Swapnasandhani Kaushik Sen, in 2006.\": 14.683004379272461, \"Marsha Albert | Marsha Albert (born 1948) is credited with being the person who jump-started the early 1960s phenomena known as Beatlemania in the United States when as a 15-year-old girl, on 17 December 1963, she introduced for the first time on American radio a song written and recorded by The Beatles titled I Want to Hold Your Hand (the Beatles' best-selling single worldwide), and that Beatles historian and author Bruce Spizer noted, in 2004, by his stating \\\"Marsha Albert's actions forced a major record company to push up the release date of a debut single from an unknown band during the holiday season, a time when record companies traditionally released no new product.\\\"\": 13.236483573913574, \"Up Against It! (Todd Rundgren album) | Up Against It! is a 1997 album by Todd Rundgren, essentially consisting of Rundgren's song demos for the Off Broadway show that were Written and Recorded by Todd from 1986-88. The project was inspired by the never-produced Up Against It which was a play originally written by Joe Orton for The Beatles.\": 12.989564895629883, \"Blood and Fire (Star Trek: The Next Generation) | \\\"Blood and Fire\\\" is an episode written by David Gerrold for possible use on \\\"\\\". The script was commissioned and written, but never actually filmed. According to Gerrold, some of the production staff, including Rick Berman, had a negative reaction to its positive depiction of an openly gay couple. Herbert Wright rewrote the script under the name \\\"Blood and Ice\\\", which also was left unproduced.\": 11.980508804321289, \"Cedar Rapids (film) | Cedar Rapids is a 2011 American comedy film directed by Miguel Arteta. The script, written by Phil Johnston, was included on the 2009 Black List, a Hollywood list of the most popular unproduced screenplays of the year.\": 11.593443870544434, \"The Beatles: The Biography | The Beatles: The Biography is the name of a 2005 biography of the 1960s rock band The Beatles written by Bob Spitz. It was first published by Little, Brown and Company on November 1, 2005.\": 11.583497047424316, \"The Illusionist (2010 film) | The Illusionist (French: L'Illusionniste ) is a 2010 French-British animated film directed by Sylvain Chomet. The film is based on an unproduced script written by French mime, director and actor Jacques Tati in 1956. Controversy surrounds Tati's motivation for the script, which was written as a personal letter to his estranged eldest daughter, Helga Marie-Jeanne Schiel in collaboration with his long-term writing partner Henri Marquet, between writing for the films \\\"Mon Oncle\\\" and \\\"Play Time\\\".\": 11.528533935546875, \"Bobby Florsheim | Robert Florsheim (born December 19, 1969) is an American screenwriter, best known for co-writing \\\"The Passion Of The Ark\\\" with Josh Stolberg, (the basis for the film \\\"Evan Almighty\\\"). Their original script was reported by Daily Variety as the highest priced spec script ever sold by unproduced screenwriters (April, 2004). In refashioning the script into \\\"Evan Almighty\\\", Universal Studios discarded the script and then hired screenwriter Steve Oedekerk, who received sole writing credit on the finished film.\": 11.23757553100586, \"The Hook (screenplay) | The Hook is an unproduced screenplay by American playwright, Arthur Miller. It was written in 1947 and was intended to be produced by Columbia Pictures Studio, Hollywood, and to be directed by Elia Kazan. The screenplay was inspired by the true story of Pete Panto, a young dockworker who stood up against the corrupt Mafia-connected union leadership. Panto was discovered dead in a pit outside New York eighteen months after his disappearance. Set in the Red Hook district of Brooklyn, \\\"The Hook\\\" is the story of Marty Ferrara, a longshoreman who is ‘ready to lay down his life, if need be, to secure one thing – his sense of personal dignity.’\": 11.222980499267578}\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "The claim states that the author of the 1960s unproduced script written for The Beatles, \"Up Against It\", and Bernard-Marie Koltès are both playwrights. The context confirms that Joe Orton wrote the script \"Up Against It\" for The Beatles, and Bernard-Marie Koltès was a French playwright. Therefore, the claim is supported by the provided context.\n", - "\n", - "[[ ## new_notes ## ]]\n", - "[]\n", - "\n", - "[[ ## titles ## ]]\n", - "[\"Up Against It\", \"Bernard-Marie Koltès\", \"Joe Orton\", \"The Beatles\"]\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "dspy.inspect_history(n=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's save our optimized program so we can use it again later." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-12-25T12:18:16.177899]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `claim` (str)\n", + "2. `notes` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `query` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## claim ## ]]\n", + "{claim}\n", + "\n", + "[[ ## notes ## ]]\n", + "{notes}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## query ## ]]\n", + "{query}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given a claim and a set of notes, generate a query that can be used to gather additional evidence or context to support or refute the claim. Think step by step to ensure the query is specific and relevant to the information provided in the notes.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "Danyang, Jiangusu and this city are both cities in China. This city was the birthplace of Chen Xiuke.\n", + "\n", + "[[ ## notes ## ]]\n", + "[1] \u00abChen Xiuke was born in Dongfang, Hainan.\u00bb\n", + "[2] \u00abDanyang is a city in Jiangsu province, China.\u00bb\n", + "[3] \u00abChen Xiuke was born in Dongfang, Hainan.\u00bb\n", + "[4] \u00abDanyang is a city in Jiangsu province, China.\u00bb\n", + "[5] \u00abDongfang is a city in Hainan province, China.\u00bb\n", + "[6] \u00abChen Xiuke was born in Dongfang, Hainan.\u00bb\n", + "[7] \u00abDanyang is a city in Jiangsu province, China.\u00bb\n", + "[8] \u00abDongfang is a city in Hainan province, China.\u00bb\n", + "[9] \u00abChen Xiuke was born in Dongfang, Hainan.\u00bb\n", + "[10] \u00abDanyang is a city in Jiangsu province, China.\u00bb\n", + "[11] \u00abDongfang is a city in Hainan province, China.\u00bb\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## query ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The claim states that Danyang, Jiangsu, and another city are both cities in China, and that this other city was the birthplace of Chen Xiuke. According to the notes, Chen Xiuke was born in Dongfang, Hainan. Therefore, the city referred to in the claim as \"this city\" is Dongfang, which is indeed a city in Hainan province, China. The claim is consistent with the information provided in the notes.\n", + "\n", + "[[ ## query ## ]]\n", + "What is the birthplace of Chen Xiuke?\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The star of Nothing to Report and Gary Barlow have a profession in common.\n", + "\n", + "[[ ## notes ## ]]\n", + "[1] \u00abChris Jericho is a musician and professional wrestler.\u00bb\n", + "[2] \u00abGary Barlow is a singer and songwriter, known for being a member of Take That.\u00bb\n", + "[3] \u00abChris Jericho is a musician and professional wrestler.\u00bb\n", + "[4] \u00abGary Barlow is a singer and songwriter, known for being a member of Take That.\u00bb\n", + "[5] \u00abChris Jericho stars in 'Nothing to Report'.\u00bb\n", + "[6] \u00abChris Jericho is a musician and professional wrestler.\u00bb\n", + "[7] \u00abGary Barlow is a singer and songwriter, known for being a member of Take That.\u00bb\n", + "[8] \u00abChris Jericho stars in 'Nothing to Report'.\u00bb\n", + "[9] \u00abChris Jericho is a musician and professional wrestler.\u00bb\n", + "[10] \u00abGary Barlow is a singer and songwriter, known for being a member of Take That.\u00bb\n", + "[11] \u00abChris Jericho stars in 'Nothing to Report'.\u00bb\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## query ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The claim suggests that the star of \"Nothing to Report\" and Gary Barlow share a profession. From the notes, we know that Chris Jericho stars in \"Nothing to Report\" and is a musician and professional wrestler. Gary Barlow is a singer and songwriter. The common profession between Chris Jericho and Gary Barlow is that they are both musicians.\n", + "\n", + "[[ ## query ## ]]\n", + "What is the profession that Chris Jericho and Gary Barlow have in common?\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\n", + "\n", + "[[ ## notes ## ]]\n", + "N/A\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## query ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "The claim states that the author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights. However, there is no information in the notes to support or refute this claim. The notes are empty, which means we cannot verify the author of the script or Bernard-Marie Kolt\u00e8s' profession.\n", + "\n", + "[[ ## query ## ]]\n", + "Who is the author of the 1960s unproduced script written for The Beatles, Up Against It?\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-12-25T12:18:16.191896]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `claim` (str)\n", + "2. `notes` (str)\n", + "3. `context` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `new_notes` (list[str])\n", + "3. `titles` (list[str])\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## claim ## ]]\n", + "{claim}\n", + "\n", + "[[ ## notes ## ]]\n", + "{notes}\n", + "\n", + "[[ ## context ## ]]\n", + "{context}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## new_notes ## ]]\n", + "{new_notes} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n", + "\n", + "[[ ## titles ## ]]\n", + "{titles} # note: the value you produce must be pareseable according to the following JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Analyze the provided `claim`, `notes`, and `context` to generate `new_notes` that further support or refute the claim, and extract relevant `titles` that represent key topics or entities from the context.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "This is an example of the task, though some input or output fields are not supplied.\n", + "\n", + "[[ ## claim ## ]]\n", + "The figure who currently holds the role Michael Noonan held from 2001 to 2002, was the Minister for Social Protection from 2016 until 2017.\n", + "\n", + "[[ ## notes ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "[[ ## context ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "[[ ## new_notes ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Michael Noonan\", \"Leader of Fine Gael\", \"Leo Varadkar\"]\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "This is an example of the task, though some input or output fields are not supplied.\n", + "\n", + "[[ ## claim ## ]]\n", + "The type of area Wiliwili are typically found in have a dominant tree species of Acacia koa. They are of the pea family.\n", + "\n", + "[[ ## notes ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "[[ ## context ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "[[ ## new_notes ## ]]\n", + "Not supplied for this particular example.\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Acacia koa\", \"Wiliwili\", \"Hawaiian tropical dry forests\"]\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The father of Zak Ov\u00e9 and A. Edward Sutherland are not both photographers.\n", + "\n", + "[[ ## notes ## ]]\n", + "[1] \u00abHorace Ov\u00e9 is a photographer, filmmaker, and writer.\u00bb\n", + "[2] \u00abA. Edward Sutherland is a film director, not a photographer.\u00bb\n", + "[3] \u00abHorace Ov\u00e9 is a photographer, filmmaker, and writer.\u00bb\n", + "[4] \u00abA. Edward Sutherland is a film director, not a photographer.\u00bb\n", + "[5] \u00abHorace Ov\u00e9 is a photographer, filmmaker, and writer.\u00bb\n", + "[6] \u00abA. Edward Sutherland is a film director, not a photographer.\u00bb\n", + "[7] \u00abHorace Ov\u00e9 is a photographer, filmmaker, and writer.\u00bb\n", + "[8] \u00abA. Edward Sutherland is a film director, not a photographer.\u00bb\n", + "\n", + "[[ ## context ## ]]\n", + "{\"Horace Ov\u00e9 | Horace Ov\u00e9, CBE (born 1939), is a British filmmaker, photographer, painter and writer, one of the leading black independent film-makers to emerge in Britain since the post-war period. Ov\u00e9 holds the \\\"Guinness World Record\\\" for being the first black British film-maker to direct a feature-length film, \\\"Pressure\\\" (1975). In its retrospective history, \\\"100 Years of Cinema\\\", the British Film Institute (BFI) declared: \\\"Horace Ov\u00e9 is undoubtedly a pioneer in Black British history and his work provides a perspective on the Black experience in Britain.\\\"\": 15.836545944213867, \"Zak Ov\u00e9 | Zak Ov\u00e9 (born 1966) is a British visual artist who works between sculpture, film and photography, living in London, UK, and Trinidad. His themes reflect \\\"his documentation of and anthropological interest in diasporic and African history, specifically that which is explored through Trinidadian carnival.\\\" In work that is \\\"filtered through his own personal and cultural upbringing, with a black Trinidadian father and white Irish mother\\\", he has exhibited widely in Europe, the United States and Africa, participating in international museum shows in London, Dakar, Paris, Dubai, Prague, Berlin, Johannesburg, Bamako and New York City. His father is the filmmaker Horace Ov\u00e9 and his sister is the actress Indra Ov\u00e9.\": 13.145259857177734, \"Playing Away | Playing Away is a 1987 TV comedy film directed by Horace Ov\u00e9, from a screenplay by Caryl Phillips. In the story, an English cricket team, fictitiously named \\\"Sneddington\\\" (based in Lavenham, Suffolk), invites a team of West Indian heritage based in Brixton (South London) to play a charity game in support of their \\\"Third World Week.\\\" According to Screenonline, \\\"The gentle comedy of manners and unexpected reversal of white and black stereotypes in \\\"Playing Away\\\" contrasts sharply with the stylistic experimentation and the militant denunciations of racial prejudice in director Horace Ov\u00e9's earlier feature, \\\"Pressure\\\" (1975).\\\" \\\" New York Times\\\" reviewer Vincent Canby called it \\\"witty and wise without being seriously disturbing for a minute\\\".\": 12.445182800292969, \"Pressure (film) | Pressure is a 1976 British drama film and the first feature-length fiction film directed by a Black film-maker in Britain. Directed by Horace Ov\u00e9, and co-written by him with Samuel Selvon, \\\"Pressure\\\" is a powerful portrait of inter-generational tensions between first- and second-generation West Indian migrants in London's Notting Hill area. According to Julia Toppin,\": 10.526924133300781, \"What a Night! (1928 film) | What a Night! (1928) is an American silent film directed by A. Edward Sutherland. The romantic comedy was written by Louise Long, from a story by Lloyd Corrigan and Grover Jones. The film stars Bebe Daniels, Neil Hamilton, and William Austin.\": 10.453304290771484, \"The Orchid House (TV serial) | The Orchid House is a four-part television serial that first aired on British television's Channel 4 from 21 February to 14 March 1991, directed by Horace Ov\u00e9. Its cast featured Diana Quick, Madge Sinclair, Nigel Terry, Elizabeth Hurley, Kate Buffery and Frances Barber, and was based on Phyllis Shand Allfrey's only novel of the same name.\": 10.413471221923828, \"Indra Ov\u00e9 | Indra Ov\u00e9 (born 1968) is a British movie and stage actress.\": 10.15088176727295, \"Henry Sutherland Edwards | Henry Sutherland Edwards (1828\u20131906) was a British journalist.\": 9.868606567382812, \"Burning an Illusion | Burning an Illusion is a 1981 British film written and directed by Menelik Shabazz, about a young British-born black woman's love life, mostly shot in London's Notting Hill and Ladbroke Grove communities. It was only the second British feature to have been made by a black director, following Horace Ov\u00e9\u2019s 1975 \\\"Pressure\\\", and is described by Stephen Bourne as \\\"the first British film to give a black woman a voice of any kind.\\\" Imruh Bakari worked with Shabazz and co-founded Kumba productions with him.\": 9.634703636169434, \"Horace Hall Edwards | Horace Hall Edwards (August 21, 1902 \u2013 January 27, 1987) was an American politician, who was mayor of Richmond, Virginia, and a candidate for governor in 1949.\": 9.451831817626953}\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The claim states that the father of Zak Ov\u00e9 and A. Edward Sutherland are not both photographers. From the context, we know that Zak Ov\u00e9's father is Horace Ov\u00e9, who is indeed a photographer among other professions. However, A. Edward Sutherland is not mentioned as a photographer in the context; he is known as a film director. Therefore, the claim is correct because Horace Ov\u00e9 is a photographer, but A. Edward Sutherland is not.\n", + "\n", + "[[ ## new_notes ## ]]\n", + "[\"Horace Ov\u00e9 is a photographer, filmmaker, and writer.\", \"A. Edward Sutherland is a film director, not a photographer.\"]\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Horace Ov\u00e9\", \"A. Edward Sutherland\", \"Zak Ov\u00e9\"]\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The summer 2016 romantic drama \"Me Before You\" is directed by Thea Sharrock. The star of the film The Lost Future (who also appears in The Hunger Games) stars as the character Will Traynor.\n", + "\n", + "[[ ## notes ## ]]\n", + "[1] \u00abSam Claflin stars as Will Traynor in 'Me Before You'.\u00bb\n", + "[2] \u00abSam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.\u00bb\n", + "[3] \u00abThea Sharrock directed 'Me Before You'.\u00bb\n", + "[4] \u00abSam Claflin stars as Will Traynor in 'Me Before You'.\u00bb\n", + "[5] \u00abSam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.\u00bb\n", + "[6] \u00abThea Sharrock directed 'Me Before You'.\u00bb\n", + "[7] \u00abSam Claflin starred in 'The Lost Future'.\u00bb\n", + "[8] \u00abSam Claflin stars as Will Traynor in 'Me Before You'.\u00bb\n", + "[9] \u00abSam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.\u00bb\n", + "[10] \u00abThea Sharrock directed 'Me Before You'.\u00bb\n", + "[11] \u00abSam Claflin starred in 'The Lost Future'.\u00bb\n", + "[12] \u00abSam Claflin stars as Will Traynor in 'Me Before You'.\u00bb\n", + "[13] \u00abSam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.\u00bb\n", + "[14] \u00abThea Sharrock directed 'Me Before You'.\u00bb\n", + "[15] \u00abSam Claflin starred in 'The Lost Future'.\u00bb\n", + "\n", + "[[ ## context ## ]]\n", + "{\"Sam Claflin | Samuel George Claflin (born 27 June 1986) is an English actor. He is known for portraying Finnick Odair in \\\"The Hunger Games\\\" film series, Philip Swift in \\\"\\\", and Will Traynor in \\\"Me Before You\\\".\": 19.94539451599121, \"Me Before You (film) | Me Before You is a 2016 romantic drama film directed by Thea Sharrock in her directorial debut and adapted by English author Jojo Moyes from her 2012 novel of the same name. The film stars Emilia Clarke, Sam Claflin, Steve Peacocke, Jenna Coleman, Charles Dance, Matthew Lewis, Janet McTeer, Vanessa Kirby and Joanna Lumley.\": 18.48834228515625, \"Look What You Did to Me | Look What You Did to Me is the debut studio album by American rapper and singer Z-Ro. It was released on June 16, 1998, by this independently distributed label Fisherboy Records. The album is entirely produced by Z-Ro alongside Rakish Jacob (aka Roc), while the guest appearances was from T.A.Z., Bam, Trae, The Fakkulty, Chris Ward and Al-D.\": 14.100790977478027, \"I Still Know What You Did Last Summer | I Still Know What You Did Last Summer is a 1998 American slasher film and a sequel to the 1997 film \\\"I Know What You Did Last Summer\\\". Directed by Danny Cannon, the film was written by Trey Callaway, and features characters originally created in Lois Duncan's 1973 novel \\\"I Know What You Did Last Summer\\\". Jennifer Love Hewitt, Freddie Prinze, Jr. and Muse Watson reprise their roles, with Brandy, Mekhi Phifer, Jennifer Esposito, and Matthew Settle joining the cast. \\\"I Still Know What You Did Last Summer\\\" continues after the events of the first film.\": 13.990736961364746, \"Tell Me What You Dream | \\\"Tell Me What You Dream\\\" is a song written by Timothy B. Schmit, Josh Leo and Vince Melamed and performed by country group Restless Heart along with saxophonist Warren Hill. The single was the group's only number one on the adult contemporary chart and despite previous country chart success, the song did not make the country top 40. \\\"Tell Me What You Dream\\\" spent two weeks at number one and peaked at number forty-three on the \\\"Billboard\\\" Hot 100.\": 13.774335861206055, \"Geraldine Jones (character) | Geraldine Jones was a fictional African American character, the most famous recurring persona of comedian Flip Wilson. Geraldine was played as a sassy liberated Southern woman who was coarsely flirty yet faithful to her (unseen) boyfriend \\\"Killer\\\". Poorly educated, she was nevertheless confident; she did not change her behavior to suit anyone. Several of Geraldine's sayings entered U.S. popular culture as catchphrases, especially \\\"When you're hot, you're hot; when you're not, you're not,\\\" \\\"The Devil made me do it,\\\" and \\\"What you see is what you get! \\\"\": 13.149161338806152, \"When Did You Stop Loving Me, When Did I Stop Loving You | When Did You Stop Loving Me, When Did I Stop Loving You\": 12.946025848388672, \"Pajama Sam 3: You Are What You Eat from Your Head to Your Feet | Pajama Sam 3: You Are What You Eat from Your Head to Your Feet\": 12.687528610229492, \"Sam Quartin | Sam Quartin is a musician, producer and actress best known for role as June Glass, starring alongside Marilyn Manson in the American movie Let Me Make You a Martyr\\\"(2017), also for \\\"By the Rivers of Babylon)\\\" and \\\"Aimy in a Cage\\\".\": 12.639335632324219, \"Why You Wanna Treat Me So Bad? | \\\"Why You Wanna Treat Me So Bad?\\\" is the US follow-up single to Prince's first big hit, \\\"I Wanna Be Your Lover\\\". It is also Prince's first rock and roll-inspired single release. It did not make the top 40 of the Billboard Hot 100 charts, although it did reach #13 on the R&B Singles charts. The lyrics are self-explanatory from the title, and deal with a cruel lover. The song prominently features guitar and bass, with the keyboard featured in a less prominent role. A highlight of the song is a soaring guitar solo at the end, played by Prince himself.\": 12.574298858642578}\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mAssistant message:\u001b[0m\n", + "\n", + "[[ ## reasoning ## ]]\n", + "The claim states that the film \"Me Before You\" is directed by Thea Sharrock and that the actor who stars as Will Traynor also appeared in \"The Lost Future\" and \"The Hunger Games\". According to the notes, Sam Claflin stars as Will Traynor in \"Me Before You\" and is known for his role as Finnick Odair in \"The Hunger Games\". Additionally, Sam Claflin starred in \"The Lost Future\". The context confirms that \"Me Before You\" is directed by Thea Sharrock and that Sam Claflin plays Will Traynor. Therefore, the claim is supported by the provided notes and context.\n", + "\n", + "[[ ## new_notes ## ]]\n", + "[\"Sam Claflin stars as Will Traynor in 'Me Before You'.\", \"Sam Claflin is known for his role as Finnick Odair in 'The Hunger Games'.\", \"Thea Sharrock directed 'Me Before You'.\", \"Sam Claflin starred in 'The Lost Future'.\"]\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Me Before You (film)\", \"Sam Claflin\", \"Thea Sharrock\", \"The Lost Future\", \"The Hunger Games\"]\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claim ## ]]\n", + "The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\n", + "\n", + "[[ ## notes ## ]]\n", + "N/A\n", + "\n", + "[[ ## context ## ]]\n", + "{\"Up Against It | Up Against It is an unproduced script by Joe Orton, written in 1967 for The Beatles at the height of their fame.\": 23.741416931152344, \"The Alien (unproduced film) | The Alien was an unproduced Indian-American science fiction film in development in the late 1960s which was eventually cancelled. It was to be directed by celebrated Indian filmmaker Satyajit Ray and co-produced by Columbia Pictures. The script was written by Ray in 1967, loosely based on Bankubabur Bandhu (\\\"Banku Babu's Friend\\\" or \\\"Mr. Banku's Friend\\\"), a Bengali science fiction story he had written in 1962 for \\\"Sandesh\\\", the Ray family magazine, which gained popularity among Bengalis in the early 1960s. \\\"Bankubabur Bandhu\\\" was eventually adapted into a television film by Satyajit Ray's son Sandip Ray, and a play by the theatre group Swapnasandhani Kaushik Sen, in 2006.\": 14.683004379272461, \"Marsha Albert | Marsha Albert (born 1948) is credited with being the person who jump-started the early 1960s phenomena known as Beatlemania in the United States when as a 15-year-old girl, on 17 December 1963, she introduced for the first time on American radio a song written and recorded by The Beatles titled I Want to Hold Your Hand (the Beatles' best-selling single worldwide), and that Beatles historian and author Bruce Spizer noted, in 2004, by his stating \\\"Marsha Albert's actions forced a major record company to push up the release date of a debut single from an unknown band during the holiday season, a time when record companies traditionally released no new product.\\\"\": 13.236483573913574, \"Up Against It! (Todd Rundgren album) | Up Against It! is a 1997 album by Todd Rundgren, essentially consisting of Rundgren's song demos for the Off Broadway show that were Written and Recorded by Todd from 1986-88. The project was inspired by the never-produced Up Against It which was a play originally written by Joe Orton for The Beatles.\": 12.989564895629883, \"Blood and Fire (Star Trek: The Next Generation) | \\\"Blood and Fire\\\" is an episode written by David Gerrold for possible use on \\\"\\\". The script was commissioned and written, but never actually filmed. According to Gerrold, some of the production staff, including Rick Berman, had a negative reaction to its positive depiction of an openly gay couple. Herbert Wright rewrote the script under the name \\\"Blood and Ice\\\", which also was left unproduced.\": 11.980508804321289, \"Cedar Rapids (film) | Cedar Rapids is a 2011 American comedy film directed by Miguel Arteta. The script, written by Phil Johnston, was included on the 2009 Black List, a Hollywood list of the most popular unproduced screenplays of the year.\": 11.593443870544434, \"The Beatles: The Biography | The Beatles: The Biography is the name of a 2005 biography of the 1960s rock band The Beatles written by Bob Spitz. It was first published by Little, Brown and Company on November 1, 2005.\": 11.583497047424316, \"The Illusionist (2010 film) | The Illusionist (French: L'Illusionniste ) is a 2010 French-British animated film directed by Sylvain Chomet. The film is based on an unproduced script written by French mime, director and actor Jacques Tati in 1956. Controversy surrounds Tati's motivation for the script, which was written as a personal letter to his estranged eldest daughter, Helga Marie-Jeanne Schiel in collaboration with his long-term writing partner Henri Marquet, between writing for the films \\\"Mon Oncle\\\" and \\\"Play Time\\\".\": 11.528533935546875, \"Bobby Florsheim | Robert Florsheim (born December 19, 1969) is an American screenwriter, best known for co-writing \\\"The Passion Of The Ark\\\" with Josh Stolberg, (the basis for the film \\\"Evan Almighty\\\"). Their original script was reported by Daily Variety as the highest priced spec script ever sold by unproduced screenwriters (April, 2004). In refashioning the script into \\\"Evan Almighty\\\", Universal Studios discarded the script and then hired screenwriter Steve Oedekerk, who received sole writing credit on the finished film.\": 11.23757553100586, \"The Hook (screenplay) | The Hook is an unproduced screenplay by American playwright, Arthur Miller. It was written in 1947 and was intended to be produced by Columbia Pictures Studio, Hollywood, and to be directed by Elia Kazan. The screenplay was inspired by the true story of Pete Panto, a young dockworker who stood up against the corrupt Mafia-connected union leadership. Panto was discovered dead in a pit outside New York eighteen months after his disappearance. Set in the Red Hook district of Brooklyn, \\\"The Hook\\\" is the story of Marty Ferrara, a longshoreman who is \u2018ready to lay down his life, if need be, to secure one thing \u2013 his sense of personal dignity.\u2019\": 11.222980499267578}\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## new_notes ## ]]` (must be formatted as a valid Python list[str]), then `[[ ## titles ## ]]` (must be formatted as a valid Python list[str]), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "The claim states that the author of the 1960s unproduced script written for The Beatles, \"Up Against It\", and Bernard-Marie Kolt\u00e8s are both playwrights. The context confirms that Joe Orton wrote the script \"Up Against It\" for The Beatles, and Bernard-Marie Kolt\u00e8s was a French playwright. Therefore, the claim is supported by the provided context.\n", + "\n", + "[[ ## new_notes ## ]]\n", + "[]\n", + "\n", + "[[ ## titles ## ]]\n", + "[\"Up Against It\", \"Bernard-Marie Kolt\u00e8s\", \"Joe Orton\", \"The Beatles\"]\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history(n=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's save our optimized program so we can use it again later." + ] + }, { - "data": { - "text/plain": [ - "['Up Against It', 'Bernard-Marie Koltès', 'The Beatles', 'Joe Orton']" + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Up Against It', 'Bernard-Marie Kolt\u00e8s', 'The Beatles', 'Joe Orton']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized.save(\"optimized_hop.json\")\n", + "\n", + "loaded_program = Hop()\n", + "loaded_program.load(\"optimized_hop.json\")\n", + "\n", + "loaded_program(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Kolt\u00e8s are both playwrights.\").titles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Saving programs in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", + "\n", + "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", + "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", + "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", + "\n", + "To save the program in MLflow, run the following code:\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "# Start an MLflow Run and save the program\n", + "with mlflow.start_run(run_name=\"optimized\"):\n", + " model_info = mlflow.dspy.log_model(\n", + " optimized,\n", + " artifact_path=\"model\", # Any name to save the program in MLflow\n", + " )\n", + "\n", + "# Load the program back from MLflow\n", + "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "optimized.save(\"optimized_hop.json\")\n", - "\n", - "loaded_program = Hop()\n", - "loaded_program.load(\"optimized_hop.json\")\n", - "\n", - "loaded_program(claim=\"The author of the 1960s unproduced script written for The Beatles, Up Against It, and Bernard-Marie Koltès are both playwrights.\").titles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Saving programs in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", - "\n", - "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", - "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", - "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", - "\n", - "To save the program in MLflow, run the following code:\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "# Start an MLflow Run and save the program\n", - "with mlflow.start_run(run_name=\"optimized\"):\n", - " model_info = mlflow.dspy.log_model(\n", - " optimized,\n", - " artifact_path=\"model\", # Any name to save the program in MLflow\n", - " )\n", - "\n", - "# Load the program back from MLflow\n", - "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jun2024_py310", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "jun2024_py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/docs/tutorials/rag/index.ipynb b/docs/docs/tutorials/rag/index.ipynb index 79c9374f87..aba88cbdf1 100644 --- a/docs/docs/tutorials/rag/index.ipynb +++ b/docs/docs/tutorials/rag/index.ipynb @@ -1,1497 +1,1497 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial: Retrieval-Augmented Generation (RAG)\n", - "\n", - "Let's walk through a quick example of **basic question answering** with and without **retrieval-augmented generation** (RAG) in DSPy. Specifically, let's build **a system for answering Tech questions**, e.g. about Linux or iPhone apps.\n", - "\n", - "Install the latest DSPy via `pip install -U dspy` and follow along. If you're looking instead for a conceptual overview of DSPy, this [recent lecture](https://www.youtube.com/live/JEMYuzrKLUw) is a good place to start." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Configuring the DSPy environment.\n", - "\n", - "Let's tell DSPy that we will use OpenAI's `gpt-4o-mini` in our modules. To authenticate, DSPy will look into your `OPENAI_API_KEY`. You can easily swap this out for [other providers or local models](https://github.com/stanfordnlp/dspy/blob/main/examples/migration.ipynb).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", - "\n", - "### MLflow DSPy Integration\n", - "\n", - "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", - "\n", - "![MLflow Trace](./mlflow-tracing-rag.png)\n", - "\n", - "1. Install MLflow\n", - "\n", - "```bash\n", - "%pip install mlflow>=2.20\n", - "```\n", - "\n", - "2. Start MLflow UI in a separate terminal\n", - "```bash\n", - "mlflow ui --port 5000\n", - "```\n", - "\n", - "3. Connect the notebook to MLflow\n", - "```python\n", - "import mlflow\n", - "\n", - "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", - "mlflow.set_experiment(\"DSPy\")\n", - "```\n", - "\n", - "4. Enabling tracing.\n", - "```python\n", - "mlflow.dspy.autolog()\n", - "```\n", - "\n", - "Once you have completed the steps above, you can see traces for each program execution on the notebook. They provide great visibility into the model's behavior and helps you understand the DSPy's concepts better throughout the tutorial.\n", - "\n", - "To kearn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "lm = dspy.LM('openai/gpt-4o-mini')\n", - "dspy.configure(lm=lm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring some basic DSPy Modules.\n", - "\n", - "You can always prompt the LM directly via `lm(prompt=\"prompt\")` or `lm(messages=[...])`. However, DSPy gives you `Modules` as a better way to define your LM functions.\n", - "\n", - "The simplest module is `dspy.Predict`. It takes a [DSPy Signature](/learn/programming/signatures), i.e. a structured input/output schema, and gives you back a callable function for the behavior you specified. Let's use the \"in-line\" notation for signatures to declare a module that takes a `question` (of type `str`) as input and produces a `response` as an output." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "In Linux, \"high memory\" and \"low memory\" refer to different regions of the system's memory address space, particularly in the context of 32-bit architectures.\n", - "\n", - "- **Low Memory**: This typically refers to the memory that is directly accessible by the kernel. In a 32-bit system, this is usually the first 896 MB of RAM (from 0 to 896 MB). The kernel can directly map this memory, making it faster for the kernel to access and manage. Low memory is used for kernel data structures and for user processes that require direct access to memory.\n", - "\n", - "- **High Memory**: This refers to the memory above the low memory limit, which is not directly accessible by the kernel in a 32-bit system. This area is typically above 896 MB. The kernel cannot directly access this memory without using special mechanisms, such as mapping it into the kernel's address space when needed. High memory is used for user processes that require more memory than what is available in low memory.\n", - "\n", - "In summary, low memory is directly accessible by the kernel, while high memory requires additional steps for the kernel to access it, especially in 32-bit systems. In 64-bit systems, this distinction is less significant as the kernel can address a much larger memory space directly.\n" - ] - } - ], - "source": [ - "qa = dspy.Predict('question: str -> response: str')\n", - "response = qa(question=\"what are high memory and low memory on linux?\")\n", - "\n", - "print(response.response)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the variable names we specified in the signature defined our input and output argument names and their role.\n", - "\n", - "Now, what did DSPy do to build this `qa` module? Nothing fancy in this example, yet. The module passed your signature, LM, and inputs to an Adapter, which is a layer that handles structuring the inputs and parsing structured outputs to fit your signature.\n", - "\n", - "Let's see it directly. You can inspect the `n` last prompts sent by DSPy easily. Alternatively, if you enabled MLflow Tracing above, you can see the full LLM interactions for each program execution in a tree view.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial: Retrieval-Augmented Generation (RAG)\n", + "\n", + "Let's walk through a quick example of **basic question answering** with and without **retrieval-augmented generation** (RAG) in DSPy. Specifically, let's build **a system for answering Tech questions**, e.g. about Linux or iPhone apps.\n", + "\n", + "Install the latest DSPy via `pip install -U dspy` and follow along. If you're looking instead for a conceptual overview of DSPy, this [recent lecture](https://www.youtube.com/live/JEMYuzrKLUw) is a good place to start." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-11-23T23:16:35.966534]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `question` (str)\n", - "\n", - "Your output fields are:\n", - "1. `response` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## question ## ]]\n", - "{question}\n", - "\n", - "[[ ## response ## ]]\n", - "{response}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `question`, produce the fields `response`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## question ## ]]\n", - "what are high memory and low memory on linux?\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## response ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## response ## ]]\n", - "In Linux, \"high memory\" and \"low memory\" refer to different regions of the system's memory address space, particularly in the context of 32-bit architectures.\n", - "\n", - "- **Low Memory**: This typically refers to the memory that is directly accessible by the kernel. In a 32-bit system, this is usually the first 896 MB of RAM (from 0 to 896 MB). The kernel can directly map this memory, making it faster for the kernel to access and manage. Low memory is used for kernel data structures and for user processes that require direct access to memory.\n", - "\n", - "- **High Memory**: This refers to the memory above the low memory limit, which is not directly accessible by the kernel in a 32-bit system. This area is typically above 896 MB. The kernel cannot directly access this memory without using special mechanisms, such as mapping it into the kernel's address space when needed. High memory is used for user processes that require more memory than what is available in low memory.\n", - "\n", - "In summary, low memory is directly accessible by the kernel, while high memory requires additional steps for the kernel to access it, especially in 32-bit systems. In 64-bit systems, this distinction is less significant as the kernel can address a much larger memory space directly.\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "dspy.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DSPy has various built-in modules, e.g. `dspy.ChainOfThought`, `dspy.ProgramOfThought`, and `dspy.ReAct`. These are interchangeable with basic `dspy.Predict`: they take your signature, which is specific to your task, and they apply general-purpose prompting techniques and inference-time strategies to it.\n", - "\n", - "For example, `dspy.ChainOfThought` is an easy way to elicit `reasoning` out of your LM before it commits to the outputs requested in your signature.\n", - "\n", - "In the example below, we'll omit `str` types (as the default type is string). You should feel free to experiment with other fields and types, e.g. try `topics: list[str]` or `is_realistic: bool`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Configuring the DSPy environment.\n", + "\n", + "Let's tell DSPy that we will use OpenAI's `gpt-4o-mini` in our modules. To authenticate, DSPy will look into your `OPENAI_API_KEY`. You can easily swap this out for [other providers or local models](https://github.com/stanfordnlp/dspy/blob/main/examples/migration.ipynb).\n" + ] + }, { - "data": { - "text/plain": [ - "Prediction(\n", - " reasoning='The placement of curly braces on their own line depends on the coding style and conventions being followed. In some programming languages and style guides, such as the Allman style, curly braces are placed on their own line to enhance readability. In contrast, other styles, like K&R style, place the opening brace on the same line as the control statement. Ultimately, it is a matter of personal or team preference, and consistency within a project is key.',\n", - " response='Curly braces can appear on their own line depending on the coding style you are following. If you prefer a style that enhances readability, such as the Allman style, then yes, they should be on their own line. However, if you are following a different style, like K&R, they may not need to be. Consistency is important, so choose a style and stick with it.'\n", - ")" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Recommended: Set up MLflow Tracing to understand what's happening under the hood.\n", + "\n", + "### MLflow DSPy Integration\n", + "\n", + "MLflow is an LLMOps tool that natively integrates with DSPy and offer explainability and experiment tracking. In this tutorial, you can use MLflow to visualize prompts and optimization progress as traces to understand the DSPy's behavior better. You can set up MLflow easily by following the four steps below.\n", + "\n", + "![MLflow Trace](./mlflow-tracing-rag.png)\n", + "\n", + "1. Install MLflow\n", + "\n", + "```bash\n", + "%pip install mlflow>=2.20\n", + "```\n", + "\n", + "2. Start MLflow UI in a separate terminal\n", + "```bash\n", + "mlflow ui --port 5000\n", + "```\n", + "\n", + "3. Connect the notebook to MLflow\n", + "```python\n", + "import mlflow\n", + "\n", + "mlflow.set_tracking_uri(\"http://localhost:5000\")\n", + "mlflow.set_experiment(\"DSPy\")\n", + "```\n", + "\n", + "4. Enabling tracing.\n", + "```python\n", + "mlflow.dspy.autolog()\n", + "```\n", + "\n", + "Once you have completed the steps above, you can see traces for each program execution on the notebook. They provide great visibility into the model's behavior and helps you understand the DSPy's concepts better throughout the tutorial.\n", + "\n", + "To kearn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cot = dspy.ChainOfThought('question -> response')\n", - "cot(question=\"should curly braces appear on their own line?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Interestingly, asking for reasoning can make the output `response` shorter in this case. Is this a good thing or a bad thing? It depends on what you need: there's no free lunch, but DSPy gives you the tools to experiment with different strategies extremely quickly.\n", - "\n", - "By the way, `dspy.ChainOfThought` is implemented in DSPy, using `dspy.Predict`. This is a good place to `dspy.inspect_history` if you're curious.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using DSPy well involves evaluation and iterative development.\n", - "\n", - "You already know a lot about DSPy at this point. If all you want is quick scripting, this much of DSPy already enables a lot. Sprinkling DSPy signatures and modules into your Python control flow is a pretty ergonomic way to just get stuff done with LMs.\n", - "\n", - "That said, you're likely here because you want to build a high-quality system and improve it over time. The way to do that in DSPy is to iterate fast by evaluating the quality of your system and using DSPy's powerful tools, e.g. Optimizers.\n", - "\n", - "## Manipulating Examples in DSPy.\n", - "\n", - "To measure the quality of your DSPy system, you need (1) a bunch of input values, like `question`s for example, and (2) a `metric` that can score the quality of an output from your system. Metrics vary widely. Some metrics need ground-truth labels of ideal outputs, e.g. for classification or question answering. Other metrics are self-supervised, e.g. checking faithfulness or lack of hallucination, perhaps using a DSPy program as a judge of these qualities.\n", - "\n", - "Let's load a dataset of questions and their (pretty long) gold answers. Since we started this notebook with the goal of building **a system for answering Tech questions**, we obtained a bunch of StackExchange-based questions and their correct answers from the [RAG-QA Arena](https://arxiv.org/abs/2407.13998) dataset.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import ujson\n", - "from dspy.utils import download\n", - "\n", - "# Download question--answer pairs from the RAG-QA Arena \"Tech\" dataset.\n", - "download(\"https://huggingface.co/dspy/cache/resolve/main/ragqa_arena_tech_examples.jsonl\")\n", - "\n", - "with open(\"ragqa_arena_tech_examples.jsonl\") as f:\n", - " data = [ujson.loads(line) for line in f]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "{'question': 'why igp is used in mpls?',\n", - " 'response': \"An IGP exchanges routing prefixes between gateways/routers. \\nWithout a routing protocol, you'd have to configure each route on every router and you'd have no dynamic updates when routes change because of link failures. \\nFuthermore, within an MPLS network, an IGP is vital for advertising the internal topology and ensuring connectivity for MP-BGP inside the network.\",\n", - " 'gold_doc_ids': [2822, 2823]}" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "\n", + "lm = dspy.LM('openai/gpt-4o-mini')\n", + "dspy.configure(lm=lm)" ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Inspect one datapoint.\n", - "data[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Given a simple dict like this, let's create a list of `dspy.Example`s, which is the datatype that carries training (or test) datapoints in DSPy.\n", - "\n", - "When you build a `dspy.Example`, you should generally specify `.with_inputs(\"field1\", \"field2\", ...)` to indicate which fields are inputs. The other fields are treated as labels or metadata.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "Example({'question': 'why are my text messages coming up as maybe?', 'response': 'This is part of the Proactivity features new with iOS 9: It looks at info in emails to see if anyone with this number sent you an email and if it finds the phone number associated with a contact from your email, it will show you \"Maybe\". \\n\\nHowever, it has been suggested there is a bug in iOS 11.2 that can result in \"Maybe\" being displayed even when \"Find Contacts in Other Apps\" is disabled.', 'gold_doc_ids': [3956, 3957, 8034]}) (input_keys={'question'})" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring some basic DSPy Modules.\n", + "\n", + "You can always prompt the LM directly via `lm(prompt=\"prompt\")` or `lm(messages=[...])`. However, DSPy gives you `Modules` as a better way to define your LM functions.\n", + "\n", + "The simplest module is `dspy.Predict`. It takes a [DSPy Signature](/learn/programming/signatures), i.e. a structured input/output schema, and gives you back a callable function for the behavior you specified. Let's use the \"in-line\" notation for signatures to declare a module that takes a `question` (of type `str`) as input and produces a `response` as an output." ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = [dspy.Example(**d).with_inputs('question') for d in data]\n", - "\n", - "# Let's pick an `example` here from the data.\n", - "example = data[2]\n", - "example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Now, let's divide the data into:\n", - "\n", - "- Training (and with it Validation) set:\n", - " - These are the splits you typically give to DSPy optimizers.\n", - " - Optimizers typically learn directly from the training examples and check their progress using the validation examples.\n", - " - It's good to have 30--300 examples for training and validation each.\n", - " - For prompt optimizers in particular, it's often better to pass _more_ validation than training.\n", - " - Below, we'll use 200 in total. MIPROv2 will split them into 20% training and 80% validation if you don't pass a valset.\n", - "\n", - "- Development and Test sets: The rest, typically on the order of 30--1000, can be used for:\n", - " - development (i.e., you can inspect them as you iterate on your system) and\n", - " - testing (final held-out evaluation).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "(200, 300, 500)" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In Linux, \"high memory\" and \"low memory\" refer to different regions of the system's memory address space, particularly in the context of 32-bit architectures.\n", + "\n", + "- **Low Memory**: This typically refers to the memory that is directly accessible by the kernel. In a 32-bit system, this is usually the first 896 MB of RAM (from 0 to 896 MB). The kernel can directly map this memory, making it faster for the kernel to access and manage. Low memory is used for kernel data structures and for user processes that require direct access to memory.\n", + "\n", + "- **High Memory**: This refers to the memory above the low memory limit, which is not directly accessible by the kernel in a 32-bit system. This area is typically above 896 MB. The kernel cannot directly access this memory without using special mechanisms, such as mapping it into the kernel's address space when needed. High memory is used for user processes that require more memory than what is available in low memory.\n", + "\n", + "In summary, low memory is directly accessible by the kernel, while high memory requires additional steps for the kernel to access it, especially in 32-bit systems. In 64-bit systems, this distinction is less significant as the kernel can address a much larger memory space directly.\n" + ] + } + ], + "source": [ + "qa = dspy.Predict('question: str -> response: str')\n", + "response = qa(question=\"what are high memory and low memory on linux?\")\n", + "\n", + "print(response.response)" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import random\n", - "\n", - "random.Random(0).shuffle(data)\n", - "trainset, devset, testset = data[:200], data[200:500], data[500:1000]\n", - "\n", - "len(trainset), len(devset), len(testset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluation in DSPy.\n", - "\n", - "What kind of metric can suit our question-answering task? There are many choices, but since the answers are long, we may ask: How well does the system response _cover_ all key facts in the gold response? And the other way around, how well is the system response _not saying things_ that aren't in the gold response?\n", - "\n", - "That metric is essentially a **semantic F1**, so let's load a `SemanticF1` metric from DSPy. This metric is actually implemented as a [very simple DSPy module](https://github.com/stanfordnlp/dspy/blob/main/dspy/evaluate/auto_evaluation.py#L21) using whatever LM we're working with." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: \t why are my text messages coming up as maybe?\n", - "\n", - "Gold Response: \t This is part of the Proactivity features new with iOS 9: It looks at info in emails to see if anyone with this number sent you an email and if it finds the phone number associated with a contact from your email, it will show you \"Maybe\". \n", - "\n", - "However, it has been suggested there is a bug in iOS 11.2 that can result in \"Maybe\" being displayed even when \"Find Contacts in Other Apps\" is disabled.\n", - "\n", - "Predicted Response: \t Your text messages are showing up as \"maybe\" because your messaging app is uncertain about the sender's identity. This typically occurs when the sender's number is not saved in your contacts or if the message is from an unknown number. To resolve this, you can save the contact in your address book or check the message settings in your app.\n", - "\n", - "Semantic F1 Score: 0.33\n" - ] - } - ], - "source": [ - "from dspy.evaluate import SemanticF1\n", - "\n", - "# Instantiate the metric.\n", - "metric = SemanticF1(decompositional=True)\n", - "\n", - "# Produce a prediction from our `cot` module, using the `example` above as input.\n", - "pred = cot(**example.inputs())\n", - "\n", - "# Compute the metric score for the prediction.\n", - "score = metric(example, pred)\n", - "\n", - "print(f\"Question: \\t {example.question}\\n\")\n", - "print(f\"Gold Response: \\t {example.response}\\n\")\n", - "print(f\"Predicted Response: \\t {pred.response}\\n\")\n", - "print(f\"Semantic F1 Score: {score:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "The final DSPy module call above actually happens inside `metric`. You might be curious how it measured the semantic F1 for this example.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the variable names we specified in the signature defined our input and output argument names and their role.\n", + "\n", + "Now, what did DSPy do to build this `qa` module? Nothing fancy in this example, yet. The module passed your signature, LM, and inputs to an Adapter, which is a layer that handles structuring the inputs and parsing structured outputs to fit your signature.\n", + "\n", + "Let's see it directly. You can inspect the `n` last prompts sent by DSPy easily. Alternatively, if you enabled MLflow Tracing above, you can see the full LLM interactions for each program execution in a tree view.\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-11-23T23:16:36.149518]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `question` (str)\n", - "2. `ground_truth` (str)\n", - "3. `system_response` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `ground_truth_key_ideas` (str): enumeration of key ideas in the ground truth\n", - "3. `system_response_key_ideas` (str): enumeration of key ideas in the system response\n", - "4. `discussion` (str): discussion of the overlap between ground truth and system response\n", - "5. `recall` (float): fraction (out of 1.0) of ground truth covered by the system response\n", - "6. `precision` (float): fraction (out of 1.0) of system response covered by the ground truth\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## question ## ]]\n", - "{question}\n", - "\n", - "[[ ## ground_truth ## ]]\n", - "{ground_truth}\n", - "\n", - "[[ ## system_response ## ]]\n", - "{system_response}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## ground_truth_key_ideas ## ]]\n", - "{ground_truth_key_ideas}\n", - "\n", - "[[ ## system_response_key_ideas ## ]]\n", - "{system_response_key_ideas}\n", - "\n", - "[[ ## discussion ## ]]\n", - "{discussion}\n", - "\n", - "[[ ## recall ## ]]\n", - "{recall} # note: the value you produce must be a single float value\n", - "\n", - "[[ ## precision ## ]]\n", - "{precision} # note: the value you produce must be a single float value\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Compare a system's response to the ground truth to compute recall and precision of key ideas.\n", - " You will first enumerate key ideas in each response, discuss their overlap, and then report recall and precision.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## question ## ]]\n", - "why are my text messages coming up as maybe?\n", - "\n", - "[[ ## ground_truth ## ]]\n", - "This is part of the Proactivity features new with iOS 9: It looks at info in emails to see if anyone with this number sent you an email and if it finds the phone number associated with a contact from your email, it will show you \"Maybe\". \n", - "\n", - "However, it has been suggested there is a bug in iOS 11.2 that can result in \"Maybe\" being displayed even when \"Find Contacts in Other Apps\" is disabled.\n", - "\n", - "[[ ## system_response ## ]]\n", - "Your text messages are showing up as \"maybe\" because your messaging app is uncertain about the sender's identity. This typically occurs when the sender's number is not saved in your contacts or if the message is from an unknown number. To resolve this, you can save the contact in your address book or check the message settings in your app.\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## ground_truth_key_ideas ## ]]`, then `[[ ## system_response_key_ideas ## ]]`, then `[[ ## discussion ## ]]`, then `[[ ## recall ## ]]` (must be formatted as a valid Python float), then `[[ ## precision ## ]]` (must be formatted as a valid Python float), and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "The system response provides a general explanation for why text messages may show up as \"maybe,\" focusing on the uncertainty of the sender's identity. However, it does not mention the specific iOS features or the potential bug in iOS 11.2 that are highlighted in the ground truth. The ground truth explains that this feature is part of iOS 9's Proactivity features and mentions a specific bug in iOS 11.2, which is not addressed in the system response.\n", - "\n", - "[[ ## ground_truth_key_ideas ## ]]\n", - "1. Proactivity features introduced in iOS 9.\n", - "2. The system checks emails to associate phone numbers with contacts.\n", - "3. \"Maybe\" is displayed if the number is not saved in contacts.\n", - "4. Mention of a bug in iOS 11.2 causing \"Maybe\" to appear incorrectly.\n", - "\n", - "[[ ## system_response_key_ideas ## ]]\n", - "1. Text messages show up as \"maybe\" due to uncertainty about the sender's identity.\n", - "2. Occurs when the sender's number is not saved in contacts or is from an unknown number.\n", - "3. Suggests saving the contact or checking message settings.\n", - "\n", - "[[ ## discussion ## ]]\n", - "There is some overlap between the ground truth and the system response regarding the uncertainty of the sender's identity and the suggestion to save the contact. However, the system response lacks specific details about the iOS features and the bug mentioned in the ground truth. The ground truth provides a more comprehensive explanation of the \"maybe\" feature, while the system response is more general and does not address the iOS version specifics.\n", - "\n", - "[[ ## recall ## ]]\n", - "0.25\n", - "\n", - "[[ ## precision ## ]]\n", - "0.5\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "dspy.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For evaluation, you could use the metric above in a simple loop and just average the score. But for nice parallelism and utilities, we can rely on `dspy.Evaluate`." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-11-23T23:16:35.966534]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `question` (str)\n", + "\n", + "Your output fields are:\n", + "1. `response` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## question ## ]]\n", + "{question}\n", + "\n", + "[[ ## response ## ]]\n", + "{response}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `question`, produce the fields `response`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## question ## ]]\n", + "what are high memory and low memory on linux?\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## response ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## response ## ]]\n", + "In Linux, \"high memory\" and \"low memory\" refer to different regions of the system's memory address space, particularly in the context of 32-bit architectures.\n", + "\n", + "- **Low Memory**: This typically refers to the memory that is directly accessible by the kernel. In a 32-bit system, this is usually the first 896 MB of RAM (from 0 to 896 MB). The kernel can directly map this memory, making it faster for the kernel to access and manage. Low memory is used for kernel data structures and for user processes that require direct access to memory.\n", + "\n", + "- **High Memory**: This refers to the memory above the low memory limit, which is not directly accessible by the kernel in a 32-bit system. This area is typically above 896 MB. The kernel cannot directly access this memory without using special mechanisms, such as mapping it into the kernel's address space when needed. High memory is used for user processes that require more memory than what is available in low memory.\n", + "\n", + "In summary, low memory is directly accessible by the kernel, while high memory requires additional steps for the kernel to access it, especially in 32-bit systems. In 64-bit systems, this distinction is less significant as the kernel can address a much larger memory space directly.\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history(n=1)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 125.68 / 300 (41.9%): 100%|██████████| 300/300 [00:00<00:00, 666.96it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DSPy has various built-in modules, e.g. `dspy.ChainOfThought`, `dspy.ProgramOfThought`, and `dspy.ReAct`. These are interchangeable with basic `dspy.Predict`: they take your signature, which is specific to your task, and they apply general-purpose prompting techniques and inference-time strategies to it.\n", + "\n", + "For example, `dspy.ChainOfThought` is an easy way to elicit `reasoning` out of your LM before it commits to the outputs requested in your signature.\n", + "\n", + "In the example below, we'll omit `str` types (as the default type is string). You should feel free to experiment with other fields and types, e.g. try `topics: list[str]` or `is_realistic: bool`.\n" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/11/23 23:16:36 INFO dspy.evaluate.evaluate: Average Metric: 125.68228336477591 / 300 (41.9%)\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " reasoning='The placement of curly braces on their own line depends on the coding style and conventions being followed. In some programming languages and style guides, such as the Allman style, curly braces are placed on their own line to enhance readability. In contrast, other styles, like K&R style, place the opening brace on the same line as the control statement. Ultimately, it is a matter of personal or team preference, and consistency within a project is key.',\n", + " response='Curly braces can appear on their own line depending on the coding style you are following. If you prefer a style that enhances readability, such as the Allman style, then yes, they should be on their own line. However, if you are following a different style, like K&R, they may not need to be. Consistency is important, so choose a style and stick with it.'\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cot = dspy.ChainOfThought('question -> response')\n", + "cot(question=\"should curly braces appear on their own line?\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Interestingly, asking for reasoning can make the output `response` shorter in this case. Is this a good thing or a bad thing? It depends on what you need: there's no free lunch, but DSPy gives you the tools to experiment with different strategies extremely quickly.\n", + "\n", + "By the way, `dspy.ChainOfThought` is implemented in DSPy, using `dspy.Predict`. This is a good place to `dspy.inspect_history` if you're curious.\n" + ] }, { - "data": { - "text/html": [ - "
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questionexample_responsegold_doc_idsreasoningpred_responseSemanticF1
0when to use c over c++, and c++ over c?If you are equally familiar with both C++ and C, it's advisable to...[733]C and C++ are both powerful programming languages, but they serve ...Use C when you need low-level access to memory, require high perfo...
1should images be stored in a git repository?One viewpoint expresses that there is no significant downside, esp...[6253, 6254, 6275, 6278, 8215]Storing images in a Git repository can be beneficial for version c...Images can be stored in a Git repository, but it's important to co...✔️ [0.444]
\n", - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using DSPy well involves evaluation and iterative development.\n", + "\n", + "You already know a lot about DSPy at this point. If all you want is quick scripting, this much of DSPy already enables a lot. Sprinkling DSPy signatures and modules into your Python control flow is a pretty ergonomic way to just get stuff done with LMs.\n", + "\n", + "That said, you're likely here because you want to build a high-quality system and improve it over time. The way to do that in DSPy is to iterate fast by evaluating the quality of your system and using DSPy's powerful tools, e.g. Optimizers.\n", + "\n", + "## Manipulating Examples in DSPy.\n", + "\n", + "To measure the quality of your DSPy system, you need (1) a bunch of input values, like `question`s for example, and (2) a `metric` that can score the quality of an output from your system. Metrics vary widely. Some metrics need ground-truth labels of ideal outputs, e.g. for classification or question answering. Other metrics are self-supervised, e.g. checking faithfulness or lack of hallucination, perhaps using a DSPy program as a judge of these qualities.\n", + "\n", + "Let's load a dataset of questions and their (pretty long) gold answers. Since we started this notebook with the goal of building **a system for answering Tech questions**, we obtained a bunch of StackExchange-based questions and their correct answers from the [RAG-QA Arena](https://arxiv.org/abs/2407.13998) dataset.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import ujson\n", + "from dspy.utils import download\n", + "\n", + "# Download question--answer pairs from the RAG-QA Arena \"Tech\" dataset.\n", + "download(\"https://huggingface.co/dspy/cache/resolve/main/ragqa_arena_tech_examples.jsonl\")\n", + "\n", + "with open(\"ragqa_arena_tech_examples.jsonl\") as f:\n", + " data = [ujson.loads(line) for line in f]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'question': 'why igp is used in mpls?',\n", + " 'response': \"An IGP exchanges routing prefixes between gateways/routers. \\nWithout a routing protocol, you'd have to configure each route on every router and you'd have no dynamic updates when routes change because of link failures. \\nFuthermore, within an MPLS network, an IGP is vital for advertising the internal topology and ensuring connectivity for MP-BGP inside the network.\",\n", + " 'gold_doc_ids': [2822, 2823]}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " question \\\n", - "0 when to use c over c++, and c++ over c? \n", - "1 should images be stored in a git repository? \n", - "\n", - " example_response \\\n", - "0 If you are equally familiar with both C++ and C, it's advisable to... \n", - "1 One viewpoint expresses that there is no significant downside, esp... \n", - "\n", - " gold_doc_ids \\\n", - "0 [733] \n", - "1 [6253, 6254, 6275, 6278, 8215] \n", - "\n", - " reasoning \\\n", - "0 C and C++ are both powerful programming languages, but they serve ... \n", - "1 Storing images in a Git repository can be beneficial for version c... \n", - "\n", - " pred_response \\\n", - "0 Use C when you need low-level access to memory, require high perfo... \n", - "1 Images can be stored in a Git repository, but it's important to co... \n", - "\n", - " SemanticF1 \n", - "0 \n", - "1 ✔️ [0.444] " + "source": [ + "# Inspect one datapoint.\n", + "data[0]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Given a simple dict like this, let's create a list of `dspy.Example`s, which is the datatype that carries training (or test) datapoints in DSPy.\n", + "\n", + "When you build a `dspy.Example`, you should generally specify `.with_inputs(\"field1\", \"field2\", ...)` to indicate which fields are inputs. The other fields are treated as labels or metadata.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'question': 'why are my text messages coming up as maybe?', 'response': 'This is part of the Proactivity features new with iOS 9: It looks at info in emails to see if anyone with this number sent you an email and if it finds the phone number associated with a contact from your email, it will show you \"Maybe\". \\n\\nHowever, it has been suggested there is a bug in iOS 11.2 that can result in \"Maybe\" being displayed even when \"Find Contacts in Other Apps\" is disabled.', 'gold_doc_ids': [3956, 3957, 8034]}) (input_keys={'question'})" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "data = [dspy.Example(**d).with_inputs('question') for d in data]\n", + "\n", + "# Let's pick an `example` here from the data.\n", + "example = data[2]\n", + "example" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "41.89" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now, let's divide the data into:\n", + "\n", + "- Training (and with it Validation) set:\n", + " - These are the splits you typically give to DSPy optimizers.\n", + " - Optimizers typically learn directly from the training examples and check their progress using the validation examples.\n", + " - It's good to have 30--300 examples for training and validation each.\n", + " - For prompt optimizers in particular, it's often better to pass _more_ validation than training.\n", + " - Below, we'll use 200 in total. MIPROv2 will split them into 20% training and 80% validation if you don't pass a valset.\n", + "\n", + "- Development and Test sets: The rest, typically on the order of 30--1000, can be used for:\n", + " - development (i.e., you can inspect them as you iterate on your system) and\n", + " - testing (final held-out evaluation).\n" ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Define an evaluator that we can re-use.\n", - "evaluate = dspy.Evaluate(devset=devset, metric=metric, num_threads=24,\n", - " display_progress=True, display_table=2)\n", - "\n", - "# Evaluate the Chain-of-Thought program.\n", - "evaluate(cot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Tracking Evaluation Results in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", - "\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "with mlflow.start_run(run_name=\"rag_evaluation\"):\n", - " evaluate = dspy.Evaluate(\n", - " devset=devset,\n", - " metric=metric,\n", - " num_threads=24,\n", - " display_progress=True,\n", - " # To record the outputs and detailed scores to MLflow\n", - " return_all_scores=True,\n", - " return_outputs=True,\n", - " )\n", - "\n", - " # Evaluate the program as usual\n", - " aggregated_score, outputs, all_scores = evaluate(cot)\n", - "\n", - "\n", - " # Log the aggregated score\n", - " mlflow.log_metric(\"semantic_f1_score\", aggregated_score)\n", - " # Log the detailed evaluation results as a table\n", - " mlflow.log_table(\n", - " {\n", - " \"Question\": [example.question for example in eval_set],\n", - " \"Gold Response\": [example.response for example in eval_set],\n", - " \"Predicted Response\": outputs,\n", - " \"Semantic F1 Score\": all_scores,\n", - " },\n", - " artifact_file=\"eval_results.json\",\n", - " )\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far, we built a very simple chain-of-thought module for question answering and evaluated it on a small dataset.\n", - "\n", - "Can we do better? In the rest of this guide, we will build a retrieval-augmented generation (RAG) program in DSPy for the same task. We'll see how this can boost the score substantially, then we'll use one of the DSPy Optimizers to _compile_ our RAG program to higher-quality prompts, raising our scores even more." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic Retrieval-Augmented Generation (RAG).\n", - "\n", - "First, let's download the corpus data that we will use for RAG search. An older version of this tutorial used the full (650,000 document) corpus. To make this very fast and cheap to run, we've downsampled the corpus to just 28,000 documents." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "download(\"https://huggingface.co/dspy/cache/resolve/main/ragqa_arena_tech_corpus.jsonl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up your system's retriever.\n", - "\n", - "As far as DSPy is concerned, you can plug in any Python code for calling tools or retrievers. Here, we'll just use OpenAI Embeddings and do top-K search locally, just for convenience.\n", - "\n", - "**Note:** The step below will require that you either do `pip install -U faiss-cpu` or pass `brute_force_threshold=30_000` to `dspy.retrievers.Embeddings` to avoid faiss." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# %pip install -U faiss-cpu # or faiss-gpu if you have a GPU" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded 28436 documents. Will encode them below.\n", - "Training a 32-byte FAISS index with 337 partitions, based on 28436 x 512-dim embeddings\n" - ] - } - ], - "source": [ - "max_characters = 6000 # for truncating >99th percentile of documents\n", - "topk_docs_to_retrieve = 5 # number of documents to retrieve per search query\n", - "\n", - "with open(\"ragqa_arena_tech_corpus.jsonl\") as f:\n", - " corpus = [ujson.loads(line)['text'][:max_characters] for line in f]\n", - " print(f\"Loaded {len(corpus)} documents. Will encode them below.\")\n", - "\n", - "embedder = dspy.Embedder('openai/text-embedding-3-small', dimensions=512)\n", - "search = dspy.retrievers.Embeddings(embedder=embedder, corpus=corpus, k=topk_docs_to_retrieve)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Build your first RAG Module.\n", - "\n", - "In the previous guide, we looked at individual DSPy modules in isolation, e.g. `dspy.Predict(\"question -> answer\")`.\n", - "\n", - "What if we want to build a DSPy _program_ that has multiple steps? The syntax below with `dspy.Module` allows you to connect a few pieces together, in this case, our retriever and a generation module, so the whole system can be optimized.\n", - "\n", - "Concretely, in the `__init__` method, you declare any sub-module you'll need, which in this case is just a `dspy.ChainOfThought('context, question -> response')` module that takes retrieved context, a question, and produces a response. In the `forward` method, you simply express any Python control flow you like, possibly using your modules. In this case, we first invoke the `search` function defined earlier and then invoke the `self.respond` ChainOfThought module.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "class RAG(dspy.Module):\n", - " def __init__(self):\n", - " self.respond = dspy.ChainOfThought('context, question -> response')\n", - "\n", - " def forward(self, question):\n", - " context = search(question).passages\n", - " return self.respond(context=context, question=question)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Let's use the RAG module.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200, 300, 500)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "random.Random(0).shuffle(data)\n", + "trainset, devset, testset = data[:200], data[200:500], data[500:1000]\n", + "\n", + "len(trainset), len(devset), len(testset)" + ] + }, { - "data": { - "text/plain": [ - "Prediction(\n", - " reasoning=\"High Memory and Low Memory in Linux refer to two segments of the kernel's memory space. Low Memory is the portion of memory that the kernel can access directly and is statically mapped at boot time. This area is typically used for kernel data structures and is always accessible to the kernel. High Memory, on the other hand, is not permanently mapped in the kernel's address space, meaning that the kernel cannot access it directly without first mapping it into its address space. High Memory is used for user-space applications and temporary data buffers. The distinction allows for better memory management and security, as user-space applications cannot directly access kernel-space memory.\",\n", - " response=\"In Linux, High Memory refers to the segment of memory that is not permanently mapped in the kernel's address space, which means the kernel must map it temporarily to access it. This area is typically used for user-space applications and temporary data buffers. Low Memory, in contrast, is the portion of memory that the kernel can access directly and is statically mapped at boot time. It is used for kernel data structures and is always accessible to the kernel. This separation enhances security by preventing user-space applications from accessing kernel-space memory directly.\"\n", - ")" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation in DSPy.\n", + "\n", + "What kind of metric can suit our question-answering task? There are many choices, but since the answers are long, we may ask: How well does the system response _cover_ all key facts in the gold response? And the other way around, how well is the system response _not saying things_ that aren't in the gold response?\n", + "\n", + "That metric is essentially a **semantic F1**, so let's load a `SemanticF1` metric from DSPy. This metric is actually implemented as a [very simple DSPy module](https://github.com/stanfordnlp/dspy/blob/main/dspy/evaluate/auto_evaluation.py#L21) using whatever LM we're working with." ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rag = RAG()\n", - "rag(question=\"what are high memory and low memory on linux?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[34m[2024-11-23T23:16:49.175612]\u001b[0m\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `context` (str)\n", - "2. `question` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `response` (str)\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## context ## ]]\n", - "{context}\n", - "\n", - "[[ ## question ## ]]\n", - "{question}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## response ## ]]\n", - "{response}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `context`, `question`, produce the fields `response`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## context ## ]]\n", - "[1] «As far as I remember, High Memory is used for application space and Low Memory for the kernel. Advantage is that (user-space) applications cant access kernel-space memory.»\n", - "[2] «HIGHMEM is a range of kernels memory space, but it is NOT memory you access but its a place where you put what you want to access. A typical 32bit Linux virtual memory map is like: 0x00000000-0xbfffffff: user process (3GB) 0xc0000000-0xffffffff: kernel space (1GB) (CPU-specific vector and whatsoever are ignored here). Linux splits the 1GB kernel space into 2 pieces, LOWMEM and HIGHMEM. The split varies from installation to installation. If an installation chooses, say, 512MB-512MB for LOW and HIGH mems, the 512MB LOWMEM (0xc0000000-0xdfffffff) is statically mapped at the kernel boot time; usually the first so many bytes of the physical memory is used for this so that virtual and physical addresses in this range have a constant offset of, say, 0xc0000000. On the other hand, the latter 512MB (HIGHMEM) has no static mapping (although you could leave pages semi-permanently mapped there, but you must do so explicitly in your driver code). Instead, pages are temporarily mapped and unmapped here so that virtual and physical addresses in this range have no consistent mapping. Typical uses of HIGHMEM include single-time data buffers.»\n", - "[3] «This is relevant to the Linux kernel; Im not sure how any Unix kernel handles this. The High Memory is the segment of memory that user-space programs can address. It cannot touch Low Memory. Low Memory is the segment of memory that the Linux kernel can address directly. If the kernel must access High Memory, it has to map it into its own address space first. There was a patch introduced recently that lets you control where the segment is. The tradeoff is that you can take addressable memory away from user space so that the kernel can have more memory that it does not have to map before using. Additional resources: http://tldp.org/HOWTO/KernelAnalysis-HOWTO-7.html http://linux-mm.org/HighMemory»\n", - "[4] «The first reference to turn to is Linux Device Drivers (available both online and in book form), particularly chapter 15 which has a section on the topic. In an ideal world, every system component would be able to map all the memory it ever needs to access. And this is the case for processes on Linux and most operating systems: a 32-bit process can only access a little less than 2^32 bytes of virtual memory (in fact about 3GB on a typical Linux 32-bit architecture). It gets difficult for the kernel, which needs to be able to map the full memory of the process whose system call its executing, plus the whole physical memory, plus any other memory-mapped hardware device. So when a 32-bit kernel needs to map more than 4GB of memory, it must be compiled with high memory support. High memory is memory which is not permanently mapped in the kernels address space. (Low memory is the opposite: it is always mapped, so you can access it in the kernel simply by dereferencing a pointer.) When you access high memory from kernel code, you need to call kmap first, to obtain a pointer from a page data structure (struct page). Calling kmap works whether the page is in high or low memory. There is also kmap_atomic which has added constraints but is more efficient on multiprocessor machines because it uses finer-grained locking. The pointer obtained through kmap is a resource: it uses up address space. Once youve finished with it, you must call kunmap (or kunmap_atomic) to free that resource; then the pointer is no longer valid, and the contents of the page cant be accessed until you call kmap again.»\n", - "[5] «/proc/meminfo will tell you how free works, but /proc/kcore can tell you what the kernel uses. From the same page: /proc/kcore This file represents the physical memory of the system and is stored in the ELF core file format. With this pseudo-file, and an unstripped kernel (/usr/src/linux/vmlinux) binary, GDB can be used to examine the current state of any kernel data structures. The total length of the file is the size of physical memory (RAM) plus 4KB. /proc/meminfo This file reports statistics about memory usage on the system. It is used by free(1) to report the amount of free and used memory (both physical and swap) on the system as well as the shared memory and buffers used by the kernel. Each line of the file consists of a parameter name, followed by a colon, the value of the parameter, and an option unit of measurement (e.g., kB). The list below describes the parameter names and the format specifier required to read the field value. Except as noted below, all of the fields have been present since at least Linux 2.6.0. Some fileds are displayed only if the kernel was configured with various options; those dependencies are noted in the list. MemTotal %lu Total usable RAM (i.e., physical RAM minus a few reserved bits and the kernel binary code). MemFree %lu The sum of LowFree+HighFree. Buffers %lu Relatively temporary storage for raw disk blocks that shouldnt get tremendously large (20MB or so). Cached %lu In-memory cache for files read from the disk (the page cache). Doesnt include SwapCached. SwapCached %lu Memory that once was swapped out, is swapped back in but still also is in the swap file. (If memory pressure is high, these pages dont need to be swapped out again because they are already in the swap file. This saves I/O.) Active %lu Memory that has been used more recently and usually not reclaimed unless absolutely necessary. Inactive %lu Memory which has been less recently used. It is more eligible to be reclaimed for other purposes. Active(anon) %lu (since Linux 2.6.28) [To be documented.] Inactive(anon) %lu (since Linux 2.6.28) [To be documented.] Active(file) %lu (since Linux 2.6.28) [To be documented.] Inactive(file) %lu (since Linux 2.6.28) [To be documented.] Unevictable %lu (since Linux 2.6.28) (From Linux 2.6.28 to 2.6.30, CONFIG_UNEVICTABLE_LRU was required.) [To be documented.] Mlocked %lu (since Linux 2.6.28) (From Linux 2.6.28 to 2.6.30, CONFIG_UNEVICTABLE_LRU was required.) [To be documented.] HighTotal %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Total amount of highmem. Highmem is all memory above ~860MB of physical memory. Highmem areas are for use by user-space programs, or for the page cache. The kernel must use tricks to access this memory, making it slower to access than lowmem. HighFree %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Amount of free highmem. LowTotal %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Total amount of lowmem. Lowmem is memory which can be used for everything that highmem can be used for, but it is also available for the kernels use for its own data structures. Among many other things, it is where everything from Slab is allocated. Bad things happen when youre out of lowmem. LowFree %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Amount of free lowmem. MmapCopy %lu (since Linux 2.6.29) (CONFIG_MMU is required.) [To be documented.] SwapTotal %lu Total amount of swap space available. SwapFree %lu Amount of swap space that is currently unused. Dirty %lu Memory which is waiting to get written back to the disk. Writeback %lu Memory which is actively being written back to the disk. AnonPages %lu (since Linux 2.6.18) Non-file backed pages mapped into user-space page tables. Mapped %lu Files which have been mmaped, such as libraries. Shmem %lu (since Linux 2.6.32) [To be documented.] Slab %lu In-kernel data structures cache. SReclaimable %lu (since Linux 2.6.19) Part of Slab, that might be reclaimed, such as caches. SUnreclaim %lu (since Linux 2.6.19) Part of Slab, that cannot be reclaimed on memory pressure. KernelStack %lu (since Linux 2.6.32) Amount of memory allocated to kernel stacks. PageTables %lu (since Linux 2.6.18) Amount of memory dedicated to the lowest level of page tables. Quicklists %lu (since Linux 2.6.27) (CONFIG_QUICKLIST is required.) [To be documented.] NFS_Unstable %lu (since Linux 2.6.18) NFS pages sent to the server, but not yet committed to stable storage. Bounce %lu (since Linux 2.6.18) Memory used for block device bounce buffers. WritebackTmp %lu (since Linux 2.6.26) Memory used by FUSE for temporary writeback buffers. CommitLimit %lu (since Linux 2.6.10) Based on the overcommit ratio (vm.overcommit_ratio), this is the total amount of memory currently available to be allocated on the system. This limit is adhered to only if strict overcommit accounting is enabled (mode 2 in /proc/sys/vm/overcommit_ratio). The CommitLimit is calculated using the following formula: CommitLimit = ([total RAM pages] - [total huge TLB pages]) * overcommit_ratio / 100 + [total swap pages] For example, on a system with 1GB of physical RAM and 7GB of swap with a overcommit_ratio of 30, this formula yields a CommitLimit of 7.3GB. For more details, see the memory overcommit documentation in the kernel source file Documentation/vm/overcommit-accounting. Committed_AS %lu The amount of memory presently allocated on the system. The committed memory is a sum of all of the memory which has been allocated by processes, even if it has not been used by them as of yet. A process which allocates 1GB of memory (using malloc(3) or similar), but touches only 300MB of that memory will show up as using only 300MB of memory even if it has the address space allocated for the entire 1GB. This 1GB is memory which has been committed to by the VM and can be used at any time by the allocating application. With strict overcommit enabled on the system (mode 2 /proc/sys/vm/overcommit_memory), allocations w»\n", - "\n", - "[[ ## question ## ]]\n", - "what are high memory and low memory on linux?\n", - "\n", - "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## response ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "High Memory and Low Memory in Linux refer to two segments of the kernel's memory space. Low Memory is the portion of memory that the kernel can access directly and is statically mapped at boot time. This area is typically used for kernel data structures and is always accessible to the kernel. High Memory, on the other hand, is not permanently mapped in the kernel's address space, meaning that the kernel cannot access it directly without first mapping it into its address space. High Memory is used for user-space applications and temporary data buffers. The distinction allows for better memory management and security, as user-space applications cannot directly access kernel-space memory.\n", - "\n", - "[[ ## response ## ]]\n", - "In Linux, High Memory refers to the segment of memory that is not permanently mapped in the kernel's address space, which means the kernel must map it temporarily to access it. This area is typically used for user-space applications and temporary data buffers. Low Memory, in contrast, is the portion of memory that the kernel can access directly and is statically mapped at boot time. It is used for kernel data structures and is always accessible to the kernel. This separation enhances security by preventing user-space applications from accessing kernel-space memory directly.\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "dspy.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Earlier with a CoT module, we got around 40% in terms of semantic F1 on our `devset`. Would this `RAG` module score better?" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: \t why are my text messages coming up as maybe?\n", + "\n", + "Gold Response: \t This is part of the Proactivity features new with iOS 9: It looks at info in emails to see if anyone with this number sent you an email and if it finds the phone number associated with a contact from your email, it will show you \"Maybe\". \n", + "\n", + "However, it has been suggested there is a bug in iOS 11.2 that can result in \"Maybe\" being displayed even when \"Find Contacts in Other Apps\" is disabled.\n", + "\n", + "Predicted Response: \t Your text messages are showing up as \"maybe\" because your messaging app is uncertain about the sender's identity. This typically occurs when the sender's number is not saved in your contacts or if the message is from an unknown number. To resolve this, you can save the contact in your address book or check the message settings in your app.\n", + "\n", + "Semantic F1 Score: 0.33\n" + ] + } + ], + "source": [ + "from dspy.evaluate import SemanticF1\n", + "\n", + "# Instantiate the metric.\n", + "metric = SemanticF1(decompositional=True)\n", + "\n", + "# Produce a prediction from our `cot` module, using the `example` above as input.\n", + "pred = cot(**example.inputs())\n", + "\n", + "# Compute the metric score for the prediction.\n", + "score = metric(example, pred)\n", + "\n", + "print(f\"Question: \\t {example.question}\\n\")\n", + "print(f\"Gold Response: \\t {example.response}\\n\")\n", + "print(f\"Predicted Response: \\t {pred.response}\\n\")\n", + "print(f\"Semantic F1 Score: {score:.2f}\")" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 166.54 / 300 (55.5%): 100%|██████████| 300/300 [00:04<00:00, 61.40it/s] " - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The final DSPy module call above actually happens inside `metric`. You might be curious how it measured the semantic F1 for this example.\n", + "\n" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/11/23 23:16:54 INFO dspy.evaluate.evaluate: Average Metric: 166.53601368289284 / 300 (55.5%)\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-11-23T23:16:36.149518]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `question` (str)\n", + "2. `ground_truth` (str)\n", + "3. `system_response` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `ground_truth_key_ideas` (str): enumeration of key ideas in the ground truth\n", + "3. `system_response_key_ideas` (str): enumeration of key ideas in the system response\n", + "4. `discussion` (str): discussion of the overlap between ground truth and system response\n", + "5. `recall` (float): fraction (out of 1.0) of ground truth covered by the system response\n", + "6. `precision` (float): fraction (out of 1.0) of system response covered by the ground truth\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## question ## ]]\n", + "{question}\n", + "\n", + "[[ ## ground_truth ## ]]\n", + "{ground_truth}\n", + "\n", + "[[ ## system_response ## ]]\n", + "{system_response}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## ground_truth_key_ideas ## ]]\n", + "{ground_truth_key_ideas}\n", + "\n", + "[[ ## system_response_key_ideas ## ]]\n", + "{system_response_key_ideas}\n", + "\n", + "[[ ## discussion ## ]]\n", + "{discussion}\n", + "\n", + "[[ ## recall ## ]]\n", + "{recall} # note: the value you produce must be a single float value\n", + "\n", + "[[ ## precision ## ]]\n", + "{precision} # note: the value you produce must be a single float value\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Compare a system's response to the ground truth to compute recall and precision of key ideas.\n", + " You will first enumerate key ideas in each response, discuss their overlap, and then report recall and precision.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## question ## ]]\n", + "why are my text messages coming up as maybe?\n", + "\n", + "[[ ## ground_truth ## ]]\n", + "This is part of the Proactivity features new with iOS 9: It looks at info in emails to see if anyone with this number sent you an email and if it finds the phone number associated with a contact from your email, it will show you \"Maybe\". \n", + "\n", + "However, it has been suggested there is a bug in iOS 11.2 that can result in \"Maybe\" being displayed even when \"Find Contacts in Other Apps\" is disabled.\n", + "\n", + "[[ ## system_response ## ]]\n", + "Your text messages are showing up as \"maybe\" because your messaging app is uncertain about the sender's identity. This typically occurs when the sender's number is not saved in your contacts or if the message is from an unknown number. To resolve this, you can save the contact in your address book or check the message settings in your app.\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## ground_truth_key_ideas ## ]]`, then `[[ ## system_response_key_ideas ## ]]`, then `[[ ## discussion ## ]]`, then `[[ ## recall ## ]]` (must be formatted as a valid Python float), then `[[ ## precision ## ]]` (must be formatted as a valid Python float), and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "The system response provides a general explanation for why text messages may show up as \"maybe,\" focusing on the uncertainty of the sender's identity. However, it does not mention the specific iOS features or the potential bug in iOS 11.2 that are highlighted in the ground truth. The ground truth explains that this feature is part of iOS 9's Proactivity features and mentions a specific bug in iOS 11.2, which is not addressed in the system response.\n", + "\n", + "[[ ## ground_truth_key_ideas ## ]]\n", + "1. Proactivity features introduced in iOS 9.\n", + "2. The system checks emails to associate phone numbers with contacts.\n", + "3. \"Maybe\" is displayed if the number is not saved in contacts.\n", + "4. Mention of a bug in iOS 11.2 causing \"Maybe\" to appear incorrectly.\n", + "\n", + "[[ ## system_response_key_ideas ## ]]\n", + "1. Text messages show up as \"maybe\" due to uncertainty about the sender's identity.\n", + "2. Occurs when the sender's number is not saved in contacts or is from an unknown number.\n", + "3. Suggests saving the contact or checking message settings.\n", + "\n", + "[[ ## discussion ## ]]\n", + "There is some overlap between the ground truth and the system response regarding the uncertainty of the sender's identity and the suggestion to save the contact. However, the system response lacks specific details about the iOS features and the bug mentioned in the ground truth. The ground truth provides a more comprehensive explanation of the \"maybe\" feature, while the system response is more general and does not address the iOS version specifics.\n", + "\n", + "[[ ## recall ## ]]\n", + "0.25\n", + "\n", + "[[ ## precision ## ]]\n", + "0.5\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history(n=1)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For evaluation, you could use the metric above in a simple loop and just average the score. But for nice parallelism and utilities, we can rely on `dspy.Evaluate`." + ] }, { - "data": { - "text/html": [ - "
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questionexample_responsegold_doc_idsreasoningpred_responseSemanticF1
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questionexample_responsegold_doc_idsreasoningpred_responseSemanticF1
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1should images be stored in a git repository?One viewpoint expresses that there is no significant downside, esp...[6253, 6254, 6275, 6278, 8215]Storing images in a Git repository can be beneficial for version c...Images can be stored in a Git repository, but it's important to co...\u2714\ufe0f [0.444]
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" + ], + "text/plain": [ + " question \\\n", + "0 when to use c over c++, and c++ over c? \n", + "1 should images be stored in a git repository? \n", + "\n", + " example_response \\\n", + "0 If you are equally familiar with both C++ and C, it's advisable to... \n", + "1 One viewpoint expresses that there is no significant downside, esp... \n", + "\n", + " gold_doc_ids \\\n", + "0 [733] \n", + "1 [6253, 6254, 6275, 6278, 8215] \n", + "\n", + " reasoning \\\n", + "0 C and C++ are both powerful programming languages, but they serve ... \n", + "1 Storing images in a Git repository can be beneficial for version c... \n", + "\n", + " pred_response \\\n", + "0 Use C when you need low-level access to memory, require high perfo... \n", + "1 Images can be stored in a Git repository, but it's important to co... \n", + "\n", + " SemanticF1 \n", + "0 \n", + "1 \u2714\ufe0f [0.444] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + "Tracking Evaluation Results in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "To track and visualize the evaluation results over time, you can record the results in MLflow Experiment.\n", + "\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "with mlflow.start_run(run_name=\"rag_evaluation\"):\n", + " evaluate = dspy.Evaluate(\n", + " devset=devset,\n", + " metric=metric,\n", + " num_threads=24,\n", + " display_progress=True,\n", + " # To record the outputs and detailed scores to MLflow\n", + " return_all_scores=True,\n", + " return_outputs=True,\n", + " )\n", + "\n", + " # Evaluate the program as usual\n", + " aggregated_score, outputs, all_scores = evaluate(cot)\n", + "\n", + "\n", + " # Log the aggregated score\n", + " mlflow.log_metric(\"semantic_f1_score\", aggregated_score)\n", + " # Log the detailed evaluation results as a table\n", + " mlflow.log_table(\n", + " {\n", + " \"Question\": [example.question for example in eval_set],\n", + " \"Gold Response\": [example.response for example in eval_set],\n", + " \"Predicted Response\": outputs,\n", + " \"Semantic F1 Score\": all_scores,\n", + " },\n", + " artifact_file=\"eval_results.json\",\n", + " )\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far, we built a very simple chain-of-thought module for question answering and evaluated it on a small dataset.\n", + "\n", + "Can we do better? In the rest of this guide, we will build a retrieval-augmented generation (RAG) program in DSPy for the same task. We'll see how this can boost the score substantially, then we'll use one of the DSPy Optimizers to _compile_ our RAG program to higher-quality prompts, raising our scores even more." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Retrieval-Augmented Generation (RAG).\n", + "\n", + "First, let's download the corpus data that we will use for RAG search. An older version of this tutorial used the full (650,000 document) corpus. To make this very fast and cheap to run, we've downsampled the corpus to just 28,000 documents." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "download(\"https://huggingface.co/dspy/cache/resolve/main/ragqa_arena_tech_corpus.jsonl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up your system's retriever.\n", + "\n", + "As far as DSPy is concerned, you can plug in any Python code for calling tools or retrievers. Here, we'll just use OpenAI Embeddings and do top-K search locally, just for convenience.\n", + "\n", + "**Note:** The step below will require that you either do `pip install -U faiss-cpu` or pass `brute_force_threshold=30_000` to `dspy.retrievers.Embeddings` to avoid faiss." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# %pip install -U faiss-cpu # or faiss-gpu if you have a GPU" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 28436 documents. Will encode them below.\n", + "Training a 32-byte FAISS index with 337 partitions, based on 28436 x 512-dim embeddings\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "max_characters = 6000 # for truncating >99th percentile of documents\n", + "topk_docs_to_retrieve = 5 # number of documents to retrieve per search query\n", + "\n", + "with open(\"ragqa_arena_tech_corpus.jsonl\") as f:\n", + " corpus = [ujson.loads(line)['text'][:max_characters] for line in f]\n", + " print(f\"Loaded {len(corpus)} documents. Will encode them below.\")\n", + "\n", + "embedder = dspy.Embedder('openai/text-embedding-3-small', dimensions=512)\n", + "search = dspy.retrievers.Embeddings(embedder=embedder, corpus=corpus, k=topk_docs_to_retrieve)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "55.51" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Build your first RAG Module.\n", + "\n", + "In the previous guide, we looked at individual DSPy modules in isolation, e.g. `dspy.Predict(\"question -> answer\")`.\n", + "\n", + "What if we want to build a DSPy _program_ that has multiple steps? The syntax below with `dspy.Module` allows you to connect a few pieces together, in this case, our retriever and a generation module, so the whole system can be optimized.\n", + "\n", + "Concretely, in the `__init__` method, you declare any sub-module you'll need, which in this case is just a `dspy.ChainOfThought('context, question -> response')` module that takes retrieved context, a question, and produces a response. In the `forward` method, you simply express any Python control flow you like, possibly using your modules. In this case, we first invoke the `search` function defined earlier and then invoke the `self.respond` ChainOfThought module.\n" ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(RAG())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using a DSPy Optimizer to improve your RAG prompt.\n", - "\n", - "Off the shelf, our `RAG` module scores 55%. What are our options to make it stronger? One of the various choices DSPy offers is optimizing the prompts in our pipeline.\n", - "\n", - "If there are many sub-modules in your program, all of them will be optimized together. In this case, there's only one: `self.respond = dspy.ChainOfThought('context, question -> response')`\n", - "\n", - "Let's set up and use DSPy's MIPRO (v2) optimizer. The run below has a cost around $1.5 (for the `medium` auto setting) and may take some 20-30 minutes depending on your number of threads." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tp = dspy.MIPROv2(metric=metric, auto=\"medium\", num_threads=24) # use fewer threads if your rate limit is small\n", - "\n", - "optimized_rag = tp.compile(RAG(), trainset=trainset,\n", - " max_bootstrapped_demos=2, max_labeled_demos=2,\n", - " requires_permission_to_run=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The prompt optimization process here is pretty systematic, you can learn about it for example in this paper. Importantly, it's not a magic button. It's very possible that it can overfit your training set for instance and not generalize well to a held-out set, making it essential that we iteratively validate our programs.\n", - "\n", - "Let's check on an example here, asking the same question to the baseline `rag = RAG()` program, which was not optimized, and to the `optimized_rag = MIPROv2(..)(..)` program, after prompt optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "You are correct that cmd+tab does not work on hidden or minimized windows. To switch back to a minimized app, you must first switch to another application and let it take focus before returning to the minimized one.\n" - ] - } - ], - "source": [ - "baseline = rag(question=\"cmd+tab does not work on hidden or minimized windows\")\n", - "print(baseline.response)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class RAG(dspy.Module):\n", + " def __init__(self):\n", + " self.respond = dspy.ChainOfThought('context, question -> response')\n", + "\n", + " def forward(self, question):\n", + " context = search(question).passages\n", + " return self.respond(context=context, question=question)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Command + Tab shortcut on macOS is designed to switch between currently open applications, but it does not directly restore minimized or hidden windows. When you use Command + Tab, it cycles through the applications that are actively running, and minimized windows do not count as active. To manage minimized windows, you can use other shortcuts or methods. For example, you can use Command + Option + H + M to hide all other applications and minimize the most recently used one. Alternatively, you can navigate to the application you want to restore using Command + Tab and then manually click on the minimized window in the Dock to bring it back to focus.\n" - ] - } - ], - "source": [ - "pred = optimized_rag(question=\"cmd+tab does not work on hidden or minimized windows\")\n", - "print(pred.response)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can use `dspy.inspect_history(n=2)` to view the RAG prompt [before optimization](https://gist.github.com/okhat/5d04648f2226e72e66e26a8cb1456ee4) and [after optimization](https://gist.github.com/okhat/79405b8889b4b07da577ee19f1a3479a).\n", - "\n", - "Concretely, in one of the runs of this notebook, the optimized prompt does the following (note that it may be different on a later rerun).\n", - "\n", - "1. Constructs the following instruction,\n", - "```text\n", - "Using the provided `context` and `question`, analyze the information step by step to generate a comprehensive and informative `response`. Ensure that the response clearly explains the concepts involved, highlights key distinctions, and addresses any complexities noted in the context.\n", - "```\n", - "\n", - "2. And includes two fully worked out RAG examples with synthetic reasoning and answers, e.g. `how to transfer whatsapp voice message to computer?`.\n", - "\n", - "Let's now evaluate on the overall devset." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Let's use the RAG module.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " reasoning=\"High Memory and Low Memory in Linux refer to two segments of the kernel's memory space. Low Memory is the portion of memory that the kernel can access directly and is statically mapped at boot time. This area is typically used for kernel data structures and is always accessible to the kernel. High Memory, on the other hand, is not permanently mapped in the kernel's address space, meaning that the kernel cannot access it directly without first mapping it into its address space. High Memory is used for user-space applications and temporary data buffers. The distinction allows for better memory management and security, as user-space applications cannot directly access kernel-space memory.\",\n", + " response=\"In Linux, High Memory refers to the segment of memory that is not permanently mapped in the kernel's address space, which means the kernel must map it temporarily to access it. This area is typically used for user-space applications and temporary data buffers. Low Memory, in contrast, is the portion of memory that the kernel can access directly and is statically mapped at boot time. It is used for kernel data structures and is always accessible to the kernel. This separation enhances security by preventing user-space applications from accessing kernel-space memory directly.\"\n", + ")" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rag = RAG()\n", + "rag(question=\"what are high memory and low memory on linux?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[34m[2024-11-23T23:16:49.175612]\u001b[0m\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `context` (str)\n", + "2. `question` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `response` (str)\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## context ## ]]\n", + "{context}\n", + "\n", + "[[ ## question ## ]]\n", + "{question}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## response ## ]]\n", + "{response}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `context`, `question`, produce the fields `response`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## context ## ]]\n", + "[1] \u00abAs far as I remember, High Memory is used for application space and Low Memory for the kernel. Advantage is that (user-space) applications cant access kernel-space memory.\u00bb\n", + "[2] \u00abHIGHMEM is a range of kernels memory space, but it is NOT memory you access but its a place where you put what you want to access. A typical 32bit Linux virtual memory map is like: 0x00000000-0xbfffffff: user process (3GB) 0xc0000000-0xffffffff: kernel space (1GB) (CPU-specific vector and whatsoever are ignored here). Linux splits the 1GB kernel space into 2 pieces, LOWMEM and HIGHMEM. The split varies from installation to installation. If an installation chooses, say, 512MB-512MB for LOW and HIGH mems, the 512MB LOWMEM (0xc0000000-0xdfffffff) is statically mapped at the kernel boot time; usually the first so many bytes of the physical memory is used for this so that virtual and physical addresses in this range have a constant offset of, say, 0xc0000000. On the other hand, the latter 512MB (HIGHMEM) has no static mapping (although you could leave pages semi-permanently mapped there, but you must do so explicitly in your driver code). Instead, pages are temporarily mapped and unmapped here so that virtual and physical addresses in this range have no consistent mapping. Typical uses of HIGHMEM include single-time data buffers.\u00bb\n", + "[3] \u00abThis is relevant to the Linux kernel; Im not sure how any Unix kernel handles this. The High Memory is the segment of memory that user-space programs can address. It cannot touch Low Memory. Low Memory is the segment of memory that the Linux kernel can address directly. If the kernel must access High Memory, it has to map it into its own address space first. There was a patch introduced recently that lets you control where the segment is. The tradeoff is that you can take addressable memory away from user space so that the kernel can have more memory that it does not have to map before using. Additional resources: http://tldp.org/HOWTO/KernelAnalysis-HOWTO-7.html http://linux-mm.org/HighMemory\u00bb\n", + "[4] \u00abThe first reference to turn to is Linux Device Drivers (available both online and in book form), particularly chapter 15 which has a section on the topic. In an ideal world, every system component would be able to map all the memory it ever needs to access. And this is the case for processes on Linux and most operating systems: a 32-bit process can only access a little less than 2^32 bytes of virtual memory (in fact about 3GB on a typical Linux 32-bit architecture). It gets difficult for the kernel, which needs to be able to map the full memory of the process whose system call its executing, plus the whole physical memory, plus any other memory-mapped hardware device. So when a 32-bit kernel needs to map more than 4GB of memory, it must be compiled with high memory support. High memory is memory which is not permanently mapped in the kernels address space. (Low memory is the opposite: it is always mapped, so you can access it in the kernel simply by dereferencing a pointer.) When you access high memory from kernel code, you need to call kmap first, to obtain a pointer from a page data structure (struct page). Calling kmap works whether the page is in high or low memory. There is also kmap_atomic which has added constraints but is more efficient on multiprocessor machines because it uses finer-grained locking. The pointer obtained through kmap is a resource: it uses up address space. Once youve finished with it, you must call kunmap (or kunmap_atomic) to free that resource; then the pointer is no longer valid, and the contents of the page cant be accessed until you call kmap again.\u00bb\n", + "[5] \u00ab/proc/meminfo will tell you how free works, but /proc/kcore can tell you what the kernel uses. From the same page: /proc/kcore This file represents the physical memory of the system and is stored in the ELF core file format. With this pseudo-file, and an unstripped kernel (/usr/src/linux/vmlinux) binary, GDB can be used to examine the current state of any kernel data structures. The total length of the file is the size of physical memory (RAM) plus 4KB. /proc/meminfo This file reports statistics about memory usage on the system. It is used by free(1) to report the amount of free and used memory (both physical and swap) on the system as well as the shared memory and buffers used by the kernel. Each line of the file consists of a parameter name, followed by a colon, the value of the parameter, and an option unit of measurement (e.g., kB). The list below describes the parameter names and the format specifier required to read the field value. Except as noted below, all of the fields have been present since at least Linux 2.6.0. Some fileds are displayed only if the kernel was configured with various options; those dependencies are noted in the list. MemTotal %lu Total usable RAM (i.e., physical RAM minus a few reserved bits and the kernel binary code). MemFree %lu The sum of LowFree+HighFree. Buffers %lu Relatively temporary storage for raw disk blocks that shouldnt get tremendously large (20MB or so). Cached %lu In-memory cache for files read from the disk (the page cache). Doesnt include SwapCached. SwapCached %lu Memory that once was swapped out, is swapped back in but still also is in the swap file. (If memory pressure is high, these pages dont need to be swapped out again because they are already in the swap file. This saves I/O.) Active %lu Memory that has been used more recently and usually not reclaimed unless absolutely necessary. Inactive %lu Memory which has been less recently used. It is more eligible to be reclaimed for other purposes. Active(anon) %lu (since Linux 2.6.28) [To be documented.] Inactive(anon) %lu (since Linux 2.6.28) [To be documented.] Active(file) %lu (since Linux 2.6.28) [To be documented.] Inactive(file) %lu (since Linux 2.6.28) [To be documented.] Unevictable %lu (since Linux 2.6.28) (From Linux 2.6.28 to 2.6.30, CONFIG_UNEVICTABLE_LRU was required.) [To be documented.] Mlocked %lu (since Linux 2.6.28) (From Linux 2.6.28 to 2.6.30, CONFIG_UNEVICTABLE_LRU was required.) [To be documented.] HighTotal %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Total amount of highmem. Highmem is all memory above ~860MB of physical memory. Highmem areas are for use by user-space programs, or for the page cache. The kernel must use tricks to access this memory, making it slower to access than lowmem. HighFree %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Amount of free highmem. LowTotal %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Total amount of lowmem. Lowmem is memory which can be used for everything that highmem can be used for, but it is also available for the kernels use for its own data structures. Among many other things, it is where everything from Slab is allocated. Bad things happen when youre out of lowmem. LowFree %lu (Starting with Linux 2.6.19, CONFIG_HIGHMEM is required.) Amount of free lowmem. MmapCopy %lu (since Linux 2.6.29) (CONFIG_MMU is required.) [To be documented.] SwapTotal %lu Total amount of swap space available. SwapFree %lu Amount of swap space that is currently unused. Dirty %lu Memory which is waiting to get written back to the disk. Writeback %lu Memory which is actively being written back to the disk. AnonPages %lu (since Linux 2.6.18) Non-file backed pages mapped into user-space page tables. Mapped %lu Files which have been mmaped, such as libraries. Shmem %lu (since Linux 2.6.32) [To be documented.] Slab %lu In-kernel data structures cache. SReclaimable %lu (since Linux 2.6.19) Part of Slab, that might be reclaimed, such as caches. SUnreclaim %lu (since Linux 2.6.19) Part of Slab, that cannot be reclaimed on memory pressure. KernelStack %lu (since Linux 2.6.32) Amount of memory allocated to kernel stacks. PageTables %lu (since Linux 2.6.18) Amount of memory dedicated to the lowest level of page tables. Quicklists %lu (since Linux 2.6.27) (CONFIG_QUICKLIST is required.) [To be documented.] NFS_Unstable %lu (since Linux 2.6.18) NFS pages sent to the server, but not yet committed to stable storage. Bounce %lu (since Linux 2.6.18) Memory used for block device bounce buffers. WritebackTmp %lu (since Linux 2.6.26) Memory used by FUSE for temporary writeback buffers. CommitLimit %lu (since Linux 2.6.10) Based on the overcommit ratio (vm.overcommit_ratio), this is the total amount of memory currently available to be allocated on the system. This limit is adhered to only if strict overcommit accounting is enabled (mode 2 in /proc/sys/vm/overcommit_ratio). The CommitLimit is calculated using the following formula: CommitLimit = ([total RAM pages] - [total huge TLB pages]) * overcommit_ratio / 100 + [total swap pages] For example, on a system with 1GB of physical RAM and 7GB of swap with a overcommit_ratio of 30, this formula yields a CommitLimit of 7.3GB. For more details, see the memory overcommit documentation in the kernel source file Documentation/vm/overcommit-accounting. Committed_AS %lu The amount of memory presently allocated on the system. The committed memory is a sum of all of the memory which has been allocated by processes, even if it has not been used by them as of yet. A process which allocates 1GB of memory (using malloc(3) or similar), but touches only 300MB of that memory will show up as using only 300MB of memory even if it has the address space allocated for the entire 1GB. This 1GB is memory which has been committed to by the VM and can be used at any time by the allocating application. With strict overcommit enabled on the system (mode 2 /proc/sys/vm/overcommit_memory), allocations w\u00bb\n", + "\n", + "[[ ## question ## ]]\n", + "what are high memory and low memory on linux?\n", + "\n", + "Respond with the corresponding output fields, starting with the field `[[ ## reasoning ## ]]`, then `[[ ## response ## ]]`, and then ending with the marker for `[[ ## completed ## ]]`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "High Memory and Low Memory in Linux refer to two segments of the kernel's memory space. Low Memory is the portion of memory that the kernel can access directly and is statically mapped at boot time. This area is typically used for kernel data structures and is always accessible to the kernel. High Memory, on the other hand, is not permanently mapped in the kernel's address space, meaning that the kernel cannot access it directly without first mapping it into its address space. High Memory is used for user-space applications and temporary data buffers. The distinction allows for better memory management and security, as user-space applications cannot directly access kernel-space memory.\n", + "\n", + "[[ ## response ## ]]\n", + "In Linux, High Memory refers to the segment of memory that is not permanently mapped in the kernel's address space, which means the kernel must map it temporarily to access it. This area is typically used for user-space applications and temporary data buffers. Low Memory, in contrast, is the portion of memory that the kernel can access directly and is statically mapped at boot time. It is used for kernel data structures and is always accessible to the kernel. This separation enhances security by preventing user-space applications from accessing kernel-space memory directly.\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history()" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 183.32 / 300 (61.1%): 100%|██████████| 300/300 [00:02<00:00, 104.48it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Earlier with a CoT module, we got around 40% in terms of semantic F1 on our `devset`. Would this `RAG` module score better?" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024/11/23 23:17:21 INFO dspy.evaluate.evaluate: Average Metric: 183.3194433591069 / 300 (61.1%)\n" - ] + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 166.54 / 300 (55.5%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [00:04<00:00, 61.40it/s] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/11/23 23:16:54 INFO dspy.evaluate.evaluate: Average Metric: 166.53601368289284 / 300 (55.5%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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questionexample_responsegold_doc_idsreasoningpred_responseSemanticF1
0when to use c over c++, and c++ over c?If you are equally familiar with both C++ and C, it's advisable to...[733]C should be used over C++ primarily in scenarios where simplicity ...Use C over C++ when working on embedded systems, requiring low-lev...\u2714\ufe0f [0.500]
1should images be stored in a git repository?One viewpoint expresses that there is no significant downside, esp...[6253, 6254, 6275, 6278, 8215]Storing images in a Git repository is generally not recommended du...While it is technically possible to store images in a Git reposito...\u2714\ufe0f [0.444]
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" + ], + "text/plain": [ + " question \\\n", + "0 when to use c over c++, and c++ over c? \n", + "1 should images be stored in a git repository? \n", + "\n", + " example_response \\\n", + "0 If you are equally familiar with both C++ and C, it's advisable to... \n", + "1 One viewpoint expresses that there is no significant downside, esp... \n", + "\n", + " gold_doc_ids \\\n", + "0 [733] \n", + "1 [6253, 6254, 6275, 6278, 8215] \n", + "\n", + " reasoning \\\n", + "0 C should be used over C++ primarily in scenarios where simplicity ... \n", + "1 Storing images in a Git repository is generally not recommended du... \n", + "\n", + " pred_response \\\n", + "0 Use C over C++ when working on embedded systems, requiring low-lev... \n", + "1 While it is technically possible to store images in a Git reposito... \n", + "\n", + " SemanticF1 \n", + "0 \u2714\ufe0f [0.500] \n", + "1 \u2714\ufe0f [0.444] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "55.51" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate(RAG())" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using a DSPy Optimizer to improve your RAG prompt.\n", + "\n", + "Off the shelf, our `RAG` module scores 55%. What are our options to make it stronger? One of the various choices DSPy offers is optimizing the prompts in our pipeline.\n", + "\n", + "If there are many sub-modules in your program, all of them will be optimized together. In this case, there's only one: `self.respond = dspy.ChainOfThought('context, question -> response')`\n", + "\n", + "Let's set up and use DSPy's MIPRO (v2) optimizer. The run below has a cost around $1.5 (for the `medium` auto setting) and may take some 20-30 minutes depending on your number of threads." + ] }, { - "data": { - "text/html": [ - "
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questionexample_responsegold_doc_idsreasoningpred_responseSemanticF1
0when to use c over c++, and c++ over c?If you are equally familiar with both C++ and C, it's advisable to...[733]The context provides insights into the strengths and weaknesses of...You should consider using C over C++ in scenarios where simplicity...✔️ [0.333]
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" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tp = dspy.MIPROv2(metric=metric, auto=\"medium\", num_threads=24) # use fewer threads if your rate limit is small\n", + "\n", + "optimized_rag = tp.compile(RAG(), trainset=trainset,\n", + " max_bootstrapped_demos=2, max_labeled_demos=2,\n", + " requires_permission_to_run=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The prompt optimization process here is pretty systematic, you can learn about it for example in this paper. Importantly, it's not a magic button. It's very possible that it can overfit your training set for instance and not generalize well to a held-out set, making it essential that we iteratively validate our programs.\n", + "\n", + "Let's check on an example here, asking the same question to the baseline `rag = RAG()` program, which was not optimized, and to the `optimized_rag = MIPROv2(..)(..)` program, after prompt optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You are correct that cmd+tab does not work on hidden or minimized windows. To switch back to a minimized app, you must first switch to another application and let it take focus before returning to the minimized one.\n" + ] + } ], - "text/plain": [ - " question \\\n", - "0 when to use c over c++, and c++ over c? \n", - "1 should images be stored in a git repository? \n", - "\n", - " example_response \\\n", - "0 If you are equally familiar with both C++ and C, it's advisable to... \n", - "1 One viewpoint expresses that there is no significant downside, esp... \n", - "\n", - " gold_doc_ids \\\n", - "0 [733] \n", - "1 [6253, 6254, 6275, 6278, 8215] \n", - "\n", - " reasoning \\\n", - "0 The context provides insights into the strengths and weaknesses of... \n", - "1 The context discusses the challenges and considerations of storing... \n", - "\n", - " pred_response \\\n", - "0 You should consider using C over C++ in scenarios where simplicity... \n", - "1 Storing images in a Git repository is generally considered bad pra... \n", - "\n", - " SemanticF1 \n", - "0 ✔️ [0.333] \n", - "1 ✔️ [0.500] " + "source": [ + "baseline = rag(question=\"cmd+tab does not work on hidden or minimized windows\")\n", + "print(baseline.response)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Command + Tab shortcut on macOS is designed to switch between currently open applications, but it does not directly restore minimized or hidden windows. When you use Command + Tab, it cycles through the applications that are actively running, and minimized windows do not count as active. To manage minimized windows, you can use other shortcuts or methods. For example, you can use Command + Option + H + M to hide all other applications and minimize the most recently used one. Alternatively, you can navigate to the application you want to restore using Command + Tab and then manually click on the minimized window in the Dock to bring it back to focus.\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "pred = optimized_rag(question=\"cmd+tab does not work on hidden or minimized windows\")\n", + "print(pred.response)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "61.11" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use `dspy.inspect_history(n=2)` to view the RAG prompt [before optimization](https://gist.github.com/okhat/5d04648f2226e72e66e26a8cb1456ee4) and [after optimization](https://gist.github.com/okhat/79405b8889b4b07da577ee19f1a3479a).\n", + "\n", + "Concretely, in one of the runs of this notebook, the optimized prompt does the following (note that it may be different on a later rerun).\n", + "\n", + "1. Constructs the following instruction,\n", + "```text\n", + "Using the provided `context` and `question`, analyze the information step by step to generate a comprehensive and informative `response`. Ensure that the response clearly explains the concepts involved, highlights key distinctions, and addresses any complexities noted in the context.\n", + "```\n", + "\n", + "2. And includes two fully worked out RAG examples with synthetic reasoning and answers, e.g. `how to transfer whatsapp voice message to computer?`.\n", + "\n", + "Let's now evaluate on the overall devset." ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(optimized_rag)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Keeping an eye on cost.\n", - "\n", - "DSPy allows you to track the cost of your programs, which can be used to monitor the cost of your calls. Here, we'll show you how to track the cost of your programs with DSPy." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "cost = sum([x['cost'] for x in lm.history if x['cost'] is not None]) # in USD, as calculated by LiteLLM for certain providers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Saving and loading.\n", - "\n", - "The optimized program has a pretty simple structure on the inside. Feel free to explore it.\n", - "\n", - "Here, we'll save `optimized_rag` so we can load it again later without having to optimize from scratch." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 183.32 / 300 (61.1%): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [00:02<00:00, 104.48it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024/11/23 23:17:21 INFO dspy.evaluate.evaluate: Average Metric: 183.3194433591069 / 300 (61.1%)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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questionexample_responsegold_doc_idsreasoningpred_responseSemanticF1
0when to use c over c++, and c++ over c?If you are equally familiar with both C++ and C, it's advisable to...[733]The context provides insights into the strengths and weaknesses of...You should consider using C over C++ in scenarios where simplicity...\u2714\ufe0f [0.333]
1should images be stored in a git repository?One viewpoint expresses that there is no significant downside, esp...[6253, 6254, 6275, 6278, 8215]The context discusses the challenges and considerations of storing...Storing images in a Git repository is generally considered bad pra...\u2714\ufe0f [0.500]
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" + ], + "text/plain": [ + " question \\\n", + "0 when to use c over c++, and c++ over c? \n", + "1 should images be stored in a git repository? \n", + "\n", + " example_response \\\n", + "0 If you are equally familiar with both C++ and C, it's advisable to... \n", + "1 One viewpoint expresses that there is no significant downside, esp... \n", + "\n", + " gold_doc_ids \\\n", + "0 [733] \n", + "1 [6253, 6254, 6275, 6278, 8215] \n", + "\n", + " reasoning \\\n", + "0 The context provides insights into the strengths and weaknesses of... \n", + "1 The context discusses the challenges and considerations of storing... \n", + "\n", + " pred_response \\\n", + "0 You should consider using C over C++ in scenarios where simplicity... \n", + "1 Storing images in a Git repository is generally considered bad pra... \n", + "\n", + " SemanticF1 \n", + "0 \u2714\ufe0f [0.333] \n", + "1 \u2714\ufe0f [0.500] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "61.11" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate(optimized_rag)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Keeping an eye on cost.\n", + "\n", + "DSPy allows you to track the cost of your programs, which can be used to monitor the cost of your calls. Here, we'll show you how to track the cost of your programs with DSPy." + ] + }, { - "data": { - "text/plain": [ - "Prediction(\n", - " reasoning='The context explains how the Command + Tab shortcut functions on macOS, particularly in relation to switching between applications. It notes that this shortcut does not bring back minimized or hidden windows directly. Instead, it cycles through applications that are currently open and visible. The information also suggests alternative methods for managing minimized windows and provides insights into how to navigate between applications effectively.',\n", - " response='The Command + Tab shortcut on macOS is designed to switch between currently open applications, but it does not directly restore minimized or hidden windows. When you use Command + Tab, it cycles through the applications that are actively running, and minimized windows do not count as active. To manage minimized windows, you can use other shortcuts or methods. For example, you can use Command + Option + H + M to hide all other applications and minimize the most recently used one. Alternatively, you can navigate to the application you want to restore using Command + Tab and then manually click on the minimized window in the Dock to bring it back to focus.'\n", - ")" + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "cost = sum([x['cost'] for x in lm.history if x['cost'] is not None]) # in USD, as calculated by LiteLLM for certain providers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving and loading.\n", + "\n", + "The optimized program has a pretty simple structure on the inside. Feel free to explore it.\n", + "\n", + "Here, we'll save `optimized_rag` so we can load it again later without having to optimize from scratch." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " reasoning='The context explains how the Command + Tab shortcut functions on macOS, particularly in relation to switching between applications. It notes that this shortcut does not bring back minimized or hidden windows directly. Instead, it cycles through applications that are currently open and visible. The information also suggests alternative methods for managing minimized windows and provides insights into how to navigate between applications effectively.',\n", + " response='The Command + Tab shortcut on macOS is designed to switch between currently open applications, but it does not directly restore minimized or hidden windows. When you use Command + Tab, it cycles through the applications that are actively running, and minimized windows do not count as active. To manage minimized windows, you can use other shortcuts or methods. For example, you can use Command + Option + H + M to hide all other applications and minimize the most recently used one. Alternatively, you can navigate to the application you want to restore using Command + Tab and then manually click on the minimized window in the Dock to bring it back to focus.'\n", + ")" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized_rag.save(\"optimized_rag.json\")\n", + "\n", + "loaded_rag = RAG()\n", + "loaded_rag.load(\"optimized_rag.json\")\n", + "\n", + "loaded_rag(question=\"cmd+tab does not work on hidden or minimized windows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Saving programs in MLflow Experiment\n", + "\n", + "
\n", + "\n", + "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", + "\n", + "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", + "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", + "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", + "\n", + "To save the program in MLflow, run the following code:\n", + "\n", + "```python\n", + "import mlflow\n", + "\n", + "# Start an MLflow Run and save the program\n", + "with mlflow.start_run(run_name=\"optimized_rag\"):\n", + " model_info = mlflow.dspy.log_model(\n", + " optimized_rag,\n", + " artifact_path=\"model\", # Any name to save the program in MLflow\n", + " )\n", + "\n", + "# Load the program back from MLflow\n", + "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", + "```\n", + "\n", + "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## What's next?\n", + "\n", + "Improving from around 42% to approximately 61% on this task, in terms of `SemanticF1`, was pretty easy.\n", + "\n", + "But DSPy gives you paths to continue iterating on the quality of your system and we have barely scratched the surface.\n", + "\n", + "In general, you have the following tools:\n", + "\n", + "1. Explore better system architectures for your program, e.g. what if we ask the LM to generate search queries for the retriever? See, e.g., the [STORM pipeline](https://arxiv.org/abs/2402.14207) built in DSPy.\n", + "2. Explore different [prompt optimizers](https://arxiv.org/abs/2406.11695) or [weight optimizers](https://arxiv.org/abs/2407.10930). See the Optimizers Docs.\n", + "3. Scale inference time compute using DSPy Optimizers, e.g. via ensembling multiple post-optimization programs.\n", + "4. Cut cost by distilling to a smaller LM, via prompt or weight optimization.\n", + "\n", + "How do you decide which ones to proceed with first?\n", + "\n", + "The first step is to look at your system outputs, which will allow you to identify the sources of lower performance if any. While doing all of this, make sure you continue to refine your metric, e.g. by optimizing against your judgments, and to collect more (or more realistic) data, e.g. from related domains or from putting a demo of your system in front of users." ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "optimized_rag.save(\"optimized_rag.json\")\n", - "\n", - "loaded_rag = RAG()\n", - "loaded_rag.load(\"optimized_rag.json\")\n", - "\n", - "loaded_rag(question=\"cmd+tab does not work on hidden or minimized windows\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Saving programs in MLflow Experiment\n", - "\n", - "
\n", - "\n", - "Instead of saving the program to a local file, you can track it in MLflow for better reproducibility and collaboration.\n", - "\n", - "1. **Dependency Management**: MLflow automatically save the frozen environment metadata along with the program to ensure reproducibility.\n", - "2. **Experiment Tracking**: With MLflow, you can track the program's performance and cost along with the program itself.\n", - "3. **Collaboration**: You can share the program and results with your team members by sharing the MLflow experiment.\n", - "\n", - "To save the program in MLflow, run the following code:\n", - "\n", - "```python\n", - "import mlflow\n", - "\n", - "# Start an MLflow Run and save the program\n", - "with mlflow.start_run(run_name=\"optimized_rag\"):\n", - " model_info = mlflow.dspy.log_model(\n", - " optimized_rag,\n", - " artifact_path=\"model\", # Any name to save the program in MLflow\n", - " )\n", - "\n", - "# Load the program back from MLflow\n", - "loaded = mlflow.dspy.load_model(model_info.model_uri)\n", - "```\n", - "\n", - "To learn more about the integration, visit [MLflow DSPy Documentation](https://mlflow.org/docs/latest/llms/dspy/index.html) as well.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## What's next?\n", - "\n", - "Improving from around 42% to approximately 61% on this task, in terms of `SemanticF1`, was pretty easy.\n", - "\n", - "But DSPy gives you paths to continue iterating on the quality of your system and we have barely scratched the surface.\n", - "\n", - "In general, you have the following tools:\n", - "\n", - "1. Explore better system architectures for your program, e.g. what if we ask the LM to generate search queries for the retriever? See, e.g., the [STORM pipeline](https://arxiv.org/abs/2402.14207) built in DSPy.\n", - "2. Explore different [prompt optimizers](https://arxiv.org/abs/2406.11695) or [weight optimizers](https://arxiv.org/abs/2407.10930). See the Optimizers Docs.\n", - "3. Scale inference time compute using DSPy Optimizers, e.g. via ensembling multiple post-optimization programs.\n", - "4. Cut cost by distilling to a smaller LM, via prompt or weight optimization.\n", - "\n", - "How do you decide which ones to proceed with first?\n", - "\n", - "The first step is to look at your system outputs, which will allow you to identify the sources of lower performance if any. While doing all of this, make sure you continue to refine your metric, e.g. by optimizing against your judgments, and to collect more (or more realistic) data, e.g. from related domains or from putting a demo of your system in front of users." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py310_sept24_user", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "py310_sept24_user", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/migration.ipynb b/examples/migration.ipynb index 6d63788e52..244691ae05 100644 --- a/examples/migration.ipynb +++ b/examples/migration.ipynb @@ -1,468 +1,468 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "### Migrating from DSPy 2.4 to 2.5\n", - "\n", - "**DSPy 2.5** focuses on improving the _pre-optimization_ developer experience, i.e. making it easier to obtain higher-quality outputs from various LMs _out of the box_ prior to setting up a DSPy optimizer." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### tl;dr Just set up the LM differently at the start.\n", - "\n", - "For most applications, you will only need to change 1-2 lines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "lm = dspy.LM('openai/gpt-4o-mini')\n", - "dspy.configure(lm=lm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The rest of your code can remain unchanged. However, make sure to experiment with your modules. Improvements to **Adapters** will affect the underlying prompts, before and after optimization, and will enforce stricter validation for LM outputs.\n", - "\n", - "If you want to dive deeper, here's are some new things and things to keep in mind when upgrading.\n", - "\n", - "1. **Use the `dspy.LM` class for setting up language models.**\n", - "2. **Invoke LMs in the same way as before.**\n", - "3. **This internally uses new DSPy `Adapters`, leading to better prompting out of the box.**\n", - "4. **Advanced: If needed, you can set up your own Adapter for LMs.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Use the `dspy.LM` class for setting up language models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Earlier versions of DSPy involved tens of clients for different LM providers, e.g. `dspy.OpenAI`, `dspy.GoogleVertexAI`, and `dspy.HFClientTGI`, etc. These are now deprecated and will be removed in DSPy 2.6.\n", - "\n", - "Instead, use `dspy.LM` to access any LM endpoint for local and remote models. This relies on [LiteLLM](https://github.com/BerriAI/litellm) to translate the different client APIs into an OpenAI-compatible interface.\n", - "\n", - "Any [provider supported in LiteLLM](https://docs.litellm.ai/docs/providers) should work with `dspy.LM`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# OpenAI, which can authenticate via OPENAI_API_KEY.\n", - "lm = dspy.LM('openai/gpt-4o-mini')\n", - "\n", - "# Anthropic, which can authenticate via ANTHROPIC_API_KEY.\n", - "anthropic_lm = dspy.LM('anthropic/claude-3-opus-20240229')\n", - "\n", - "# You can also pass auth information directly.\n", - "anthropic_lm = dspy.LM('anthropic/claude-3-opus-20240229', api_key=\"..\", api_base=\"..\")\n", - "\n", - "# Cohere, which can authenticate via COHERE_API_KEY.\n", - "cohere_lm = dspy.LM('cohere/command-nightly')\n", - "\n", - "# Databricks, which can authenticate via DATABRICKS_API_KEY & DATABRICKS_API_BASE (or automatically on a DB workspace).\n", - "databricks_llama3 = dspy.LM('databricks/databricks-meta-llama-3-1-70b-instruct')\n", - "\n", - "# or many others." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Any OpenAI-compatible endpoint is easy to set up with an `openai/` prefix as well. This works great for open LMs from HuggingFace hosted locally with SGLang, VLLM, or HF Text-Generation-Inference." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "sglang_port = 7501\n", - "sglang_url = f\"http://localhost:{sglang_port}/v1\"\n", - "sglang_llama = dspy.LM(\"openai/meta-llama/Meta-Llama-3-8B-Instruct\", api_base=sglang_url)\n", - "\n", - "# You could also use text mode, in which the prompts are *not* formatted as messages.\n", - "sglang_llama_text = dspy.LM(\"openai/meta-llama/Meta-Llama-3-8B-Instruct\", api_base=sglang_url, model_type='text')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Configuring LM attributes\n", - "\n", - "For any LM, you can configure any of the following attributes at initialization or per call." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "gpt_4o_mini = dspy.LM('openai/gpt-4o-mini', temperature=0.9, max_tokens=3000, stop=None, cache=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Caching.\n", - "\n", - "Old clients still exist but you'll get a deprecation warning. They will be removed in DSPy 2.6." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Invoke LMs in the same way as before." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Invoking the LM directly with a prompt is the same as before." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['2 + 2 equals 4.']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lm(\"What is 2+2?\", temperature=0.9)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For chat LMs, you can pass a list of messages." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['2 + 2 equals 4.']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lm(messages=[{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", - " {\"role\": \"user\", \"content\": \"What is 2+2?\"}])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using DSPy modules is the same as before." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Prediction(\n", - " reasoning='To solve the equation 2 + 2, we simply add the two numbers together. The number 2 represents a quantity, and when we add another 2 to it, we are combining these quantities. Therefore, 2 + 2 equals 4.',\n", - " answer='4'\n", - ")" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "module = dspy.ChainOfThought('question -> answer')\n", - "module(question=\"What is 2+2?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Every LM object maintains the history of its interactions, including inputs, outputs, token usage (and $$$ cost), and metadata." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['prompt', 'messages', 'kwargs', 'response', 'outputs', 'usage', 'cost'])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(lm.history) # e.g., 3 calls to the LM\n", - "\n", - "lm.history[-1].keys() # access the last call to the LM, with all metadata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) This internally uses new DSPy `Adapters`, leading to better prompting out of the box.\n", - "\n", - "Prompt optimizers in DSPy generate and tune the _instructions_ or the _examples_ in the prompts corresponding to your Signatures. DSPy 2.5 introduces **Adapters** as a layer between Signatures and LMs, responsible for formatting these pieces (Signature I/O fields, instructions, and examples) as well as generating and parsing the outputs. In DSPy 2.5, the default Adapters are now more natively aware of chat LMs and are responsible for enforcing types, building on earlier experimental features from `TypedPredictor` and `configure(experimental=True)`. In our experience, this change tends to deliver more consistent pre-optimization quality from various LMs, especially for sophisticated Signatures." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Prediction(\n", - " reasoning='The first claim states that \"Python was released in 1991,\" which is true. Python was indeed first released by Guido van Rossum in February 1991. The second claim states that \"Python is a compiled language.\" This is false; Python is primarily an interpreted language, although it can be compiled to bytecode, it is not considered a compiled language in the traditional sense like C or Java.',\n", - " verdicts=[True, False]\n", - ")" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fact_checking = dspy.ChainOfThought('claims -> verdicts: list[bool]')\n", - "fact_checking(claims=[\"Python was released in 1991.\", \"Python is a compiled language.\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can as usual inspect the last prompt (and output) from an LM call." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\u001b[31mSystem message:\u001b[0m\n", - "\n", - "Your input fields are:\n", - "1. `claims` (str)\n", - "\n", - "Your output fields are:\n", - "1. `reasoning` (str)\n", - "2. `verdicts` (list[bool])\n", - "\n", - "All interactions will be structured in the following way, with the appropriate values filled in.\n", - "\n", - "[[ ## claims ## ]]\n", - "{claims}\n", - "\n", - "[[ ## reasoning ## ]]\n", - "{reasoning}\n", - "\n", - "[[ ## verdicts ## ]]\n", - "{verdicts}\n", - "\n", - "[[ ## completed ## ]]\n", - "\n", - "In adhering to this structure, your objective is: \n", - " Given the fields `claims`, produce the fields `verdicts`.\n", - "\n", - "\n", - "\u001b[31mUser message:\u001b[0m\n", - "\n", - "[[ ## claims ## ]]\n", - "[1] «Python was released in 1991.»\n", - "[2] «Python is a compiled language.»\n", - "\n", - "Respond with the corresponding output fields, starting with the field `reasoning`, then `verdicts`, and then ending with the marker for `completed`.\n", - "\n", - "\n", - "\u001b[31mResponse:\u001b[0m\n", - "\n", - "\u001b[32m[[ ## reasoning ## ]]\n", - "The first claim states that \"Python was released in 1991,\" which is true. Python was indeed first released by Guido van Rossum in February 1991. The second claim states that \"Python is a compiled language.\" This is false; Python is primarily an interpreted language, although it can be compiled to bytecode, it is not considered a compiled language in the traditional sense like C or Java.\n", - "\n", - "[[ ## verdicts ## ]]\n", - "[True, False]\n", - "\n", - "[[ ## completed ## ]]\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "### Migrating from DSPy 2.4 to 2.5\n", + "\n", + "**DSPy 2.5** focuses on improving the _pre-optimization_ developer experience, i.e. making it easier to obtain higher-quality outputs from various LMs _out of the box_ prior to setting up a DSPy optimizer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### tl;dr Just set up the LM differently at the start.\n", + "\n", + "For most applications, you will only need to change 1-2 lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "\n", + "lm = dspy.LM('openai/gpt-4o-mini')\n", + "dspy.configure(lm=lm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rest of your code can remain unchanged. However, make sure to experiment with your modules. Improvements to **Adapters** will affect the underlying prompts, before and after optimization, and will enforce stricter validation for LM outputs.\n", + "\n", + "If you want to dive deeper, here's are some new things and things to keep in mind when upgrading.\n", + "\n", + "1. **Use the `dspy.LM` class for setting up language models.**\n", + "2. **Invoke LMs in the same way as before.**\n", + "3. **This internally uses new DSPy `Adapters`, leading to better prompting out of the box.**\n", + "4. **Advanced: If needed, you can set up your own Adapter for LMs.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Use the `dspy.LM` class for setting up language models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Earlier versions of DSPy involved tens of clients for different LM providers, e.g. `dspy.OpenAI`, `dspy.GoogleVertexAI`, and `dspy.HFClientTGI`, etc. These are now deprecated and will be removed in DSPy 2.6.\n", + "\n", + "Instead, use `dspy.LM` to access any LM endpoint for local and remote models. This relies on [LiteLLM](https://github.com/BerriAI/litellm) to translate the different client APIs into an OpenAI-compatible interface.\n", + "\n", + "Any [provider supported in LiteLLM](https://docs.litellm.ai/docs/providers) should work with `dspy.LM`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenAI, which can authenticate via OPENAI_API_KEY.\n", + "lm = dspy.LM('openai/gpt-4o-mini')\n", + "\n", + "# Anthropic, which can authenticate via ANTHROPIC_API_KEY.\n", + "anthropic_lm = dspy.LM('anthropic/claude-3-opus-20240229')\n", + "\n", + "# You can also pass auth information directly.\n", + "anthropic_lm = dspy.LM('anthropic/claude-3-opus-20240229', api_key=\"..\", api_base=\"..\")\n", + "\n", + "# Cohere, which can authenticate via COHERE_API_KEY.\n", + "cohere_lm = dspy.LM('cohere/command-nightly')\n", + "\n", + "# Databricks, which can authenticate via DATABRICKS_API_KEY & DATABRICKS_API_BASE (or automatically on a DB workspace).\n", + "databricks_llama3 = dspy.LM('databricks/databricks-meta-llama-3-1-70b-instruct')\n", + "\n", + "# or many others." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any OpenAI-compatible endpoint is easy to set up with an `openai/` prefix as well. This works great for open LMs from HuggingFace hosted locally with SGLang, VLLM, or HF Text-Generation-Inference." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "sglang_port = 7501\n", + "sglang_url = f\"http://localhost:{sglang_port}/v1\"\n", + "sglang_llama = dspy.LM(\"openai/meta-llama/Meta-Llama-3-8B-Instruct\", api_base=sglang_url)\n", + "\n", + "# You could also use text mode, in which the prompts are *not* formatted as messages.\n", + "sglang_llama_text = dspy.LM(\"openai/meta-llama/Meta-Llama-3-8B-Instruct\", api_base=sglang_url, model_type='text')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Configuring LM attributes\n", + "\n", + "For any LM, you can configure any of the following attributes at initialization or per call." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "gpt_4o_mini = dspy.LM('openai/gpt-4o-mini', temperature=0.9, max_tokens=3000, stop=None, cache=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Caching.\n", + "\n", + "Old clients still exist but you'll get a deprecation warning. They will be removed in DSPy 2.6." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Invoke LMs in the same way as before." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Invoking the LM directly with a prompt is the same as before." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2 + 2 equals 4.']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm(\"What is 2+2?\", temperature=0.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For chat LMs, you can pass a list of messages." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2 + 2 equals 4.']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm(messages=[{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"What is 2+2?\"}])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using DSPy modules is the same as before." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " reasoning='To solve the equation 2 + 2, we simply add the two numbers together. The number 2 represents a quantity, and when we add another 2 to it, we are combining these quantities. Therefore, 2 + 2 equals 4.',\n", + " answer='4'\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "module = dspy.ChainOfThought('question -> answer')\n", + "module(question=\"What is 2+2?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Every LM object maintains the history of its interactions, including inputs, outputs, token usage (and $$$ cost), and metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['prompt', 'messages', 'kwargs', 'response', 'outputs', 'usage', 'cost'])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(lm.history) # e.g., 3 calls to the LM\n", + "\n", + "lm.history[-1].keys() # access the last call to the LM, with all metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) This internally uses new DSPy `Adapters`, leading to better prompting out of the box.\n", + "\n", + "Prompt optimizers in DSPy generate and tune the _instructions_ or the _examples_ in the prompts corresponding to your Signatures. DSPy 2.5 introduces **Adapters** as a layer between Signatures and LMs, responsible for formatting these pieces (Signature I/O fields, instructions, and examples) as well as generating and parsing the outputs. In DSPy 2.5, the default Adapters are now more natively aware of chat LMs and are responsible for enforcing types, building on earlier experimental features from `TypedPredictor` and `configure(experimental=True)`. In our experience, this change tends to deliver more consistent pre-optimization quality from various LMs, especially for sophisticated Signatures." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " reasoning='The first claim states that \"Python was released in 1991,\" which is true. Python was indeed first released by Guido van Rossum in February 1991. The second claim states that \"Python is a compiled language.\" This is false; Python is primarily an interpreted language, although it can be compiled to bytecode, it is not considered a compiled language in the traditional sense like C or Java.',\n", + " verdicts=[True, False]\n", + ")" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fact_checking = dspy.ChainOfThought('claims -> verdicts: list[bool]')\n", + "fact_checking(claims=[\"Python was released in 1991.\", \"Python is a compiled language.\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can as usual inspect the last prompt (and output) from an LM call." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\u001b[31mSystem message:\u001b[0m\n", + "\n", + "Your input fields are:\n", + "1. `claims` (str)\n", + "\n", + "Your output fields are:\n", + "1. `reasoning` (str)\n", + "2. `verdicts` (list[bool])\n", + "\n", + "All interactions will be structured in the following way, with the appropriate values filled in.\n", + "\n", + "[[ ## claims ## ]]\n", + "{claims}\n", + "\n", + "[[ ## reasoning ## ]]\n", + "{reasoning}\n", + "\n", + "[[ ## verdicts ## ]]\n", + "{verdicts}\n", + "\n", + "[[ ## completed ## ]]\n", + "\n", + "In adhering to this structure, your objective is: \n", + " Given the fields `claims`, produce the fields `verdicts`.\n", + "\n", + "\n", + "\u001b[31mUser message:\u001b[0m\n", + "\n", + "[[ ## claims ## ]]\n", + "[1] \u00abPython was released in 1991.\u00bb\n", + "[2] \u00abPython is a compiled language.\u00bb\n", + "\n", + "Respond with the corresponding output fields, starting with the field `reasoning`, then `verdicts`, and then ending with the marker for `completed`.\n", + "\n", + "\n", + "\u001b[31mResponse:\u001b[0m\n", + "\n", + "\u001b[32m[[ ## reasoning ## ]]\n", + "The first claim states that \"Python was released in 1991,\" which is true. Python was indeed first released by Guido van Rossum in February 1991. The second claim states that \"Python is a compiled language.\" This is false; Python is primarily an interpreted language, although it can be compiled to bytecode, it is not considered a compiled language in the traditional sense like C or Java.\n", + "\n", + "[[ ## verdicts ## ]]\n", + "[True, False]\n", + "\n", + "[[ ## completed ## ]]\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "dspy.inspect_history()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Stricter validation for LM outputs.\n", + "\n", + "Previously, only `TypedPredictor`s had strict validation for LM outputs. This is now in all DSPy modules." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "When parsing the output: Expected dict_keys(['reasoning', 'output']) but got dict_keys(['reasoning'])\n" + ] + } + ], + "source": [ + "# a module with insufficient tokens to format the output\n", + "bad_module = dspy.ChainOfThought('input -> output: list[str]', max_tokens=5)\n", + "\n", + "try:\n", + " bad_module(input='oops')\n", + "except ValueError as e:\n", + " print(\"When parsing the output:\", e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are currently considering a \"soft failure\" mode, which fills failed fields with `None`s for cases that don't require explicit exception handling. We are also working on more accessible exceptions and error messages in future 2.5.* releases." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Advanced: If needed, you can set up your own Adapter for LMs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adapters are the first step towards DSPy optimizers that tune the templates themselves. They also make it much easier for you to adapt the templates to your needs programatically, though that remains discouraged for most applications since such effort is often best spent on a program's control flow or optimization. At a minimum, an Adapter defines a method `format(signature, demos, inputs)` that prepares a list of messages (or a prompt string) and a method `parse(signature, completion)` that extract a `dict` of output fields. You can also overwrite `__call__(lm, lm_kwargs, signature, demos, inputs)` to refine the generation or decoding logic for your signatures." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "dspy.configure(adapter=dspy.ChatAdapter()) # ChatAdapter is the default adapter, but you can override it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5) What's next?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next few weeks, we will release into DSPy 2.5 a number of new updates, including: (1) optimizable adapters, smarter retries, better exceptions, support for images, support for multi-turn signatures, improved finetuning and assertions, concurrent with other ongoing efforts from the [DSPy Roadmap](https://github.com/stanfordnlp/dspy/blob/main/docs/docs/roadmap.md), which include more powerful optimizers, human-in-the-loop processes, etc." + ] } - ], - "source": [ - "dspy.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Stricter validation for LM outputs.\n", - "\n", - "Previously, only `TypedPredictor`s had strict validation for LM outputs. This is now in all DSPy modules." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "When parsing the output: Expected dict_keys(['reasoning', 'output']) but got dict_keys(['reasoning'])\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "jun2024_py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" } - ], - "source": [ - "# a module with insufficient tokens to format the output\n", - "bad_module = dspy.ChainOfThought('input -> output: list[str]', max_tokens=5)\n", - "\n", - "try:\n", - " bad_module(input='oops')\n", - "except ValueError as e:\n", - " print(\"When parsing the output:\", e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are currently considering a \"soft failure\" mode, which fills failed fields with `None`s for cases that don't require explicit exception handling. We are also working on more accessible exceptions and error messages in future 2.5.* releases." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Advanced: If needed, you can set up your own Adapter for LMs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adapters are the first step towards DSPy optimizers that tune the templates themselves. They also make it much easier for you to adapt the templates to your needs programatically, though that remains discouraged for most applications since such effort is often best spent on a program's control flow or optimization. At a minimum, an Adapter defines a method `format(signature, demos, inputs)` that prepares a list of messages (or a prompt string) and a method `parse(signature, completion)` that extract a `dict` of output fields. You can also overwrite `__call__(lm, lm_kwargs, signature, demos, inputs)` to refine the generation or decoding logic for your signatures." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "dspy.configure(adapter=dspy.ChatAdapter()) # ChatAdapter is the default adapter, but you can override it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5) What's next?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next few weeks, we will release into DSPy 2.5 a number of new updates, including: (1) optimizable adapters, smarter retries, better exceptions, support for images, support for multi-turn signatures, improved finetuning and assertions, concurrent with other ongoing efforts from the [DSPy Roadmap](https://github.com/stanfordnlp/dspy/blob/main/docs/docs/roadmap.md), which include more powerful optimizers, human-in-the-loop processes, etc." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jun2024_py310", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/agents/avatar_langchain_tools.ipynb b/examples/outdated_v2.4_examples/agents/avatar_langchain_tools.ipynb index fad371c12e..cc442792b8 100644 --- a/examples/outdated_v2.4_examples/agents/avatar_langchain_tools.ipynb +++ b/examples/outdated_v2.4_examples/agents/avatar_langchain_tools.ipynb @@ -1,465 +1,465 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%env SERPER_API_KEY=YOUR_SERPER_API_KEY\n", - "%env OPENAI_API_KEY=YOUR_OPENAI_API_KEY" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%env SERPER_API_KEY=YOUR_SERPER_API_KEY\n", + "%env OPENAI_API_KEY=YOUR_OPENAI_API_KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting Up\n", + "\n", + "The aim of this notebook is to showcase how one can use Langchain Agents using `Avatar` Module and optimize the actor using `AvatarOptimizer` optimizer for each of the toolset for the datasets. We'll be testing our module over 3 datasets:\n", + "\n", + "* ArxivQA\n", + "* SearchAQ\n", + "\n", + "Before loading our datasets and going to the execution part, we'll need to configure the `lm` in `dspy.settings`. For the purpose of this notebook we'll be using `gpt-4o-mini`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import dspy\n", + "\n", + "dspy.settings.configure(\n", + " lm=dspy.OpenAI(\n", + " model=\"gpt-4o-mini\",\n", + " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", + " max_tokens=4000,\n", + " temperature=0,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining Signature\n", + "\n", + "Over all the three datasets the nature of problem is essentially a QA type so we'll create similar signatures `SearchQASignature` and `ArxivQASignature`. The only difference between them is `ArxivQASignature` takes `paper_id` as input too. This is mainly for Arxiv API tool." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SearchQASignature(dspy.Signature):\n", + " \"\"\"You will be given a question. Your task is to answer the question.\"\"\"\n", + " \n", + " question: str = dspy.InputField(\n", + " prefix=\"Question:\",\n", + " desc=\"question to ask\",\n", + " format=lambda x: x.strip(),\n", + " )\n", + " answer: str = dspy.OutputField(\n", + " prefix=\"Answer:\",\n", + " desc=\"answer to the question\",\n", + " )\n", + "\n", + "class ArxivQASignature(dspy.Signature):\n", + " \"\"\"You will be given a question and an Arxiv Paper ID. Your task is to answer the question.\"\"\"\n", + " \n", + " question: str = dspy.InputField(\n", + " prefix=\"Question:\",\n", + " desc=\"question to ask\",\n", + " format=lambda x: x.strip(),\n", + " )\n", + " paper_id: str = dspy.InputField(\n", + " prefix=\"Paper ID:\",\n", + " desc=\"Arxiv Paper ID\",\n", + " )\n", + " answer: str = dspy.OutputField(\n", + " prefix=\"Answer:\",\n", + " desc=\"answer to the question\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading Datasets\n", + "\n", + "We'll be loading three datasets to evaluate our model on them. We'll be using `searchqa` and `arxiv_qa` datasets for the purpose of this notebook. We can use DSPy `DataLoader` to load these datasets from HuggingFace to DSPy friendly format of list of `Example`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from random import sample\n", + "from dspy.datasets import DataLoader\n", + "\n", + "dl = DataLoader()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "searchqa = dl.from_huggingface(\n", + " \"lucadiliello/searchqa\",\n", + " split=\"train\",\n", + " input_keys=(\"question\",),\n", + ")\n", + "\n", + "arxiv_qa = dl.from_huggingface(\n", + " \"taesiri/arxiv_qa\",\n", + " split=\"train\",\n", + " input_keys=(\"question\", \"paper_id\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to demonstration purposes we'll operate on a subset of training and testing dataset. We'll be using 200 examples for training set and 100 examples for testing set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "# Set a random seed for reproducibility\n", + "random.seed(42)\n", + "\n", + "\n", + "sqa_train = [\n", + " dspy.Example(question=example.question, answer=\",\".join(example.answers)).with_inputs(\"question\")\n", + " for example in sample(searchqa, 200)\n", + "]\n", + "sqa_test = [\n", + " dspy.Example(question=example.question, answer=\",\".join(example.answers)).with_inputs(\"question\")\n", + " for example in sample(searchqa, 100)\n", + "]\n", + "\n", + "aqa_train = [\n", + " dspy.Example(question=example.question, paper_id=example.paper_id, answer=example.answer).with_inputs(\"question\", \"paper_id\")\n", + " for example in sample(arxiv_qa, 200)\n", + "]\n", + "aqa_test = [\n", + " dspy.Example(question=example.question, paper_id=example.paper_id, answer=example.answer).with_inputs(\"question\", \"paper_id\")\n", + " for example in sample(arxiv_qa, 100)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting Up Tools\n", + "\n", + "We'll setup `Avatar` modules for both signatures and all the `tools` can be used by each of the dataset i.e. `searchqa` and `arxiv_qa`. `Tool` is a pydantic model that Avatar expects the `tools` to be composed as more specifically it have 4 fields:\n", + "\n", + "* `name` : Name of the tool\n", + "* `input_type` : Type of input the tool accepts\n", + "* `output_type` : Type of output the tool returns\n", + "* `tool` : The actual tool object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dspy.predict.avatar import Tool, Avatar\n", + "from langchain_community.utilities import GoogleSerperAPIWrapper, ArxivAPIWrapper\n", + "\n", + "tools = [\n", + " Tool(\n", + " tool=GoogleSerperAPIWrapper(),\n", + " name=\"WEB_SEARCH\",\n", + " desc=\"If you have a question, you can use this tool to search the web for the answer.\"\n", + " ),\n", + " Tool(\n", + " tool=ArxivAPIWrapper(),\n", + " name=\"ARXIV_SEARCH\",\n", + " desc=\"Pass the arxiv paper id to get the paper information.\",\n", + " input_type=\"Arxiv Paper ID\",\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have defined our `tools`, we can now create an `Avatar` object by passing the `tools` and `signature`. It takes 2 more optional parameters `verbose` and `max_iters`. `verbose` is used to display the logs and `max_iters` is used to control the number of iterations in multi step execution. \n", + "\n", + "An avatar agent stops the tool usage iteration once it reaches `max_iters` or when it prompts `Finish`. You can also create custom tools too, all you need to make sure is:\n", + "\n", + "* You pass is a class object.\n", + "* Implements `__init__` and `run` method.\n", + "* Must take 1 string a input and returns 1 string as output.\n", + "\n", + "If your tool doesn't return or takes input a string then you can make a custom wrapper to take care of that for now. In future we'll try to enable a diverse tool usage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arxiv_agent = Avatar(\n", + " tools=tools,\n", + " signature=ArxivQASignature,\n", + " verbose=True,\n", + ")\n", + "\n", + "search_agent = Avatar(\n", + " tools=tools,\n", + " signature=SearchQASignature,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation\n", + "\n", + "Open enden QA tasks are hard to evaluate on rigid metrics like exact match. So, we'll be using an improvised LLM as Judge for the evaluation of our model on test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class Evaluator(dspy.Signature):\n", + " \"\"\"Please act as an impartial judge and evaluate the quality of the responses provided by multiple AI assistants to the user question displayed below. You should choose the assistant that offers a better user experience by interacting with the user more effectively and efficiently, and providing a correct final response to the user's question.\n", + " \n", + "Rules:\n", + "1. Avoid Position Biases: Ensure that the order in which the responses were presented does not influence your decision. Evaluate each response on its own merits.\n", + "2. Length of Responses: Do not let the length of the responses affect your evaluation. Focus on the quality and relevance of the response. A good response is targeted and addresses the user's needs effectively, rather than simply being detailed.\n", + "3. Objectivity: Be as objective as possible. Consider the user's perspective and overall experience with each assistant.\"\"\"\n", + " \n", + " question: str = dspy.InputField(\n", + " prefix=\"Question:\",\n", + " desc=\"question to ask\",\n", + " )\n", + " reference_answer: str = dspy.InputField(\n", + " prefix=\"Reference Answer:\",\n", + " desc=\"Answer to the question given by the model.\",\n", + " )\n", + " answer: str = dspy.InputField(\n", + " prefix=\"Answer:\",\n", + " desc=\"Answer to the question given by the model.\",\n", + " )\n", + " rationale: str = dspy.OutputField(\n", + " prefix=\"Rationale:\",\n", + " desc=\"Explanation of why the answer is correct or incorrect.\",\n", + " )\n", + " is_correct: bool = dspy.OutputField(\n", + " prefix=\"Correct:\",\n", + " desc=\"Whether the answer is correct.\",\n", + " )\n", + "\n", + "\n", + "evaluator = dspy.TypedPredictor(Evaluator)\n", + "\n", + "\n", + "def metric(example, prediction, trace=None):\n", + " return int(\n", + " evaluator(\n", + " question=example.question,\n", + " answer=prediction.answer,\n", + " reference_answer=example.answer\n", + " ).is_correct\n", + " ) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For evaluation we can't use `dspy.Evaluate`, reason being that `Avatar` changes it's signature per iteration by adding the actions and it's results to it as fields. So we can create our own hacky thread safe evaluator for it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tqdm\n", + "\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "def process_example(example, signature):\n", + " try:\n", + " avatar = Avatar(\n", + " signature,\n", + " tools=tools,\n", + " verbose=False,\n", + " )\n", + " prediction = avatar(**example.inputs().toDict())\n", + "\n", + " return metric(example, prediction)\n", + " except Exception as e:\n", + " print(e)\n", + " return 0\n", + "\n", + "\n", + "def multi_thread_executor(test_set, signature, num_threads=60):\n", + " total_score = 0\n", + " total_examples = len(test_set)\n", + "\n", + " with ThreadPoolExecutor(max_workers=num_threads) as executor:\n", + " futures = [executor.submit(process_example, example, signature) for example in test_set]\n", + "\n", + " for future in tqdm.tqdm(futures, total=total_examples, desc=\"Processing examples\"):\n", + " total_score += future.result()\n", + "\n", + " avg_metric = total_score / total_examples\n", + " return avg_metric" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sqa_score = multi_thread_executor(sqa_test, SearchQASignature)\n", + "print(f\"Average Score on SearchQA: {sqa_score:.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aqa_score = multi_thread_executor(aqa_test, ArxivQASignature)\n", + "print(f\"Average Score on ArxivQA: {aqa_score:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization\n", + "\n", + "For the optimization of the `Actor` we'll be using `AvatarOptimizer`. It's a DSPy implementation of the [Avatar](https://github.com/zou-group/avatar/) method that optimizes the `Actor` for the given `tools` using a comparator module that optimizes Actor instruction. Note, that Actor is the Module that directs tool execution and flow, it's not the signature that we are passing. It doesn't optimize the instruction of the signature we pass. It takes the following parameters:\n", + "\n", + "* `metric`: Metric that we'll be optimizing for\n", + "* `max_iters`: Maximum number of iterations for the optimizer\n", + "* `lower_bound`: Lower bound for the metric to classify example as negative\n", + "* `upper_bound`: Upper bound for the metric to classify example as positive\n", + "* `max_positive_inputs`: Maximum number of positive inputs sampled for comparator\n", + "* `max_negative_inputs`: Maximum number of negative inputs sampled for comparator\n", + "* `optimize_for`: Whether we want to maximize the metric or minimize it during optimization\n", + "\n", + "Once the optimizer is done we can get the optimized actor and use it for the evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dspy.teleprompt import AvatarOptimizer\n", + "\n", + "teleprompter = AvatarOptimizer(\n", + " metric=metric,\n", + " max_iters=1,\n", + " max_negative_inputs=10,\n", + " max_positive_inputs=10,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "optimized_arxiv_agent = teleprompter.compile(\n", + " student=arxiv_agent,\n", + " trainset=aqa_train\n", + ")\n", + "\n", + "optimized_search_agent = teleprompter.compile(\n", + " student=search_agent,\n", + " trainset=sqa_train\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can evaluate our actor module, for this we've provided an implementation of thread safe evaluator that we above as part of class method of `AvatarOptimizer`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter.thread_safe_evaluator(aqa_test, optimized_arxiv_agent)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter.thread_safe_evaluator(sqa_test, optimized_search_agent)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dspy-74wouE_3-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting Up\n", - "\n", - "The aim of this notebook is to showcase how one can use Langchain Agents using `Avatar` Module and optimize the actor using `AvatarOptimizer` optimizer for each of the toolset for the datasets. We'll be testing our module over 3 datasets:\n", - "\n", - "* ArxivQA\n", - "* SearchAQ\n", - "\n", - "Before loading our datasets and going to the execution part, we'll need to configure the `lm` in `dspy.settings`. For the purpose of this notebook we'll be using `gpt-4o-mini`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import dspy\n", - "\n", - "dspy.settings.configure(\n", - " lm=dspy.OpenAI(\n", - " model=\"gpt-4o-mini\",\n", - " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", - " max_tokens=4000,\n", - " temperature=0,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining Signature\n", - "\n", - "Over all the three datasets the nature of problem is essentially a QA type so we'll create similar signatures `SearchQASignature` and `ArxivQASignature`. The only difference between them is `ArxivQASignature` takes `paper_id` as input too. This is mainly for Arxiv API tool." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class SearchQASignature(dspy.Signature):\n", - " \"\"\"You will be given a question. Your task is to answer the question.\"\"\"\n", - " \n", - " question: str = dspy.InputField(\n", - " prefix=\"Question:\",\n", - " desc=\"question to ask\",\n", - " format=lambda x: x.strip(),\n", - " )\n", - " answer: str = dspy.OutputField(\n", - " prefix=\"Answer:\",\n", - " desc=\"answer to the question\",\n", - " )\n", - "\n", - "class ArxivQASignature(dspy.Signature):\n", - " \"\"\"You will be given a question and an Arxiv Paper ID. Your task is to answer the question.\"\"\"\n", - " \n", - " question: str = dspy.InputField(\n", - " prefix=\"Question:\",\n", - " desc=\"question to ask\",\n", - " format=lambda x: x.strip(),\n", - " )\n", - " paper_id: str = dspy.InputField(\n", - " prefix=\"Paper ID:\",\n", - " desc=\"Arxiv Paper ID\",\n", - " )\n", - " answer: str = dspy.OutputField(\n", - " prefix=\"Answer:\",\n", - " desc=\"answer to the question\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading Datasets\n", - "\n", - "We'll be loading three datasets to evaluate our model on them. We'll be using `searchqa` and `arxiv_qa` datasets for the purpose of this notebook. We can use DSPy `DataLoader` to load these datasets from HuggingFace to DSPy friendly format of list of `Example`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from random import sample\n", - "from dspy.datasets import DataLoader\n", - "\n", - "dl = DataLoader()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "searchqa = dl.from_huggingface(\n", - " \"lucadiliello/searchqa\",\n", - " split=\"train\",\n", - " input_keys=(\"question\",),\n", - ")\n", - "\n", - "arxiv_qa = dl.from_huggingface(\n", - " \"taesiri/arxiv_qa\",\n", - " split=\"train\",\n", - " input_keys=(\"question\", \"paper_id\"),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Due to demonstration purposes we'll operate on a subset of training and testing dataset. We'll be using 200 examples for training set and 100 examples for testing set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "\n", - "# Set a random seed for reproducibility\n", - "random.seed(42)\n", - "\n", - "\n", - "sqa_train = [\n", - " dspy.Example(question=example.question, answer=\",\".join(example.answers)).with_inputs(\"question\")\n", - " for example in sample(searchqa, 200)\n", - "]\n", - "sqa_test = [\n", - " dspy.Example(question=example.question, answer=\",\".join(example.answers)).with_inputs(\"question\")\n", - " for example in sample(searchqa, 100)\n", - "]\n", - "\n", - "aqa_train = [\n", - " dspy.Example(question=example.question, paper_id=example.paper_id, answer=example.answer).with_inputs(\"question\", \"paper_id\")\n", - " for example in sample(arxiv_qa, 200)\n", - "]\n", - "aqa_test = [\n", - " dspy.Example(question=example.question, paper_id=example.paper_id, answer=example.answer).with_inputs(\"question\", \"paper_id\")\n", - " for example in sample(arxiv_qa, 100)\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting Up Tools\n", - "\n", - "We'll setup `Avatar` modules for both signatures and all the `tools` can be used by each of the dataset i.e. `searchqa` and `arxiv_qa`. `Tool` is a pydantic model that Avatar expects the `tools` to be composed as more specifically it have 4 fields:\n", - "\n", - "* `name` : Name of the tool\n", - "* `input_type` : Type of input the tool accepts\n", - "* `output_type` : Type of output the tool returns\n", - "* `tool` : The actual tool object" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from dspy.predict.avatar import Tool, Avatar\n", - "from langchain_community.utilities import GoogleSerperAPIWrapper, ArxivAPIWrapper\n", - "\n", - "tools = [\n", - " Tool(\n", - " tool=GoogleSerperAPIWrapper(),\n", - " name=\"WEB_SEARCH\",\n", - " desc=\"If you have a question, you can use this tool to search the web for the answer.\"\n", - " ),\n", - " Tool(\n", - " tool=ArxivAPIWrapper(),\n", - " name=\"ARXIV_SEARCH\",\n", - " desc=\"Pass the arxiv paper id to get the paper information.\",\n", - " input_type=\"Arxiv Paper ID\",\n", - " ),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once we have defined our `tools`, we can now create an `Avatar` object by passing the `tools` and `signature`. It takes 2 more optional parameters `verbose` and `max_iters`. `verbose` is used to display the logs and `max_iters` is used to control the number of iterations in multi step execution. \n", - "\n", - "An avatar agent stops the tool usage iteration once it reaches `max_iters` or when it prompts `Finish`. You can also create custom tools too, all you need to make sure is:\n", - "\n", - "* You pass is a class object.\n", - "* Implements `__init__` and `run` method.\n", - "* Must take 1 string a input and returns 1 string as output.\n", - "\n", - "If your tool doesn't return or takes input a string then you can make a custom wrapper to take care of that for now. In future we'll try to enable a diverse tool usage." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arxiv_agent = Avatar(\n", - " tools=tools,\n", - " signature=ArxivQASignature,\n", - " verbose=True,\n", - ")\n", - "\n", - "search_agent = Avatar(\n", - " tools=tools,\n", - " signature=SearchQASignature,\n", - " verbose=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluation\n", - "\n", - "Open enden QA tasks are hard to evaluate on rigid metrics like exact match. So, we'll be using an improvised LLM as Judge for the evaluation of our model on test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Evaluator(dspy.Signature):\n", - " \"\"\"Please act as an impartial judge and evaluate the quality of the responses provided by multiple AI assistants to the user question displayed below. You should choose the assistant that offers a better user experience by interacting with the user more effectively and efficiently, and providing a correct final response to the user's question.\n", - " \n", - "Rules:\n", - "1. Avoid Position Biases: Ensure that the order in which the responses were presented does not influence your decision. Evaluate each response on its own merits.\n", - "2. Length of Responses: Do not let the length of the responses affect your evaluation. Focus on the quality and relevance of the response. A good response is targeted and addresses the user's needs effectively, rather than simply being detailed.\n", - "3. Objectivity: Be as objective as possible. Consider the user's perspective and overall experience with each assistant.\"\"\"\n", - " \n", - " question: str = dspy.InputField(\n", - " prefix=\"Question:\",\n", - " desc=\"question to ask\",\n", - " )\n", - " reference_answer: str = dspy.InputField(\n", - " prefix=\"Reference Answer:\",\n", - " desc=\"Answer to the question given by the model.\",\n", - " )\n", - " answer: str = dspy.InputField(\n", - " prefix=\"Answer:\",\n", - " desc=\"Answer to the question given by the model.\",\n", - " )\n", - " rationale: str = dspy.OutputField(\n", - " prefix=\"Rationale:\",\n", - " desc=\"Explanation of why the answer is correct or incorrect.\",\n", - " )\n", - " is_correct: bool = dspy.OutputField(\n", - " prefix=\"Correct:\",\n", - " desc=\"Whether the answer is correct.\",\n", - " )\n", - "\n", - "\n", - "evaluator = dspy.TypedPredictor(Evaluator)\n", - "\n", - "\n", - "def metric(example, prediction, trace=None):\n", - " return int(\n", - " evaluator(\n", - " question=example.question,\n", - " answer=prediction.answer,\n", - " reference_answer=example.answer\n", - " ).is_correct\n", - " ) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For evaluation we can't use `dspy.Evaluate`, reason being that `Avatar` changes it's signature per iteration by adding the actions and it's results to it as fields. So we can create our own hacky thread safe evaluator for it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tqdm\n", - "\n", - "from concurrent.futures import ThreadPoolExecutor\n", - "\n", - "def process_example(example, signature):\n", - " try:\n", - " avatar = Avatar(\n", - " signature,\n", - " tools=tools,\n", - " verbose=False,\n", - " )\n", - " prediction = avatar(**example.inputs().toDict())\n", - "\n", - " return metric(example, prediction)\n", - " except Exception as e:\n", - " print(e)\n", - " return 0\n", - "\n", - "\n", - "def multi_thread_executor(test_set, signature, num_threads=60):\n", - " total_score = 0\n", - " total_examples = len(test_set)\n", - "\n", - " with ThreadPoolExecutor(max_workers=num_threads) as executor:\n", - " futures = [executor.submit(process_example, example, signature) for example in test_set]\n", - "\n", - " for future in tqdm.tqdm(futures, total=total_examples, desc=\"Processing examples\"):\n", - " total_score += future.result()\n", - "\n", - " avg_metric = total_score / total_examples\n", - " return avg_metric" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sqa_score = multi_thread_executor(sqa_test, SearchQASignature)\n", - "print(f\"Average Score on SearchQA: {sqa_score:.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aqa_score = multi_thread_executor(aqa_test, ArxivQASignature)\n", - "print(f\"Average Score on ArxivQA: {aqa_score:.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization\n", - "\n", - "For the optimization of the `Actor` we'll be using `AvatarOptimizer`. It's a DSPy implementation of the [Avatar](https://github.com/zou-group/avatar/) method that optimizes the `Actor` for the given `tools` using a comparator module that optimizes Actor instruction. Note, that Actor is the Module that directs tool execution and flow, it's not the signature that we are passing. It doesn't optimize the instruction of the signature we pass. It takes the following parameters:\n", - "\n", - "* `metric`: Metric that we'll be optimizing for\n", - "* `max_iters`: Maximum number of iterations for the optimizer\n", - "* `lower_bound`: Lower bound for the metric to classify example as negative\n", - "* `upper_bound`: Upper bound for the metric to classify example as positive\n", - "* `max_positive_inputs`: Maximum number of positive inputs sampled for comparator\n", - "* `max_negative_inputs`: Maximum number of negative inputs sampled for comparator\n", - "* `optimize_for`: Whether we want to maximize the metric or minimize it during optimization\n", - "\n", - "Once the optimizer is done we can get the optimized actor and use it for the evaluation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from dspy.teleprompt import AvatarOptimizer\n", - "\n", - "teleprompter = AvatarOptimizer(\n", - " metric=metric,\n", - " max_iters=1,\n", - " max_negative_inputs=10,\n", - " max_positive_inputs=10,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimized_arxiv_agent = teleprompter.compile(\n", - " student=arxiv_agent,\n", - " trainset=aqa_train\n", - ")\n", - "\n", - "optimized_search_agent = teleprompter.compile(\n", - " student=search_agent,\n", - " trainset=sqa_train\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can evaluate our actor module, for this we've provided an implementation of thread safe evaluator that we above as part of class method of `AvatarOptimizer`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter.thread_safe_evaluator(aqa_test, optimized_arxiv_agent)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter.thread_safe_evaluator(sqa_test, optimized_search_agent)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dspy-74wouE_3-py3.10", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/agents/multi_agent.ipynb b/examples/outdated_v2.4_examples/agents/multi_agent.ipynb index 6ca9540551..5a69d734f3 100644 --- a/examples/outdated_v2.4_examples/agents/multi_agent.ipynb +++ b/examples/outdated_v2.4_examples/agents/multi_agent.ipynb @@ -1,893 +1,893 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "### Multi-Agent DSPy Programs: Bootstrapping & Aggregating Multiple `ReAct` Agents\n", - "\n", - "This is a quick (somewhat advanced) example of DSPy. You're given a hard QA task and an agent architecture (`dspy.ReAct`), how do you get high scores without tinkering with prompts?\n", - "\n", - "There are many ways, but this notebook shows one complex strategy that DSPy makes near-trivial to achieve: we'll automatically bootstrap five different highly-effective prompts for ReAct, then optimize an aggregator that combines their powers.\n", - "\n", - "As is usually the case with DSPy, the code to do this is probably shorter than describing it in English, so let's jump right into that." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 0) TLDR.\n", - "\n", - "We'll build a ReAct agent in DSPy that scores 30% accuracy on a retrieval-based question answering task.\n", - "\n", - "Then, we'll optimize it with `BootstrapFewShotWithRandomSearch` to get 46% accuracy.\n", - "\n", - "Then, we'll build a multi-agent aggregator over five different optimized versions of the agent.\n", - "\n", - "Our unoptimized aggregator will score 26%. It doesn't understand the task. Hence, we'll optimize the aggregator too.\n", - "\n", - "We'll end up with an optimized multi-agent system that scores a whopping 60% accuracy on the same task.\n", - "\n", - "The core portion of the code to do this can be fit into 10 lines of DSPy, but we'll sprinkle some short explanations below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Setting Up.\n", - "\n", - "We'll configure the language model (GPT-3.5) and the retrieval model (ColBERTv2 over Wikipedia)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.datasets.hotpotqa import HotPotQA\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", - "\n", - "gpt3 = dspy.OpenAI('gpt-3.5-turbo-0125', max_tokens=1000)\n", - "colbert = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "dspy.configure(lm=gpt3, rm=colbert)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Loading some data.\n", - "\n", - "We'll load 150 examples for training (`trainset`), 50 examples for validation & optimization (`valset`), and 300 examples for evaluation (`devset`)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "### Multi-Agent DSPy Programs: Bootstrapping & Aggregating Multiple `ReAct` Agents\n", + "\n", + "This is a quick (somewhat advanced) example of DSPy. You're given a hard QA task and an agent architecture (`dspy.ReAct`), how do you get high scores without tinkering with prompts?\n", + "\n", + "There are many ways, but this notebook shows one complex strategy that DSPy makes near-trivial to achieve: we'll automatically bootstrap five different highly-effective prompts for ReAct, then optimize an aggregator that combines their powers.\n", + "\n", + "As is usually the case with DSPy, the code to do this is probably shorter than describing it in English, so let's jump right into that." ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=300, test_size=0)\n", - "trainset = [x.with_inputs('question') for x in dataset.train[0:150]]\n", - "valset = [x.with_inputs('question') for x in dataset.train[150:200]]\n", - "devset = [x.with_inputs('question') for x in dataset.dev]\n", - "\n", - "# show an example datapoint; it's just a question-answer pair\n", - "trainset[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) ReAct Agent.\n", - "\n", - "Our agent will just be a DSPy ReAct agent that takes a `question` and outputs the `answer` by using a ColBERTv2 retrieval tool." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "agent = dspy.ReAct(\"question -> answer\", tools=[dspy.Retrieve(k=1)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's evaluate this **unoptimized** ReAct agent on the `devset`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 91 / 300 (30.3): 100%|██████████| 300/300 [00:01<00:00, 161.84it/s]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0) TLDR.\n", + "\n", + "We'll build a ReAct agent in DSPy that scores 30% accuracy on a retrieval-based question answering task.\n", + "\n", + "Then, we'll optimize it with `BootstrapFewShotWithRandomSearch` to get 46% accuracy.\n", + "\n", + "Then, we'll build a multi-agent aggregator over five different optimized versions of the agent.\n", + "\n", + "Our unoptimized aggregator will score 26%. It doesn't understand the task. Hence, we'll optimize the aggregator too.\n", + "\n", + "We'll end up with an optimized multi-agent system that scores a whopping 60% accuracy on the same task.\n", + "\n", + "The core portion of the code to do this can be fit into 10 lines of DSPy, but we'll sprinkle some short explanations below." + ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}[['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No, Cangzhou is in the Hebei province, while Qionghai is in the Hainan province of China.False
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}[[\"2017 NHL Expansion Draft | The 2017 NHL Expansion Draft was an expansion draft conducted by the National Hockey League on June 18–20, 2017 to...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}[['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...Tweed RiverFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}[['Æthelweard (son of Alfred) | Æthelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.']]King Alfred the Great✔️ [True]
\n" - ], - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Setting Up.\n", + "\n", + "We'll configure the language model (GPT-3.5) and the retrieval model (ColBERTv2 over Wikipedia)." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " - ], - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.datasets.hotpotqa import HotPotQA\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "\n", + "gpt3 = dspy.OpenAI('gpt-3.5-turbo-0125', max_tokens=1000)\n", + "colbert = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "dspy.configure(lm=gpt3, rm=colbert)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "30.33" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Loading some data.\n", + "\n", + "We'll load 150 examples for training (`trainset`), 50 examples for validation & optimization (`valset`), and 300 examples for evaluation (`devset`)." ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Set up an evaluator on the first 300 examples of the devset.\n", - "config = dict(num_threads=8, display_progress=True, display_table=5)\n", - "evaluate = Evaluate(devset=devset, metric=dspy.evaluate.answer_exact_match, **config)\n", - "\n", - "evaluate(agent)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Optimized ReAct.\n", - "\n", - "Let's use DSPy's simple `BootstrapFewShotWithRandomSearch` optimizer to create successful examples of the ReAct program and attempt to optimize the prompts using those constructed examples. In the future, we could try more sophisticated DSPy optimizers too, like `MIPRO`.\n", - "\n", - "We'll bootstrap 20 programs that way. Examples will be bootstrapped starting from the `trainset` and optimized over our tiny `valset`. We'll evaluate later on the `devset`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 50 (28.0): 100%|██████████| 50/50 [00:00<00:00, 151.32it/s]\n", - "Average Metric: 14 / 50 (28.0): 100%|██████████| 50/50 [00:00<00:00, 1191.35it/s]\n", - " 4%|▍ | 6/150 [00:00<00:00, 216.36it/s]\n", - "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 158.43it/s]\n", - " 3%|▎ | 4/150 [00:00<00:00, 258.73it/s]\n", - "Average Metric: 21 / 50 (42.0): 100%|██████████| 50/50 [00:00<00:00, 184.63it/s]\n", - " 3%|▎ | 4/150 [00:00<00:01, 125.61it/s]\n", - "Average Metric: 24 / 50 (48.0): 100%|██████████| 50/50 [00:00<00:00, 130.39it/s]\n", - " 1%|▏ | 2/150 [00:00<00:00, 213.13it/s]\n", - "Average Metric: 20 / 50 (40.0): 100%|██████████| 50/50 [00:00<00:00, 158.40it/s]\n", - " 3%|▎ | 4/150 [00:00<00:00, 387.38it/s]\n", - "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 168.50it/s]\n", - " 4%|▍ | 6/150 [00:00<00:00, 201.99it/s]\n", - "Average Metric: 12 / 50 (24.0): 100%|██████████| 50/50 [00:00<00:00, 152.09it/s]\n", - " 6%|▌ | 9/150 [00:00<00:00, 203.21it/s]\n", - "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 231.82it/s]\n", - " 8%|▊ | 12/150 [00:00<00:00, 275.48it/s]\n", - "Average Metric: 16 / 50 (32.0): 100%|██████████| 50/50 [00:00<00:00, 165.50it/s]\n", - " 9%|▊ | 13/150 [00:00<00:00, 238.67it/s]\n", - "Average Metric: 20 / 50 (40.0): 100%|██████████| 50/50 [00:00<00:00, 167.18it/s]\n", - " 1%| | 1/150 [00:00<00:00, 624.06it/s]\n", - "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 963.47it/s] \n", - " 3%|▎ | 5/150 [00:00<00:00, 335.88it/s]\n", - "Average Metric: 21 / 50 (42.0): 100%|██████████| 50/50 [00:00<00:00, 173.26it/s]\n", - " 1%| | 1/150 [00:00<00:00, 196.26it/s]\n", - "Average Metric: 25 / 50 (50.0): 100%|██████████| 50/50 [00:00<00:00, 156.35it/s]\n", - " 7%|▋ | 10/150 [00:00<00:00, 415.36it/s]\n", - "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 180.18it/s]\n", - " 11%|█▏ | 17/150 [00:00<00:00, 265.33it/s]\n", - "Average Metric: 23 / 50 (46.0): 100%|██████████| 50/50 [00:00<00:00, 94.46it/s] \n", - " 4%|▍ | 6/150 [00:00<00:00, 273.37it/s]\n", - "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 193.94it/s]\n", - " 1%|▏ | 2/150 [00:00<00:00, 290.68it/s]\n", - "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 1055.39it/s]\n", - " 1%| | 1/150 [00:00<00:00, 268.11it/s]\n", - "Average Metric: 16 / 50 (32.0): 100%|██████████| 50/50 [00:00<00:00, 148.74it/s]\n", - " 3%|▎ | 4/150 [00:00<00:00, 204.06it/s]\n", - "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 183.84it/s]\n", - " 9%|▉ | 14/150 [00:00<00:00, 343.80it/s]\n", - "Average Metric: 23 / 50 (46.0): 100%|██████████| 50/50 [00:00<00:00, 148.82it/s]\n", - " 1%|▏ | 2/150 [00:00<00:00, 590.25it/s]\n", - "Average Metric: 17 / 50 (34.0): 100%|██████████| 50/50 [00:00<00:00, 223.29it/s]\n", - " 1%| | 1/150 [00:00<00:00, 169.02it/s]\n", - "Average Metric: 15 / 50 (30.0): 100%|██████████| 50/50 [00:00<00:00, 128.94it/s]\n" - ] - } - ], - "source": [ - "config = dict(max_bootstrapped_demos=2, max_labeled_demos=0, num_candidate_programs=20, num_threads=8)\n", - "tp = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match, **config)\n", - "optimized_react = tp.compile(agent, trainset=trainset, valset=valset)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=300, test_size=0)\n", + "trainset = [x.with_inputs('question') for x in dataset.train[0:150]]\n", + "valset = [x.with_inputs('question') for x in dataset.train[150:200]]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]\n", + "\n", + "# show an example datapoint; it's just a question-answer pair\n", + "trainset[0]" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 138 / 300 (46.0): 100%|██████████| 300/300 [00:00<00:00, 512.74it/s]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) ReAct Agent.\n", + "\n", + "Our agent will just be a DSPy ReAct agent that takes a `question` and outputs the `answer` by using a ColBERTv2 retrieval tool." + ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}[['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...no✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}[['2017–18 Pittsburgh Penguins season | The 2017–18 Pittsburgh Penguins season will be the 51st season for the National Hockey League ice hockey team that was...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}[['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...Crichton Collegiate Church is located in Midlothian, Scotland, near the hamlet of Crichton, about 7.5 miles south of Edinburgh.False
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}[['Æthelweard (son of Alfred) | Æthelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.'], ['Æthelstan of Kent |...Alfred the GreatFalse
\n" - ], - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "agent = dspy.ReAct(\"question -> answer\", tools=[dspy.Retrieve(k=1)])" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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\n", - " " - ], - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate this **unoptimized** ReAct agent on the `devset`." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "46.0" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 91 / 300 (30.3): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [00:01<00:00, 161.84it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}[['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No, Cangzhou is in the Hebei province, while Qionghai is in the Hainan province of China.False
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017\u201318 Pittsburgh Penguins season'}[[\"2017 NHL Expansion Draft | The 2017 NHL Expansion Draft was an expansion draft conducted by the National Hockey League on June 18\u201320, 2017 to...National Hockey League\u2714\ufe0f [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}[['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman\u2714\ufe0f [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...Tweed RiverFalse
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'Ealhswith', '\u00c6thelweard (son of Alfred)'}[['\u00c6thelweard (son of Alfred) | \u00c6thelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.']]King Alfred the Great\u2714\ufe0f [True]
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " ... 295 more rows not displayed ...\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "30.33" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set up an evaluator on the first 300 examples of the devset.\n", + "config = dict(num_threads=8, display_progress=True, display_table=5)\n", + "evaluate = Evaluate(devset=devset, metric=dspy.evaluate.answer_exact_match, **config)\n", + "\n", + "evaluate(agent)" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(optimized_react)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5) Zero-Shot Aggregator.\n", - "\n", - "Let's now extract the best five bootstrapped ReAct programs. We'll build a simple DSPy aggregator that runs all of them then produces a final answer." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from dsp.utils import flatten, deduplicate\n", - "\n", - "# the best-performing five ReAct programs from the optimization process\n", - "AGENTS = [x[-1] for x in optimized_react.candidate_programs[:5]]\n", - "\n", - "class Aggregator(dspy.Module):\n", - "\tdef __init__(self, temperature=0.0):\n", - "\t\tself.aggregate = dspy.ChainOfThought('context, question -> answer')\n", - "\t\tself.temperature = temperature\n", - "\n", - "\tdef forward(self, question):\n", - "\t\t# Run all five agents with high temperature, then extract and deduplicate their observed contexts\n", - "\t\twith dspy.context(lm=gpt3.copy(temperature=self.temperature)):\n", - "\t\t\tpreds = [agent(question=question) for agent in AGENTS]\n", - "\t\t\tcontext = deduplicate(flatten([flatten(p.observations) for p in preds]))\n", - "\n", - "\t\t# Run the aggregation step to produce a final answer\n", - "\t\treturn self.aggregate(context=context, question=question)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's quickly evaluate the aggregator prior to optimization." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 78 / 300 (26.0): 100%|██████████| 300/300 [00:06<00:00, 45.38it/s]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Optimized ReAct.\n", + "\n", + "Let's use DSPy's simple `BootstrapFewShotWithRandomSearch` optimizer to create successful examples of the ReAct program and attempt to optimize the prompts using those constructed examples. In the future, we could try more sophisticated DSPy optimizers too, like `MIPRO`.\n", + "\n", + "We'll bootstrap 20 programs that way. Examples will be bootstrapped starting from the `trainset` and optimized over our tiny `valset`. We'll evaluate later on the `devset`." + ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}determine if both Cangzhou and Qionghai are in the Hebei province of China. We need to carefully analyze the information provided in the context to...No, only Cangzhou is in the Hebei province of China. Qionghai is located in Hainan province.False
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}produce the answer. We know that Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season. Looking at the context provided, we...The 2017 NHL Expansion Draft conducted by the National Hockey League filled the roster of the Vegas Golden Knights, including selecting Marc-Andre Fleury for the...False
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}identify the retired Canadian professional ice hockey player and current general manager of the Tampa Bay Lightning of the National Hockey League (NHL) whose retirement...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}identify the river near the Crichton Collegiate Church. We know that the church is situated in Midlothian, Scotland, and the River Esk flows through Midlothian...The River EskFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}produce the answer. We know from the context that Ealhswith had a son named Æthelweard in the 10th century A.D. Now, looking at the information...King Alfred the Great✔️ [True]
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 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}[['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...no\u2714\ufe0f [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017\u201318 Pittsburgh Penguins season'}[['2017\u201318 Pittsburgh Penguins season | The 2017\u201318 Pittsburgh Penguins season will be the 51st season for the National Hockey League ice hockey team that was...National Hockey League\u2714\ufe0f [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}[['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman\u2714\ufe0f [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...Crichton Collegiate Church is located in Midlothian, Scotland, near the hamlet of Crichton, about 7.5 miles south of Edinburgh.False
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'Ealhswith', '\u00c6thelweard (son of Alfred)'}[['\u00c6thelweard (son of Alfred) | \u00c6thelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.'], ['\u00c6thelstan of Kent |...Alfred the GreatFalse
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We'll build a simple DSPy aggregator that runs all of them then produces a final answer." ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aggregator = Aggregator()\n", - "evaluate(aggregator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6) Optimized Aggregator." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 16 / 50 (32.0): 100%|██████████| 50/50 [00:00<00:00, 153.98it/s]\n", - "Average Metric: 27 / 50 (54.0): 100%|██████████| 50/50 [00:00<00:00, 82.75it/s]\n", - " 3%|▎ | 4/150 [00:00<00:03, 45.32it/s]\n", - "Average Metric: 28 / 50 (56.0): 100%|██████████| 50/50 [00:00<00:00, 156.28it/s]\n", - " 1%|▏ | 2/150 [00:00<00:03, 39.99it/s]\n", - "Average Metric: 28 / 50 (56.0): 100%|██████████| 50/50 [00:00<00:00, 162.26it/s]\n", - " 1%| | 1/150 [00:00<00:02, 51.23it/s]\n", - "Average Metric: 26 / 50 (52.0): 100%|██████████| 50/50 [00:00<00:00, 158.64it/s]\n", - " 1%| | 1/150 [00:00<00:00, 155.47it/s]\n", - "Average Metric: 28 / 50 (56.0): 100%|██████████| 50/50 [00:00<00:00, 159.96it/s]\n", - " 1%| | 1/150 [00:00<00:04, 31.56it/s]\n", - "Average Metric: 27 / 50 (54.0): 100%|██████████| 50/50 [00:00<00:00, 143.11it/s]\n", - " 1%| | 1/150 [00:00<00:03, 43.19it/s]\n", - "Average Metric: 29 / 50 (58.0): 100%|██████████| 50/50 [00:00<00:00, 163.95it/s]\n", - " 1%|▏ | 2/150 [00:00<00:04, 31.94it/s]\n", - "Average Metric: 25 / 50 (50.0): 100%|██████████| 50/50 [00:00<00:00, 197.13it/s]\n", - " 2%|▏ | 3/150 [00:00<00:04, 35.74it/s]\n", - "Average Metric: 27 / 50 (54.0): 100%|██████████| 50/50 [00:00<00:00, 161.65it/s]\n", - " 5%|▍ | 7/150 [00:00<00:03, 40.74it/s]\n", - "Average Metric: 29 / 50 (58.0): 100%|██████████| 50/50 [00:00<00:00, 176.77it/s]\n", - " 1%| | 1/150 [00:00<00:02, 65.18it/s]\n", - "Average Metric: 28 / 50 (56.0): 100%|██████████| 50/50 [00:00<00:00, 153.30it/s]\n", - " 2%|▏ | 3/150 [00:00<00:02, 59.95it/s]\n", - "Average Metric: 30 / 50 (60.0): 100%|██████████| 50/50 [00:00<00:00, 148.52it/s]\n" - ] - } - ], - "source": [ - "kwargs = dict(max_bootstrapped_demos=2, max_labeled_demos=6, num_candidate_programs=10, num_threads=8)\n", - "tp = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match, **kwargs)\n", - "optimized_aggregator = tp.compile(aggregator, trainset=trainset, valset=valset)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from dsp.utils import flatten, deduplicate\n", + "\n", + "# the best-performing five ReAct programs from the optimization process\n", + "AGENTS = [x[-1] for x in optimized_react.candidate_programs[:5]]\n", + "\n", + "class Aggregator(dspy.Module):\n", + "\tdef __init__(self, temperature=0.0):\n", + "\t\tself.aggregate = dspy.ChainOfThought('context, question -> answer')\n", + "\t\tself.temperature = temperature\n", + "\n", + "\tdef forward(self, question):\n", + "\t\t# Run all five agents with high temperature, then extract and deduplicate their observed contexts\n", + "\t\twith dspy.context(lm=gpt3.copy(temperature=self.temperature)):\n", + "\t\t\tpreds = [agent(question=question) for agent in AGENTS]\n", + "\t\t\tcontext = deduplicate(flatten([flatten(p.observations) for p in preds]))\n", + "\n", + "\t\t# Run the aggregation step to produce a final answer\n", + "\t\treturn self.aggregate(context=context, question=question)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's quickly evaluate the aggregator prior to optimization." + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 180 / 300 (60.0): 100%|██████████| 300/300 [00:07<00:00, 42.10it/s]\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 78 / 300 (26.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [00:06<00:00, 45.38it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}determine if both Cangzhou and Qionghai are in the Hebei province of China. We need to carefully analyze the information provided in the context to...No, only Cangzhou is in the Hebei province of China. Qionghai is located in Hainan province.False
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017\u201318 Pittsburgh Penguins season'}produce the answer. We know that Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season. Looking at the context provided, we...The 2017 NHL Expansion Draft conducted by the National Hockey League filled the roster of the Vegas Golden Knights, including selecting Marc-Andre Fleury for the...False
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}identify the retired Canadian professional ice hockey player and current general manager of the Tampa Bay Lightning of the National Hockey League (NHL) whose retirement...Steve Yzerman\u2714\ufe0f [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}identify the river near the Crichton Collegiate Church. We know that the church is situated in Midlothian, Scotland, and the River Esk flows through Midlothian...The River EskFalse
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'Ealhswith', '\u00c6thelweard (son of Alfred)'}produce the answer. We know from the context that Ealhswith had a son named \u00c6thelweard in the 10th century A.D. Now, looking at the information...King Alfred the Great\u2714\ufe0f [True]
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 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}produce the answer. From the context, we know that Cangzhou is a prefecture-level city in eastern Hebei province, while Qionghai is one of the seven...no✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}produce the answer. From the context, we know that Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season. The draft that...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}produce the answer. We know from the context that Steve Yzerman is a Canadian retired professional ice hockey player and the current general manager of...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}produce the answer. We know that Crichton Collegiate Church is located in Midlothian, Scotland, near the hamlet of Crichton. Since it is close to Edinburgh,...River EskFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}produce the answer. From the context, we know that Ealhswith was the wife of King Alfred the Great. Therefore, in the 10th Century A.D., Ealhswith...King Alfred the Great✔️ [True]
\n" + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 16 / 50 (32.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 153.98it/s]\n", + "Average Metric: 27 / 50 (54.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 82.75it/s]\n", + " 3%|\u258e | 4/150 [00:00<00:03, 45.32it/s]\n", + "Average Metric: 28 / 50 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 156.28it/s]\n", + " 1%|\u258f | 2/150 [00:00<00:03, 39.99it/s]\n", + "Average Metric: 28 / 50 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 162.26it/s]\n", + " 1%| | 1/150 [00:00<00:02, 51.23it/s]\n", + "Average Metric: 26 / 50 (52.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 158.64it/s]\n", + " 1%| | 1/150 [00:00<00:00, 155.47it/s]\n", + "Average Metric: 28 / 50 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 159.96it/s]\n", + " 1%| | 1/150 [00:00<00:04, 31.56it/s]\n", + "Average Metric: 27 / 50 (54.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 143.11it/s]\n", + " 1%| | 1/150 [00:00<00:03, 43.19it/s]\n", + "Average Metric: 29 / 50 (58.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 163.95it/s]\n", + " 1%|\u258f | 2/150 [00:00<00:04, 31.94it/s]\n", + "Average Metric: 25 / 50 (50.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 197.13it/s]\n", + " 2%|\u258f | 3/150 [00:00<00:04, 35.74it/s]\n", + "Average Metric: 27 / 50 (54.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 161.65it/s]\n", + " 5%|\u258d | 7/150 [00:00<00:03, 40.74it/s]\n", + "Average Metric: 29 / 50 (58.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 176.77it/s]\n", + " 1%| | 1/150 [00:00<00:02, 65.18it/s]\n", + "Average Metric: 28 / 50 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 153.30it/s]\n", + " 2%|\u258f | 3/150 [00:00<00:02, 59.95it/s]\n", + "Average Metric: 30 / 50 (60.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 148.52it/s]\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "kwargs = dict(max_bootstrapped_demos=2, max_labeled_demos=6, num_candidate_programs=10, num_threads=8)\n", + "tp = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match, **kwargs)\n", + "optimized_aggregator = tp.compile(aggregator, trainset=trainset, valset=valset)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}produce the answer. From the context, we know that Cangzhou is a prefecture-level city in eastern Hebei province, while Qionghai is one of the seven...no\u2714\ufe0f [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017\u201318 Pittsburgh Penguins season'}produce the answer. From the context, we know that Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season. The draft that...National Hockey League\u2714\ufe0f [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}produce the answer. We know from the context that Steve Yzerman is a Canadian retired professional ice hockey player and the current general manager of...Steve Yzerman\u2714\ufe0f [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}produce the answer. We know that Crichton Collegiate Church is located in Midlothian, Scotland, near the hamlet of Crichton. Since it is close to Edinburgh,...River EskFalse
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'Ealhswith', '\u00c6thelweard (son of Alfred)'}produce the answer. From the context, we know that Ealhswith was the wife of King Alfred the Great. Therefore, in the 10th Century A.D., Ealhswith...King Alfred the Great\u2714\ufe0f [True]
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "60.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "optimized_aggregator2 = optimized_aggregator.deepcopy()\n", + "optimized_aggregator2.temperature = 0.7\n", + "\n", + "evaluate(optimized_aggregator2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7) Conclusion.\n", + "\n", + "Normally, we like to release notebooks with pre-computed caches and to inspect the prompts with `gpt3.inspect_history` to explore the behavior of optimization. See the intro notebook (or any of the Colab notebooks on the README) for such annotated examples!\n", + "\n", + "To keep the current release super quick, Omar will extend this notebook into an annotated version if there's significant interest." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "60.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8) Post-Conclusion Note.\n", + "\n", + "With a little bit of syntactic sugar, the main code in this notebook could be as short as 10 lines excluding whitespace:\n", + "\n", + "```python\n", + "agent = dspy.ReAct(\"question -> answer\", tools=[dspy.Retrieve(k=1)])\n", + "\n", + "optimizer = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match)\n", + "optimized_react = optimizer.compile(agent, trainset=trainset, valset=valset)\n", + "\n", + "class Aggregator(dspy.Module):\n", + "\tdef __init__(self):\n", + "\t\tself.aggregate = dspy.ChainOfThought('context, question -> answer')\n", + "\n", + "\tdef forward(self, question):\n", + " preds = [agent(question=question) for agent in optimized_react.best_programs[:5]]\n", + "\t\treturn self.aggregate(context=deduplicate(flatten([p.observations for p in preds])), question=question)\n", + "\t\n", + "optimized_aggregator = optimizer.compile(aggregator, trainset=trainset, valset=valset)\n", + "\n", + "# Use it!\n", + "optimized_aggregator(question=\"How many storeys are in the castle that David Gregory inherited?\")\n", + "```" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "optimized_aggregator2 = optimized_aggregator.deepcopy()\n", - "optimized_aggregator2.temperature = 0.7\n", - "\n", - "evaluate(optimized_aggregator2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7) Conclusion.\n", - "\n", - "Normally, we like to release notebooks with pre-computed caches and to inspect the prompts with `gpt3.inspect_history` to explore the behavior of optimization. See the intro notebook (or any of the Colab notebooks on the README) for such annotated examples!\n", - "\n", - "To keep the current release super quick, Omar will extend this notebook into an annotated version if there's significant interest." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8) Post-Conclusion Note.\n", - "\n", - "With a little bit of syntactic sugar, the main code in this notebook could be as short as 10 lines excluding whitespace:\n", - "\n", - "```python\n", - "agent = dspy.ReAct(\"question -> answer\", tools=[dspy.Retrieve(k=1)])\n", - "\n", - "optimizer = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match)\n", - "optimized_react = optimizer.compile(agent, trainset=trainset, valset=valset)\n", - "\n", - "class Aggregator(dspy.Module):\n", - "\tdef __init__(self):\n", - "\t\tself.aggregate = dspy.ChainOfThought('context, question -> answer')\n", - "\n", - "\tdef forward(self, question):\n", - " preds = [agent(question=question) for agent in optimized_react.best_programs[:5]]\n", - "\t\treturn self.aggregate(context=deduplicate(flatten([p.observations for p in preds])), question=question)\n", - "\t\n", - "optimized_aggregator = optimizer.compile(aggregator, trainset=trainset, valset=valset)\n", - "\n", - "# Use it!\n", - "optimized_aggregator(question=\"How many storeys are in the castle that David Gregory inherited?\")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py310", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/dataloaders/dataloaders_dolly.ipynb b/examples/outdated_v2.4_examples/dataloaders/dataloaders_dolly.ipynb index e8c19f2496..17b86d6949 100644 --- a/examples/outdated_v2.4_examples/dataloaders/dataloaders_dolly.ipynb +++ b/examples/outdated_v2.4_examples/dataloaders/dataloaders_dolly.ipynb @@ -1,512 +1,512 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "i9ZPeh5Gk2pX" - }, - "source": [ - 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)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FH5S5Xm1lMC-" - }, - "source": [ - "In the **DSPy** framework, the primary data type utilized for interactions with various modules `Example`. Given the huge repository of datasets available through Hugging Face, there's a significant opportunity to leverage these resources within DSPy. However, to make these datasets compatible and fully utilizable, they need to be converted into the `Example` data format.\n", - "\n", - "Moreover, CSV files are a common format for storing and exchanging data, recognized for their simplicity and wide application across various domains. The conversion of data from CSV format into the `Example` format is a necessary requirement. But can we we do something to avoid this tedious process?\n", - "\n", - "Introducing `DataLoaders`, a module aimed at simplifying the task of importing dataset from diverse sources, including HuggingFace and CSV files, into the `Example` format. `DataLoaders` are designed to abstract away the complexities and boilerplate involved in data conversion, offering a user-friendly interface for loading, transforming, and preparing data for use within the DSPy framework. This addition significantly enhances the ease of use of DSPy." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RqSZB2MqsTAQ" - }, - "source": [ - "# Setting Up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XZ2MimQMkyjA" - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "# Set up the cache for this notebook\n", - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " # !pip install dspy-ai\n", - " !pip install -e $repo_path" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PtBml664suSx" - }, - "source": [ - "# Data Loading Using `DataLoader`\n", - "\n", - "Currently `DataLoader` provides support for data loading for following sources:\n", - "\n", - "* HuggingFace using `from_huggingface()` method.\n", - "* CSV Files using `from_csv()` method.\n", - "\n", - "We'll start by initializing the `DataLoader` object that takes no arguments, so we can just create an object without arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "UeB7AEvhshfe" - }, - "outputs": [], - "source": [ - "from dspy.datasets import DataLoader\n", - "\n", - "dl = DataLoader()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RR8vp-IWv-N0" - }, - "source": [ - "## Loading from HuggingFace\n", - "\n", - "To load data from Hugging Face we have `from_huggingface` method which loads a dataset from Hugging Face's datasets library and processes it according to the split configuration.\n", - "\n", - "**Parameters:**\n", - "* `dataset_name (str)`: The name of the dataset to load.\n", - "* `*args`: Positional arguments passed to the load_dataset function.\n", - "* `input_keys (Tuple[str], optional)`: Tuple of input keys to be used in the dataset.\n", - "* `fields (Tuple[str], optional)`: Tuple of fields to include from the dataset. If None, all fields are included.\n", - "* `**kwargs`: Keyword arguments passed to the load_dataset function.\n", - "\n", - "**Returns:**\n", - "\n", - "* A dictionary mapping split names to lists of `dspy.Example` objects, or a list of `dspy.Example` objects if split is a string." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "OslWdJbMvVYU" - }, - "outputs": [], - "source": [ - "code_alpaca = dl.from_huggingface(\n", - " \"HuggingFaceH4/CodeAlpaca_20K\",\n", - " split = [\"train\", \"test\"], # returns Dictionary with train, test keys\n", - " input_keys = (\"prompt\",),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RRyYfcTUF121" - }, - "source": [ - "Once the above code is executed the data would be returned in a dict with keys `train`, and `test` each containing the List of `Example` for each split based on the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "i9ZPeh5Gk2pX" + }, + "source": [ + 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)" + ] }, - "id": "CIj3Tkj6zWTq", - "outputId": "2d8bd9bb-566a-41ec-b0ee-fa225514d1c7" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Keys present in the returned dict: ['train', 'test']\n", - "Number of examples in train set: 18019\n", - "Number of examples in test set: 2003\n" - ] - } - ], - "source": [ - "print(f\"Keys present in the returned dict: {list(code_alpaca.keys())}\")\n", - "\n", - "print(f\"Number of examples in train set: {len(code_alpaca['train'])}\")\n", - "print(f\"Number of examples in test set: {len(code_alpaca['test'])}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NAqtNAUwuKvD" - }, - "source": [ - "You can also pass a single split or a read instruction or anything that `load_dataset` allows you to pass and the conversion to `dspy.Example` would be taken care of by Dataloader." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "FH5S5Xm1lMC-" + }, + "source": [ + "In the **DSPy** framework, the primary data type utilized for interactions with various modules `Example`. Given the huge repository of datasets available through Hugging Face, there's a significant opportunity to leverage these resources within DSPy. However, to make these datasets compatible and fully utilizable, they need to be converted into the `Example` data format.\n", + "\n", + "Moreover, CSV files are a common format for storing and exchanging data, recognized for their simplicity and wide application across various domains. The conversion of data from CSV format into the `Example` format is a necessary requirement. But can we we do something to avoid this tedious process?\n", + "\n", + "Introducing `DataLoaders`, a module aimed at simplifying the task of importing dataset from diverse sources, including HuggingFace and CSV files, into the `Example` format. `DataLoaders` are designed to abstract away the complexities and boilerplate involved in data conversion, offering a user-friendly interface for loading, transforming, and preparing data for use within the DSPy framework. This addition significantly enhances the ease of use of DSPy." + ] }, - "id": "1LX1B8qUlW2E", - "outputId": "0b6160e7-7839-4a79-e820-08e4e6fcd80c" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of examples in split: 18019\n" - ] - } - ], - "source": [ - "code_alpaca = dl.from_huggingface(\n", - " \"HuggingFaceH4/CodeAlpaca_20K\",\n", - " split = \"train\",\n", - ")\n", - "\n", - "print(f\"Number of examples in split: {len(code_alpaca)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9Y14ZZ0Vlh_6" - }, - "source": [ - "As you can see if the `split` argumnent is a string it will return a List of `dspy.Example` instead of mapping because there is only one split in it. However, HF Dataset can create slices of dataset too and the same concept can be applied here too!" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "RqSZB2MqsTAQ" + }, + "source": [ + "# Setting Up" + ] }, - "id": "GpS-zSTUlhXq", - "outputId": "cedb796e-e315-4a67-c8ed-ca6dd1082ce1" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of examples in split: 14415\n", - "Number of examples in split: 10811\n" - ] - } - ], - "source": [ - "code_alpaca_80 = dl.from_huggingface(\n", - " \"HuggingFaceH4/CodeAlpaca_20K\",\n", - " split = \"train[:80%]\",\n", - ")\n", - "\n", - "print(f\"Number of examples in split: {len(code_alpaca_80)}\")\n", - "\n", - "code_alpaca_20_80 = dl.from_huggingface(\n", - " \"HuggingFaceH4/CodeAlpaca_20K\",\n", - " split = \"train[20%:80%]\",\n", - ")\n", - "\n", - "print(f\"Number of examples in split: {len(code_alpaca_20_80)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jc6IDdc8mSbU" - }, - "source": [ - "Cool right! In the same way we can use `ReadInstruction` to create even more complicated splits. That aside, if you need to load a dataset from a subset(if any) you can pass it as a positional argument to the method. Similar to how you do it in `load_dataset`." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XZ2MimQMkyjA" + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "# Set up the cache for this notebook\n", + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " # !pip install dspy-ai\n", + " !pip install -e $repo_path" + ] }, - "id": "iVYAFxQAmR1M", - "outputId": "c6fbcf18-0029-4355-b359-2bc06e604428" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Keys present in the returned dict: ['train', 'test']\n", - "Number of examples in train set: 7473\n", - "Number of examples in test set: 1319\n" - ] - } - ], - "source": [ - "gms8k = dl.from_huggingface(\n", - " \"gsm8k\",\n", - " \"main\",\n", - " input_keys = (\"question\",),\n", - ")\n", - "\n", - "print(f\"Keys present in the returned dict: {list(gms8k.keys())}\")\n", - "\n", - "print(f\"Number of examples in train set: {len(gms8k['train'])}\")\n", - "print(f\"Number of examples in test set: {len(gms8k['test'])}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WJ78GF1kPn9N" - }, - "source": [ - "## Loading from CSV\n", - "\n", - "The from_csv function in the DataLoader class makes it easy to load data from CSV files for machine learning projects. You just need to tell it where your CSV file is and which parts of the data you're interested in by listing the columns you want. This function turns your CSV data into a list of examples that you can use right away with DSPy modules.\n", - "\n", - "**Parameters:**\n", - "* `file_path (str)`: The path to the CSV file.\n", - "* `fields (List[str], optional)`: List of fields to include from the dataset. If None, all fields are included.\n", - "* `input_keys (Tuple[str], optional)`: Tuple of input keys to be used in the dataset.\n", - "\n", - "**Returns:**\n", - "* A list of `dspy.Example` objects." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "tkK14QOVQwph" - }, - "outputs": [], - "source": [ - "dolly_100_dataset = dl.from_csv(\n", - " \"dolly_subset_100_rows.csv\",\n", - " fields=[\"instruction\", \"context\", \"response\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "q2Zcn1X2RPr_" - }, - "source": [ - "Once the above code is executed the data would be returned in a dict with keys `train`, `dev`, and `test` containing the List of `Example` for each split based on the configuration of size and seed set in the object during initialization" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "PtBml664suSx" + }, + "source": [ + "# Data Loading Using `DataLoader`\n", + "\n", + "Currently `DataLoader` provides support for data loading for following sources:\n", + "\n", + "* HuggingFace using `from_huggingface()` method.\n", + "* CSV Files using `from_csv()` method.\n", + "\n", + "We'll start by initializing the `DataLoader` object that takes no arguments, so we can just create an object without arguments." + ] }, - "id": "TIrNy_wrQ4B5", - "outputId": "49e4c90b-e5af-4f86-f865-a553f7f520a7" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of examples in train set: 100\n" - ] - } - ], - "source": [ - "print(f\"Number of examples in train set: {len(dolly_100_dataset)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "UeB7AEvhshfe" + }, + "outputs": [], + "source": [ + "from dspy.datasets import DataLoader\n", + "\n", + "dl = DataLoader()" + ] }, - "id": "r4K2TWkRWdDR", - "outputId": "1ac2cd38-4ecf-452d-ef91-b6f25fd45b65" - }, - "outputs": [ { - "data": { - "text/plain": [ - "Example({'instruction': 'Which of these animals can be pets? Wolf, dog, rabbit, squirrel, cat, lion.', 'context': None, 'response': 'While most animals can be domesticated, dogs, rabbits and cats are common pets.'}) (input_keys={()})" + "cell_type": "markdown", + "metadata": { + "id": "RR8vp-IWv-N0" + }, + "source": [ + "## Loading from HuggingFace\n", + "\n", + "To load data from Hugging Face we have `from_huggingface` method which loads a dataset from Hugging Face's datasets library and processes it according to the split configuration.\n", + "\n", + "**Parameters:**\n", + "* `dataset_name (str)`: The name of the dataset to load.\n", + "* `*args`: Positional arguments passed to the load_dataset function.\n", + "* `input_keys (Tuple[str], optional)`: Tuple of input keys to be used in the dataset.\n", + "* `fields (Tuple[str], optional)`: Tuple of fields to include from the dataset. If None, all fields are included.\n", + "* `**kwargs`: Keyword arguments passed to the load_dataset function.\n", + "\n", + "**Returns:**\n", + "\n", + "* A dictionary mapping split names to lists of `dspy.Example` objects, or a list of `dspy.Example` objects if split is a string." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "OslWdJbMvVYU" + }, + "outputs": [], + "source": [ + "code_alpaca = dl.from_huggingface(\n", + " \"HuggingFaceH4/CodeAlpaca_20K\",\n", + " split = [\"train\", \"test\"], # returns Dictionary with train, test keys\n", + " input_keys = (\"prompt\",),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RRyYfcTUF121" + }, + "source": [ + "Once the above code is executed the data would be returned in a dict with keys `train`, and `test` each containing the List of `Example` for each split based on the dataset:" ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dolly_100_dataset[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NkCG01E5p6VX" - }, - "source": [ - "# Manipulating Dataset with `DataLoader`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jEEt4kI8qBkQ" - }, - "source": [ - "## Sampling Chunks\n", - "\n", - "To sample chunks of data from your dataset, you can use the sample method of the DataLoader class. This method allows you to randomly pick a specified number of examples from your dataset. For example, if you want to sample 5 examples from a dataset `gsm8k` we loaded above, you can use the following code:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "DOv929yUhRT7", - "outputId": "5156fbd9-ae98-4414-cfcb-ced25a29e01d" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of examples in sampled chunk: 5\n" - ] + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CIj3Tkj6zWTq", + "outputId": "2d8bd9bb-566a-41ec-b0ee-fa225514d1c7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys present in the returned dict: ['train', 'test']\n", + "Number of examples in train set: 18019\n", + "Number of examples in test set: 2003\n" + ] + } + ], + "source": [ + "print(f\"Keys present in the returned dict: {list(code_alpaca.keys())}\")\n", + "\n", + "print(f\"Number of examples in train set: {len(code_alpaca['train'])}\")\n", + "print(f\"Number of examples in test set: {len(code_alpaca['test'])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NAqtNAUwuKvD" + }, + "source": [ + "You can also pass a single split or a read instruction or anything that `load_dataset` allows you to pass and the conversion to `dspy.Example` would be taken care of by Dataloader." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1LX1B8qUlW2E", + "outputId": "0b6160e7-7839-4a79-e820-08e4e6fcd80c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples in split: 18019\n" + ] + } + ], + "source": [ + "code_alpaca = dl.from_huggingface(\n", + " \"HuggingFaceH4/CodeAlpaca_20K\",\n", + " split = \"train\",\n", + ")\n", + "\n", + "print(f\"Number of examples in split: {len(code_alpaca)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Y14ZZ0Vlh_6" + }, + "source": [ + "As you can see if the `split` argumnent is a string it will return a List of `dspy.Example` instead of mapping because there is only one split in it. However, HF Dataset can create slices of dataset too and the same concept can be applied here too!" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GpS-zSTUlhXq", + "outputId": "cedb796e-e315-4a67-c8ed-ca6dd1082ce1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples in split: 14415\n", + "Number of examples in split: 10811\n" + ] + } + ], + "source": [ + "code_alpaca_80 = dl.from_huggingface(\n", + " \"HuggingFaceH4/CodeAlpaca_20K\",\n", + " split = \"train[:80%]\",\n", + ")\n", + "\n", + "print(f\"Number of examples in split: {len(code_alpaca_80)}\")\n", + "\n", + "code_alpaca_20_80 = dl.from_huggingface(\n", + " \"HuggingFaceH4/CodeAlpaca_20K\",\n", + " split = \"train[20%:80%]\",\n", + ")\n", + "\n", + "print(f\"Number of examples in split: {len(code_alpaca_20_80)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jc6IDdc8mSbU" + }, + "source": [ + "Cool right! In the same way we can use `ReadInstruction` to create even more complicated splits. That aside, if you need to load a dataset from a subset(if any) you can pass it as a positional argument to the method. Similar to how you do it in `load_dataset`." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iVYAFxQAmR1M", + "outputId": "c6fbcf18-0029-4355-b359-2bc06e604428" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keys present in the returned dict: ['train', 'test']\n", + "Number of examples in train set: 7473\n", + "Number of examples in test set: 1319\n" + ] + } + ], + "source": [ + "gms8k = dl.from_huggingface(\n", + " \"gsm8k\",\n", + " \"main\",\n", + " input_keys = (\"question\",),\n", + ")\n", + "\n", + "print(f\"Keys present in the returned dict: {list(gms8k.keys())}\")\n", + "\n", + "print(f\"Number of examples in train set: {len(gms8k['train'])}\")\n", + "print(f\"Number of examples in test set: {len(gms8k['test'])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WJ78GF1kPn9N" + }, + "source": [ + "## Loading from CSV\n", + "\n", + "The from_csv function in the DataLoader class makes it easy to load data from CSV files for machine learning projects. You just need to tell it where your CSV file is and which parts of the data you're interested in by listing the columns you want. This function turns your CSV data into a list of examples that you can use right away with DSPy modules.\n", + "\n", + "**Parameters:**\n", + "* `file_path (str)`: The path to the CSV file.\n", + "* `fields (List[str], optional)`: List of fields to include from the dataset. If None, all fields are included.\n", + "* `input_keys (Tuple[str], optional)`: Tuple of input keys to be used in the dataset.\n", + "\n", + "**Returns:**\n", + "* A list of `dspy.Example` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "tkK14QOVQwph" + }, + "outputs": [], + "source": [ + "dolly_100_dataset = dl.from_csv(\n", + " \"dolly_subset_100_rows.csv\",\n", + " fields=[\"instruction\", \"context\", \"response\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q2Zcn1X2RPr_" + }, + "source": [ + "Once the above code is executed the data would be returned in a dict with keys `train`, `dev`, and `test` containing the List of `Example` for each split based on the configuration of size and seed set in the object during initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TIrNy_wrQ4B5", + "outputId": "49e4c90b-e5af-4f86-f865-a553f7f520a7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples in train set: 100\n" + ] + } + ], + "source": [ + "print(f\"Number of examples in train set: {len(dolly_100_dataset)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r4K2TWkRWdDR", + "outputId": "1ac2cd38-4ecf-452d-ef91-b6f25fd45b65" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'instruction': 'Which of these animals can be pets? Wolf, dog, rabbit, squirrel, cat, lion.', 'context': None, 'response': 'While most animals can be domesticated, dogs, rabbits and cats are common pets.'}) (input_keys={()})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dolly_100_dataset[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NkCG01E5p6VX" + }, + "source": [ + "# Manipulating Dataset with `DataLoader`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jEEt4kI8qBkQ" + }, + "source": [ + "## Sampling Chunks\n", + "\n", + "To sample chunks of data from your dataset, you can use the sample method of the DataLoader class. This method allows you to randomly pick a specified number of examples from your dataset. For example, if you want to sample 5 examples from a dataset `gsm8k` we loaded above, you can use the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DOv929yUhRT7", + "outputId": "5156fbd9-ae98-4414-cfcb-ced25a29e01d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of examples in sampled chunk: 5\n" + ] + } + ], + "source": [ + "sampled_example = dl.sample(dataset=gms8k[\"train\"], n=5)\n", + "\n", + "print(f\"Number of examples in sampled chunk: {len(sampled_example)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3IKtN0Qfyhiu" + }, + "source": [ + "This will give you a random set of 5 examples from `gms8k` dataset's `train` split that you can use for quick testing or analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hZyOGFjzqERb" + }, + "source": [ + "## Splitting Dataset\n", + "\n", + "To split your dataset into training and testing sets, the DataLoader class provides the `train_test_split` method. This method divides your dataset based on the proportions you specify. For instance, if you have a dataset `dolly_100_dataset` and you want to split it with 80% of the data for training and 20% for testing, you can use the code like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "djGamczdqIjn", + "outputId": "a2fe4161-df45-48bb-dd87-d9abf1e6f6a1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of total examples in dataset: 100\n", + "Number of examples in train set: 80\n", + "Number of examples in test set: 20\n" + ] + } + ], + "source": [ + "splits = dl.train_test_split(dataset=dolly_100_dataset, train_size=0.8)\n", + "train_dataset = splits['train']\n", + "test_dataset = splits['test']\n", + "\n", + "print(f\"Number of total examples in dataset: {len(dolly_100_dataset)}\")\n", + "print(f\"Number of examples in train set: {len(train_dataset)}\")\n", + "print(f\"Number of examples in test set: {len(test_dataset)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bwDoKE3rytsD" + }, + "source": [ + "Now, `train_dataset` will contain 80% of the examples for training your model, and `test_dataset` will contain the remaining 20% for testing its performance." + ] } - ], - "source": [ - "sampled_example = dl.sample(dataset=gms8k[\"train\"], n=5)\n", - "\n", - "print(f\"Number of examples in sampled chunk: {len(sampled_example)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3IKtN0Qfyhiu" - }, - "source": [ - "This will give you a random set of 5 examples from `gms8k` dataset's `train` split that you can use for quick testing or analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hZyOGFjzqERb" - }, - "source": [ - "## Splitting Dataset\n", - "\n", - "To split your dataset into training and testing sets, the DataLoader class provides the `train_test_split` method. This method divides your dataset based on the proportions you specify. For instance, if you have a dataset `dolly_100_dataset` and you want to split it with 80% of the data for training and 20% for testing, you can use the code like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { + ], + "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "collapsed_sections": [ + "RqSZB2MqsTAQ" + ], + "provenance": [], + "toc_visible": true }, - "id": "djGamczdqIjn", - "outputId": "a2fe4161-df45-48bb-dd87-d9abf1e6f6a1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of total examples in dataset: 100\n", - "Number of examples in train set: 80\n", - "Number of examples in test set: 20\n" - ] + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.12" } - ], - "source": [ - "splits = dl.train_test_split(dataset=dolly_100_dataset, train_size=0.8)\n", - "train_dataset = splits['train']\n", - "test_dataset = splits['test']\n", - "\n", - "print(f\"Number of total examples in dataset: {len(dolly_100_dataset)}\")\n", - "print(f\"Number of examples in train set: {len(train_dataset)}\")\n", - "print(f\"Number of examples in test set: {len(test_dataset)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bwDoKE3rytsD" - }, - "source": [ - "Now, `train_dataset` will contain 80% of the examples for training your model, and `test_dataset` will contain the remaining 20% for testing its performance." - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [ - "RqSZB2MqsTAQ" - ], - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" }, - "language_info": { - "name": "python", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/examples/outdated_v2.4_examples/finetune/_unpolished_finetune_demo.ipynb b/examples/outdated_v2.4_examples/finetune/_unpolished_finetune_demo.ipynb index bde92e4e43..4c11e78caa 100644 --- a/examples/outdated_v2.4_examples/finetune/_unpolished_finetune_demo.ipynb +++ b/examples/outdated_v2.4_examples/finetune/_unpolished_finetune_demo.ipynb @@ -1,1505 +1,1505 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/scr-ssd/dilara/.miniconda3/envs/dspyprod/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "# Enable reloading on code changes\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "# Setting the environment variables\n", - "import os # noqa\n", - "\n", - "# os.environ[\"DSPY_CACHEDIR\"] =\n", - "# os.environ[\"DSP_CACHEDIR\"] =\n", - "# os.environ[\"OPENAI_API_KEY\"] =\n", - "\n", - "# Import the library\n", - "import dspy\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## I. Showcasing `LM.finetune()`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[\"The average distance from the Earth to the Moon is about 238,855 miles (384,400 kilometers). However, this distance can vary slightly due to the Moon's elliptical orbit, ranging from approximately 225,623 miles (363,104 kilometers) at its closest (perigee) to about 252,088 miles (405,696 kilometers) at its farthest (apogee).\"]" + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scr-ssd/dilara/.miniconda3/envs/dspyprod/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "# Enable reloading on code changes\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# Setting the environment variables\n", + "import os # noqa\n", + "\n", + "# os.environ[\"DSPY_CACHEDIR\"] =\n", + "# os.environ[\"DSP_CACHEDIR\"] =\n", + "# os.environ[\"OPENAI_API_KEY\"] =\n", + "\n", + "# Import the library\n", + "import dspy\n" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Example call to an LM before fine-tuning\n", - "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", - "lm(\"How far is the Moon from Earth?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", - "dspy.settings.experimental = True" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Finetune] Validating the data format" - ] }, { - "data": { - "text/plain": [ - "dspy.clients.openai.TrainingJobOpenAI" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## I. Showcasing `LM.finetune()`" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[Finetune] Saving the data to a file\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Finetune] Uploading the data to the provider\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Finetune] Start remote training\n", - "[Finetune] Wait for training to complete\n", - "[Finetune] Get trained model if the run was a success\n" - ] - } - ], - "source": [ - "# Let's construct a dummy dataset\n", - "message = {\n", - " \"messages\": [\n", - " {\"role\": \"system\", \"content\": \"Marv is a factual chatbot that is also sarcastic.\"},\n", - " {\"role\": \"user\", \"content\": \"How far is the Moon from Earth?\"},\n", - " {\"role\": \"assistant\", \"content\": \"384,400 kilometers\"},\n", - " ]\n", - "}\n", - "training_data = [message] * 20\n", - "\n", - "# Let's finetune the model\n", - "train_kwargs = {\n", - " \"n_epochs\": 1,\n", - "}\n", - "\n", - "job = lm.finetune(\n", - " train_data=training_data,\n", - " train_kwargs=train_kwargs,\n", - " data_format=\"chat\", # Could be left empty, inferred from \"lm.model_type\" as a default\n", - ")\n", - "type(job)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Running the cell below immediately after the cell above returns `False`, indicating that the job is not done." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# This will return False until the job is complete\n", - "job.done()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once started, a `job` object can be polled for status, assuming that a provider has implemented the status checking.\n", - "Note: It takes a bit for the `job.done()` to update once `job.status()` turns to succeeded." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TrainingStatus.pending\n", - "TrainingStatus.pending\n", - "TrainingStatus.pending\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.running\n", - "TrainingStatus.succeeded\n", - "TrainingStatus.succeeded\n", - "TrainingStatus.succeeded\n" - ] - } - ], - "source": [ - "while not job.done():\n", - " print(job.status())\n", - " time.sleep(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Base model: gpt-4o-mini-2024-07-18\n", - "Fine-tuned model: ft:gpt-4o-mini-2024-07-18:stanford::AOHSK6y9\n" - ] - } - ], - "source": [ - "# Once the job is complete, the fine-tuned LM can be obtained via job.result()\n", - "finetuned_lm = job.result()\n", - "print(finetuned_lm)\n", - "\n", - "# We can look at the model IDs to ensure that the fine-tuned model is different\n", - "print(f\"Base model: {lm.model}\")\n", - "print(f\"Fine-tuned model: {finetuned_lm.model}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['384,400 kilometers']" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import time" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We can check how the fine-tuned LM responds to the query we used for\n", - "# fine-tuning.\n", - "finetuned_lm(\"How far is the Moon from Earth?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## II. LM fine-tuning with a custom `Provider`" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "from typing import Any, Dict, List, Optional\n", - "from dspy.clients.provider import Provider, TrainingJob, DataFormat\n", - "\n", - "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", - "dspy.settings.experimental = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define a custom provider with a dummy fine-tune method." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "class CustomProvider(Provider):\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.finetunable = True\n", - "\n", - " @staticmethod\n", - " def finetune(\n", - " job: TrainingJob,\n", - " model: str,\n", - " train_data: List[Dict[str, Any]],\n", - " train_kwargs: Optional[Dict[str, Any]] = None,\n", - " data_format: Optional[DataFormat] = None,\n", - " ) -> str:\n", - "\n", - " # Fake fine-tuning\n", - " print(\"Fake fine-tuning has started!!\")\n", - " time.sleep(15)\n", - " print(\"Done\")\n", - "\n", - " # Return the new model name; we are hard-coding an OpenAI model as a\n", - " # demo placeholder\n", - " model = \"ft:gpt-4o-mini-2024-07-18:stanford::AMDsC653\"\n", - " return model\n", - " \n", - " # # We could also override the launch/kill methods if needed\n", - " # def launch(model: str, launch_kwargs: dict):\n", - " # pass\n", - "\n", - " # def kill(model: str, launch_kwargs: dict):\n", - " # pass\n", - "\n", - "\n", - "# We could also create a custom TrainingJob class to implement\n", - "# .status() and .cancel() methods, but we don't have to." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "`launch()` is called for the auto-launched model openai/MyAmazingCustomModel -- no action is taken!\n", - "`kill()` is called for the auto-launched model openai/MyAmazingCustomModel -- no action is taken!\n" - ] - } - ], - "source": [ - "# We could also pass launch_kwargs if this model needs to be launched before\n", - "# use, assuming that the launch and kill methods are implemented by the\n", - "# custom provider.\n", - "launch_kwargs = {\n", - " \"gpu\": 1,\n", - " \"max_prompt_length\": 1000,\n", - "}\n", - "\n", - "# Create the LM we want to fine-tune, using a dummy model name\n", - "model = \"openai/MyAmazingCustomModel\"\n", - "provider = CustomProvider()\n", - "lm = dspy.LM(model, provider=provider, launch_kwargs=launch_kwargs)\n", - "lm.launch()\n", - "\n", - "# Query the model -- commented out because the model is not real\n", - "# lm(\"How far is the Moon from Earth?\")\n", - "\n", - "# kill the model once done\n", - "lm.kill()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fake fine-tuning has started!!" - ] }, { - "data": { - "text/plain": [ - "dspy.clients.provider.TrainingJob" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\"The average distance from the Earth to the Moon is about 238,855 miles (384,400 kilometers). However, this distance can vary slightly due to the Moon's elliptical orbit, ranging from approximately 225,623 miles (363,104 kilometers) at its closest (perigee) to about 252,088 miles (405,696 kilometers) at its farthest (apogee).\"]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example call to an LM before fine-tuning\n", + "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", + "lm(\"How far is the Moon from Earth?\")" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# Try fine-tune\n", - "dspy.settings.experimental = True\n", - "\n", - "# Let's construct a dummy dataset\n", - "message = {\n", - " \"messages\": [\n", - " {\"role\": \"system\", \"content\": \"Marv is a factual chatbot that is also sarcastic.\"},\n", - " {\"role\": \"user\", \"content\": \"How far is the Moon from Earth?\"},\n", - " {\"role\": \"assistant\", \"content\": \"384,400 kilometers\"},\n", - " ]\n", - "}\n", - "training_data = [message] * 20\n", - "\n", - "# Let's finetune the model\n", - "train_kwargs = {\n", - " \"n_epochs\": 1,\n", - "}\n", - "\n", - "job = lm.finetune(\n", - " train_data=training_data,\n", - " train_kwargs=train_kwargs,\n", - " data_format=\"chat\"\n", - ")\n", - "type(job)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0s] job.done(): False\n", - "[20s] job.done(): True\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done\n" - ] - } - ], - "source": [ - "# Running the command below immediately after the cell above returns `False`, indicating that the job is not done.\n", - "print(f\"[0s] job.done(): {job.done()}\")\n", - "\n", - "# Wait\n", - "time.sleep(20)\n", - "\n", - "# Check again\n", - "print(f\"[20s] job.done(): {job.done()}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can access the fine-tuned model as before" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['384,400 kilometers']" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", + "dspy.settings.experimental = True" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lm = job.result()\n", - "lm(\"How far is the Moon from Earth?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## III. Showcasing `BootstrapFinetune`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### i. Task Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Example setup using HotPotQA" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from dspy.datasets import HotPotQA\n", - "from dspy.evaluate import Evaluate # noqa\n", - "from dsp.utils.utils import deduplicate # noqa\n", - "\n", - "\n", - "# We are setting the experimental flag to True to make use of the fine-tuning\n", - "# features that are still in development.\n", - "dspy.settings.configure(experimental=True)\n", - "\n", - "# Define the program\n", - "class BasicMH(dspy.Module):\n", - " def __init__(self, passages_per_hop=3, num_hops=2):\n", - " super().__init__()\n", - " self.num_hops = 2\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_query = [dspy.ChainOfThought(\"context, question -> search_query\") for _ in range(self.num_hops)]\n", - " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " context = []\n", - " \n", - " for hop in range(self.num_hops):\n", - " search_query = self.generate_query[hop](context=context, question=question).search_query\n", - " passages = self.retrieve(search_query).passages\n", - " context = deduplicate(context + passages)\n", - "\n", - " answer = self.generate_answer(context=context, question=question).copy(context=context)\n", - " return answer\n", - "\n", - "# Prepare the dataset\n", - "TRAIN_SIZE = 1000\n", - "DEV_SIZE = 500\n", - "dataset = HotPotQA(train_seed=1, eval_seed=2023, test_size=0, only_hard_examples=True)\n", - "trainset = [x.with_inputs('question') for x in dataset.train][:TRAIN_SIZE]\n", - "devset = [x.with_inputs('question') for x in dataset.dev][:DEV_SIZE]\n", - "\n", - "# Prepare the metric and evaluator\n", - "NUM_THREADS = 12\n", - "metric = dspy.evaluate.answer_exact_match\n", - "evaluate = Evaluate(devset=devset, metric=metric, num_threads=NUM_THREADS, display_progress=True)\n", - "\n", - "# Prepare the retriever model\n", - "COLBERT_V2_ENDPOINT = \"http://20.102.90.50:2017/wiki17_abstracts\"\n", - "retriever = dspy.ColBERTv2(url=COLBERT_V2_ENDPOINT)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ii. Demo of `BootstrapFinetune`" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", - "dspy.settings.experimental = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cell below shows the different ways the `BootstrapFinetune` can be optimized." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# (1) BSFT can be initilized with no arguments!\n", - "weight_optimizer = dspy.BootstrapFinetune()\n", - "\n", - "# (2) Better to optimize with a metric to be used for filtering data before\n", - "# fine-tuning\n", - "weight_optimizer = dspy.BootstrapFinetune(\n", - " metric=metric\n", - ")\n", - "\n", - "# (3) Bootstrap fine-tune accepts other parameters as well, as shown below\n", - "train_kwargs = {\n", - " \"n_epochs\": 1,\n", - "}\n", - "adapter = dspy.ChatAdapter()\n", - "\n", - "weight_optimizer = dspy.BootstrapFinetune(\n", - " metric=metric, # Can be left empty, leads to no filtering\n", - " multitask=True, # We can also handle False!\n", - " train_kwargs=train_kwargs, # Can be left empty\n", - " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", - " exclude_demos=False, # Can be left empty\n", - " num_threads = 1, # Can be left empty\n", - ")\n", - "\n", - "\n", - "# (4) The adapter and train_kwargs arguments could be passed as dictionaries\n", - "# mapping LMs to their respective adapters/train_kwargs. This is useful when the\n", - "# predictors of the program point to different LMs.\n", - "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", - "adapter = dspy.ChatAdapter()\n", - "\n", - "train_kwargs = {\n", - " lm: {\n", - " \"n_epochs\": 1,\n", - " },\n", - " # lm2: train_kwargs2,\n", - "}\n", - "adapter = {\n", - " lm: adapter,\n", - " # lm2: adapter2,\n", - "}\n", - "\n", - "weight_optimizer = dspy.BootstrapFinetune(\n", - " metric=metric, # Can be left empty, leads to no filtering\n", - " multitask=True, # We can also handle False!\n", - " train_kwargs=train_kwargs, # Can be left empty\n", - " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", - " exclude_demos=False, # Can be left empty\n", - " num_threads = 1, # Can be left empty\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cell below shows an example of running `BootstrapFinetune`" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preparing the student and teacher programs...\n", - "Ensuring that the student is not compiled.\n", - "No teacher provided. Using a copy of the student program as the teacher.\n", - "Bootstrapping data...\n", - "Average Metric: 5 / 10 (50.0): 100%|██████████| 10/10 [00:00<00:00, 416.94it/s]\n", - "Preparing the train data...\n", - "Collected data for 10 examples\n", - "After filtering for score, 5 examples remain\n", - "Using 15 data points for fine-tuning the model: gpt-4o-mini-2024-07-18\n", - "Starting LM fine-tuning...\n", - "1 fine-tuning job(s) to start.\n", - "Starting 1 fine-tuning jobs...\n", - "[OpenAI Provider] Validating the data format\n", - "[OpenAI Provider] Saving the data to a file\n", - "[OpenAI Provider] Data saved to /scr-ssd/dilara/.cache/dspy-new/finetune/4fd944783dbb3639.jsonl\n", - "[OpenAI Provider] Uploading the data to the provider\n", - "[OpenAI Provider] Start remote training\n", - "[OpenAI Provider] Job started with the OpenAI Job ID ftjob-gUXOgSEqEyV3v9Q6JgAqic9G\n", - "[OpenAI Provider] Wait for training to complete\n", - "[OpenAI Provider] Attempting to retrieve the trained model\n", - "[OpenAI Provider] Model retrieved: ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n", - "Job 1/1 completed.\n", - "Updating the student program with the fine-tuned LMs...\n", - "BootstrapFinetune has finished compiling the student program.\n" - ] - } - ], - "source": [ - "# Using method (3) from above to create a weight-optimized program\n", - "train_kwargs = {\n", - " \"n_epochs\": 1,\n", - "}\n", - "adapter = dspy.ChatAdapter()\n", - "\n", - "weight_optimizer = dspy.BootstrapFinetune(\n", - " metric=metric, # Can be left empty, leads to no filtering\n", - " multitask=True, # We can also handle False!\n", - " train_kwargs=train_kwargs, # Can be left empty\n", - " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", - " exclude_demos=False, # Can be left empty\n", - " num_threads = 1, # Can be left empty\n", - ")\n", - "\n", - "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", - "small_trainset = trainset[:10] # Use a small subset of the training data\n", - "\n", - "with dspy.context(lm=lm, rm=retriever):\n", - " weight_optimized_program = weight_optimizer.compile(\n", - " student=BasicMH(),\n", - " trainset=small_trainset,\n", - " teacher=None, # Doesn't need to be set, student is used as the teacher by default\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n", - "ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n", - "ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n" - ] - } - ], - "source": [ - "for p in weight_optimized_program.predictors():\n", - " print(p.lm.model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IV. Demo of `BetterTogether`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### i. Task Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Example setup using HotPotQA" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from dspy.datasets import HotPotQA\n", - "from dspy.evaluate import Evaluate # noqa\n", - "from dsp.utils.utils import deduplicate # noqa\n", - "\n", - "\n", - "# We are setting the experimental flag to True to make use of the fine-tuning\n", - "# features that are still in development.\n", - "dspy.settings.configure(experimental=True)\n", - "\n", - "# Define the program\n", - "class BasicMH(dspy.Module):\n", - " def __init__(self, passages_per_hop=3, num_hops=2):\n", - " super().__init__()\n", - " self.num_hops = 2\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_query = [dspy.ChainOfThought(\"context, question -> search_query\") for _ in range(self.num_hops)]\n", - " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " context = []\n", - " \n", - " for hop in range(self.num_hops):\n", - " search_query = self.generate_query[hop](context=context, question=question).search_query\n", - " passages = self.retrieve(search_query).passages\n", - " context = deduplicate(context + passages)\n", - "\n", - " answer = self.generate_answer(context=context, question=question).copy(context=context)\n", - " return answer\n", - "\n", - "# Prepare the dataset\n", - "TRAIN_SIZE = 1000\n", - "DEV_SIZE = 500\n", - "dataset = HotPotQA(train_seed=1, eval_seed=2023, test_size=0, only_hard_examples=True)\n", - "trainset = [x.with_inputs('question') for x in dataset.train][:TRAIN_SIZE]\n", - "devset = [x.with_inputs('question') for x in dataset.dev][:DEV_SIZE]\n", - "\n", - "# Prepare the metric and evaluator\n", - "NUM_THREADS = 12\n", - "metric = dspy.evaluate.answer_exact_match\n", - "evaluate = Evaluate(devset=devset, metric=metric, num_threads=NUM_THREADS, display_progress=True)\n", - "\n", - "# Prepare the retriever model\n", - "COLBERT_V2_ENDPOINT = \"http://20.102.90.50:2017/wiki17_abstracts\"\n", - "retriever = dspy.ColBERTv2(url=COLBERT_V2_ENDPOINT)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ii. Demo" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", - "dspy.settings.experimental = True\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Going to sample between 1 and 4 traces per predictor.\n", - "Will attempt to bootstrap 16 candidate sets.\n" - ] - } - ], - "source": [ - "# (1) The only required argument we require for BetterTogether is the metric\n", - "better_together = dspy.BetterTogether(\n", - " metric=metric # This is the only metric we require!\n", - " # We could also consider not requiring it if BootstrapFewShotWithRandomSearch is modified.\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Going to sample between 1 and 3 traces per predictor.\n", - "Will attempt to bootstrap 6 candidate sets.\n" - ] - } - ], - "source": [ - "# (2) We can also pass the weight and prompt optimizers we initialized\n", - "train_kwargs = {\n", - " \"n_epochs\": 1,\n", - "}\n", - "adapter = dspy.ChatAdapter()\n", - "\n", - "weight_optimizer = dspy.BootstrapFinetune(\n", - " metric=metric, # Can be left empty, leads to no filtering\n", - " multitask=True, # We can also handle False!\n", - " train_kwargs=train_kwargs, # Can be left empty\n", - " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", - " exclude_demos=True, # We are dropping the demos for fine-tuning \n", - " num_threads = 1, # Can be left empty\n", - ")\n", - "\n", - "prompt_optimizer = dspy.BootstrapFewShotWithRandomSearch(\n", - " metric=metric,\n", - " max_bootstrapped_demos=3,\n", - " max_labeled_demos=3,\n", - " num_candidate_programs=6,\n", - " num_threads=6\n", - ")\n", - "\n", - "# Initialize BetterTogether\n", - "better_together = dspy.BetterTogether(\n", - " metric=metric,\n", - " weight_optimizer=weight_optimizer, # Can be left empty\n", - " prompt_optimizer=prompt_optimizer, # Can be left empty\n", - " seed=2023, # Can be left empty\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[BetterTogether] Validating the strategy\n", - "[BetterTogether] Preparing the student program...\n", - "Ensuring that the student is not compiled\n", - "[BetterTogether] Compiling the student program...\n", - "[BetterTogether] Step 1 of 3 - Strategy `p`\n", - "[BetterTogether] Shuffling the trainset...\n", - "[BetterTogether] Preparing for prompt optimization...\n", - "[BetterTogether] Launching the program LMs for sampling...\n", - "`launch()` is called for the auto-launched model `gpt-4o-mini-2024-07-18` -- no action is taken!\n", - "[BetterTogether] Compiling the prompt optimizer...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 2 / 5 (40.0): 100%|██████████| 5/5 [00:02<00:00, 2.05it/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New best score: 40.0 for seed -3\n", - "Scores so far: [40.0]\n", - "Best score so far: 40.0\n" - ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 2 / 5 (40.0): 100%|██████████| 5/5 [00:02<00:00, 2.30it/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [40.0, 40.0]\n", - "Best score so far: 40.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 11%|█ | 5/45 [00:11<01:28, 2.21s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 3 full traces after 6 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 5 (20.0): 100%|██████████| 5/5 [00:06<00:00, 1.25s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [40.0, 40.0, 20.0]\n", - "Best score so far: 40.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 11%|█ | 5/45 [00:18<02:25, 3.63s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 6 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 5 (20.0): 100%|██████████| 5/5 [00:07<00:00, 1.43s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [40.0, 40.0, 20.0, 20.0]\n", - "Best score so far: 40.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 7%|▋ | 3/45 [00:05<01:10, 1.67s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 4 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 2 / 5 (40.0): 100%|██████████| 5/5 [00:05<00:00, 1.11s/it] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0]\n", - "Best score so far: 40.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|▏ | 1/45 [00:02<01:32, 2.10s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 5 (20.0): 100%|██████████| 5/5 [00:09<00:00, 1.84s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0, 20.0]\n", - "Best score so far: 40.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|▏ | 1/45 [00:01<01:04, 1.46s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 5 (20.0): 100%|██████████| 5/5 [00:06<00:00, 1.28s/it] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0, 20.0, 20.0]\n", - "Best score so far: 40.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|▏ | 1/45 [00:04<03:36, 4.91s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 2 / 5 (40.0): 100%|██████████| 5/5 [00:08<00:00, 1.60s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0, 20.0, 20.0, 40.0]\n", - "Best score so far: 40.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 16%|█▌ | 7/45 [00:11<01:02, 1.65s/it]\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Finetune] Validating the data format" + ] + }, + { + "data": { + "text/plain": [ + "dspy.clients.openai.TrainingJobOpenAI" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[Finetune] Saving the data to a file\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Finetune] Uploading the data to the provider\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Finetune] Start remote training\n", + "[Finetune] Wait for training to complete\n", + "[Finetune] Get trained model if the run was a success\n" + ] + } + ], + "source": [ + "# Let's construct a dummy dataset\n", + "message = {\n", + " \"messages\": [\n", + " {\"role\": \"system\", \"content\": \"Marv is a factual chatbot that is also sarcastic.\"},\n", + " {\"role\": \"user\", \"content\": \"How far is the Moon from Earth?\"},\n", + " {\"role\": \"assistant\", \"content\": \"384,400 kilometers\"},\n", + " ]\n", + "}\n", + "training_data = [message] * 20\n", + "\n", + "# Let's finetune the model\n", + "train_kwargs = {\n", + " \"n_epochs\": 1,\n", + "}\n", + "\n", + "job = lm.finetune(\n", + " train_data=training_data,\n", + " train_kwargs=train_kwargs,\n", + " data_format=\"chat\", # Could be left empty, inferred from \"lm.model_type\" as a default\n", + ")\n", + "type(job)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 3 full traces after 8 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Running the cell below immediately after the cell above returns `False`, indicating that the job is not done." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 5 (60.0): 100%|██████████| 5/5 [00:06<00:00, 1.37s/it] \n" - ] + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This will return False until the job is complete\n", + "job.done()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "New best score: 60.0 for seed 5\n", - "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0, 20.0, 20.0, 40.0, 60.0]\n", - "Best score so far: 60.0\n", - "9 candidate programs found.\n", - "[BetterTogether] Killing the LMs used for sampling...\n", - "`kill()` is called for the auto-launched model `gpt-4o-mini-2024-07-18` -- no action is taken!\n", - "[BetterTogether] Step 2 of 3 - Strategy `p -> w`\n", - "[BetterTogether] Shuffling the trainset...\n", - "[BetterTogether] Preparing for weight optimization...\n", - "[BetterTogether] Compiling the weight optimizer...\n", - "[BootstrapFinetune] Preparing the student and teacher programs...\n", - "Ensuring that the student is not compiled\n", - "No teacher provided. Using a copy of the student program as the teacher.\n", - "[BootstrapFinetune] Bootstrapping data...\n", - "Average Metric: 28 / 50 (56.0): 100%|██████████| 50/50 [03:55<00:00, 4.72s/it]\n", - "[BootstrapFinetune] Preparing the train data...\n", - "[BootstrapFinetune] Collected data for 50 examples\n", - "[BootstrapFinetune] After filtering for score, 28 examples remain\n", - "Using 84 data points for fine-tuning the model: gpt-4o-mini-2024-07-18\n", - "[BootstrapFinetune] Starting LM fine-tuning...\n", - "[BootstrapFinetune] 1 fine-tuning job(s) to start\n", - "[BootstrapFinetune] Starting 1 fine-tuning jobs...\n", - "[OpenAI Provider] Validating the data format\n", - "[OpenAI Provider] Saving the data to a file\n", - "[OpenAI Provider] Data saved to /scr-ssd/dilara/.cache/dspy-new/finetune/c4bb7c084d3a7ad3.jsonl\n", - "[OpenAI Provider] Uploading the data to the provider\n", - "[OpenAI Provider] Starting remote training\n", - "[OpenAI Provider] Job started with the OpenAI Job ID ftjob-75MiMHGY9xW1gNMGZSs5ht8v\n", - "[OpenAI Provider] Waiting for training to complete\n", - "[OpenAI Provider] Attempting to retrieve the trained model\n", - "[OpenAI Provider] Model retrieved: ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n", - "Job 1/1 completed.\n", - "[BootstrapFinetune] Updating the student program with the fine-tuned LMs...\n", - "[BootstrapFinetune] BootstrapFinetune has finished compiling the student program\n", - "[BetterTogether] Step 3 of 3 - Strategy `p -> w -> p`\n", - "[BetterTogether] Shuffling the trainset...\n", - "[BetterTogether] Preparing for prompt optimization...\n", - "[BetterTogether] Launching the program LMs for sampling...\n", - "`launch()` is called for the auto-launched model `ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8` -- no action is taken!\n", - "[BetterTogether] Compiling the prompt optimizer...\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once started, a `job` object can be polled for status, assuming that a provider has implemented the status checking.\n", + "Note: It takes a bit for the `job.done()` to update once `job.status()` turns to succeeded." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 5 (20.0): 100%|██████████| 5/5 [00:02<00:00, 2.42it/s] \n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TrainingStatus.pending\n", + "TrainingStatus.pending\n", + "TrainingStatus.pending\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.running\n", + "TrainingStatus.succeeded\n", + "TrainingStatus.succeeded\n", + "TrainingStatus.succeeded\n" + ] + } + ], + "source": [ + "while not job.done():\n", + " print(job.status())\n", + " time.sleep(10)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "New best score: 20.0 for seed -3\n", - "Scores so far: [20.0]\n", - "Best score so far: 20.0\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Base model: gpt-4o-mini-2024-07-18\n", + "Fine-tuned model: ft:gpt-4o-mini-2024-07-18:stanford::AOHSK6y9\n" + ] + } + ], + "source": [ + "# Once the job is complete, the fine-tuned LM can be obtained via job.result()\n", + "finetuned_lm = job.result()\n", + "print(finetuned_lm)\n", + "\n", + "# We can look at the model IDs to ensure that the fine-tuned model is different\n", + "print(f\"Base model: {lm.model}\")\n", + "print(f\"Fine-tuned model: {finetuned_lm.model}\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 5 (60.0): 100%|██████████| 5/5 [00:01<00:00, 3.57it/s]\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['384,400 kilometers']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can check how the fine-tuned LM responds to the query we used for\n", + "# fine-tuning.\n", + "finetuned_lm(\"How far is the Moon from Earth?\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "New best score: 60.0 for seed -2\n", - "Scores so far: [20.0, 60.0]\n", - "Best score so far: 60.0\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## II. LM fine-tuning with a custom `Provider`" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 9%|▉ | 4/45 [00:08<01:22, 2.02s/it]\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from typing import Any, Dict, List, Optional\n", + "from dspy.clients.provider import Provider, TrainingJob, DataFormat\n", + "\n", + "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", + "dspy.settings.experimental = True" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 3 full traces after 5 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we define a custom provider with a dummy fine-tune method." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 5 (60.0): 100%|██████████| 5/5 [00:06<00:00, 1.21s/it]\n" - ] + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "class CustomProvider(Provider):\n", + "\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.finetunable = True\n", + "\n", + " @staticmethod\n", + " def finetune(\n", + " job: TrainingJob,\n", + " model: str,\n", + " train_data: List[Dict[str, Any]],\n", + " train_kwargs: Optional[Dict[str, Any]] = None,\n", + " data_format: Optional[DataFormat] = None,\n", + " ) -> str:\n", + "\n", + " # Fake fine-tuning\n", + " print(\"Fake fine-tuning has started!!\")\n", + " time.sleep(15)\n", + " print(\"Done\")\n", + "\n", + " # Return the new model name; we are hard-coding an OpenAI model as a\n", + " # demo placeholder\n", + " model = \"ft:gpt-4o-mini-2024-07-18:stanford::AMDsC653\"\n", + " return model\n", + " \n", + " # # We could also override the launch/kill methods if needed\n", + " # def launch(model: str, launch_kwargs: dict):\n", + " # pass\n", + "\n", + " # def kill(model: str, launch_kwargs: dict):\n", + " # pass\n", + "\n", + "\n", + "# We could also create a custom TrainingJob class to implement\n", + "# .status() and .cancel() methods, but we don't have to." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [20.0, 60.0, 60.0]\n", - "Best score so far: 60.0\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "`launch()` is called for the auto-launched model openai/MyAmazingCustomModel -- no action is taken!\n", + "`kill()` is called for the auto-launched model openai/MyAmazingCustomModel -- no action is taken!\n" + ] + } + ], + "source": [ + "# We could also pass launch_kwargs if this model needs to be launched before\n", + "# use, assuming that the launch and kill methods are implemented by the\n", + "# custom provider.\n", + "launch_kwargs = {\n", + " \"gpu\": 1,\n", + " \"max_prompt_length\": 1000,\n", + "}\n", + "\n", + "# Create the LM we want to fine-tune, using a dummy model name\n", + "model = \"openai/MyAmazingCustomModel\"\n", + "provider = CustomProvider()\n", + "lm = dspy.LM(model, provider=provider, launch_kwargs=launch_kwargs)\n", + "lm.launch()\n", + "\n", + "# Query the model -- commented out because the model is not real\n", + "# lm(\"How far is the Moon from Earth?\")\n", + "\n", + "# kill the model once done\n", + "lm.kill()" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4%|▍ | 2/45 [00:02<01:03, 1.48s/it]\n" - ] + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fake fine-tuning has started!!" + ] + }, + { + "data": { + "text/plain": [ + "dspy.clients.provider.TrainingJob" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Try fine-tune\n", + "dspy.settings.experimental = True\n", + "\n", + "# Let's construct a dummy dataset\n", + "message = {\n", + " \"messages\": [\n", + " {\"role\": \"system\", \"content\": \"Marv is a factual chatbot that is also sarcastic.\"},\n", + " {\"role\": \"user\", \"content\": \"How far is the Moon from Earth?\"},\n", + " {\"role\": \"assistant\", \"content\": \"384,400 kilometers\"},\n", + " ]\n", + "}\n", + "training_data = [message] * 20\n", + "\n", + "# Let's finetune the model\n", + "train_kwargs = {\n", + " \"n_epochs\": 1,\n", + "}\n", + "\n", + "job = lm.finetune(\n", + " train_data=training_data,\n", + " train_kwargs=train_kwargs,\n", + " data_format=\"chat\"\n", + ")\n", + "type(job)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 3 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0s] job.done(): False\n", + "[20s] job.done(): True\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done\n" + ] + } + ], + "source": [ + "# Running the command below immediately after the cell above returns `False`, indicating that the job is not done.\n", + "print(f\"[0s] job.done(): {job.done()}\")\n", + "\n", + "# Wait\n", + "time.sleep(20)\n", + "\n", + "# Check again\n", + "print(f\"[20s] job.done(): {job.done()}\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 5 (80.0): 100%|██████████| 5/5 [00:06<00:00, 1.22s/it] \n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can access the fine-tuned model as before" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "New best score: 80.0 for seed 0\n", - "Scores so far: [20.0, 60.0, 60.0, 80.0]\n", - "Best score so far: 80.0\n" - ] + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['384,400 kilometers']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm = job.result()\n", + "lm(\"How far is the Moon from Earth?\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|▏ | 1/45 [00:01<00:58, 1.32s/it]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## III. Showcasing `BootstrapFinetune`" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### i. Task Setup" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 5 (20.0): 100%|██████████| 5/5 [00:06<00:00, 1.39s/it]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example setup using HotPotQA" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0]\n", - "Best score so far: 80.0\n" - ] + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.datasets import HotPotQA\n", + "from dspy.evaluate import Evaluate # noqa\n", + "from dsp.utils.utils import deduplicate # noqa\n", + "\n", + "\n", + "# We are setting the experimental flag to True to make use of the fine-tuning\n", + "# features that are still in development.\n", + "dspy.settings.configure(experimental=True)\n", + "\n", + "# Define the program\n", + "class BasicMH(dspy.Module):\n", + " def __init__(self, passages_per_hop=3, num_hops=2):\n", + " super().__init__()\n", + " self.num_hops = 2\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_query = [dspy.ChainOfThought(\"context, question -> search_query\") for _ in range(self.num_hops)]\n", + " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " context = []\n", + " \n", + " for hop in range(self.num_hops):\n", + " search_query = self.generate_query[hop](context=context, question=question).search_query\n", + " passages = self.retrieve(search_query).passages\n", + " context = deduplicate(context + passages)\n", + "\n", + " answer = self.generate_answer(context=context, question=question).copy(context=context)\n", + " return answer\n", + "\n", + "# Prepare the dataset\n", + "TRAIN_SIZE = 1000\n", + "DEV_SIZE = 500\n", + "dataset = HotPotQA(train_seed=1, eval_seed=2023, test_size=0, only_hard_examples=True)\n", + "trainset = [x.with_inputs('question') for x in dataset.train][:TRAIN_SIZE]\n", + "devset = [x.with_inputs('question') for x in dataset.dev][:DEV_SIZE]\n", + "\n", + "# Prepare the metric and evaluator\n", + "NUM_THREADS = 12\n", + "metric = dspy.evaluate.answer_exact_match\n", + "evaluate = Evaluate(devset=devset, metric=metric, num_threads=NUM_THREADS, display_progress=True)\n", + "\n", + "# Prepare the retriever model\n", + "COLBERT_V2_ENDPOINT = \"http://20.102.90.50:2017/wiki17_abstracts\"\n", + "retriever = dspy.ColBERTv2(url=COLBERT_V2_ENDPOINT)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 7%|▋ | 3/45 [00:04<01:00, 1.43s/it]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ii. Demo of `BootstrapFinetune`" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 4 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", + "dspy.settings.experimental = True" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 5 (60.0): 100%|██████████| 5/5 [00:06<00:00, 1.27s/it] \n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cell below shows the different ways the `BootstrapFinetune` can be optimized." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0]\n", - "Best score so far: 80.0\n" - ] + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# (1) BSFT can be initilized with no arguments!\n", + "weight_optimizer = dspy.BootstrapFinetune()\n", + "\n", + "# (2) Better to optimize with a metric to be used for filtering data before\n", + "# fine-tuning\n", + "weight_optimizer = dspy.BootstrapFinetune(\n", + " metric=metric\n", + ")\n", + "\n", + "# (3) Bootstrap fine-tune accepts other parameters as well, as shown below\n", + "train_kwargs = {\n", + " \"n_epochs\": 1,\n", + "}\n", + "adapter = dspy.ChatAdapter()\n", + "\n", + "weight_optimizer = dspy.BootstrapFinetune(\n", + " metric=metric, # Can be left empty, leads to no filtering\n", + " multitask=True, # We can also handle False!\n", + " train_kwargs=train_kwargs, # Can be left empty\n", + " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", + " exclude_demos=False, # Can be left empty\n", + " num_threads = 1, # Can be left empty\n", + ")\n", + "\n", + "\n", + "# (4) The adapter and train_kwargs arguments could be passed as dictionaries\n", + "# mapping LMs to their respective adapters/train_kwargs. This is useful when the\n", + "# predictors of the program point to different LMs.\n", + "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", + "adapter = dspy.ChatAdapter()\n", + "\n", + "train_kwargs = {\n", + " lm: {\n", + " \"n_epochs\": 1,\n", + " },\n", + " # lm2: train_kwargs2,\n", + "}\n", + "adapter = {\n", + " lm: adapter,\n", + " # lm2: adapter2,\n", + "}\n", + "\n", + "weight_optimizer = dspy.BootstrapFinetune(\n", + " metric=metric, # Can be left empty, leads to no filtering\n", + " multitask=True, # We can also handle False!\n", + " train_kwargs=train_kwargs, # Can be left empty\n", + " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", + " exclude_demos=False, # Can be left empty\n", + " num_threads = 1, # Can be left empty\n", + ")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|▏ | 1/45 [00:01<00:47, 1.07s/it]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cell below shows an example of running `BootstrapFinetune`" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparing the student and teacher programs...\n", + "Ensuring that the student is not compiled.\n", + "No teacher provided. Using a copy of the student program as the teacher.\n", + "Bootstrapping data...\n", + "Average Metric: 5 / 10 (50.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10/10 [00:00<00:00, 416.94it/s]\n", + "Preparing the train data...\n", + "Collected data for 10 examples\n", + "After filtering for score, 5 examples remain\n", + "Using 15 data points for fine-tuning the model: gpt-4o-mini-2024-07-18\n", + "Starting LM fine-tuning...\n", + "1 fine-tuning job(s) to start.\n", + "Starting 1 fine-tuning jobs...\n", + "[OpenAI Provider] Validating the data format\n", + "[OpenAI Provider] Saving the data to a file\n", + "[OpenAI Provider] Data saved to /scr-ssd/dilara/.cache/dspy-new/finetune/4fd944783dbb3639.jsonl\n", + "[OpenAI Provider] Uploading the data to the provider\n", + "[OpenAI Provider] Start remote training\n", + "[OpenAI Provider] Job started with the OpenAI Job ID ftjob-gUXOgSEqEyV3v9Q6JgAqic9G\n", + "[OpenAI Provider] Wait for training to complete\n", + "[OpenAI Provider] Attempting to retrieve the trained model\n", + "[OpenAI Provider] Model retrieved: ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n", + "Job 1/1 completed.\n", + "Updating the student program with the fine-tuned LMs...\n", + "BootstrapFinetune has finished compiling the student program.\n" + ] + } + ], + "source": [ + "# Using method (3) from above to create a weight-optimized program\n", + "train_kwargs = {\n", + " \"n_epochs\": 1,\n", + "}\n", + "adapter = dspy.ChatAdapter()\n", + "\n", + "weight_optimizer = dspy.BootstrapFinetune(\n", + " metric=metric, # Can be left empty, leads to no filtering\n", + " multitask=True, # We can also handle False!\n", + " train_kwargs=train_kwargs, # Can be left empty\n", + " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", + " exclude_demos=False, # Can be left empty\n", + " num_threads = 1, # Can be left empty\n", + ")\n", + "\n", + "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", + "small_trainset = trainset[:10] # Use a small subset of the training data\n", + "\n", + "with dspy.context(lm=lm, rm=retriever):\n", + " weight_optimized_program = weight_optimizer.compile(\n", + " student=BasicMH(),\n", + " trainset=small_trainset,\n", + " teacher=None, # Doesn't need to be set, student is used as the teacher by default\n", + " )" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 5 (60.0): 100%|██████████| 5/5 [00:07<00:00, 1.43s/it] \n" - ] + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n", + "ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n", + "ft:gpt-4o-mini-2024-07-18:stanford::AOI4YHf2\n" + ] + } + ], + "source": [ + "for p in weight_optimized_program.predictors():\n", + " print(p.lm.model)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0, 60.0]\n", - "Best score so far: 80.0\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IV. Demo of `BetterTogether`" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|▏ | 1/45 [00:03<02:19, 3.17s/it]\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### i. Task Setup" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example setup using HotPotQA" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 5 (60.0): 100%|██████████| 5/5 [00:05<00:00, 1.18s/it]\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.datasets import HotPotQA\n", + "from dspy.evaluate import Evaluate # noqa\n", + "from dsp.utils.utils import deduplicate # noqa\n", + "\n", + "\n", + "# We are setting the experimental flag to True to make use of the fine-tuning\n", + "# features that are still in development.\n", + "dspy.settings.configure(experimental=True)\n", + "\n", + "# Define the program\n", + "class BasicMH(dspy.Module):\n", + " def __init__(self, passages_per_hop=3, num_hops=2):\n", + " super().__init__()\n", + " self.num_hops = 2\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_query = [dspy.ChainOfThought(\"context, question -> search_query\") for _ in range(self.num_hops)]\n", + " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " context = []\n", + " \n", + " for hop in range(self.num_hops):\n", + " search_query = self.generate_query[hop](context=context, question=question).search_query\n", + " passages = self.retrieve(search_query).passages\n", + " context = deduplicate(context + passages)\n", + "\n", + " answer = self.generate_answer(context=context, question=question).copy(context=context)\n", + " return answer\n", + "\n", + "# Prepare the dataset\n", + "TRAIN_SIZE = 1000\n", + "DEV_SIZE = 500\n", + "dataset = HotPotQA(train_seed=1, eval_seed=2023, test_size=0, only_hard_examples=True)\n", + "trainset = [x.with_inputs('question') for x in dataset.train][:TRAIN_SIZE]\n", + "devset = [x.with_inputs('question') for x in dataset.dev][:DEV_SIZE]\n", + "\n", + "# Prepare the metric and evaluator\n", + "NUM_THREADS = 12\n", + "metric = dspy.evaluate.answer_exact_match\n", + "evaluate = Evaluate(devset=devset, metric=metric, num_threads=NUM_THREADS, display_progress=True)\n", + "\n", + "# Prepare the retriever model\n", + "COLBERT_V2_ENDPOINT = \"http://20.102.90.50:2017/wiki17_abstracts\"\n", + "retriever = dspy.ColBERTv2(url=COLBERT_V2_ENDPOINT)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0, 60.0, 60.0]\n", - "Best score so far: 80.0\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ii. Demo" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 11%|█ | 5/45 [00:14<01:58, 2.96s/it]\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Using LM.finetune(), BSFT, and BetterTogether requires this flag\n", + "dspy.settings.experimental = True\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 3 full traces after 6 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Going to sample between 1 and 4 traces per predictor.\n", + "Will attempt to bootstrap 16 candidate sets.\n" + ] + } + ], + "source": [ + "# (1) The only required argument we require for BetterTogether is the metric\n", + "better_together = dspy.BetterTogether(\n", + " metric=metric # This is the only metric we require!\n", + " # We could also consider not requiring it if BootstrapFewShotWithRandomSearch is modified.\n", + ")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 5 (60.0): 100%|██████████| 5/5 [00:06<00:00, 1.31s/it] " - ] + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Going to sample between 1 and 3 traces per predictor.\n", + "Will attempt to bootstrap 6 candidate sets.\n" + ] + } + ], + "source": [ + "# (2) We can also pass the weight and prompt optimizers we initialized\n", + "train_kwargs = {\n", + " \"n_epochs\": 1,\n", + "}\n", + "adapter = dspy.ChatAdapter()\n", + "\n", + "weight_optimizer = dspy.BootstrapFinetune(\n", + " metric=metric, # Can be left empty, leads to no filtering\n", + " multitask=True, # We can also handle False!\n", + " train_kwargs=train_kwargs, # Can be left empty\n", + " adapter=adapter, # Can be left empty, leads to adapters inferred from the LM\n", + " exclude_demos=True, # We are dropping the demos for fine-tuning \n", + " num_threads = 1, # Can be left empty\n", + ")\n", + "\n", + "prompt_optimizer = dspy.BootstrapFewShotWithRandomSearch(\n", + " metric=metric,\n", + " max_bootstrapped_demos=3,\n", + " max_labeled_demos=3,\n", + " num_candidate_programs=6,\n", + " num_threads=6\n", + ")\n", + "\n", + "# Initialize BetterTogether\n", + "better_together = dspy.BetterTogether(\n", + " metric=metric,\n", + " weight_optimizer=weight_optimizer, # Can be left empty\n", + " prompt_optimizer=prompt_optimizer, # Can be left empty\n", + " seed=2023, # Can be left empty\n", + ")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0, 60.0, 60.0, 60.0]\n", - "Best score so far: 80.0\n", - "9 candidate programs found.\n", - "[BetterTogether] Killing the LMs used for sampling...\n", - "`kill()` is called for the auto-launched model `ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8` -- no action is taken!\n", - "[BetterTogether] BetterTogether has finished compiling the student program.\n" - ] + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[BetterTogether] Validating the strategy\n", + "[BetterTogether] Preparing the student program...\n", + "Ensuring that the student is not compiled\n", + "[BetterTogether] Compiling the student program...\n", + "[BetterTogether] Step 1 of 3 - Strategy `p`\n", + "[BetterTogether] Shuffling the trainset...\n", + "[BetterTogether] Preparing for prompt optimization...\n", + "[BetterTogether] Launching the program LMs for sampling...\n", + "`launch()` is called for the auto-launched model `gpt-4o-mini-2024-07-18` -- no action is taken!\n", + "[BetterTogether] Compiling the prompt optimizer...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 2 / 5 (40.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:02<00:00, 2.05it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New best score: 40.0 for seed -3\n", + "Scores so far: [40.0]\n", + "Best score so far: 40.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 2 / 5 (40.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:02<00:00, 2.30it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [40.0, 40.0]\n", + "Best score so far: 40.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 11%|\u2588 | 5/45 [00:11<01:28, 2.21s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 3 full traces after 6 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 5 (20.0): 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+ "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 4 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 2 / 5 (40.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:05<00:00, 1.11s/it] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0]\n", + "Best score so far: 40.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u258f | 1/45 [00:02<01:32, 2.10s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 5 (20.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:09<00:00, 1.84s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0, 20.0]\n", + "Best score so far: 40.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u258f | 1/45 [00:01<01:04, 1.46s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 5 (20.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:06<00:00, 1.28s/it] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [40.0, 40.0, 20.0, 20.0, 40.0, 20.0, 20.0]\n", + "Best score so far: 40.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u258f | 1/45 [00:04<03:36, 4.91s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + 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"Best score so far: 60.0\n", + "9 candidate programs found.\n", + "[BetterTogether] Killing the LMs used for sampling...\n", + "`kill()` is called for the auto-launched model `gpt-4o-mini-2024-07-18` -- no action is taken!\n", + "[BetterTogether] Step 2 of 3 - Strategy `p -> w`\n", + "[BetterTogether] Shuffling the trainset...\n", + "[BetterTogether] Preparing for weight optimization...\n", + "[BetterTogether] Compiling the weight optimizer...\n", + "[BootstrapFinetune] Preparing the student and teacher programs...\n", + "Ensuring that the student is not compiled\n", + "No teacher provided. Using a copy of the student program as the teacher.\n", + "[BootstrapFinetune] Bootstrapping data...\n", + "Average Metric: 28 / 50 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [03:55<00:00, 4.72s/it]\n", + "[BootstrapFinetune] Preparing the train data...\n", + "[BootstrapFinetune] Collected data for 50 examples\n", + "[BootstrapFinetune] After filtering for score, 28 examples remain\n", + "Using 84 data points for fine-tuning the model: gpt-4o-mini-2024-07-18\n", + "[BootstrapFinetune] Starting LM fine-tuning...\n", + "[BootstrapFinetune] 1 fine-tuning job(s) to start\n", + "[BootstrapFinetune] Starting 1 fine-tuning jobs...\n", + "[OpenAI Provider] Validating the data format\n", + "[OpenAI Provider] Saving the data to a file\n", + "[OpenAI Provider] Data saved to /scr-ssd/dilara/.cache/dspy-new/finetune/c4bb7c084d3a7ad3.jsonl\n", + "[OpenAI Provider] Uploading the data to the provider\n", + "[OpenAI Provider] Starting remote training\n", + "[OpenAI Provider] Job started with the OpenAI Job ID ftjob-75MiMHGY9xW1gNMGZSs5ht8v\n", + "[OpenAI Provider] Waiting for training to complete\n", + "[OpenAI Provider] Attempting to retrieve the trained model\n", + "[OpenAI Provider] Model retrieved: ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n", + "Job 1/1 completed.\n", + "[BootstrapFinetune] Updating the student program with the fine-tuned LMs...\n", + "[BootstrapFinetune] BootstrapFinetune has finished compiling the student program\n", + "[BetterTogether] Step 3 of 3 - Strategy `p -> w -> p`\n", + "[BetterTogether] Shuffling the trainset...\n", + "[BetterTogether] Preparing for prompt optimization...\n", + "[BetterTogether] Launching the program LMs for sampling...\n", + "`launch()` is called for the auto-launched model `ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8` -- no action is taken!\n", + "[BetterTogether] Compiling the prompt optimizer...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 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"name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0]\n", + "Best score so far: 80.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u258f | 1/45 [00:01<00:47, 1.07s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 5 (60.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:07<00:00, 1.43s/it] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0, 60.0]\n", + "Best score so far: 80.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u258f | 1/45 [00:03<02:19, 3.17s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 5 (60.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:05<00:00, 1.18s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0, 60.0, 60.0]\n", + "Best score so far: 80.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 11%|\u2588 | 5/45 [00:14<01:58, 2.96s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 3 full traces after 6 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 5 (60.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [00:06<00:00, 1.31s/it] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores so far: [20.0, 60.0, 60.0, 80.0, 20.0, 60.0, 60.0, 60.0, 60.0]\n", + "Best score so far: 80.0\n", + "9 candidate programs found.\n", + "[BetterTogether] Killing the LMs used for sampling...\n", + "`kill()` is called for the auto-launched model `ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8` -- no action is taken!\n", + "[BetterTogether] BetterTogether has finished compiling the student program.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Running BetterTogether on a small dataset\n", + "\n", + "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", + "small_trainset = trainset[:50] # Use a small subset of the training data\n", + "\n", + "with dspy.context(lm=lm, rm=retriever):\n", + " optimized_program = better_together.compile(\n", + " student=BasicMH(),\n", + " trainset=small_trainset,\n", + " strategy=\"p -> w -> p\",\n", + " valset_ratio=0.1\n", + " )" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n", + "ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n", + "ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n" + ] + } + ], + "source": [ + "for p in optimized_program.predictors():\n", + " print(p.lm.model)" + ] } - ], - "source": [ - "# Running BetterTogether on a small dataset\n", - "\n", - "lm = dspy.LM('gpt-4o-mini-2024-07-18')\n", - "small_trainset = trainset[:50] # Use a small subset of the training data\n", - "\n", - "with dspy.context(lm=lm, rm=retriever):\n", - " optimized_program = better_together.compile(\n", - " student=BasicMH(),\n", - " trainset=small_trainset,\n", - " strategy=\"p -> w -> p\",\n", - " valset_ratio=0.1\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n", - "ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n", - "ft:gpt-4o-mini-2024-07-18:stanford::AOIn7Dm8\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "dspy25", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" } - ], - "source": [ - "for p in optimized_program.predictors():\n", - " print(p.lm.model)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dspy25", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.19" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/integrations/clarifai/clarifai_llm_retriever_example.ipynb b/examples/outdated_v2.4_examples/integrations/clarifai/clarifai_llm_retriever_example.ipynb index b385a9abcc..99a6d5a6c8 100644 --- a/examples/outdated_v2.4_examples/integrations/clarifai/clarifai_llm_retriever_example.ipynb +++ b/examples/outdated_v2.4_examples/integrations/clarifai/clarifai_llm_retriever_example.ipynb @@ -1,467 +1,467 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DSPy-Clarifai lm and retriever example notebook\n", - "\n", - "This notebook will walk you through on the integration of clarifai into DSPy which enables the DSPy users to leverage clarifai capabilities of calling llm models from clarifai platform and to utilize clarifai app as retriever for their vector search use cases." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install clarifai" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install dspy-ai" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import necessary packages" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from dspy.retrieve.clarifai_rm import ClarifaiRM " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize clarifai app id, user id and PAT.\n", - "Create an AI app in clarifai portal in < 1 min by following this link [getting started](https://docs.clarifai.com/clarifai-basics/quick-start/your-first-predictions).\n", - "\n", - "You can browse the portal to obtain [MODEL URL](https://clarifai.com/explore/models) for different models in clarifai community." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#for the demo we are going with llama2-70b-chat\n", - "MODEL_URL = \"https://clarifai.com/meta/Llama-2/models/llama2-70b-chat\" \n", - "PAT = \"CLARIFAI_PAT\"\n", - "USER_ID = \"YOUR_ID\"\n", - "APP_ID = \"YOUR_APP\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data ingestion into clarifai vectordatabase\n", - "\n", - "To use clarifai as retriever all you have to do is ingest the documents into clarifai app that serves as your vectordatabase to retrieve similar documents.\n", - "To simplify the ingestion, we are utilising the clarifaivectordatabase integration for ingestion." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#run this block to ingest the documents into clarifai app as chunks.\n", - "# if you encounter any issue, make sure to run `pip install langchain`\n", - "\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.document_loaders import TextLoader\n", - "from langchain.vectorstores import Clarifai as clarifaivectorstore\n", - "\n", - "loader = TextLoader(\"YOUR_TEXT_FILE\") #replace with your file path\n", - "documents = loader.load()\n", - "text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=200)\n", - "docs = text_splitter.split_documents(documents)\n", - "\n", - "clarifai_vector_db = clarifaivectorstore.from_documents(\n", - " user_id=USER_ID,\n", - " app_id=APP_ID,\n", - " documents=docs,\n", - " pat=PAT\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize LLM class" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure to pass all the model parameters in inference_params field of clarifaiLLM class. " - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "llm=dspy.Clarifai(model=MODEL_URL, api_key=PAT, n=2, inference_params={\"max_tokens\":100,'temperature':0.6})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initialize Clarifai Retriever model class\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "retriever_model=ClarifaiRM(clarifai_user_id=USER_ID, clarfiai_app_id=APP_ID, clarifai_pat=PAT, k=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "configure dspy with llm and rm models." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "dspy.settings.configure(lm=llm, rm=retriever_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example: dspy.signature and dspy.module with clairfaiLLM" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NEGATIVE\n", - "\n", - "\n" - ] - } - ], - "source": [ - "sentence = \"disney again ransacks its archives for a quick-buck sequel .\" # example from the SST-2 dataset.\n", - "\n", - "classify = dspy.Predict('sentence -> sentiment')\n", - "print(classify(sentence=sentence).sentiment)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example: Quick glimpse into how our retriever works when a query is passed to the dspy.Retrieve class\n", - "\n", - "Here we have used the rulebook of Formula student Germany competition.\n", - "\n", - "link : https://www.formulastudent.de/fileadmin/user_upload/all/2024/rules/FS-Rules_2024_v1.0.pdf \n", - "\n", - "We have used the .txt version of the file for our demo." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "retrieve = dspy.Retrieve()\n", - "topK_passages = retrieve(\"can I test my vehicle engine in pit?\").passages" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neutral.\\n• All driven wheels are removed.\\n• Connectors to all injectors and ignition coils are detached.\\n• A fire extinguisher must be placed next to the engine.\\n\\nA 6.9\\n\\nFueling and Oil\\n\\nA 6.9.1\\n\\nFueling may only take place at the fuel station and must be conducted by officials only.\\n\\nA 6.9.2\\n\\nOpen fuel containers are not permitted at the competition.\\n\\nA 6.9.3\\n\\nWaste oil must be taken to the fuel station for disposal.\\n\\nA 6.10\\n\\n[EV ONLY ] Working on the Vehicle\\n\\nA 6.10.1\\n\\nAll activities require the TSAL to be green.\\n\\nA 6.10.2\\n\\nA prominent manual sign indicating the “TSAL green” state must be present whenever the\\nLVS is switched off and the requirements for an only green TSAL according to EV 4.10 are\\nmet.\\n\\nA 6.10.3', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neutral.\\n• All driven wheels are removed.\\n• Connectors to all injectors and ignition coils are detached.\\n• A fire extinguisher must be placed next to the engine.\\n\\nA 6.9\\n\\nFueling and Oil\\n\\nA 6.9.1\\n\\nFueling may only take place at the fuel station and must be conducted by officials only.\\n\\nA 6.9.2\\n\\nOpen fuel containers are not permitted at the competition.\\n\\nA 6.9.3\\n\\nWaste oil must be taken to the fuel station for disposal.\\n\\nA 6.10\\n\\n[EV ONLY ] Working on the Vehicle\\n\\nA 6.10.1\\n\\nAll activities require the TSAL to be green.\\n\\nA 6.10.2\\n\\nA prominent manual sign indicating the “TSAL green” state must be present whenever the\\nLVS is switched off and the requirements for an only green TSAL according to EV 4.10 are\\nmet.\\n\\nA 6.10.3']\n" - ] - } - ], - "source": [ - "print(topK_passages)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## RAG dspy module using clarifai as retriever" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generally to construct a module in dspy, you might need to define \n", - "\n", - "Signature: \n", - "explain the input and output fields in an intuitive way with just few words.\n", - "(\"question\"-> \"answer\")\n", - "\n", - "Module:\n", - "Module can be something where you put the signatures into action by defining a certain module which compiles and generate response for you for the given query." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construct a signaturre class, which defines the input fields and output fields needed. \n", - "Also, give docstrings and description in verbose, so that the dspy signature could understand the context and compile best prompt for the usecase." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "class GenerateAnswer(dspy.Signature):\n", - " \"\"\"Think and Answer questions based on the context provided.\"\"\"\n", - "\n", - " context = dspy.InputField(desc=\"may contain relevant facts about user query\")\n", - " question = dspy.InputField(desc=\"User query\")\n", - " answer = dspy.OutputField(desc=\"Answer in one or two lines\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define the module with the actions needs to be performed, here we are showing a small RAG use case where we are retrieving similar contexts using our retriever class and generating response based on the factual context using one of the DSPy module `ChainOfThought`." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "class RAG(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " self.retrieve = dspy.Retrieve()\n", - " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", - " \n", - " def forward(self, question):\n", - " context = self.retrieve(question).passages\n", - " prediction = self.generate_answer(context=context, question=question)\n", - " return dspy.Prediction(context=context, answer=prediction.answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are passing our query and retrieving relevant chunks using clarifai retriever and based on factual evidence, model is able to generate response." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: can I test my vehicle engine in pit before inspection?\n", - "Predicted Answer: No, you cannot test your vehicle engine in the pit before inspection.\n", - "Retrieved Contexts (truncated): ['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neut...', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neut...']\n" - ] - } - ], - "source": [ - "# Ask any question you like to this RAG program.\n", - "my_question = \"can I test my vehicle engine in pit before inspection?\"\n", - "\n", - "# Get the prediction. This contains `pred.context` and `pred.answer`.\n", - "Rag_obj= RAG()\n", - "predict_response_llama70b=Rag_obj(my_question)\n", - "\n", - "# Print the contexts and the answer.\n", - "print(f\"Question: {my_question}\")\n", - "print(f\"Predicted Answer: {predict_response_llama70b.answer}\")\n", - "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_llama70b.context]}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now we will compare our RAG DSPy module with different community models from clarifai and comapare responses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mistral-7b Instruct" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "mistral_lm = dspy.Clarifai(model=\"https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct\", api_key=PAT, n=2, inference_params={'temperature':0.6})\n", - "dspy.settings.configure(lm=mistral_lm, rm=retriever_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: can I test my vehicle engine in pit before inspection?\n", - "Predicted Answer: Reasoning: According to the context, cranking engines in the pits is allowed only when the vehicle has passed mechanical inspection.\n", - "\n", - "Answer: No, you cannot test your vehicle engine in pit before inspection.\n", - "Retrieved Contexts (truncated): ['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neut...', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neut...']\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DSPy-Clarifai lm and retriever example notebook\n", + "\n", + "This notebook will walk you through on the integration of clarifai into DSPy which enables the DSPy users to leverage clarifai capabilities of calling llm models from clarifai platform and to utilize clarifai app as retriever for their vector search use cases." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install clarifai" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install dspy-ai" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.retrieve.clarifai_rm import ClarifaiRM " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize clarifai app id, user id and PAT.\n", + "Create an AI app in clarifai portal in < 1 min by following this link [getting started](https://docs.clarifai.com/clarifai-basics/quick-start/your-first-predictions).\n", + "\n", + "You can browse the portal to obtain [MODEL URL](https://clarifai.com/explore/models) for different models in clarifai community." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#for the demo we are going with llama2-70b-chat\n", + "MODEL_URL = \"https://clarifai.com/meta/Llama-2/models/llama2-70b-chat\" \n", + "PAT = \"CLARIFAI_PAT\"\n", + "USER_ID = \"YOUR_ID\"\n", + "APP_ID = \"YOUR_APP\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data ingestion into clarifai vectordatabase\n", + "\n", + "To use clarifai as retriever all you have to do is ingest the documents into clarifai app that serves as your vectordatabase to retrieve similar documents.\n", + "To simplify the ingestion, we are utilising the clarifaivectordatabase integration for ingestion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#run this block to ingest the documents into clarifai app as chunks.\n", + "# if you encounter any issue, make sure to run `pip install langchain`\n", + "\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain.document_loaders import TextLoader\n", + "from langchain.vectorstores import Clarifai as clarifaivectorstore\n", + "\n", + "loader = TextLoader(\"YOUR_TEXT_FILE\") #replace with your file path\n", + "documents = loader.load()\n", + "text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=200)\n", + "docs = text_splitter.split_documents(documents)\n", + "\n", + "clarifai_vector_db = clarifaivectorstore.from_documents(\n", + " user_id=USER_ID,\n", + " app_id=APP_ID,\n", + " documents=docs,\n", + " pat=PAT\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize LLM class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure to pass all the model parameters in inference_params field of clarifaiLLM class. " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "llm=dspy.Clarifai(model=MODEL_URL, api_key=PAT, n=2, inference_params={\"max_tokens\":100,'temperature':0.6})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize Clarifai Retriever model class\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "retriever_model=ClarifaiRM(clarifai_user_id=USER_ID, clarfiai_app_id=APP_ID, clarifai_pat=PAT, k=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "configure dspy with llm and rm models." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "dspy.settings.configure(lm=llm, rm=retriever_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: dspy.signature and dspy.module with clairfaiLLM" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NEGATIVE\n", + "\n", + "\n" + ] + } + ], + "source": [ + "sentence = \"disney again ransacks its archives for a quick-buck sequel .\" # example from the SST-2 dataset.\n", + "\n", + "classify = dspy.Predict('sentence -> sentiment')\n", + "print(classify(sentence=sentence).sentiment)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: Quick glimpse into how our retriever works when a query is passed to the dspy.Retrieve class\n", + "\n", + "Here we have used the rulebook of Formula student Germany competition.\n", + "\n", + "link : https://www.formulastudent.de/fileadmin/user_upload/all/2024/rules/FS-Rules_2024_v1.0.pdf \n", + "\n", + "We have used the .txt version of the file for our demo." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "retrieve = dspy.Retrieve()\n", + "topK_passages = retrieve(\"can I test my vehicle engine in pit?\").passages" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neutral.\\n\u2022 All driven wheels are removed.\\n\u2022 Connectors to all injectors and ignition coils are detached.\\n\u2022 A fire extinguisher must be placed next to the engine.\\n\\nA 6.9\\n\\nFueling and Oil\\n\\nA 6.9.1\\n\\nFueling may only take place at the fuel station and must be conducted by officials only.\\n\\nA 6.9.2\\n\\nOpen fuel containers are not permitted at the competition.\\n\\nA 6.9.3\\n\\nWaste oil must be taken to the fuel station for disposal.\\n\\nA 6.10\\n\\n[EV ONLY ] Working on the Vehicle\\n\\nA 6.10.1\\n\\nAll activities require the TSAL to be green.\\n\\nA 6.10.2\\n\\nA prominent manual sign indicating the \u201cTSAL green\u201d state must be present whenever the\\nLVS is switched off and the requirements for an only green TSAL according to EV 4.10 are\\nmet.\\n\\nA 6.10.3', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neutral.\\n\u2022 All driven wheels are removed.\\n\u2022 Connectors to all injectors and ignition coils are detached.\\n\u2022 A fire extinguisher must be placed next to the engine.\\n\\nA 6.9\\n\\nFueling and Oil\\n\\nA 6.9.1\\n\\nFueling may only take place at the fuel station and must be conducted by officials only.\\n\\nA 6.9.2\\n\\nOpen fuel containers are not permitted at the competition.\\n\\nA 6.9.3\\n\\nWaste oil must be taken to the fuel station for disposal.\\n\\nA 6.10\\n\\n[EV ONLY ] Working on the Vehicle\\n\\nA 6.10.1\\n\\nAll activities require the TSAL to be green.\\n\\nA 6.10.2\\n\\nA prominent manual sign indicating the \u201cTSAL green\u201d state must be present whenever the\\nLVS is switched off and the requirements for an only green TSAL according to EV 4.10 are\\nmet.\\n\\nA 6.10.3']\n" + ] + } + ], + "source": [ + "print(topK_passages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RAG dspy module using clarifai as retriever" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generally to construct a module in dspy, you might need to define \n", + "\n", + "Signature: \n", + "explain the input and output fields in an intuitive way with just few words.\n", + "(\"question\"-> \"answer\")\n", + "\n", + "Module:\n", + "Module can be something where you put the signatures into action by defining a certain module which compiles and generate response for you for the given query." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construct a signaturre class, which defines the input fields and output fields needed. \n", + "Also, give docstrings and description in verbose, so that the dspy signature could understand the context and compile best prompt for the usecase." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "class GenerateAnswer(dspy.Signature):\n", + " \"\"\"Think and Answer questions based on the context provided.\"\"\"\n", + "\n", + " context = dspy.InputField(desc=\"may contain relevant facts about user query\")\n", + " question = dspy.InputField(desc=\"User query\")\n", + " answer = dspy.OutputField(desc=\"Answer in one or two lines\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the module with the actions needs to be performed, here we are showing a small RAG use case where we are retrieving similar contexts using our retriever class and generating response based on the factual context using one of the DSPy module `ChainOfThought`." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "class RAG(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " self.retrieve = dspy.Retrieve()\n", + " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", + " \n", + " def forward(self, question):\n", + " context = self.retrieve(question).passages\n", + " prediction = self.generate_answer(context=context, question=question)\n", + " return dspy.Prediction(context=context, answer=prediction.answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are passing our query and retrieving relevant chunks using clarifai retriever and based on factual evidence, model is able to generate response." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: can I test my vehicle engine in pit before inspection?\n", + "Predicted Answer: No, you cannot test your vehicle engine in the pit before inspection.\n", + "Retrieved Contexts (truncated): ['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neut...', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neut...']\n" + ] + } + ], + "source": [ + "# Ask any question you like to this RAG program.\n", + "my_question = \"can I test my vehicle engine in pit before inspection?\"\n", + "\n", + "# Get the prediction. This contains `pred.context` and `pred.answer`.\n", + "Rag_obj= RAG()\n", + "predict_response_llama70b=Rag_obj(my_question)\n", + "\n", + "# Print the contexts and the answer.\n", + "print(f\"Question: {my_question}\")\n", + "print(f\"Predicted Answer: {predict_response_llama70b.answer}\")\n", + "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_llama70b.context]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now we will compare our RAG DSPy module with different community models from clarifai and comapare responses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mistral-7b Instruct" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "mistral_lm = dspy.Clarifai(model=\"https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct\", api_key=PAT, n=2, inference_params={'temperature':0.6})\n", + "dspy.settings.configure(lm=mistral_lm, rm=retriever_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: can I test my vehicle engine in pit before inspection?\n", + "Predicted Answer: Reasoning: According to the context, cranking engines in the pits is allowed only when the vehicle has passed mechanical inspection.\n", + "\n", + "Answer: No, you cannot test your vehicle engine in pit before inspection.\n", + "Retrieved Contexts (truncated): ['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neut...', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neut...']\n" + ] + } + ], + "source": [ + "my_question = \"can I test my vehicle engine in pit before inspection?\"\n", + "Rag_obj= RAG()\n", + "predict_response_mistral=Rag_obj(my_question)\n", + "\n", + "print(f\"Question: {my_question}\")\n", + "print(f\"Predicted Answer: {predict_response_mistral.answer}\")\n", + "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_mistral.context]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gemini Pro" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "gemini_lm = dspy.Clarifai(model=\"https://clarifai.com/gcp/generate/models/gemini-pro\", api_key=PAT, n=2)\n", + "dspy.settings.configure(lm=gemini_lm, rm=retriever_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: can I test my vehicle engine in pit before inspection?\n", + "Predicted Answer: No, you can't test your vehicle engine in the pits before inspection.\n", + "Retrieved Contexts (truncated): ['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neut...', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n\u2022 The vehicle has passed mechanical inspection.\\n\u2022 The driven axles are securely jacked up.\\n\u2022 Gearbox is in neut...']\n" + ] + } + ], + "source": [ + "my_question = \"can I test my vehicle engine in pit before inspection?\"\n", + "Rag_obj= RAG()\n", + "predict_response_gemini=Rag_obj(my_question)\n", + "\n", + "print(f\"Question: {my_question}\")\n", + "print(f\"Predicted Answer: {predict_response_gemini.answer}\")\n", + "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_gemini.context]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clarifai enables you to test your dspy module with different llm models and compare the response as it is crucial part of prompt engineering to test and achieve the right combination of llm models with right prompt." + ] } - ], - "source": [ - "my_question = \"can I test my vehicle engine in pit before inspection?\"\n", - "Rag_obj= RAG()\n", - "predict_response_mistral=Rag_obj(my_question)\n", - "\n", - "print(f\"Question: {my_question}\")\n", - "print(f\"Predicted Answer: {predict_response_mistral.answer}\")\n", - "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_mistral.context]}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gemini Pro" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "gemini_lm = dspy.Clarifai(model=\"https://clarifai.com/gcp/generate/models/gemini-pro\", api_key=PAT, n=2)\n", - "dspy.settings.configure(lm=gemini_lm, rm=retriever_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: can I test my vehicle engine in pit before inspection?\n", - "Predicted Answer: No, you can't test your vehicle engine in the pits before inspection.\n", - "Retrieved Contexts (truncated): ['A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neut...', 'A 6.8.3\\n\\nCranking engines in the pits is allowed, when the following conditions are met:\\n• The vehicle has passed mechanical inspection.\\n• The driven axles are securely jacked up.\\n• Gearbox is in neut...']\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "my_question = \"can I test my vehicle engine in pit before inspection?\"\n", - "Rag_obj= RAG()\n", - "predict_response_gemini=Rag_obj(my_question)\n", - "\n", - "print(f\"Question: {my_question}\")\n", - "print(f\"Predicted Answer: {predict_response_gemini.answer}\")\n", - "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_gemini.context]}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clarifai enables you to test your dspy module with different llm models and compare the response as it is crucial part of prompt engineering to test and achieve the right combination of llm models with right prompt." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/integrations/colbert/colbert_local.ipynb b/examples/outdated_v2.4_examples/integrations/colbert/colbert_local.ipynb index f5eb881a23..a75a7cbc6b 100644 --- a/examples/outdated_v2.4_examples/integrations/colbert/colbert_local.ipynb +++ b/examples/outdated_v2.4_examples/integrations/colbert/colbert_local.ipynb @@ -1,404 +1,404 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IN THIS NOTEBOOK, WE WILL EXPLORE THE COLBERT AS A RERANKER AND RETRIEVER IN LOCAL MODE. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* If you want to build a server from your colbert local index, please refer [here](https://github.com/stanford-futuredata/ColBERT/blob/main/server.py)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from colbert.infra.config import ColBERTConfig" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "# You can set this environment variable for debugging purposes\n", - "os.environ['COLBERT_LOAD_TORCH_EXTENSION_VERBOSE'] = \"True\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's review the colbert config class" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# You can view the different attributes of the colbert config by uncommenting cell below\n", - "# for k,v in ColBERTConfig().__dict__.items():\n", - "# print(f\"{k} --> {v}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "passages = [\"It's a piece of cake.\", \"Don't put off until tomorrow what you can do today.\", 'To kill two birds with one stone.', 'Actions speak louder than words.', 'Honesty is the best policy.', 'If you want something done right, do it yourself.', 'The best things in life are free.', \"Don't count your chickens before they hatch.\", 'She sells seashells by the seashore.', 'Practice makes perfect.', \"Where there's a will, there's a way.\", 'Absence makes the heart grow fonder.', 'When the going gets tough, the tough get going.', 'A journey of a thousand miles begins with a single step.', \"You can't have your cake and eat it too.\", \"If you can't beat them, join them.\", 'Keep your friends close and your enemies closer.', \"Don't put all your eggs in one basket.\", \"All's fair in love and war.\", 'Every dog has its day.', 'All good things must come to an end.', 'Once bitten, twice shy.', \"The apple doesn't fall far from the tree.\", 'A penny saved is a penny earned.', \"Don't bite the hand that feeds you.\", 'You reap what you sow.', 'An apple a day keeps the doctor away.', \"One man's trash is another man's treasure.\", 'The squeaky wheel gets the grease.', 'A picture is worth a thousand words.', 'Fortune favors the bold.', 'Practice what you preach.', 'A watched pot never boils.', 'No pain, no gain.', \"You can't make an omelet without breaking eggs.\", \"There's no place like home.\", 'Ask and you shall receive.', 'Let sleeping dogs lie.', 'If the shoe fits, wear it.', 'Every cloud has a silver lining.', 'Look before you leap.', 'The more, the merrier.', 'The grass is always greener on the other side.', 'Beauty is only skin deep.', \"Two wrongs don't make a right.\", 'Beauty is in the eye of the beholder.', 'Necessity is the mother of invention.', 'Out of sight, out of mind.', 'Patience is a virtue.', 'Curiosity killed the cat.', \"If at first you don't succeed, try, try again.\", \"Beggars can't be choosers.\", 'Too many cooks spoil the broth.', 'Easy come, easy go.', \"Don't cry over spilled milk.\", \"There's no such thing as a free lunch.\", 'A bird in the hand is worth two in the bush.', 'Good things come to those who wait.', 'The quick brown fox jumps over the lazy dog.', 'It takes two to tango.', 'A friend in need is a friend indeed.', 'Like father, like son.', 'Let bygones be bygones.', 'Kill two birds with one stone.', 'A penny for your thoughts.', 'I am the master of my fate, I am the captain of my soul.', 'The pen is mightier than the sword.', 'When in Rome, do as the Romans do.', \"Rome wasn't built in a day.\", \"You can't judge a book by its cover.\", \"It's raining cats and dogs.\", 'Make hay while the sun shines.', \"It's better to be safe than sorry.\", 'The early bird catches the worm.', 'To be or not to be, that is the question.', 'Better late than never.']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## This tutorial is running from the `examples/integrations/tutorials folder`, hence we need to add the system path for dspy\n", - "\n", - "* If you have installed the dspy package, then you don't need to run the below cell" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append(\"../../..\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## COLBERT AS RETRIEVER" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "colbert_config = ColBERTConfig()\n", - "colbert_config.index_name = \"Colbert-RM\"\n", - "colbert_config.experiment = \"Colbert-Experiment\"\n", - "colbert_config.checkpoint = \"colbert-ir/colbertv2.0\"\n", - "colbert_retriever = dspy.ColBERTv2RetrieverLocal(\n", - " passages = passages,load_only=False,\n", - " colbert_config=colbert_config\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#CONFIGURE COLBERT IN DSPY\n", - "dspy.settings.configure(rm=colbert_retriever)\n", - "\n", - "retrieved_docs = dspy.Retrieve(k=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "DeprecationWarning: 'dspy.Retrieve' for reranking has been deprecated, please use dspy.RetrieveThenRerank. The reranking is ignored here. In the future this will raise an error.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IN THIS NOTEBOOK, WE WILL EXPLORE THE COLBERT AS A RERANKER AND RETRIEVER IN LOCAL MODE. " + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "#> QueryTokenizer.tensorize(batch_text[0], batch_background[0], bsize) ==\n", - "#> Input: . What is the meaning of life?, \t\t True, \t\t None\n", - "#> Output IDs: torch.Size([32]), tensor([ 101, 1, 2054, 2003, 1996, 3574, 1997, 2166, 1029, 102, 103, 103,\n", - " 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103,\n", - " 103, 103, 103, 103, 103, 103, 103, 103], device='cuda:0')\n", - "#> Output Mask: torch.Size([32]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')\n", - "\n" - ] - } - ], - "source": [ - "pred = retrieved_docs(\n", - " \"What is the meaning of life?\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* If you want to build a server from your colbert local index, please refer [here](https://github.com/stanford-futuredata/ColBERT/blob/main/server.py)" + ] + }, { - "data": { - "text/plain": [ - "Prediction(\n", - " score=[nan, nan, nan, nan, nan],\n", - " pid=[33, 6, 47, 74, 48],\n", - " passages=['No pain, no gain.', 'The best things in life are free.', 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'Patience is a virtue.']\n", - ")" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from colbert.infra.config import ColBERTConfig" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "DeprecationWarning: 'dspy.Retrieve' for reranking has been deprecated, please use dspy.RetrieveThenRerank. The reranking is ignored here. In the future this will raise an error.\n" - ] - } - ], - "source": [ - "multiple_pred = retrieved_docs(\n", - " [\"What is the meaning of life?\",\"Meaning of pain?\"],by_prob=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# You can set this environment variable for debugging purposes\n", + "os.environ['COLBERT_LOAD_TORCH_EXTENSION_VERBOSE'] = \"True\"" + ] + }, { - "data": { - "text/plain": [ - "[Prediction(\n", - " score=[nan, nan, nan, nan, nan],\n", - " pid=[33, 6, 47, 74, 48],\n", - " passages=['No pain, no gain.', 'The best things in life are free.', 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'Patience is a virtue.']\n", - " ),\n", - " Prediction(\n", - " score=[nan, nan, nan, nan, nan],\n", - " pid=[16, 0, 47, 74, 26],\n", - " passages=['Keep your friends close and your enemies closer.', \"It's a piece of cake.\", 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'An apple a day keeps the doctor away.']\n", - " )]" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's review the colbert config class" ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multiple_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## COLBERT AS RERANKER" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "colbert_config = ColBERTConfig()\n", - "colbert_config.index_name = 'colbert-ir-index'\n", - "colbert_reranker = dspy.ColBERTv2RerankerLocal(\n", - " checkpoint='colbert-ir/colbertv2.0',colbert_config=colbert_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "dspy.settings.configure(rm=colbert_retriever,reranker=colbert_reranker)\n", - "\n", - "retrieve_rerank = dspy.RetrieveThenRerank(k=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "pred = retrieve_rerank(\n", - " [\"What is the meaning of life?\",\"Meaning of pain?\"]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "[Prediction(\n", - " score=[nan, nan, nan, nan, nan],\n", - " pid=[6, 48, 74, 47, 33],\n", - " rerank_score=[15.8359375, 14.2109375, 12.5703125, 11.7890625, 9.1796875],\n", - " passages=['The best things in life are free.', 'Patience is a virtue.', 'To be or not to be, that is the question.', 'Out of sight, out of mind.', 'No pain, no gain.']\n", - " ),\n", - " Prediction(\n", - " score=[nan, nan, nan, nan, nan],\n", - " pid=[33, 0, 47, 74, 16],\n", - " rerank_score=[19.828125, 12.2890625, 11.171875, 9.09375, 6.8984375],\n", - " passages=['No pain, no gain.', \"It's a piece of cake.\", 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'Keep your friends close and your enemies closer.']\n", - " )]" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# You can view the different attributes of the colbert config by uncommenting cell below\n", + "# for k,v in ColBERTConfig().__dict__.items():\n", + "# print(f\"{k} --> {v}\")" ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## YOU CAN ALSO COLBERT RERANKER AS STANDALONE MODEL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install tabulate" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tabulate\n", - "\n", - "scores_arr = colbert_reranker(\n", - " \"What is the meaning of life and pain?\",\n", - " # Pass a subset of passages\n", - " passages[:10]\n", - ")\n", - "\n", - "tabulate_data = []\n", - "for idx in np.argsort(scores_arr)[::-1]:\n", - " # print(f\"Passage = {passages[idx]} --> Score = {scores_arr[idx]}\")\n", - " tabulate_data.append([passages[idx],scores_arr[idx]])\n", - "\n", - "table = tabulate.tabulate(tabulate_data,tablefmt=\"html\",headers={'sentence','score'})" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
score sentence
The best things in life are free. 12.5156
It's a piece of cake. 10
Practice makes perfect. 8.27344
Honesty is the best policy. 7.57422
To kill two birds with one stone. 7.51953
Actions speak louder than words. 7.05469
If you want something done right, do it yourself. 6.52344
Don't put off until tomorrow what you can do today. 3.78711
She sells seashells by the seashore. 2.77148
Don't count your chickens before they hatch. 1.82227
" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "passages = [\"It's a piece of cake.\", \"Don't put off until tomorrow what you can do today.\", 'To kill two birds with one stone.', 'Actions speak louder than words.', 'Honesty is the best policy.', 'If you want something done right, do it yourself.', 'The best things in life are free.', \"Don't count your chickens before they hatch.\", 'She sells seashells by the seashore.', 'Practice makes perfect.', \"Where there's a will, there's a way.\", 'Absence makes the heart grow fonder.', 'When the going gets tough, the tough get going.', 'A journey of a thousand miles begins with a single step.', \"You can't have your cake and eat it too.\", \"If you can't beat them, join them.\", 'Keep your friends close and your enemies closer.', \"Don't put all your eggs in one basket.\", \"All's fair in love and war.\", 'Every dog has its day.', 'All good things must come to an end.', 'Once bitten, twice shy.', \"The apple doesn't fall far from the tree.\", 'A penny saved is a penny earned.', \"Don't bite the hand that feeds you.\", 'You reap what you sow.', 'An apple a day keeps the doctor away.', \"One man's trash is another man's treasure.\", 'The squeaky wheel gets the grease.', 'A picture is worth a thousand words.', 'Fortune favors the bold.', 'Practice what you preach.', 'A watched pot never boils.', 'No pain, no gain.', \"You can't make an omelet without breaking eggs.\", \"There's no place like home.\", 'Ask and you shall receive.', 'Let sleeping dogs lie.', 'If the shoe fits, wear it.', 'Every cloud has a silver lining.', 'Look before you leap.', 'The more, the merrier.', 'The grass is always greener on the other side.', 'Beauty is only skin deep.', \"Two wrongs don't make a right.\", 'Beauty is in the eye of the beholder.', 'Necessity is the mother of invention.', 'Out of sight, out of mind.', 'Patience is a virtue.', 'Curiosity killed the cat.', \"If at first you don't succeed, try, try again.\", \"Beggars can't be choosers.\", 'Too many cooks spoil the broth.', 'Easy come, easy go.', \"Don't cry over spilled milk.\", \"There's no such thing as a free lunch.\", 'A bird in the hand is worth two in the bush.', 'Good things come to those who wait.', 'The quick brown fox jumps over the lazy dog.', 'It takes two to tango.', 'A friend in need is a friend indeed.', 'Like father, like son.', 'Let bygones be bygones.', 'Kill two birds with one stone.', 'A penny for your thoughts.', 'I am the master of my fate, I am the captain of my soul.', 'The pen is mightier than the sword.', 'When in Rome, do as the Romans do.', \"Rome wasn't built in a day.\", \"You can't judge a book by its cover.\", \"It's raining cats and dogs.\", 'Make hay while the sun shines.', \"It's better to be safe than sorry.\", 'The early bird catches the worm.', 'To be or not to be, that is the question.', 'Better late than never.']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This tutorial is running from the `examples/integrations/tutorials folder`, hence we need to add the system path for dspy\n", + "\n", + "* If you have installed the dspy package, then you don't need to run the below cell" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../../..\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## COLBERT AS RETRIEVER" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "colbert_config = ColBERTConfig()\n", + "colbert_config.index_name = \"Colbert-RM\"\n", + "colbert_config.experiment = \"Colbert-Experiment\"\n", + "colbert_config.checkpoint = \"colbert-ir/colbertv2.0\"\n", + "colbert_retriever = dspy.ColBERTv2RetrieverLocal(\n", + " passages = passages,load_only=False,\n", + " colbert_config=colbert_config\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#CONFIGURE COLBERT IN DSPY\n", + "dspy.settings.configure(rm=colbert_retriever)\n", + "\n", + "retrieved_docs = dspy.Retrieve(k=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DeprecationWarning: 'dspy.Retrieve' for reranking has been deprecated, please use dspy.RetrieveThenRerank. The reranking is ignored here. In the future this will raise an error.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "#> QueryTokenizer.tensorize(batch_text[0], batch_background[0], bsize) ==\n", + "#> Input: . What is the meaning of life?, \t\t True, \t\t None\n", + "#> Output IDs: torch.Size([32]), tensor([ 101, 1, 2054, 2003, 1996, 3574, 1997, 2166, 1029, 102, 103, 103,\n", + " 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103,\n", + " 103, 103, 103, 103, 103, 103, 103, 103], device='cuda:0')\n", + "#> Output Mask: torch.Size([32]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')\n", + "\n" + ] + } + ], + "source": [ + "pred = retrieved_docs(\n", + " \"What is the meaning of life?\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " score=[nan, nan, nan, nan, nan],\n", + " pid=[33, 6, 47, 74, 48],\n", + " passages=['No pain, no gain.', 'The best things in life are free.', 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'Patience is a virtue.']\n", + ")" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DeprecationWarning: 'dspy.Retrieve' for reranking has been deprecated, please use dspy.RetrieveThenRerank. The reranking is ignored here. In the future this will raise an error.\n" + ] + } + ], + "source": [ + "multiple_pred = retrieved_docs(\n", + " [\"What is the meaning of life?\",\"Meaning of pain?\"],by_prob=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Prediction(\n", + " score=[nan, nan, nan, nan, nan],\n", + " pid=[33, 6, 47, 74, 48],\n", + " passages=['No pain, no gain.', 'The best things in life are free.', 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'Patience is a virtue.']\n", + " ),\n", + " Prediction(\n", + " score=[nan, nan, nan, nan, nan],\n", + " pid=[16, 0, 47, 74, 26],\n", + " passages=['Keep your friends close and your enemies closer.', \"It's a piece of cake.\", 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'An apple a day keeps the doctor away.']\n", + " )]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "multiple_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## COLBERT AS RERANKER" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "colbert_config = ColBERTConfig()\n", + "colbert_config.index_name = 'colbert-ir-index'\n", + "colbert_reranker = dspy.ColBERTv2RerankerLocal(\n", + " checkpoint='colbert-ir/colbertv2.0',colbert_config=colbert_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "dspy.settings.configure(rm=colbert_retriever,reranker=colbert_reranker)\n", + "\n", + "retrieve_rerank = dspy.RetrieveThenRerank(k=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "pred = retrieve_rerank(\n", + " [\"What is the meaning of life?\",\"Meaning of pain?\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Prediction(\n", + " score=[nan, nan, nan, nan, nan],\n", + " pid=[6, 48, 74, 47, 33],\n", + " rerank_score=[15.8359375, 14.2109375, 12.5703125, 11.7890625, 9.1796875],\n", + " passages=['The best things in life are free.', 'Patience is a virtue.', 'To be or not to be, that is the question.', 'Out of sight, out of mind.', 'No pain, no gain.']\n", + " ),\n", + " Prediction(\n", + " score=[nan, nan, nan, nan, nan],\n", + " pid=[33, 0, 47, 74, 16],\n", + " rerank_score=[19.828125, 12.2890625, 11.171875, 9.09375, 6.8984375],\n", + " passages=['No pain, no gain.', \"It's a piece of cake.\", 'Out of sight, out of mind.', 'To be or not to be, that is the question.', 'Keep your friends close and your enemies closer.']\n", + " )]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## YOU CAN ALSO COLBERT RERANKER AS STANDALONE MODEL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install tabulate" ] - }, - "metadata": {}, - "output_type": "display_data" + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tabulate\n", + "\n", + "scores_arr = colbert_reranker(\n", + " \"What is the meaning of life and pain?\",\n", + " # Pass a subset of passages\n", + " passages[:10]\n", + ")\n", + "\n", + "tabulate_data = []\n", + "for idx in np.argsort(scores_arr)[::-1]:\n", + " # print(f\"Passage = {passages[idx]} --> Score = {scores_arr[idx]}\")\n", + " tabulate_data.append([passages[idx],scores_arr[idx]])\n", + "\n", + "table = tabulate.tabulate(tabulate_data,tablefmt=\"html\",headers={'sentence','score'})" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
score sentence
The best things in life are free. 12.5156
It's a piece of cake. 10
Practice makes perfect. 8.27344
Honesty is the best policy. 7.57422
To kill two birds with one stone. 7.51953
Actions speak louder than words. 7.05469
If you want something done right, do it yourself. 6.52344
Don't put off until tomorrow what you can do today. 3.78711
She sells seashells by the seashore. 2.77148
Don't count your chickens before they hatch. 1.82227
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import HTML, display\n", + "display(HTML(table))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } - ], - "source": [ - "from IPython.display import HTML, display\n", - "display(HTML(table))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/intro.ipynb b/examples/outdated_v2.4_examples/intro.ipynb index abe410f386..cef38f997a 100644 --- a/examples/outdated_v2.4_examples/intro.ipynb +++ b/examples/outdated_v2.4_examples/intro.ipynb @@ -1,1960 +1,1960 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "## **DSPy**: Programming with Foundation Models\n", - "\n", - "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook introduces the **DSPy** framework for **Programming with Foundation Models**, i.e., language models (LMs) and retrieval models (RMs).\n", - "\n", - "**DSPy** emphasizes programming over prompting. It unifies techniques for **prompting** and **fine-tuning** LMs as well as improving them with **reasoning** and **tool/retrieval augmentation**, all expressed through a _minimalistic set of Pythonic operations that compose and learn_.\n", - "\n", - "**DSPy** provides **composable and declarative modules** for instructing LMs in a familiar Pythonic syntax. On top of that, **DSPy** introduces an **automatic compiler that teaches LMs** how to conduct the declarative steps in your program. The **DSPy compiler** will internally _trace_ your program and then **craft high-quality prompts for large LMs (or train automatic finetunes for small LMs)** to teach them the steps of your task." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 0] Setting Up\n", - "\n", - "As we'll start to see below, **DSPy** can routinely teach powerful models like `GPT-3.5` and local models like `T5-base` or `Llama2-13b` to be much more reliable at complex tasks. **DSPy** will compile the _same program_ into different few-shot prompts and/or finetunes for each LM.\n", - "\n", - "Let's begin by setting things up. The snippet below will also install **DSPy** if it's not there already." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "# Set up the cache for this notebook\n", - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " !pip install dspy-ai==2.4.17\n", - " !pip install openai~=0.28.1\n", - " # !pip install -e $repo_path\n", - "\n", - "import dspy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1] Getting Started\n", - "\n", - "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models. In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the retriever `ColBERTv2`.\n", - "\n", - "To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", - "\n", - "**Note:** _If you want to run this notebook without changing the examples, you don't need an API key. All examples are already cached internally so you can inspect them!_" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "turbo = dspy.OpenAI(model='gpt-3.5-turbo')\n", - "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "\n", - "dspy.settings.configure(lm=turbo, rm=colbertv2_wiki17_abstracts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the last line above, we configure **DSPy** to use the turbo LM and the ColBERTv2 retriever (over Wikipedia 2017 abstracts) by default. This will be easy to overwrite for local parts of our programs if needed." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### A word on the workflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can build your own **DSPy programs** for various tasks, e.g., question answering, information extraction, or text-to-SQL.\n", - "\n", - "Whatever the task, the general workflow is:\n", - "\n", - "1. **Collect a little bit of data.** Define examples of the inputs and outputs of your program (e.g., questions and their answers). This could just be a handful of quick examples you wrote down. If large datasets exist, the more the merrier!\n", - "1. **Write your program.** Define the modules (i.e., sub-tasks) of your program and the way they should interact together to solve your task.\n", - "1. **Define some validation logic.** What makes for a good run of your program? Maybe the answers need to have a certain length or stick to a particular format? Specify the logic that checks that.\n", - "1. **Compile!** Ask **DSPy** to _compile_ your program using your data. The compiler will use your data and validation logic to optimize your program (e.g., prompts and modules) so it's efficient and effective!\n", - "1. **Iterate.** Repeat the process by improving your data, program, validation, or by using more advanced features of the **DSPy** compiler.\n", - "\n", - "Let's now see this in action." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2] Task Examples\n", - "\n", - "**DSPy** accommodates a wide variety of applications and tasks. **In this intro notebook, we will work on the example task of multi-hop question answering (QA).**\n", - "\n", - "Other notebooks and tutorials will present different tasks. Now, let us load a tiny sample from the HotPotQA multi-hop dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(20, 50)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dspy.datasets import HotPotQA\n", - "\n", - "# Load the dataset.\n", - "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0)\n", - "\n", - "# Tell DSPy that the 'question' field is the input. Any other fields are labels and/or metadata.\n", - "trainset = [x.with_inputs('question') for x in dataset.train]\n", - "devset = [x.with_inputs('question') for x in dataset.dev]\n", - "\n", - "len(trainset), len(devset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We just loaded `trainset` (20 examples) and `devset` (50 examples). Each example in our **training set** contains just a **question** and its (human-annotated) **answer**.\n", - "\n", - "**DSPy** typically requires very minimal labeling. Whereas your pipeline may involve six or seven complex steps, you only need labels for the initial question and the final answer. **DSPy** will bootstrap any intermediate labels needed to support your pipeline. If you change your pipeline in any way, the data bootstrapped will change accordingly!\n", - "\n", - "Now, let's look at some data examples." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: At My Window was released by which American singer-songwriter?\n", - "Answer: John Townes Van Zandt\n" - ] - } - ], - "source": [ - "train_example = trainset[0]\n", - "print(f\"Question: {train_example.question}\")\n", - "print(f\"Answer: {train_example.answer}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examples in the **dev set** contain a third field, namely, **titles** of relevant Wikipedia articles. This is not essential but, for the sake of this intro, it'll help us get a sense of how well our programs are doing." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", - "Answer: English\n", - "Relevant Wikipedia Titles: {'Robert Irvine', 'Restaurant: Impossible'}\n" - ] - } - ], - "source": [ - "dev_example = devset[18]\n", - "print(f\"Question: {dev_example.question}\")\n", - "print(f\"Answer: {dev_example.answer}\")\n", - "print(f\"Relevant Wikipedia Titles: {dev_example.gold_titles}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After loading the raw data, we'd applied `x.with_inputs('question')` to each example to tell **DSPy** that our input field in each example will be just `question`. Any other fields are labels or metadata that are not given to the system." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "For this dataset, training examples have input keys ['question'] and label keys ['answer']\n", - "For this dataset, dev examples have input keys ['question'] and label keys ['answer', 'gold_titles']\n" - ] - } - ], - "source": [ - "print(f\"For this dataset, training examples have input keys {train_example.inputs().keys()} and label keys {train_example.labels().keys()}\")\n", - "print(f\"For this dataset, dev examples have input keys {dev_example.inputs().keys()} and label keys {dev_example.labels().keys()}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that there's nothing special about the HotPotQA dataset: it's just a list of examples.\n", - "\n", - "You can define your own examples as below. A future notebook will guide you through creating your own data in unusual or data-scarce settings, which is a context where **DSPy** excels.\n", - "\n", - "```\n", - "dspy.Example(field1=value, field2=value2, ...)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3] Building Blocks\n", - "\n", - "In **DSPy**, we will maintain a clean separation between **defining your modules in a declarative way** and **calling them in a pipeline to solve the task**.\n", - "\n", - "This allows you to focus on the information flow of your pipeline. **DSPy** will then take your program and automatically optimize **how to prompt** (or finetune) LMs **for your particular pipeline** so it works well.\n", - "\n", - "If you have experience with PyTorch, you can think of DSPy as the PyTorch of the foundation model space. Before we see this in action, let's first understand some key pieces." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Using the Language Model: **Signatures** & **Predictors**\n", - "\n", - "Every call to the LM in a **DSPy** program needs to have a **Signature**.\n", - "\n", - "A signature consists of three simple elements:\n", - "\n", - "- A minimal description of the sub-task the LM is supposed to solve.\n", - "- A description of one or more input fields (e.g., input question) that we will give to the LM.\n", - "- A description of one or more output fields (e.g., the question's answer) that we will expect from the LM.\n", - "\n", - "Let's define a simple signature for basic question answering." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "class BasicQA(dspy.Signature):\n", - " \"\"\"Answer questions with short factoid answers.\"\"\"\n", - "\n", - " question = dspy.InputField()\n", - " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In `BasicQA`, the docstring describes the sub-task here (i.e., answering questions). Each `InputField` or `OutputField` can optionally contain a description `desc` too. When it's not given, it's inferred from the field's name (e.g., `question`).\n", - "\n", - "Notice that there isn't anything special about this signature in **DSPy**. We can just as easily define a signature that takes a long snippet from a PDF and outputs structured information, for instance.\n", - "\n", - "Anyway, now that we have a signature, let's define and use a **Predictor**. A predictor is a module that knows how to use the LM to implement a signature. Importantly, predictors can **learn** to fit their behavior to the task!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", - "Predicted Answer: American\n" - ] - } - ], - "source": [ - "# Define the predictor.\n", - "generate_answer = dspy.Predict(BasicQA)\n", - "\n", - "# Call the predictor on a particular input.\n", - "pred = generate_answer(question=dev_example.question)\n", - "\n", - "# Print the input and the prediction.\n", - "print(f\"Question: {dev_example.question}\")\n", - "print(f\"Predicted Answer: {pred.answer}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the example above, we asked the predictor about the the chef featured in \"Restaurant: Impossible\". The model outputs an answer (\"American\").\n", - "\n", - "For visibility, we can inspect how this extremely basic predictor implemented our signature. Let's inspect the history of our LM (**turbo**)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Answer questions with short factoid answers.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: ${question}\n", - "Answer: often between 1 and 5 words\n", - "\n", - "---\n", - "\n", - "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", - "Answer:\u001b[32m American\u001b[0m\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "turbo.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It happens that this chef is both British and American, but we have no way of knowing if the model just guessed \"American\" because it's a common answer. In general, adding **retrieval** and **learning** will help the LM be more factual, and we'll explore this in a minute!\n", - "\n", - "But before we do that, how about we _just_ change the predictor? It would be nice to allow the model to elicit a chain of thought along with the prediction." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", - "Thought: We know that the chef and restaurateur featured in Restaurant: Impossible is Robert Irvine.\n", - "Predicted Answer: British\n" - ] - } - ], - "source": [ - "# Define the predictor. Notice we're just changing the class. The signature BasicQA is unchanged.\n", - "generate_answer_with_chain_of_thought = dspy.ChainOfThought(BasicQA)\n", - "\n", - "# Call the predictor on the same input.\n", - "pred = generate_answer_with_chain_of_thought(question=dev_example.question)\n", - "\n", - "# Print the input, the chain of thought, and the prediction.\n", - "print(f\"Question: {dev_example.question}\")\n", - "print(f\"Thought: {pred.rationale.split('.', 1)[1].strip()}\")\n", - "print(f\"Predicted Answer: {pred.answer}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is indeed a better answer: the model figures out that the chef in question is **Robert Irvine** and correctly identifies that he's British.\n", - "\n", - "These predictors (`dspy.Predict` and `dspy.ChainOfThought`) can be applied to _any_ signature. As we'll see below, they can also be optimized to learn from your data and validation logic." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Using the Retrieval Model\n", - "\n", - "Using the retriever is pretty simple. A module `dspy.Retrieve(k)` will search for the top-`k` passages that match a given query.\n", - "\n", - "By default, this will use the retriever we configured at the top of this notebook, namely, ColBERTv2 over a Wikipedia index." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 3 passages for question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible? \n", - " ------------------------------ \n", - "\n", - "1] Restaurant: Impossible | Restaurant: Impossible is an American reality television series, featuring chef and restaurateur Robert Irvine, that aired on Food Network from 2011 to 2016. \n", - "\n", - "2] Jean Joho | Jean Joho is a French-American chef and restaurateur. He is chef/proprietor of Everest in Chicago (founded in 1986), Paris Club Bistro & Bar and Studio Paris in Chicago, The Eiffel Tower Restaurant in Las Vegas, and Brasserie JO in Boston. \n", - "\n", - "3] List of Restaurant: Impossible episodes | This is the list of the episodes for the American cooking and reality television series \"Restaurant Impossible\", produced by Food Network. The premise of the series is that within two days and on a budget of $10,000, celebrity chef Robert Irvine renovates a failing American restaurant with the goal of helping to restore it to profitability and prominence. Irvine is assisted by a designer (usually Taniya Nayak, Cheryl Torrenueva, or Lynn Keagan, but sometimes Vanessa De Leon, Krista Watterworth, Yvette Irene, or Nicole Faccuito), along with general contractor Tom Bury, who sometimes does double duty as both general contractor and designer. After assessing the problems with the restaurant, Robert Irvine typically creates a plan for the new decor, oversees the cleaning of the restaurant, reduces the size of the menu and improves the food, develops a promotional activity, educates the restaurant's owners, or trains the staff, as needed by each restaurant. \n", - "\n" - ] - } - ], - "source": [ - "retrieve = dspy.Retrieve(k=3)\n", - "topK_passages = retrieve(dev_example.question).passages\n", - "\n", - "print(f\"Top {retrieve.k} passages for question: {dev_example.question} \\n\", '-' * 30, '\\n')\n", - "\n", - "for idx, passage in enumerate(topK_passages):\n", - " print(f'{idx+1}]', passage, '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Feel free to any other queries you like." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'History of the FIFA World Cup | The FIFA World Cup was first held in 1930, when FIFA president Jules Rimet decided to stage an international football tournament. The inaugural edition, held in 1930, was contested as a final tournament of only thirteen teams invited by the organization. Since then, the World Cup has experienced successive expansions and format remodeling to its current 32-team final tournament preceded by a two-year qualifying process, involving over 200 teams from around the world.'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "retrieve(\"When was the first FIFA World Cup held?\").passages[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4] Program 1: Basic Retrieval-Augmented Generation (“RAG”)\n", - "\n", - "Let's define our first complete program for this task. We'll build a retrieval-augmented pipeline for answer generation.\n", - "\n", - "Given a question, we'll search for the top-3 passages in Wikipedia and then feed them as context for answer generation.\n", - "\n", - "Let's start by defining this signature: `context, question --> answer`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "class GenerateAnswer(dspy.Signature):\n", - " \"\"\"Answer questions with short factoid answers.\"\"\"\n", - "\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " question = dspy.InputField()\n", - " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great. Now let's define the actual program. This is a class that inherits from `dspy.Module`.\n", - "\n", - "It needs two methods:\n", - "\n", - "- The `__init__` method will simply declare the sub-modules it needs: `dspy.Retrieve` and `dspy.ChainOfThought`. The latter is defined to implement our `GenerateAnswer` signature.\n", - "- The `forward` method will describe the control flow of answering the question using the modules we have." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "class RAG(dspy.Module):\n", - " def __init__(self, num_passages=3):\n", - " super().__init__()\n", - "\n", - " self.retrieve = dspy.Retrieve(k=num_passages)\n", - " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", - " \n", - " def forward(self, question):\n", - " context = self.retrieve(question).passages\n", - " prediction = self.generate_answer(context=context, question=question)\n", - " return dspy.Prediction(context=context, answer=prediction.answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Compiling the RAG program\n", - "\n", - "Having defined this program, let's now **compile** it. Compiling a program will update the parameters stored in each module. In our setting, this is primarily in the form of collecting and selecting good demonstrations for inclusion in your prompt(s).\n", - "\n", - "Compiling depends on three things:\n", - "\n", - "1. **A training set.** We'll just use our 20 question–answer examples from `trainset` above.\n", - "1. **A metric for validation.** We'll define a quick `validate_context_and_answer` that checks that the predicted answer is correct. It'll also check that the retrieved context does actually contain that answer.\n", - "1. **A specific teleprompter.** The **DSPy** compiler includes a number of **teleprompters** that can optimize your programs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Teleprompters:** Teleprompters are powerful optimizers that can take any program and learn to bootstrap and select effective prompts for its modules. Hence the name, which means \"prompting at a distance\".\n", - "\n", - "Different teleprompters offer various tradeoffs in terms of how much they optimize cost versus quality, etc. We will use a simple default `BootstrapFewShot` in this notebook.\n", - "\n", - "\n", - "_If you're into analogies, you could think of this as your training data, your loss function, and your optimizer in a standard DNN supervised learning setup. Whereas SGD is a basic optimizer, there are more sophisticated (and more expensive!) ones like Adam or RMSProp._" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 50%|█████ | 10/20 [00:00<00:00, 121.99it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 11 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from dspy.teleprompt import BootstrapFewShot\n", - "\n", - "# Validation logic: check that the predicted answer is correct.\n", - "# Also check that the retrieved context does actually contain that answer.\n", - "def validate_context_and_answer(example, pred, trace=None):\n", - " answer_EM = dspy.evaluate.answer_exact_match(example, pred)\n", - " answer_PM = dspy.evaluate.answer_passage_match(example, pred)\n", - " return answer_EM and answer_PM\n", - "\n", - "# Set up a basic teleprompter, which will compile our RAG program.\n", - "teleprompter = BootstrapFewShot(metric=validate_context_and_answer)\n", - "\n", - "# Compile!\n", - "compiled_rag = teleprompter.compile(RAG(), trainset=trainset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've compiled our RAG program, let's try it out." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: What castle did David Gregory inherit?\n", - "Predicted Answer: Kinnairdy Castle\n", - "Retrieved Contexts (truncated): ['David Gregory (physician) | David Gregory (20 December 1625 – 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinn...', 'Gregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: Γρηγόριος Ταρχανειώτης , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 t...', 'David Gregory (mathematician) | David Gregory (originally spelt Gregorie) FRS (? 1659 – 10 October 1708) was a Scottish mathematician and astronomer. He was professor of mathematics at the University ...']\n" - ] - } - ], - "source": [ - "# Ask any question you like to this simple RAG program.\n", - "my_question = \"What castle did David Gregory inherit?\"\n", - "\n", - "# Get the prediction. This contains `pred.context` and `pred.answer`.\n", - "pred = compiled_rag(my_question)\n", - "\n", - "# Print the contexts and the answer.\n", - "print(f\"Question: {my_question}\")\n", - "print(f\"Predicted Answer: {pred.answer}\")\n", - "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Excellent. How about we inspect the last prompt for the LM?" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Answer questions with short factoid answers.\n", - "\n", - "---\n", - "\n", - "Question: At My Window was released by which American singer-songwriter?\n", - "Answer: John Townes Van Zandt\n", - "\n", - "Question: \"Everything Has Changed\" is a song from an album released under which record label ?\n", - "Answer: Big Machine Records\n", - "\n", - "Question: The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?\n", - "Answer: 1950\n", - "\n", - "Question: Which Pakistani cricket umpire who won 3 consecutive ICC umpire of the year awards in 2009, 2010, and 2011 will be in the ICC World Twenty20?\n", - "Answer: Aleem Sarwar Dar\n", - "\n", - "Question: Having the combination of excellent foot speed and bat speed helped Eric Davis, create what kind of outfield for the Los Angeles Dodgers?\n", - "Answer: \"Outfield of Dreams\"\n", - "\n", - "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", - "Answer: Aleksandr Danilovich Aleksandrov\n", - "\n", - "Question: The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?\n", - "Answer: 2010\n", - "\n", - "Question: Tombstone stared an actor born May 17, 1955 known as who?\n", - "Answer: Bill Paxton\n", - "\n", - "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", - "Answer: 1874\n", - "\n", - "Question: which American actor was Candace Kita guest starred with\n", - "Answer: Bill Murray\n", - "\n", - "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", - "Answer: The Empire State Building\n", - "\n", - "Question: Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?\n", - "Answer: Buena Vista Distribution\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: may contain relevant facts\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: often between 1 and 5 words\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Tae Kwon Do Times | Tae Kwon Do Times is a magazine devoted to the martial art of taekwondo, and is published in the United States of America. While the title suggests that it focuses on taekwondo exclusively, the magazine also covers other Korean martial arts. \"Tae Kwon Do Times\" has published articles by a wide range of authors, including He-Young Kimm, Thomas Kurz, Scott Shaw, and Mark Van Schuyver.»\n", - "[2] «Kwon Tae-man | Kwon Tae-man (born 1941) was an early Korean hapkido practitioner and a pioneer of the art, first in Korea and then in the United States. He formed one of the earliest dojang's for hapkido in the United States in Torrance, California, and has been featured in many magazine articles promoting the art.»\n", - "[3] «Hee Il Cho | Cho Hee Il (born October 13, 1940) is a prominent Korean-American master of taekwondo, holding the rank of 9th \"dan\" in the martial art. He has written 11 martial art books, produced 70 martial art training videos, and has appeared on more than 70 martial arts magazine covers. Cho won several national and international competitions as a taekwondo competitor, and has appeared in several films, including \"Fight to Win\", \"Best of the Best\", \"Bloodsport II\", and \"Bloodsport III\". He founded the Action International Martial Arts Association (AIMAA) in 1980, and is its President. Cho is a member of both \"Black Belt\" magazine's Hall of Fame and \"Tae Kwon Do Times\" magazine's Hall of Fame.»\n", - "\n", - "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know from the context that \"Tae Kwon Do Times\" is a magazine that covers taekwondo and other Korean martial arts. It has published articles by authors like Scott Shaw. On the other hand, there is no information about Southwest Art magazine in the context.\n", - "\n", - "Answer: Tae Kwon Do Times\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Rosario Dawson | Rosario Isabel Dawson (born May 9, 1979) is an American actress, producer, singer, comic book writer, and political activist. She made her film debut in the 1995 teen drama \"Kids\". Her subsequent film roles include \"He Got Game\", \"Men in Black II\", \"25th Hour\", \"Rent\", \"Sin City\", \"Death Proof\", \"Seven Pounds\", \"\", and \"Top Five\". Dawson has also provided voice-over work for Disney and DC.»\n", - "[2] «Sarai Gonzalez | Sarai Isaura Gonzalez (born 2005) is an American Latina child actress who made her professional debut at the age of 11 on the Spanish-language \"\"Soy Yo\"\" (\"That's Me\") music video by Bomba Estéreo. Cast as a \"nerdy\" tween with a \"sassy\" and \"confident\" attitude, her performance turned her into a \"Latina icon\" for \"female empowerment, identity and self-worth\". She subsequently appeared in two get out the vote videos for Latinos in advance of the 2016 United States elections.»\n", - "[3] «Gabriela (2001 film) | Gabriela is a 2001 American romance film, starring Seidy Lopez in the title role alongside Jaime Gomez as her admirer Mike. The film has been cited as an inspiration behind the Premiere Weekend Club, which supports Latino film-making.»\n", - "\n", - "Question: Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the actress made her film debut in 1995 and co-founded Voto Latino.\n", - "\n", - "Answer: Rosario Dawson\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Battle of Kursk | The Battle of Kursk was a Second World War engagement between German and Soviet forces on the Eastern Front near Kursk (450 km south-west of Moscow) in the Soviet Union during July and August 1943. The battle began with the launch of the German offensive, Operation Citadel (German: \"Unternehmen Zitadelle\" ), on 5 July, which had the objective of pinching off the Kursk salient with attacks on the base of the salient from north and south simultaneously. After the German offensive stalled on the northern side of the salient, on 12 July the Soviets commenced their Kursk Strategic Offensive Operation with the launch of Operation Kutuzov (Russian: Кутузов ) against the rear of the German forces in the northern side. On the southern side, the Soviets also launched powerful counterattacks the same day, one of which led to a large armoured clash, the Battle of Prokhorovka. On 3 August, the Soviets began the second phase of the Kursk Strategic Offensive Operation with the launch of Operation Polkovodets Rumyantsev (Russian: Полководец Румянцев ) against the German forces in the southern side of the Kursk salient.»\n", - "[2] «Operation Mars | Operation Mars, also known as the Second Rzhev-Sychevka Offensive Operation (Russian: Вторая Ржевско-Сычёвская наступательная операция), was the codename for an offensive launched by Soviet forces against German forces during World War II. It took place between 25 November and 20 December 1942 around the Rzhev salient in the vicinity of Moscow.»\n", - "[3] «Kholm Pocket | The Kholm Pocket (German: \"Kessel von Cholm\" ; Russian: Холмский котёл ) was the name given for the encirclement of German troops by the Red Army around Kholm south of Leningrad, during World War II on the Eastern Front, from 23 January 1942 until 5 May 1942. A much larger pocket was simultaneously surrounded in Demyansk, about 100 km to the northeast. These were the results of German retreat following their defeat during the Battle of Moscow.»\n", - "\n", - "Question: What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the German offensive that started the Battle of Kursk was called Operation Citadel.\n", - "\n", - "Answer: Operation Citadel\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Kerry Condon | Kerry Condon (born 4 January 1983) is an Irish television and film actress, best known for her role as Octavia of the Julii in the HBO/BBC series \"Rome,\" as Stacey Ehrmantraut in AMC's \"Better Call Saul\" and as the voice of F.R.I.D.A.Y. in various films in the Marvel Cinematic Universe. She is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\"»\n", - "[2] «Corona Riccardo | Corona Riccardo (c. 1878October 15, 1917) was an Italian born American actress who had a brief Broadway stage career before leaving to become a wife and mother. Born in Naples she came to acting in 1894 playing a Mexican girl in a play at the Empire Theatre. Wilson Barrett engaged her for a role in his play \"The Sign of the Cross\" which he took on tour of the United States. Riccardo played the role of Ancaria and later played Berenice in the same play. Robert B. Mantell in 1898 who struck by her beauty also cast her in two Shakespeare plays, \"Romeo and Juliet\" and \"Othello\". Author Lewis Strang writing in 1899 said Riccardo was the most promising actress in America at the time. Towards the end of 1898 Mantell chose her for another Shakespeare part, Ophelia im Hamlet. Afterwards she was due to join Augustin Daly's Theatre Company but Daly died in 1899. In 1899 she gained her biggest fame by playing Iras in the first stage production of Ben-Hur.»\n", - "[3] «Judi Dench | Dame Judith Olivia \"Judi\" Dench, {'1': \", '2': \", '3': \", '4': \"} (born 9 December 1934) is an English actress and author. Dench made her professional debut in 1957 with the Old Vic Company. Over the following few years, she performed in several of Shakespeare's plays in such roles as Ophelia in \"Hamlet\", Juliet in \"Romeo and Juliet\", and Lady Macbeth in \"Macbeth\". Although most of her work during this period was in theatre, she also branched into film work and won a BAFTA Award as Most Promising Newcomer. She drew strong reviews for her leading role in the musical \"Cabaret\" in 1968.»\n", - "\n", - "Question: Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the actress we are looking for is the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" We also know that she acted in the short film The Shore.\n", - "\n", - "Answer: Kerry Condon\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «David Gregory (physician) | David Gregory (20 December 1625 – 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.»\n", - "[2] «Gregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: Γρηγόριος Ταρχανειώτης , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 to 1006. In December 999, and again on February 2, 1002, he reinstituted and confirmed the possessions of the abbey and monks of Monte Cassino in Ascoli. In 1004, he fortified and expanded the castle of Dragonara on the Fortore. He gave it three circular towers and one square one. He also strengthened Lucera.»\n", - "[3] «David Gregory (mathematician) | David Gregory (originally spelt Gregorie) FRS (? 1659 – 10 October 1708) was a Scottish mathematician and astronomer. He was professor of mathematics at the University of Edinburgh, Savilian Professor of Astronomy at the University of Oxford, and a commentator on Isaac Newton's \"Principia\".»\n", - "\n", - "Question: What castle did David Gregory inherit?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know that David Gregory inherited a castle. The name of the castle is Kinnairdy Castle.\n", - "\n", - "Answer: Kinnairdy Castle\u001b[0m\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "turbo.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Even though we haven't written any of this detailed demonstrations, we see that **DSPy** was able to bootstrap this 3,000 token prompt for **3-shot retrieval augmented generation with hard negative passages and chain of thought** from our extremely simple program.\n", - "\n", - "This illustrates the power of composition and learning. Of course, this was just generated by a particular teleprompter, which may or may not be perfect in each setting. As you'll see in **DSPy**, there is a large but systematic space of options you have to optimize and validate the quality and cost of your programs.\n", - "\n", - "If you're so inclined, you can easily inspect the learned objects themselves." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "generate_answer\n", - "Example({'augmented': True, 'context': ['Tae Kwon Do Times | Tae Kwon Do Times is a magazine devoted to the martial art of taekwondo, and is published in the United States of America. While the title suggests that it focuses on taekwondo exclusively, the magazine also covers other Korean martial arts. \"Tae Kwon Do Times\" has published articles by a wide range of authors, including He-Young Kimm, Thomas Kurz, Scott Shaw, and Mark Van Schuyver.', \"Kwon Tae-man | Kwon Tae-man (born 1941) was an early Korean hapkido practitioner and a pioneer of the art, first in Korea and then in the United States. He formed one of the earliest dojang's for hapkido in the United States in Torrance, California, and has been featured in many magazine articles promoting the art.\", 'Hee Il Cho | Cho Hee Il (born October 13, 1940) is a prominent Korean-American master of taekwondo, holding the rank of 9th \"dan\" in the martial art. He has written 11 martial art books, produced 70 martial art training videos, and has appeared on more than 70 martial arts magazine covers. Cho won several national and international competitions as a taekwondo competitor, and has appeared in several films, including \"Fight to Win\", \"Best of the Best\", \"Bloodsport II\", and \"Bloodsport III\". He founded the Action International Martial Arts Association (AIMAA) in 1980, and is its President. Cho is a member of both \"Black Belt\" magazine\\'s Hall of Fame and \"Tae Kwon Do Times\" magazine\\'s Hall of Fame.'], 'question': 'Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?', 'rationale': 'produce the answer. We know from the context that \"Tae Kwon Do Times\" is a magazine that covers taekwondo and other Korean martial arts. It has published articles by authors like Scott Shaw. On the other hand, there is no information about Southwest Art magazine in the context.', 'answer': 'Tae Kwon Do Times'}) (input_keys=None)\n", - "\n" - ] - } - ], - "source": [ - "for name, parameter in compiled_rag.named_predictors():\n", - " print(name)\n", - " print(parameter.demos[0])\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Evaluating the Answers\n", - "\n", - "We can now evaluate our `compiled_rag` program on the dev set. Of course, this tiny set is _not_ meant to be a reliable benchmark, but it'll be instructive to use it for illustration.\n", - "\n", - "For a start, let's evaluate the accuracy (exact match) of the predicted answer." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 22 / 50 (44.0): 100%|██████████| 50/50 [00:00<00:00, 116.45it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 22 / 50 (44.0%)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answergold_titlescontextpred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}['2017–18 Pittsburgh Penguins season | The 2017–18 Pittsburgh Penguins season will be the 51st season for the National Hockey League ice hockey team that was...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Castle', 'Crichton Collegiate Church'}[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne✔️ [True]
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}[\"Æthelweard of East Anglia | Æthelweard (died 854) was a 9th-century king of East Anglia, the long-lived Anglo-Saxon kingdom which today includes the English counties...King Alfred the Great✔️ [True]
\n" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "## **DSPy**: Programming with Foundation Models\n", + "\n", + "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/intro.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook introduces the **DSPy** framework for **Programming with Foundation Models**, i.e., language models (LMs) and retrieval models (RMs).\n", + "\n", + "**DSPy** emphasizes programming over prompting. It unifies techniques for **prompting** and **fine-tuning** LMs as well as improving them with **reasoning** and **tool/retrieval augmentation**, all expressed through a _minimalistic set of Pythonic operations that compose and learn_.\n", + "\n", + "**DSPy** provides **composable and declarative modules** for instructing LMs in a familiar Pythonic syntax. On top of that, **DSPy** introduces an **automatic compiler that teaches LMs** how to conduct the declarative steps in your program. The **DSPy compiler** will internally _trace_ your program and then **craft high-quality prompts for large LMs (or train automatic finetunes for small LMs)** to teach them the steps of your task." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0] Setting Up\n", + "\n", + "As we'll start to see below, **DSPy** can routinely teach powerful models like `GPT-3.5` and local models like `T5-base` or `Llama2-13b` to be much more reliable at complex tasks. **DSPy** will compile the _same program_ into different few-shot prompts and/or finetunes for each LM.\n", + "\n", + "Let's begin by setting things up. The snippet below will also install **DSPy** if it's not there already." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "# Set up the cache for this notebook\n", + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " !pip install dspy-ai==2.4.17\n", + " !pip install openai~=0.28.1\n", + " # !pip install -e $repo_path\n", + "\n", + "import dspy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1] Getting Started\n", + "\n", + "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models. In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the retriever `ColBERTv2`.\n", + "\n", + "To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", + "\n", + "**Note:** _If you want to run this notebook without changing the examples, you don't need an API key. All examples are already cached internally so you can inspect them!_" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "turbo = dspy.OpenAI(model='gpt-3.5-turbo')\n", + "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "\n", + "dspy.settings.configure(lm=turbo, rm=colbertv2_wiki17_abstracts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the last line above, we configure **DSPy** to use the turbo LM and the ColBERTv2 retriever (over Wikipedia 2017 abstracts) by default. This will be easy to overwrite for local parts of our programs if needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### A word on the workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can build your own **DSPy programs** for various tasks, e.g., question answering, information extraction, or text-to-SQL.\n", + "\n", + "Whatever the task, the general workflow is:\n", + "\n", + "1. **Collect a little bit of data.** Define examples of the inputs and outputs of your program (e.g., questions and their answers). This could just be a handful of quick examples you wrote down. If large datasets exist, the more the merrier!\n", + "1. **Write your program.** Define the modules (i.e., sub-tasks) of your program and the way they should interact together to solve your task.\n", + "1. **Define some validation logic.** What makes for a good run of your program? Maybe the answers need to have a certain length or stick to a particular format? Specify the logic that checks that.\n", + "1. **Compile!** Ask **DSPy** to _compile_ your program using your data. The compiler will use your data and validation logic to optimize your program (e.g., prompts and modules) so it's efficient and effective!\n", + "1. **Iterate.** Repeat the process by improving your data, program, validation, or by using more advanced features of the **DSPy** compiler.\n", + "\n", + "Let's now see this in action." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2] Task Examples\n", + "\n", + "**DSPy** accommodates a wide variety of applications and tasks. **In this intro notebook, we will work on the example task of multi-hop question answering (QA).**\n", + "\n", + "Other notebooks and tutorials will present different tasks. Now, let us load a tiny sample from the HotPotQA multi-hop dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20, 50)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "source": [ + "from dspy.datasets import HotPotQA\n", + "\n", + "# Load the dataset.\n", + "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0)\n", + "\n", + "# Tell DSPy that the 'question' field is the input. Any other fields are labels and/or metadata.\n", + "trainset = [x.with_inputs('question') for x in dataset.train]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]\n", + "\n", + "len(trainset), len(devset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We just loaded `trainset` (20 examples) and `devset` (50 examples). Each example in our **training set** contains just a **question** and its (human-annotated) **answer**.\n", + "\n", + "**DSPy** typically requires very minimal labeling. Whereas your pipeline may involve six or seven complex steps, you only need labels for the initial question and the final answer. **DSPy** will bootstrap any intermediate labels needed to support your pipeline. If you change your pipeline in any way, the data bootstrapped will change accordingly!\n", + "\n", + "Now, let's look at some data examples." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: At My Window was released by which American singer-songwriter?\n", + "Answer: John Townes Van Zandt\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "train_example = trainset[0]\n", + "print(f\"Question: {train_example.question}\")\n", + "print(f\"Answer: {train_example.answer}\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "44.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examples in the **dev set** contain a third field, namely, **titles** of relevant Wikipedia articles. This is not essential but, for the sake of this intro, it'll help us get a sense of how well our programs are doing." ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dspy.evaluate.evaluate import Evaluate\n", - "\n", - "# Set up the `evaluate_on_hotpotqa` function. We'll use this many times below.\n", - "evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - "\n", - "# Evaluate the `compiled_rag` program with the `answer_exact_match` metric.\n", - "metric = dspy.evaluate.answer_exact_match\n", - "evaluate_on_hotpotqa(compiled_rag, metric=metric)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Evaluating the Retrieval\n", - "\n", - "It may also be instructive to look at the accuracy of retrieval. There are multiple ways to do this. Often, we can just check whether the retrieved passages contain the answer.\n", - "\n", - "That said, since our dev set includes the gold titles that should be retrieved, we can just use these here." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 13 / 50 (26.0): 100%|██████████| 50/50 [00:00<00:00, 671.76it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 13 / 50 (26.0%)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answergold_titlescontextpred_answergold_passages_retrieved
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No❌ [False]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}['2017–18 Pittsburgh Penguins season | The 2017–18 Pittsburgh Penguins season will be the 51st season for the National Hockey League ice hockey team that was...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Castle', 'Crichton Collegiate Church'}[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne✔️ [True]
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}[\"Æthelweard of East Anglia | Æthelweard (died 854) was a 9th-century king of East Anglia, the long-lived Anglo-Saxon kingdom which today includes the English counties...King Alfred the Great❌ [False]
\n" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", + "Answer: English\n", + "Relevant Wikipedia Titles: {'Robert Irvine', 'Restaurant: Impossible'}\n" + ] + } ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "source": [ + "dev_example = devset[18]\n", + "print(f\"Question: {dev_example.question}\")\n", + "print(f\"Answer: {dev_example.answer}\")\n", + "print(f\"Relevant Wikipedia Titles: {dev_example.gold_titles}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After loading the raw data, we'd applied `x.with_inputs('question')` to each example to tell **DSPy** that our input field in each example will be just `question`. Any other fields are labels or metadata that are not given to the system." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For this dataset, training examples have input keys ['question'] and label keys ['answer']\n", + "For this dataset, dev examples have input keys ['question'] and label keys ['answer', 'gold_titles']\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "print(f\"For this dataset, training examples have input keys {train_example.inputs().keys()} and label keys {train_example.labels().keys()}\")\n", + "print(f\"For this dataset, dev examples have input keys {dev_example.inputs().keys()} and label keys {dev_example.labels().keys()}\")" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def gold_passages_retrieved(example, pred, trace=None):\n", - " gold_titles = set(map(dspy.evaluate.normalize_text, example['gold_titles']))\n", - " found_titles = set(map(dspy.evaluate.normalize_text, [c.split(' | ')[0] for c in pred.context]))\n", - "\n", - " return gold_titles.issubset(found_titles)\n", - "\n", - "compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although this simple `compiled_rag` program is able to answer a decent fraction of the questions correctly (on this tiny set, over 40%), the quality of retrieval is much lower.\n", - "\n", - "This potentially suggests that the LM is often relying on the knowledge it memorized during training to answer questions. To address this weak retrieval, let's explore a second program that involves more advanced search behavior." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5] Program 2: Multi-Hop Search (“Baleen”)\n", - "\n", - "From exploring the harder questions in the training/dev sets, it becomes clear that a single search query is often not enough for this task. For instance, this can be seen when a question ask about, say, the birth city of the writer of \"Right Back At It Again\". A search query identifies the author correctly as \"Jeremy McKinnon\", but it wouldn't figure out when he was born.\n", - "\n", - "The standard approach for this challenge in the retrieval-augmented NLP literature is to build multi-hop search systems, like GoldEn (Qi et al., 2019) and Baleen (Khattab et al., 2021). These systems read the retrieved results and then generate additional queries to gather additional information if necessary. Using **DSPy**, we can easily simulate such systems in a few lines of code.\n", - "\n", - "\n", - "We'll still use the `GenerateAnswer` signature from the RAG implementation above. All we need now is a **signature** for the \"hop\" behavior: taking some partial context and a question, generate a search query to find missing information." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "class GenerateSearchQuery(dspy.Signature):\n", - " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", - "\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " question = dspy.InputField()\n", - " query = dspy.OutputField()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: We could have written `context = GenerateAnswer.signature.context` to avoid duplicating the description of the `context` field.\n", - "\n", - "Now, let's define the program itself `SimplifiedBaleen`. There are many possible ways to implement this, but we'll keep this version down to the key elements for simplicity." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "from dsp.utils import deduplicate\n", - "\n", - "class SimplifiedBaleen(dspy.Module):\n", - " def __init__(self, passages_per_hop=3, max_hops=2):\n", - " super().__init__()\n", - "\n", - " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", - " self.max_hops = max_hops\n", - " \n", - " def forward(self, question):\n", - " context = []\n", - " \n", - " for hop in range(self.max_hops):\n", - " query = self.generate_query[hop](context=context, question=question).query\n", - " passages = self.retrieve(query).passages\n", - " context = deduplicate(context + passages)\n", - "\n", - " pred = self.generate_answer(context=context, question=question)\n", - " return dspy.Prediction(context=context, answer=pred.answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the `__init__` method defines a few key sub-modules:\n", - "\n", - "- **generate_query**: For each hop, we will have one `dspy.ChainOfThought` predictor with the `GenerateSearchQuery` signature.\n", - "- **retrieve**: This module will do the actual search, using the generated queries.\n", - "- **generate_answer**: This `dspy.Predict` module will be used after all the search steps. It has a `GenerateAnswer`, to actually produce an answer.\n", - "\n", - "The `forward` method uses these sub-modules in simple control flow.\n", - "\n", - "1. First, we'll loop up to `self.max_hops` times.\n", - "1. In each iteration, we'll generate a search query using the predictor at `self.generate_query[hop]`.\n", - "1. We'll retrieve the top-k passages using that query.\n", - "1. We'll add the (deduplicated) passages to our accumulator of `context`.\n", - "1. After the loop, we'll use `self.generate_answer` to produce an answer.\n", - "1. We'll return a prediction with the retrieved `context` and predicted `answer`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Inspect the zero-shot version of the Baleen program\n", - "\n", - "We will also compile this program shortly. But, before that, we can try it out in a \"zero-shot\" setting (i.e., without any compilation).\n", - "\n", - "Using a program in zero-shot (uncompiled) setting doesn't mean that quality will be bad. It just means that we're bottlenecked directly by the reliability of the underlying LM to understand our sub-tasks from minimal instructions.\n", - "\n", - "This is often just fine when using the most expensive/powerful models (e.g., GPT-4) on the easiest and most standard tasks (e.g., answering simple questions about popular entities).\n", - "\n", - "However, a zero-shot approach quickly falls short for more specialized tasks, for novel domains/settings, and for more efficient (or open) models. **DSPy** can help you in all of these settings." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: How many storeys are in the castle that David Gregory inherited?\n", - "Predicted Answer: five\n", - "Retrieved Contexts (truncated): ['David Gregory (physician) | David Gregory (20 December 1625 – 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinn...', 'The Boleyn Inheritance | The Boleyn Inheritance is a novel by British author Philippa Gregory which was first published in 2006. It is a direct sequel to her previous novel \"The Other Boleyn Girl,\" an...', 'Gregory of Gaeta | Gregory was the Duke of Gaeta from 963 until his death. He was the second son of Docibilis II of Gaeta and his wife Orania. He succeeded his brother John II, who had left only daugh...', 'Kinnairdy Castle | Kinnairdy Castle is a tower house, having five storeys and a garret, two miles south of Aberchirder, Aberdeenshire, Scotland. The alternative name is Old Kinnairdy....', 'Kinnaird Head | Kinnaird Head (Scottish Gaelic: \"An Ceann Àrd\" , \"high headland\") is a headland projecting into the North Sea, within the town of Fraserburgh, Aberdeenshire on the east coast of Scotla...', 'Kinnaird Castle, Brechin | Kinnaird Castle is a 15th-century castle in Angus, Scotland. The castle has been home to the Carnegie family, the Earl of Southesk, for more than 600 years....']\n" - ] - } - ], - "source": [ - "# Ask any question you like to this simple RAG program.\n", - "my_question = \"How many storeys are in the castle that David Gregory inherited?\"\n", - "\n", - "# Get the prediction. This contains `pred.context` and `pred.answer`.\n", - "uncompiled_baleen = SimplifiedBaleen() # uncompiled (i.e., zero-shot) program\n", - "pred = uncompiled_baleen(my_question)\n", - "\n", - "# Print the contexts and the answer.\n", - "print(f\"Question: {my_question}\")\n", - "print(f\"Predicted Answer: {pred.answer}\")\n", - "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's inspect the last **three** calls to the LM (i.e., generating the first hop's query, generating the second hop's query, and generating the answer)." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Write a simple search query that will help answer a complex question.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: may contain relevant facts\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", - "\n", - "Query: ${query}\n", - "\n", - "---\n", - "\n", - "Context: N/A\n", - "\n", - "Question: How many storeys are in the castle that David Gregory inherited?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer to this question. First, we need to find information about David Gregory and the castle he inherited. Then, we can search for details about the castle's architecture or any historical records that mention the number of storeys.\n", - "\n", - "Query: \"David Gregory castle inheritance\"\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Write a simple search query that will help answer a complex question.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: may contain relevant facts\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", - "\n", - "Query: ${query}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «David Gregory (physician) | David Gregory (20 December 1625 – 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.»\n", - "[2] «The Boleyn Inheritance | The Boleyn Inheritance is a novel by British author Philippa Gregory which was first published in 2006. It is a direct sequel to her previous novel \"The Other Boleyn Girl,\" and one of the additions to her six-part series on the Tudor royals. (The other titles in the series are \"The Constant Princess,\" \"The Queen's Fool,\" \"The Virgin's Lover,and The Other Queen.\") * The novel is told through the first-person narratives of – Anne of Cleves, Katherine Howard, and Jane Boleyn, who was mentioned in \"The Other Boleyn Girl.\" It covers a period from 1539 until 1542 and chronicles the fourth and fifth marriages of King Henry VIII of England.»\n", - "[3] «Gregory of Gaeta | Gregory was the Duke of Gaeta from 963 until his death. He was the second son of Docibilis II of Gaeta and his wife Orania. He succeeded his brother John II, who had left only daughters. Gregory rapidly depleted the \"publicum\" (public land) of the Duchy of Gaeta by doling it out to family members as grants. Gregory disappears from the records in 964 and was succeeded by his younger brother Marinus of Fondi over the heads of his three sons. It is possible that there was an internal power struggle between factions of the Docibilan family and that Gregory was forced out. On the other hand, perhaps he died and his sons fought a losing battle for their inheritance to Gaeta.»\n", - "\n", - "Question: How many storeys are in the castle that David Gregory inherited?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m produce the query. We know that David Gregory inherited Kinnairdy Castle, so we need to find information about the castle and its characteristics.\n", - "\n", - "Query: \"Kinnairdy Castle number of storeys\"\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Answer questions with short factoid answers.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: may contain relevant facts\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: often between 1 and 5 words\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «David Gregory (physician) | David Gregory (20 December 1625 – 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.»\n", - "[2] «The Boleyn Inheritance | The Boleyn Inheritance is a novel by British author Philippa Gregory which was first published in 2006. It is a direct sequel to her previous novel \"The Other Boleyn Girl,\" and one of the additions to her six-part series on the Tudor royals. (The other titles in the series are \"The Constant Princess,\" \"The Queen's Fool,\" \"The Virgin's Lover,and The Other Queen.\") * The novel is told through the first-person narratives of – Anne of Cleves, Katherine Howard, and Jane Boleyn, who was mentioned in \"The Other Boleyn Girl.\" It covers a period from 1539 until 1542 and chronicles the fourth and fifth marriages of King Henry VIII of England.»\n", - "[3] «Gregory of Gaeta | Gregory was the Duke of Gaeta from 963 until his death. He was the second son of Docibilis II of Gaeta and his wife Orania. He succeeded his brother John II, who had left only daughters. Gregory rapidly depleted the \"publicum\" (public land) of the Duchy of Gaeta by doling it out to family members as grants. Gregory disappears from the records in 964 and was succeeded by his younger brother Marinus of Fondi over the heads of his three sons. It is possible that there was an internal power struggle between factions of the Docibilan family and that Gregory was forced out. On the other hand, perhaps he died and his sons fought a losing battle for their inheritance to Gaeta.»\n", - "[4] «Kinnairdy Castle | Kinnairdy Castle is a tower house, having five storeys and a garret, two miles south of Aberchirder, Aberdeenshire, Scotland. The alternative name is Old Kinnairdy.»\n", - "[5] «Kinnaird Head | Kinnaird Head (Scottish Gaelic: \"An Ceann Àrd\" , \"high headland\") is a headland projecting into the North Sea, within the town of Fraserburgh, Aberdeenshire on the east coast of Scotland. The 16th-century Kinnaird Castle was converted in 1787 for use as the Kinnaird Head Lighthouse, the first lighthouse in Scotland to be lit by the Commissioners of Northern Lights. Kinnaird Castle and the nearby Winetower were described by W. Douglas Simpson as two of the nine castles of the Knuckle, referring to the rocky headland of north-east Aberdeenshire. Both buildings are category A listed buildings.»\n", - "[6] «Kinnaird Castle, Brechin | Kinnaird Castle is a 15th-century castle in Angus, Scotland. The castle has been home to the Carnegie family, the Earl of Southesk, for more than 600 years.»\n", - "\n", - "Question: How many storeys are in the castle that David Gregory inherited?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know from the context that David Gregory inherited Kinnairdy Castle. \n", - "\n", - "Answer: five\u001b[0m\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "turbo.inspect_history(n=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Compiling the Baleen program\n", - "\n", - "Now is the time to compile our multi-hop (`SimplifiedBaleen`) program.\n", - "\n", - "We will first define our validation logic, which will simply require that:\n", - "\n", - "- The predicted answer matches the gold answer.\n", - "- The retrieved context contains the gold answer.\n", - "- None of the generated queries is rambling (i.e., none exceeds 100 characters in length).\n", - "- None of the generated queries is roughly repeated (i.e., none is within 0.8 or higher F1 score of earlier queries)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def validate_context_and_answer_and_hops(example, pred, trace=None):\n", - " if not dspy.evaluate.answer_exact_match(example, pred): return False\n", - " if not dspy.evaluate.answer_passage_match(example, pred): return False\n", - "\n", - " hops = [example.question] + [outputs.query for *_, outputs in trace if 'query' in outputs]\n", - "\n", - " if max([len(h) for h in hops]) > 100: return False\n", - " if any(dspy.evaluate.answer_exact_match_str(hops[idx], hops[:idx], frac=0.8) for idx in range(2, len(hops))): return False\n", - "\n", - " return True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Like we did for RAG, we'll use one of the most basic teleprompters in **DSPy**, namely, `BootstrapFewShot`." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [00:00<00:00, 64.11it/s]\n" - ] - } - ], - "source": [ - "teleprompter = BootstrapFewShot(metric=validate_context_and_answer_and_hops)\n", - "compiled_baleen = teleprompter.compile(SimplifiedBaleen(), teacher=SimplifiedBaleen(passages_per_hop=2), trainset=trainset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Evaluating the Retrieval\n", - "\n", - "Earlier, it appeared like our simple RAG program was not very effective at finding all evidence required for answering each question. Is this resolved by the adding some extra steps in the `forward` function of `SimplifiedBaleen`? What about compiling, does it help for that? \n", - "\n", - "The answer for these questions is not always going to be obvious. However, **DSPy** makes it extremely easy to try many diverse approaches with minimal effort.\n", - "\n", - "Let's evaluate the quality of retrieval of our compiled and uncompiled Baleen pipelines!" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "uncompiled_baleen_retrieval_score = evaluate_on_hotpotqa(uncompiled_baleen, metric=gold_passages_retrieved)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 30 / 50 (60.0): 100%|██████████| 50/50 [00:00<00:00, 54.98it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 30 / 50 (60.0%)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answergold_titlescontextpred_answergold_passages_retrieved
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}[\"2017 NHL Expansion Draft | The 2017 NHL Expansion Draft was an expansion draft conducted by the National Hockey League on June 18–20, 2017 to...National Hockey League (NHL)❌ [False]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}['List of Tampa Bay Lightning general managers | The Tampa Bay Lightning are an American professional ice hockey team based in Tampa, Florida. They play...Steve Yzerman❌ [False]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Castle', 'Crichton Collegiate Church'}[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne✔️ [True]
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}['Æthelweard (son of Alfred) | Æthelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.', 'Æthelred the Unready |...King Alfred the Great❌ [False]
\n" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that there's nothing special about the HotPotQA dataset: it's just a list of examples.\n", + "\n", + "You can define your own examples as below. A future notebook will guide you through creating your own data in unusual or data-scarce settings, which is a context where **DSPy** excels.\n", + "\n", + "```\n", + "dspy.Example(field1=value, field2=value2, ...)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3] Building Blocks\n", + "\n", + "In **DSPy**, we will maintain a clean separation between **defining your modules in a declarative way** and **calling them in a pipeline to solve the task**.\n", + "\n", + "This allows you to focus on the information flow of your pipeline. **DSPy** will then take your program and automatically optimize **how to prompt** (or finetune) LMs **for your particular pipeline** so it works well.\n", + "\n", + "If you have experience with PyTorch, you can think of DSPy as the PyTorch of the foundation model space. Before we see this in action, let's first understand some key pieces." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Using the Language Model: **Signatures** & **Predictors**\n", + "\n", + "Every call to the LM in a **DSPy** program needs to have a **Signature**.\n", + "\n", + "A signature consists of three simple elements:\n", + "\n", + "- A minimal description of the sub-task the LM is supposed to solve.\n", + "- A description of one or more input fields (e.g., input question) that we will give to the LM.\n", + "- A description of one or more output fields (e.g., the question's answer) that we will expect from the LM.\n", + "\n", + "Let's define a simple signature for basic question answering." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class BasicQA(dspy.Signature):\n", + " \"\"\"Answer questions with short factoid answers.\"\"\"\n", + "\n", + " question = dspy.InputField()\n", + " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In `BasicQA`, the docstring describes the sub-task here (i.e., answering questions). Each `InputField` or `OutputField` can optionally contain a description `desc` too. When it's not given, it's inferred from the field's name (e.g., `question`).\n", + "\n", + "Notice that there isn't anything special about this signature in **DSPy**. We can just as easily define a signature that takes a long snippet from a PDF and outputs structured information, for instance.\n", + "\n", + "Anyway, now that we have a signature, let's define and use a **Predictor**. A predictor is a module that knows how to use the LM to implement a signature. Importantly, predictors can **learn** to fit their behavior to the task!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", + "Predicted Answer: American\n" + ] + } ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " ... 45 more rows not displayed ...\n", - "
\n", - " " + "source": [ + "# Define the predictor.\n", + "generate_answer = dspy.Predict(BasicQA)\n", + "\n", + "# Call the predictor on a particular input.\n", + "pred = generate_answer(question=dev_example.question)\n", + "\n", + "# Print the input and the prediction.\n", + "print(f\"Question: {dev_example.question}\")\n", + "print(f\"Predicted Answer: {pred.answer}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the example above, we asked the predictor about the the chef featured in \"Restaurant: Impossible\". The model outputs an answer (\"American\").\n", + "\n", + "For visibility, we can inspect how this extremely basic predictor implemented our signature. Let's inspect the history of our LM (**turbo**)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Answer questions with short factoid answers.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Question: ${question}\n", + "Answer: often between 1 and 5 words\n", + "\n", + "---\n", + "\n", + "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", + "Answer:\u001b[32m American\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "turbo.inspect_history(n=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It happens that this chef is both British and American, but we have no way of knowing if the model just guessed \"American\" because it's a common answer. In general, adding **retrieval** and **learning** will help the LM be more factual, and we'll explore this in a minute!\n", + "\n", + "But before we do that, how about we _just_ change the predictor? It would be nice to allow the model to elicit a chain of thought along with the prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible?\n", + "Thought: We know that the chef and restaurateur featured in Restaurant: Impossible is Robert Irvine.\n", + "Predicted Answer: British\n" + ] + } + ], + "source": [ + "# Define the predictor. Notice we're just changing the class. The signature BasicQA is unchanged.\n", + "generate_answer_with_chain_of_thought = dspy.ChainOfThought(BasicQA)\n", + "\n", + "# Call the predictor on the same input.\n", + "pred = generate_answer_with_chain_of_thought(question=dev_example.question)\n", + "\n", + "# Print the input, the chain of thought, and the prediction.\n", + "print(f\"Question: {dev_example.question}\")\n", + "print(f\"Thought: {pred.rationale.split('.', 1)[1].strip()}\")\n", + "print(f\"Predicted Answer: {pred.answer}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is indeed a better answer: the model figures out that the chef in question is **Robert Irvine** and correctly identifies that he's British.\n", + "\n", + "These predictors (`dspy.Predict` and `dspy.ChainOfThought`) can be applied to _any_ signature. As we'll see below, they can also be optimized to learn from your data and validation logic." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Using the Retrieval Model\n", + "\n", + "Using the retriever is pretty simple. A module `dspy.Retrieve(k)` will search for the top-`k` passages that match a given query.\n", + "\n", + "By default, this will use the retriever we configured at the top of this notebook, namely, ColBERTv2 over a Wikipedia index." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 3 passages for question: What is the nationality of the chef and restaurateur featured in Restaurant: Impossible? \n", + " ------------------------------ \n", + "\n", + "1] Restaurant: Impossible | Restaurant: Impossible is an American reality television series, featuring chef and restaurateur Robert Irvine, that aired on Food Network from 2011 to 2016. \n", + "\n", + "2] Jean Joho | Jean Joho is a French-American chef and restaurateur. He is chef/proprietor of Everest in Chicago (founded in 1986), Paris Club Bistro & Bar and Studio Paris in Chicago, The Eiffel Tower Restaurant in Las Vegas, and Brasserie JO in Boston. \n", + "\n", + "3] List of Restaurant: Impossible episodes | This is the list of the episodes for the American cooking and reality television series \"Restaurant Impossible\", produced by Food Network. The premise of the series is that within two days and on a budget of $10,000, celebrity chef Robert Irvine renovates a failing American restaurant with the goal of helping to restore it to profitability and prominence. Irvine is assisted by a designer (usually Taniya Nayak, Cheryl Torrenueva, or Lynn Keagan, but sometimes Vanessa De Leon, Krista Watterworth, Yvette Irene, or Nicole Faccuito), along with general contractor Tom Bury, who sometimes does double duty as both general contractor and designer. After assessing the problems with the restaurant, Robert Irvine typically creates a plan for the new decor, oversees the cleaning of the restaurant, reduces the size of the menu and improves the food, develops a promotional activity, educates the restaurant's owners, or trains the staff, as needed by each restaurant. \n", + "\n" + ] + } + ], + "source": [ + "retrieve = dspy.Retrieve(k=3)\n", + "topK_passages = retrieve(dev_example.question).passages\n", + "\n", + "print(f\"Top {retrieve.k} passages for question: {dev_example.question} \\n\", '-' * 30, '\\n')\n", + "\n", + "for idx, passage in enumerate(topK_passages):\n", + " print(f'{idx+1}]', passage, '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Feel free to any other queries you like." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'History of the FIFA World Cup | The FIFA World Cup was first held in 1930, when FIFA president Jules Rimet decided to stage an international football tournament. The inaugural edition, held in 1930, was contested as a final tournament of only thirteen teams invited by the organization. Since then, the World Cup has experienced successive expansions and format remodeling to its current 32-team final tournament preceded by a two-year qualifying process, involving over 200 teams from around the world.'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retrieve(\"When was the first FIFA World Cup held?\").passages[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4] Program 1: Basic Retrieval-Augmented Generation (\u201cRAG\u201d)\n", + "\n", + "Let's define our first complete program for this task. We'll build a retrieval-augmented pipeline for answer generation.\n", + "\n", + "Given a question, we'll search for the top-3 passages in Wikipedia and then feed them as context for answer generation.\n", + "\n", + "Let's start by defining this signature: `context, question --> answer`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "class GenerateAnswer(dspy.Signature):\n", + " \"\"\"Answer questions with short factoid answers.\"\"\"\n", + "\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " question = dspy.InputField()\n", + " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great. Now let's define the actual program. This is a class that inherits from `dspy.Module`.\n", + "\n", + "It needs two methods:\n", + "\n", + "- The `__init__` method will simply declare the sub-modules it needs: `dspy.Retrieve` and `dspy.ChainOfThought`. The latter is defined to implement our `GenerateAnswer` signature.\n", + "- The `forward` method will describe the control flow of answering the question using the modules we have." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "class RAG(dspy.Module):\n", + " def __init__(self, num_passages=3):\n", + " super().__init__()\n", + "\n", + " self.retrieve = dspy.Retrieve(k=num_passages)\n", + " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", + " \n", + " def forward(self, question):\n", + " context = self.retrieve(question).passages\n", + " prediction = self.generate_answer(context=context, question=question)\n", + " return dspy.Prediction(context=context, answer=prediction.answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Compiling the RAG program\n", + "\n", + "Having defined this program, let's now **compile** it. Compiling a program will update the parameters stored in each module. In our setting, this is primarily in the form of collecting and selecting good demonstrations for inclusion in your prompt(s).\n", + "\n", + "Compiling depends on three things:\n", + "\n", + "1. **A training set.** We'll just use our 20 question\u2013answer examples from `trainset` above.\n", + "1. **A metric for validation.** We'll define a quick `validate_context_and_answer` that checks that the predicted answer is correct. It'll also check that the retrieved context does actually contain that answer.\n", + "1. **A specific teleprompter.** The **DSPy** compiler includes a number of **teleprompters** that can optimize your programs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Teleprompters:** Teleprompters are powerful optimizers that can take any program and learn to bootstrap and select effective prompts for its modules. Hence the name, which means \"prompting at a distance\".\n", + "\n", + "Different teleprompters offer various tradeoffs in terms of how much they optimize cost versus quality, etc. We will use a simple default `BootstrapFewShot` in this notebook.\n", + "\n", + "\n", + "_If you're into analogies, you could think of this as your training data, your loss function, and your optimizer in a standard DNN supervised learning setup. Whereas SGD is a basic optimizer, there are more sophisticated (and more expensive!) ones like Adam or RMSProp._" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|\u2588\u2588\u2588\u2588\u2588 | 10/20 [00:00<00:00, 121.99it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 11 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from dspy.teleprompt import BootstrapFewShot\n", + "\n", + "# Validation logic: check that the predicted answer is correct.\n", + "# Also check that the retrieved context does actually contain that answer.\n", + "def validate_context_and_answer(example, pred, trace=None):\n", + " answer_EM = dspy.evaluate.answer_exact_match(example, pred)\n", + " answer_PM = dspy.evaluate.answer_passage_match(example, pred)\n", + " return answer_EM and answer_PM\n", + "\n", + "# Set up a basic teleprompter, which will compile our RAG program.\n", + "teleprompter = BootstrapFewShot(metric=validate_context_and_answer)\n", + "\n", + "# Compile!\n", + "compiled_rag = teleprompter.compile(RAG(), trainset=trainset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've compiled our RAG program, let's try it out." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: What castle did David Gregory inherit?\n", + "Predicted Answer: Kinnairdy Castle\n", + "Retrieved Contexts (truncated): ['David Gregory (physician) | David Gregory (20 December 1625 \u2013 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinn...', 'Gregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: \u0393\u03c1\u03b7\u03b3\u03cc\u03c1\u03b9\u03bf\u03c2 \u03a4\u03b1\u03c1\u03c7\u03b1\u03bd\u03b5\u03b9\u03ce\u03c4\u03b7\u03c2 , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 t...', 'David Gregory (mathematician) | David Gregory (originally spelt Gregorie) FRS (? 1659 \u2013 10 October 1708) was a Scottish mathematician and astronomer. He was professor of mathematics at the University ...']\n" + ] + } + ], + "source": [ + "# Ask any question you like to this simple RAG program.\n", + "my_question = \"What castle did David Gregory inherit?\"\n", + "\n", + "# Get the prediction. This contains `pred.context` and `pred.answer`.\n", + "pred = compiled_rag(my_question)\n", + "\n", + "# Print the contexts and the answer.\n", + "print(f\"Question: {my_question}\")\n", + "print(f\"Predicted Answer: {pred.answer}\")\n", + "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excellent. How about we inspect the last prompt for the LM?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Answer questions with short factoid answers.\n", + "\n", + "---\n", + "\n", + "Question: At My Window was released by which American singer-songwriter?\n", + "Answer: John Townes Van Zandt\n", + "\n", + "Question: \"Everything Has Changed\" is a song from an album released under which record label ?\n", + "Answer: Big Machine Records\n", + "\n", + "Question: The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?\n", + "Answer: 1950\n", + "\n", + "Question: Which Pakistani cricket umpire who won 3 consecutive ICC umpire of the year awards in 2009, 2010, and 2011 will be in the ICC World Twenty20?\n", + "Answer: Aleem Sarwar Dar\n", + "\n", + "Question: Having the combination of excellent foot speed and bat speed helped Eric Davis, create what kind of outfield for the Los Angeles Dodgers?\n", + "Answer: \"Outfield of Dreams\"\n", + "\n", + "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", + "Answer: Aleksandr Danilovich Aleksandrov\n", + "\n", + "Question: The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?\n", + "Answer: 2010\n", + "\n", + "Question: Tombstone stared an actor born May 17, 1955 known as who?\n", + "Answer: Bill Paxton\n", + "\n", + "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", + "Answer: 1874\n", + "\n", + "Question: which American actor was Candace Kita guest starred with\n", + "Answer: Bill Murray\n", + "\n", + "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", + "Answer: The Empire State Building\n", + "\n", + "Question: Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?\n", + "Answer: Buena Vista Distribution\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: may contain relevant facts\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: often between 1 and 5 words\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abTae Kwon Do Times | Tae Kwon Do Times is a magazine devoted to the martial art of taekwondo, and is published in the United States of America. While the title suggests that it focuses on taekwondo exclusively, the magazine also covers other Korean martial arts. \"Tae Kwon Do Times\" has published articles by a wide range of authors, including He-Young Kimm, Thomas Kurz, Scott Shaw, and Mark Van Schuyver.\u00bb\n", + "[2] \u00abKwon Tae-man | Kwon Tae-man (born 1941) was an early Korean hapkido practitioner and a pioneer of the art, first in Korea and then in the United States. He formed one of the earliest dojang's for hapkido in the United States in Torrance, California, and has been featured in many magazine articles promoting the art.\u00bb\n", + "[3] \u00abHee Il Cho | Cho Hee Il (born October 13, 1940) is a prominent Korean-American master of taekwondo, holding the rank of 9th \"dan\" in the martial art. He has written 11 martial art books, produced 70 martial art training videos, and has appeared on more than 70 martial arts magazine covers. Cho won several national and international competitions as a taekwondo competitor, and has appeared in several films, including \"Fight to Win\", \"Best of the Best\", \"Bloodsport II\", and \"Bloodsport III\". He founded the Action International Martial Arts Association (AIMAA) in 1980, and is its President. Cho is a member of both \"Black Belt\" magazine's Hall of Fame and \"Tae Kwon Do Times\" magazine's Hall of Fame.\u00bb\n", + "\n", + "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know from the context that \"Tae Kwon Do Times\" is a magazine that covers taekwondo and other Korean martial arts. It has published articles by authors like Scott Shaw. On the other hand, there is no information about Southwest Art magazine in the context.\n", + "\n", + "Answer: Tae Kwon Do Times\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abRosario Dawson | Rosario Isabel Dawson (born May 9, 1979) is an American actress, producer, singer, comic book writer, and political activist. She made her film debut in the 1995 teen drama \"Kids\". Her subsequent film roles include \"He Got Game\", \"Men in Black II\", \"25th Hour\", \"Rent\", \"Sin City\", \"Death Proof\", \"Seven Pounds\", \"\", and \"Top Five\". Dawson has also provided voice-over work for Disney and DC.\u00bb\n", + "[2] \u00abSarai Gonzalez | Sarai Isaura Gonzalez (born 2005) is an American Latina child actress who made her professional debut at the age of 11 on the Spanish-language \"\"Soy Yo\"\" (\"That's Me\") music video by Bomba Est\u00e9reo. Cast as a \"nerdy\" tween with a \"sassy\" and \"confident\" attitude, her performance turned her into a \"Latina icon\" for \"female empowerment, identity and self-worth\". She subsequently appeared in two get out the vote videos for Latinos in advance of the 2016 United States elections.\u00bb\n", + "[3] \u00abGabriela (2001 film) | Gabriela is a 2001 American romance film, starring Seidy Lopez in the title role alongside Jaime Gomez as her admirer Mike. The film has been cited as an inspiration behind the Premiere Weekend Club, which supports Latino film-making.\u00bb\n", + "\n", + "Question: Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the actress made her film debut in 1995 and co-founded Voto Latino.\n", + "\n", + "Answer: Rosario Dawson\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abBattle of Kursk | The Battle of Kursk was a Second World War engagement between German and Soviet forces on the Eastern Front near Kursk (450 km south-west of Moscow) in the Soviet Union during July and August 1943. The battle began with the launch of the German offensive, Operation Citadel (German: \"Unternehmen Zitadelle\" ), on 5 July, which had the objective of pinching off the Kursk salient with attacks on the base of the salient from north and south simultaneously. After the German offensive stalled on the northern side of the salient, on 12 July the Soviets commenced their Kursk Strategic Offensive Operation with the launch of Operation Kutuzov (Russian: \u041a\u0443\u0442\u0443\u0437\u043e\u0432 ) against the rear of the German forces in the northern side. On the southern side, the Soviets also launched powerful counterattacks the same day, one of which led to a large armoured clash, the Battle of Prokhorovka. On 3 August, the Soviets began the second phase of the Kursk Strategic Offensive Operation with the launch of Operation Polkovodets Rumyantsev (Russian: \u041f\u043e\u043b\u043a\u043e\u0432\u043e\u0434\u0435\u0446 \u0420\u0443\u043c\u044f\u043d\u0446\u0435\u0432 ) against the German forces in the southern side of the Kursk salient.\u00bb\n", + "[2] \u00abOperation Mars | Operation Mars, also known as the Second Rzhev-Sychevka Offensive Operation (Russian: \u0412\u0442\u043e\u0440\u0430\u044f \u0420\u0436\u0435\u0432\u0441\u043a\u043e-\u0421\u044b\u0447\u0451\u0432\u0441\u043a\u0430\u044f \u043d\u0430\u0441\u0442\u0443\u043f\u0430\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u044f), was the codename for an offensive launched by Soviet forces against German forces during World War II. It took place between 25 November and 20 December 1942 around the Rzhev salient in the vicinity of Moscow.\u00bb\n", + "[3] \u00abKholm Pocket | The Kholm Pocket (German: \"Kessel von Cholm\" ; Russian: \u0425\u043e\u043b\u043c\u0441\u043a\u0438\u0439 \u043a\u043e\u0442\u0451\u043b ) was the name given for the encirclement of German troops by the Red Army around Kholm south of Leningrad, during World War II on the Eastern Front, from 23 January 1942 until 5 May 1942. A much larger pocket was simultaneously surrounded in Demyansk, about 100 km to the northeast. These were the results of German retreat following their defeat during the Battle of Moscow.\u00bb\n", + "\n", + "Question: What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the German offensive that started the Battle of Kursk was called Operation Citadel.\n", + "\n", + "Answer: Operation Citadel\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abKerry Condon | Kerry Condon (born 4 January 1983) is an Irish television and film actress, best known for her role as Octavia of the Julii in the HBO/BBC series \"Rome,\" as Stacey Ehrmantraut in AMC's \"Better Call Saul\" and as the voice of F.R.I.D.A.Y. in various films in the Marvel Cinematic Universe. She is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\"\u00bb\n", + "[2] \u00abCorona Riccardo | Corona Riccardo (c. 1878October 15, 1917) was an Italian born American actress who had a brief Broadway stage career before leaving to become a wife and mother. Born in Naples she came to acting in 1894 playing a Mexican girl in a play at the Empire Theatre. Wilson Barrett engaged her for a role in his play \"The Sign of the Cross\" which he took on tour of the United States. Riccardo played the role of Ancaria and later played Berenice in the same play. Robert B. Mantell in 1898 who struck by her beauty also cast her in two Shakespeare plays, \"Romeo and Juliet\" and \"Othello\". Author Lewis Strang writing in 1899 said Riccardo was the most promising actress in America at the time. Towards the end of 1898 Mantell chose her for another Shakespeare part, Ophelia im Hamlet. Afterwards she was due to join Augustin Daly's Theatre Company but Daly died in 1899. In 1899 she gained her biggest fame by playing Iras in the first stage production of Ben-Hur.\u00bb\n", + "[3] \u00abJudi Dench | Dame Judith Olivia \"Judi\" Dench, {'1': \", '2': \", '3': \", '4': \"} (born 9 December 1934) is an English actress and author. Dench made her professional debut in 1957 with the Old Vic Company. Over the following few years, she performed in several of Shakespeare's plays in such roles as Ophelia in \"Hamlet\", Juliet in \"Romeo and Juliet\", and Lady Macbeth in \"Macbeth\". Although most of her work during this period was in theatre, she also branched into film work and won a BAFTA Award as Most Promising Newcomer. She drew strong reviews for her leading role in the musical \"Cabaret\" in 1968.\u00bb\n", + "\n", + "Question: Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the actress we are looking for is the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" We also know that she acted in the short film The Shore.\n", + "\n", + "Answer: Kerry Condon\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abDavid Gregory (physician) | David Gregory (20 December 1625 \u2013 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.\u00bb\n", + "[2] \u00abGregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: \u0393\u03c1\u03b7\u03b3\u03cc\u03c1\u03b9\u03bf\u03c2 \u03a4\u03b1\u03c1\u03c7\u03b1\u03bd\u03b5\u03b9\u03ce\u03c4\u03b7\u03c2 , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 to 1006. In December 999, and again on February 2, 1002, he reinstituted and confirmed the possessions of the abbey and monks of Monte Cassino in Ascoli. In 1004, he fortified and expanded the castle of Dragonara on the Fortore. He gave it three circular towers and one square one. He also strengthened Lucera.\u00bb\n", + "[3] \u00abDavid Gregory (mathematician) | David Gregory (originally spelt Gregorie) FRS (? 1659 \u2013 10 October 1708) was a Scottish mathematician and astronomer. He was professor of mathematics at the University of Edinburgh, Savilian Professor of Astronomy at the University of Oxford, and a commentator on Isaac Newton's \"Principia\".\u00bb\n", + "\n", + "Question: What castle did David Gregory inherit?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know that David Gregory inherited a castle. The name of the castle is Kinnairdy Castle.\n", + "\n", + "Answer: Kinnairdy Castle\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "turbo.inspect_history(n=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though we haven't written any of this detailed demonstrations, we see that **DSPy** was able to bootstrap this 3,000 token prompt for **3-shot retrieval augmented generation with hard negative passages and chain of thought** from our extremely simple program.\n", + "\n", + "This illustrates the power of composition and learning. Of course, this was just generated by a particular teleprompter, which may or may not be perfect in each setting. As you'll see in **DSPy**, there is a large but systematic space of options you have to optimize and validate the quality and cost of your programs.\n", + "\n", + "If you're so inclined, you can easily inspect the learned objects themselves." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generate_answer\n", + "Example({'augmented': True, 'context': ['Tae Kwon Do Times | Tae Kwon Do Times is a magazine devoted to the martial art of taekwondo, and is published in the United States of America. While the title suggests that it focuses on taekwondo exclusively, the magazine also covers other Korean martial arts. \"Tae Kwon Do Times\" has published articles by a wide range of authors, including He-Young Kimm, Thomas Kurz, Scott Shaw, and Mark Van Schuyver.', \"Kwon Tae-man | Kwon Tae-man (born 1941) was an early Korean hapkido practitioner and a pioneer of the art, first in Korea and then in the United States. He formed one of the earliest dojang's for hapkido in the United States in Torrance, California, and has been featured in many magazine articles promoting the art.\", 'Hee Il Cho | Cho Hee Il (born October 13, 1940) is a prominent Korean-American master of taekwondo, holding the rank of 9th \"dan\" in the martial art. He has written 11 martial art books, produced 70 martial art training videos, and has appeared on more than 70 martial arts magazine covers. Cho won several national and international competitions as a taekwondo competitor, and has appeared in several films, including \"Fight to Win\", \"Best of the Best\", \"Bloodsport II\", and \"Bloodsport III\". He founded the Action International Martial Arts Association (AIMAA) in 1980, and is its President. Cho is a member of both \"Black Belt\" magazine\\'s Hall of Fame and \"Tae Kwon Do Times\" magazine\\'s Hall of Fame.'], 'question': 'Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?', 'rationale': 'produce the answer. We know from the context that \"Tae Kwon Do Times\" is a magazine that covers taekwondo and other Korean martial arts. It has published articles by authors like Scott Shaw. On the other hand, there is no information about Southwest Art magazine in the context.', 'answer': 'Tae Kwon Do Times'}) (input_keys=None)\n", + "\n" + ] + } + ], + "source": [ + "for name, parameter in compiled_rag.named_predictors():\n", + " print(name)\n", + " print(parameter.demos[0])\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Evaluating the Answers\n", + "\n", + "We can now evaluate our `compiled_rag` program on the dev set. Of course, this tiny set is _not_ meant to be a reliable benchmark, but it'll be instructive to use it for illustration.\n", + "\n", + "For a start, let's evaluate the accuracy (exact match) of the predicted answer." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 22 / 50 (44.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 116.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 22 / 50 (44.0%)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answergold_titlescontextpred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No\u2714\ufe0f [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017\u201318 Pittsburgh Penguins season'}['2017\u201318 Pittsburgh Penguins season | The 2017\u201318 Pittsburgh Penguins season will be the 51st season for the National Hockey League ice hockey team that was...National Hockey League\u2714\ufe0f [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman\u2714\ufe0f [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Castle', 'Crichton Collegiate Church'}[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne\u2714\ufe0f [True]
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'\u00c6thelweard (son of Alfred)', 'Ealhswith'}[\"\u00c6thelweard of East Anglia | \u00c6thelweard (died 854) was a 9th-century king of East Anglia, the long-lived Anglo-Saxon kingdom which today includes the English counties...King Alfred the Great\u2714\ufe0f [True]
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 questionexample_answergold_titlescontextpred_answergold_passages_retrieved
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No\u274c [False]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017\u201318 Pittsburgh Penguins season'}['2017\u201318 Pittsburgh Penguins season | The 2017\u201318 Pittsburgh Penguins season will be the 51st season for the National Hockey League ice hockey team that was...National Hockey League\u2714\ufe0f [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman\u2714\ufe0f [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Castle', 'Crichton Collegiate Church'}[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne\u2714\ufe0f [True]
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'\u00c6thelweard (son of Alfred)', 'Ealhswith'}[\"\u00c6thelweard of East Anglia | \u00c6thelweard (died 854) was a 9th-century king of East Anglia, the long-lived Anglo-Saxon kingdom which today includes the English counties...King Alfred the Great\u274c [False]
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def gold_passages_retrieved(example, pred, trace=None):\n", + " gold_titles = set(map(dspy.evaluate.normalize_text, example['gold_titles']))\n", + " found_titles = set(map(dspy.evaluate.normalize_text, [c.split(' | ')[0] for c in pred.context]))\n", + "\n", + " return gold_titles.issubset(found_titles)\n", + "\n", + "compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although this simple `compiled_rag` program is able to answer a decent fraction of the questions correctly (on this tiny set, over 40%), the quality of retrieval is much lower.\n", + "\n", + "This potentially suggests that the LM is often relying on the knowledge it memorized during training to answer questions. To address this weak retrieval, let's explore a second program that involves more advanced search behavior." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5] Program 2: Multi-Hop Search (\u201cBaleen\u201d)\n", + "\n", + "From exploring the harder questions in the training/dev sets, it becomes clear that a single search query is often not enough for this task. For instance, this can be seen when a question ask about, say, the birth city of the writer of \"Right Back At It Again\". A search query identifies the author correctly as \"Jeremy McKinnon\", but it wouldn't figure out when he was born.\n", + "\n", + "The standard approach for this challenge in the retrieval-augmented NLP literature is to build multi-hop search systems, like GoldEn (Qi et al., 2019) and Baleen (Khattab et al., 2021). These systems read the retrieved results and then generate additional queries to gather additional information if necessary. Using **DSPy**, we can easily simulate such systems in a few lines of code.\n", + "\n", + "\n", + "We'll still use the `GenerateAnswer` signature from the RAG implementation above. All we need now is a **signature** for the \"hop\" behavior: taking some partial context and a question, generate a search query to find missing information." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "class GenerateSearchQuery(dspy.Signature):\n", + " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", + "\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " question = dspy.InputField()\n", + " query = dspy.OutputField()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: We could have written `context = GenerateAnswer.signature.context` to avoid duplicating the description of the `context` field.\n", + "\n", + "Now, let's define the program itself `SimplifiedBaleen`. There are many possible ways to implement this, but we'll keep this version down to the key elements for simplicity." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from dsp.utils import deduplicate\n", + "\n", + "class SimplifiedBaleen(dspy.Module):\n", + " def __init__(self, passages_per_hop=3, max_hops=2):\n", + " super().__init__()\n", + "\n", + " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", + " self.max_hops = max_hops\n", + " \n", + " def forward(self, question):\n", + " context = []\n", + " \n", + " for hop in range(self.max_hops):\n", + " query = self.generate_query[hop](context=context, question=question).query\n", + " passages = self.retrieve(query).passages\n", + " context = deduplicate(context + passages)\n", + "\n", + " pred = self.generate_answer(context=context, question=question)\n", + " return dspy.Prediction(context=context, answer=pred.answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the `__init__` method defines a few key sub-modules:\n", + "\n", + "- **generate_query**: For each hop, we will have one `dspy.ChainOfThought` predictor with the `GenerateSearchQuery` signature.\n", + "- **retrieve**: This module will do the actual search, using the generated queries.\n", + "- **generate_answer**: This `dspy.Predict` module will be used after all the search steps. It has a `GenerateAnswer`, to actually produce an answer.\n", + "\n", + "The `forward` method uses these sub-modules in simple control flow.\n", + "\n", + "1. First, we'll loop up to `self.max_hops` times.\n", + "1. In each iteration, we'll generate a search query using the predictor at `self.generate_query[hop]`.\n", + "1. We'll retrieve the top-k passages using that query.\n", + "1. We'll add the (deduplicated) passages to our accumulator of `context`.\n", + "1. After the loop, we'll use `self.generate_answer` to produce an answer.\n", + "1. We'll return a prediction with the retrieved `context` and predicted `answer`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Inspect the zero-shot version of the Baleen program\n", + "\n", + "We will also compile this program shortly. But, before that, we can try it out in a \"zero-shot\" setting (i.e., without any compilation).\n", + "\n", + "Using a program in zero-shot (uncompiled) setting doesn't mean that quality will be bad. It just means that we're bottlenecked directly by the reliability of the underlying LM to understand our sub-tasks from minimal instructions.\n", + "\n", + "This is often just fine when using the most expensive/powerful models (e.g., GPT-4) on the easiest and most standard tasks (e.g., answering simple questions about popular entities).\n", + "\n", + "However, a zero-shot approach quickly falls short for more specialized tasks, for novel domains/settings, and for more efficient (or open) models. **DSPy** can help you in all of these settings." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: How many storeys are in the castle that David Gregory inherited?\n", + "Predicted Answer: five\n", + "Retrieved Contexts (truncated): ['David Gregory (physician) | David Gregory (20 December 1625 \u2013 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinn...', 'The Boleyn Inheritance | The Boleyn Inheritance is a novel by British author Philippa Gregory which was first published in 2006. It is a direct sequel to her previous novel \"The Other Boleyn Girl,\" an...', 'Gregory of Gaeta | Gregory was the Duke of Gaeta from 963 until his death. He was the second son of Docibilis II of Gaeta and his wife Orania. He succeeded his brother John II, who had left only daugh...', 'Kinnairdy Castle | Kinnairdy Castle is a tower house, having five storeys and a garret, two miles south of Aberchirder, Aberdeenshire, Scotland. The alternative name is Old Kinnairdy....', 'Kinnaird Head | Kinnaird Head (Scottish Gaelic: \"An Ceann \u00c0rd\" , \"high headland\") is a headland projecting into the North Sea, within the town of Fraserburgh, Aberdeenshire on the east coast of Scotla...', 'Kinnaird Castle, Brechin | Kinnaird Castle is a 15th-century castle in Angus, Scotland. The castle has been home to the Carnegie family, the Earl of Southesk, for more than 600 years....']\n" + ] + } + ], + "source": [ + "# Ask any question you like to this simple RAG program.\n", + "my_question = \"How many storeys are in the castle that David Gregory inherited?\"\n", + "\n", + "# Get the prediction. This contains `pred.context` and `pred.answer`.\n", + "uncompiled_baleen = SimplifiedBaleen() # uncompiled (i.e., zero-shot) program\n", + "pred = uncompiled_baleen(my_question)\n", + "\n", + "# Print the contexts and the answer.\n", + "print(f\"Question: {my_question}\")\n", + "print(f\"Predicted Answer: {pred.answer}\")\n", + "print(f\"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's inspect the last **three** calls to the LM (i.e., generating the first hop's query, generating the second hop's query, and generating the answer)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Write a simple search query that will help answer a complex question.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: may contain relevant facts\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", + "\n", + "Query: ${query}\n", + "\n", + "---\n", + "\n", + "Context: N/A\n", + "\n", + "Question: How many storeys are in the castle that David Gregory inherited?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m find the answer to this question. First, we need to find information about David Gregory and the castle he inherited. Then, we can search for details about the castle's architecture or any historical records that mention the number of storeys.\n", + "\n", + "Query: \"David Gregory castle inheritance\"\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Write a simple search query that will help answer a complex question.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: may contain relevant facts\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", + "\n", + "Query: ${query}\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abDavid Gregory (physician) | David Gregory (20 December 1625 \u2013 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.\u00bb\n", + "[2] \u00abThe Boleyn Inheritance | The Boleyn Inheritance is a novel by British author Philippa Gregory which was first published in 2006. It is a direct sequel to her previous novel \"The Other Boleyn Girl,\" and one of the additions to her six-part series on the Tudor royals. (The other titles in the series are \"The Constant Princess,\" \"The Queen's Fool,\" \"The Virgin's Lover,and The Other Queen.\") * The novel is told through the first-person narratives of \u2013 Anne of Cleves, Katherine Howard, and Jane Boleyn, who was mentioned in \"The Other Boleyn Girl.\" It covers a period from 1539 until 1542 and chronicles the fourth and fifth marriages of King Henry VIII of England.\u00bb\n", + "[3] \u00abGregory of Gaeta | Gregory was the Duke of Gaeta from 963 until his death. He was the second son of Docibilis II of Gaeta and his wife Orania. He succeeded his brother John II, who had left only daughters. Gregory rapidly depleted the \"publicum\" (public land) of the Duchy of Gaeta by doling it out to family members as grants. Gregory disappears from the records in 964 and was succeeded by his younger brother Marinus of Fondi over the heads of his three sons. It is possible that there was an internal power struggle between factions of the Docibilan family and that Gregory was forced out. On the other hand, perhaps he died and his sons fought a losing battle for their inheritance to Gaeta.\u00bb\n", + "\n", + "Question: How many storeys are in the castle that David Gregory inherited?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m produce the query. We know that David Gregory inherited Kinnairdy Castle, so we need to find information about the castle and its characteristics.\n", + "\n", + "Query: \"Kinnairdy Castle number of storeys\"\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Answer questions with short factoid answers.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: may contain relevant facts\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: often between 1 and 5 words\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abDavid Gregory (physician) | David Gregory (20 December 1625 \u2013 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.\u00bb\n", + "[2] \u00abThe Boleyn Inheritance | The Boleyn Inheritance is a novel by British author Philippa Gregory which was first published in 2006. It is a direct sequel to her previous novel \"The Other Boleyn Girl,\" and one of the additions to her six-part series on the Tudor royals. (The other titles in the series are \"The Constant Princess,\" \"The Queen's Fool,\" \"The Virgin's Lover,and The Other Queen.\") * The novel is told through the first-person narratives of \u2013 Anne of Cleves, Katherine Howard, and Jane Boleyn, who was mentioned in \"The Other Boleyn Girl.\" It covers a period from 1539 until 1542 and chronicles the fourth and fifth marriages of King Henry VIII of England.\u00bb\n", + "[3] \u00abGregory of Gaeta | Gregory was the Duke of Gaeta from 963 until his death. He was the second son of Docibilis II of Gaeta and his wife Orania. He succeeded his brother John II, who had left only daughters. Gregory rapidly depleted the \"publicum\" (public land) of the Duchy of Gaeta by doling it out to family members as grants. Gregory disappears from the records in 964 and was succeeded by his younger brother Marinus of Fondi over the heads of his three sons. It is possible that there was an internal power struggle between factions of the Docibilan family and that Gregory was forced out. On the other hand, perhaps he died and his sons fought a losing battle for their inheritance to Gaeta.\u00bb\n", + "[4] \u00abKinnairdy Castle | Kinnairdy Castle is a tower house, having five storeys and a garret, two miles south of Aberchirder, Aberdeenshire, Scotland. The alternative name is Old Kinnairdy.\u00bb\n", + "[5] \u00abKinnaird Head | Kinnaird Head (Scottish Gaelic: \"An Ceann \u00c0rd\" , \"high headland\") is a headland projecting into the North Sea, within the town of Fraserburgh, Aberdeenshire on the east coast of Scotland. The 16th-century Kinnaird Castle was converted in 1787 for use as the Kinnaird Head Lighthouse, the first lighthouse in Scotland to be lit by the Commissioners of Northern Lights. Kinnaird Castle and the nearby Winetower were described by W. Douglas Simpson as two of the nine castles of the Knuckle, referring to the rocky headland of north-east Aberdeenshire. Both buildings are category A listed buildings.\u00bb\n", + "[6] \u00abKinnaird Castle, Brechin | Kinnaird Castle is a 15th-century castle in Angus, Scotland. The castle has been home to the Carnegie family, the Earl of Southesk, for more than 600 years.\u00bb\n", + "\n", + "Question: How many storeys are in the castle that David Gregory inherited?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know from the context that David Gregory inherited Kinnairdy Castle. \n", + "\n", + "Answer: five\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "turbo.inspect_history(n=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Compiling the Baleen program\n", + "\n", + "Now is the time to compile our multi-hop (`SimplifiedBaleen`) program.\n", + "\n", + "We will first define our validation logic, which will simply require that:\n", + "\n", + "- The predicted answer matches the gold answer.\n", + "- The retrieved context contains the gold answer.\n", + "- None of the generated queries is rambling (i.e., none exceeds 100 characters in length).\n", + "- None of the generated queries is roughly repeated (i.e., none is within 0.8 or higher F1 score of earlier queries)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def validate_context_and_answer_and_hops(example, pred, trace=None):\n", + " if not dspy.evaluate.answer_exact_match(example, pred): return False\n", + " if not dspy.evaluate.answer_passage_match(example, pred): return False\n", + "\n", + " hops = [example.question] + [outputs.query for *_, outputs in trace if 'query' in outputs]\n", + "\n", + " if max([len(h) for h in hops]) > 100: return False\n", + " if any(dspy.evaluate.answer_exact_match_str(hops[idx], hops[:idx], frac=0.8) for idx in range(2, len(hops))): return False\n", + "\n", + " return True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like we did for RAG, we'll use one of the most basic teleprompters in **DSPy**, namely, `BootstrapFewShot`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:00<00:00, 64.11it/s]\n" + ] + } + ], + "source": [ + "teleprompter = BootstrapFewShot(metric=validate_context_and_answer_and_hops)\n", + "compiled_baleen = teleprompter.compile(SimplifiedBaleen(), teacher=SimplifiedBaleen(passages_per_hop=2), trainset=trainset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Evaluating the Retrieval\n", + "\n", + "Earlier, it appeared like our simple RAG program was not very effective at finding all evidence required for answering each question. Is this resolved by the adding some extra steps in the `forward` function of `SimplifiedBaleen`? What about compiling, does it help for that? \n", + "\n", + "The answer for these questions is not always going to be obvious. However, **DSPy** makes it extremely easy to try many diverse approaches with minimal effort.\n", + "\n", + "Let's evaluate the quality of retrieval of our compiled and uncompiled Baleen pipelines!" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "uncompiled_baleen_retrieval_score = evaluate_on_hotpotqa(uncompiled_baleen, metric=gold_passages_retrieved)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 30 / 50 (60.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 54.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 30 / 50 (60.0%)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answergold_titlescontextpred_answergold_passages_retrieved
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No\u2714\ufe0f [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017\u201318 Pittsburgh Penguins season'}[\"2017 NHL Expansion Draft | The 2017 NHL Expansion Draft was an expansion draft conducted by the National Hockey League on June 18\u201320, 2017 to...National Hockey League (NHL)\u274c [False]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}['List of Tampa Bay Lightning general managers | The Tampa Bay Lightning are an American professional ice hockey team based in Tampa, Florida. They play...Steve Yzerman\u274c [False]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Castle', 'Crichton Collegiate Church'}[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne\u2714\ufe0f [True]
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'\u00c6thelweard (son of Alfred)', 'Ealhswith'}['\u00c6thelweard (son of Alfred) | \u00c6thelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.', '\u00c6thelred the Unready |...King Alfred the Great\u274c [False]
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " ... 45 more rows not displayed ...\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "compiled_baleen_retrieval_score = evaluate_on_hotpotqa(compiled_baleen, metric=gold_passages_retrieved)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "## Retrieval Score for RAG: 26.0\n", + "## Retrieval Score for uncompiled Baleen: 36.0\n", + "## Retrieval Score for compiled Baleen: 60.0\n" + ] + } + ], + "source": [ + "print(f\"## Retrieval Score for RAG: {compiled_rag_retrieval_score}\") # note that for RAG, compilation has no effect on the retrieval step\n", + "print(f\"## Retrieval Score for uncompiled Baleen: {uncompiled_baleen_retrieval_score}\")\n", + "print(f\"## Retrieval Score for compiled Baleen: {compiled_baleen_retrieval_score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excellent! There might be something to this compiled, multi-hop program then. But this is far from all you can do: **DSPy** gives you a clean space of composable operators to deal with any shortcomings you see.\n", + "\n", + "We can inspect a few concrete examples. If we see failure causes, we can:\n", + "\n", + "1. Expand our pipeline by using additional sub-modules (e.g., maybe summarize after retrieval?)\n", + "1. Modify our pipeline by using more complex logic (e.g., maybe we need to break out of the multi-hop loop if we found all information we need?) \n", + "1. Refine our validation logic (e.g., maybe use a metric that use a second **DSPy** program to do the answer evaluation, instead of relying on strict string matching)\n", + "1. Use a different teleprompter to optimize your pipeline more aggressively.\n", + "1. Add more or better training examples!\n", + "\n", + "\n", + "Or, if you really want, we can tweak the descriptions in the Signatures we use in your program to make them more precisely suited for their sub-tasks. This is akin to prompt engineering and should be a final resort, given the other powerful options that **DSPy** gives us!" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Write a simple search query that will help answer a complex question.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: may contain relevant facts\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", + "\n", + "Query: ${query}\n", + "\n", + "---\n", + "\n", + "Context: N/A\n", + "\n", + "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", + "\n", + "Reasoning: Let's think step by step in order to produce the query. We know that the FA Charity Shield is an annual football match played in England between the winners of the previous season's Premier League and FA Cup. In this case, we are looking for the year when Manchester City played against a specific club in the 1972 FA Charity Shield. To find this information, we can search for the history of the FA Charity Shield and the teams that participated in the 1972 edition.\n", + "\n", + "Query: \"History of FA Charity Shield 1972\"\n", + "\n", + "---\n", + "\n", + "Context: N/A\n", + "\n", + "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the query. We need to find the heights of both buildings and compare them.\n", + "\n", + "Query: \"height of Empire State Building\" OR \"height of Bank of America Tower\"\n", + "\n", + "---\n", + "\n", + "Context: N/A\n", + "\n", + "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the query. We can search for the birth dates of both Aleksandr Danilovich Aleksandrov and Anatoly Fomenko and compare them to determine who is older.\n", + "\n", + "Query: \"Birth date Aleksandr Danilovich Aleksandrov\" \"Birth date Anatoly Fomenko\"\n", + "\n", + "---\n", + "\n", + "Context: N/A\n", + "\n", + "Question: How many storeys are in the castle that David Gregory inherited?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m produce the query. We need to find information about the castle that David Gregory inherited and determine the number of storeys it has.\n", + "\n", + "Query: \"Castle inherited by David Gregory number of storeys\"\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Write a simple search query that will help answer a complex question.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: may contain relevant facts\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", + "\n", + "Query: ${query}\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00ab1972 FA Charity Shield | The 1972 FA Charity Shield was contested between Manchester City and Aston Villa.\u00bb\n", + "[2] \u00ab1971 FA Charity Shield | The 1971 FA Charity Shield was a football match between Leicester City and Liverpool at Filbert Street on Saturday 7 August 1971.\u00bb\n", + "\n", + "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", + "\n", + "Reasoning: Let's think step by step in order to produce the query. We know that the 1972 FA Charity Shield was contested between Manchester City and Aston Villa. To find out the year the club founded that played against Manchester City, we need to search for the founding year of Aston Villa.\n", + "\n", + "Query: \"Aston Villa founding year\"\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abEmpire State Building | The Empire State Building is a 102-story skyscraper located on Fifth Avenue between West 33rd and 34th Streets in Midtown, Manhattan, New York City. It has a roof height of 1,250 feet (381 m), and with its antenna included, it stands a total of 1454 ft tall. Its name is derived from the nickname for New York, the Empire State.\u00bb\n", + "[2] \u00abBank of America Plaza (Atlanta) | Bank of America Plaza is a skyscraper located in between Midtown Atlanta and Downtown Atlanta. At 312 m , the tower is the 96th-tallest building in the world. It is the 14th tallest building in the U.S., the tallest building in Georgia and the tallest building in any U.S. state capital, overtaking the 250 m (820 ft), 50 story One Atlantic Center in height, which previously held the record as Georgia's tallest building. It has 55 stories of office space and was completed in 1992, when it was called NationsBank Plaza. Originally intended to be the headquarters for Citizens & Southern National Bank (which merged with Sovran Bank during construction), it became NationsBank's property following its formation in the 1991 hostile takeover of C&S/Sovran by NCNB.\u00bb\n", + "\n", + "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", + "\n", + "Reasoning: Let's think step by step in order to answer the question. We know that the Empire State Building has a roof height of 1,250 feet and a total height of 1,454 feet including its antenna. On the other hand, the Bank of America Plaza in Atlanta is 312 meters tall. To compare the heights of the two buildings, we need to convert the height of the Bank of America Plaza from meters to feet.\n", + "\n", + "Query: \"Convert 312 meters to feet\"\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abAleksandr Danilovich Aleksandrov | Aleksandr Danilovich Aleksandrov (Russian: \u0410\u043b\u0435\u043a\u0441\u0430\u0301\u043d\u0434\u0440 \u0414\u0430\u043d\u0438\u0301\u043b\u043e\u0432\u0438\u0447 \u0410\u043b\u0435\u043a\u0441\u0430\u0301\u043d\u0434\u0440\u043e\u0432 , alternative transliterations: \"Alexandr\" or \"Alexander\" (first name), and \"Alexandrov\" (last name)) (August 4, 1912 \u2013 July 27, 1999), was a Soviet/Russian mathematician, physicist, philosopher and mountaineer.\u00bb\n", + "[2] \u00abAleksandr Pavlovich Aleksandrov | Aleksandr Pavlovich Aleksandrov (Russian: \u0410\u043b\u0435\u043a\u0441\u0430\u043d\u0434\u0440 \u041f\u0430\u0432\u043b\u043e\u0432\u0438\u0447 \u0410\u043b\u0435\u043a\u0441\u0430\u043d\u0434\u0440\u043e\u0432 ; born February 20, 1943) is a former Soviet cosmonaut and twice Hero of the Soviet Union (November 23, 1983 and December 29, 1987).\u00bb\n", + "\n", + "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the query. We know that Aleksandr Danilovich Aleksandrov was born on August 4, 1912, but we don't have information about Anatoly Fomenko's birthdate. To find out who is older, we need to compare their birthdates.\n", + "\n", + "Query: \"Anatoly Fomenko birthdate\"\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abDavid Gregory (physician) | David Gregory (20 December 1625 \u2013 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.\u00bb\n", + "[2] \u00abGregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: \u0393\u03c1\u03b7\u03b3\u03cc\u03c1\u03b9\u03bf\u03c2 \u03a4\u03b1\u03c1\u03c7\u03b1\u03bd\u03b5\u03b9\u03ce\u03c4\u03b7\u03c2 , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 to 1006. In December 999, and again on February 2, 1002, he reinstituted and confirmed the possessions of the abbey and monks of Monte Cassino in Ascoli. In 1004, he fortified and expanded the castle of Dragonara on the Fortore. He gave it three circular towers and one square one. He also strengthened Lucera.\u00bb\n", + "[3] \u00abGregory Parsloe-Parsloe | Sir Gregory Parsloe-Parsloe, 7th Baronet is a fictional character from the Blandings stories of P. G. Wodehouse. The seventh Baronet, who resides at Matchingham Hall, he is the son of the Very Reverend Dean Parsloe-Parsloe and is the rival and enemy of Lord Emsworth, master of Blandings Castle.\u00bb\n", + "\n", + "Question: How many storeys are in the castle that David Gregory inherited?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m produce the query. We know that David Gregory inherited Kinnairdy Castle and that he had three of his twenty-nine children become mathematics professors. To find out how many storeys are in the castle, we need to search for information about Kinnairdy Castle.\n", + "\n", + "Query: \"Kinnairdy Castle number of storeys\"\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Answer questions with short factoid answers.\n", + "\n", + "---\n", + "\n", + "Question: The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?\n", + "Answer: 2010\n", + "\n", + "Question: On the coast of what ocean is the birthplace of Diogal Sakho?\n", + "Answer: Atlantic\n", + "\n", + "Question: Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?\n", + "Answer: Buena Vista Distribution\n", + "\n", + "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", + "Answer: Tae Kwon Do Times\n", + "\n", + "Question: Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?\n", + "Answer: Rosario Dawson\n", + "\n", + "Question: Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?\n", + "Answer: Kerry Condon\n", + "\n", + "Question: which American actor was Candace Kita guest starred with\n", + "Answer: Bill Murray\n", + "\n", + "Question: \"Everything Has Changed\" is a song from an album released under which record label ?\n", + "Answer: Big Machine Records\n", + "\n", + "Question: Tombstone stared an actor born May 17, 1955 known as who?\n", + "Answer: Bill Paxton\n", + "\n", + "Question: Which of these publications was most recently published, Who Put the Bomp or Self?\n", + "Answer: Self\n", + "\n", + "Question: What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?\n", + "Answer: Operation Citadel\n", + "\n", + "Question: Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where?\n", + "Answer: space\n", + "\n", + "Question: Having the combination of excellent foot speed and bat speed helped Eric Davis, create what kind of outfield for the Los Angeles Dodgers?\n", + "Answer: \"Outfield of Dreams\"\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: may contain relevant facts\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: often between 1 and 5 words\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00ab1972 FA Charity Shield | The 1972 FA Charity Shield was contested between Manchester City and Aston Villa.\u00bb\n", + "[2] \u00ab1971 FA Charity Shield | The 1971 FA Charity Shield was a football match between Leicester City and Liverpool at Filbert Street on Saturday 7 August 1971.\u00bb\n", + "[3] \u00ab1896\u201397 Aston Villa F.C. season | The 1896-87 season was Aston Villa's ninth season in the Football League since being one of its 12 founding members in 1888.\u00bb\n", + "[4] \u00abList of Aston Villa F.C. seasons | This is a list of seasons played by Aston Villa Football Club in English and European football, from 1879 (the year of the club's first FA Cup entry) to the most recent completed season. Aston Villa football club was founded in March, 1874, by members of the Villa Cross Wesleyan Chapel in Aston. Throughout the 1870s Aston Villa played a small amount of games. At least one game, against Aston Brook St Mary's was played with one half under Rugby rules and the other under football rules. In the 1880s the game became more formalised and in 1888, William McGregor formed the Football League with 11 other clubs.\u00bb\n", + "\n", + "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the 1972 FA Charity Shield was contested between Manchester City and Aston Villa. According to the context, Aston Villa Football Club was founded in March, 1874.\n", + "\n", + "Answer: 1874\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abEmpire State Building | The Empire State Building is a 102-story skyscraper located on Fifth Avenue between West 33rd and 34th Streets in Midtown, Manhattan, New York City. It has a roof height of 1,250 feet (381 m), and with its antenna included, it stands a total of 1454 ft tall. Its name is derived from the nickname for New York, the Empire State.\u00bb\n", + "[2] \u00abBank of America Plaza (Atlanta) | Bank of America Plaza is a skyscraper located in between Midtown Atlanta and Downtown Atlanta. At 312 m , the tower is the 96th-tallest building in the world. It is the 14th tallest building in the U.S., the tallest building in Georgia and the tallest building in any U.S. state capital, overtaking the 250 m (820 ft), 50 story One Atlantic Center in height, which previously held the record as Georgia's tallest building. It has 55 stories of office space and was completed in 1992, when it was called NationsBank Plaza. Originally intended to be the headquarters for Citizens & Southern National Bank (which merged with Sovran Bank during construction), it became NationsBank's property following its formation in the 1991 hostile takeover of C&S/Sovran by NCNB.\u00bb\n", + "[3] \u00abFoot (unit) | The foot ( feet; abbreviation: ft; symbol: \u2032, the prime symbol) is a unit of length in the imperial and US customary systems of measurement. Since 1959, both units have been defined by international agreement as equivalent to 0.3048 meters exactly. In both systems, the foot comprises 12 inches and three feet compose a yard.\u00bb\n", + "[4] \u00abPentameter | Pentameter (from Greek: \u03c0\u03b5\u03bd\u03c4\u03ac\u03bc\u03b5\u03c4\u03c1\u03bf\u03c2 - 'measuring five (feet)') is a poetic meter. \u0410 poem is said to be written in a particular pentameter when the lines of the poem have the length of five feet, where 'foot' is a combination of a particular number (1 or 2) of unstressed (or weak) syllables and a stressed (or strong) syllable. Depending on the pattern of feet, pentameter can be iambic (one of three two-syllable meters alongside trochaic and spondaic) or dactylic (one of two three-syllable meters alongside anapestic) (see links below).\u00bb\n", + "\n", + "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the Empire State Building has a roof height of 1,250 feet and with its antenna included, it stands a total of 1,454 feet tall. On the other hand, the Bank of America Plaza in Atlanta is 312 meters tall, which is approximately 1,024 feet. Therefore, the Empire State Building is taller than the Bank of America Plaza.\n", + "\n", + "Answer: Empire State Building\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abAleksandr Danilovich Aleksandrov | Aleksandr Danilovich Aleksandrov (Russian: \u0410\u043b\u0435\u043a\u0441\u0430\u0301\u043d\u0434\u0440 \u0414\u0430\u043d\u0438\u0301\u043b\u043e\u0432\u0438\u0447 \u0410\u043b\u0435\u043a\u0441\u0430\u0301\u043d\u0434\u0440\u043e\u0432 , alternative transliterations: \"Alexandr\" or \"Alexander\" (first name), and \"Alexandrov\" (last name)) (August 4, 1912 \u2013 July 27, 1999), was a Soviet/Russian mathematician, physicist, philosopher and mountaineer.\u00bb\n", + "[2] \u00abAleksandr Pavlovich Aleksandrov | Aleksandr Pavlovich Aleksandrov (Russian: \u0410\u043b\u0435\u043a\u0441\u0430\u043d\u0434\u0440 \u041f\u0430\u0432\u043b\u043e\u0432\u0438\u0447 \u0410\u043b\u0435\u043a\u0441\u0430\u043d\u0434\u0440\u043e\u0432 ; born February 20, 1943) is a former Soviet cosmonaut and twice Hero of the Soviet Union (November 23, 1983 and December 29, 1987).\u00bb\n", + "[3] \u00abAnatoly Fomenko | Anatoly Timofeevich Fomenko (Russian: \u0410\u043d\u0430\u0442\u043e\u0301\u043b\u0438\u0439 \u0422\u0438\u043c\u043e\u0444\u0435\u0301\u0435\u0432\u0438\u0447 \u0424\u043e\u043c\u0435\u0301\u043d\u043a\u043e ) (born 13 March 1945 in Stalino, USSR) is a Soviet and Russian mathematician, professor at Moscow State University, well known as a topologist, and a member of the Russian Academy of Sciences. He is author of a pseudoscientific theory known as New Chronology. He is also a member of the Russian Academy of Natural Sciences (1991).\u00bb\n", + "[4] \u00abSemyon Fomin | Semyon Anatolyevich Fomin (Russian: \u0421\u0435\u043c\u0451\u043d \u0410\u043d\u0430\u0442\u043e\u043b\u044c\u0435\u0432\u0438\u0447 \u0424\u043e\u043c\u0438\u043d ; born 10 January 1989) is a Russian professional footballer. He plays as a midfielder for FC Luch-Energiya Vladivostok.\u00bb\n", + "\n", + "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that Aleksandr Danilovich Aleksandrov was born on August 4, 1912, and Anatoly Fomenko was born on March 13, 1945. Therefore, Aleksandr Danilovich Aleksandrov is older.\n", + "\n", + "Answer: Aleksandr Danilovich Aleksandrov\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abDavid Gregory (physician) | David Gregory (20 December 1625 \u2013 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.\u00bb\n", + "[2] \u00abGregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: \u0393\u03c1\u03b7\u03b3\u03cc\u03c1\u03b9\u03bf\u03c2 \u03a4\u03b1\u03c1\u03c7\u03b1\u03bd\u03b5\u03b9\u03ce\u03c4\u03b7\u03c2 , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 to 1006. In December 999, and again on February 2, 1002, he reinstituted and confirmed the possessions of the abbey and monks of Monte Cassino in Ascoli. In 1004, he fortified and expanded the castle of Dragonara on the Fortore. He gave it three circular towers and one square one. He also strengthened Lucera.\u00bb\n", + "[3] \u00abGregory Parsloe-Parsloe | Sir Gregory Parsloe-Parsloe, 7th Baronet is a fictional character from the Blandings stories of P. G. Wodehouse. The seventh Baronet, who resides at Matchingham Hall, he is the son of the Very Reverend Dean Parsloe-Parsloe and is the rival and enemy of Lord Emsworth, master of Blandings Castle.\u00bb\n", + "[4] \u00abKinnairdy Castle | Kinnairdy Castle is a tower house, having five storeys and a garret, two miles south of Aberchirder, Aberdeenshire, Scotland. The alternative name is Old Kinnairdy.\u00bb\n", + "[5] \u00abKinnaird Head | Kinnaird Head (Scottish Gaelic: \"An Ceann \u00c0rd\" , \"high headland\") is a headland projecting into the North Sea, within the town of Fraserburgh, Aberdeenshire on the east coast of Scotland. The 16th-century Kinnaird Castle was converted in 1787 for use as the Kinnaird Head Lighthouse, the first lighthouse in Scotland to be lit by the Commissioners of Northern Lights. Kinnaird Castle and the nearby Winetower were described by W. Douglas Simpson as two of the nine castles of the Knuckle, referring to the rocky headland of north-east Aberdeenshire. Both buildings are category A listed buildings.\u00bb\n", + "[6] \u00abKinnaird Castle, Brechin | Kinnaird Castle is a 15th-century castle in Angus, Scotland. The castle has been home to the Carnegie family, the Earl of Southesk, for more than 600 years.\u00bb\n", + "\n", + "Question: How many storeys are in the castle that David Gregory inherited?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know that David Gregory inherited Kinnairdy Castle. According to the context, Kinnairdy Castle is a tower house with five storeys and a garret.\n", + "\n", + "Answer: Five\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "compiled_baleen(\"How many storeys are in the castle that David Gregory inherited?\")\n", + "turbo.inspect_history(n=3)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "compiled_baleen_retrieval_score = evaluate_on_hotpotqa(compiled_baleen, metric=gold_passages_retrieved)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "## Retrieval Score for RAG: 26.0\n", - "## Retrieval Score for uncompiled Baleen: 36.0\n", - "## Retrieval Score for compiled Baleen: 60.0\n" - ] - } - ], - "source": [ - "print(f\"## Retrieval Score for RAG: {compiled_rag_retrieval_score}\") # note that for RAG, compilation has no effect on the retrieval step\n", - "print(f\"## Retrieval Score for uncompiled Baleen: {uncompiled_baleen_retrieval_score}\")\n", - "print(f\"## Retrieval Score for compiled Baleen: {compiled_baleen_retrieval_score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Excellent! There might be something to this compiled, multi-hop program then. But this is far from all you can do: **DSPy** gives you a clean space of composable operators to deal with any shortcomings you see.\n", - "\n", - "We can inspect a few concrete examples. If we see failure causes, we can:\n", - "\n", - "1. Expand our pipeline by using additional sub-modules (e.g., maybe summarize after retrieval?)\n", - "1. Modify our pipeline by using more complex logic (e.g., maybe we need to break out of the multi-hop loop if we found all information we need?) \n", - "1. Refine our validation logic (e.g., maybe use a metric that use a second **DSPy** program to do the answer evaluation, instead of relying on strict string matching)\n", - "1. Use a different teleprompter to optimize your pipeline more aggressively.\n", - "1. Add more or better training examples!\n", - "\n", - "\n", - "Or, if you really want, we can tweak the descriptions in the Signatures we use in your program to make them more precisely suited for their sub-tasks. This is akin to prompt engineering and should be a final resort, given the other powerful options that **DSPy** gives us!" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Write a simple search query that will help answer a complex question.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: may contain relevant facts\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", - "\n", - "Query: ${query}\n", - "\n", - "---\n", - "\n", - "Context: N/A\n", - "\n", - "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", - "\n", - "Reasoning: Let's think step by step in order to produce the query. We know that the FA Charity Shield is an annual football match played in England between the winners of the previous season's Premier League and FA Cup. In this case, we are looking for the year when Manchester City played against a specific club in the 1972 FA Charity Shield. To find this information, we can search for the history of the FA Charity Shield and the teams that participated in the 1972 edition.\n", - "\n", - "Query: \"History of FA Charity Shield 1972\"\n", - "\n", - "---\n", - "\n", - "Context: N/A\n", - "\n", - "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the query. We need to find the heights of both buildings and compare them.\n", - "\n", - "Query: \"height of Empire State Building\" OR \"height of Bank of America Tower\"\n", - "\n", - "---\n", - "\n", - "Context: N/A\n", - "\n", - "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the query. We can search for the birth dates of both Aleksandr Danilovich Aleksandrov and Anatoly Fomenko and compare them to determine who is older.\n", - "\n", - "Query: \"Birth date Aleksandr Danilovich Aleksandrov\" \"Birth date Anatoly Fomenko\"\n", - "\n", - "---\n", - "\n", - "Context: N/A\n", - "\n", - "Question: How many storeys are in the castle that David Gregory inherited?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m produce the query. We need to find information about the castle that David Gregory inherited and determine the number of storeys it has.\n", - "\n", - "Query: \"Castle inherited by David Gregory number of storeys\"\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Write a simple search query that will help answer a complex question.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: may contain relevant facts\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the query}. We ...\n", - "\n", - "Query: ${query}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «1972 FA Charity Shield | The 1972 FA Charity Shield was contested between Manchester City and Aston Villa.»\n", - "[2] «1971 FA Charity Shield | The 1971 FA Charity Shield was a football match between Leicester City and Liverpool at Filbert Street on Saturday 7 August 1971.»\n", - "\n", - "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", - "\n", - "Reasoning: Let's think step by step in order to produce the query. We know that the 1972 FA Charity Shield was contested between Manchester City and Aston Villa. To find out the year the club founded that played against Manchester City, we need to search for the founding year of Aston Villa.\n", - "\n", - "Query: \"Aston Villa founding year\"\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Empire State Building | The Empire State Building is a 102-story skyscraper located on Fifth Avenue between West 33rd and 34th Streets in Midtown, Manhattan, New York City. It has a roof height of 1,250 feet (381 m), and with its antenna included, it stands a total of 1454 ft tall. Its name is derived from the nickname for New York, the Empire State.»\n", - "[2] «Bank of America Plaza (Atlanta) | Bank of America Plaza is a skyscraper located in between Midtown Atlanta and Downtown Atlanta. At 312 m , the tower is the 96th-tallest building in the world. It is the 14th tallest building in the U.S., the tallest building in Georgia and the tallest building in any U.S. state capital, overtaking the 250 m (820 ft), 50 story One Atlantic Center in height, which previously held the record as Georgia's tallest building. It has 55 stories of office space and was completed in 1992, when it was called NationsBank Plaza. Originally intended to be the headquarters for Citizens & Southern National Bank (which merged with Sovran Bank during construction), it became NationsBank's property following its formation in the 1991 hostile takeover of C&S/Sovran by NCNB.»\n", - "\n", - "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", - "\n", - "Reasoning: Let's think step by step in order to answer the question. We know that the Empire State Building has a roof height of 1,250 feet and a total height of 1,454 feet including its antenna. On the other hand, the Bank of America Plaza in Atlanta is 312 meters tall. To compare the heights of the two buildings, we need to convert the height of the Bank of America Plaza from meters to feet.\n", - "\n", - "Query: \"Convert 312 meters to feet\"\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Aleksandr Danilovich Aleksandrov | Aleksandr Danilovich Aleksandrov (Russian: Алекса́ндр Дани́лович Алекса́ндров , alternative transliterations: \"Alexandr\" or \"Alexander\" (first name), and \"Alexandrov\" (last name)) (August 4, 1912 – July 27, 1999), was a Soviet/Russian mathematician, physicist, philosopher and mountaineer.»\n", - "[2] «Aleksandr Pavlovich Aleksandrov | Aleksandr Pavlovich Aleksandrov (Russian: Александр Павлович Александров ; born February 20, 1943) is a former Soviet cosmonaut and twice Hero of the Soviet Union (November 23, 1983 and December 29, 1987).»\n", - "\n", - "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the query. We know that Aleksandr Danilovich Aleksandrov was born on August 4, 1912, but we don't have information about Anatoly Fomenko's birthdate. To find out who is older, we need to compare their birthdates.\n", - "\n", - "Query: \"Anatoly Fomenko birthdate\"\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «David Gregory (physician) | David Gregory (20 December 1625 – 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.»\n", - "[2] «Gregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: Γρηγόριος Ταρχανειώτης , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 to 1006. In December 999, and again on February 2, 1002, he reinstituted and confirmed the possessions of the abbey and monks of Monte Cassino in Ascoli. In 1004, he fortified and expanded the castle of Dragonara on the Fortore. He gave it three circular towers and one square one. He also strengthened Lucera.»\n", - "[3] «Gregory Parsloe-Parsloe | Sir Gregory Parsloe-Parsloe, 7th Baronet is a fictional character from the Blandings stories of P. G. Wodehouse. The seventh Baronet, who resides at Matchingham Hall, he is the son of the Very Reverend Dean Parsloe-Parsloe and is the rival and enemy of Lord Emsworth, master of Blandings Castle.»\n", - "\n", - "Question: How many storeys are in the castle that David Gregory inherited?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m produce the query. We know that David Gregory inherited Kinnairdy Castle and that he had three of his twenty-nine children become mathematics professors. To find out how many storeys are in the castle, we need to search for information about Kinnairdy Castle.\n", - "\n", - "Query: \"Kinnairdy Castle number of storeys\"\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Answer questions with short factoid answers.\n", - "\n", - "---\n", - "\n", - "Question: The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?\n", - "Answer: 2010\n", - "\n", - "Question: On the coast of what ocean is the birthplace of Diogal Sakho?\n", - "Answer: Atlantic\n", - "\n", - "Question: Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?\n", - "Answer: Buena Vista Distribution\n", - "\n", - "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", - "Answer: Tae Kwon Do Times\n", - "\n", - "Question: Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?\n", - "Answer: Rosario Dawson\n", - "\n", - "Question: Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?\n", - "Answer: Kerry Condon\n", - "\n", - "Question: which American actor was Candace Kita guest starred with\n", - "Answer: Bill Murray\n", - "\n", - "Question: \"Everything Has Changed\" is a song from an album released under which record label ?\n", - "Answer: Big Machine Records\n", - "\n", - "Question: Tombstone stared an actor born May 17, 1955 known as who?\n", - "Answer: Bill Paxton\n", - "\n", - "Question: Which of these publications was most recently published, Who Put the Bomp or Self?\n", - "Answer: Self\n", - "\n", - "Question: What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?\n", - "Answer: Operation Citadel\n", - "\n", - "Question: Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where?\n", - "Answer: space\n", - "\n", - "Question: Having the combination of excellent foot speed and bat speed helped Eric Davis, create what kind of outfield for the Los Angeles Dodgers?\n", - "Answer: \"Outfield of Dreams\"\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: may contain relevant facts\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: often between 1 and 5 words\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «1972 FA Charity Shield | The 1972 FA Charity Shield was contested between Manchester City and Aston Villa.»\n", - "[2] «1971 FA Charity Shield | The 1971 FA Charity Shield was a football match between Leicester City and Liverpool at Filbert Street on Saturday 7 August 1971.»\n", - "[3] «1896–97 Aston Villa F.C. season | The 1896-87 season was Aston Villa's ninth season in the Football League since being one of its 12 founding members in 1888.»\n", - "[4] «List of Aston Villa F.C. seasons | This is a list of seasons played by Aston Villa Football Club in English and European football, from 1879 (the year of the club's first FA Cup entry) to the most recent completed season. Aston Villa football club was founded in March, 1874, by members of the Villa Cross Wesleyan Chapel in Aston. Throughout the 1870s Aston Villa played a small amount of games. At least one game, against Aston Brook St Mary's was played with one half under Rugby rules and the other under football rules. In the 1880s the game became more formalised and in 1888, William McGregor formed the Football League with 11 other clubs.»\n", - "\n", - "Question: In what year was the club founded that played Manchester City in the 1972 FA Charity Shield\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the 1972 FA Charity Shield was contested between Manchester City and Aston Villa. According to the context, Aston Villa Football Club was founded in March, 1874.\n", - "\n", - "Answer: 1874\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Empire State Building | The Empire State Building is a 102-story skyscraper located on Fifth Avenue between West 33rd and 34th Streets in Midtown, Manhattan, New York City. It has a roof height of 1,250 feet (381 m), and with its antenna included, it stands a total of 1454 ft tall. Its name is derived from the nickname for New York, the Empire State.»\n", - "[2] «Bank of America Plaza (Atlanta) | Bank of America Plaza is a skyscraper located in between Midtown Atlanta and Downtown Atlanta. At 312 m , the tower is the 96th-tallest building in the world. It is the 14th tallest building in the U.S., the tallest building in Georgia and the tallest building in any U.S. state capital, overtaking the 250 m (820 ft), 50 story One Atlantic Center in height, which previously held the record as Georgia's tallest building. It has 55 stories of office space and was completed in 1992, when it was called NationsBank Plaza. Originally intended to be the headquarters for Citizens & Southern National Bank (which merged with Sovran Bank during construction), it became NationsBank's property following its formation in the 1991 hostile takeover of C&S/Sovran by NCNB.»\n", - "[3] «Foot (unit) | The foot ( feet; abbreviation: ft; symbol: ′, the prime symbol) is a unit of length in the imperial and US customary systems of measurement. Since 1959, both units have been defined by international agreement as equivalent to 0.3048 meters exactly. In both systems, the foot comprises 12 inches and three feet compose a yard.»\n", - "[4] «Pentameter | Pentameter (from Greek: πεντάμετρος - 'measuring five (feet)') is a poetic meter. А poem is said to be written in a particular pentameter when the lines of the poem have the length of five feet, where 'foot' is a combination of a particular number (1 or 2) of unstressed (or weak) syllables and a stressed (or strong) syllable. Depending on the pattern of feet, pentameter can be iambic (one of three two-syllable meters alongside trochaic and spondaic) or dactylic (one of two three-syllable meters alongside anapestic) (see links below).»\n", - "\n", - "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the Empire State Building has a roof height of 1,250 feet and with its antenna included, it stands a total of 1,454 feet tall. On the other hand, the Bank of America Plaza in Atlanta is 312 meters tall, which is approximately 1,024 feet. Therefore, the Empire State Building is taller than the Bank of America Plaza.\n", - "\n", - "Answer: Empire State Building\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Aleksandr Danilovich Aleksandrov | Aleksandr Danilovich Aleksandrov (Russian: Алекса́ндр Дани́лович Алекса́ндров , alternative transliterations: \"Alexandr\" or \"Alexander\" (first name), and \"Alexandrov\" (last name)) (August 4, 1912 – July 27, 1999), was a Soviet/Russian mathematician, physicist, philosopher and mountaineer.»\n", - "[2] «Aleksandr Pavlovich Aleksandrov | Aleksandr Pavlovich Aleksandrov (Russian: Александр Павлович Александров ; born February 20, 1943) is a former Soviet cosmonaut and twice Hero of the Soviet Union (November 23, 1983 and December 29, 1987).»\n", - "[3] «Anatoly Fomenko | Anatoly Timofeevich Fomenko (Russian: Анато́лий Тимофе́евич Фоме́нко ) (born 13 March 1945 in Stalino, USSR) is a Soviet and Russian mathematician, professor at Moscow State University, well known as a topologist, and a member of the Russian Academy of Sciences. He is author of a pseudoscientific theory known as New Chronology. He is also a member of the Russian Academy of Natural Sciences (1991).»\n", - "[4] «Semyon Fomin | Semyon Anatolyevich Fomin (Russian: Семён Анатольевич Фомин ; born 10 January 1989) is a Russian professional footballer. He plays as a midfielder for FC Luch-Energiya Vladivostok.»\n", - "\n", - "Question: Who is older, Aleksandr Danilovich Aleksandrov or Anatoly Fomenko?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that Aleksandr Danilovich Aleksandrov was born on August 4, 1912, and Anatoly Fomenko was born on March 13, 1945. Therefore, Aleksandr Danilovich Aleksandrov is older.\n", - "\n", - "Answer: Aleksandr Danilovich Aleksandrov\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «David Gregory (physician) | David Gregory (20 December 1625 – 1720) was a Scottish physician and inventor. His surname is sometimes spelt as Gregorie, the original Scottish spelling. He inherited Kinnairdy Castle in 1664. Three of his twenty-nine children became mathematics professors. He is credited with inventing a military cannon that Isaac Newton described as \"being destructive to the human species\". Copies and details of the model no longer exist. Gregory's use of a barometer to predict farming-related weather conditions led him to be accused of witchcraft by Presbyterian ministers from Aberdeen, although he was never convicted.»\n", - "[2] «Gregory Tarchaneiotes | Gregory Tarchaneiotes (Greek: Γρηγόριος Ταρχανειώτης , Italian: \"Gregorio Tracanioto\" or \"Tracamoto\" ) was a \"protospatharius\" and the long-reigning catepan of Italy from 998 to 1006. In December 999, and again on February 2, 1002, he reinstituted and confirmed the possessions of the abbey and monks of Monte Cassino in Ascoli. In 1004, he fortified and expanded the castle of Dragonara on the Fortore. He gave it three circular towers and one square one. He also strengthened Lucera.»\n", - "[3] «Gregory Parsloe-Parsloe | Sir Gregory Parsloe-Parsloe, 7th Baronet is a fictional character from the Blandings stories of P. G. Wodehouse. The seventh Baronet, who resides at Matchingham Hall, he is the son of the Very Reverend Dean Parsloe-Parsloe and is the rival and enemy of Lord Emsworth, master of Blandings Castle.»\n", - "[4] «Kinnairdy Castle | Kinnairdy Castle is a tower house, having five storeys and a garret, two miles south of Aberchirder, Aberdeenshire, Scotland. The alternative name is Old Kinnairdy.»\n", - "[5] «Kinnaird Head | Kinnaird Head (Scottish Gaelic: \"An Ceann Àrd\" , \"high headland\") is a headland projecting into the North Sea, within the town of Fraserburgh, Aberdeenshire on the east coast of Scotland. The 16th-century Kinnaird Castle was converted in 1787 for use as the Kinnaird Head Lighthouse, the first lighthouse in Scotland to be lit by the Commissioners of Northern Lights. Kinnaird Castle and the nearby Winetower were described by W. Douglas Simpson as two of the nine castles of the Knuckle, referring to the rocky headland of north-east Aberdeenshire. Both buildings are category A listed buildings.»\n", - "[6] «Kinnaird Castle, Brechin | Kinnaird Castle is a 15th-century castle in Angus, Scotland. The castle has been home to the Carnegie family, the Earl of Southesk, for more than 600 years.»\n", - "\n", - "Question: How many storeys are in the castle that David Gregory inherited?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know that David Gregory inherited Kinnairdy Castle. According to the context, Kinnairdy Castle is a tower house with five storeys and a garret.\n", - "\n", - "Answer: Five\u001b[0m\n", - "\n", - "\n", - "\n" - ] } - ], - "source": [ - "compiled_baleen(\"How many storeys are in the castle that David Gregory inherited?\")\n", - "turbo.inspect_history(n=3)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" + ], + "metadata": { + "kernelspec": { + "display_name": "py39", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/knn.ipynb b/examples/outdated_v2.4_examples/knn.ipynb index 450f2d459c..e139cc4607 100644 --- a/examples/outdated_v2.4_examples/knn.ipynb +++ b/examples/outdated_v2.4_examples/knn.ipynb @@ -1,1163 +1,1163 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DSPy KNN few-shot example \n", - "This notebook shows how KNN few-shot can be implemented with DSPy using the **KNNFewShot** teleprompter. To illustrate, we use the HotPotQA dataset. Please see [intro.ipynb](../intro.ipynb) for other example use cases of DSPy.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"creds.json\", \"r\") as creds:\n", - " api_key = json.loads(creds.read())[\"openai_key\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)\n", - "dspy.settings.configure(lm=lm)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from dspy.datasets import HotPotQA\n", - "\n", - "# Load the dataset.\n", - "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0)\n", - "\n", - "trainset = [x.with_inputs('question') for x in dataset.train]\n", - "devset = [x.with_inputs('question') for x in dataset.dev]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})\n", - "Question: At My Window was released by which American singer-songwriter?\n", - "Answer: John Townes Van Zandt\n" - ] - } - ], - "source": [ - "train_example = trainset[0]\n", - "print(train_example)\n", - "print(f\"Question: {train_example.question}\")\n", - "print(f\"Answer: {train_example.answer}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "class BasicQA(dspy.Signature):\n", - " \"\"\"Answer questions with short factoid answers.\"\"\"\n", - "\n", - " question = dspy.InputField()\n", - " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "class BasicQABot(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " self.generate = dspy.Predict(BasicQA)\n", - "\n", - " def forward(self,question):\n", - " prediction = self.generate(question = question)\n", - " return dspy.Prediction(answer = prediction.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "'Alfred the Great'" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DSPy KNN few-shot example \n", + "This notebook shows how KNN few-shot can be implemented with DSPy using the **KNNFewShot** teleprompter. To illustrate, we use the HotPotQA dataset. Please see [intro.ipynb](../intro.ipynb) for other example use cases of DSPy.\n" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qa_bot = BasicQABot()\n", - "pred = qa_bot.forward(\"In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?\")\n", - "pred.answer" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from dspy.teleprompt import KNNFewShot\n", - "\n", - "knn_teleprompter = KNNFewShot(7, trainset)\n", - "compiled_knn = knn_teleprompter.compile(BasicQABot(), trainset=trainset)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:02<00:02, 1.44it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n", - "Question: Are both Cangzhou and Qionghai in the Hebei province of China?\n", - "Expected answer: no\n", - "Prediction: No\n" - ] - } - ], - "source": [ - "example = devset[0]\n", - "pred = compiled_knn(question = example.question)\n", - "print(\"Question: \", example.question)\n", - "print(\"Expected answer: \", example.answer)\n", - "print(\"Prediction: \", pred.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Answer questions with short factoid answers.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: ${question}\n", - "Answer: often between 1 and 5 words\n", - "\n", - "---\n", - "\n", - "Question: On the coast of what ocean is the birthplace of Diogal Sakho?\n", - "Answer: Atlantic Ocean\n", - "\n", - "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", - "Answer: Empire State Building\n", - "\n", - "Question: Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where?\n", - "Answer: Space\n", - "\n", - "Question: Which Pakistani cricket umpire who won 3 consecutive ICC umpire of the year awards in 2009, 2010, and 2011 will be in the ICC World Twenty20?\n", - "Answer: Aleem Dar\n", - "\n", - "Question: What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?\n", - "Answer: Operation Citadel\n", - "\n", - "Question: Which of these publications was most recently published, Who Put the Bomp or Self?\n", - "Answer: Self\n", - "\n", - "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", - "Answer: Tae Kwon Do Times\n", - "\n", - "---\n", - "\n", - "Question: Are both Cangzhou and Qionghai in the Hebei province of China?\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "lm.inspect_history(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:03<00:02, 1.28it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:03<00:02, 1.06it/s] | 1/50 [00:03<03:01, 3.70s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:02<00:01, 1.57it/s] | 2/50 [00:08<03:14, 4.05s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:03<00:02, 1.16it/s] | 3/50 [00:11<02:58, 3.79s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] - }, - { - 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] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "import json" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"creds.json\", \"r\") as creds:\n", + " api_key = json.loads(creds.read())[\"openai_key\"]" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:02<00:02, 1.35it/s]██▍| 47/50 [03:14<00:14, 4.74s/it]\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)\n", + "dspy.settings.configure(lm=lm)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from dspy.datasets import HotPotQA\n", + "\n", + "# Load the dataset.\n", + "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0)\n", + "\n", + "trainset = [x.with_inputs('question') for x in dataset.train]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]\n" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:03<00:02, 1.17it/s]██▌| 48/50 [03:17<00:08, 4.38s/it]\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})\n", + "Question: At My Window was released by which American singer-songwriter?\n", + "Answer: John Townes Van Zandt\n" + ] + } + ], + "source": [ + "train_example = trainset[0]\n", + "print(train_example)\n", + "print(f\"Question: {train_example.question}\")\n", + "print(f\"Answer: {train_example.answer}\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class BasicQA(dspy.Signature):\n", + " \"\"\"Answer questions with short factoid answers.\"\"\"\n", + "\n", + " question = dspy.InputField()\n", + " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:04<00:03, 1.02s/it]██▊| 49/50 [03:21<00:04, 4.28s/it]\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class BasicQABot(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " self.generate = dspy.Predict(BasicQA)\n", + "\n", + " def forward(self,question):\n", + " prediction = self.generate(question = question)\n", + " return dspy.Prediction(answer = prediction.answer)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Alfred the Great'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qa_bot = BasicQABot()\n", + "pred = qa_bot.forward(\"In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?\")\n", + "pred.answer" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 23 / 50 (46.0): 100%|██████████| 50/50 [03:26<00:00, 4.13s/it]\n", - "/home/jovyan/scdc/project-vaqa-autosuggest/Query-Generation-exploratory/dspy/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from dspy.teleprompt import KNNFewShot\n", + "\n", + "knn_teleprompter = KNNFewShot(7, trainset)\n", + "compiled_knn = knn_teleprompter.compile(BasicQABot(), trainset=trainset)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 23 / 50 (46.0%)\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:02<00:02, 1.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n", + "Question: Are both Cangzhou and Qionghai in the Hebei province of China?\n", + "Expected answer: no\n", + "Prediction: No\n" + ] + } + ], + "source": [ + "example = devset[0]\n", + "pred = compiled_knn(question = example.question)\n", + "print(\"Question: \", example.question)\n", + "print(\"Expected answer: \", example.answer)\n", + "print(\"Prediction: \", pred.answer)" + ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answergold_titlespred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}No✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017–18 Pittsburgh Penguins season', '2017 NHL Expansion Draft'}National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}Tyne River❌ [False]
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}Alfred the Great❌ [False]
\n" + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Answer questions with short factoid answers.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Question: ${question}\n", + "Answer: often between 1 and 5 words\n", + "\n", + "---\n", + "\n", + "Question: On the coast of what ocean is the birthplace of Diogal Sakho?\n", + "Answer: Atlantic Ocean\n", + "\n", + "Question: Which is taller, the Empire State Building or the Bank of America Tower?\n", + "Answer: Empire State Building\n", + "\n", + "Question: Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where?\n", + "Answer: Space\n", + "\n", + "Question: Which Pakistani cricket umpire who won 3 consecutive ICC umpire of the year awards in 2009, 2010, and 2011 will be in the ICC World Twenty20?\n", + "Answer: Aleem Dar\n", + "\n", + "Question: What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?\n", + "Answer: Operation Citadel\n", + "\n", + "Question: Which of these publications was most recently published, Who Put the Bomp or Self?\n", + "Answer: Self\n", + "\n", + "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", + "Answer: Tae Kwon Do Times\n", + "\n", + "---\n", + "\n", + "Question: Are both Cangzhou and Qionghai in the Hebei province of China?\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "lm.inspect_history(1)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "
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+ }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:03<00:02, 1.16it/s] | 3/50 [00:11<02:58, 3.79s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:03<00:02, 1.30it/s] | 4/50 [00:15<02:56, 3.84s/it] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:02<00:01, 1.52it/s] | 5/50 [00:19<02:49, 3.78s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": 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}, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:03<00:02, 1.08it/s] | 25/50 [01:39<01:47, 4.32s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:03<00:02, 1.14it/s] | 26/50 [01:44<01:44, 4.36s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:04<00:03, 1.22s/it] | 27/50 [01:49<01:43, 4.50s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": 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 questionexample_answergold_titlespred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}No\u2714\ufe0f [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017\u201318 Pittsburgh Penguins season', '2017 NHL Expansion Draft'}National Hockey League\u2714\ufe0f [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006\u201307 Detroit Red Wings season', 'Steve Yzerman'}Steve Yzerman\u2714\ufe0f [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}Tyne River\u274c [False]
4In the 10th Century A.D. Ealhswith had a son called \u00c6thelweard by which English king?King Alfred the Great{'\u00c6thelweard (son of Alfred)', 'Ealhswith'}Alfred the Great\u274c [False]
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True \n", + " 10 What year d... 79 AD {Curse of t... 79 AD True \n", + " 11 Is the 72nd... the oldest {First Unit... Oldest True \n", + " 12 Was Stanisl... not {Stanis\u0142aw ... Yes False \n", + " 13 Which film ... Del Lord {Wang Xiaos... Wang Xiaoshuai False \n", + " 14 Lord North ... Jonathan Wi... {Jonathan A... Jonathan Ai... False \n", + " 15 What is the... Marche {Marche, Po... Marche True \n", + " 16 William Hug... 7,402 at th... {Kosciusko,... Unknown False \n", + " 17 What do stu... design thei... {Gallatin S... Study Art False \n", + " 18 What is the... English {Restaurant... British False \n", + " 19 What Americ... Robert F. Chew {Robert F. ... Wood Harris False \n", + " 20 What city i... Manchester {Toby Sawye... London False \n", + " 21 Who was bor... Deepa Mehta {Tony Kaye ... Tony Kaye False \n", + " 22 What is the... the good ma... {Boise Town... The Bon March\u00e9 False \n", + " 23 Who did Liz... Christine C... {Lizzette R... Christine C... True \n", + " 24 What was th... William Str... {P. T. Barn... Zerah Colburn False \n", + " 25 Which battl... Battle of t... {Meuse-Argo... Battle of t... True \n", + " 26 What cricke... Ian Botham {Ian Botham... Terry Alderman False \n", + " 27 What is the... defensive a... {1982 NC St... Deceased False \n", + " 28 Which Scott... Ewan McGregor {Come What ... Ewan McGregor True \n", + " 29 Where have ... space {Frank De W... Space True \n", + " 30 The origina... Maria Yermo... {Wild Honey... Maria Yermo... True \n", + " 31 Are Roswell... no {Pago Pago ... No True \n", + " 32 Untold: The... the voice o... {Marv Alber... Gus Johnson False \n", + " 33 Are Walt Di... yes {Sacro GRA,... No False \n", + " 34 What is the... Hamas {Status of ... Hamas True \n", + " 35 What album ... 1989 {Wildest Dr... 1989 True \n", + " 36 Which is co... Apera {Apera, Gun... Apera True \n", + " 37 Do The Drum... no {Pussy Galo... No True \n", + " 38 What is the... Exon {Banded Bro... UoE False \n", + " 39 Are both Be... yes {Len Wisema... Yes True \n", + " 40 Steven Cuit... Bill Melendez {Steven C. ... Bill Melendez True \n", + " 41 Shark Creek... Clarence River {Clarence R... Clarence River True \n", + " 42 Who was the... Pixar {Finding Do... Lindsey Col... False \n", + " 43 Who purchas... Renault {Benetton F... Jean Todt False \n", + " 44 Fredrick La... Cadwalader ... {Frederick ... Cadwalader ... True \n", + " 45 Gordon Warn... \"Forza Ital... {Franco Zef... Democratic ... False \n", + " 46 Andr\u00e9 Zucca... the Wehrmacht {Andr\u00e9 Zucc... The Propaga... False \n", + " 47 Both Bill P... cricketer {Bill Ponsf... Cricket False \n", + " 48 Suzana S. ... Danny Wallace {Yes Man (f... James Ellroy False \n", + " 49 In what cit... Portland {Election L... New York False )" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "from dspy.evaluate.evaluate import Evaluate\n", + "\n", + "# Set up the `evaluate_on_hotpotqa` function. We'll use this many times below.\n", + "evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + "\n", + "# Evaluate the `compiled_knn` program with the `answer_exact_match` metric.\n", + "metric = dspy.evaluate.answer_exact_match\n", + "\n", + " \n", + "evaluate_on_hotpotqa(compiled_knn, metric)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "(46.0,\n", - " question example_answer gold_titles pred_answer answer_exact_match\n", - " 0 Are both Ca... no {Cangzhou, ... No True \n", - " 1 Who conduct... National Ho... {2017–18 Pi... National Ho... True \n", - " 2 The Wings e... Steve Yzerman {2006–07 De... Steve Yzerman True \n", - " 3 What river ... the River Tyne {Crichton C... Tyne River False \n", - " 4 In the 10th... King Alfred... {Æthelweard... Alfred the ... False \n", - " 5 The Newark ... Port Author... {Newark Air... Port Author... True \n", - " 6 Where did a... Bundesliga {Claudio Pi... Peru False \n", - " 7 Are both Ch... no {Chico Muni... No True \n", - " 8 In which Ma... Waldo Count... {Stockton S... Waldo County False \n", - " 9 Which 90s r... The Afghan ... {Gene (band... The Afghan ... True \n", - " 10 What year d... 79 AD {Curse of t... 79 AD True \n", - " 11 Is the 72nd... the oldest {First Unit... Oldest True \n", - " 12 Was Stanisl... not {Stanisław ... Yes False \n", - " 13 Which film ... Del Lord {Wang Xiaos... Wang Xiaoshuai False \n", - " 14 Lord North ... Jonathan Wi... {Jonathan A... Jonathan Ai... False \n", - " 15 What is the... Marche {Marche, Po... Marche True \n", - " 16 William Hug... 7,402 at th... {Kosciusko,... Unknown False \n", - " 17 What do stu... design thei... {Gallatin S... Study Art False \n", - " 18 What is the... English {Restaurant... British False \n", - " 19 What Americ... Robert F. Chew {Robert F. ... Wood Harris False \n", - " 20 What city i... Manchester {Toby Sawye... London False \n", - " 21 Who was bor... Deepa Mehta {Tony Kaye ... Tony Kaye False \n", - " 22 What is the... the good ma... {Boise Town... The Bon Marché False \n", - " 23 Who did Liz... Christine C... {Lizzette R... Christine C... True \n", - " 24 What was th... William Str... {P. T. Barn... Zerah Colburn False \n", - " 25 Which battl... Battle of t... {Meuse-Argo... Battle of t... True \n", - " 26 What cricke... Ian Botham {Ian Botham... Terry Alderman False \n", - " 27 What is the... defensive a... {1982 NC St... Deceased False \n", - " 28 Which Scott... Ewan McGregor {Come What ... Ewan McGregor True \n", - " 29 Where have ... space {Frank De W... Space True \n", - " 30 The origina... Maria Yermo... {Wild Honey... Maria Yermo... True \n", - " 31 Are Roswell... no {Pago Pago ... No True \n", - " 32 Untold: The... the voice o... {Marv Alber... Gus Johnson False \n", - " 33 Are Walt Di... yes {Sacro GRA,... No False \n", - " 34 What is the... Hamas {Status of ... Hamas True \n", - " 35 What album ... 1989 {Wildest Dr... 1989 True \n", - " 36 Which is co... Apera {Apera, Gun... Apera True \n", - " 37 Do The Drum... no {Pussy Galo... No True \n", - " 38 What is the... Exon {Banded Bro... UoE False \n", - " 39 Are both Be... yes {Len Wisema... Yes True \n", - " 40 Steven Cuit... Bill Melendez {Steven C. ... Bill Melendez True \n", - " 41 Shark Creek... Clarence River {Clarence R... Clarence River True \n", - " 42 Who was the... Pixar {Finding Do... Lindsey Col... False \n", - " 43 Who purchas... Renault {Benetton F... Jean Todt False \n", - " 44 Fredrick La... Cadwalader ... {Frederick ... Cadwalader ... True \n", - " 45 Gordon Warn... \"Forza Ital... {Franco Zef... Democratic ... False \n", - " 46 André Zucca... the Wehrmacht {André Zucc... The Propaga... False \n", - " 47 Both Bill P... cricketer {Bill Ponsf... Cricket False \n", - " 48 Suzana S. ... Danny Wallace {Yes Man (f... James Ellroy False \n", - " 49 In what cit... Portland {Election L... New York False )" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.18 64-bit ('pipenv')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "1d3dd57b136d599382d2667f19e9aed8a929301e79008dd54c75c965d79714ea" + } } - ], - "source": [ - "from dspy.evaluate.evaluate import Evaluate\n", - "\n", - "# Set up the `evaluate_on_hotpotqa` function. We'll use this many times below.\n", - "evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - "\n", - "# Evaluate the `compiled_knn` program with the `answer_exact_match` metric.\n", - "metric = dspy.evaluate.answer_exact_match\n", - "\n", - " \n", - "evaluate_on_hotpotqa(compiled_knn, metric)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.18 64-bit ('pipenv')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "1d3dd57b136d599382d2667f19e9aed8a929301e79008dd54c75c965d79714ea" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/llamaindex/dspy_llamaindex_rag.ipynb b/examples/outdated_v2.4_examples/llamaindex/dspy_llamaindex_rag.ipynb index 712c4b6a04..1d71bd5e57 100644 --- a/examples/outdated_v2.4_examples/llamaindex/dspy_llamaindex_rag.ipynb +++ b/examples/outdated_v2.4_examples/llamaindex/dspy_llamaindex_rag.ipynb @@ -1,879 +1,879 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "f8eb12c8", - "metadata": {}, - "source": [ - "# DEPRECATION WARNING\n", - "\n", - "This integration with LlamaIndex is no longer supported.\n", - "\n", - "\n", - "----" - ] - }, - { - "cell_type": "markdown", - "id": "849dbd89-ce04-4a18-84fb-c19f3db5504a", - "metadata": {}, - "source": [ - "# Building optimized RAG with LlamaIndex + DSPy\n", - "\n", - "This notebook provides a comprehensive overview of LlamaIndex + DSPy integrations.\n", - "\n", - "We show **three** core integrations:\n", - "1. **Build and optimize Query Pipelines with DSPy predictors**: The first section shows you how to write DSPy code to define signatures for LLM inputs/outputs. Then port over these components to overall workflows within LlamaIndex Query pipelines, and then end-to-end optimize the entire system.\n", - "\n", - "2. **Build and optimize Query Pipelines with Existing Prompts**: Instead of writing DSPy signatures, you can just define a LlamaIndex prompt template, and our converter will auto-optimize it for you.\n", - "\n", - "3. **Port over DSPy-Optimized Prompts to any LlamaIndex Module**: Possible through our `DSPyPromptTemplate` - translate an optimized prompt through DSPy into any module that requires prompts in LlamaIndex." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa558b8d", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install llama-index==0.10.44" - ] - }, - { - "cell_type": "markdown", - "id": "3d223313-af5b-4155-8896-c24aa4cb6925", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Define the LLM setting for DSPy (note: this is separate from using the LlamaIndex LLMs), and also the answer signature." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c5c50d5e-7046-40c5-bf3e-a8d2a2a2c6f8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "turbo = dspy.OpenAI(model='gpt-3.5-turbo')\n", - "dspy.settings.configure(lm=turbo)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ac68be4f-36b1-4054-99dd-707ce42f61a4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import dspy\n", - "\n", - "class GenerateAnswer(dspy.Signature):\n", - " \"\"\"Answer questions with short factoid answers.\"\"\"\n", - "\n", - " context_str = dspy.InputField(desc=\"contains relevant facts\")\n", - " query_str = dspy.InputField()\n", - " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" - ] - }, - { - "cell_type": "markdown", - "id": "eec2a981-2ee1-497a-b675-15e1029183f6", - "metadata": {}, - "source": [ - "## [Part 1] Build and Optimize a Query Pipeline with DSPy Modules\n", - "\n", - "Use our DSPy query components to plugin DSPy prompts/LLMs, stitch together with our query pipeline abstraction.\n", - "\n", - "Any query pipeline can be plugged into our `LlamaIndexModule`. We can then let DSPy optimize the entire thing e2e." - ] - }, - { - "cell_type": "markdown", - "id": "113f0637-993f-4bef-bd8a-1122951cf7f8", - "metadata": {}, - "source": [ - "#### Load Data, Build Index" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "55c9d434-c8f6-403e-9499-36059eb09907", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-06-17 23:54:09-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8001::154, 2606:50c0:8002::154, 2606:50c0:8000::154, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8001::154|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 75042 (73K) [text/plain]\n", - "Saving to: ‘paul_graham_essay.txt’\n", - "\n", - "paul_graham_essay.t 100%[===================>] 73.28K --.-KB/s in 0.01s \n", - "\n", - "2024-06-17 23:54:10 (7.48 MB/s) - ‘paul_graham_essay.txt’ saved [75042/75042]\n", - "\n" - ] - } - ], - "source": [ - "# port it over to another index (paul graham example) \n", - "\n", - "!wget https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt -O paul_graham_essay.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "367a7985-2f89-47be-b2af-14fef2e845c9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n", - "\n", - "reader = SimpleDirectoryReader(input_files=[\"paul_graham_essay.txt\"])\n", - "docs = reader.load_data()\n", - "\n", - "index = VectorStoreIndex.from_documents(docs)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c9a68920-6517-465b-8c0c-4c6e0b8390a3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "retriever = index.as_retriever(similarity_top_k=2)" - ] - }, - { - "cell_type": "markdown", - "id": "a8d43c6c-9dee-4cbc-b39d-5d8ad617b6ea", - "metadata": {}, - "source": [ - "#### Build Query Pipeline\n", - "\n", - "Replace the synthesis piece with the DSPy component (make sure GenerateAnswer matches signature of inputs/outputs)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ac7df48b-9369-4f17-8542-0f8afd9e621e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from llama_index.core.query_pipeline import QueryPipeline as QP, InputComponent, FnComponent\n", - "from dspy.predict.llamaindex import DSPyComponent, LlamaIndexModule\n", - "\n", - "dspy_component = DSPyComponent(\n", - " dspy.ChainOfThought(GenerateAnswer)\n", - ")\n", - "\n", - "retriever_post = FnComponent(\n", - " lambda contexts: \"\\n\\n\".join([n.get_content() for n in contexts])\n", - ")\n", - "\n", - "\n", - "p = QP(verbose=True)\n", - "p.add_modules(\n", - " {\n", - " \"input\": InputComponent(),\n", - " \"retriever\": retriever,\n", - " \"retriever_post\": retriever_post,\n", - " \"synthesizer\": dspy_component,\n", - " }\n", - ")\n", - "p.add_link(\"input\", \"retriever\")\n", - "p.add_link(\"retriever\", \"retriever_post\")\n", - "p.add_link(\"input\", \"synthesizer\", dest_key=\"query_str\")\n", - "p.add_link(\"retriever_post\", \"synthesizer\", dest_key=\"context_str\")\n", - "\n", - "\n", - "dspy_qp = LlamaIndexModule(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b457f1db-3315-476c-8673-bf19ad6e1657", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", - "query_str: what did the author do in YC\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", - "input: what did the author do in YC\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", - "contexts: [NodeWithScore(node=TextNode(id_='49a290f5-5f29-413c-97e7-9fdf15169cf4', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", - "query_str: what did the author do in YC\n", - "context_str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, what...\n", - "\n", - "\u001b[0m" - ] - } - ], - "source": [ - "output = dspy_qp(query_str=\"what did the author do in YC\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7b78501f-e281-4e68-bdb6-1eeca6bf2464", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Prediction(\n", - " answer='Worked with startups, funded them.'\n", - ")" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output" - ] - }, - { - "cell_type": "markdown", - "id": "691d5d86-1ce0-46fd-9fbc-eca68043c935", - "metadata": {}, - "source": [ - "#### Optimize Query Pipeline\n", - "\n", - "Let's try optimizing the query pipeline with few-shot examples.\n", - "\n", - "We define a toy dataset with two examples. We then use our `SemanticSimilarityEvaluator` to define a custom eval function to pass to the DSPy teleprompter.\n", - "- Because our passing threshold is set to very low, every example should pass with a reasonable LLM. \n", - "- What this practically means is that all training examples will be added as few-shot examples to the prompt." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "ec3a783c-2025-4b1c-9f5b-cdcde7211ace", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from dspy import Example\n", - "\n", - "train_examples = [\n", - " Example(query_str=\"What did the author do growing up?\", answer=\"The author wrote short stories and also worked on programming.\"),\n", - " Example(query_str=\"What did the author do during his time at YC?\", answer=\"organizing a Summer Founders Program, funding startups, writing essays, working on a new version of Arc, creating Hacker News, and developing internal software for YC\")\n", - "]\n", - "\n", - "train_examples = [t.with_inputs(\"query_str\") for t in train_examples]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2e43a18c-5569-4947-a02d-08f52331b134", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import nest_asyncio\n", - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "502cc403-8ebc-4bbf-be7c-c15385909b7a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00 Running module input with input: \n", - "query_str: What did the author do growing up?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", - "input: What did the author do growing up?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", - "contexts: [NodeWithScore(node=TextNode(id_='20a73a69-f604-450e-b07d-cace28b471a5', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", - "query_str: What did the author do growing up?\n", - "context_str: What I Worked On\n", - "\n", - "February 2021\n", - "\n", - "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to...\n", - "\n", - "\u001b[0m" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 50%|████████████████████████████████ | 1/2 [00:00<00:00, 1.85it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", - "query_str: What did the author do during his time at YC?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", - "input: What did the author do during his time at YC?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", - "contexts: [NodeWithScore(node=TextNode(id_='49a290f5-5f29-413c-97e7-9fdf15169cf4', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", - "query_str: What did the author do during his time at YC?\n", - "context_str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, what...\n", - "\n", - "\u001b[0m" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.07it/s]\n" - ] - } - ], - "source": [ - "from dspy.teleprompt import BootstrapFewShot\n", - "from llama_index.core.evaluation import SemanticSimilarityEvaluator\n", - "\n", - "evaluator = SemanticSimilarityEvaluator(similarity_threshold=0.5)\n", - "\n", - "# Validation logic: check that the predicted answer is correct.\n", - "# Also check that the retrieved context does actually contain that answer.\n", - "def validate_context_and_answer(example, pred, trace=None):\n", - " result = evaluator.evaluate(response=pred.answer, reference=example.answer)\n", - " return result.passing\n", - "\n", - "# Set up a basic teleprompter, which will compile our RAG program.\n", - "teleprompter = BootstrapFewShot(max_labeled_demos=0, metric=validate_context_and_answer)\n", - "\n", - "# Compile!\n", - "compiled_dspy_qp = teleprompter.compile(dspy_qp, trainset=train_examples)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "3a147c3e-febe-43fa-bca5-152432d3662b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", - "query_str: How did PG meet Jessica Livingston?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", - "input: How did PG meet Jessica Livingston?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", - "contexts: [NodeWithScore(node=TextNode(id_='465a8143-6740-4bbd-8e6c-bb5eba4bb5a3', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", - "query_str: How did PG meet Jessica Livingston?\n", - "context_str: Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I al...\n", - "\n", - "\u001b[0m" - ] - }, - { - "data": { - "text/plain": [ - "Prediction(\n", - " answer='Met at a party in 2003.'\n", - ")" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# test this out \n", - "compiled_dspy_qp(query_str=\"How did PG meet Jessica Livingston?\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96a2ba4e-6677-44e7-a474-db382ad1a56a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# [optional]: inspect history\n", - "turbo.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "id": "9895ceff-5aeb-4285-8ede-99bb35a50a45", - "metadata": {}, - "source": [ - "## [Part 2] Build and Optimize Query Pipelines with Existing Prompts\n", - "\n", - "Build a query pipeline similar to the previous section. But instead of directly using DSPy signatures/predictors, we can build DSPyComponent modules from LlamaIndex prompts directly. \n", - "\n", - "This allows you to write any LlamaIndex prompt and trust that it'll be optimized in DSPy." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "8efff3a9-77d9-4a89-a0f7-207d63e73ab0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from llama_index.core.prompts import PromptTemplate\n", - "\n", - "# let's try a fun prompt that writes in Shakespeare! \n", - "qa_prompt_template = PromptTemplate(\"\"\"\\\n", - "Context information is below.\n", - "---------------------\n", - "{context_str}\n", - "---------------------\n", - "Given the context information and not prior knowledge, \\\n", - "answer the query.\n", - "\n", - "Write in the style of a Shakespearean sonnet.\n", - "\n", - "Query: {query_str}\n", - "Answer: \n", - "\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "a84aa7b2-b9b8-4740-b862-9073470c31c2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from llama_index.core.query_pipeline import QueryPipeline as QP, InputComponent, FnComponent\n", - "from dspy.predict.llamaindex import DSPyComponent, LlamaIndexModule\n", - "\n", - "dspy_component = DSPyComponent.from_prompt(qa_prompt_template)\n", - "\n", - "retriever_post = FnComponent(\n", - " lambda contexts: \"\\n\\n\".join([n.get_content() for n in contexts])\n", - ")\n", - "\n", - "\n", - "p = QP(verbose=True)\n", - "p.add_modules(\n", - " {\n", - " \"input\": InputComponent(),\n", - " \"retriever\": retriever,\n", - " \"retriever_post\": retriever_post,\n", - " \"synthesizer\": dspy_component,\n", - " }\n", - ")\n", - "p.add_link(\"input\", \"retriever\")\n", - "p.add_link(\"retriever\", \"retriever_post\")\n", - "p.add_link(\"input\", \"synthesizer\", dest_key=\"query_str\")\n", - "p.add_link(\"retriever_post\", \"synthesizer\", dest_key=\"context_str\")\n", - "\n", - "\n", - "dspy_qp = LlamaIndexModule(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "e23b7662-cb87-4906-bbaf-e2ffd74e20d8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "StringSignature(context_str, query_str -> sonnet_answer\n", - " instructions='Essential Instructions: Provide an answer to the query based solely on the context information provided. The response should be written in the style of a Shakespearean sonnet, which typically consists of 14 lines written in iambic pentameter, with a rhyme scheme of ABABCDCDEFEFGG.'\n", - " context_str = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context Str:', 'desc': '${context_str}'})\n", - " query_str = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Query Str:', 'desc': '${query_str}'})\n", - " sonnet_answer = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'output', 'prefix': 'Sonnet Answer:', 'desc': '${sonnet_answer}'})\n", - ")" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the inferred signature\n", - "dspy_component.predict_module.signature" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "6044855f-cdd2-43f0-9d2e-e7ba64a28e27", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00 Running module input with input: \n", - "query_str: What did the author do growing up?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", - "input: What did the author do growing up?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", - "contexts: [NodeWithScore(node=TextNode(id_='20a73a69-f604-450e-b07d-cace28b471a5', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", - "query_str: What did the author do growing up?\n", - "context_str: What I Worked On\n", - "\n", - "February 2021\n", - "\n", - "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to...\n", - "\n", - "\u001b[0m" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 50%|████████████████████████████████ | 1/2 [00:00<00:00, 1.94it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", - "query_str: What did the author do during his time at YC?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", - "input: What did the author do during his time at YC?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", - "contexts: [NodeWithScore(node=TextNode(id_='49a290f5-5f29-413c-97e7-9fdf15169cf4', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", - "query_str: What did the author do during his time at YC?\n", - "context_str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, what...\n", - "\n", - "\u001b[0m" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.95it/s]\n" - ] - } - ], - "source": [ - "from dspy.teleprompt import BootstrapFewShot\n", - "from llama_index.core.evaluation import SemanticSimilarityEvaluator\n", - "from dspy import Example\n", - "\n", - "output_key = \"sonnet_answer\"\n", - "train_example_dicts = [\n", - " {\"query_str\": \"What did the author do growing up?\", output_key: \"The author wrote short stories and also worked on programming.\"},\n", - " {\"query_str\": \"What did the author do during his time at YC?\", output_key: \"organizing a Summer Founders Program, funding startups, writing essays, working on a new version of Arc, creating Hacker News, and developing internal software for YC\"}\n", - "]\n", - "train_examples = [Example(**t).with_inputs(\"query_str\") for t in train_example_dicts]\n", - "\n", - "evaluator = SemanticSimilarityEvaluator(similarity_threshold=0.5)\n", - "# Validation logic: check that the predicted answer is correct.\n", - "# Also check that the retrieved context does actually contain that answer.\n", - "def validate_context_and_answer(example, pred, trace=None):\n", - " result = evaluator.evaluate(response=getattr(pred, output_key), reference=getattr(example, output_key))\n", - " return result.passing\n", - "\n", - "# Set up a basic teleprompter, which will compile our RAG program.\n", - "teleprompter = BootstrapFewShot(max_labeled_demos=0, metric=validate_context_and_answer)\n", - "\n", - "# Compile!\n", - "compiled_dspy_qp = teleprompter.compile(dspy_qp, trainset=train_examples)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "16fcb28c-c442-4f1d-8876-9c6c6c47eda0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", - "query_str: How did PG meet Jessica Livingston?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", - "input: How did PG meet Jessica Livingston?\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", - "contexts: [NodeWithScore(node=TextNode(id_='465a8143-6740-4bbd-8e6c-bb5eba4bb5a3', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", - "query_str: How did PG meet Jessica Livingston?\n", - "context_str: Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I al...\n", - "\n", - "\u001b[0m" - ] - }, - { - "data": { - "text/plain": [ - "Prediction(\n", - " sonnet_answer=\"In the midst of a party, bright and gay,\\nA clever scheme brought guests together, true.\\nAmong them, Jessica, in a charming way,\\nCaught the author's eye, a friendship grew.\\n\\nShe, a marketer in a bank of old,\\nDiscovered startup tales, colorful and bold.\\nAs the bank faced troubles, she sought anew,\\nVenture capital's flaws came into view.\\n\\nTheir paths converged on a fateful night,\\nAt the corner of Garden and Walker streets.\\nA decision made, a future bright,\\nTo start an investment firm, their feats.\\n\\nThus, through ignorance and boldness, they began,\\nA journey in angel investing, a novel plan.\"\n", - ")" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# test this out \n", - "compiled_dspy_qp(query_str=\"How did PG meet Jessica Livingston?\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2b1b5f7-66d0-49d2-9bae-ca94f12a124d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# [optional]: inspect the optimized prompt \n", - "turbo.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "id": "d4daae77-bf36-418f-9a80-c9c0fb6f20ec", - "metadata": {}, - "source": [ - "## [Part 3] Port over Optimized Prompts to LlamaIndex using the DSPy Prompt Template\n", - "\n", - "Extract out a prompt from an existing compiled DSPy module, and then port it over to any LlamaIndex pipeline! \n", - "\n", - "In the example below we use our `DSPyPromptTemplate` to extract out the compiled few-shot prompt from the optimized query pipeline. \n", - "\n", - "We then plug it into a separate query engine over the PG essay." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "8180ea2b-412a-41a6-a707-f2945d9dcda0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from dspy.predict.llamaindex import DSPyPromptTemplate\n", - "\n", - "# NOTE: you cannot do DSPyPromptTemplate(dspy_component.predict_module) - the predict_module is replaced.\n", - "qa_prompt_tmpl = DSPyPromptTemplate(compiled_dspy_qp.query_pipeline.module_dict[\"synthesizer\"].predict_module)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "e3c86e63-f225-4540-b1a2-a48a13e29756", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Answer questions with short factoid answers.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context Str: contains relevant facts\n", - "Query Str: ${query_str}\n", - "Answer: often between 1 and 5 words\n", - "\n", - "---\n", - "\n", - "Context Str: What I Worked On February 2021 Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights. The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer. I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear. With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1] The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer. Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter. Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored. I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI. AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words. I didn't want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he'd found such a spectacular way to get out of grad school. Then one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn't have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay \"Yes, I think so. I'll give you something to read in a few days.\" I picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There's a whole world there that's barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely. Meanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went. I'd applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design. Toward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they'd sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I'd done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian. Only stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don't know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2] I'm only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn't require the faculty to teach anything, and in return the faculty wouldn't require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines. Our model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She'd copy an obscure old painting out of a book, and then he'd take the copy and maltreat it to make it look old. [3] While I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time.\n", - "Query Str: What did the author do growing up?\n", - "Answer: wrote short stories, programmed on an IBM 1401 in 9th grade, built a microcomputer from a kit, wrote simple games and a word processor on a TRS-80, studied philosophy in college, switched to AI, and painted still lives at the Accademia di Belli Arti\n", - "\n", - "---\n", - "\n", - "Context Str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it. There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\" He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I'd better work very hard. One day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn't the last cool thing you do.\" At the time I didn't understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life's work or I'd have to leave eventually. And it wasn't, so I would. In the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else. I asked Jessica if she wanted to be president, but she didn't, so we decided we'd try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn't be controlled by the founders. So if Sam said yes, we'd let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners. When we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned. She died on January 15, 2014. We knew this was coming, but it was still hard when it did. I kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.) What should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I don't think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs. The deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16] Fairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them. As YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another's customers. We used to refer jokingly to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates. I had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things. In the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn't startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one's intellectual curiosity. HN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I'd had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one's work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17] As well as HN, I wrote all of YC's internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn't have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC. YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it. There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\"\n", - "Query Str: What did the author do during his time at YC?\n", - "Answer: Worked with startups, solved their problems, wrote essays, and developed internal software.\n", - "\n", - "---\n", - "\n", - "Context Str: this is my context\n", - "Query Str: hello?\n", - "Answer:\n" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "f8eb12c8", + "metadata": {}, + "source": [ + "# DEPRECATION WARNING\n", + "\n", + "This integration with LlamaIndex is no longer supported.\n", + "\n", + "\n", + "----" + ] + }, + { + "cell_type": "markdown", + "id": "849dbd89-ce04-4a18-84fb-c19f3db5504a", + "metadata": {}, + "source": [ + "# Building optimized RAG with LlamaIndex + DSPy\n", + "\n", + "This notebook provides a comprehensive overview of LlamaIndex + DSPy integrations.\n", + "\n", + "We show **three** core integrations:\n", + "1. **Build and optimize Query Pipelines with DSPy predictors**: The first section shows you how to write DSPy code to define signatures for LLM inputs/outputs. Then port over these components to overall workflows within LlamaIndex Query pipelines, and then end-to-end optimize the entire system.\n", + "\n", + "2. **Build and optimize Query Pipelines with Existing Prompts**: Instead of writing DSPy signatures, you can just define a LlamaIndex prompt template, and our converter will auto-optimize it for you.\n", + "\n", + "3. **Port over DSPy-Optimized Prompts to any LlamaIndex Module**: Possible through our `DSPyPromptTemplate` - translate an optimized prompt through DSPy into any module that requires prompts in LlamaIndex." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa558b8d", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index==0.10.44" + ] + }, + { + "cell_type": "markdown", + "id": "3d223313-af5b-4155-8896-c24aa4cb6925", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Define the LLM setting for DSPy (note: this is separate from using the LlamaIndex LLMs), and also the answer signature." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c5c50d5e-7046-40c5-bf3e-a8d2a2a2c6f8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import dspy\n", + "\n", + "turbo = dspy.OpenAI(model='gpt-3.5-turbo')\n", + "dspy.settings.configure(lm=turbo)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ac68be4f-36b1-4054-99dd-707ce42f61a4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import dspy\n", + "\n", + "class GenerateAnswer(dspy.Signature):\n", + " \"\"\"Answer questions with short factoid answers.\"\"\"\n", + "\n", + " context_str = dspy.InputField(desc=\"contains relevant facts\")\n", + " query_str = dspy.InputField()\n", + " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")" + ] + }, + { + "cell_type": "markdown", + "id": "eec2a981-2ee1-497a-b675-15e1029183f6", + "metadata": {}, + "source": [ + "## [Part 1] Build and Optimize a Query Pipeline with DSPy Modules\n", + "\n", + "Use our DSPy query components to plugin DSPy prompts/LLMs, stitch together with our query pipeline abstraction.\n", + "\n", + "Any query pipeline can be plugged into our `LlamaIndexModule`. We can then let DSPy optimize the entire thing e2e." + ] + }, + { + "cell_type": "markdown", + "id": "113f0637-993f-4bef-bd8a-1122951cf7f8", + "metadata": {}, + "source": [ + "#### Load Data, Build Index" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "55c9d434-c8f6-403e-9499-36059eb09907", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-06-17 23:54:09-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8001::154, 2606:50c0:8002::154, 2606:50c0:8000::154, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8001::154|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: \u2018paul_graham_essay.txt\u2019\n", + "\n", + "paul_graham_essay.t 100%[===================>] 73.28K --.-KB/s in 0.01s \n", + "\n", + "2024-06-17 23:54:10 (7.48 MB/s) - \u2018paul_graham_essay.txt\u2019 saved [75042/75042]\n", + "\n" + ] + } + ], + "source": [ + "# port it over to another index (paul graham example) \n", + "\n", + "!wget https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt -O paul_graham_essay.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "367a7985-2f89-47be-b2af-14fef2e845c9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n", + "\n", + "reader = SimpleDirectoryReader(input_files=[\"paul_graham_essay.txt\"])\n", + "docs = reader.load_data()\n", + "\n", + "index = VectorStoreIndex.from_documents(docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c9a68920-6517-465b-8c0c-4c6e0b8390a3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "retriever = index.as_retriever(similarity_top_k=2)" + ] + }, + { + "cell_type": "markdown", + "id": "a8d43c6c-9dee-4cbc-b39d-5d8ad617b6ea", + "metadata": {}, + "source": [ + "#### Build Query Pipeline\n", + "\n", + "Replace the synthesis piece with the DSPy component (make sure GenerateAnswer matches signature of inputs/outputs)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ac7df48b-9369-4f17-8542-0f8afd9e621e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from llama_index.core.query_pipeline import QueryPipeline as QP, InputComponent, FnComponent\n", + "from dspy.predict.llamaindex import DSPyComponent, LlamaIndexModule\n", + "\n", + "dspy_component = DSPyComponent(\n", + " dspy.ChainOfThought(GenerateAnswer)\n", + ")\n", + "\n", + "retriever_post = FnComponent(\n", + " lambda contexts: \"\\n\\n\".join([n.get_content() for n in contexts])\n", + ")\n", + "\n", + "\n", + "p = QP(verbose=True)\n", + "p.add_modules(\n", + " {\n", + " \"input\": InputComponent(),\n", + " \"retriever\": retriever,\n", + " \"retriever_post\": retriever_post,\n", + " \"synthesizer\": dspy_component,\n", + " }\n", + ")\n", + "p.add_link(\"input\", \"retriever\")\n", + "p.add_link(\"retriever\", \"retriever_post\")\n", + "p.add_link(\"input\", \"synthesizer\", dest_key=\"query_str\")\n", + "p.add_link(\"retriever_post\", \"synthesizer\", dest_key=\"context_str\")\n", + "\n", + "\n", + "dspy_qp = LlamaIndexModule(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b457f1db-3315-476c-8673-bf19ad6e1657", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", + "query_str: what did the author do in YC\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", + "input: what did the author do in YC\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", + "contexts: [NodeWithScore(node=TextNode(id_='49a290f5-5f29-413c-97e7-9fdf15169cf4', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", + "query_str: what did the author do in YC\n", + "context_str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, what...\n", + "\n", + "\u001b[0m" + ] + } + ], + "source": [ + "output = dspy_qp(query_str=\"what did the author do in YC\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7b78501f-e281-4e68-bdb6-1eeca6bf2464", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " answer='Worked with startups, funded them.'\n", + ")" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output" + ] + }, + { + "cell_type": "markdown", + "id": "691d5d86-1ce0-46fd-9fbc-eca68043c935", + "metadata": {}, + "source": [ + "#### Optimize Query Pipeline\n", + "\n", + "Let's try optimizing the query pipeline with few-shot examples.\n", + "\n", + "We define a toy dataset with two examples. We then use our `SemanticSimilarityEvaluator` to define a custom eval function to pass to the DSPy teleprompter.\n", + "- Because our passing threshold is set to very low, every example should pass with a reasonable LLM. \n", + "- What this practically means is that all training examples will be added as few-shot examples to the prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ec3a783c-2025-4b1c-9f5b-cdcde7211ace", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from dspy import Example\n", + "\n", + "train_examples = [\n", + " Example(query_str=\"What did the author do growing up?\", answer=\"The author wrote short stories and also worked on programming.\"),\n", + " Example(query_str=\"What did the author do during his time at YC?\", answer=\"organizing a Summer Founders Program, funding startups, writing essays, working on a new version of Arc, creating Hacker News, and developing internal software for YC\")\n", + "]\n", + "\n", + "train_examples = [t.with_inputs(\"query_str\") for t in train_examples]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "2e43a18c-5569-4947-a02d-08f52331b134", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "502cc403-8ebc-4bbf-be7c-c15385909b7a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:00 Running module input with input: \n", + "query_str: What did the author do growing up?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", + "input: What did the author do growing up?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", + "contexts: [NodeWithScore(node=TextNode(id_='20a73a69-f604-450e-b07d-cace28b471a5', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", + "query_str: What did the author do growing up?\n", + "context_str: What I Worked On\n", + "\n", + "February 2021\n", + "\n", + "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to...\n", + "\n", + "\u001b[0m" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 1/2 [00:00<00:00, 1.85it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", + "query_str: What did the author do during his time at YC?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", + "input: What did the author do during his time at YC?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", + "contexts: [NodeWithScore(node=TextNode(id_='49a290f5-5f29-413c-97e7-9fdf15169cf4', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", + "query_str: What did the author do during his time at YC?\n", + "context_str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, what...\n", + "\n", + "\u001b[0m" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:00<00:00, 2.07it/s]\n" + ] + } + ], + "source": [ + "from dspy.teleprompt import BootstrapFewShot\n", + "from llama_index.core.evaluation import SemanticSimilarityEvaluator\n", + "\n", + "evaluator = SemanticSimilarityEvaluator(similarity_threshold=0.5)\n", + "\n", + "# Validation logic: check that the predicted answer is correct.\n", + "# Also check that the retrieved context does actually contain that answer.\n", + "def validate_context_and_answer(example, pred, trace=None):\n", + " result = evaluator.evaluate(response=pred.answer, reference=example.answer)\n", + " return result.passing\n", + "\n", + "# Set up a basic teleprompter, which will compile our RAG program.\n", + "teleprompter = BootstrapFewShot(max_labeled_demos=0, metric=validate_context_and_answer)\n", + "\n", + "# Compile!\n", + "compiled_dspy_qp = teleprompter.compile(dspy_qp, trainset=train_examples)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3a147c3e-febe-43fa-bca5-152432d3662b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", + "query_str: How did PG meet Jessica Livingston?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", + "input: How did PG meet Jessica Livingston?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", + "contexts: [NodeWithScore(node=TextNode(id_='465a8143-6740-4bbd-8e6c-bb5eba4bb5a3', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", + "query_str: How did PG meet Jessica Livingston?\n", + "context_str: Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I al...\n", + "\n", + "\u001b[0m" + ] + }, + { + "data": { + "text/plain": [ + "Prediction(\n", + " answer='Met at a party in 2003.'\n", + ")" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# test this out \n", + "compiled_dspy_qp(query_str=\"How did PG meet Jessica Livingston?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96a2ba4e-6677-44e7-a474-db382ad1a56a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# [optional]: inspect history\n", + "turbo.inspect_history(n=1)" + ] + }, + { + "cell_type": "markdown", + "id": "9895ceff-5aeb-4285-8ede-99bb35a50a45", + "metadata": {}, + "source": [ + "## [Part 2] Build and Optimize Query Pipelines with Existing Prompts\n", + "\n", + "Build a query pipeline similar to the previous section. But instead of directly using DSPy signatures/predictors, we can build DSPyComponent modules from LlamaIndex prompts directly. \n", + "\n", + "This allows you to write any LlamaIndex prompt and trust that it'll be optimized in DSPy." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8efff3a9-77d9-4a89-a0f7-207d63e73ab0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from llama_index.core.prompts import PromptTemplate\n", + "\n", + "# let's try a fun prompt that writes in Shakespeare! \n", + "qa_prompt_template = PromptTemplate(\"\"\"\\\n", + "Context information is below.\n", + "---------------------\n", + "{context_str}\n", + "---------------------\n", + "Given the context information and not prior knowledge, \\\n", + "answer the query.\n", + "\n", + "Write in the style of a Shakespearean sonnet.\n", + "\n", + "Query: {query_str}\n", + "Answer: \n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a84aa7b2-b9b8-4740-b862-9073470c31c2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from llama_index.core.query_pipeline import QueryPipeline as QP, InputComponent, FnComponent\n", + "from dspy.predict.llamaindex import DSPyComponent, LlamaIndexModule\n", + "\n", + "dspy_component = DSPyComponent.from_prompt(qa_prompt_template)\n", + "\n", + "retriever_post = FnComponent(\n", + " lambda contexts: \"\\n\\n\".join([n.get_content() for n in contexts])\n", + ")\n", + "\n", + "\n", + "p = QP(verbose=True)\n", + "p.add_modules(\n", + " {\n", + " \"input\": InputComponent(),\n", + " \"retriever\": retriever,\n", + " \"retriever_post\": retriever_post,\n", + " \"synthesizer\": dspy_component,\n", + " }\n", + ")\n", + "p.add_link(\"input\", \"retriever\")\n", + "p.add_link(\"retriever\", \"retriever_post\")\n", + "p.add_link(\"input\", \"synthesizer\", dest_key=\"query_str\")\n", + "p.add_link(\"retriever_post\", \"synthesizer\", dest_key=\"context_str\")\n", + "\n", + "\n", + "dspy_qp = LlamaIndexModule(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e23b7662-cb87-4906-bbaf-e2ffd74e20d8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "StringSignature(context_str, query_str -> sonnet_answer\n", + " instructions='Essential Instructions: Provide an answer to the query based solely on the context information provided. The response should be written in the style of a Shakespearean sonnet, which typically consists of 14 lines written in iambic pentameter, with a rhyme scheme of ABABCDCDEFEFGG.'\n", + " context_str = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context Str:', 'desc': '${context_str}'})\n", + " query_str = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Query Str:', 'desc': '${query_str}'})\n", + " sonnet_answer = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'output', 'prefix': 'Sonnet Answer:', 'desc': '${sonnet_answer}'})\n", + ")" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the inferred signature\n", + "dspy_component.predict_module.signature" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "6044855f-cdd2-43f0-9d2e-e7ba64a28e27", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:00 Running module input with input: \n", + "query_str: What did the author do growing up?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", + "input: What did the author do growing up?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", + "contexts: [NodeWithScore(node=TextNode(id_='20a73a69-f604-450e-b07d-cace28b471a5', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", + "query_str: What did the author do growing up?\n", + "context_str: What I Worked On\n", + "\n", + "February 2021\n", + "\n", + "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to...\n", + "\n", + "\u001b[0m" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 1/2 [00:00<00:00, 1.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", + "query_str: What did the author do during his time at YC?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", + "input: What did the author do during his time at YC?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", + "contexts: [NodeWithScore(node=TextNode(id_='49a290f5-5f29-413c-97e7-9fdf15169cf4', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", + "query_str: What did the author do during his time at YC?\n", + "context_str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, what...\n", + "\n", + "\u001b[0m" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:01<00:00, 1.95it/s]\n" + ] + } + ], + "source": [ + "from dspy.teleprompt import BootstrapFewShot\n", + "from llama_index.core.evaluation import SemanticSimilarityEvaluator\n", + "from dspy import Example\n", + "\n", + "output_key = \"sonnet_answer\"\n", + "train_example_dicts = [\n", + " {\"query_str\": \"What did the author do growing up?\", output_key: \"The author wrote short stories and also worked on programming.\"},\n", + " {\"query_str\": \"What did the author do during his time at YC?\", output_key: \"organizing a Summer Founders Program, funding startups, writing essays, working on a new version of Arc, creating Hacker News, and developing internal software for YC\"}\n", + "]\n", + "train_examples = [Example(**t).with_inputs(\"query_str\") for t in train_example_dicts]\n", + "\n", + "evaluator = SemanticSimilarityEvaluator(similarity_threshold=0.5)\n", + "# Validation logic: check that the predicted answer is correct.\n", + "# Also check that the retrieved context does actually contain that answer.\n", + "def validate_context_and_answer(example, pred, trace=None):\n", + " result = evaluator.evaluate(response=getattr(pred, output_key), reference=getattr(example, output_key))\n", + " return result.passing\n", + "\n", + "# Set up a basic teleprompter, which will compile our RAG program.\n", + "teleprompter = BootstrapFewShot(max_labeled_demos=0, metric=validate_context_and_answer)\n", + "\n", + "# Compile!\n", + "compiled_dspy_qp = teleprompter.compile(dspy_qp, trainset=train_examples)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "16fcb28c-c442-4f1d-8876-9c6c6c47eda0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", + "query_str: How did PG meet Jessica Livingston?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever with input: \n", + "input: How did PG meet Jessica Livingston?\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module retriever_post with input: \n", + "contexts: [NodeWithScore(node=TextNode(id_='465a8143-6740-4bbd-8e6c-bb5eba4bb5a3', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain'...\n", + "\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module synthesizer with input: \n", + "query_str: How did PG meet Jessica Livingston?\n", + "context_str: Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I al...\n", + "\n", + "\u001b[0m" + ] + }, + { + "data": { + "text/plain": [ + "Prediction(\n", + " sonnet_answer=\"In the midst of a party, bright and gay,\\nA clever scheme brought guests together, true.\\nAmong them, Jessica, in a charming way,\\nCaught the author's eye, a friendship grew.\\n\\nShe, a marketer in a bank of old,\\nDiscovered startup tales, colorful and bold.\\nAs the bank faced troubles, she sought anew,\\nVenture capital's flaws came into view.\\n\\nTheir paths converged on a fateful night,\\nAt the corner of Garden and Walker streets.\\nA decision made, a future bright,\\nTo start an investment firm, their feats.\\n\\nThus, through ignorance and boldness, they began,\\nA journey in angel investing, a novel plan.\"\n", + ")" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# test this out \n", + "compiled_dspy_qp(query_str=\"How did PG meet Jessica Livingston?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2b1b5f7-66d0-49d2-9bae-ca94f12a124d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# [optional]: inspect the optimized prompt \n", + "turbo.inspect_history(n=1)" + ] + }, + { + "cell_type": "markdown", + "id": "d4daae77-bf36-418f-9a80-c9c0fb6f20ec", + "metadata": {}, + "source": [ + "## [Part 3] Port over Optimized Prompts to LlamaIndex using the DSPy Prompt Template\n", + "\n", + "Extract out a prompt from an existing compiled DSPy module, and then port it over to any LlamaIndex pipeline! \n", + "\n", + "In the example below we use our `DSPyPromptTemplate` to extract out the compiled few-shot prompt from the optimized query pipeline. \n", + "\n", + "We then plug it into a separate query engine over the PG essay." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8180ea2b-412a-41a6-a707-f2945d9dcda0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from dspy.predict.llamaindex import DSPyPromptTemplate\n", + "\n", + "# NOTE: you cannot do DSPyPromptTemplate(dspy_component.predict_module) - the predict_module is replaced.\n", + "qa_prompt_tmpl = DSPyPromptTemplate(compiled_dspy_qp.query_pipeline.module_dict[\"synthesizer\"].predict_module)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e3c86e63-f225-4540-b1a2-a48a13e29756", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Answer questions with short factoid answers.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context Str: contains relevant facts\n", + "Query Str: ${query_str}\n", + "Answer: often between 1 and 5 words\n", + "\n", + "---\n", + "\n", + "Context Str: What I Worked On February 2021 Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines \u2014 CPU, disk drives, printer, card reader \u2014 sitting up on a raised floor under bright fluorescent lights. The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer. I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear. With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1] The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer. Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter. Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored. I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI. AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words. I didn't want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he'd found such a spectacular way to get out of grad school. Then one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn't have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay \"Yes, I think so. I'll give you something to read in a few days.\" I picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There's a whole world there that's barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely. Meanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went. I'd applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design. Toward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they'd sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I'd done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian. Only stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don't know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2] I'm only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn't require the faculty to teach anything, and in return the faculty wouldn't require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines. Our model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She'd copy an obscure old painting out of a book, and then he'd take the copy and maltreat it to make it look old. [3] While I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time.\n", + "Query Str: What did the author do growing up?\n", + "Answer: wrote short stories, programmed on an IBM 1401 in 9th grade, built a microcomputer from a kit, wrote simple games and a word processor on a TRS-80, studied philosophy in college, switched to AI, and painted still lives at the Accademia di Belli Arti\n", + "\n", + "---\n", + "\n", + "Context Str: YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it. There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\" He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I'd better work very hard. One day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn't the last cool thing you do.\" At the time I didn't understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life's work or I'd have to leave eventually. And it wasn't, so I would. In the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else. I asked Jessica if she wanted to be president, but she didn't, so we decided we'd try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn't be controlled by the founders. So if Sam said yes, we'd let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners. When we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned. She died on January 15, 2014. We knew this was coming, but it was still hard when it did. I kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.) What should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I don't think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs. The deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16] Fairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them. As YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another's customers. We used to refer jokingly to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates. I had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things. In the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn't startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one's intellectual curiosity. HN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I'd had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one's work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17] As well as HN, I wrote all of YC's internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn't have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC. YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it. There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\"\n", + "Query Str: What did the author do during his time at YC?\n", + "Answer: Worked with startups, solved their problems, wrote essays, and developed internal software.\n", + "\n", + "---\n", + "\n", + "Context Str: this is my context\n", + "Query Str: hello?\n", + "Answer:\n" + ] + } + ], + "source": [ + "print(qa_prompt_tmpl.format(query_str=\"hello?\", context_str=\"this is my context\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "f3097d9c-89e3-4d89-ae8d-6a57bd4fda0b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "query_engine = index.as_query_engine(\n", + " text_qa_template=qa_prompt_tmpl\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "df7b8d58-ddb0-468c-854f-bf475134d653", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "response = query_engine.query(\"what did the author do at RISD?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "2a93b21d-2766-404e-833f-5a1cb3bf8891", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What did the author do after dropping out of RISD?\n", + "Answer: Moved to New York, painted, and wrote a book on Lisp.\n" + ] + } + ], + "source": [ + "print(str(response))" + ] } - ], - "source": [ - "print(qa_prompt_tmpl.format(query_str=\"hello?\", context_str=\"this is my context\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f3097d9c-89e3-4d89-ae8d-6a57bd4fda0b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "query_engine = index.as_query_engine(\n", - " text_qa_template=qa_prompt_tmpl\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "df7b8d58-ddb0-468c-854f-bf475134d653", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "response = query_engine.query(\"what did the author do at RISD?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "2a93b21d-2766-404e-833f-5a1cb3bf8891", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What did the author do after dropping out of RISD?\n", - "Answer: Moved to New York, painted, and wrote a book on Lisp.\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "llama_index_v3", + "language": "python", + "name": "llama_index_v3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" } - ], - "source": [ - "print(str(response))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "llama_index_v3", - "language": "python", - "name": "llama_index_v3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/outdated_v2.4_examples/longformqa/longformqa_assertions.ipynb b/examples/outdated_v2.4_examples/longformqa/longformqa_assertions.ipynb index 3d649569d3..0701c704b1 100644 --- a/examples/outdated_v2.4_examples/longformqa/longformqa_assertions.ipynb +++ b/examples/outdated_v2.4_examples/longformqa/longformqa_assertions.ipynb @@ -1,651 +1,651 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "\n", - "## **DSPy Assertions**: Asserting Computational Constraints on Foundation \n", - "\n", - "### **LongFormQA**: Generating long-form length responses to answer questions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/longformqa/longformqa_assertions.ipynb)\n", - "\n", - "This notebook builds upon the foundational concepts of the **DSPy** framework, as introduced in our previous tutorial (see [intro.ipynb](./intro.ipynb) for a refresher). DSPy overs a novel programming-centric approach to utilizing language and retrieval models. It offers a unique blend of prompting, reasoning, fine-tuning, and tool augmentation, all encapsulated under a minimalistic Python syntax. \n", - "\n", - "In this advancement of DSPy, we introduce **Assertions**, a feature with the capability to declare computational constraints within DSPy programs. This allows programmers to specify natural-language rules for valid outputs, guiding the behavior of language model calls during both compiling and inference stages. \n", - "\n", - "Our approach harnesses Pythonic style of assertions while meshing backtracking logic to ensure autonomous self-correction and refinement of language model calls. By accounting for past outputs and passing forward relevant feedback and guidelines for self-correction, this feature offers a significant leap in DSPy with enhanced control over program behavior.\n", - "\n", - "This notebook demonstrates the utility of assertions on specific downstream examples, extending the Multi-Hop Question-Answering task from the [intro.ipynb](./intro.ipynb) to long-form paragraph generation with citations to answer questions. We demonstrate the performance benefits of integrating assertions to ensure the inclusion of citations in a predefined format and the faithfulness of generated text to its cited references. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 0] Setting Up\n", - "Let's begin by setting things up." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will install **DSPy** if it's not there already." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "import regex as re\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - " \n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " !pip install dspy-ai\n", - " !pip install openai~=0.28.1\n", - " !pip install -e $repo_path\n", - "\n", - "import dspy\n", - "from dspy.predict import Retry\n", - "from dspy.datasets import HotPotQA\n", - "\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", - "from dsp.utils import normalize_text\n", - "from dspy.primitives.assertions import assert_transform_module, backtrack_handler\n", - "\n", - "%cd dspy/examples/longformqa\n", - "\n", - "from utils import extract_text_by_citation, citations_check" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import openai\n", - "openai.api_key = os.getenv('OPENAI_API_KEY')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1] Getting Started\n", - "\n", - "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models. In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the retriever `ColBERTv2`.\n", - "\n", - "To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", - "\n", - "We configure **DSPy** to use the turbo LM and the ColBERTv2 retriever (over Wikipedia 2017 abstracts) by default. This can be overwritten for local parts of programs if needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", - "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", - "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2] Dataset\n", - "\n", - "Now, let's load a sample from the HotPotQA multi-hop dataset for our tasks. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", - "trainset = [x.with_inputs('question') for x in dataset.train]\n", - "devset = [x.with_inputs('question') for x in dataset.dev]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We just loaded `trainset` (300 examples) and `devset` (300 examples). Each example in our **training set** contains just a **question,** its corresponding (human-annotated) **answer**, and the **gold titles**. These gold titles represent titles of relevant Wikipedia articles that contain supporting facts necessary to answering the question. \n", - "\n", - "After loading the datasets, we'd applied `x.with_inputs('question')` to each example to tell **DSPy** that our input field in each example will be just `question`. Any other fields are labels not given to the system.\n", - "\n", - "Now, let's look at some data examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_example = trainset[0]\n", - "print(f\"Question: {train_example.question}\")\n", - "print(f\"Answer: {train_example.answer}\")\n", - "print(f\"Relevant Wikipedia Titles: {train_example.gold_titles}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dev_example = devset[18]\n", - "print(f\"Question: {dev_example.question}\")\n", - "print(f\"Answer: {dev_example.answer}\")\n", - "print(f\"Relevant Wikipedia Titles: {dev_example.gold_titles}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3] LongFormQA with Citations\n", - "\n", - "Let's define our first complete program for this task. We extend the `Multi-Hop QA` program, shifting the answer generation focus from short phrases of 1-5 words to comprehensive paragraphs that include citations. \n", - "\n", - "The `LongFormQA` module reflects the iterative multi-hop generation process in query generation, passage retrieval, and context assembly. The `GenerateCitedParagraph` layer then takes the context state alongside the question to generate a paragraph with relevant reference citations to the context. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this program, we aim to generate paragraphs that adhere the following guidelines:\n", - "1. Every 1-2 sentences in the paragraph are followed by citations in the intended format **\"{text}... [source_num].\"**\n", - "2. Every text segment preceding a citation is faithful to the referenced source passage. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from dsp.utils import deduplicate\n", - "\n", - "class GenerateSearchQuery(dspy.Signature):\n", - " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " question = dspy.InputField()\n", - " query = dspy.OutputField()\n", - "\n", - "class GenerateCitedParagraph(dspy.Signature):\n", - " \"\"\"Generate a paragraph with citations.\"\"\"\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " question = dspy.InputField()\n", - " paragraph = dspy.OutputField(desc=\"includes citations\")\n", - "\n", - "class LongFormQA(dspy.Module):\n", - " def __init__(self, passages_per_hop=3, max_hops=2):\n", - " super().__init__()\n", - " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph)\n", - " self.max_hops = max_hops\n", - " \n", - " def forward(self, question):\n", - " context = []\n", - " for hop in range(self.max_hops):\n", - " query = self.generate_query[hop](context=context, question=question).query\n", - " passages = self.retrieve(query).passages\n", - " context = deduplicate(context + passages)\n", - " pred = self.generate_cited_paragraph(context=context, question=question)\n", - " pred = dspy.Prediction(context=context, paragraph=pred.paragraph)\n", - " return pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4] Evaluation\n", - "\n", - "We now define our evaluation metrics, **Intrinsic** and **Extrinsic** quality checks:\n", - "\n", - "#### Intrinsic Metrics: passing internal computational constraints is the goal \n", - "\n", - "**Faithfulness (per Citation)**: To verify the accuracy of each citation in the generated text, we utilize another **DSPy** program: `ChainOfThought` of `CheckCitationFaithfulness`. This module takes segments of text preceding each citation and its corresponding context passage and determines whether the text accurately reflects the facts of the context. This validation process involves a language model call for each citation, ensuring that each reference in the generated paragraph is factually consistent with its reference source." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class CheckCitationFaithfulness(dspy.Signature):\n", - " \"\"\"Verify that the text is based on the provided context.\"\"\"\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " text = dspy.InputField(desc=\"between 1 to 2 sentences\")\n", - " faithfulness = dspy.OutputField(desc=\"boolean indicating if text is faithful to context\")\n", - "\n", - "def citation_faithfulness(example, pred, trace):\n", - " paragraph, context = pred.paragraph, pred.context\n", - " citation_dict = extract_text_by_citation(paragraph)\n", - " if not citation_dict:\n", - " return False, None\n", - " context_dict = {str(i): context[i].split(' | ')[1] for i in range(len(context))}\n", - " faithfulness_results = []\n", - " unfaithful_citations = []\n", - " check_citation_faithfulness = dspy.ChainOfThought(CheckCitationFaithfulness)\n", - " for citation_num, texts in citation_dict.items():\n", - " if citation_num not in context_dict:\n", - " continue\n", - " current_context = context_dict[citation_num]\n", - " for text in texts:\n", - " try:\n", - " result = check_citation_faithfulness(context=current_context, text=text)\n", - " is_faithful = result.faithfulness.lower() == 'true'\n", - " faithfulness_results.append(is_faithful)\n", - " if not is_faithful:\n", - " unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'context': current_context})\n", - " except ValueError as e:\n", - " faithfulness_results.append(False)\n", - " unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'error': str(e)})\n", - " final_faithfulness = all(faithfulness_results)\n", - " if not faithfulness_results:\n", - " return False, None\n", - " return final_faithfulness, unfaithful_citations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Extrinsic Metrics: Assess the overall quality and effectiveness of generated output on downstream task:\n", - "\n", - "- **Citation Precision**: Measures proportion of cited 'gold titles' in generated paragraph from all cited titles for datapoint. \n", - "- **Citation Recall**: Measures proportion of cited 'gold titles' in generated paragraph from all 'gold titles' for datapoint.\n", - "- **Answer Inclusion**: Evaluates whether generated paragraph with citations accurately incorporates the 'gold' answer for datapoint. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def extract_cited_titles_from_paragraph(paragraph, context):\n", - " cited_indices = [int(m.group(1)) for m in re.finditer(r'\\[(\\d+)\\]\\.', paragraph)]\n", - " cited_indices = [index - 1 for index in cited_indices if index <= len(context)]\n", - " cited_titles = [context[index].split(' | ')[0] for index in cited_indices]\n", - " return cited_titles\n", - "\n", - "def calculate_recall(example, pred, trace=None):\n", - " gold_titles = set(example['gold_titles'])\n", - " found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context))\n", - " intersection = gold_titles.intersection(found_cited_titles)\n", - " recall = len(intersection) / len(gold_titles) if gold_titles else 0\n", - " return recall\n", - "\n", - "def calculate_precision(example, pred, trace=None):\n", - " gold_titles = set(example['gold_titles'])\n", - " found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context))\n", - " intersection = gold_titles.intersection(found_cited_titles)\n", - " precision = len(intersection) / len(found_cited_titles) if found_cited_titles else 0\n", - " return precision\n", - "\n", - "def answer_correctness(example, pred, trace=None):\n", - " assert hasattr(example, 'answer'), \"Example does not have 'answer'.\"\n", - " normalized_context = normalize_text(pred.paragraph)\n", - " if isinstance(example.answer, str):\n", - " gold_answers = [example.answer]\n", - " elif isinstance(example.answer, list):\n", - " gold_answers = example.answer\n", - " else:\n", - " raise ValueError(\"'example.answer' is not string or list.\")\n", - " return 1 if any(normalize_text(answer) in normalized_context for answer in gold_answers) else 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now evaluate our program on these metrics over our devset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(module):\n", - " correctness_values = []\n", - " recall_values = []\n", - " precision_values = []\n", - " citation_faithfulness_values = []\n", - " for i in range(len(devset)):\n", - " example = devset[i]\n", - " try:\n", - " pred = module(question=example.question)\n", - " correctness_values.append(answer_correctness(example, pred)) \n", - " citation_faithfulness_score, _ = citation_faithfulness(None, pred, None)\n", - " citation_faithfulness_values.append(citation_faithfulness_score)\n", - " recall = calculate_recall(example, pred)\n", - " precision = calculate_precision(example, pred)\n", - " recall_values.append(recall)\n", - " precision_values.append(precision)\n", - " except Exception as e:\n", - " print(f\"Failed generation with error: {e}\")\n", - "\n", - " average_correctness = sum(correctness_values) / len(devset) if correctness_values else 0\n", - " average_recall = sum(recall_values) / len(devset) if recall_values else 0\n", - " average_precision = sum(precision_values) / len(devset) if precision_values else 0\n", - " average_citation_faithfulness = sum(citation_faithfulness_values) / len(devset) if citation_faithfulness_values else 0\n", - "\n", - " print(f\"Average Correctness: {average_correctness}\")\n", - " print(f\"Average Recall: {average_recall}\")\n", - " print(f\"Average Precision: {average_precision}\")\n", - " print(f\"Average Citation Faithfulness: {average_citation_faithfulness}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "longformqa = LongFormQA()\n", - "evaluate(longformqa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at an example paragraph generation:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: What is the name of this region of Italy, referring to the medieval March of Ancona and nearby marches of Camerino and Fermo, where the comune Pollenza is located?\n", - "Predicted Paragraph: The region of Italy that refers to the medieval March of Ancona and nearby marches of Camerino and Fermo is called Marche. This name is derived from the plural form of \"marca,\" which originally denoted the frontier territories established during the Middle Ages, particularly the March of Ancona (Marche) (1). Today, Marche is recognized not only for its historical significance but also for its rich shoemaking tradition, producing some of the finest Italian footwear (1). Within this region lies the comune of Pollenza, located approximately 40 km southwest of Ancona, further illustrating the geographical and cultural significance of Marche (3).\n", - "Citation Faithfulness: False\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "\n", + "## **DSPy Assertions**: Asserting Computational Constraints on Foundation \n", + "\n", + "### **LongFormQA**: Generating long-form length responses to answer questions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/longformqa/longformqa_assertions.ipynb)\n", + "\n", + "This notebook builds upon the foundational concepts of the **DSPy** framework, as introduced in our previous tutorial (see [intro.ipynb](./intro.ipynb) for a refresher). DSPy overs a novel programming-centric approach to utilizing language and retrieval models. It offers a unique blend of prompting, reasoning, fine-tuning, and tool augmentation, all encapsulated under a minimalistic Python syntax. \n", + "\n", + "In this advancement of DSPy, we introduce **Assertions**, a feature with the capability to declare computational constraints within DSPy programs. This allows programmers to specify natural-language rules for valid outputs, guiding the behavior of language model calls during both compiling and inference stages. \n", + "\n", + "Our approach harnesses Pythonic style of assertions while meshing backtracking logic to ensure autonomous self-correction and refinement of language model calls. By accounting for past outputs and passing forward relevant feedback and guidelines for self-correction, this feature offers a significant leap in DSPy with enhanced control over program behavior.\n", + "\n", + "This notebook demonstrates the utility of assertions on specific downstream examples, extending the Multi-Hop Question-Answering task from the [intro.ipynb](./intro.ipynb) to long-form paragraph generation with citations to answer questions. We demonstrate the performance benefits of integrating assertions to ensure the inclusion of citations in a predefined format and the faithfulness of generated text to its cited references. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0] Setting Up\n", + "Let's begin by setting things up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will install **DSPy** if it's not there already." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "import regex as re\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + " \n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " !pip install dspy-ai\n", + " !pip install openai~=0.28.1\n", + " !pip install -e $repo_path\n", + "\n", + "import dspy\n", + "from dspy.predict import Retry\n", + "from dspy.datasets import HotPotQA\n", + "\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "from dsp.utils import normalize_text\n", + "from dspy.primitives.assertions import assert_transform_module, backtrack_handler\n", + "\n", + "%cd dspy/examples/longformqa\n", + "\n", + "from utils import extract_text_by_citation, citations_check" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "openai.api_key = os.getenv('OPENAI_API_KEY')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1] Getting Started\n", + "\n", + "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models. In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`) and the retriever `ColBERTv2`.\n", + "\n", + "To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", + "\n", + "We configure **DSPy** to use the turbo LM and the ColBERTv2 retriever (over Wikipedia 2017 abstracts) by default. This can be overwritten for local parts of programs if needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", + "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", + "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2] Dataset\n", + "\n", + "Now, let's load a sample from the HotPotQA multi-hop dataset for our tasks. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", + "trainset = [x.with_inputs('question') for x in dataset.train]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We just loaded `trainset` (300 examples) and `devset` (300 examples). Each example in our **training set** contains just a **question,** its corresponding (human-annotated) **answer**, and the **gold titles**. These gold titles represent titles of relevant Wikipedia articles that contain supporting facts necessary to answering the question. \n", + "\n", + "After loading the datasets, we'd applied `x.with_inputs('question')` to each example to tell **DSPy** that our input field in each example will be just `question`. Any other fields are labels not given to the system.\n", + "\n", + "Now, let's look at some data examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_example = trainset[0]\n", + "print(f\"Question: {train_example.question}\")\n", + "print(f\"Answer: {train_example.answer}\")\n", + "print(f\"Relevant Wikipedia Titles: {train_example.gold_titles}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dev_example = devset[18]\n", + "print(f\"Question: {dev_example.question}\")\n", + "print(f\"Answer: {dev_example.answer}\")\n", + "print(f\"Relevant Wikipedia Titles: {dev_example.gold_titles}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3] LongFormQA with Citations\n", + "\n", + "Let's define our first complete program for this task. We extend the `Multi-Hop QA` program, shifting the answer generation focus from short phrases of 1-5 words to comprehensive paragraphs that include citations. \n", + "\n", + "The `LongFormQA` module reflects the iterative multi-hop generation process in query generation, passage retrieval, and context assembly. The `GenerateCitedParagraph` layer then takes the context state alongside the question to generate a paragraph with relevant reference citations to the context. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this program, we aim to generate paragraphs that adhere the following guidelines:\n", + "1. Every 1-2 sentences in the paragraph are followed by citations in the intended format **\"{text}... [source_num].\"**\n", + "2. Every text segment preceding a citation is faithful to the referenced source passage. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dsp.utils import deduplicate\n", + "\n", + "class GenerateSearchQuery(dspy.Signature):\n", + " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " question = dspy.InputField()\n", + " query = dspy.OutputField()\n", + "\n", + "class GenerateCitedParagraph(dspy.Signature):\n", + " \"\"\"Generate a paragraph with citations.\"\"\"\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " question = dspy.InputField()\n", + " paragraph = dspy.OutputField(desc=\"includes citations\")\n", + "\n", + "class LongFormQA(dspy.Module):\n", + " def __init__(self, passages_per_hop=3, max_hops=2):\n", + " super().__init__()\n", + " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph)\n", + " self.max_hops = max_hops\n", + " \n", + " def forward(self, question):\n", + " context = []\n", + " for hop in range(self.max_hops):\n", + " query = self.generate_query[hop](context=context, question=question).query\n", + " passages = self.retrieve(query).passages\n", + " context = deduplicate(context + passages)\n", + " pred = self.generate_cited_paragraph(context=context, question=question)\n", + " pred = dspy.Prediction(context=context, paragraph=pred.paragraph)\n", + " return pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4] Evaluation\n", + "\n", + "We now define our evaluation metrics, **Intrinsic** and **Extrinsic** quality checks:\n", + "\n", + "#### Intrinsic Metrics: passing internal computational constraints is the goal \n", + "\n", + "**Faithfulness (per Citation)**: To verify the accuracy of each citation in the generated text, we utilize another **DSPy** program: `ChainOfThought` of `CheckCitationFaithfulness`. This module takes segments of text preceding each citation and its corresponding context passage and determines whether the text accurately reflects the facts of the context. This validation process involves a language model call for each citation, ensuring that each reference in the generated paragraph is factually consistent with its reference source." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class CheckCitationFaithfulness(dspy.Signature):\n", + " \"\"\"Verify that the text is based on the provided context.\"\"\"\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " text = dspy.InputField(desc=\"between 1 to 2 sentences\")\n", + " faithfulness = dspy.OutputField(desc=\"boolean indicating if text is faithful to context\")\n", + "\n", + "def citation_faithfulness(example, pred, trace):\n", + " paragraph, context = pred.paragraph, pred.context\n", + " citation_dict = extract_text_by_citation(paragraph)\n", + " if not citation_dict:\n", + " return False, None\n", + " context_dict = {str(i): context[i].split(' | ')[1] for i in range(len(context))}\n", + " faithfulness_results = []\n", + " unfaithful_citations = []\n", + " check_citation_faithfulness = dspy.ChainOfThought(CheckCitationFaithfulness)\n", + " for citation_num, texts in citation_dict.items():\n", + " if citation_num not in context_dict:\n", + " continue\n", + " current_context = context_dict[citation_num]\n", + " for text in texts:\n", + " try:\n", + " result = check_citation_faithfulness(context=current_context, text=text)\n", + " is_faithful = result.faithfulness.lower() == 'true'\n", + " faithfulness_results.append(is_faithful)\n", + " if not is_faithful:\n", + " unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'context': current_context})\n", + " except ValueError as e:\n", + " faithfulness_results.append(False)\n", + " unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'error': str(e)})\n", + " final_faithfulness = all(faithfulness_results)\n", + " if not faithfulness_results:\n", + " return False, None\n", + " return final_faithfulness, unfaithful_citations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extrinsic Metrics: Assess the overall quality and effectiveness of generated output on downstream task:\n", + "\n", + "- **Citation Precision**: Measures proportion of cited 'gold titles' in generated paragraph from all cited titles for datapoint. \n", + "- **Citation Recall**: Measures proportion of cited 'gold titles' in generated paragraph from all 'gold titles' for datapoint.\n", + "- **Answer Inclusion**: Evaluates whether generated paragraph with citations accurately incorporates the 'gold' answer for datapoint. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_cited_titles_from_paragraph(paragraph, context):\n", + " cited_indices = [int(m.group(1)) for m in re.finditer(r'\\[(\\d+)\\]\\.', paragraph)]\n", + " cited_indices = [index - 1 for index in cited_indices if index <= len(context)]\n", + " cited_titles = [context[index].split(' | ')[0] for index in cited_indices]\n", + " return cited_titles\n", + "\n", + "def calculate_recall(example, pred, trace=None):\n", + " gold_titles = set(example['gold_titles'])\n", + " found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context))\n", + " intersection = gold_titles.intersection(found_cited_titles)\n", + " recall = len(intersection) / len(gold_titles) if gold_titles else 0\n", + " return recall\n", + "\n", + "def calculate_precision(example, pred, trace=None):\n", + " gold_titles = set(example['gold_titles'])\n", + " found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context))\n", + " intersection = gold_titles.intersection(found_cited_titles)\n", + " precision = len(intersection) / len(found_cited_titles) if found_cited_titles else 0\n", + " return precision\n", + "\n", + "def answer_correctness(example, pred, trace=None):\n", + " assert hasattr(example, 'answer'), \"Example does not have 'answer'.\"\n", + " normalized_context = normalize_text(pred.paragraph)\n", + " if isinstance(example.answer, str):\n", + " gold_answers = [example.answer]\n", + " elif isinstance(example.answer, list):\n", + " gold_answers = example.answer\n", + " else:\n", + " raise ValueError(\"'example.answer' is not string or list.\")\n", + " return 1 if any(normalize_text(answer) in normalized_context for answer in gold_answers) else 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now evaluate our program on these metrics over our devset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate(module):\n", + " correctness_values = []\n", + " recall_values = []\n", + " precision_values = []\n", + " citation_faithfulness_values = []\n", + " for i in range(len(devset)):\n", + " example = devset[i]\n", + " try:\n", + " pred = module(question=example.question)\n", + " correctness_values.append(answer_correctness(example, pred)) \n", + " citation_faithfulness_score, _ = citation_faithfulness(None, pred, None)\n", + " citation_faithfulness_values.append(citation_faithfulness_score)\n", + " recall = calculate_recall(example, pred)\n", + " precision = calculate_precision(example, pred)\n", + " recall_values.append(recall)\n", + " precision_values.append(precision)\n", + " except Exception as e:\n", + " print(f\"Failed generation with error: {e}\")\n", + "\n", + " average_correctness = sum(correctness_values) / len(devset) if correctness_values else 0\n", + " average_recall = sum(recall_values) / len(devset) if recall_values else 0\n", + " average_precision = sum(precision_values) / len(devset) if precision_values else 0\n", + " average_citation_faithfulness = sum(citation_faithfulness_values) / len(devset) if citation_faithfulness_values else 0\n", + "\n", + " print(f\"Average Correctness: {average_correctness}\")\n", + " print(f\"Average Recall: {average_recall}\")\n", + " print(f\"Average Precision: {average_precision}\")\n", + " print(f\"Average Citation Faithfulness: {average_citation_faithfulness}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "longformqa = LongFormQA()\n", + "evaluate(longformqa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at an example paragraph generation:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: What is the name of this region of Italy, referring to the medieval March of Ancona and nearby marches of Camerino and Fermo, where the comune Pollenza is located?\n", + "Predicted Paragraph: The region of Italy that refers to the medieval March of Ancona and nearby marches of Camerino and Fermo is called Marche. This name is derived from the plural form of \"marca,\" which originally denoted the frontier territories established during the Middle Ages, particularly the March of Ancona (Marche) (1). Today, Marche is recognized not only for its historical significance but also for its rich shoemaking tradition, producing some of the finest Italian footwear (1). Within this region lies the comune of Pollenza, located approximately 40 km southwest of Ancona, further illustrating the geographical and cultural significance of Marche (3).\n", + "Citation Faithfulness: False\n" + ] + } + ], + "source": [ + "question = devset[15].question\n", + "pred = longformqa(question)\n", + "citation_faithfulness_score, _ = citation_faithfulness(None, pred, None)\n", + "\n", + "print(f\"Question: {question}\")\n", + "print(f\"Predicted Paragraph: {pred.paragraph}\")\n", + "print(f\"Citation Faithfulness: {citation_faithfulness_score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the generated paragraph does not properly include citations as intended in the format of \"[source]\". \n", + "\n", + "Additionally, we see that not all included citations are faithful to their preceding text." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5] Introducing Assertions: LongFormQAWithAssertions\n", + "\n", + "To correct these errors, we introduce **Assertions** to impose clear computational constraints within our program.\n", + "\n", + "DSPy provides two key mechanisms for **Assertions**:\n", + "\n", + "- **`dspy.Assert`**: This mandates that the program must satisfy the given assertion, raising an Exception otherwise. This is important when enforcing non-negotiable constraints within the program.\n", + "- **`dspy.Suggest`**: Unlike `Assert`, `Suggest` is more flexible. It encourages the program to meet the assertion but allows the program to continue even if the assertion is not satisfied. This is particularly useful for guiding the program towards desired outcomes without halting execution for non-critical issues.\n", + "\n", + "Since our goal is indeed to evaluate the program on the defined metrics, let's utilize the `dspy.Suggest` assertion. \n", + "\n", + "The syntax for `dspy.Suggest` is as follows:\n", + "```python\n", + "dspy.Suggest(validation_function(model_outputs): bool, instruction_message: str)\n", + "```\n", + "\n", + "Let's add assertions to abide by the computational constraints defined above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class LongFormQAWithAssertions(dspy.Module):\n", + " def __init__(self, passages_per_hop=3, max_hops=2):\n", + " super().__init__()\n", + " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph)\n", + " self.max_hops = max_hops\n", + " \n", + " def forward(self, question):\n", + " context = []\n", + " for hop in range(self.max_hops):\n", + " query = self.generate_query[hop](context=context, question=question).query\n", + " passages = self.retrieve(query).passages\n", + " context = deduplicate(context + passages)\n", + " pred = self.generate_cited_paragraph(context=context, question=question)\n", + " pred = dspy.Prediction(context=context, paragraph=pred.paragraph)\n", + " dspy.Suggest(citations_check(pred.paragraph), \"Make sure every 1-2 sentences has citations. If any 1-2 sentences lack citations, add them in 'text... [x].' format.\", target_module=self.generate_cited_paragraph)\n", + " _, unfaithful_outputs = citation_faithfulness(None, pred, None)\n", + " if unfaithful_outputs:\n", + " unfaithful_pairs = [(output['text'], output['context']) for output in unfaithful_outputs]\n", + " for _, context in unfaithful_pairs:\n", + " dspy.Suggest(len(unfaithful_pairs) == 0, f\"Make sure your output is based on the following context: '{context}'.\", target_module=self.generate_cited_paragraph)\n", + " else:\n", + " return pred\n", + " return pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We include assertions that simply reiterate our computational constraints and now allow the `LongFormQA` program to execute and adhere to these guidelines under the hood. \n", + "\n", + "Since we want to impose these assertions on the paragraph generation, we can pass in the `GenerateCitedParagraph` signature to indicate the `target_module` for the assertion handling to identify. \n", + "\n", + "In the first **Assertion**, we validate the output paragraph to ensure citations are included every 1-2 sentences. If this validation returns False, the assertion backtracking logic is activated the feedback instruction: **\"Ensure each 1-2 sentences include citations in 'text... [x].' format.\"**\n", + "\n", + "In the second **Assertion**, we now utilize the `CheckCitationFaithfulness` program to validate the accuracy of each cited references, looping over text segments denoted in the generated paragraph. In cases of unfaithful citations, it sends the feedback instruction alongside the context as: **\"Ensure your output aligns with this context: '{context}'.\"** This ensures the assertion backtracking has the relevant information and specific context it needs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now evaluate our `LongFormQAWithAssertions` program over the devset.\n", + "\n", + "Note that this requires wrapping the module with the `Retry` module which handles the backtracking logic. This wrapped module is then passed to the `assert_transform_module` function to prepare and execute the backtracking logic. This is passed alongside the `backtrack_handler` which configures the backtracking logic to account for the feedback messages passed in to the `dspy.Suggest` statements within the program." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "longformqa_with_assertions = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler) \n", + "evaluate(longformqa_with_assertions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the same example from above with the `LongFormQAWithAssertions` program:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: What is the name of this region of Italy, referring to the medieval March of Ancona and nearby marches of Camerino and Fermo, where the comune Pollenza is located?\n", + "Predicted Paragraph: The region of Italy that refers to the medieval March of Ancona and nearby marches of Camerino and Fermo is called Marche [1]. This name is derived from the plural form of \"marca,\" which originally denoted the frontier territories established during the Middle Ages, particularly the March of Ancona [2]. Today, Marche is recognized not only for its historical significance but also for its rich shoemaking tradition, producing some of the finest Italian footwear [1]. Within this region lies the comune of Pollenza, located approximately 40 km southwest of Ancona and about 9 km southwest of Macerata, further illustrating the geographical and cultural significance of Marche [3].\n", + "Citation Faithfulness: True\n" + ] + } + ], + "source": [ + "question = devset[15].question\n", + "pred = longformqa_with_assertions(question)\n", + "citation_faithfulness_score, _ = citation_faithfulness(None, pred, None)\n", + "\n", + "print(f\"Question: {question}\")\n", + "print(f\"Predicted Paragraph: {pred.paragraph}\")\n", + "print(f\"Citation Faithfulness: {citation_faithfulness_score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now see that both computational constraints are indeed met. Every 1-2 sentences includes a citation and from our `citation_faithfulness` check, we see that each reference is also faithful to its preceding text. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6] Compilation With Assertions\n", + "\n", + "We can also leverage **DSPy**'s advanced compiling features to enhance our program's performance. \n", + "\n", + "For this, we utilize the `BootstrapFewShotWithRandomSearch` teleprompter, which automatically incorporates few-shot demonstrations and conducts a random search over a candidate set to output the best compiled program. We evaluate this over the `answer_correctness` metric as our ultimate goal is indeed to generate correct answers to the `HotPotQA` questions from the paragraphs, aiming to optimize both intrinsic and extrinsic metrics as a result. \n", + "\n", + "Let's evaluate this on the LongFormQA program first:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "longformqa = LongFormQA()\n", + "teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "cited_longformqa = teleprompter.compile(student = longformqa, teacher = longformqa, trainset=trainset, valset=devset[:25])\n", + "evaluate(cited_longformqa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate this with assertions. \n", + "\n", + "**Note** The pipeline here lies in compiling with **Assertions** to give the teleprompter correct bootstrapped examples by the `answer_correctness` metric and then 'teaching' the student with these correct examples. This is represented by passing `LongFormQA()` as the student and `LongFormQAWithAssertions()` as the teacher." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "longformqa = LongFormQA()\n", + "teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "cited_longformqa_teacher = teleprompter.compile(student=longformqa, teacher = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler), trainset=trainset, valset=devset[:25])\n", + "evaluate(cited_longformqa_teacher)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note** This pipeline on the other hand sets both the teacher and student with `LongFormQAWithAssertions()` to ensure the teacher correctly instructs the student with the right bootstrapped examples and the student has the chance to self-correct with **Assertions** for any examples that are still deemed incorrect." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "longformqa = LongFormQA()\n", + "teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "cited_longformqa_student_teacher = teleprompter.compile(student=assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler), teacher = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler), trainset=trainset, valset=devset[:25])\n", + "evaluate(cited_longformqa_student_teacher)" + ] } - ], - "source": [ - "question = devset[15].question\n", - "pred = longformqa(question)\n", - "citation_faithfulness_score, _ = citation_faithfulness(None, pred, None)\n", - "\n", - "print(f\"Question: {question}\")\n", - "print(f\"Predicted Paragraph: {pred.paragraph}\")\n", - "print(f\"Citation Faithfulness: {citation_faithfulness_score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the generated paragraph does not properly include citations as intended in the format of \"[source]\". \n", - "\n", - "Additionally, we see that not all included citations are faithful to their preceding text." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5] Introducing Assertions: LongFormQAWithAssertions\n", - "\n", - "To correct these errors, we introduce **Assertions** to impose clear computational constraints within our program.\n", - "\n", - "DSPy provides two key mechanisms for **Assertions**:\n", - "\n", - "- **`dspy.Assert`**: This mandates that the program must satisfy the given assertion, raising an Exception otherwise. This is important when enforcing non-negotiable constraints within the program.\n", - "- **`dspy.Suggest`**: Unlike `Assert`, `Suggest` is more flexible. It encourages the program to meet the assertion but allows the program to continue even if the assertion is not satisfied. This is particularly useful for guiding the program towards desired outcomes without halting execution for non-critical issues.\n", - "\n", - "Since our goal is indeed to evaluate the program on the defined metrics, let's utilize the `dspy.Suggest` assertion. \n", - "\n", - "The syntax for `dspy.Suggest` is as follows:\n", - "```python\n", - "dspy.Suggest(validation_function(model_outputs): bool, instruction_message: str)\n", - "```\n", - "\n", - "Let's add assertions to abide by the computational constraints defined above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class LongFormQAWithAssertions(dspy.Module):\n", - " def __init__(self, passages_per_hop=3, max_hops=2):\n", - " super().__init__()\n", - " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph)\n", - " self.max_hops = max_hops\n", - " \n", - " def forward(self, question):\n", - " context = []\n", - " for hop in range(self.max_hops):\n", - " query = self.generate_query[hop](context=context, question=question).query\n", - " passages = self.retrieve(query).passages\n", - " context = deduplicate(context + passages)\n", - " pred = self.generate_cited_paragraph(context=context, question=question)\n", - " pred = dspy.Prediction(context=context, paragraph=pred.paragraph)\n", - " dspy.Suggest(citations_check(pred.paragraph), \"Make sure every 1-2 sentences has citations. If any 1-2 sentences lack citations, add them in 'text... [x].' format.\", target_module=self.generate_cited_paragraph)\n", - " _, unfaithful_outputs = citation_faithfulness(None, pred, None)\n", - " if unfaithful_outputs:\n", - " unfaithful_pairs = [(output['text'], output['context']) for output in unfaithful_outputs]\n", - " for _, context in unfaithful_pairs:\n", - " dspy.Suggest(len(unfaithful_pairs) == 0, f\"Make sure your output is based on the following context: '{context}'.\", target_module=self.generate_cited_paragraph)\n", - " else:\n", - " return pred\n", - " return pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We include assertions that simply reiterate our computational constraints and now allow the `LongFormQA` program to execute and adhere to these guidelines under the hood. \n", - "\n", - "Since we want to impose these assertions on the paragraph generation, we can pass in the `GenerateCitedParagraph` signature to indicate the `target_module` for the assertion handling to identify. \n", - "\n", - "In the first **Assertion**, we validate the output paragraph to ensure citations are included every 1-2 sentences. If this validation returns False, the assertion backtracking logic is activated the feedback instruction: **\"Ensure each 1-2 sentences include citations in 'text... [x].' format.\"**\n", - "\n", - "In the second **Assertion**, we now utilize the `CheckCitationFaithfulness` program to validate the accuracy of each cited references, looping over text segments denoted in the generated paragraph. In cases of unfaithful citations, it sends the feedback instruction alongside the context as: **\"Ensure your output aligns with this context: '{context}'.\"** This ensures the assertion backtracking has the relevant information and specific context it needs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now evaluate our `LongFormQAWithAssertions` program over the devset.\n", - "\n", - "Note that this requires wrapping the module with the `Retry` module which handles the backtracking logic. This wrapped module is then passed to the `assert_transform_module` function to prepare and execute the backtracking logic. This is passed alongside the `backtrack_handler` which configures the backtracking logic to account for the feedback messages passed in to the `dspy.Suggest` statements within the program." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "longformqa_with_assertions = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler) \n", - "evaluate(longformqa_with_assertions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the same example from above with the `LongFormQAWithAssertions` program:" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Question: What is the name of this region of Italy, referring to the medieval March of Ancona and nearby marches of Camerino and Fermo, where the comune Pollenza is located?\n", - "Predicted Paragraph: The region of Italy that refers to the medieval March of Ancona and nearby marches of Camerino and Fermo is called Marche [1]. This name is derived from the plural form of \"marca,\" which originally denoted the frontier territories established during the Middle Ages, particularly the March of Ancona [2]. Today, Marche is recognized not only for its historical significance but also for its rich shoemaking tradition, producing some of the finest Italian footwear [1]. Within this region lies the comune of Pollenza, located approximately 40 km southwest of Ancona and about 9 km southwest of Macerata, further illustrating the geographical and cultural significance of Marche [3].\n", - "Citation Faithfulness: True\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "dspy_dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "question = devset[15].question\n", - "pred = longformqa_with_assertions(question)\n", - "citation_faithfulness_score, _ = citation_faithfulness(None, pred, None)\n", - "\n", - "print(f\"Question: {question}\")\n", - "print(f\"Predicted Paragraph: {pred.paragraph}\")\n", - "print(f\"Citation Faithfulness: {citation_faithfulness_score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now see that both computational constraints are indeed met. Every 1-2 sentences includes a citation and from our `citation_faithfulness` check, we see that each reference is also faithful to its preceding text. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6] Compilation With Assertions\n", - "\n", - "We can also leverage **DSPy**'s advanced compiling features to enhance our program's performance. \n", - "\n", - "For this, we utilize the `BootstrapFewShotWithRandomSearch` teleprompter, which automatically incorporates few-shot demonstrations and conducts a random search over a candidate set to output the best compiled program. We evaluate this over the `answer_correctness` metric as our ultimate goal is indeed to generate correct answers to the `HotPotQA` questions from the paragraphs, aiming to optimize both intrinsic and extrinsic metrics as a result. \n", - "\n", - "Let's evaluate this on the LongFormQA program first:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "longformqa = LongFormQA()\n", - "teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "cited_longformqa = teleprompter.compile(student = longformqa, teacher = longformqa, trainset=trainset, valset=devset[:25])\n", - "evaluate(cited_longformqa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's evaluate this with assertions. \n", - "\n", - "**Note** The pipeline here lies in compiling with **Assertions** to give the teleprompter correct bootstrapped examples by the `answer_correctness` metric and then 'teaching' the student with these correct examples. This is represented by passing `LongFormQA()` as the student and `LongFormQAWithAssertions()` as the teacher." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "longformqa = LongFormQA()\n", - "teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "cited_longformqa_teacher = teleprompter.compile(student=longformqa, teacher = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler), trainset=trainset, valset=devset[:25])\n", - "evaluate(cited_longformqa_teacher)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note** This pipeline on the other hand sets both the teacher and student with `LongFormQAWithAssertions()` to ensure the teacher correctly instructs the student with the right bootstrapped examples and the student has the chance to self-correct with **Assertions** for any examples that are still deemed incorrect." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "longformqa = LongFormQA()\n", - "teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "cited_longformqa_student_teacher = teleprompter.compile(student=assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler), teacher = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler), trainset=trainset, valset=devset[:25])\n", - "evaluate(cited_longformqa_student_teacher)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dspy_dev", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/math/gsm8k/CoT.ipynb b/examples/outdated_v2.4_examples/math/gsm8k/CoT.ipynb index 0ad4f4573b..85bf24b178 100644 --- a/examples/outdated_v2.4_examples/math/gsm8k/CoT.ipynb +++ b/examples/outdated_v2.4_examples/math/gsm8k/CoT.ipynb @@ -1,231 +1,231 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# %load_ext autoreload\n", - "# %autoreload 2\n", - "# import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy')\n", - "\n", - "import dspy\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.datasets.gsm8k import GSM8K, gsm8k_metric\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 7473/7473 [00:00<00:00, 39790.63it/s]\n", - "100%|██████████| 1319/1319 [00:00<00:00, 40274.36it/s]\n" - ] - } - ], - "source": [ - "gms8k = GSM8K()\n", - "turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct', max_tokens=250)\n", - "\n", - "trainset, devset = gms8k.train, gms8k.dev\n", - "\n", - "dspy.settings.configure(lm=turbo)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "NUM_THREADS = 4\n", - "evaluate = Evaluate(devset=devset[:], metric=gsm8k_metric, num_threads=NUM_THREADS, display_progress=True, display_table=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class CoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.prog = dspy.ChainOfThought(\"question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " return self.prog(question=question)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "RUN_FROM_SCRATCH = False\n", - "\n", - "if RUN_FROM_SCRATCH:\n", - " config = dict(max_bootstrapped_demos=8, max_labeled_demos=8, num_candidate_programs=10, num_threads=NUM_THREADS)\n", - " teleprompter = BootstrapFewShotWithRandomSearch(metric=gsm8k_metric, **config)\n", - " cot_bs = teleprompter.compile(CoT(), trainset=trainset, valset=devset)\n", - " # cot_bs.save('turbo_8_8_10_gsm8k_200_300.json')\n", - "else:\n", - " cot_bs = CoT()\n", - " cot_bs.load('turbo_8_8_10_gsm8k_200_300.json')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "# import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy')\n", + "\n", + "import dspy\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.datasets.gsm8k import GSM8K, gsm8k_metric\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 241 / 300 (80.3): 100%|██████████| 300/300 [00:00<00:00, 433.24it/s]\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7473/7473 [00:00<00:00, 39790.63it/s]\n", + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1319/1319 [00:00<00:00, 40274.36it/s]\n" + ] + } + ], + "source": [ + "gms8k = GSM8K()\n", + "turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct', max_tokens=250)\n", + "\n", + "trainset, devset = gms8k.train, gms8k.dev\n", + "\n", + "dspy.settings.configure(lm=turbo)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 241 / 300 (80.3%)\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "NUM_THREADS = 4\n", + "evaluate = Evaluate(devset=devset[:], metric=gsm8k_metric, num_threads=NUM_THREADS, display_progress=True, display_table=0)" + ] }, { - "data": { - "text/plain": [ - "80.33" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class CoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.prog = dspy.ChainOfThought(\"question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " return self.prog(question=question)" ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate(cot_bs, devset=devset[:])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Given the fields `question`, produce the fields `answer`.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: ${question}\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Question: Mark is baking bread. He has to let it rise for 120 minutes twice. He also needs to spend 10 minutes kneading it and 30 minutes baking it. How many minutes does it take Mark to finish making the bread?\n", - "Reasoning: Let's think step by step in order to find the total time it takes Mark to finish making the bread. We know that he has to let the bread rise for 120 minutes twice, which is 240 minutes. He also needs to spend 10 minutes kneading it and 30 minutes baking it. Therefore, the total time it takes Mark to finish making the bread is 240 + 10 + 30 = 280 minutes.\n", - "Answer: 280\n", - "\n", - "---\n", - "\n", - "Question: Ben has $2000 for his business operations costs. He orders goods from his supplier and writes them a cheque for $600. His debtor pays him $800 from the purchases they had made on credit. Mr. Ben then decides to do equipment maintenance and spends $1200 on the whole operation. How much money is Mr. Ben remaining with?\n", - "Reasoning: Let's think step by step in order to find the remaining amount of money. We first start with the total amount of money Mr. Ben has, which is $2000. Then, we subtract the amount he spent on goods from his supplier, which is $600. Next, we add the amount he received from his debtor, which is $800. Finally, we subtract the amount he spent on equipment maintenance, which is $1200. We can represent this as $2000 - $600 + $800 - $1200 = $1000.\n", - "Answer: 1000\n", - "\n", - "---\n", - "\n", - "Question: Debbie works at a post office packing boxes to mail. Each large box takes 4 feet of packing tape to seal, each medium box takes 2 feet of packing tape to seal, and each small box takes 1 foot of packing tape to seal. Each box also takes 1 foot of packing tape to stick the address label on. Debbie packed two large boxes, eight medium boxes, and five small boxes this afternoon. How much tape did she use?\n", - "Reasoning: Let's think step by step in order to find the total amount of tape Debbie used. We know that she used 4 feet of tape for each large box, 2 feet of tape for each medium box, and 1 foot of tape for each small box. Therefore, we can calculate the total amount of tape used for each type of box by multiplying the number of boxes by the amount of tape needed for each box. For the large boxes, she used 4 feet of tape for each of the 2 boxes, so she used 8 feet of tape. For the medium boxes, she used 2 feet of tape for each of the 8 boxes, so she used 16 feet of tape. For the small boxes, she used 1 foot of tape for each of the 5 boxes, so she used 5 feet of tape. In total, she used 8 + 16 + 5 = 29 feet of tape. Additionally, she used 1 foot of tape for each box to stick the address label, so she used 15 feet of tape for the 15 boxes. Therefore, the total amount of tape she used is 29 + 15 = 44 feet.\n", - "Answer: 44 feet\n", - "\n", - "---\n", - "\n", - "Question: Heloise has dogs and cats in the ratio of 10:17, with the total number of pets being 189. If she gives 10 dogs to her friend Janet, how many dogs does she remain with altogether?\n", - "Reasoning: Let's think step by step in order to find the number of dogs Heloise remains with. We know that the total number of pets is 189, and the ratio of dogs to cats is 10:17. This means that for every 10 dogs, there are 17 cats. If we divide 189 by 27 (10+17), we get 7. This means that for every 7 sets of 10 dogs and 17 cats, there are 189 pets. Since we are only interested in the number of dogs, we can multiply 7 by 10 to get 70 dogs. If Heloise gives 10 dogs to Janet, she will remain with 70-10 = 60 dogs.\n", - "Answer: 60\n", - "\n", - "---\n", - "\n", - "Question: Jorge is 24 years younger than Simon. In 2005, Jorge is 16 years old. In 2010, how old would Simon be?\n", - "Answer: 45\n", - "\n", - "---\n", - "\n", - "Question: Sally is selling boxes of crackers for her scout troop's fund-raiser. If she sells 50% more on Sunday than she sold on Saturday, then she'll have sold a total of 150 boxes on the two days. How many boxes did she sell on Saturday?\n", - "Answer: 60\n", - "\n", - "---\n", - "\n", - "Question: Audrey is 7 years older than Heracles. In 3 years, Audrey will be twice as old as Heracles is now. How old is Heracles now?\n", - "Answer: 10\n", - "\n", - "---\n", - "\n", - "Question: Billy is breeding mice for an experiment. He starts with 8 mice, who each have 6 pups. When the pups grow up, all the mice have another 6 pups. Then each adult mouse eats 2 of their pups due to the stress of overcrowding. How many mice are left?\n", - "Answer: 280\n", - "\n", - "---\n", - "\n", - "Question: Cloud 9 Diving Company has taken individual bookings worth $12,000 and group bookings worth $16,000. Some people have cancelled at the last minute. $1600 has had to be returned to them. How much money has the sky diving company taken altogether?\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the total amount of money the sky diving company has taken. We know that they have taken individual bookings worth $12,000 and group bookings worth $16,000, for a total of $28,000. However, they had to return $1600 to some people who cancelled at the last minute. Therefore, the total amount of money they have taken is $28,000 - $1600 = $26,400.\n", - "Answer: 26400\u001b[0m\n", - "\n", - "\n", - "\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "RUN_FROM_SCRATCH = False\n", + "\n", + "if RUN_FROM_SCRATCH:\n", + " config = dict(max_bootstrapped_demos=8, max_labeled_demos=8, num_candidate_programs=10, num_threads=NUM_THREADS)\n", + " teleprompter = BootstrapFewShotWithRandomSearch(metric=gsm8k_metric, **config)\n", + " cot_bs = teleprompter.compile(CoT(), trainset=trainset, valset=devset)\n", + " # cot_bs.save('turbo_8_8_10_gsm8k_200_300.json')\n", + "else:\n", + " cot_bs = CoT()\n", + " cot_bs.load('turbo_8_8_10_gsm8k_200_300.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 241 / 300 (80.3): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 300/300 [00:00<00:00, 433.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 241 / 300 (80.3%)\n" + ] + }, + { + "data": { + "text/plain": [ + "80.33" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate(cot_bs, devset=devset[:])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Given the fields `question`, produce the fields `answer`.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Question: ${question}\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "Answer: ${answer}\n", + "\n", + "---\n", + "\n", + "Question: Mark is baking bread. He has to let it rise for 120 minutes twice. He also needs to spend 10 minutes kneading it and 30 minutes baking it. How many minutes does it take Mark to finish making the bread?\n", + "Reasoning: Let's think step by step in order to find the total time it takes Mark to finish making the bread. We know that he has to let the bread rise for 120 minutes twice, which is 240 minutes. He also needs to spend 10 minutes kneading it and 30 minutes baking it. Therefore, the total time it takes Mark to finish making the bread is 240 + 10 + 30 = 280 minutes.\n", + "Answer: 280\n", + "\n", + "---\n", + "\n", + "Question: Ben has $2000 for his business operations costs. He orders goods from his supplier and writes them a cheque for $600. His debtor pays him $800 from the purchases they had made on credit. Mr. Ben then decides to do equipment maintenance and spends $1200 on the whole operation. How much money is Mr. Ben remaining with?\n", + "Reasoning: Let's think step by step in order to find the remaining amount of money. We first start with the total amount of money Mr. Ben has, which is $2000. Then, we subtract the amount he spent on goods from his supplier, which is $600. Next, we add the amount he received from his debtor, which is $800. Finally, we subtract the amount he spent on equipment maintenance, which is $1200. We can represent this as $2000 - $600 + $800 - $1200 = $1000.\n", + "Answer: 1000\n", + "\n", + "---\n", + "\n", + "Question: Debbie works at a post office packing boxes to mail. Each large box takes 4 feet of packing tape to seal, each medium box takes 2 feet of packing tape to seal, and each small box takes 1 foot of packing tape to seal. Each box also takes 1 foot of packing tape to stick the address label on. Debbie packed two large boxes, eight medium boxes, and five small boxes this afternoon. How much tape did she use?\n", + "Reasoning: Let's think step by step in order to find the total amount of tape Debbie used. We know that she used 4 feet of tape for each large box, 2 feet of tape for each medium box, and 1 foot of tape for each small box. Therefore, we can calculate the total amount of tape used for each type of box by multiplying the number of boxes by the amount of tape needed for each box. For the large boxes, she used 4 feet of tape for each of the 2 boxes, so she used 8 feet of tape. For the medium boxes, she used 2 feet of tape for each of the 8 boxes, so she used 16 feet of tape. For the small boxes, she used 1 foot of tape for each of the 5 boxes, so she used 5 feet of tape. In total, she used 8 + 16 + 5 = 29 feet of tape. Additionally, she used 1 foot of tape for each box to stick the address label, so she used 15 feet of tape for the 15 boxes. Therefore, the total amount of tape she used is 29 + 15 = 44 feet.\n", + "Answer: 44 feet\n", + "\n", + "---\n", + "\n", + "Question: Heloise has dogs and cats in the ratio of 10:17, with the total number of pets being 189. If she gives 10 dogs to her friend Janet, how many dogs does she remain with altogether?\n", + "Reasoning: Let's think step by step in order to find the number of dogs Heloise remains with. We know that the total number of pets is 189, and the ratio of dogs to cats is 10:17. This means that for every 10 dogs, there are 17 cats. If we divide 189 by 27 (10+17), we get 7. This means that for every 7 sets of 10 dogs and 17 cats, there are 189 pets. Since we are only interested in the number of dogs, we can multiply 7 by 10 to get 70 dogs. If Heloise gives 10 dogs to Janet, she will remain with 70-10 = 60 dogs.\n", + "Answer: 60\n", + "\n", + "---\n", + "\n", + "Question: Jorge is 24 years younger than Simon. In 2005, Jorge is 16 years old. In 2010, how old would Simon be?\n", + "Answer: 45\n", + "\n", + "---\n", + "\n", + "Question: Sally is selling boxes of crackers for her scout troop's fund-raiser. If she sells 50% more on Sunday than she sold on Saturday, then she'll have sold a total of 150 boxes on the two days. How many boxes did she sell on Saturday?\n", + "Answer: 60\n", + "\n", + "---\n", + "\n", + "Question: Audrey is 7 years older than Heracles. In 3 years, Audrey will be twice as old as Heracles is now. How old is Heracles now?\n", + "Answer: 10\n", + "\n", + "---\n", + "\n", + "Question: Billy is breeding mice for an experiment. He starts with 8 mice, who each have 6 pups. When the pups grow up, all the mice have another 6 pups. Then each adult mouse eats 2 of their pups due to the stress of overcrowding. How many mice are left?\n", + "Answer: 280\n", + "\n", + "---\n", + "\n", + "Question: Cloud 9 Diving Company has taken individual bookings worth $12,000 and group bookings worth $16,000. Some people have cancelled at the last minute. $1600 has had to be returned to them. How much money has the sky diving company taken altogether?\n", + "Reasoning: Let's think step by step in order to\u001b[32m find the total amount of money the sky diving company has taken. We know that they have taken individual bookings worth $12,000 and group bookings worth $16,000, for a total of $28,000. However, they had to return $1600 to some people who cancelled at the last minute. Therefore, the total amount of money they have taken is $28,000 - $1600 = $26,400.\n", + "Answer: 26400\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "turbo.inspect_history(n=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "turbo.inspect_history(n=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39_aug2023_dspy", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" + ], + "metadata": { + "kernelspec": { + "display_name": "py39_aug2023_dspy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/math/gsm8k/gsm8k_assertions.ipynb b/examples/outdated_v2.4_examples/math/gsm8k/gsm8k_assertions.ipynb index bd92ad7682..ac6db4221f 100644 --- a/examples/outdated_v2.4_examples/math/gsm8k/gsm8k_assertions.ipynb +++ b/examples/outdated_v2.4_examples/math/gsm8k/gsm8k_assertions.ipynb @@ -1,659 +1,659 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "zdbPo0MbQ4R7" - }, - "source": [ - "\"DSPy7\n", - "\n", - "### **SolveGSM8k**: Solving grade school math problems using DSPy\n", - "\n", - "\n", - " \"Open\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6Aq1HjnZdV0e" - }, - "source": [ - "This notebook builds upon the foundational concepts of the DSPy framework. DSPy overs a novel programming-centric approach to utilizing language and retrieval models. It offers a unique blend of prompting, reasoning, fine-tuning, and tool augmentation, all encapsulated under a minimalistic Python syntax.\n", - "\n", - "We will focus on three parts:\n", - "\n", - "\n", - "1. Define a DSPy program and evaluate its performance.\n", - "3. Constrain DSPy program's behavior with runtime DSPy assertions and suggestions.\n", - "2. Optimize the DSPy program with in-context learning and prompt tuning.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NckBcbZN9p6y" - }, - "outputs": [], - "source": [ - "# Set up your API key for OpenAI\n", - "import os\n", - "os.environ[\"OPENAI_API_KEY\"]=\"Paste your key here\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ghG4tCe3e-kQ" - }, - "source": [ - "### Step -1. **Installing Cache and DSPy** (Run the collapsed cells)\n", - "\n", - "The cells are collapsed by default. Running the following cells will set up cache and install all dependencies and DSPy.\n", - "\n", - "The first cell ensures all the following LM calls in this notebook will be using the cached OpenAI's API result. Removing this step might sigificantly increase the running time of all the following DSPy programs depending on your OpenAI account setup.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gXLDcxrLPgBO" - }, - "outputs": [], - "source": [ - "# This cell sets up the cache (pre-computed OpenAI call results).\n", - "!rm -r gsm8k_cache || true\n", - "!rm -r dspy || true\n", - "!git clone https://github.com/Shangyint/gsm8k_cache.git\n", - "\n", - "import os\n", - "repo_clone_path = '/content/gsm8k_cache'\n", - "\n", - "# Check if '/content' is writable\n", - "if not os.access('/content', os.W_OK):\n", - " # If '/content' is not writable, choose an alternative directory\n", - " # Example: using a directory relative to the current working directory\n", - " repo_clone_path = os.path.join(os.getcwd(), 'gsm8k_cache')\n", - "\n", - "# Set up the cache for this notebook\n", - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = repo_clone_path" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BiS0I8Oc9HDC" - }, - "source": [ - "Before we start, we install DSPy and all dependencies." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "u8WPRleVRkln" - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import os\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install git+https://github.com/stanfordnlp/dspy.git\n", - " !pip install openai~=0.28.1\n", - "\n", - "from rich import print\n", - "import dspy\n", - "\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.datasets.gsm8k import GSM8K, gsm8k_metric\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xJcHuvwZ-rUk" - }, - "source": [ - "### Step 0. **Getting Started** (Run the collapsed cells)\n", - "\n", - "We'll start by importing our dataset GSM8K, a dataset containing 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. We preshuffled the dataset, and divided it into three smaller sets - train set, dev set (validation set), and test set. For simplicity, we will be using the train set and dev set.\n", - "\n", - "If you would like to inspect the dataset and see how we setup DSPy to use OpenAI gpt3, expand this step.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ZQDVRJ4wgFQx" - }, - "outputs": [], - "source": [ - "gms8k = GSM8K()\n", - "trainset, devset = gms8k.train, gms8k.dev\n", - "len(trainset), len(devset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cYMPUeeGgg9_" - }, - "source": [ - "Now we can inspect some examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "x3XWN_wfhgGW" - }, - "outputs": [], - "source": [ - "math_problem_example = devset[10]\n", - "\n", - "print(f\"Question: {math_problem_example.question}\\n\")\n", - "print(f\"Gold Reasoning: {math_problem_example.gold_reasoning}\\n\")\n", - "print(f\"Answer: {math_problem_example.answer}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "js5bCUuYcAks" - }, - "source": [ - "Then we set up the language model (LM). **DSPy** supports multiple API and local models. In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`).\n", - "\n", - "We configure **DSPy** to use the turbo LM (`gpt-3.5-turbo`) by default. This can be overwritten for local parts of programs if needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "DBQg4TQBS7GH" - }, - "outputs": [], - "source": [ - "turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500)\n", - "dspy.settings.configure(lm=turbo)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bCTCd0l1kQda" - }, - "source": [ - "### Step 1. **First DSPy program**\n", - "\n", - "In **DSPy**, we will maintain a clean separation between **defining your modules in a declarative way** and **calling them in a pipeline to solve the task**.\n", - "\n", - "This allows you to focus on the information flow of your pipeline. **DSPy** will then take your program and automatically optimize **how to prompt** (or finetune) LMs **for your particular pipeline** so it works well.\n", - "\n", - "If you have experience with PyTorch, you can think of DSPy as the PyTorch of the foundation model space. Before we see this in action, let's first understand some key pieces." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p8Mth2j8kgVS" - }, - "source": [ - "##### Using the Language Model: **Signatures** & **Predictors**\n", - "\n", - "Every call to the LM in a **DSPy** program needs to have a **Signature**.\n", - "\n", - "A signature consists of three simple elements:\n", - "\n", - "- A minimal description of the sub-task the LM is supposed to solve.\n", - "- A description of one or more input fields (e.g., input question) that we will give to the LM.\n", - "- A description of one or more output fields (e.g., the question's answer) that we will expect from the LM.\n", - "\n", - "Let's define a simple signature for basic math problem solving." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Ue7--j2Tk5qe" - }, - "outputs": [], - "source": [ - "class SimpleMathSignature(dspy.Signature):\n", - " \"\"\"Answer the math question.\"\"\"\n", - "\n", - " question = dspy.InputField(desc=\"A simple math question.\")\n", - " answer = dspy.OutputField(desc=\"The answer to the math question.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P-lX8Grsl9wT" - }, - "source": [ - "In `SimpleMathSignature`, the docstring describes the sub-task here (i.e., answering math questions). Each `InputField` or `OutputField` can optionally contain a description `desc` too. When it's not given, it's inferred from the field's name (e.g., `question`).\n", - "\n", - "Notice that there isn't anything special about this signature in **DSPy**. We can just as easily define a signature that takes a long snippet from a PDF and outputs structured information, for instance.\n", - "\n", - "One trick for DSPy signature is that when it only contains simple fields performing straightforward tasks, we can replace the whole class definition with a syntactic sugar `question -> answer`. Now, lets define our first DSPy program with DSPy predictor. A predictor is a module that knows how to use the LM to implement a signature. Importantly, predictors can learn to fit their behavior to the task!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "D3A1CH3foAFv" - }, - "outputs": [], - "source": [ - "basic_math_solver = dspy.Predict(\"question -> answer\") # Alternatively, we can write dspy.Predict(SimpleMathSignature)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_GBP5jBUpRQP" - }, - "source": [ - "**`DSPy.Predict`** is the simplest DSPy predictor. Now, we can call this minimal _program_ with a hand crafted question:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "w1rZSbu2pQY3" - }, - "outputs": [], - "source": [ - "prediction = basic_math_solver(question=\"What is 1+1+1?\")\n", - "\n", - "print(f\"Answer: {prediction.answer}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WxFlRkxVqPw_" - }, - "source": [ - "In the example above, we asked the predictor a simple math question \"What is 1+1+1?\". The model outputs an answer (\"3\").\n", - "\n", - "For visibility, we can inspect how this extremely basic predictor implemented our signature. Let's inspect the history of our LM (**turbo**)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7qMBWy-8qO0t" - }, - "outputs": [], - "source": [ - "_ = turbo.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VllCRN6tq-ty" - }, - "source": [ - "Great. Now let's define the actual program. This is a class that inherits from `dspy.Module`.\n", - "\n", - "It needs two methods:\n", - "\n", - "- The `__init__` method will simply declare the sub-modules it needs: This time, we will be using a fancier predictor that implementes Chain-of-Thought prompting `dspy.ChainOfThought`. `dspy.ChainOfThought` will add another field called \"rationale\" as output to help the model think step-by-step.\n", - "- The `forward` method will describe the control flow of answering the question using the modules we have (here, we just have one)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kpxVuUblVvP7" - }, - "outputs": [], - "source": [ - "class SimpleMathSolver(dspy.Module):\n", - " def __init__(self):\n", - " self.prog = dspy.ChainOfThought(\"question -> answer\")\n", - "\n", - " def forward(self, question):\n", - " pred = self.prog(question=question)\n", - " return pred\n", - "\n", - "simple_math_solver = SimpleMathSolver()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sUmsMEGtrjm7" - }, - "source": [ - "#### **Exercise**\n", - "Create your own math problem, use the math solver we just defined. Then, inspect the trace of the LM with `turbo.inspect_history` to see what has changed compared to the `dspy.Predict` predictor." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cXUH1WaOsIEr" - }, - "outputs": [], - "source": [ - "### Fill this code cell" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pVC0KPuvsoMD" - }, - "source": [ - "We can now evaluate our simple math solver on the validation set.\n", - "\n", - "For a start, let's evaluate the accuracy of the predicted answer. We provide a simple metric function called `gsm8k_metric`, which essentially extract the numerical answer from the model input." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "EBOdvzCGTBYD" - }, - "outputs": [], - "source": [ - "evaluate = Evaluate(devset=devset[:], metric=gsm8k_metric, num_threads=16, display_progress=True, display_table=5)\n", - "\n", - "evaluate(simple_math_solver)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CNFkEVkvtaWz" - }, - "source": [ - "### Step 2. **Adding constraints with DSPy Assertions**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rFKOzC4NxkSe" - }, - "source": [ - "We have **61.67%** on our validation set, not bad! But we also noticed two things: 1). many answers are sentences rather than the numerical result we want. Although we are able to parse most of the answers within `gsm8k_metric`, generating irrevalent tokens as answers might negatively affect the overall accuracy; 2). some of the reasoning might not contain the desired computational steps as in the example below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "EMrMDwRXAdqi" - }, - "outputs": [], - "source": [ - "simple_math_solver(devset[0].question)\n", - "_ = turbo.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "90BxOnIrAgnD" - }, - "source": [ - "Forturnately, in DSPy, we can utilize a simple yet powerful construct called **LM Assertions** to constrain the output of LMs. For example, here, we can say:\n", - "\n", - "```python\n", - "dspy.Suggest(len(pred.answer) < 10, \"Your Answer should be a number.\")\n", - "```\n", - "\n", - "This suggestion tells the DSPy runtime that we expect the answer of our math solver to be short, and if the LM failed to yield such an answer, we instruct the LM that \"Your Answer should be a number.\"\n", - "\n", - "LM assertions in DSPy could either be a hard constraint `Assert` or a soft constraint `Suggest`. LM assertions accept two argument, one is the predicate to be tested, similar to that of traditional assertions; then, we also require an additional \"error message\" to guide the language model to refine itself when failing.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W2dBQ-g3YvO9" - }, - "source": [ - "#### Math Solver with Suggestions\n", - "We can encode the two observations we have into two suggestions, and add them to the `SimpleMathSolver`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "UDCY7Jw3ys86" - }, - "outputs": [], - "source": [ - "def extract_number(question):\n", - " numbers = [int(s) for s in question.split() if s.isdigit()]\n", - " return numbers\n", - "\n", - "def has_numbers(rationale, numbers):\n", - " for number in numbers:\n", - " if str(number) not in rationale:\n", - " return False, number\n", - " return True, None\n", - "\n", - "class SimpleMathSolverWithSuggest(dspy.Module):\n", - " def __init__(self):\n", - " self.prog = dspy.ChainOfThought(\"question -> answer\")\n", - "\n", - " def forward(self, question):\n", - " pred = self.prog(question=question)\n", - " rationale_has_numbers, missing_number = has_numbers(pred.rationale, extract_number(question))\n", - " dspy.Suggest(rationale_has_numbers, f\"Your Reasoning should contain {missing_number}.\")\n", - " dspy.Suggest(len(pred.answer) < 10, \"Your Answer should be a number.\")\n", - " return pred\n", - "\n", - "simple_math_solver_suggest = SimpleMathSolverWithSuggest().activate_assertions()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VWuOTcUIZLtj" - }, - "source": [ - "Now we can rerun our math solver on the first question, and see how LM assertions in DSPy internally fix these errors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bANEA2jmFXpM" - }, - "outputs": [], - "source": [ - "simple_math_solver_suggest(devset[0].question)\n", - "_ = turbo.inspect_history(n=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jzHHqOA0Zb9x" - }, - "source": [ - "Finally, let's evaluate the performance of `simple_math_solver_suggest`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ASzhftKkZbHx" - }, - "outputs": [], - "source": [ - "evaluate(simple_math_solver_suggest)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "35xoJc9lye7x" - }, - "source": [ - "### Step 3. **Compiling DSPy programs with optimizers**\n", - "\n", - "Another cool thing to do with DSPy is optimizers!\n", - "\n", - "A DSPy optimizer is an algorithm that can tune the parameters of a DSPy program (i.e., the prompts and/or the LM weights) to maximize the metrics you specify, like accuracy.\n", - "\n", - "There are many built-in optimizers in DSPy, which apply vastly different strategies. A typical DSPy optimizer takes three things:\n", - "\n", - "1. Your **DSPy program**. This may be a single module (e.g., dspy.Predict) or a complex multi-module program.\n", - "\n", - "2. Your **metric**. This is a function that evaluates the output of your program, and assigns it a score (higher is better).\n", - "\n", - "3. A few **training inputs**. This may be very small (i.e., only 5 or 10 examples) and incomplete (only inputs to your program, without any labels).\n", - "\n", - "If you happen to have a lot of data, DSPy can leverage that. But you can start small and get strong results.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FV5worzCvkyA" - }, - "source": [ - "In this turtorial, we demonstrate one DSPy optimizer called `BootstrapFewShotWithRandomSearch`, which bootstraps demonstrations from the training set and search for the best combination of demonstrations. Two things to note here:\n", - "1. Most optimizers work with LM assertions.\n", - "2. This step is time/compute intensive. Therefore we cached the API calls. The good thing is, once you optmized the program, you can save the compiled DSPy program and reuse it later!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xF4Y2LMxnR1j" - }, - "outputs": [], - "source": [ - "optimizer = BootstrapFewShotWithRandomSearch(gsm8k_metric, max_bootstrapped_demos=3, max_labeled_demos=6, num_candidate_programs=6)\n", - "\n", - "compiled_prog = optimizer.compile(student=simple_math_solver, trainset=trainset[:], valset=devset[:100])\n", - "compiled_prog_suggest = optimizer.compile(student=simple_math_solver_suggest, trainset=trainset[:], valset=devset[:100])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xiKwKBPJnEN6" - }, - "outputs": [], - "source": [ - "# Evaluating compiled program\n", - "evaluate(compiled_prog)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nRRDcsV-AF8_" - }, - "outputs": [], - "source": [ - "# Evaluating compiled program with suggestions\n", - "evaluate(compiled_prog_suggest)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Mz4u8j-k2lNY" - }, - "source": [ - "Now we inspect on our previous example, and see how the optmizer tunes the prompt:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7Esd1Ry92vIt" - }, - "outputs": [], - "source": [ - "compiled_prog(devset[0].question)\n", - "_ = turbo.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UGG4S1TpUnFz" - }, - "source": [ - "### More DSPy turtorials\n", - "\n", - "1. [Intro to DSPy](https://github.com/stanfordnlp/dspy/blob/main/intro.ipynb)\n", - "2. [DSPy Assertions](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/longformqa/longformqa_assertions.ipynb)\n", - "3. [Quiz Generation](https://github.com/stanfordnlp/dspy/blob/main/examples/quiz/quiz_assertions.ipynb)\n", - "4. ... more on [DSPy github](https://github.com/stanfordnlp/dspy)\n", - "\n", - "#### Contact: Shangyin Tan (shangyin@berkeley.edu)" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [ - "ghG4tCe3e-kQ", - "xJcHuvwZ-rUk" - ], - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 0 + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "zdbPo0MbQ4R7" + }, + "source": [ + "\"DSPy7\n", + "\n", + "### **SolveGSM8k**: Solving grade school math problems using DSPy\n", + "\n", + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Aq1HjnZdV0e" + }, + "source": [ + "This notebook builds upon the foundational concepts of the DSPy framework. DSPy overs a novel programming-centric approach to utilizing language and retrieval models. It offers a unique blend of prompting, reasoning, fine-tuning, and tool augmentation, all encapsulated under a minimalistic Python syntax.\n", + "\n", + "We will focus on three parts:\n", + "\n", + "\n", + "1. Define a DSPy program and evaluate its performance.\n", + "3. Constrain DSPy program's behavior with runtime DSPy assertions and suggestions.\n", + "2. Optimize the DSPy program with in-context learning and prompt tuning.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NckBcbZN9p6y" + }, + "outputs": [], + "source": [ + "# Set up your API key for OpenAI\n", + "import os\n", + "os.environ[\"OPENAI_API_KEY\"]=\"Paste your key here\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ghG4tCe3e-kQ" + }, + "source": [ + "### Step -1. **Installing Cache and DSPy** (Run the collapsed cells)\n", + "\n", + "The cells are collapsed by default. Running the following cells will set up cache and install all dependencies and DSPy.\n", + "\n", + "The first cell ensures all the following LM calls in this notebook will be using the cached OpenAI's API result. Removing this step might sigificantly increase the running time of all the following DSPy programs depending on your OpenAI account setup.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gXLDcxrLPgBO" + }, + "outputs": [], + "source": [ + "# This cell sets up the cache (pre-computed OpenAI call results).\n", + "!rm -r gsm8k_cache || true\n", + "!rm -r dspy || true\n", + "!git clone https://github.com/Shangyint/gsm8k_cache.git\n", + "\n", + "import os\n", + "repo_clone_path = '/content/gsm8k_cache'\n", + "\n", + "# Check if '/content' is writable\n", + "if not os.access('/content', os.W_OK):\n", + " # If '/content' is not writable, choose an alternative directory\n", + " # Example: using a directory relative to the current working directory\n", + " repo_clone_path = os.path.join(os.getcwd(), 'gsm8k_cache')\n", + "\n", + "# Set up the cache for this notebook\n", + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = repo_clone_path" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BiS0I8Oc9HDC" + }, + "source": [ + "Before we start, we install DSPy and all dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "u8WPRleVRkln" + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import os\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install git+https://github.com/stanfordnlp/dspy.git\n", + " !pip install openai~=0.28.1\n", + "\n", + "from rich import print\n", + "import dspy\n", + "\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.datasets.gsm8k import GSM8K, gsm8k_metric\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xJcHuvwZ-rUk" + }, + "source": [ + "### Step 0. **Getting Started** (Run the collapsed cells)\n", + "\n", + "We'll start by importing our dataset GSM8K, a dataset containing 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. We preshuffled the dataset, and divided it into three smaller sets - train set, dev set (validation set), and test set. For simplicity, we will be using the train set and dev set.\n", + "\n", + "If you would like to inspect the dataset and see how we setup DSPy to use OpenAI gpt3, expand this step.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZQDVRJ4wgFQx" + }, + "outputs": [], + "source": [ + "gms8k = GSM8K()\n", + "trainset, devset = gms8k.train, gms8k.dev\n", + "len(trainset), len(devset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cYMPUeeGgg9_" + }, + "source": [ + "Now we can inspect some examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x3XWN_wfhgGW" + }, + "outputs": [], + "source": [ + "math_problem_example = devset[10]\n", + "\n", + "print(f\"Question: {math_problem_example.question}\\n\")\n", + "print(f\"Gold Reasoning: {math_problem_example.gold_reasoning}\\n\")\n", + "print(f\"Answer: {math_problem_example.answer}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "js5bCUuYcAks" + }, + "source": [ + "Then we set up the language model (LM). **DSPy** supports multiple API and local models. In this notebook, we'll work with GPT-3.5 (`gpt-3.5-turbo`).\n", + "\n", + "We configure **DSPy** to use the turbo LM (`gpt-3.5-turbo`) by default. This can be overwritten for local parts of programs if needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DBQg4TQBS7GH" + }, + "outputs": [], + "source": [ + "turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500)\n", + "dspy.settings.configure(lm=turbo)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bCTCd0l1kQda" + }, + "source": [ + "### Step 1. **First DSPy program**\n", + "\n", + "In **DSPy**, we will maintain a clean separation between **defining your modules in a declarative way** and **calling them in a pipeline to solve the task**.\n", + "\n", + "This allows you to focus on the information flow of your pipeline. **DSPy** will then take your program and automatically optimize **how to prompt** (or finetune) LMs **for your particular pipeline** so it works well.\n", + "\n", + "If you have experience with PyTorch, you can think of DSPy as the PyTorch of the foundation model space. Before we see this in action, let's first understand some key pieces." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p8Mth2j8kgVS" + }, + "source": [ + "##### Using the Language Model: **Signatures** & **Predictors**\n", + "\n", + "Every call to the LM in a **DSPy** program needs to have a **Signature**.\n", + "\n", + "A signature consists of three simple elements:\n", + "\n", + "- A minimal description of the sub-task the LM is supposed to solve.\n", + "- A description of one or more input fields (e.g., input question) that we will give to the LM.\n", + "- A description of one or more output fields (e.g., the question's answer) that we will expect from the LM.\n", + "\n", + "Let's define a simple signature for basic math problem solving." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ue7--j2Tk5qe" + }, + "outputs": [], + "source": [ + "class SimpleMathSignature(dspy.Signature):\n", + " \"\"\"Answer the math question.\"\"\"\n", + "\n", + " question = dspy.InputField(desc=\"A simple math question.\")\n", + " answer = dspy.OutputField(desc=\"The answer to the math question.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P-lX8Grsl9wT" + }, + "source": [ + "In `SimpleMathSignature`, the docstring describes the sub-task here (i.e., answering math questions). Each `InputField` or `OutputField` can optionally contain a description `desc` too. When it's not given, it's inferred from the field's name (e.g., `question`).\n", + "\n", + "Notice that there isn't anything special about this signature in **DSPy**. We can just as easily define a signature that takes a long snippet from a PDF and outputs structured information, for instance.\n", + "\n", + "One trick for DSPy signature is that when it only contains simple fields performing straightforward tasks, we can replace the whole class definition with a syntactic sugar `question -> answer`. Now, lets define our first DSPy program with DSPy predictor. A predictor is a module that knows how to use the LM to implement a signature. Importantly, predictors can learn to fit their behavior to the task!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "D3A1CH3foAFv" + }, + "outputs": [], + "source": [ + "basic_math_solver = dspy.Predict(\"question -> answer\") # Alternatively, we can write dspy.Predict(SimpleMathSignature)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_GBP5jBUpRQP" + }, + "source": [ + "**`DSPy.Predict`** is the simplest DSPy predictor. Now, we can call this minimal _program_ with a hand crafted question:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "w1rZSbu2pQY3" + }, + "outputs": [], + "source": [ + "prediction = basic_math_solver(question=\"What is 1+1+1?\")\n", + "\n", + "print(f\"Answer: {prediction.answer}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WxFlRkxVqPw_" + }, + "source": [ + "In the example above, we asked the predictor a simple math question \"What is 1+1+1?\". The model outputs an answer (\"3\").\n", + "\n", + "For visibility, we can inspect how this extremely basic predictor implemented our signature. Let's inspect the history of our LM (**turbo**)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7qMBWy-8qO0t" + }, + "outputs": [], + "source": [ + "_ = turbo.inspect_history(n=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VllCRN6tq-ty" + }, + "source": [ + "Great. Now let's define the actual program. This is a class that inherits from `dspy.Module`.\n", + "\n", + "It needs two methods:\n", + "\n", + "- The `__init__` method will simply declare the sub-modules it needs: This time, we will be using a fancier predictor that implementes Chain-of-Thought prompting `dspy.ChainOfThought`. `dspy.ChainOfThought` will add another field called \"rationale\" as output to help the model think step-by-step.\n", + "- The `forward` method will describe the control flow of answering the question using the modules we have (here, we just have one)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kpxVuUblVvP7" + }, + "outputs": [], + "source": [ + "class SimpleMathSolver(dspy.Module):\n", + " def __init__(self):\n", + " self.prog = dspy.ChainOfThought(\"question -> answer\")\n", + "\n", + " def forward(self, question):\n", + " pred = self.prog(question=question)\n", + " return pred\n", + "\n", + "simple_math_solver = SimpleMathSolver()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sUmsMEGtrjm7" + }, + "source": [ + "#### **Exercise**\n", + "Create your own math problem, use the math solver we just defined. Then, inspect the trace of the LM with `turbo.inspect_history` to see what has changed compared to the `dspy.Predict` predictor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cXUH1WaOsIEr" + }, + "outputs": [], + "source": [ + "### Fill this code cell" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pVC0KPuvsoMD" + }, + "source": [ + "We can now evaluate our simple math solver on the validation set.\n", + "\n", + "For a start, let's evaluate the accuracy of the predicted answer. We provide a simple metric function called `gsm8k_metric`, which essentially extract the numerical answer from the model input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EBOdvzCGTBYD" + }, + "outputs": [], + "source": [ + "evaluate = Evaluate(devset=devset[:], metric=gsm8k_metric, num_threads=16, display_progress=True, display_table=5)\n", + "\n", + "evaluate(simple_math_solver)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CNFkEVkvtaWz" + }, + "source": [ + "### Step 2. **Adding constraints with DSPy Assertions**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFKOzC4NxkSe" + }, + "source": [ + "We have **61.67%** on our validation set, not bad! But we also noticed two things: 1). many answers are sentences rather than the numerical result we want. Although we are able to parse most of the answers within `gsm8k_metric`, generating irrevalent tokens as answers might negatively affect the overall accuracy; 2). some of the reasoning might not contain the desired computational steps as in the example below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EMrMDwRXAdqi" + }, + "outputs": [], + "source": [ + "simple_math_solver(devset[0].question)\n", + "_ = turbo.inspect_history()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "90BxOnIrAgnD" + }, + "source": [ + "Forturnately, in DSPy, we can utilize a simple yet powerful construct called **LM Assertions** to constrain the output of LMs. For example, here, we can say:\n", + "\n", + "```python\n", + "dspy.Suggest(len(pred.answer) < 10, \"Your Answer should be a number.\")\n", + "```\n", + "\n", + "This suggestion tells the DSPy runtime that we expect the answer of our math solver to be short, and if the LM failed to yield such an answer, we instruct the LM that \"Your Answer should be a number.\"\n", + "\n", + "LM assertions in DSPy could either be a hard constraint `Assert` or a soft constraint `Suggest`. LM assertions accept two argument, one is the predicate to be tested, similar to that of traditional assertions; then, we also require an additional \"error message\" to guide the language model to refine itself when failing.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W2dBQ-g3YvO9" + }, + "source": [ + "#### Math Solver with Suggestions\n", + "We can encode the two observations we have into two suggestions, and add them to the `SimpleMathSolver`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UDCY7Jw3ys86" + }, + "outputs": [], + "source": [ + "def extract_number(question):\n", + " numbers = [int(s) for s in question.split() if s.isdigit()]\n", + " return numbers\n", + "\n", + "def has_numbers(rationale, numbers):\n", + " for number in numbers:\n", + " if str(number) not in rationale:\n", + " return False, number\n", + " return True, None\n", + "\n", + "class SimpleMathSolverWithSuggest(dspy.Module):\n", + " def __init__(self):\n", + " self.prog = dspy.ChainOfThought(\"question -> answer\")\n", + "\n", + " def forward(self, question):\n", + " pred = self.prog(question=question)\n", + " rationale_has_numbers, missing_number = has_numbers(pred.rationale, extract_number(question))\n", + " dspy.Suggest(rationale_has_numbers, f\"Your Reasoning should contain {missing_number}.\")\n", + " dspy.Suggest(len(pred.answer) < 10, \"Your Answer should be a number.\")\n", + " return pred\n", + "\n", + "simple_math_solver_suggest = SimpleMathSolverWithSuggest().activate_assertions()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VWuOTcUIZLtj" + }, + "source": [ + "Now we can rerun our math solver on the first question, and see how LM assertions in DSPy internally fix these errors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bANEA2jmFXpM" + }, + "outputs": [], + "source": [ + "simple_math_solver_suggest(devset[0].question)\n", + "_ = turbo.inspect_history(n=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jzHHqOA0Zb9x" + }, + "source": [ + "Finally, let's evaluate the performance of `simple_math_solver_suggest`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ASzhftKkZbHx" + }, + "outputs": [], + "source": [ + "evaluate(simple_math_solver_suggest)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "35xoJc9lye7x" + }, + "source": [ + "### Step 3. **Compiling DSPy programs with optimizers**\n", + "\n", + "Another cool thing to do with DSPy is optimizers!\n", + "\n", + "A DSPy optimizer is an algorithm that can tune the parameters of a DSPy program (i.e., the prompts and/or the LM weights) to maximize the metrics you specify, like accuracy.\n", + "\n", + "There are many built-in optimizers in DSPy, which apply vastly different strategies. A typical DSPy optimizer takes three things:\n", + "\n", + "1. Your **DSPy program**. This may be a single module (e.g., dspy.Predict) or a complex multi-module program.\n", + "\n", + "2. Your **metric**. This is a function that evaluates the output of your program, and assigns it a score (higher is better).\n", + "\n", + "3. A few **training inputs**. This may be very small (i.e., only 5 or 10 examples) and incomplete (only inputs to your program, without any labels).\n", + "\n", + "If you happen to have a lot of data, DSPy can leverage that. But you can start small and get strong results.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FV5worzCvkyA" + }, + "source": [ + "In this turtorial, we demonstrate one DSPy optimizer called `BootstrapFewShotWithRandomSearch`, which bootstraps demonstrations from the training set and search for the best combination of demonstrations. Two things to note here:\n", + "1. Most optimizers work with LM assertions.\n", + "2. This step is time/compute intensive. Therefore we cached the API calls. The good thing is, once you optmized the program, you can save the compiled DSPy program and reuse it later!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xF4Y2LMxnR1j" + }, + "outputs": [], + "source": [ + "optimizer = BootstrapFewShotWithRandomSearch(gsm8k_metric, max_bootstrapped_demos=3, max_labeled_demos=6, num_candidate_programs=6)\n", + "\n", + "compiled_prog = optimizer.compile(student=simple_math_solver, trainset=trainset[:], valset=devset[:100])\n", + "compiled_prog_suggest = optimizer.compile(student=simple_math_solver_suggest, trainset=trainset[:], valset=devset[:100])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xiKwKBPJnEN6" + }, + "outputs": [], + "source": [ + "# Evaluating compiled program\n", + "evaluate(compiled_prog)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nRRDcsV-AF8_" + }, + "outputs": [], + "source": [ + "# Evaluating compiled program with suggestions\n", + "evaluate(compiled_prog_suggest)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mz4u8j-k2lNY" + }, + "source": [ + "Now we inspect on our previous example, and see how the optmizer tunes the prompt:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7Esd1Ry92vIt" + }, + "outputs": [], + "source": [ + "compiled_prog(devset[0].question)\n", + "_ = turbo.inspect_history()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UGG4S1TpUnFz" + }, + "source": [ + "### More DSPy turtorials\n", + "\n", + "1. [Intro to DSPy](https://github.com/stanfordnlp/dspy/blob/main/intro.ipynb)\n", + "2. [DSPy Assertions](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/longformqa/longformqa_assertions.ipynb)\n", + "3. [Quiz Generation](https://github.com/stanfordnlp/dspy/blob/main/examples/quiz/quiz_assertions.ipynb)\n", + "4. ... more on [DSPy github](https://github.com/stanfordnlp/dspy)\n", + "\n", + "#### Contact: Shangyin Tan (shangyin@berkeley.edu)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "ghG4tCe3e-kQ", + "xJcHuvwZ-rUk" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json b/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json index 9b6cc109b9..165bde8d9f 100644 --- a/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json +++ b/examples/outdated_v2.4_examples/math/gsm8k/turbo_8_8_10_gsm8k_200_300.json @@ -1,53 +1,53 @@ { "prog": { - "lm": null, - "traces": [], - "train": [], "demos": [ { + "answer": "280", "augmented": true, "question": "Mark is baking bread. He has to let it rise for 120 minutes twice. He also needs to spend 10 minutes kneading it and 30 minutes baking it. How many minutes does it take Mark to finish making the bread?", - "rationale": "find the total time it takes Mark to finish making the bread. We know that he has to let the bread rise for 120 minutes twice, which is 240 minutes. He also needs to spend 10 minutes kneading it and 30 minutes baking it. Therefore, the total time it takes Mark to finish making the bread is 240 + 10 + 30 = 280 minutes.", - "answer": "280" + "rationale": "find the total time it takes Mark to finish making the bread. We know that he has to let the bread rise for 120 minutes twice, which is 240 minutes. He also needs to spend 10 minutes kneading it and 30 minutes baking it. Therefore, the total time it takes Mark to finish making the bread is 240 + 10 + 30 = 280 minutes." }, { + "answer": "1000", "augmented": true, "question": "Ben has $2000 for his business operations costs. He orders goods from his supplier and writes them a cheque for $600. His debtor pays him $800 from the purchases they had made on credit. Mr. Ben then decides to do equipment maintenance and spends $1200 on the whole operation. How much money is Mr. Ben remaining with?", - "rationale": "find the remaining amount of money. We first start with the total amount of money Mr. Ben has, which is $2000. Then, we subtract the amount he spent on goods from his supplier, which is $600. Next, we add the amount he received from his debtor, which is $800. Finally, we subtract the amount he spent on equipment maintenance, which is $1200. We can represent this as $2000 - $600 + $800 - $1200 = $1000.", - "answer": "1000" + "rationale": "find the remaining amount of money. We first start with the total amount of money Mr. Ben has, which is $2000. Then, we subtract the amount he spent on goods from his supplier, which is $600. Next, we add the amount he received from his debtor, which is $800. Finally, we subtract the amount he spent on equipment maintenance, which is $1200. We can represent this as $2000 - $600 + $800 - $1200 = $1000." }, { + "answer": "44 feet", "augmented": true, "question": "Debbie works at a post office packing boxes to mail. Each large box takes 4 feet of packing tape to seal, each medium box takes 2 feet of packing tape to seal, and each small box takes 1 foot of packing tape to seal. Each box also takes 1 foot of packing tape to stick the address label on. Debbie packed two large boxes, eight medium boxes, and five small boxes this afternoon. How much tape did she use?", - "rationale": "find the total amount of tape Debbie used. We know that she used 4 feet of tape for each large box, 2 feet of tape for each medium box, and 1 foot of tape for each small box. Therefore, we can calculate the total amount of tape used for each type of box by multiplying the number of boxes by the amount of tape needed for each box. For the large boxes, she used 4 feet of tape for each of the 2 boxes, so she used 8 feet of tape. For the medium boxes, she used 2 feet of tape for each of the 8 boxes, so she used 16 feet of tape. For the small boxes, she used 1 foot of tape for each of the 5 boxes, so she used 5 feet of tape. In total, she used 8 + 16 + 5 = 29 feet of tape. Additionally, she used 1 foot of tape for each box to stick the address label, so she used 15 feet of tape for the 15 boxes. Therefore, the total amount of tape she used is 29 + 15 = 44 feet.", - "answer": "44 feet" + "rationale": "find the total amount of tape Debbie used. We know that she used 4 feet of tape for each large box, 2 feet of tape for each medium box, and 1 foot of tape for each small box. Therefore, we can calculate the total amount of tape used for each type of box by multiplying the number of boxes by the amount of tape needed for each box. For the large boxes, she used 4 feet of tape for each of the 2 boxes, so she used 8 feet of tape. For the medium boxes, she used 2 feet of tape for each of the 8 boxes, so she used 16 feet of tape. For the small boxes, she used 1 foot of tape for each of the 5 boxes, so she used 5 feet of tape. In total, she used 8 + 16 + 5 = 29 feet of tape. Additionally, she used 1 foot of tape for each box to stick the address label, so she used 15 feet of tape for the 15 boxes. Therefore, the total amount of tape she used is 29 + 15 = 44 feet." }, { + "answer": "60", "augmented": true, "question": "Heloise has dogs and cats in the ratio of 10:17, with the total number of pets being 189. If she gives 10 dogs to her friend Janet, how many dogs does she remain with altogether?", - "rationale": "find the number of dogs Heloise remains with. We know that the total number of pets is 189, and the ratio of dogs to cats is 10:17. This means that for every 10 dogs, there are 17 cats. If we divide 189 by 27 (10+17), we get 7. This means that for every 7 sets of 10 dogs and 17 cats, there are 189 pets. Since we are only interested in the number of dogs, we can multiply 7 by 10 to get 70 dogs. If Heloise gives 10 dogs to Janet, she will remain with 70-10 = 60 dogs.", - "answer": "60" + "rationale": "find the number of dogs Heloise remains with. We know that the total number of pets is 189, and the ratio of dogs to cats is 10:17. This means that for every 10 dogs, there are 17 cats. If we divide 189 by 27 (10+17), we get 7. This means that for every 7 sets of 10 dogs and 17 cats, there are 189 pets. Since we are only interested in the number of dogs, we can multiply 7 by 10 to get 70 dogs. If Heloise gives 10 dogs to Janet, she will remain with 70-10 = 60 dogs." }, { - "question": "Jorge is 24 years younger than Simon. In 2005, Jorge is 16 years old. In 2010, how old would Simon be?", + "answer": "45", "gold_reasoning": "In 2005, Simon was 16+24 = <<16+24=40>>40 years old. There are 2010-2005 = <<2010-2005=5>>5 years between 2005 and 2010. In 2010 Simon would be 40+5 = 45 years old.", - "answer": "45" + "question": "Jorge is 24 years younger than Simon. In 2005, Jorge is 16 years old. In 2010, how old would Simon be?" }, { - "question": "Sally is selling boxes of crackers for her scout troop's fund-raiser. If she sells 50% more on Sunday than she sold on Saturday, then she'll have sold a total of 150 boxes on the two days. How many boxes did she sell on Saturday?", + "answer": "60", "gold_reasoning": "Let S be the number of boxes sold on Saturday. S+1.5S=150 2.5S=150 S=<<60=60>>60 Sally sold 60 boxes on Saturday.", - "answer": "60" + "question": "Sally is selling boxes of crackers for her scout troop's fund-raiser. If she sells 50% more on Sunday than she sold on Saturday, then she'll have sold a total of 150 boxes on the two days. How many boxes did she sell on Saturday?" }, { - "question": "Audrey is 7 years older than Heracles. In 3 years, Audrey will be twice as old as Heracles is now. How old is Heracles now?", + "answer": "10", "gold_reasoning": "Since Audrey is older than Heracles by 7 years, in 3 years he will be 10 years older than Heracles' current age In 3 years he will be double Heracles' current age 10 * 2 = <<10*2=20>>20 Heracles is <<10=10>>10", - "answer": "10" + "question": "Audrey is 7 years older than Heracles. In 3 years, Audrey will be twice as old as Heracles is now. How old is Heracles now?" }, { - "question": "Billy is breeding mice for an experiment. He starts with 8 mice, who each have 6 pups. When the pups grow up, all the mice have another 6 pups. Then each adult mouse eats 2 of their pups due to the stress of overcrowding. How many mice are left?", - "gold_reasoning": "First find the number of pups in the first generation: 8 mice * 6 pups\/mouse = <<8*6=48>>48 pups Then add the number of adult mice to find the total number of mice: 48 pups + 8 mice = <<48+8=56>>56 mice Then find the number of pups per mouse that survive from the second generation: 6 pups\/mouse - 2 pups\/mouse = <<6-2=4>>4 pups\/mouse Then multiply that number by the number of adult mice to find the number of pups in the second generation: 56 mice * 4 pups\/mouse = <<56*4=224>>224 mice Then add the number of adult mice to the number of second-generation pups to find the total number of mice: 224 mice + 56 mice = <<224+56=280>>280 mice", - "answer": "280" + "answer": "280", + "gold_reasoning": "First find the number of pups in the first generation: 8 mice * 6 pups/mouse = <<8*6=48>>48 pups Then add the number of adult mice to find the total number of mice: 48 pups + 8 mice = <<48+8=56>>56 mice Then find the number of pups per mouse that survive from the second generation: 6 pups/mouse - 2 pups/mouse = <<6-2=4>>4 pups/mouse Then multiply that number by the number of adult mice to find the number of pups in the second generation: 56 mice * 4 pups/mouse = <<56*4=224>>224 mice Then add the number of adult mice to the number of second-generation pups to find the total number of mice: 224 mice + 56 mice = <<224+56=280>>280 mice", + "question": "Billy is breeding mice for an experiment. He starts with 8 mice, who each have 6 pups. When the pups grow up, all the mice have another 6 pups. Then each adult mouse eats 2 of their pups due to the stress of overcrowding. How many mice are left?" } - ] + ], + "lm": null, + "traces": [], + "train": [] } } diff --git a/examples/outdated_v2.4_examples/multi-input-output/beginner-multi-input-output.ipynb b/examples/outdated_v2.4_examples/multi-input-output/beginner-multi-input-output.ipynb index 60fef87e38..5165087945 100644 --- a/examples/outdated_v2.4_examples/multi-input-output/beginner-multi-input-output.ipynb +++ b/examples/outdated_v2.4_examples/multi-input-output/beginner-multi-input-output.ipynb @@ -1,1105 +1,1105 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DSPy Tutorial: Building a Code Plagiarism Detector\n", - "\n", - "If you've ever felt intimidated by DSPy, don't worry—it might look complex at first glance, but it's actually quite approachable. This tutorial will walk you through the process of building a project, providing a clear, step-by-step approach to understanding and implementing DSPy concepts.\n", - "\n", - "## TLDR 🚀\n", - "\n", - "We're going to build a system for code plagiarism detection. Our goal is to compare two input code files, determine if plagiarism has occurred, and provide an explanation for the result. \n", - "\n", - "This project will showcase:\n", - "\n", - "- Multiple inputs and outputs\n", - "- Double validation techniques\n", - "\n", - "I strongly recommend reading the [DSPy Cheatsheet](https://dspy.ai/cheatsheet/) it will help you with quick start.\n", - "\n", - "## How to Start?\n", - "\n", - "A highly effective practice I've found to be game-changing when starting any DSPy project is to answer [these 8 key questions](https://dspy-docs.vercel.app/docs/building-blocks/solving_your_task). This exercise helps you develop a clear vision for your project before diving into the code.\n", - "\n", - "Here's an example of how your answers might look:\n", - "1. **Define your task**\n", - " - Expected input: Two input code files (strings containing plain code) to be compared.\n", - " - Expected output:\n", - " - Plagiarism detection result (Yes/No)\n", - " - Explanation/justification of the result\n", - " - Quality and Cost Specifications: Cost is not a concern; quality is the main priority. We want to try different models.\n", - "\n", - "2. **Define your pipeline**\n", - " - We don't need any external tools or document retrieval. It will be a simple chain-of-thought step, as we want to evaluate LLM capabilities for plagiarism detection.\n", - "\n", - "3. **Explore a few examples**\n", - " - We explored LLM capabilities for plagiarism detection using a few examples with ChatGPT and Claude, yielding promising results.\n", - "\n", - "4. **Define your data**\n", - " - We are working with a dataset from the publication: [Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation](https://github.com/oscarkarnalim/sourcecodeplagiarismdataset/blob/master/IR-Plag-Dataset.zip)\n", - " - We selected a subset and manually labeled the dataset with our output labels. This dataset should be used for training and testing, while the rest of the original dataset should be used for evaluation.\n", - " - Dataset: [train.csv](/data/train.tsv) (65 samples)\n", - " - When you don't have labeled dataset, it is good idea to try hand-labeling a few examples to get a sense of the task. It will help you to understand the task better and also increase the quality of program.\n", - "\n", - "5. **Define your metric**\n", - " - We are dealing with a **classification problem**, so we will use accuracy as our main metric. \n", - " - Our metric will be simple: if pred_label == true_label then 1 else 0.\n", - " - As second evaluation we will be evaluating the quality of the explanation via secondary LLM.\n", - "\n", - "6. **Collect preliminary \"zero-shot\" evaluations**\n", - " - Done in code.\n", - "\n", - "7. **Compile with a DSPy optimizer**\n", - " - We don't want to update weights of the LLM, so we are looking at optimizers such as:\n", - " - BootstrapFewShot\n", - " - BootstrapFewShotWithRandomSearch\n", - " - MIPRO\n", - " - ...\n", - "\n", - "8. **Iterate**\n", - " - Regroup and attack again!\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import re\n", - "\n", - "import dspy\n", - "import pandas as pd\n", - "from dotenv import load_dotenv\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.teleprompt import (\n", - " BootstrapFewShot,\n", - " BootstrapFewShotWithRandomSearch,\n", - " KNNFewShot,\n", - " MIPROv2,\n", - ")\n", - "\n", - "# load your environment variables from .env file\n", - "load_dotenv()\n", - "\n", - "# azure-openai model deployment\n", - "AZURE_OPENAI_KEY = os.getenv(\"AZURE_OPENAI_KEY\")\n", - "AZURE_OPENAI_ENDPOINT = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n", - "AZURE_OPENAI_DEPLOYMENT = os.getenv(\"AZURE_OPENAI_DEPLOYMENT\")\n", - "AZURE_OPENAI_VERSION = os.getenv(\"AZURE_OPENAI_VERSION\")\n", - "\n", - "# openai model deployment\n", - "\n", - "OPENAI_MODEL = os.getenv(\"OPENAI_MODEL\")\n", - "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n", - "\n", - "# ollama deployment\n", - "OLLAMA_URL = os.getenv(\"OLLAMA_URL\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 0. Load dataset\n", - "\n", - "Our first task is to load our dataset. Each entry in our dataset will consist of the following components:\n", - "\n", - "* `sample_1`: The first code sample to be analyzed\n", - "* `sample_2`: The second code sample to be compared against the first\n", - "* `plagiarized`: A boolean value (True if plagiarism is detected, False otherwise)\n", - "* `reason`: A detailed explanation of the plagiarism detection result\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/html": [ - "
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311public class T1 { public static void main(Str...* * To change this license header, choose Lice...TrueThe two code samples are nearly identical in t...
421public class T1 { public static void main(Str.../* * To change this license header, choose Li...TrueThe two code samples contain identical main me...
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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DSPy Tutorial: Building a Code Plagiarism Detector\n", + "\n", + "If you've ever felt intimidated by DSPy, don't worry\u2014it might look complex at first glance, but it's actually quite approachable. This tutorial will walk you through the process of building a project, providing a clear, step-by-step approach to understanding and implementing DSPy concepts.\n", + "\n", + "## TLDR \ud83d\ude80\n", + "\n", + "We're going to build a system for code plagiarism detection. Our goal is to compare two input code files, determine if plagiarism has occurred, and provide an explanation for the result. \n", + "\n", + "This project will showcase:\n", + "\n", + "- Multiple inputs and outputs\n", + "- Double validation techniques\n", + "\n", + "I strongly recommend reading the [DSPy Cheatsheet](https://dspy.ai/cheatsheet/) it will help you with quick start.\n", + "\n", + "## How to Start?\n", + "\n", + "A highly effective practice I've found to be game-changing when starting any DSPy project is to answer [these 8 key questions](https://dspy-docs.vercel.app/docs/building-blocks/solving_your_task). This exercise helps you develop a clear vision for your project before diving into the code.\n", + "\n", + "Here's an example of how your answers might look:\n", + "1. **Define your task**\n", + " - Expected input: Two input code files (strings containing plain code) to be compared.\n", + " - Expected output:\n", + " - Plagiarism detection result (Yes/No)\n", + " - Explanation/justification of the result\n", + " - Quality and Cost Specifications: Cost is not a concern; quality is the main priority. We want to try different models.\n", + "\n", + "2. **Define your pipeline**\n", + " - We don't need any external tools or document retrieval. It will be a simple chain-of-thought step, as we want to evaluate LLM capabilities for plagiarism detection.\n", + "\n", + "3. **Explore a few examples**\n", + " - We explored LLM capabilities for plagiarism detection using a few examples with ChatGPT and Claude, yielding promising results.\n", + "\n", + "4. **Define your data**\n", + " - We are working with a dataset from the publication: [Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation](https://github.com/oscarkarnalim/sourcecodeplagiarismdataset/blob/master/IR-Plag-Dataset.zip)\n", + " - We selected a subset and manually labeled the dataset with our output labels. This dataset should be used for training and testing, while the rest of the original dataset should be used for evaluation.\n", + " - Dataset: [train.csv](/data/train.tsv) (65 samples)\n", + " - When you don't have labeled dataset, it is good idea to try hand-labeling a few examples to get a sense of the task. It will help you to understand the task better and also increase the quality of program.\n", + "\n", + "5. **Define your metric**\n", + " - We are dealing with a **classification problem**, so we will use accuracy as our main metric. \n", + " - Our metric will be simple: if pred_label == true_label then 1 else 0.\n", + " - As second evaluation we will be evaluating the quality of the explanation via secondary LLM.\n", + "\n", + "6. **Collect preliminary \"zero-shot\" evaluations**\n", + " - Done in code.\n", + "\n", + "7. **Compile with a DSPy optimizer**\n", + " - We don't want to update weights of the LLM, so we are looking at optimizers such as:\n", + " - BootstrapFewShot\n", + " - BootstrapFewShotWithRandomSearch\n", + " - MIPRO\n", + " - ...\n", + "\n", + "8. **Iterate**\n", + " - Regroup and attack again!\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import re\n", + "\n", + "import dspy\n", + "import pandas as pd\n", + "from dotenv import load_dotenv\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.teleprompt import (\n", + " BootstrapFewShot,\n", + " BootstrapFewShotWithRandomSearch,\n", + " KNNFewShot,\n", + " MIPROv2,\n", + ")\n", + "\n", + "# load your environment variables from .env file\n", + "load_dotenv()\n", + "\n", + "# azure-openai model deployment\n", + "AZURE_OPENAI_KEY = os.getenv(\"AZURE_OPENAI_KEY\")\n", + "AZURE_OPENAI_ENDPOINT = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n", + "AZURE_OPENAI_DEPLOYMENT = os.getenv(\"AZURE_OPENAI_DEPLOYMENT\")\n", + "AZURE_OPENAI_VERSION = os.getenv(\"AZURE_OPENAI_VERSION\")\n", + "\n", + "# openai model deployment\n", + "\n", + "OPENAI_MODEL = os.getenv(\"OPENAI_MODEL\")\n", + "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n", + "\n", + "# ollama deployment\n", + "OLLAMA_URL = os.getenv(\"OLLAMA_URL\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 0. Load dataset\n", + "\n", + "Our first task is to load our dataset. Each entry in our dataset will consist of the following components:\n", + "\n", + "* `sample_1`: The first code sample to be analyzed\n", + "* `sample_2`: The second code sample to be compared against the first\n", + "* `plagiarized`: A boolean value (True if plagiarism is detected, False otherwise)\n", + "* `reason`: A detailed explanation of the plagiarism detection result\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Lcasesample_1sample_2plagiarizedreason
001public class T1 { public static void main(Str.../* * To change this license header, choose Li...FalseThe two code samples, while producing similar ...
101public class T1 { public static void main(Str.../** * * @author 65FBEF05E01FAC390CB3FA073FB3...FalseThe code samples demonstrate different approac...
201public class T1 { public static void main(Str.../** * * @author CB6AB3315634A1E4D11B091BA48B...FalseThe two code samples produce the same output b...
311public class T1 { public static void main(Str...* * To change this license header, choose Lice...TrueThe two code samples are nearly identical in t...
421public class T1 { public static void main(Str.../* * To change this license header, choose Li...TrueThe two code samples contain identical main me...
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" + ], + "text/plain": [ + " L case sample_1 \\\n", + "0 0 1 public class T1 { public static void main(Str... \n", + "1 0 1 public class T1 { public static void main(Str... \n", + "2 0 1 public class T1 { public static void main(Str... \n", + "3 1 1 public class T1 { public static void main(Str... \n", + "4 2 1 public class T1 { public static void main(Str... \n", + "\n", + " sample_2 plagiarized \\\n", + "0 /* * To change this license header, choose Li... False \n", + "1 /** * * @author 65FBEF05E01FAC390CB3FA073FB3... False \n", + "2 /** * * @author CB6AB3315634A1E4D11B091BA48B... False \n", + "3 * * To change this license header, choose Lice... True \n", + "4 /* * To change this license header, choose Li... True \n", + "\n", + " reason \n", + "0 The two code samples, while producing similar ... \n", + "1 The code samples demonstrate different approac... \n", + "2 The two code samples produce the same output b... \n", + "3 The two code samples are nearly identical in t... \n", + "4 The two code samples contain identical main me... " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " L case sample_1 \\\n", - "0 0 1 public class T1 { public static void main(Str... \n", - "1 0 1 public class T1 { public static void main(Str... \n", - "2 0 1 public class T1 { public static void main(Str... \n", - "3 1 1 public class T1 { public static void main(Str... \n", - "4 2 1 public class T1 { public static void main(Str... \n", - "\n", - " sample_2 plagiarized \\\n", - "0 /* * To change this license header, choose Li... False \n", - "1 /** * * @author 65FBEF05E01FAC390CB3FA073FB3... False \n", - "2 /** * * @author CB6AB3315634A1E4D11B091BA48B... False \n", - "3 * * To change this license header, choose Lice... True \n", - "4 /* * To change this license header, choose Li... True \n", - "\n", - " reason \n", - "0 The two code samples, while producing similar ... \n", - "1 The code samples demonstrate different approac... \n", - "2 The two code samples produce the same output b... \n", - "3 The two code samples are nearly identical in t... \n", - "4 The two code samples contain identical main me... " + "source": [ + "df = pd.read_csv(\"https://raw.githubusercontent.com/williambrach/LLM-plagiarism-check/main/data/train.tsv\", sep=\"\\t\")\n", + "df.head()" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(\"https://raw.githubusercontent.com/williambrach/LLM-plagiarism-check/main/data/train.tsv\", sep=\"\\t\")\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Prepare dataset\n", - "\n", - "DSPy utilizes special objects called [`Example`](https://dspy-docs.vercel.app/docs/deep-dive/data-handling/examples#creating-an-example) to structure and process data. Our next task is to convert our raw dataset into a collection of these `Example` objects.\n", - "\n", - "## Creating Custom Example Objects\n", - "\n", - "For our plagiarism detection system, we'll create `Example` objects with the following attributes:\n", - "- `code_sample_1`: The first code sample to analyze\n", - "- `code_sample_2`: The second code sample to compare\n", - "- `plagiarized`: Boolean indicating whether plagiarism was detected\n", - "- `explanation`: Detailed reasoning for the plagiarism decision\n", - "\n", - "## Specifying Inputs\n", - "\n", - "It's crucial to inform DSPy which attributes serve as inputs for our model. We accomplish this using the `.with_inputs()` method. In our case, we'll specify:\n", - "\n", - "```python\n", - ".with_inputs(\"code_sample_1\", \"code_sample_2\")\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "def create_example(row: pd.Series) -> dspy.Example:\n", - " return dspy.Example(\n", - " code_sample_1=row[\"sample_1\"],\n", - " code_sample_2=row[\"sample_2\"],\n", - " plagiarized=\"Yes\" if row[\"plagiarized\"] else \"No\",\n", - " explanation=row[\"reason\"],\n", - " ).with_inputs(\"code_sample_1\", \"code_sample_2\")\n", - "\n", - "\n", - "examples = []\n", - "for _, row in df.iterrows():\n", - " example = create_example(row)\n", - " examples.append(example)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. DSPy setup\n", - "\n", - "DSPy is designed to be compatible with a variety of language models and their respective clients. For this tutorial, we will primarily utilize GPT-4 through the [Azure client](https://github.com/stanfordnlp/dspy/blob/main/dsp/modules/azure_openai.py). However, to demonstrate DSPy's flexibility, we will also provide configuration examples for other popular options:\n", - "\n", - "1. [OpenAI client](https://dspy-docs.vercel.app/docs/deep-dive/language_model_clients/remote_models/OpenAI)\n", - "2. Local models via [Ollama](https://github.com/stanfordnlp/dspy/blob/main/dsp/modules/ollama.py)\n", - "\n", - "## Custom Client Implementation\n", - "\n", - "If your preferred language model client is not natively supported by DSPy, you have the option to implement a custom client. For detailed instructions on creating a custom client, please refer to this [comprehensive guide - custom-lm-client](https://dspy-docs.vercel.app/docs/deep-dive/language_model_clients/custom-lm-client).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# client for AzureOpenAI\n", - "lm = dspy.AzureOpenAI(\n", - " api_base=AZURE_OPENAI_ENDPOINT,\n", - " api_version=AZURE_OPENAI_VERSION,\n", - " deployment_id=AZURE_OPENAI_DEPLOYMENT,\n", - " api_key=AZURE_OPENAI_KEY,\n", - ")\n", - "\n", - "# client for OpenAI\n", - "# lm = dspy.OpenAI(\n", - "# api_key=OPENAI_API_KEY,\n", - "# model=OPENAI_MODEL,\n", - "# )\n", - "\n", - "# client for Ollama\n", - "# model_name = \"llama3.1\"\n", - "# lm = dspy.OllamaLocal(base_url=OLLAMA_URL, model=model_name)\n", - "\n", - "dspy.settings.configure(lm=lm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Setting up signature and module\n", - "\n", - "\n", - "## Core DSPy Components\n", - "\n", - "[dspy.Signature](https://dspy-docs.vercel.app/docs/building-blocks/signatures) and [dspy.Module](https://dspy-docs.vercel.app/docs/building-blocks/modules) are fundamental building blocks for DSPy programs:\n", - "\n", - "- **Signature**: A declarative specification of the input/output behavior of a DSPy module.\n", - "- **Module**: A building block for programs that leverage Language Models (LMs).\n", - "\n", - "## Types of DSPy Modules\n", - "\n", - "DSPy offers various module types, each serving different purposes:\n", - "\n", - "1. [dspy.Predict](https://dspy-docs.vercel.app/api/modules/Predict)\n", - " - Basic predictor\n", - " - Maintains the original signature\n", - " - Handles key forms of learning (storing instructions, demonstrations, and LM updates)\n", - " - Most similar to direct LM usage\n", - "\n", - "2. [dspy.ChainOfThought](https://dspy-docs.vercel.app/api/modules/ChainOfThought)\n", - " - Enhances the LM to think step-by-step before producing the final response\n", - " - Modifies the signature to incorporate intermediate reasoning steps\n", - "\n", - "3. [Additional Advanced Modules](https://dspy-docs.vercel.app/api/category/modules)\n", - " - DSPy library offers a range of more specialized modules for complex tasks\n", - "\n", - "### Recommendation for starting\n", - "\n", - "For those new to DSPy, it's advisable to start with `dspy.Predict`. Its simplicity makes it ideal for understanding the basics of DSPy operation. Once you've successfully implemented your program using `dspy.Predict`, you can explore more advanced modules like `dspy.ChainOfThought` to potentially enhance your model's performance.\n", - "\n", - "For an overview of other prompting techniques beyond zero-shot learning, refer to the [Prompting Guide](https://www.promptingguide.ai/techniques). This resource covers various methods that can enhance your DSPy applications as you progress." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "class PlagiarismSignature(dspy.Signature):\n", - " # Clarify something about the nature of the task (expressed below as a docstring)! \n", - " \"\"\"Detect if two code samples are plagiarized. In plagiarized field answer only : Yes if the code samples are plagiarized, No otherwise. In explanation field add the reason why the code samples are/ are not plagiarized.\"\"\"\n", - "\n", - " # Supply hints on the nature of an input field, expressed as a desc keyword argument for dspy.InputField.\n", - " code_sample_1 = dspy.InputField(desc=\"The first code sample to compare\")\n", - " code_sample_2 = dspy.InputField(desc=\"The second code sample to compare\")\n", - "\n", - " # Supply constraints on an output field, expressed as a desc keyword argument for dspy.OutputField.\n", - " explanation = dspy.OutputField(\n", - " desc=\"Explanation or reason why the code samples are/ are not plagiarized\"\n", - " )\n", - " plagiarized = dspy.OutputField(\n", - " desc=\"Yes/No indicating if code samples are plagiarized\"\n", - " )\n", - "\n", - "\n", - "class PlagiarismCoT(dspy.Module):\n", - " def __init__(self) -> None:\n", - " super().__init__()\n", - "\n", - " # self.prog = dspy.ChainOfThought(PlagiarismSignature)\n", - " self.prog = dspy.Predict(PlagiarismSignature)\n", - "\n", - " def forward(self, code_sample_1: str, code_sample_2: str) -> PlagiarismSignature:\n", - "\n", - " # here you can do any processing you want, calling your function, etc.\n", - " # modifying your code, inputs etc.\n", - " # similar to pytorch forward function\n", - "\n", - " # returned signature object \n", - " prediction = self.prog(code_sample_1=code_sample_1, code_sample_2=code_sample_2)\n", - " return prediction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1 Test your module with zero-shot program" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "plagiarized : Yes\n", - "explanation : Explanation: The two code samples are identical in every aspect, including the class name, method name, and the repeated print statements. There are no differences in the structure, logic, or content of the code.\n" - ] - } - ], - "source": [ - "output = PlagiarismCoT()(examples[0].code_sample_1, examples[0].code_sample_1)\n", - "print(f\"plagiarized : {output.plagiarized}\")\n", - "print(f\"explanation : {output.explanation}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Create metric for your programs\n", - "\n", - "Creating evaluation metric is a crucial component of the DSPy pipeline. This step is often the most complex function in your DSPy application.\n", - "\n", - "### Metric Structure\n", - "\n", - "In DSPy, metrics (or evaluation functions) must adhere to the following structure:\n", - "\n", - "1. **Arguments**: \n", - " - Two required arguments, both of type `dspy.Example`:\n", - " - The ground truth example from the dataset\n", - " - The predicted example from the model\n", - " - An optional third argument called `trace`\n", - "\n", - "2. **Return Value**: \n", - " - A numerical score (float, int, or bool)\n", - " - For binary metrics, return `True` for correct predictions and `False` for incorrect ones\n", - "\n", - "For more detailed information on creating metrics in DSPy, refer to the [official documentation on metrics](https://dspy-docs.vercel.app/docs/building-blocks/metrics)." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "def validate_answer(\n", - " example: dspy.Example, pred: PlagiarismSignature, trace: object = None\n", - ") -> bool:\n", - " \"\"\"\n", - " Validate the predicted plagiarism answer against the example answer.\n", - "\n", - " This function compares the predicted plagiarism answer with the example answer,\n", - " focusing on a simple \"yes\" or \"no\" response. It extracts the core answer from\n", - " the prediction, handling potential variations in formatting and capitalization.\n", - "\n", - " Parameters:\n", - " - example (dspy.Example): The example object containing the correct answer.\n", - " - pred (PlagiarismSignature): The prediction object containing the model's answer.\n", - " - trace (object, optional): Unused parameter, kept for compatibility.\n", - "\n", - " Returns:\n", - " - bool: True if the predicted answer matches the example answer, False otherwise.\n", - "\n", - " The function returns False if either the predicted or example answer is None,\n", - " or if any exception occurs during the validation process.\n", - " \"\"\"\n", - " try:\n", - " if pred.plagiarized is None:\n", - " return False\n", - "\n", - " # Extract the first line of the predicted answer, convert to lowercase\n", - " pred_plag = pred.plagiarized.strip().lower().split(\"\\n\")[0]\n", - "\n", - " # Define a regex pattern to match \"yes\" or \"no\"\n", - " yes_no_pattern = r\"\\b(yes|no)\\b\"\n", - "\n", - " # Search for the pattern in the predicted answer\n", - " match = re.search(yes_no_pattern, pred_plag)\n", - "\n", - " # If a match is found, use it; otherwise, use the entire predicted answer\n", - " extracted_answer = match.group(1) if match else pred.plagiarized.strip().lower()\n", - "\n", - " if example.plagiarized is None:\n", - " return False\n", - "\n", - " score = (\n", - " True if extracted_answer == example.plagiarized.strip().lower() else False\n", - " )\n", - " except Exception:\n", - " score = False\n", - " return score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Test your metric with evaluate\n", - "\n", - "Now we can evaluate zero-shot program with our `validate_answer` function. This will be bottom baseline for our program." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Prepare dataset\n", + "\n", + "DSPy utilizes special objects called [`Example`](https://dspy-docs.vercel.app/docs/deep-dive/data-handling/examples#creating-an-example) to structure and process data. Our next task is to convert our raw dataset into a collection of these `Example` objects.\n", + "\n", + "## Creating Custom Example Objects\n", + "\n", + "For our plagiarism detection system, we'll create `Example` objects with the following attributes:\n", + "- `code_sample_1`: The first code sample to analyze\n", + "- `code_sample_2`: The second code sample to compare\n", + "- `plagiarized`: Boolean indicating whether plagiarism was detected\n", + "- `explanation`: Detailed reasoning for the plagiarism decision\n", + "\n", + "## Specifying Inputs\n", + "\n", + "It's crucial to inform DSPy which attributes serve as inputs for our model. We accomplish this using the `.with_inputs()` method. In our case, we'll specify:\n", + "\n", + "```python\n", + ".with_inputs(\"code_sample_1\", \"code_sample_2\")\n", + "```\n" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 39 / 63 (61.9): 100%|██████████| 63/63 [00:00<00:00, 1136.76it/s]\n" - ] - } - ], - "source": [ - "evaluate = Evaluate(\n", - " devset=examples,\n", - " metric=validate_answer,\n", - " num_threads=4,\n", - " display_progress=True,\n", - " display_table=0,\n", - ")\n", - "\n", - "# zero-shot evaluation on examples\n", - "score = evaluate(PlagiarismCoT())" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def create_example(row: pd.Series) -> dspy.Example:\n", + " return dspy.Example(\n", + " code_sample_1=row[\"sample_1\"],\n", + " code_sample_2=row[\"sample_2\"],\n", + " plagiarized=\"Yes\" if row[\"plagiarized\"] else \"No\",\n", + " explanation=row[\"reason\"],\n", + " ).with_inputs(\"code_sample_1\", \"code_sample_2\")\n", + "\n", + "\n", + "examples = []\n", + "for _, row in df.iterrows():\n", + " example = create_example(row)\n", + " examples.append(example)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Zero-shot score: 61.9\n" - ] - } - ], - "source": [ - "# score for zero-shot evaluation with `validate_answer` function\n", - "print(f\"Zero-shot score: {score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 When you can't use simple programmatic metrics and you need more brain power\n", - "\n", - "In our metric, we are only evaluating the `plagiarized` field. We are not evaluating the `explanation` field. If we wanted to evaluate the `explanation`, which is a string (reasoning), we could use a text similarity metric (cosine similarity, for example) or another LLM to evaluate it.\n", - "\n", - "The advantage of using another LLM is that we can evaluate the explanation in a more human-like way, but be careful with this approach. It could be very expensive and add more time to your program's runtime.\n", - "\n", - "More examples of implementing this type of metric can be found in the [tweets example](https://github.com/stanfordnlp/dspy/blob/main/examples/tweets/tweet_metric.py)." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the signature for automatic assessments.\n", - "class Assess(dspy.Signature):\n", - " \"\"\"Assess the similarity of a of inputs. Answer only Yes if inputs are similar or No if not.\"\"\"\n", - "\n", - " original_explanation = dspy.InputField()\n", - " predicted_reasoning = dspy.InputField()\n", - " similar = dspy.OutputField(desc=\"Yes if inputs are similar or No if not\")\n", - "\n", - "\n", - "gpt_judge = dspy.AzureOpenAI(\n", - " api_base=AZURE_OPENAI_ENDPOINT,\n", - " api_version=AZURE_OPENAI_VERSION,\n", - " deployment_id=AZURE_OPENAI_DEPLOYMENT,\n", - " api_key=AZURE_OPENAI_KEY,\n", - ")\n", - "\n", - "\n", - "def validate_answer_with_explanation(\n", - " example: dspy.Example, pred: PlagiarismSignature, trace: object = None\n", - ") -> bool:\n", - "\n", - " # Extract the true explanation from the example\n", - " true_explanation = example.explanation\n", - "\n", - " # Extract the predicted explanation from the prediction\n", - " pred_explanation = pred.explanation\n", - "\n", - " # Use a language model (gpt_judge) to assess the similarity of explanations\n", - " with dspy.context(lm=gpt_judge):\n", - " # Create an Assess object to compare the explanations\n", - " similar = dspy.Predict(Assess)(\n", - " original_explanation=true_explanation, predicted_reasoning=pred_explanation\n", - " )\n", - "\n", - " # Check if the explanations are deemed similar (converting to lowercase for case-insensitive comparison)\n", - " similar_score = similar.similar.lower() == \"yes\"\n", - "\n", - " # Validate the plagiarism answer using the existing validate_answer function\n", - " plagiarized_score = validate_answer(example, pred, trace)\n", - "\n", - " # Return True only if both the explanation is similar and the plagiarism answer is correct\n", - " return similar_score and plagiarized_score" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. DSPy setup\n", + "\n", + "DSPy is designed to be compatible with a variety of language models and their respective clients. For this tutorial, we will primarily utilize GPT-4 through the [Azure client](https://github.com/stanfordnlp/dspy/blob/main/dsp/modules/azure_openai.py). However, to demonstrate DSPy's flexibility, we will also provide configuration examples for other popular options:\n", + "\n", + "1. [OpenAI client](https://dspy-docs.vercel.app/docs/deep-dive/language_model_clients/remote_models/OpenAI)\n", + "2. Local models via [Ollama](https://github.com/stanfordnlp/dspy/blob/main/dsp/modules/ollama.py)\n", + "\n", + "## Custom Client Implementation\n", + "\n", + "If your preferred language model client is not natively supported by DSPy, you have the option to implement a custom client. For detailed instructions on creating a custom client, please refer to this [comprehensive guide - custom-lm-client](https://dspy-docs.vercel.app/docs/deep-dive/language_model_clients/custom-lm-client).\n" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 37 / 63 (58.7): 100%|██████████| 63/63 [00:00<00:00, 1220.76it/s]\n" - ] - } - ], - "source": [ - "evaluate_with_explanation = Evaluate(\n", - " devset=examples,\n", - " metric=validate_answer_with_explanation,\n", - " num_threads=4,\n", - " display_progress=True,\n", - " display_table=0,\n", - ")\n", - "\n", - "# zero-shot evaluation on examples\n", - "score = evaluate_with_explanation(PlagiarismCoT())" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# client for AzureOpenAI\n", + "lm = dspy.AzureOpenAI(\n", + " api_base=AZURE_OPENAI_ENDPOINT,\n", + " api_version=AZURE_OPENAI_VERSION,\n", + " deployment_id=AZURE_OPENAI_DEPLOYMENT,\n", + " api_key=AZURE_OPENAI_KEY,\n", + ")\n", + "\n", + "# client for OpenAI\n", + "# lm = dspy.OpenAI(\n", + "# api_key=OPENAI_API_KEY,\n", + "# model=OPENAI_MODEL,\n", + "# )\n", + "\n", + "# client for Ollama\n", + "# model_name = \"llama3.1\"\n", + "# lm = dspy.OllamaLocal(base_url=OLLAMA_URL, model=model_name)\n", + "\n", + "dspy.settings.configure(lm=lm)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Zero-shot score for validate_answer_with_explanation : 58.73\n" - ] - } - ], - "source": [ - "# score for zero-shot evaluation with `validate_answer_with_explanation` function\n", - "print(f\"Zero-shot score for validate_answer_with_explanation : {score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. DSPy Magic - Optimizers\n", - "\n", - "## TLDR on Optimizers\n", - "\n", - "Optimizers in DSPy are powerful tools that handle prompt engineering for you. Think of them as \"functions\" that automate and improve the process of crafting effective prompts. There are several types of optimizers available in DSPy, but in this tutorial, we'll focus on the first two:\n", - "\n", - "1. **Automatic Few-Shot Learning**: \n", - " - Adds examples to your prompts.\n", - " - Key optimizers to explore: `BootstrapFewShot` and `BootstrapFewShotWithRandomSearch`\n", - "\n", - "2. **Automatic Instruction Optimization**: \n", - " - Produces optimal instructions for the prompt.\n", - " - In the case of MIPRO, also optimizes the set of few-shot examples.\n", - " - Key optimizer to explore: [`MIPROv2`](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/mipro_optimizer_v2.py)\n", - "\n", - "3. Automatic Finetuning:\n", - " - Used to fine-tune the underlying Language Model(s).\n", - "\n", - "4. Program Transformations:\n", - " - Ensembles a set of DSPy programs and either uses the full set or randomly samples a subset into a single program.\n", - "\n", - "If you're new to optimizers and unsure where to start, `BootstrapFewShotWithRandomSearch` is a safe and effective choice for beginners.\n", - "\n", - "## Read the Documentation!\n", - "\n", - "For more detailed information about optimizers, please refer to the [official DSPy documentation on optimizers](https://dspy-docs.vercel.app/docs/building-blocks/optimizers)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.1 Example of BootstrapFewShot\n", - "\n", - "[BooststrapFewShot docs](https://dspy-docs.vercel.app/docs/deep-dive/teleprompter/bootstrap-fewshot), [BootstrapFewShot class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/bootstrap.py) " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Setting up signature and module\n", + "\n", + "\n", + "## Core DSPy Components\n", + "\n", + "[dspy.Signature](https://dspy-docs.vercel.app/docs/building-blocks/signatures) and [dspy.Module](https://dspy-docs.vercel.app/docs/building-blocks/modules) are fundamental building blocks for DSPy programs:\n", + "\n", + "- **Signature**: A declarative specification of the input/output behavior of a DSPy module.\n", + "- **Module**: A building block for programs that leverage Language Models (LMs).\n", + "\n", + "## Types of DSPy Modules\n", + "\n", + "DSPy offers various module types, each serving different purposes:\n", + "\n", + "1. [dspy.Predict](https://dspy-docs.vercel.app/api/modules/Predict)\n", + " - Basic predictor\n", + " - Maintains the original signature\n", + " - Handles key forms of learning (storing instructions, demonstrations, and LM updates)\n", + " - Most similar to direct LM usage\n", + "\n", + "2. [dspy.ChainOfThought](https://dspy-docs.vercel.app/api/modules/ChainOfThought)\n", + " - Enhances the LM to think step-by-step before producing the final response\n", + " - Modifies the signature to incorporate intermediate reasoning steps\n", + "\n", + "3. [Additional Advanced Modules](https://dspy-docs.vercel.app/api/category/modules)\n", + " - DSPy library offers a range of more specialized modules for complex tasks\n", + "\n", + "### Recommendation for starting\n", + "\n", + "For those new to DSPy, it's advisable to start with `dspy.Predict`. Its simplicity makes it ideal for understanding the basics of DSPy operation. Once you've successfully implemented your program using `dspy.Predict`, you can explore more advanced modules like `dspy.ChainOfThought` to potentially enhance your model's performance.\n", + "\n", + "For an overview of other prompting techniques beyond zero-shot learning, refer to the [Prompting Guide](https://www.promptingguide.ai/techniques). This resource covers various methods that can enhance your DSPy applications as you progress." + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 6%|▋ | 4/63 [00:00<00:00, 3197.49it/s]" - ] + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "class PlagiarismSignature(dspy.Signature):\n", + " # Clarify something about the nature of the task (expressed below as a docstring)! \n", + " \"\"\"Detect if two code samples are plagiarized. In plagiarized field answer only : Yes if the code samples are plagiarized, No otherwise. In explanation field add the reason why the code samples are/ are not plagiarized.\"\"\"\n", + "\n", + " # Supply hints on the nature of an input field, expressed as a desc keyword argument for dspy.InputField.\n", + " code_sample_1 = dspy.InputField(desc=\"The first code sample to compare\")\n", + " code_sample_2 = dspy.InputField(desc=\"The second code sample to compare\")\n", + "\n", + " # Supply constraints on an output field, expressed as a desc keyword argument for dspy.OutputField.\n", + " explanation = dspy.OutputField(\n", + " desc=\"Explanation or reason why the code samples are/ are not plagiarized\"\n", + " )\n", + " plagiarized = dspy.OutputField(\n", + " desc=\"Yes/No indicating if code samples are plagiarized\"\n", + " )\n", + "\n", + "\n", + "class PlagiarismCoT(dspy.Module):\n", + " def __init__(self) -> None:\n", + " super().__init__()\n", + "\n", + " # self.prog = dspy.ChainOfThought(PlagiarismSignature)\n", + " self.prog = dspy.Predict(PlagiarismSignature)\n", + "\n", + " def forward(self, code_sample_1: str, code_sample_2: str) -> PlagiarismSignature:\n", + "\n", + " # here you can do any processing you want, calling your function, etc.\n", + " # modifying your code, inputs etc.\n", + " # similar to pytorch forward function\n", + "\n", + " # returned signature object \n", + " prediction = self.prog(code_sample_1=code_sample_1, code_sample_2=code_sample_2)\n", + " return prediction" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 Test your module with zero-shot program" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Average Metric: 35 / 63 (55.6): 100%|██████████| 63/63 [00:00<00:00, 2608.37it/s]\n" - ] + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "plagiarized : Yes\n", + "explanation : Explanation: The two code samples are identical in every aspect, including the class name, method name, and the repeated print statements. There are no differences in the structure, logic, or content of the code.\n" + ] + } + ], + "source": [ + "output = PlagiarismCoT()(examples[0].code_sample_1, examples[0].code_sample_1)\n", + "print(f\"plagiarized : {output.plagiarized}\")\n", + "print(f\"explanation : {output.explanation}\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('prog', Predict(PlagiarismSignature(code_sample_1, code_sample_2 -> explanation, plagiarized\n", - " instructions='Detect if two code samples are plagiarized. In plagiarized field answer only : Yes if the code samples are plagiarized, No otherwise. In explenation field add the reason why the code samples are/ are not plagiarized.'\n", - " code_sample_1 = Field(annotation=str required=True json_schema_extra={'desc': 'The first code sample to compare', '__dspy_field_type': 'input', 'prefix': 'Code Sample 1:'})\n", - " code_sample_2 = Field(annotation=str required=True json_schema_extra={'desc': 'The second code sample to compare', '__dspy_field_type': 'input', 'prefix': 'Code Sample 2:'})\n", - " explanation = Field(annotation=str required=True json_schema_extra={'desc': 'Explanation or reason why the code samples are/ are not plagiarized', '__dspy_field_type': 'output', 'prefix': 'Explanation:'})\n", - " plagiarized = Field(annotation=str required=True json_schema_extra={'desc': 'Yes/No indicating if code samples are plagiarized', '__dspy_field_type': 'output', 'prefix': 'Plagiarized:'})\n", - ")))]\n" - ] - } - ], - "source": [ - "config = {\"max_bootstrapped_demos\": 4, \"max_labeled_demos\": 4}\n", - "\n", - "optimizer = BootstrapFewShot(metric=validate_answer, **config)\n", - "optimized_program = optimizer.compile(PlagiarismCoT(), trainset=examples)\n", - "score = evaluate(optimized_program)\n", - "optimized_program.save(\"BootstrapFewShot_program.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Create metric for your programs\n", + "\n", + "Creating evaluation metric is a crucial component of the DSPy pipeline. This step is often the most complex function in your DSPy application.\n", + "\n", + "### Metric Structure\n", + "\n", + "In DSPy, metrics (or evaluation functions) must adhere to the following structure:\n", + "\n", + "1. **Arguments**: \n", + " - Two required arguments, both of type `dspy.Example`:\n", + " - The ground truth example from the dataset\n", + " - The predicted example from the model\n", + " - An optional third argument called `trace`\n", + "\n", + "2. **Return Value**: \n", + " - A numerical score (float, int, or bool)\n", + " - For binary metrics, return `True` for correct predictions and `False` for incorrect ones\n", + "\n", + "For more detailed information on creating metrics in DSPy, refer to the [official documentation on metrics](https://dspy-docs.vercel.app/docs/building-blocks/metrics)." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "BootstrapFewShot score for validate_answer : 55.56\n" - ] - } - ], - "source": [ - "# score for BootstrapFewShot evaluation with `validate_answer` function\n", - "print(f\"BootstrapFewShot score : {score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.2 Example of BootstrapFewShotWithRandomSearch\n", - "\n", - "[BootstrapFewShotWithRandomSearch class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/random_search.py) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "config = {\n", - " \"max_bootstrapped_demos\": 4,\n", - " \"max_labeled_demos\": 4,\n", - " \"num_candidate_programs\": 5,\n", - " \"num_threads\": 4,\n", - "}\n", - "\n", - "optimizer = BootstrapFewShotWithRandomSearch(metric=validate_answer, **config)\n", - "optimized_program = optimizer.compile(PlagiarismCoT(), trainset=examples)\n", - "score = evaluate(optimized_program)\n", - "optimized_program.save(\"BootstrapFewShotWithRandomSearch_program.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def validate_answer(\n", + " example: dspy.Example, pred: PlagiarismSignature, trace: object = None\n", + ") -> bool:\n", + " \"\"\"\n", + " Validate the predicted plagiarism answer against the example answer.\n", + "\n", + " This function compares the predicted plagiarism answer with the example answer,\n", + " focusing on a simple \"yes\" or \"no\" response. It extracts the core answer from\n", + " the prediction, handling potential variations in formatting and capitalization.\n", + "\n", + " Parameters:\n", + " - example (dspy.Example): The example object containing the correct answer.\n", + " - pred (PlagiarismSignature): The prediction object containing the model's answer.\n", + " - trace (object, optional): Unused parameter, kept for compatibility.\n", + "\n", + " Returns:\n", + " - bool: True if the predicted answer matches the example answer, False otherwise.\n", + "\n", + " The function returns False if either the predicted or example answer is None,\n", + " or if any exception occurs during the validation process.\n", + " \"\"\"\n", + " try:\n", + " if pred.plagiarized is None:\n", + " return False\n", + "\n", + " # Extract the first line of the predicted answer, convert to lowercase\n", + " pred_plag = pred.plagiarized.strip().lower().split(\"\\n\")[0]\n", + "\n", + " # Define a regex pattern to match \"yes\" or \"no\"\n", + " yes_no_pattern = r\"\\b(yes|no)\\b\"\n", + "\n", + " # Search for the pattern in the predicted answer\n", + " match = re.search(yes_no_pattern, pred_plag)\n", + "\n", + " # If a match is found, use it; otherwise, use the entire predicted answer\n", + " extracted_answer = match.group(1) if match else pred.plagiarized.strip().lower()\n", + "\n", + " if example.plagiarized is None:\n", + " return False\n", + "\n", + " score = (\n", + " True if extracted_answer == example.plagiarized.strip().lower() else False\n", + " )\n", + " except Exception:\n", + " score = False\n", + " return score" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "BootstrapFewShotWithRandomSearch score for validate_answer : 76.19\n" - ] - } - ], - "source": [ - "# score for BootstrapFewShotWithRandomSearch evaluation with `validate_answer` function\n", - "print(f\"BootstrapFewShotWithRandomSearch score : {score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.3 Example of KNNFewShot\n", - "\n", - "[KNNFewShot class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/knn_fewshot.py) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "k = 3\n", - "optimizer = KNNFewShot(k, examples)\n", - "optimized_program = optimizer.compile(PlagiarismCoT(), trainset=examples)\n", - "score = evaluate(optimized_program)\n", - "optimized_program.save(\"KNNFewShot_program.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Test your metric with evaluate\n", + "\n", + "Now we can evaluate zero-shot program with our `validate_answer` function. This will be bottom baseline for our program." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "KNNFewShot score : 85.71\n" - ] - } - ], - "source": [ - "# score for KNNFewShot evaluation with `validate_answer` function\n", - "print(f\"KNNFewShot score : {score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.4 Example of MIPROv2\n", - "\n", - "[MIPROv2 paper](https://arxiv.org/abs/2406.11695), [MIPROv2 class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/mipro_optimizer_v2.py) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n = 5 # The number of instructions and fewshot examples that we will generate and optimize over\n", - "batches = 20 # The number of optimization trials to be run (we will test out a new combination of instructions and fewshot examples in each trial)\n", - "temperature = 1 # The temperature configured for generating new instructions\n", - "eval_kwargs = {\"num_threads\": 4, \"display_progress\": False, \"display_table\": 0}\n", - "optimizer = MIPROv2(\n", - " prompt_model=lm,\n", - " task_model=lm,\n", - " metric=validate_answer,\n", - " num_candidates=n,\n", - " init_temperature=temperature,\n", - " verbose=True,\n", - ")\n", - "optimized_program = optimizer.compile(\n", - " PlagiarismCoT(),\n", - " trainset=examples,\n", - " num_batches=batches,\n", - " max_bootstrapped_demos=16,\n", - " max_labeled_demos=16,\n", - " requires_permission_to_run=False,\n", - " eval_kwargs=eval_kwargs,\n", - ")\n", - "score = evaluate(optimized_program)\n", - "optimized_program.save(\"MIPROv2_program.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 39 / 63 (61.9): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 63/63 [00:00<00:00, 1136.76it/s]\n" + ] + } + ], + "source": [ + "evaluate = Evaluate(\n", + " devset=examples,\n", + " metric=validate_answer,\n", + " num_threads=4,\n", + " display_progress=True,\n", + " display_table=0,\n", + ")\n", + "\n", + "# zero-shot evaluation on examples\n", + "score = evaluate(PlagiarismCoT())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Zero-shot score: 61.9\n" + ] + } + ], + "source": [ + "# score for zero-shot evaluation with `validate_answer` function\n", + "print(f\"Zero-shot score: {score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 When you can't use simple programmatic metrics and you need more brain power\n", + "\n", + "In our metric, we are only evaluating the `plagiarized` field. We are not evaluating the `explanation` field. If we wanted to evaluate the `explanation`, which is a string (reasoning), we could use a text similarity metric (cosine similarity, for example) or another LLM to evaluate it.\n", + "\n", + "The advantage of using another LLM is that we can evaluate the explanation in a more human-like way, but be careful with this approach. It could be very expensive and add more time to your program's runtime.\n", + "\n", + "More examples of implementing this type of metric can be found in the [tweets example](https://github.com/stanfordnlp/dspy/blob/main/examples/tweets/tweet_metric.py)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the signature for automatic assessments.\n", + "class Assess(dspy.Signature):\n", + " \"\"\"Assess the similarity of a of inputs. Answer only Yes if inputs are similar or No if not.\"\"\"\n", + "\n", + " original_explanation = dspy.InputField()\n", + " predicted_reasoning = dspy.InputField()\n", + " similar = dspy.OutputField(desc=\"Yes if inputs are similar or No if not\")\n", + "\n", + "\n", + "gpt_judge = dspy.AzureOpenAI(\n", + " api_base=AZURE_OPENAI_ENDPOINT,\n", + " api_version=AZURE_OPENAI_VERSION,\n", + " deployment_id=AZURE_OPENAI_DEPLOYMENT,\n", + " api_key=AZURE_OPENAI_KEY,\n", + ")\n", + "\n", + "\n", + "def validate_answer_with_explanation(\n", + " example: dspy.Example, pred: PlagiarismSignature, trace: object = None\n", + ") -> bool:\n", + "\n", + " # Extract the true explanation from the example\n", + " true_explanation = example.explanation\n", + "\n", + " # Extract the predicted explanation from the prediction\n", + " pred_explanation = pred.explanation\n", + "\n", + " # Use a language model (gpt_judge) to assess the similarity of explanations\n", + " with dspy.context(lm=gpt_judge):\n", + " # Create an Assess object to compare the explanations\n", + " similar = dspy.Predict(Assess)(\n", + " original_explanation=true_explanation, predicted_reasoning=pred_explanation\n", + " )\n", + "\n", + " # Check if the explanations are deemed similar (converting to lowercase for case-insensitive comparison)\n", + " similar_score = similar.similar.lower() == \"yes\"\n", + "\n", + " # Validate the plagiarism answer using the existing validate_answer function\n", + " plagiarized_score = validate_answer(example, pred, trace)\n", + "\n", + " # Return True only if both the explanation is similar and the plagiarism answer is correct\n", + " return similar_score and plagiarized_score" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 37 / 63 (58.7): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 63/63 [00:00<00:00, 1220.76it/s]\n" + ] + } + ], + "source": [ + "evaluate_with_explanation = Evaluate(\n", + " devset=examples,\n", + " metric=validate_answer_with_explanation,\n", + " num_threads=4,\n", + " display_progress=True,\n", + " display_table=0,\n", + ")\n", + "\n", + "# zero-shot evaluation on examples\n", + "score = evaluate_with_explanation(PlagiarismCoT())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Zero-shot score for validate_answer_with_explanation : 58.73\n" + ] + } + ], + "source": [ + "# score for zero-shot evaluation with `validate_answer_with_explanation` function\n", + "print(f\"Zero-shot score for validate_answer_with_explanation : {score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. DSPy Magic - Optimizers\n", + "\n", + "## TLDR on Optimizers\n", + "\n", + "Optimizers in DSPy are powerful tools that handle prompt engineering for you. Think of them as \"functions\" that automate and improve the process of crafting effective prompts. There are several types of optimizers available in DSPy, but in this tutorial, we'll focus on the first two:\n", + "\n", + "1. **Automatic Few-Shot Learning**: \n", + " - Adds examples to your prompts.\n", + " - Key optimizers to explore: `BootstrapFewShot` and `BootstrapFewShotWithRandomSearch`\n", + "\n", + "2. **Automatic Instruction Optimization**: \n", + " - Produces optimal instructions for the prompt.\n", + " - In the case of MIPRO, also optimizes the set of few-shot examples.\n", + " - Key optimizer to explore: [`MIPROv2`](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/mipro_optimizer_v2.py)\n", + "\n", + "3. Automatic Finetuning:\n", + " - Used to fine-tune the underlying Language Model(s).\n", + "\n", + "4. Program Transformations:\n", + " - Ensembles a set of DSPy programs and either uses the full set or randomly samples a subset into a single program.\n", + "\n", + "If you're new to optimizers and unsure where to start, `BootstrapFewShotWithRandomSearch` is a safe and effective choice for beginners.\n", + "\n", + "## Read the Documentation!\n", + "\n", + "For more detailed information about optimizers, please refer to the [official DSPy documentation on optimizers](https://dspy-docs.vercel.app/docs/building-blocks/optimizers)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.1 Example of BootstrapFewShot\n", + "\n", + "[BooststrapFewShot docs](https://dspy-docs.vercel.app/docs/deep-dive/teleprompter/bootstrap-fewshot), [BootstrapFewShot class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/bootstrap.py) " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 6%|\u258b | 4/63 [00:00<00:00, 3197.49it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Average Metric: 35 / 63 (55.6): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 63/63 [00:00<00:00, 2608.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('prog', Predict(PlagiarismSignature(code_sample_1, code_sample_2 -> explanation, plagiarized\n", + " instructions='Detect if two code samples are plagiarized. In plagiarized field answer only : Yes if the code samples are plagiarized, No otherwise. In explenation field add the reason why the code samples are/ are not plagiarized.'\n", + " code_sample_1 = Field(annotation=str required=True json_schema_extra={'desc': 'The first code sample to compare', '__dspy_field_type': 'input', 'prefix': 'Code Sample 1:'})\n", + " code_sample_2 = Field(annotation=str required=True json_schema_extra={'desc': 'The second code sample to compare', '__dspy_field_type': 'input', 'prefix': 'Code Sample 2:'})\n", + " explanation = Field(annotation=str required=True json_schema_extra={'desc': 'Explanation or reason why the code samples are/ are not plagiarized', '__dspy_field_type': 'output', 'prefix': 'Explanation:'})\n", + " plagiarized = Field(annotation=str required=True json_schema_extra={'desc': 'Yes/No indicating if code samples are plagiarized', '__dspy_field_type': 'output', 'prefix': 'Plagiarized:'})\n", + ")))]\n" + ] + } + ], + "source": [ + "config = {\"max_bootstrapped_demos\": 4, \"max_labeled_demos\": 4}\n", + "\n", + "optimizer = BootstrapFewShot(metric=validate_answer, **config)\n", + "optimized_program = optimizer.compile(PlagiarismCoT(), trainset=examples)\n", + "score = evaluate(optimized_program)\n", + "optimized_program.save(\"BootstrapFewShot_program.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BootstrapFewShot score for validate_answer : 55.56\n" + ] + } + ], + "source": [ + "# score for BootstrapFewShot evaluation with `validate_answer` function\n", + "print(f\"BootstrapFewShot score : {score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.2 Example of BootstrapFewShotWithRandomSearch\n", + "\n", + "[BootstrapFewShotWithRandomSearch class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/random_search.py) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = {\n", + " \"max_bootstrapped_demos\": 4,\n", + " \"max_labeled_demos\": 4,\n", + " \"num_candidate_programs\": 5,\n", + " \"num_threads\": 4,\n", + "}\n", + "\n", + "optimizer = BootstrapFewShotWithRandomSearch(metric=validate_answer, **config)\n", + "optimized_program = optimizer.compile(PlagiarismCoT(), trainset=examples)\n", + "score = evaluate(optimized_program)\n", + "optimized_program.save(\"BootstrapFewShotWithRandomSearch_program.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BootstrapFewShotWithRandomSearch score for validate_answer : 76.19\n" + ] + } + ], + "source": [ + "# score for BootstrapFewShotWithRandomSearch evaluation with `validate_answer` function\n", + "print(f\"BootstrapFewShotWithRandomSearch score : {score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.3 Example of KNNFewShot\n", + "\n", + "[KNNFewShot class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/knn_fewshot.py) " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "MIPROv2 score : 87.85\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "k = 3\n", + "optimizer = KNNFewShot(k, examples)\n", + "optimized_program = optimizer.compile(PlagiarismCoT(), trainset=examples)\n", + "score = evaluate(optimized_program)\n", + "optimized_program.save(\"KNNFewShot_program.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNNFewShot score : 85.71\n" + ] + } + ], + "source": [ + "# score for KNNFewShot evaluation with `validate_answer` function\n", + "print(f\"KNNFewShot score : {score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.4 Example of MIPROv2\n", + "\n", + "[MIPROv2 paper](https://arxiv.org/abs/2406.11695), [MIPROv2 class](https://github.com/stanfordnlp/dspy/blob/main/dspy/teleprompt/mipro_optimizer_v2.py) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n = 5 # The number of instructions and fewshot examples that we will generate and optimize over\n", + "batches = 20 # The number of optimization trials to be run (we will test out a new combination of instructions and fewshot examples in each trial)\n", + "temperature = 1 # The temperature configured for generating new instructions\n", + "eval_kwargs = {\"num_threads\": 4, \"display_progress\": False, \"display_table\": 0}\n", + "optimizer = MIPROv2(\n", + " prompt_model=lm,\n", + " task_model=lm,\n", + " metric=validate_answer,\n", + " num_candidates=n,\n", + " init_temperature=temperature,\n", + " verbose=True,\n", + ")\n", + "optimized_program = optimizer.compile(\n", + " PlagiarismCoT(),\n", + " trainset=examples,\n", + " num_batches=batches,\n", + " max_bootstrapped_demos=16,\n", + " max_labeled_demos=16,\n", + " requires_permission_to_run=False,\n", + " eval_kwargs=eval_kwargs,\n", + ")\n", + "score = evaluate(optimized_program)\n", + "optimized_program.save(\"MIPROv2_program.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MIPROv2 score : 87.85\n" + ] + } + ], + "source": [ + "# score for MIPROv2 evaluation with `validate_answer` function\n", + "print(f\"MIPROv2 score : {score}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6. Loading a Saved Program\n", + "\n", + "Loading a program from a saved file is a straightforward process in DSPy. Here's how you can do it:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "loaded_program = PlagiarismCoT()\n", + "loaded_program.load(path=\"BootstrapFewShotWithRandomSearch_program.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1 Deploying a DSPy Program\n", + "\n", + "Deploying a DSPy program in a production environment can be challenging, as DSPy is primarily designed for research and experimentation at the moment. However, there are two effective methods to successfully deploy a DSPy program in production:\n", + "\n", + "### 6.1.1 Using DSPy as a Library\n", + "\n", + "This method involves saving, loading, and using your DSPy program directly:\n", + "\n", + "1. Save your DSPy program to a file using the `.save()` method.\n", + "2. Load your DSPy program from the file using the `.load()` method.\n", + "3. Use the loaded program to make predictions in your production environment.\n", + "\n", + "### 6.1.2 Extracting Prompts and Calling Your LLM Directly\n", + "\n", + "This approach involves:\n", + "\n", + "1. Extracting the optimized prompts from your DSPy program.\n", + "2. Implementing these prompts in your production code.\n", + "3. Calling your Language Model (LLM) directly with the extracted prompts.\n", + "\n", + "Each method has its advantages and may be more suitable depending on your specific use case and production environment constraints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dummy call of your program\n", + "res = loaded_program(examples[0].code_sample_1, examples[0].code_sample_2)\n", + "\n", + "# find last call in your llm\n", + "last_prompt = lm.inspect_history(n=1)\n", + "with open(\"inspect_history.txt\", \"w\") as f:\n", + " f.write(str(last_prompt))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Refining Your Prompt\n", + "\n", + "You can now streamline your prompt by removing unnecessary parameters and utilizing Python's string formatting to incorporate your inputs. Here's an enhanced version of the prompt:\n", + "\n", + "```python\n", + "prompt = f\"\"\"\n", + "Detect if two code samples are plagiarized. Answer only 'Yes' in the plagiarized field if the code samples are plagiarized, 'No' otherwise. Provide the reasoning in the explanation field.\n", + "\n", + "Format:\n", + "Code Sample 1: [First code sample to compare]\n", + "Code Sample 2: [Second code sample to compare]\n", + "\n", + "Reasoning: Let's analyze step by step to determine if the code samples are plagiarized...\n", + "\n", + "Explanation: [Detailed explanation of why the code samples are or are not plagiarized]\n", + "\n", + "Plagiarized: [Yes/No]\n", + "\n", + "Example:\n", + "Code Sample 1: \n", + "import java.util.Scanner;\n", + "public class T3 {{\n", + " public static void main(String[] args) {{\n", + " Scanner input = new Scanner(System.in);\n", + " System.out.print(\"Enter weight in pounds: \");\n", + " double weight = input.nextDouble();\n", + " System.out.print(\"Enter feet: \");\n", + " double feet = input.nextDouble();\n", + " System.out.print(\"Enter inches: \");\n", + " double inches = input.nextDouble();\n", + " double height = feet * 12 + inches;\n", + " double bmi = weight * 0.45359237 / ((height * 0.0254) * (height * 0.0254));\n", + " System.out.println(\"BMI is \" + bmi);\n", + " if (bmi < 18.5) System.out.println(\"Underweight\");\n", + " else if (bmi < 25) System.out.println(\"Normal\");\n", + " else if (bmi < 30) System.out.println(\"Overweight\");\n", + " else System.out.println(\"Obese\");\n", + " }}\n", + "}}\n", + "\n", + "Code Sample 2:\n", + "import java.util.*;\n", + "public class L2 {{\n", + " public static void main(String[] args) {{\n", + " Scanner sc = new Scanner(System.in);\n", + " System.out.print(\"Enter weight in pounds: \");\n", + " double berat = sc.nextDouble();\n", + " System.out.print(\"Enter feet: \");\n", + " double feet = sc.nextDouble();\n", + " System.out.print(\"Enter inches: \");\n", + " double inci = sc.nextDouble();\n", + " double tinggi = feet * 12 + inci;\n", + " double bmi = berat * 0.45359237 / ((tinggi * 0.0254) * (tinggi * 0.0254));\n", + " System.out.println(\"BMI is \" + bmi);\n", + " if (bmi < 18.5) {{ System.out.println(\"Underweight\"); }}\n", + " else if (bmi < 25) {{ System.out.println(\"Normal\"); }}\n", + " else if (bmi < 30) {{ System.out.println(\"Overweight\"); }}\n", + " else {{ System.out.println(\"Obese\"); }}\n", + " }}\n", + "}}\n", + "\n", + "Reasoning: Let's analyze the code samples step by step to determine if they are plagiarized...\n", + "\n", + "Explanation: The two code samples are nearly identical in structure, logic, and even specific constants used, with only minor differences in variable names and formatting. This level of similarity is extremely unlikely to occur independently and strongly indicates that one sample was copied from the other or both were derived from a common source.\n", + "\n", + "Plagiarized: Yes\n", + "\n", + "Now, analyze the following code samples:\n", + "\n", + "Code Sample 1: {code_sample_1}\n", + "\n", + "Code Sample 2: {code_sample_2}\n", + "\n", + "Reasoning: Let's analyze step by step to determine if the code samples are plagiarized...\n", + "\"\"\"\n", + "```\n", + "**Just be aware that you need to do your own parsing of the output of the LLM.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thanks! \ud83d\ude4c\n", + "\n", + "In case of any questions, feel free to reach out to me: \n", + "William Brach - [@williambrach](https://x.com/williambrach) - william.brach@stuba.sk" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" } - ], - "source": [ - "# score for MIPROv2 evaluation with `validate_answer` function\n", - "print(f\"MIPROv2 score : {score}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6. Loading a Saved Program\n", - "\n", - "Loading a program from a saved file is a straightforward process in DSPy. Here's how you can do it:" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "loaded_program = PlagiarismCoT()\n", - "loaded_program.load(path=\"BootstrapFewShotWithRandomSearch_program.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.1 Deploying a DSPy Program\n", - "\n", - "Deploying a DSPy program in a production environment can be challenging, as DSPy is primarily designed for research and experimentation at the moment. However, there are two effective methods to successfully deploy a DSPy program in production:\n", - "\n", - "### 6.1.1 Using DSPy as a Library\n", - "\n", - "This method involves saving, loading, and using your DSPy program directly:\n", - "\n", - "1. Save your DSPy program to a file using the `.save()` method.\n", - "2. Load your DSPy program from the file using the `.load()` method.\n", - "3. Use the loaded program to make predictions in your production environment.\n", - "\n", - "### 6.1.2 Extracting Prompts and Calling Your LLM Directly\n", - "\n", - "This approach involves:\n", - "\n", - "1. Extracting the optimized prompts from your DSPy program.\n", - "2. Implementing these prompts in your production code.\n", - "3. Calling your Language Model (LLM) directly with the extracted prompts.\n", - "\n", - "Each method has its advantages and may be more suitable depending on your specific use case and production environment constraints." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# dummy call of your program\n", - "res = loaded_program(examples[0].code_sample_1, examples[0].code_sample_2)\n", - "\n", - "# find last call in your llm\n", - "last_prompt = lm.inspect_history(n=1)\n", - "with open(\"inspect_history.txt\", \"w\") as f:\n", - " f.write(str(last_prompt))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Refining Your Prompt\n", - "\n", - "You can now streamline your prompt by removing unnecessary parameters and utilizing Python's string formatting to incorporate your inputs. Here's an enhanced version of the prompt:\n", - "\n", - "```python\n", - "prompt = f\"\"\"\n", - "Detect if two code samples are plagiarized. Answer only 'Yes' in the plagiarized field if the code samples are plagiarized, 'No' otherwise. Provide the reasoning in the explanation field.\n", - "\n", - "Format:\n", - "Code Sample 1: [First code sample to compare]\n", - "Code Sample 2: [Second code sample to compare]\n", - "\n", - "Reasoning: Let's analyze step by step to determine if the code samples are plagiarized...\n", - "\n", - "Explanation: [Detailed explanation of why the code samples are or are not plagiarized]\n", - "\n", - "Plagiarized: [Yes/No]\n", - "\n", - "Example:\n", - "Code Sample 1: \n", - "import java.util.Scanner;\n", - "public class T3 {{\n", - " public static void main(String[] args) {{\n", - " Scanner input = new Scanner(System.in);\n", - " System.out.print(\"Enter weight in pounds: \");\n", - " double weight = input.nextDouble();\n", - " System.out.print(\"Enter feet: \");\n", - " double feet = input.nextDouble();\n", - " System.out.print(\"Enter inches: \");\n", - " double inches = input.nextDouble();\n", - " double height = feet * 12 + inches;\n", - " double bmi = weight * 0.45359237 / ((height * 0.0254) * (height * 0.0254));\n", - " System.out.println(\"BMI is \" + bmi);\n", - " if (bmi < 18.5) System.out.println(\"Underweight\");\n", - " else if (bmi < 25) System.out.println(\"Normal\");\n", - " else if (bmi < 30) System.out.println(\"Overweight\");\n", - " else System.out.println(\"Obese\");\n", - " }}\n", - "}}\n", - "\n", - "Code Sample 2:\n", - "import java.util.*;\n", - "public class L2 {{\n", - " public static void main(String[] args) {{\n", - " Scanner sc = new Scanner(System.in);\n", - " System.out.print(\"Enter weight in pounds: \");\n", - " double berat = sc.nextDouble();\n", - " System.out.print(\"Enter feet: \");\n", - " double feet = sc.nextDouble();\n", - " System.out.print(\"Enter inches: \");\n", - " double inci = sc.nextDouble();\n", - " double tinggi = feet * 12 + inci;\n", - " double bmi = berat * 0.45359237 / ((tinggi * 0.0254) * (tinggi * 0.0254));\n", - " System.out.println(\"BMI is \" + bmi);\n", - " if (bmi < 18.5) {{ System.out.println(\"Underweight\"); }}\n", - " else if (bmi < 25) {{ System.out.println(\"Normal\"); }}\n", - " else if (bmi < 30) {{ System.out.println(\"Overweight\"); }}\n", - " else {{ System.out.println(\"Obese\"); }}\n", - " }}\n", - "}}\n", - "\n", - "Reasoning: Let's analyze the code samples step by step to determine if they are plagiarized...\n", - "\n", - "Explanation: The two code samples are nearly identical in structure, logic, and even specific constants used, with only minor differences in variable names and formatting. This level of similarity is extremely unlikely to occur independently and strongly indicates that one sample was copied from the other or both were derived from a common source.\n", - "\n", - "Plagiarized: Yes\n", - "\n", - "Now, analyze the following code samples:\n", - "\n", - "Code Sample 1: {code_sample_1}\n", - "\n", - "Code Sample 2: {code_sample_2}\n", - "\n", - "Reasoning: Let's analyze step by step to determine if the code samples are plagiarized...\n", - "\"\"\"\n", - "```\n", - "**Just be aware that you need to do your own parsing of the output of the LLM.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thanks! 🙌\n", - "\n", - "In case of any questions, feel free to reach out to me: \n", - "William Brach - [@williambrach](https://x.com/williambrach) - william.brach@stuba.sk" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.19" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json b/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json index 8bd963da36..930dc59b5b 100644 --- a/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json +++ b/examples/outdated_v2.4_examples/nli/scone/scone-cot_fewshot-turbo-gpt4-demos.json @@ -1,64 +1,64 @@ { "generate_answer": { - "lm": null, - "traces": [], - "train": [], "demos": [ { + "answer": "No", "augmented": true, "context": "It is not true that there is not a single person walking in the city.", "question": "Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?", - "rationale": "produce the answer. We know that the double negative in the context implies that there is at least one person walking in the city. However, the context does not provide any information about the status or occupation of the person walking in the city. Therefore, we cannot logically conclude that the person walking in the city is a celebrity.", - "answer": "No" + "rationale": "produce the answer. We know that the double negative in the context implies that there is at least one person walking in the city. However, the context does not provide any information about the status or occupation of the person walking in the city. Therefore, we cannot logically conclude that the person walking in the city is a celebrity." }, { + "answer": "Yes", "augmented": true, "context": "the boy, not girl, will play an trombone, but not for another week", "question": "Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?", - "rationale": "produce the answer. We know that the boy will play a trombone, which is a type of instrument. The context specifies that this will happen not for another week, which means it will happen in the future, but not immediately. The gender of the person is also specified as a boy, not a girl.", - "answer": "Yes" + "rationale": "produce the answer. We know that the boy will play a trombone, which is a type of instrument. The context specifies that this will happen not for another week, which means it will happen in the future, but not immediately. The gender of the person is also specified as a boy, not a girl." }, { + "answer": "Yes", "augmented": true, "context": "A man is not holding anything in his hands.", "question": "Can we logically conclude for sure that a man is not holding beverages in his hands?", - "rationale": "produce the answer. We know that the man is not holding anything in his hands. Beverages are a subset of \"anything.\" Therefore, if he is not holding anything, he is also not holding beverages.", - "answer": "Yes" + "rationale": "produce the answer. We know that the man is not holding anything in his hands. Beverages are a subset of \"anything.\" Therefore, if he is not holding anything, he is also not holding beverages." }, { + "answer": "Yes", "augmented": true, "context": "There is not a boat nearby.", "question": "Can we logically conclude for sure that there is not a speedboat nearby?", - "rationale": "produce the answer. We know that there is not a boat nearby. A speedboat is a type of boat. If there are no boats nearby, then there cannot be a speedboat nearby either, because a speedboat is included in the category of boats.", - "answer": "Yes" + "rationale": "produce the answer. We know that there is not a boat nearby. A speedboat is a type of boat. If there are no boats nearby, then there cannot be a speedboat nearby either, because a speedboat is included in the category of boats." }, { + "answer": "Yes", "augmented": true, "context": "The man is not listening to music.", "question": "Can we logically conclude for sure that the man is not listening to rockabilly?", - "rationale": "produce the answer. We know that the man is not listening to music. Rockabilly is a genre of music. If he is not listening to music at all, then he cannot be listening to rockabilly, which is a specific type of music.", - "answer": "Yes" + "rationale": "produce the answer. We know that the man is not listening to music. Rockabilly is a genre of music. If he is not listening to music at all, then he cannot be listening to rockabilly, which is a specific type of music." }, { + "answer": "Yes", "augmented": true, "context": "There is not a single person walking in the city.", "question": "Can we logically conclude for sure that there is not a single mover walking in the city?", - "rationale": "produce the answer. We know that there is not a single person walking in the city. A mover is a type of person. If there are no people walking in the city, then there cannot be a mover walking in the city either, because a mover is included in the broader category of people.", - "answer": "Yes" + "rationale": "produce the answer. We know that there is not a single person walking in the city. A mover is a type of person. If there are no people walking in the city, then there cannot be a mover walking in the city either, because a mover is included in the broader category of people." }, { + "answer": "No", "augmented": true, "context": "a man walks not so confidently, but he does not drop eggplants", "question": "Can we logically conclude for sure that a man walks not so confidently, but he does not drop produce?", - "rationale": "produce the answer. We know that the man does not drop eggplants, which are a type of produce. However, the statement does not provide information about other types of produce. Therefore, we cannot conclude that he does not drop any type of produce, only that he does not drop eggplants specifically.", - "answer": "No" + "rationale": "produce the answer. We know that the man does not drop eggplants, which are a type of produce. However, the statement does not provide information about other types of produce. Therefore, we cannot conclude that he does not drop any type of produce, only that he does not drop eggplants specifically." }, { - "context": "The girl who is not here is not wearing any jewelry at all.", - "question": "Can we logically conclude for sure that the girl who is not here is not wearing any ringlets at all?", "answer": "Yes", - "category": "one_scoped_one_not_scoped" + "category": "one_scoped_one_not_scoped", + "context": "The girl who is not here is not wearing any jewelry at all.", + "question": "Can we logically conclude for sure that the girl who is not here is not wearing any ringlets at all?" } - ] + ], + "lm": null, + "traces": [], + "train": [] } } diff --git a/examples/outdated_v2.4_examples/nli/scone/scone.ipynb b/examples/outdated_v2.4_examples/nli/scone/scone.ipynb index 17cb708329..667e618abf 100644 --- a/examples/outdated_v2.4_examples/nli/scone/scone.ipynb +++ b/examples/outdated_v2.4_examples/nli/scone/scone.ipynb @@ -1,1085 +1,1085 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "55e51f55-ce98-4cbd-86a2-a433a7d51d3c", - "metadata": {}, - "source": [ - "# Using GPT-4 to bootstrap few-shot CoT demonstations for GPT-3.5" - ] - }, - { - "cell_type": "markdown", - "id": "994104c5-7564-4aef-a6a2-e44d0049a23b", - "metadata": {}, - "source": [ - "The [Scoped Negation (ScoNe) benchmark of She et al. (2023)](https://aclanthology.org/2023.acl-short.154/) seeks to stress-test models on their ability to reason about negation. In the original paper, the `text-davinci-002` and `text-davinci-003` models were more or less at chance on the hardest ScoNe categories.\n", - "\n", - "This notebook starts with a very simple Chain-of-Thought-based module for ScoNe. `gpt-3.5-turbo` is at chance on the \"one scoping negation\" category (one of the two hardest in ScoNe) using this simple program. \n", - "\n", - "We figured that bootstrapping demonstrations would help, but `turbo` struggled to create good demonstrations that included CoT steps. When we switched to using `gpt4-turbo` just to create these demonstrations (which involves under 50 calls to that model), `turbo` regularly achieved 85–90% accuracy. **This is a single compilation step using `dspy.BootstrapFewShotWithRandomSearch`.**" - ] - }, - { - "cell_type": "markdown", - "id": "d155ab52-564d-4a20-8973-be86a82a231a", - "metadata": {}, - "source": [ - "## Set-up" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "fb53ae9e-532a-4fb5-8099-4d21738538f6", - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "import os\n", - "import pandas as pd\n", - "import random\n", - "\n", - "import dspy\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "96e24f6b-67e5-4b20-b9d4-0e6f0d7f06c7", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join('.', 'cache')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "92f83973-1e23-476f-bf02-c80f30a9911f", - "metadata": {}, - "outputs": [], - "source": [ - "# We'll rely on turbo for everything except bootstrapping CoT demos:\n", - "\n", - "turbo = dspy.OpenAI(model='gpt-3.5-turbo-1106', max_tokens=250, model_type='chat')\n", - "\n", - "dspy.settings.configure(lm=turbo)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d0680085-df6d-4729-b952-6a5a69b00519", - "metadata": {}, - "outputs": [], - "source": [ - "# GPT-4 will be used only to bootstrap CoT demos:\n", - "\n", - "gpt4T = dspy.OpenAI(model='gpt-4-1106-preview', max_tokens=350, model_type='chat')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "17761e10-01b1-414f-962c-5efdbdb81fcc", - "metadata": {}, - "outputs": [], - "source": [ - "# Toggling this to true will redo the bootstrapping process. When\n", - "# it is set to False, the existing demonstrations will be used but\n", - "# turbo will still be used to evaluate the zero-shot and full programs.\n", - "RUN_FROM_SCRATCH = False" - ] - }, - { - "cell_type": "markdown", - "id": "e9b08d0c-e6df-4d6e-8aed-a0eeb08039fd", - "metadata": {}, - "source": [ - "## ScoNe" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "68c340a3-b2a7-4b53-aff7-636ad36dddca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cloning into 'ScoNe'...\n", - "remote: Enumerating objects: 77, done.\u001b[K\n", - "remote: Counting objects: 100% (77/77), done.\u001b[K\n", - "remote: Compressing objects: 100% (55/55), done.\u001b[K\n", - "remote: Total 77 (delta 42), reused 42 (delta 20), pack-reused 0\u001b[K\n", - "Receiving objects: 100% (77/77), 116.25 KiB | 1.21 MiB/s, done.\n", - "Resolving deltas: 100% (42/42), done.\n" - ] - } - ], - "source": [ - "!git clone https://github.com/selenashe/ScoNe.git" - ] - }, - { - "cell_type": "markdown", - "id": "d2973a74-63a4-4913-ae3b-9a9ffb631293", - "metadata": {}, - "source": [ - "### Data loader" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4fb01601-ee8e-4b92-902b-18a89236e750", - "metadata": {}, - "outputs": [], - "source": [ - "def load_scone(dirname):\n", - " dfs = []\n", - " for filename in glob.glob(dirname + \"/*.csv\"):\n", - " df = pd.read_csv(filename, index_col=0)\n", - " df['category'] = os.path.basename(filename).replace(\".csv\", \"\")\n", - " dfs.append(df)\n", - " data_df = pd.concat(dfs)\n", - "\n", - " def as_example(row):\n", - " # The 'one_scoped' file is from an earlier dataset, MoNLI, and\n", - " # so is formatted a bit differently:\n", - " suffix = '' if row['category'] == 'one_scoped' else '_edited'\n", - " # Reformat the hypothesis to be an embedded clause in a question:\n", - " hkey = 'sentence2' + suffix\n", - " question = row[hkey][0].lower() + row[hkey][1: ].strip(\".\")\n", - " question = f\"Can we logically conclude for sure that {question}?\"\n", - " # Binary task formulation:\n", - " label = \"Yes\" if row['gold_label' + suffix] == 'entailment' else \"No\"\n", - " return dspy.Example({\n", - " \"context\": row['sentence1' + suffix],\n", - " \"question\": question,\n", - " \"answer\": label,\n", - " \"category\": row['category']\n", - " }).with_inputs(\"context\", \"question\")\n", - "\n", - " return list(data_df.apply(as_example, axis=1).values)" - ] - }, - { - "cell_type": "markdown", - "id": "e1464863-00af-4eb1-b3a6-874830462a7e", - "metadata": {}, - "source": [ - "### Train and dev samples" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8d6ae528-fea9-4692-92f5-275aa8989e01", - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "(200, 50)" + "cell_type": "markdown", + "id": "55e51f55-ce98-4cbd-86a2-a433a7d51d3c", + "metadata": {}, + "source": [ + "# Using GPT-4 to bootstrap few-shot CoT demonstations for GPT-3.5" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_train = load_scone(\"ScoNe/scone_nli/train\")\n", - "\n", - "random.seed(1)\n", - "random.shuffle(all_train)\n", - "\n", - "# 200 random train, 50 random dev:\n", - "train, dev = all_train[: 200], all_train[200: 250]\n", - "\n", - "len(train), len(dev)" - ] - }, - { - "cell_type": "markdown", - "id": "5fae6481-7f79-4616-9261-6c0590df636c", - "metadata": {}, - "source": [ - "### Test" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "89eec431-1e24-41d3-bb6a-0a36c8ed3e14", - "metadata": {}, - "outputs": [], - "source": [ - "random.seed(1)\n", - "\n", - "test = load_scone(dirname=\"ScoNe/scone_nli/test\")\n", - "\n", - "# We're developing a system for the full ScoNe benchmark, but we'll\n", - "# evaluate only on one of the hardest and most informative ScoNe\n", - "# categories for now -- examples with a single negation that plays\n", - "# a crucial role in the reasoning:\n", - "test = [ex for ex in test if ex.category == \"one_scoped\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "24938d16-4ea9-4f5b-838a-db7546e34585", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "No 100\n", - "Yes 100\n", - "dtype: int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.Series([ex.answer for ex in test]).value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "d103da69-86ef-4666-9bec-70473dd7055b", - "metadata": {}, - "source": [ - "## Evaluation tools" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "81c207d3-8a47-4df7-b8e5-2b3182fe6c1a", - "metadata": {}, - "outputs": [], - "source": [ - "scone_accuracy = dspy.evaluate.metrics.answer_exact_match" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6380249a-b4cd-4910-a832-7ab7e9300e38", - "metadata": {}, - "outputs": [], - "source": [ - "evaluator = Evaluate(devset=test, num_threads=1, display_progress=True, display_table=0)" - ] - }, - { - "cell_type": "markdown", - "id": "fef0552b-d5c5-4717-8b1d-c020f6e6eee0", - "metadata": {}, - "source": [ - "## Zero-shot CoT" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d385da93-cda6-44b0-bae6-dca95501fb83", - "metadata": {}, - "outputs": [], - "source": [ - "class ScoNeSignature(dspy.Signature):\n", - " (\"\"\"You are given some context (a premise) and a question (a hypothesis). \"\"\"\n", - " \"\"\"You must indicate with Yes/No answer whether we can logically \"\"\"\n", - " \"\"\"conclude the hypothesis from the premise.\"\"\")\n", - "\n", - " context = dspy.InputField()\n", - " question = dspy.InputField()\n", - " answer = dspy.OutputField(desc=\"Yes or No\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "882d8cbb-94d3-4058-9639-1bd8502acae5", - "metadata": {}, - "outputs": [], - "source": [ - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "398bdece-5a40-42fe-9250-6fe54346ff51", - "metadata": {}, - "outputs": [], - "source": [ - "cot_zeroshot = ScoNeCoT()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f799ec4d-70ce-4e5c-a0d7-c349bdab33d5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 100 / 200 (50.0): 100%|█████████████████████████| 200/200 [00:00<00:00, 733.75it/s]" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 100 / 200 (50.0%)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/plain": [ - "50.0" + "cell_type": "markdown", + "id": "994104c5-7564-4aef-a6a2-e44d0049a23b", + "metadata": {}, + "source": [ + "The [Scoped Negation (ScoNe) benchmark of She et al. (2023)](https://aclanthology.org/2023.acl-short.154/) seeks to stress-test models on their ability to reason about negation. In the original paper, the `text-davinci-002` and `text-davinci-003` models were more or less at chance on the hardest ScoNe categories.\n", + "\n", + "This notebook starts with a very simple Chain-of-Thought-based module for ScoNe. `gpt-3.5-turbo` is at chance on the \"one scoping negation\" category (one of the two hardest in ScoNe) using this simple program. \n", + "\n", + "We figured that bootstrapping demonstrations would help, but `turbo` struggled to create good demonstrations that included CoT steps. When we switched to using `gpt4-turbo` just to create these demonstrations (which involves under 50 calls to that model), `turbo` regularly achieved 85\u201390% accuracy. **This is a single compilation step using `dspy.BootstrapFewShotWithRandomSearch`.**" ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluator(cot_zeroshot, metric=scone_accuracy)" - ] - }, - { - "cell_type": "markdown", - "id": "92fe5ded-3c8e-456c-be57-8ffa014d2920", - "metadata": {}, - "source": [ - "## Optimized few-shot with bootstrapped demonstrations" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "57b783f2-c848-4f7a-b5bf-91c723f7e5d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Going to sample between 1 and 8 traces per predictor.\n", - "Will attempt to train 10 candidate sets.\n" - ] - } - ], - "source": [ - "bootstrap_optimizer = BootstrapFewShotWithRandomSearch(\n", - " max_bootstrapped_demos=8,\n", - " max_labeled_demos=8,\n", - " num_candidate_programs=10,\n", - " num_threads=8,\n", - " metric=scone_accuracy,\n", - " teacher_settings=dict(lm=gpt4T))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6809523b-2173-4e70-af1e-854d1328a3cb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 24 / 50 (48.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1096.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 24 / 50 (48.0%)\n", - "Score: 48.0 for set: [0]\n", - "New best score: 48.0 for seed -3\n", - "Scores so far: [48.0]\n", - "Best score: 48.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 25 / 50 (50.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1034.71it/s]\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 25 / 50 (50.0%)\n", - "Score: 50.0 for set: [8]\n", - "New best score: 50.0 for seed -2\n", - "Scores so far: [48.0, 50.0]\n", - "Best score: 50.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 6%|███▎ | 11/200 [00:00<00:00, 899.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 8 full traces after 12 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 27 / 50 (54.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1225.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 27 / 50 (54.0%)\n", - "Score: 54.0 for set: [8]\n", - "New best score: 54.0 for seed -1\n", - "Scores so far: [48.0, 50.0, 54.0]\n", - "Best score: 54.0\n", - "Average of max per entry across top 1 scores: 0.54\n", - "Average of max per entry across top 2 scores: 0.7\n", - "Average of max per entry across top 3 scores: 0.76\n", - "Average of max per entry across top 5 scores: 0.76\n", - "Average of max per entry across top 8 scores: 0.76\n", - "Average of max per entry across top 9999 scores: 0.76\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4%|██▊ | 9/200 [00:00<00:00, 815.06it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 7 full traces after 10 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 37 / 50 (74.0): 100%|█████████████████████████████| 50/50 [00:00<00:00, 884.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 37 / 50 (74.0%)\n", - "Score: 74.0 for set: [8]\n", - "New best score: 74.0 for seed 0\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.78\n", - "Average of max per entry across top 3 scores: 0.86\n", - "Average of max per entry across top 5 scores: 0.92\n", - "Average of max per entry across top 8 scores: 0.92\n", - "Average of max per entry across top 9999 scores: 0.92\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|█▏ | 4/200 [00:00<00:00, 309.09it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 3 full traces after 5 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 28 / 50 (56.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1111.93it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 28 / 50 (56.0%)\n", - "Score: 56.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.8\n", - "Average of max per entry across top 3 scores: 0.82\n", - "Average of max per entry across top 5 scores: 0.92\n", - "Average of max per entry across top 8 scores: 0.92\n", - "Average of max per entry across top 9999 scores: 0.92\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%|▎ | 1/200 [00:00<00:00, 712.23it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 31 / 50 (62.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1043.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 31 / 50 (62.0%)\n", - "Score: 62.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.86\n", - "Average of max per entry across top 3 scores: 0.9\n", - "Average of max per entry across top 5 scores: 0.94\n", - "Average of max per entry across top 8 scores: 0.94\n", - "Average of max per entry across top 9999 scores: 0.94\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|█▏ | 4/200 [00:00<00:00, 837.65it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 23 / 50 (46.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1104.00it/s]\n" - ] + "cell_type": "markdown", + "id": "d155ab52-564d-4a20-8973-be86a82a231a", + "metadata": {}, + "source": [ + "## Set-up" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 23 / 50 (46.0%)\n", - "Score: 46.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.86\n", - "Average of max per entry across top 3 scores: 0.9\n", - "Average of max per entry across top 5 scores: 0.94\n", - "Average of max per entry across top 8 scores: 0.96\n", - "Average of max per entry across top 9999 scores: 0.96\n" - ] + "cell_type": "code", + "execution_count": 1, + "id": "fb53ae9e-532a-4fb5-8099-4d21738538f6", + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import os\n", + "import pandas as pd\n", + "import random\n", + "\n", + "import dspy\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|█▏ | 4/200 [00:00<00:00, 802.55it/s]\n" - ] + "cell_type": "code", + "execution_count": 2, + "id": "96e24f6b-67e5-4b20-b9d4-0e6f0d7f06c7", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join('.', 'cache')" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 5 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 3, + "id": "92f83973-1e23-476f-bf02-c80f30a9911f", + "metadata": {}, + "outputs": [], + "source": [ + "# We'll rely on turbo for everything except bootstrapping CoT demos:\n", + "\n", + "turbo = dspy.OpenAI(model='gpt-3.5-turbo-1106', max_tokens=250, model_type='chat')\n", + "\n", + "dspy.settings.configure(lm=turbo)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 34 / 50 (68.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1116.66it/s]\n" - ] + "cell_type": "code", + "execution_count": 4, + "id": "d0680085-df6d-4729-b952-6a5a69b00519", + "metadata": {}, + "outputs": [], + "source": [ + "# GPT-4 will be used only to bootstrap CoT demos:\n", + "\n", + "gpt4T = dspy.OpenAI(model='gpt-4-1106-preview', max_tokens=350, model_type='chat')" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 34 / 50 (68.0%)\n", - "Score: 68.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.92\n", - "Average of max per entry across top 3 scores: 0.98\n", - "Average of max per entry across top 5 scores: 0.98\n", - "Average of max per entry across top 8 scores: 1.0\n", - "Average of max per entry across top 9999 scores: 1.0\n" - ] + "cell_type": "code", + "execution_count": 5, + "id": "17761e10-01b1-414f-962c-5efdbdb81fcc", + "metadata": {}, + "outputs": [], + "source": [ + "# Toggling this to true will redo the bootstrapping process. When\n", + "# it is set to False, the existing demonstrations will be used but\n", + "# turbo will still be used to evaluate the zero-shot and full programs.\n", + "RUN_FROM_SCRATCH = False" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|█▌ | 5/200 [00:00<00:00, 855.28it/s]\n" - ] + "cell_type": "markdown", + "id": "e9b08d0c-e6df-4d6e-8aed-a0eeb08039fd", + "metadata": {}, + "source": [ + "## ScoNe" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 5 full traces after 6 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 6, + "id": "68c340a3-b2a7-4b53-aff7-636ad36dddca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'ScoNe'...\n", + "remote: Enumerating objects: 77, done.\u001b[K\n", + "remote: Counting objects: 100% (77/77), done.\u001b[K\n", + "remote: Compressing objects: 100% (55/55), done.\u001b[K\n", + "remote: Total 77 (delta 42), reused 42 (delta 20), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (77/77), 116.25 KiB | 1.21 MiB/s, done.\n", + "Resolving deltas: 100% (42/42), done.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/selenashe/ScoNe.git" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 30 / 50 (60.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1148.03it/s]\n" - ] + "cell_type": "markdown", + "id": "d2973a74-63a4-4913-ae3b-9a9ffb631293", + "metadata": {}, + "source": [ + "### Data loader" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 30 / 50 (60.0%)\n", - "Score: 60.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.92\n", - "Average of max per entry across top 3 scores: 0.98\n", - "Average of max per entry across top 5 scores: 0.98\n", - "Average of max per entry across top 8 scores: 1.0\n", - "Average of max per entry across top 9999 scores: 1.0\n" - ] + "cell_type": "code", + "execution_count": 7, + "id": "4fb01601-ee8e-4b92-902b-18a89236e750", + "metadata": {}, + "outputs": [], + "source": [ + "def load_scone(dirname):\n", + " dfs = []\n", + " for filename in glob.glob(dirname + \"/*.csv\"):\n", + " df = pd.read_csv(filename, index_col=0)\n", + " df['category'] = os.path.basename(filename).replace(\".csv\", \"\")\n", + " dfs.append(df)\n", + " data_df = pd.concat(dfs)\n", + "\n", + " def as_example(row):\n", + " # The 'one_scoped' file is from an earlier dataset, MoNLI, and\n", + " # so is formatted a bit differently:\n", + " suffix = '' if row['category'] == 'one_scoped' else '_edited'\n", + " # Reformat the hypothesis to be an embedded clause in a question:\n", + " hkey = 'sentence2' + suffix\n", + " question = row[hkey][0].lower() + row[hkey][1: ].strip(\".\")\n", + " question = f\"Can we logically conclude for sure that {question}?\"\n", + " # Binary task formulation:\n", + " label = \"Yes\" if row['gold_label' + suffix] == 'entailment' else \"No\"\n", + " return dspy.Example({\n", + " \"context\": row['sentence1' + suffix],\n", + " \"question\": question,\n", + " \"answer\": label,\n", + " \"category\": row['category']\n", + " }).with_inputs(\"context\", \"question\")\n", + "\n", + " return list(data_df.apply(as_example, axis=1).values)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 1%|▌ | 2/200 [00:00<00:00, 723.34it/s]\n" - ] + "cell_type": "markdown", + "id": "e1464863-00af-4eb1-b3a6-874830462a7e", + "metadata": {}, + "source": [ + "### Train and dev samples" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 3 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 8, + "id": "8d6ae528-fea9-4692-92f5-275aa8989e01", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200, 50)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_train = load_scone(\"ScoNe/scone_nli/train\")\n", + "\n", + "random.seed(1)\n", + "random.shuffle(all_train)\n", + "\n", + "# 200 random train, 50 random dev:\n", + "train, dev = all_train[: 200], all_train[200: 250]\n", + "\n", + "len(train), len(dev)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 27 / 50 (54.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1109.09it/s]\n" - ] + "cell_type": "markdown", + "id": "5fae6481-7f79-4616-9261-6c0590df636c", + "metadata": {}, + "source": [ + "### Test" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 27 / 50 (54.0%)\n", - "Score: 54.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.92\n", - "Average of max per entry across top 3 scores: 0.98\n", - "Average of max per entry across top 5 scores: 0.98\n", - "Average of max per entry across top 8 scores: 1.0\n", - "Average of max per entry across top 9999 scores: 1.0\n" - ] + "cell_type": "code", + "execution_count": 9, + "id": "89eec431-1e24-41d3-bb6a-0a36c8ed3e14", + "metadata": {}, + "outputs": [], + "source": [ + "random.seed(1)\n", + "\n", + "test = load_scone(dirname=\"ScoNe/scone_nli/test\")\n", + "\n", + "# We're developing a system for the full ScoNe benchmark, but we'll\n", + "# evaluate only on one of the hardest and most informative ScoNe\n", + "# categories for now -- examples with a single negation that plays\n", + "# a crucial role in the reasoning:\n", + "test = [ex for ex in test if ex.category == \"one_scoped\"]" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 3%|█▊ | 6/200 [00:00<00:00, 828.15it/s]\n" - ] + "cell_type": "code", + "execution_count": 10, + "id": "24938d16-4ea9-4f5b-838a-db7546e34585", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "No 100\n", + "Yes 100\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([ex.answer for ex in test]).value_counts()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 6 full traces after 7 examples in round 0.\n" - ] + "cell_type": "markdown", + "id": "d103da69-86ef-4666-9bec-70473dd7055b", + "metadata": {}, + "source": [ + "## Evaluation tools" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 28 / 50 (56.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1036.51it/s]\n" - ] + "cell_type": "code", + "execution_count": 11, + "id": "81c207d3-8a47-4df7-b8e5-2b3182fe6c1a", + "metadata": {}, + "outputs": [], + "source": [ + "scone_accuracy = dspy.evaluate.metrics.answer_exact_match" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 28 / 50 (56.0%)\n", - "Score: 56.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0, 56.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.92\n", - "Average of max per entry across top 3 scores: 0.98\n", - "Average of max per entry across top 5 scores: 0.98\n", - "Average of max per entry across top 8 scores: 1.0\n", - "Average of max per entry across top 9999 scores: 1.0\n" - ] + "cell_type": "code", + "execution_count": 12, + "id": "6380249a-b4cd-4910-a832-7ab7e9300e38", + "metadata": {}, + "outputs": [], + "source": [ + "evaluator = Evaluate(devset=test, num_threads=1, display_progress=True, display_table=0)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|█▌ | 5/200 [00:00<00:00, 790.78it/s]\n" - ] + "cell_type": "markdown", + "id": "fef0552b-d5c5-4717-8b1d-c020f6e6eee0", + "metadata": {}, + "source": [ + "## Zero-shot CoT" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 4 full traces after 6 examples in round 0.\n" - ] + "cell_type": "code", + "execution_count": 13, + "id": "d385da93-cda6-44b0-bae6-dca95501fb83", + "metadata": {}, + "outputs": [], + "source": [ + "class ScoNeSignature(dspy.Signature):\n", + " (\"\"\"You are given some context (a premise) and a question (a hypothesis). \"\"\"\n", + " \"\"\"You must indicate with Yes/No answer whether we can logically \"\"\"\n", + " \"\"\"conclude the hypothesis from the premise.\"\"\")\n", + "\n", + " context = dspy.InputField()\n", + " question = dspy.InputField()\n", + " answer = dspy.OutputField(desc=\"Yes or No\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 25 / 50 (50.0): 100%|████████████████████████████| 50/50 [00:00<00:00, 1128.36it/s]\n" - ] + "cell_type": "code", + "execution_count": 14, + "id": "882d8cbb-94d3-4058-9639-1bd8502acae5", + "metadata": {}, + "outputs": [], + "source": [ + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 25 / 50 (50.0%)\n", - "Score: 50.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0, 56.0, 50.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.92\n", - "Average of max per entry across top 3 scores: 0.98\n", - "Average of max per entry across top 5 scores: 0.98\n", - "Average of max per entry across top 8 scores: 1.0\n", - "Average of max per entry across top 9999 scores: 1.0\n" - ] + "cell_type": "code", + "execution_count": 15, + "id": "398bdece-5a40-42fe-9250-6fe54346ff51", + "metadata": {}, + "outputs": [], + "source": [ + "cot_zeroshot = ScoNeCoT()" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4%|██▍ | 8/200 [00:00<00:00, 845.75it/s]\n" - ] + "cell_type": "code", + "execution_count": 16, + "id": "f799ec4d-70ce-4e5c-a0d7-c349bdab33d5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 100 / 200 (50.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 733.75it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 100 / 200 (50.0%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "50.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator(cot_zeroshot, metric=scone_accuracy)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 8 full traces after 9 examples in round 0.\n" - ] + "cell_type": "markdown", + "id": "92fe5ded-3c8e-456c-be57-8ffa014d2920", + "metadata": {}, + "source": [ + "## Optimized few-shot with bootstrapped demonstrations" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 31 / 50 (62.0): 100%|█████████████████████████████| 50/50 [00:00<00:00, 921.83it/s]" - ] + "cell_type": "code", + "execution_count": 17, + "id": "57b783f2-c848-4f7a-b5bf-91c723f7e5d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Going to sample between 1 and 8 traces per predictor.\n", + "Will attempt to train 10 candidate sets.\n" + ] + } + ], + "source": [ + "bootstrap_optimizer = BootstrapFewShotWithRandomSearch(\n", + " max_bootstrapped_demos=8,\n", + " max_labeled_demos=8,\n", + " num_candidate_programs=10,\n", + " num_threads=8,\n", + " metric=scone_accuracy,\n", + " teacher_settings=dict(lm=gpt4T))" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 31 / 50 (62.0%)\n", - "Score: 62.0 for set: [8]\n", - "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0, 56.0, 50.0, 62.0]\n", - "Best score: 74.0\n", - "Average of max per entry across top 1 scores: 0.74\n", - "Average of max per entry across top 2 scores: 0.92\n", - "Average of max per entry across top 3 scores: 0.98\n", - "Average of max per entry across top 5 scores: 0.98\n", - "Average of max per entry across top 8 scores: 1.0\n", - "Average of max per entry across top 9999 scores: 1.0\n", - "13 candidate programs found.\n" - ] + "cell_type": "code", + "execution_count": 18, + "id": "6809523b-2173-4e70-af1e-854d1328a3cb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 24 / 50 (48.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1096.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 24 / 50 (48.0%)\n", + "Score: 48.0 for set: [0]\n", + "New best score: 48.0 for seed -3\n", + "Scores so far: [48.0]\n", + "Best score: 48.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 25 / 50 (50.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1034.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 25 / 50 (50.0%)\n", + "Score: 50.0 for set: [8]\n", + "New best score: 50.0 for seed -2\n", + "Scores so far: [48.0, 50.0]\n", + "Best score: 50.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 6%|\u2588\u2588\u2588\u258e | 11/200 [00:00<00:00, 899.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 8 full traces after 12 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 27 / 50 (54.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1225.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 27 / 50 (54.0%)\n", + "Score: 54.0 for set: [8]\n", + "New best score: 54.0 for seed -1\n", + "Scores so far: [48.0, 50.0, 54.0]\n", + "Best score: 54.0\n", + "Average of max per entry across top 1 scores: 0.54\n", + "Average of max per entry across top 2 scores: 0.7\n", + "Average of max per entry across top 3 scores: 0.76\n", + "Average of max per entry across top 5 scores: 0.76\n", + "Average of max per entry across top 8 scores: 0.76\n", + "Average of max per entry across top 9999 scores: 0.76\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 4%|\u2588\u2588\u258a | 9/200 [00:00<00:00, 815.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 7 full traces after 10 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 37 / 50 (74.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 884.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 37 / 50 (74.0%)\n", + "Score: 74.0 for set: [8]\n", + "New best score: 74.0 for seed 0\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.78\n", + "Average of max per entry across top 3 scores: 0.86\n", + "Average of max per entry across top 5 scores: 0.92\n", + "Average of max per entry across top 8 scores: 0.92\n", + "Average of max per entry across top 9999 scores: 0.92\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u2588\u258f | 4/200 [00:00<00:00, 309.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 3 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 28 / 50 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1111.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 28 / 50 (56.0%)\n", + "Score: 56.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.8\n", + "Average of max per entry across top 3 scores: 0.82\n", + "Average of max per entry across top 5 scores: 0.92\n", + "Average of max per entry across top 8 scores: 0.92\n", + "Average of max per entry across top 9999 scores: 0.92\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%|\u258e | 1/200 [00:00<00:00, 712.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 31 / 50 (62.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1043.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 31 / 50 (62.0%)\n", + "Score: 62.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.86\n", + "Average of max per entry across top 3 scores: 0.9\n", + "Average of max per entry across top 5 scores: 0.94\n", + "Average of max per entry across top 8 scores: 0.94\n", + "Average of max per entry across top 9999 scores: 0.94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u2588\u258f | 4/200 [00:00<00:00, 837.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 23 / 50 (46.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1104.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 23 / 50 (46.0%)\n", + "Score: 46.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.86\n", + "Average of max per entry across top 3 scores: 0.9\n", + "Average of max per entry across top 5 scores: 0.94\n", + "Average of max per entry across top 8 scores: 0.96\n", + "Average of max per entry across top 9999 scores: 0.96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u2588\u258f | 4/200 [00:00<00:00, 802.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 34 / 50 (68.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1116.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 34 / 50 (68.0%)\n", + "Score: 68.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.92\n", + "Average of max per entry across top 3 scores: 0.98\n", + "Average of max per entry across top 5 scores: 0.98\n", + "Average of max per entry across top 8 scores: 1.0\n", + "Average of max per entry across top 9999 scores: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u2588\u258c | 5/200 [00:00<00:00, 855.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 5 full traces after 6 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 30 / 50 (60.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1148.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 30 / 50 (60.0%)\n", + "Score: 60.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.92\n", + "Average of max per entry across top 3 scores: 0.98\n", + "Average of max per entry across top 5 scores: 0.98\n", + "Average of max per entry across top 8 scores: 1.0\n", + "Average of max per entry across top 9999 scores: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1%|\u258c | 2/200 [00:00<00:00, 723.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 2 full traces after 3 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 27 / 50 (54.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1109.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 27 / 50 (54.0%)\n", + "Score: 54.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.92\n", + "Average of max per entry across top 3 scores: 0.98\n", + "Average of max per entry across top 5 scores: 0.98\n", + "Average of max per entry across top 8 scores: 1.0\n", + "Average of max per entry across top 9999 scores: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 3%|\u2588\u258a | 6/200 [00:00<00:00, 828.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 6 full traces after 7 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 28 / 50 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1036.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 28 / 50 (56.0%)\n", + "Score: 56.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0, 56.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.92\n", + "Average of max per entry across top 3 scores: 0.98\n", + "Average of max per entry across top 5 scores: 0.98\n", + "Average of max per entry across top 8 scores: 1.0\n", + "Average of max per entry across top 9999 scores: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u2588\u258c | 5/200 [00:00<00:00, 790.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 4 full traces after 6 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 25 / 50 (50.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 1128.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 25 / 50 (50.0%)\n", + "Score: 50.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0, 56.0, 50.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.92\n", + "Average of max per entry across top 3 scores: 0.98\n", + "Average of max per entry across top 5 scores: 0.98\n", + "Average of max per entry across top 8 scores: 1.0\n", + "Average of max per entry across top 9999 scores: 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 4%|\u2588\u2588\u258d | 8/200 [00:00<00:00, 845.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 8 full traces after 9 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 31 / 50 (62.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 921.83it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 31 / 50 (62.0%)\n", + "Score: 62.0 for set: [8]\n", + "Scores so far: [48.0, 50.0, 54.0, 74.0, 56.0, 62.0, 46.0, 68.0, 60.0, 54.0, 56.0, 50.0, 62.0]\n", + "Best score: 74.0\n", + "Average of max per entry across top 1 scores: 0.74\n", + "Average of max per entry across top 2 scores: 0.92\n", + "Average of max per entry across top 3 scores: 0.98\n", + "Average of max per entry across top 5 scores: 0.98\n", + "Average of max per entry across top 8 scores: 1.0\n", + "Average of max per entry across top 9999 scores: 1.0\n", + "13 candidate programs found.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "if RUN_FROM_SCRATCH:\n", + " cot_fewshot = bootstrap_optimizer.compile(cot_zeroshot, trainset=train, valset=dev)\n", + "else:\n", + " cot_fewshot = ScoNeCoT()\n", + " cot_fewshot.load(\"scone-cot_fewshot-turbo-gpt4-demos.json\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "if RUN_FROM_SCRATCH:\n", - " cot_fewshot = bootstrap_optimizer.compile(cot_zeroshot, trainset=train, valset=dev)\n", - "else:\n", - " cot_fewshot = ScoNeCoT()\n", - " cot_fewshot.load(\"scone-cot_fewshot-turbo-gpt4-demos.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3256d089-d09c-440a-a059-0ec871728713", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 171 / 200 (85.5): 100%|█████████████████████████| 200/200 [00:00<00:00, 557.50it/s]" - ] + "cell_type": "code", + "execution_count": 19, + "id": "3256d089-d09c-440a-a059-0ec871728713", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 171 / 200 (85.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 557.50it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 171 / 200 (85.5%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "85.5" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator(cot_fewshot, metric=scone_accuracy)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 171 / 200 (85.5%)\n" - ] + "cell_type": "code", + "execution_count": 20, + "id": "58730a3a-bd11-4e6a-9945-1800ee47232b", + "metadata": {}, + "outputs": [], + "source": [ + "cot_fewshot.save(\"scone-cot_fewshot-turbo-gpt4-demos.json\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "id": "f67c97cf-c089-49b9-b5ef-ac220d9caf7f", + "metadata": {}, + "source": [ + "## Example prompt with prediction" + ] }, { - "data": { - "text/plain": [ - "85.5" + "cell_type": "code", + "execution_count": 21, + "id": "12b76b5f-5ca8-4de7-9afe-d8e6ab3174f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "You are given some context (a premise) and a question (a hypothesis). You must indicate with Yes/No answer whether we can logically conclude the hypothesis from the premise.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the double negative in the context implies that there is at least one person walking in the city. However, the context does not provide any information about the status or occupation of the person walking in the city. Therefore, we cannot logically conclude that the person walking in the city is a celebrity.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: the boy, not girl, will play an trombone, but not for another week\n", + "\n", + "Question: Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the boy will play a trombone, which is a type of instrument. The context specifies that this will happen not for another week, which means it will happen in the future, but not immediately. The gender of the person is also specified as a boy, not a girl.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding beverages in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not holding anything in his hands. Beverages are a subset of \"anything.\" Therefore, if he is not holding anything, he is also not holding beverages.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: There is not a boat nearby.\n", + "\n", + "Question: Can we logically conclude for sure that there is not a speedboat nearby?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is not a boat nearby. A speedboat is a type of boat. If there are no boats nearby, then there cannot be a speedboat nearby either, because a speedboat is included in the category of boats.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: The man is not listening to music.\n", + "\n", + "Question: Can we logically conclude for sure that the man is not listening to rockabilly?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not listening to music. Rockabilly is a genre of music. If he is not listening to music at all, then he cannot be listening to rockabilly, which is a specific type of music.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that there is not a single mover walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is not a single person walking in the city. A mover is a type of person. If there are no people walking in the city, then there cannot be a mover walking in the city either, because a mover is included in the broader category of people.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: a man walks not so confidently, but he does not drop eggplants\n", + "\n", + "Question: Can we logically conclude for sure that a man walks not so confidently, but he does not drop produce?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man does not drop eggplants, which are a type of produce. However, the statement does not provide information about other types of produce. Therefore, we cannot conclude that he does not drop any type of produce, only that he does not drop eggplants specifically.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: The girl who is not here is not wearing any jewelry at all.\n", + "Question: Can we logically conclude for sure that the girl who is not here is not wearing any ringlets at all?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: The man is not steering a sedan\n", + "\n", + "Question: Can we logically conclude for sure that the man is not steering a car?\n", + "\n", + "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know that the man is not steering a sedan, which is a specific type of car. However, the context does not provide any information about whether he is steering any other type of car. Therefore, we cannot logically conclude that he is not steering a car at all.\n", + "\n", + "Answer: No\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "turbo.inspect_history(n=1)" ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "evaluator(cot_fewshot, metric=scone_accuracy)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "58730a3a-bd11-4e6a-9945-1800ee47232b", - "metadata": {}, - "outputs": [], - "source": [ - "cot_fewshot.save(\"scone-cot_fewshot-turbo-gpt4-demos.json\")" - ] - }, - { - "cell_type": "markdown", - "id": "f67c97cf-c089-49b9-b5ef-ac220d9caf7f", - "metadata": {}, - "source": [ - "## Example prompt with prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "12b76b5f-5ca8-4de7-9afe-d8e6ab3174f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "You are given some context (a premise) and a question (a hypothesis). You must indicate with Yes/No answer whether we can logically conclude the hypothesis from the premise.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the double negative in the context implies that there is at least one person walking in the city. However, the context does not provide any information about the status or occupation of the person walking in the city. Therefore, we cannot logically conclude that the person walking in the city is a celebrity.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: the boy, not girl, will play an trombone, but not for another week\n", - "\n", - "Question: Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the boy will play a trombone, which is a type of instrument. The context specifies that this will happen not for another week, which means it will happen in the future, but not immediately. The gender of the person is also specified as a boy, not a girl.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding beverages in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not holding anything in his hands. Beverages are a subset of \"anything.\" Therefore, if he is not holding anything, he is also not holding beverages.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: There is not a boat nearby.\n", - "\n", - "Question: Can we logically conclude for sure that there is not a speedboat nearby?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is not a boat nearby. A speedboat is a type of boat. If there are no boats nearby, then there cannot be a speedboat nearby either, because a speedboat is included in the category of boats.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: The man is not listening to music.\n", - "\n", - "Question: Can we logically conclude for sure that the man is not listening to rockabilly?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not listening to music. Rockabilly is a genre of music. If he is not listening to music at all, then he cannot be listening to rockabilly, which is a specific type of music.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that there is not a single mover walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is not a single person walking in the city. A mover is a type of person. If there are no people walking in the city, then there cannot be a mover walking in the city either, because a mover is included in the broader category of people.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: a man walks not so confidently, but he does not drop eggplants\n", - "\n", - "Question: Can we logically conclude for sure that a man walks not so confidently, but he does not drop produce?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man does not drop eggplants, which are a type of produce. However, the statement does not provide information about other types of produce. Therefore, we cannot conclude that he does not drop any type of produce, only that he does not drop eggplants specifically.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: The girl who is not here is not wearing any jewelry at all.\n", - "Question: Can we logically conclude for sure that the girl who is not here is not wearing any ringlets at all?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: The man is not steering a sedan\n", - "\n", - "Question: Can we logically conclude for sure that the man is not steering a car?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m produce the answer. We know that the man is not steering a sedan, which is a specific type of car. However, the context does not provide any information about whether he is steering any other type of car. Therefore, we cannot logically conclude that he is not steering a car at all.\n", - "\n", - "Answer: No\u001b[0m\n", - "\n", - "\n", - "\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" } - ], - "source": [ - "turbo.inspect_history(n=1)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/outdated_v2.4_examples/nli/scone/scone_with_MIPRO.ipynb b/examples/outdated_v2.4_examples/nli/scone/scone_with_MIPRO.ipynb index 55780ed1a4..470a6b0ea2 100644 --- a/examples/outdated_v2.4_examples/nli/scone/scone_with_MIPRO.ipynb +++ b/examples/outdated_v2.4_examples/nli/scone/scone_with_MIPRO.ipynb @@ -1,8416 +1,8416 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "li3F9kMOqZHz" - }, - "source": [ - "\"DSPy7" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3wEDck3ZqZH0" - }, - "source": [ - "# Using __Multi-stage Instruction Proposal & Optimization (MIPROv2)__ in DSPy\n", - "[![colab-badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/nli/scone/scone_with_MIPRO.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7DzBCQ0UqZH0" - }, - "source": [ - "### FAQ 🙋\n", - "#### 1) How does MIPRO work?\n", - "At a high level, the MIPRO program optimizer works by first __proposing__ candidate fewshot example sets and instructions for each prompt in your program, and then __optimizing__ over these fewshot example sets and instructions as hyperparameters for a specified number of batches. Each batch, the optimizer evaluates different combinations of prompts on a subset of training inputs, which allows it to learn which combinations yield the best performance.\n", - "\n", - "#### 2) How much will MIPRO cost me to run?\n", - "Note that __this notebook__ is free to run, because all LM calls have been cached. However, when using an optimizer on your own program, here is a breakdown of the upper bound of the number of calls to the task model and prompt model respectively:\n", - "\n", - "- **Task model calls**: MIPRO makes up to __O(TxPxM)__ task model calls if you run without minibatching, where T is the number of batches, P is the number of prompts in the program, and M is the size of the train set. This is because the model is evaluating the program on the train set each batch. If you run **with minibatching** you can reduce calls even further to __O(TxPxB)__ where **B** is the minibatch size. Note that every few steps (a parameter you set) MIPRO will also run a full eval over all **M** examples.\n", - "\n", - "- **Prompt model calls**: MIPRO makes up to N*P+10+(P+1) prompt model calls, where N is the number of instruction / fewshot example set candidates to generate for each prompt, and P is the number of prompts in the program. The extra 10 calls comes from generating a summary of the data in the training set, which we use in the meta prompt to create better instructions. The extra (P+1) comes from program summarization where the proposer LLM will look at the program code and try to describe what each module does and what the whole program does.\n", - "\n", - "#### 3) How should I configure the hyperparameters?\n", - "We have yet to run full hyperparameter sweeps with MIPRO, but based off of initial experimintation, we'd recommend the following:\n", - "- __Batch num__: Gains can be seen after about 20-30 batches. However, 100-200 batches can help with adding on additional marginal gains.\n", - "- __num candidates__: This hyperparameter controls the number of candidate prompts and fewshot example sets that are generated to optimize over. With more batches and less prompts to optimize, we can set n to be higher, as we have more batches to explore different combinations of prompts. If your program has between 2-3 modules and is the `num_batches=30`, we'd recommend ~`n=10`. If n is higher (say `n=100`), then we can go higher to ~`n=15`. If you have a program with only 1 module and are keeping the program 0-shot (ie. no fewshot examples), then `num_batches` should be set to equal `n`, because each batch can explore a new instruction.\n", - "- __Training set size__: Between 100 and 500 training examples are recommended, however MIPROv2 can still work well with fewer. Increasing the training set size can help prevent overfitting, and only slightly increases cost for the full evaluation runs.\n", - "\n", - "#### 4) What should I do if I want to reduce the cost?\n", - "You can always update hyperparameters accordingly, such as using a smaller train set, using less batches, or using a program with less modules. You should take advantage of minibatching.\n", - "Alternatively, one strategy would be to optimize using a cheaper task model (ie. locally hosted Llama-3), as initial experiments have shown that prompts optimized for a smaller model also transfer to working well on a larger model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SgTc-CutqZH1" - }, - "source": [ - "### 0] Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5Vo4Tb9srSow" - }, - "source": [ - "First, we will install __DSPy__ if it's not there already. We'll also __load in the cached requests__ for this tasks, so that we don't actually need to call any LMs for this notebook. We'll also load in our pre optimized program from hugging face to inspect later." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JpijP_d7qZH2", - "outputId": "c641a4b1-05f5-45cc-d715-4347c526b576" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - "\n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " !pip install dspy-ai==2.4.17\n", - " !pip install openai~=1.12\n", - " !pip install -e $repo_path\n", - " !pip install --upgrade cloudpickle==3.0.0\n", - "\n", - "from huggingface_hub import hf_hub_download\n", - "import zipfile\n", - "\n", - "repo_id = 'MichaelR207/MIPRO_notebook_cache_scone'\n", - "cache_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='MIPRO_notebook_cache.zip')\n", - "compiled_program_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='compiled_program.dspy')\n", - "trial_logs_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='trial_logs.pickle')\n", - "\n", - "with zipfile.ZipFile(cache_file_path, 'r') as zip_ref:\n", - " zip_ref.extractall(\".\")\n", - "\n", - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = f\"{os.getcwd()}/MIPRO_notebook_cache\"\n", - "\n", - "import dspy\n", - "import pandas as pd\n", - "import random\n", - "from dspy.evaluate import Evaluate" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "U-DaFCBvqZH2" - }, - "source": [ - "We will also specify the __prompt LM model__ (in this case GPT 3.5), the __task LM model__ (Llama 3 8B)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "UHWzGRVgqZH2" - }, - "outputs": [], - "source": [ - "\n", - "### NOTE: if you'd like to run this code without a cache, you can remove these lines to configure your OPEN AI key ###\n", - "# os.environ['OPENAI_API_KEY'] = \"TODO: ADD YOUR OPEN AI KEY HERE\"\n", - "# openai.api_key = os.environ.get('OPENAI_API_KEY')\n", - "# openai.api_base = \"https://api.openai.com/v1\"\n", - "\n", - "prompt_model_name = \"gpt-3.5-turbo-1106\"\n", - "task_model_name = \"meta-llama/Meta-Llama-3-8B\"\n", - "\n", - "prompt_model = dspy.OpenAI(model=prompt_model_name, max_tokens=1000, stop=[\"\\n\\n\", \"\\n---\"])\n", - "task_model = dspy.HFClientVLLM(\n", - " model=task_model_name,\n", - " port=7410,\n", - " url=[\"http://future-hgx-2:7500\", \"http://future-hgx-2:7501\", \"http://future-hgx-2:7502\", \"http://future-hgx-2:7503\", \"http://future-hgx-2:7504\", \"http://future-hgx-2:7505\", \"http://future-hgx-2:7506\", \"http://future-hgx-2:7507\"],\n", - " max_tokens=1000,\n", - " stop=[\"\\n\\n\", \"\\n---\", \"assistant\"],\n", - ")\n", - "\n", - "dspy.settings.configure(lm=task_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BFoPwDrUqZH2" - }, - "source": [ - "### 1] Define Task" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s4Cyb1JtqZH2" - }, - "source": [ - "Here, we'll define the program that we'd like to run, which is a multihop [...] (we can say that it was loosely inspired by a certain paper). We additionally load in the data, and define how we'd like to evaluate this task." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fatal: destination path 'ScoNe' already exists and is not an empty directory.\n" - ] - } - ], - "source": [ - "!git clone https://github.com/selenashe/ScoNe.git" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class ScoNeSignature(dspy.Signature):\n", - " (\"\"\"context, question -> answer\"\"\")\n", - "\n", - " context = dspy.InputField()\n", - " question = dspy.InputField()\n", - " answer = dspy.OutputField(desc=\"Yes or No\")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)" - 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" dfs = []\n", - " filenames = ['one_not_scoped.csv', 'one_scoped.csv', 'no_negation.csv', 'one_scoped_one_not_scoped.csv', 'two_scoped.csv', 'two_not_scoped.csv']\n", - "\n", - " for filename in filenames:\n", - " filename = os.path.join(dirname, filename)\n", - " df = pd.read_csv(filename, index_col=0)\n", - " df['category'] = os.path.basename(filename).replace(\".csv\", \"\")\n", - " dfs.append(df)\n", - " data_df = pd.concat(dfs)\n", - "\n", - " def as_example(row):\n", - " # The 'one_scoped' file is from an earlier dataset, MoNLI, and\n", - " # so is formatted a bit differently:\n", - " suffix = '' if row['category'] == 'one_scoped' else '_edited'\n", - " # Reformat the hypothesis to be an embedded clause in a question:\n", - " hkey = 'sentence2' + suffix\n", - " question = row[hkey][0].lower() + row[hkey][1: ].strip(\".\")\n", - " question = f\"Can we logically conclude for sure that {question}?\"\n", - " # Binary task formulation:\n", - " label = \"Yes\" if row['gold_label' + suffix] == 'entailment' else \"No\"\n", - " return dspy.Example({\n", - " \"context\": row['sentence1' + suffix],\n", - " \"question\": question,\n", - " \"answer\": label,\n", - " \"category\": row['category'],\n", - " }).with_inputs(\"context\", \"question\")\n", - " return list(data_df.apply(as_example, axis=1).values)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Load and configure the datasets.\n", - "all_train = load_scone(\"ScoNe/scone_nli/train\")\n", - "\n", - "random.seed(1)\n", - "random.shuffle(all_train)\n", - "\n", - "# 1000 random train, 500 random dev:\n", - "trainset, valset, testset = all_train[: 200], all_train[200: 400], all_train[400: 600]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up metrics\n", - "NUM_THREADS = 10\n", - "metric = dspy.evaluate.answer_exact_match\n", - "\n", - "kwargs = dict(num_threads=NUM_THREADS, display_progress=True)\n", - "evaluate = Evaluate(devset=valset, metric=metric, **kwargs)\n", - "\n", - "program = ScoNeCoT()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hRGld1C1qZH3" - }, - "source": [ - "### 2] Baseline Evaluation\n", - "Now, we'll quickly evaluate our baseline program so that we can see how the performance using the Prompt Optimizer compares. We should see performance of about __58.0%__ on our trainset, __49.5%__ on our valset, and __55.0%__ on our testset." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MU2aHQBTqZH3", - "outputId": "786ba2f2-ae0a-4c68-b602-d601fb5a5aa5" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 116 / 200 (58.0): 100%|██████████| 200/200 [00:00<00:00, 907.05it/s]\n", - "Average Metric: 99 / 200 (49.5): 100%|██████████| 200/200 [00:00<00:00, 862.68it/s]\n", - "Average Metric: 110 / 200 (55.0): 100%|██████████| 200/200 [00:00<00:00, 647.97it/s]\n" - ] - } - ], - "source": [ - "baseline_train_score = evaluate(program,devset=trainset)\n", - "baseline_eval_score = evaluate(program, devset=valset)\n", - "baseline_test_score = evaluate(program, devset=testset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cjCoL27yqZH3" - }, - "source": [ - "### 3] Optimizing with MIPRO" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eEGOKjXprc7z" - }, - "source": [ - "Now let's get into the key method in this notebook - optimizing our program with MIPRO!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G04L5j9iqZH3" - }, - "source": [ - "#### 3a] Compile Program\n", - "First, we'll get our optimized program. By default, we set `LOAD_PRECOMPILED_PROGRAM` to `True`, so that you can quickly access a program we've precompiled for you. However, if you wish to optimize yourself, `LOAD_PRECOMPILED_PROGRAM` can be set to `False` (though please note that this will require adding in your own LM API keys in the __Setup__ section above).\n", - "\n", - "MIPRO only needs a metric, DSPy module, and training set to see huge gains on your task! You can instantiate a MIPRO Optimizer and compile in just two lines:\n", - "```python\n", - "teleprompter = MIPROv2(prompt_model=prompt_model, task_model=task_model, metric=metric, num_candidates=N, init_temperature=temperature)\n", - "compiled_program = teleprompter.compile(program, trainset=trainset, valset=valset, num_batches=batches, max_bootstrapped_demos=1,max_labeled_demos=2, eval_kwargs=eval_kwargs)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "NVfMJ_FpBlSI" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/michaelryan/Documents/School/Stanford/Research/dspy_official/dspy/examples/nli/scone/MIPRO_notebook_cache/compiler\n", - "[Example({'context': 'The cowboy did not tell other people that he fall off a horse at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a bronco at the competition?', 'answer': 'No', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The woman not crying is not wearing rings.', 'question': 'Can we logically conclude for sure that the woman not crying is not wearing jewelry?', 'answer': 'No', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The people were not trying to keep their voices down so they woke a woman who is not indoors.', 'question': 'Can we logically conclude for sure that the people were not trying to keep their voices down so they woke a lady who is not indoors?', 'answer': 'No', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'It is a lie that the man is not listening to bluegrass.', 'question': 'Can we logically conclude for sure that it is a lie that the man is not listening to music?', 'answer': 'Yes', 'category': 'two_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'There is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single African walking in the city?', 'answer': 'Yes', 'category': 'one_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The man does not dress up when listening to music.', 'question': 'Can we logically conclude for sure that the man does not dress up when listening to bluegrass?', 'answer': 'No', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The man is not listening to opera.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'}), Example({'context': \"There is a person walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a volunteer walking in the city when it's not dark?\", 'answer': 'No', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The girl who is not here is not wearing any jewelry at all.', 'question': 'Can we logically conclude for sure that the girl who is not here is not wearing any rings at all?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'the boy, not girl, will play an piccolo, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'})]\n", - "\u001b[93m\u001b[1mWARNING: Projected Language Model (LM) Calls\u001b[0m\n", - "\n", - "Please be advised that based on the parameters you have set, the maximum number of LM calls is projected as follows:\n", - "\n", - "\n", - "\u001b[93m- Prompt Model: \u001b[94m\u001b[1m10\u001b[0m\u001b[93m data summarizer calls + \u001b[94m\u001b[1m10\u001b[0m\u001b[93m * \u001b[94m\u001b[1m1\u001b[0m\u001b[93m lm calls in program + (\u001b[94m\u001b[1m2\u001b[0m\u001b[93m) lm calls in program aware proposer = \u001b[94m\u001b[1m22\u001b[0m\u001b[93m prompt model calls\u001b[0m\n", - "\u001b[93m- Task Model: \u001b[94m\u001b[1m25\u001b[0m\u001b[93m examples in minibatch * \u001b[94m\u001b[1m30\u001b[0m\u001b[93m batches + \u001b[94m\u001b[1m200\u001b[0m\u001b[93m examples in train set * \u001b[94m\u001b[1m3\u001b[0m\u001b[93m full evals = \u001b[94m\u001b[1m1350\u001b[0m\u001b[93m task model calls\u001b[0m\n", - "\n", - "\u001b[93m\u001b[1mEstimated Cost Calculation:\u001b[0m\n", - "\n", - "\u001b[93mTotal Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token) \n", - " + (Number of calls to prompt model * (Avg Input Token Length per Call * Task Prompt Price per Input Token + Avg Output Token Length per Call * Prompt Model Price per Output Token).\u001b[0m\n", - "\n", - "For a preliminary estimate of potential costs, we recommend you perform your own calculations based on the task\n", - "and prompt models you intend to use. If the projected costs exceed your budget or expectations, you may consider:\n", - "\n", - "\u001b[93m- Reducing the number of trials (`num_batches`), the size of the trainset, or the number of LM calls in your program.\u001b[0m\n", - "\u001b[93m- Using a cheaper task model to optimize the prompt.\u001b[0m\n", - "To proceed with the execution of this program, please confirm by typing \u001b[94m'y'\u001b[0m for yes or \u001b[94m'n'\u001b[0m for no.\n", - "\n", - "If you would like to bypass this confirmation step in future executions, set the \u001b[93m`requires_permission_to_run`\u001b[0m flag to \u001b[93m`False` when calling compile.\u001b[0m\n", - "\n", - "\u001b[93mAwaiting your input...\u001b[0m\n", - "\n", - "SOURCE CODE: ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "b: 10\n", - "e 'NoneType' object has no attribute 'write'. using observations from past round for a summary.\n", - "summary: Prediction(\n", - " summary='The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.'\n", - ")\n", - "DATA SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 1/200 [00:00<00:00, 650.99it/s]\n", - " 2%|▏ | 3/200 [00:00<00:00, 1005.35it/s]\n", - " 0%| | 1/200 [00:00<00:00, 750.19it/s]\n", - " 0%| | 1/200 [00:00<00:00, 686.69it/s]\n", - " 0%| | 1/200 [00:00<00:00, 488.96it/s]\n", - " 1%| | 2/200 [00:00<00:00, 738.24it/s]\n", - " 0%| | 1/200 [00:00<00:00, 762.74it/s]\n", - " 0%| | 1/200 [00:00<00:00, 409.00it/s]\n", - "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/optuna/samplers/_tpe/sampler.py:295: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future.\n", - " warnings.warn(\n", - "[I 2024-06-21 01:05:59,906] A new study created in memory with name: no-name-361c2f94-d354-463c-bbbe-c93d529a0192\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using a randomly generated configuration for our grounded proposer.\n", - "Selected tip: description\n", - "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", - "task_demos \n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S): \n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", - "task_demos \n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S): \n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "PROGRAM DESCRIPTION: This program is designed to solve logical reasoning tasks by using language models. It takes in a context and a question as input, and then generates an answer based on the logical reasoning of the given context and question. The program uses a Chain of Thought model to process the input and produce the answer. The example provided uses the program to determine the logical conclusion of a given statement about people walking in the city, demonstrating its ability to reason and generate responses based on the input context and question.\n", - "task_demos Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "Answer: No\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: This program is designed to solve logical reasoning tasks by using language models. It takes in a context and a question as input, and then generates an answer based on the logical reasoning of the given context and question. The program uses a Chain of Thought model to process the input and produce the answer. The example provided uses the program to determine the logical conclusion of a given statement about people walking in the city, demonstrating its ability to reason and generate responses based on the input context and question.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "Answer: No\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\"\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of logical reasoning and inference based on a given context and question. It uses a language model to generate an answer to the question based on the provided context. The program takes the context and question as input and returns a \"yes\" or \"no\" answer. In the example provided, the program uses the given context and question to produce a \"no\" answer, indicating that a logical conclusion cannot be made. The program seems to work by passing the context and question through a language model to generate the answer.\n", - "task_demos Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", - "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", - "Answer: No\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of logical reasoning and inference based on a given context and question. It uses a language model to generate an answer to the question based on the provided context. The program takes the context and question as input and returns a \"yes\" or \"no\" answer. In the example provided, the program uses the given context and question to produce a \"no\" answer, indicating that a logical conclusion cannot be made. The program seems to work by passing the context and question through a language model to generate the answer.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", - "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", - "Answer: No\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on a given context and question. The program takes a context and a question as input and uses a language model to generate an answer, which is either \"Yes\" or \"No\" based on logical inference. The program uses a Chain of Thought model to process the input and generate the answer. In the example provided, the program successfully processes the context and question to infer a logical conclusion and generate the answer \"Yes.\" Overall, the program is designed to use language models for logical reasoning tasks and inference.\n", - "task_demos Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", - "Answer: Yes\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on a given context and question. The program takes a context and a question as input and uses a language model to generate an answer, which is either \"Yes\" or \"No\" based on logical inference. The program uses a Chain of Thought model to process the input and generate the answer. In the example provided, the program successfully processes the context and question to infer a logical conclusion and generate the answer \"Yes.\" Overall, the program is designed to use language models for logical reasoning tasks and inference.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", - "Answer: Yes\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", - "PROGRAM DESCRIPTION: The program is designed to solve a task of answering yes or no questions based on a given context. It appears to work by taking in a context and a question as inputs, and then using a language model to generate an answer to the question based on the provided context. In the example given, the program uses the provided context and question to generate a logical step-by-step reasoning and ultimately provide an answer of \"No\" to the question. This suggests that the program is designed to solve tasks that require logical reasoning and inference based on textual information.\n", - "task_demos Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", - "Answer: No\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: The program is designed to solve a task of answering yes or no questions based on a given context. It appears to work by taking in a context and a question as inputs, and then using a language model to generate an answer to the question based on the provided context. In the example given, the program uses the provided context and question to generate a logical step-by-step reasoning and ultimately provide an answer of \"No\" to the question. This suggests that the program is designed to solve tasks that require logical reasoning and inference based on textual information.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", - "Answer: No\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve contextual reasoning tasks by using language models. It takes in a context and a question, and uses a language model to generate a yes or no answer based on the given context and question. In the provided example, the program is used to determine if it can be logically concluded that there is a kayak nearby based on the given context. The program uses a chain of thought to process the input and generate the answer, taking into account the context and question provided. It uses the language model to reason through the context and question in order to provide the appropriate yes or no answer.\n", - "task_demos Context: There is a boat nearby\n", - "Question: Can we logically conclude for sure that there is a kayak nearby?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", - "Answer: No\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve contextual reasoning tasks by using language models. It takes in a context and a question, and uses a language model to generate a yes or no answer based on the given context and question. In the provided example, the program is used to determine if it can be logically concluded that there is a kayak nearby based on the given context. The program uses a chain of thought to process the input and generate the answer, taking into account the context and question provided. It uses the language model to reason through the context and question in order to provide the appropriate yes or no answer.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: There is a boat nearby\n", - "Question: Can we logically conclude for sure that there is a kayak nearby?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", - "Answer: No\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "PROGRAM DESCRIPTION: This program is designed to solve tasks related to logical reasoning and inference based on a given context and question. It appears to work by taking in a context and a question as input, and then using a language model to generate an answer based on logical inference from the context. The `ScoNeSignature` function takes in the context and question as input, and returns an answer (either \"Yes\" or \"No) based on the logical reasoning process. The `ScoNeCoT` class sets up a chain of thought using the `ScoNeSignature` function to generate the answer based on the input context and question. The example provided demonstrates how the program uses the given context and question to generate a logical answer, \"Yes\".\n", - "task_demos Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "Answer: Yes\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: This program is designed to solve tasks related to logical reasoning and inference based on a given context and question. It appears to work by taking in a context and a question as input, and then using a language model to generate an answer based on logical inference from the context. The `ScoNeSignature` function takes in the context and question as input, and returns an answer (either \"Yes\" or \"No) based on the logical reasoning process. The `ScoNeCoT` class sets up a chain of thought using the `ScoNeSignature` function to generate the answer based on the input context and question. The example provided demonstrates how the program uses the given context and question to generate a logical answer, \"Yes\".\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "Answer: Yes\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of answering yes or no questions based on a given context and question. The program takes in a context and a question as input and uses a language model to generate an answer. It seems to work by taking the input context and question, passing it through the ScoNeSignature function, and using the ChainOfThought method to generate the answer based on the input context and question. The example provided shows how the program can be used to answer a yes or no question based on a given context and question.\n", - "task_demos Context: There is a athlete walking in the city when it's not dark.\n", - "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", - "Answer: Yes\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of answering yes or no questions based on a given context and question. The program takes in a context and a question as input and uses a language model to generate an answer. It seems to work by taking the input context and question, passing it through the ScoNeSignature function, and using the ChainOfThought method to generate the answer based on the input context and question. The example provided shows how the program can be used to answer a yes or no question based on a given context and question.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: There is a athlete walking in the city when it's not dark.\n", - "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", - "Answer: Yes\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on given context and question. The program takes in a context and a question as input, and the goal is to produce a logical conclusion or answer based on the given context and question. It uses a language model to generate the answer by reasoning through the given context and question. The example provided demonstrates how the program works by taking in a context and a question and generating a logical answer based on the reasoning process.\n", - "task_demos Context: A man is not holding anything in his hands.\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "Answer: Yes\n", - "\n", - "\n", - "\n", - "\n", - "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "DATASET SUMMARY: A description of the dataset that we are using.\n", - "\n", - "PROGRAM CODE: Language model program designed to solve a particular task.\n", - "\n", - "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", - "\n", - "MODULE: The module to create an instruction for.\n", - "\n", - "TASK DEMO(S): Example inputs/outputs of our module.\n", - "\n", - "BASIC INSTRUCTION: Basic instruction.\n", - "\n", - "TIP: A suggestion for how to go about generating the new instruction.\n", - "\n", - "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", - "\n", - "---\n", - "\n", - "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", - "\n", - "PROGRAM CODE:\n", - "ScoNeSignature(context, question -> answer\n", - " instructions='context, question -> answer'\n", - " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", - " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", - " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", - ")\n", - "\n", - "class ScoNeCoT(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - " def forward(self, context, question):\n", - " return self.generate_answer(context=context, question=question)\n", - "\n", - "\n", - "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on given context and question. The program takes in a context and a question as input, and the goal is to produce a logical conclusion or answer based on the given context and question. It uses a language model to generate the answer by reasoning through the given context and question. The example provided demonstrates how the program works by taking in a context and a question and generating a logical answer based on the reasoning process.\n", - "\n", - "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", - "\n", - "TASK DEMO(S):\n", - "Context: A man is not holding anything in his hands.\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "Answer: Yes\n", - "\n", - "\n", - "BASIC INSTRUCTION: context, question -> answer\n", - "\n", - "TIP: Make sure your instruction is very informative and descriptive.\n", - "\n", - "PROPOSED INSTRUCTION:\u001b[32m Given the context and question, generate a logical answer based on the reasoning process.\u001b[0m\n", - "\n", - "\n", - "\n", - "PROPOSED INSTRUCTION: Given the context and question, generate a logical answer based on the reasoning process.\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 13 / 25 (52.0): 100%|██████████| 25/25 [00:00<00:00, 654.31it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 1175.20it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 52.0\n", - "Best Combination: 1,{0: [[], [Example({'context': 'the boy, not girl, will play an horn, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The person did not like raspberries.', 'question': 'Can we logically conclude for sure that the person did not like fruit?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?', 'rationale': 'produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.', 'answer': 'No'}) (input_keys=None), Example({'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not standing on top of a ladder that is leaned against a not so tall tree.', 'question': 'Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?', 'rationale': \"produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.', 'question': 'Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'rationale': 'produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The diver has not seen any fish on his dive that did not go too deep.', 'question': 'Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'rationale': 'produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.', 'answer': 'No'}) (input_keys=None), Example({'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'There is a boat nearby', 'question': 'Can we logically conclude for sure that there is a kayak nearby?', 'rationale': \"produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'the boy plays an instrument', 'question': 'Can we logically conclude for sure that the boy plays an flute?', 'answer': 'No', 'category': 'no_negation'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The men were outside speaking loudly so as to wake the bridesmaid indoors', 'question': 'Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?', 'rationale': 'produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'There is not a single person not walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single matriarch not walking in the city?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': \"There is a athlete walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a person walking in the city when it's not dark?\", 'rationale': 'produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The man is not listening to techno.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not holding anything in his hands.', 'question': 'Can we logically conclude for sure that a man is not holding tongues in his hands?', 'rationale': \"produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\", 'answer': 'Yes'}) (input_keys=None), Example({'context': 'To believe that a man walks confidently not dropping produce is to believe a falsity.', 'question': 'Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})]]} with Mean = 52.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Average Metric: 113 / 200 (56.5): 100%|██████████| 200/200 [00:00<00:00, 587.61it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UPDATING BEST SCORE WITH 56.5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 98 / 200 (49.0): 100%|██████████| 200/200 [00:00<00:00, 660.76it/s]\n", - "[I 2024-06-21 01:06:00,667] Trial 0 finished with value: 52.0 and parameters: {'0_predictor_instruction': 1, '0_predictor_demos': 2}. Best is trial 0 with value: 52.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 11 / 25 (44.0): 100%|██████████| 25/25 [00:00<00:00, 712.57it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 251.76it/s]\n", - "[I 2024-06-21 01:06:00,762] Trial 1 finished with value: 44.0 and parameters: {'0_predictor_instruction': 6, '0_predictor_demos': 2}. Best is trial 0 with value: 52.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 44.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 25 (56.0): 100%|██████████| 25/25 [00:00<00:00, 500.19it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 76.44it/s]\n", - "[I 2024-06-21 01:06:00,877] Trial 2 finished with value: 56.0 and parameters: {'0_predictor_instruction': 8, '0_predictor_demos': 6}. Best is trial 2 with value: 56.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", - "\n", - "---\n", - "\n", - "Context: the boy plays an instrument\n", - "Question: Can we logically conclude for sure that the boy plays an flute?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a boat nearby\n", - "\n", - "Question: Can we logically conclude for sure that there is a kayak nearby?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city, but we don't know anything about the type of person. It could be a slaver, but it could also be a tourist, a local, or a homeless person. We can't logically conclude that it is a slaver for sure.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", - "\n", - "---\n", - "\n", - "Context: the boy plays an instrument\n", - "Question: Can we logically conclude for sure that the boy plays an flute?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a boat nearby\n", - "\n", - "Question: Can we logically conclude for sure that there is a kayak nearby?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city, but we don't know anything about the type of person. It could be a slaver, but it could also be a tourist, a local, or a homeless person. We can't logically conclude that it is a slaver for sure.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 56.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 17 / 25 (68.0): 100%|██████████| 25/25 [00:00<00:00, 1802.73it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 71.35it/s]\n", - "[I 2024-06-21 01:06:01,019] Trial 3 finished with value: 68.0 and parameters: {'0_predictor_instruction': 4, '0_predictor_demos': 5}. Best is trial 3 with value: 68.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there would be a slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there would be a slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 68.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 25 (56.0): 100%|██████████| 25/25 [00:00<00:00, 1270.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 280.50it/s]\n", - "[I 2024-06-21 01:06:01,124] Trial 4 finished with value: 56.0 and parameters: {'0_predictor_instruction': 3, '0_predictor_demos': 8}. Best is trial 3 with value: 68.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", - "\n", - "---\n", - "\n", - "Context: The man is not listening to techno.\n", - "Question: Can we logically conclude for sure that the man is not listening to music?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a athlete walking in the city when it's not dark.\n", - "\n", - "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", - "\n", - "---\n", - "\n", - "Context: The man is not listening to techno.\n", - "Question: Can we logically conclude for sure that the man is not listening to music?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a athlete walking in the city when it's not dark.\n", - "\n", - "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 56.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 18 / 25 (72.0): 100%|██████████| 25/25 [00:00<00:00, 903.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 139.84it/s]\n", - "[I 2024-06-21 01:06:01,205] Trial 5 finished with value: 72.0 and parameters: {'0_predictor_instruction': 2, '0_predictor_demos': 3}. Best is trial 5 with value: 72.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", - "\n", - "---\n", - "\n", - "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", - "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We can't conclude that this person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", - "\n", - "---\n", - "\n", - "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", - "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We can't conclude that this person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 72.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 25 (56.0): 100%|██████████| 25/25 [00:00<00:00, 938.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 282.88it/s]\n", - "[I 2024-06-21 01:06:01,283] Trial 6 finished with value: 56.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 5}. Best is trial 5 with value: 72.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there is a slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there is a slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 56.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 25 (56.0): 100%|██████████| 25/25 [00:00<00:00, 1269.37it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 326.99it/s]\n", - "[I 2024-06-21 01:06:01,363] Trial 7 finished with value: 56.0 and parameters: {'0_predictor_instruction': 7, '0_predictor_demos': 4}. Best is trial 5 with value: 72.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Context: The diver has not seen any fish on his dive that did not go too deep.\n", - "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Slaver is a type of person, so it is a type of person that is walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Context: The diver has not seen any fish on his dive that did not go too deep.\n", - "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Slaver is a type of person, so it is a type of person that is walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 56.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: context, question -> answer\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 18 / 25 (72.0): 100%|██████████| 25/25 [00:00<00:00, 907.74it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 448.83it/s]\n", - "[I 2024-06-21 01:06:01,431] Trial 8 finished with value: 72.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 7}. Best is trial 5 with value: 72.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 72.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 20 / 25 (80.0): 100%|██████████| 25/25 [00:00<00:00, 846.48it/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 116.36it/s]\n", - "[I 2024-06-21 01:06:01,509] Trial 9 finished with value: 80.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 80.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 19 / 25 (76.0): 100%|██████████| 25/25 [00:00<00:00, 202.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 315.15it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 76.0\n", - "Best Combination: 2,{0: [[], [Example({'context': 'the boy, not girl, will play an horn, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The person did not like raspberries.', 'question': 'Can we logically conclude for sure that the person did not like fruit?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?', 'rationale': 'produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.', 'answer': 'No'}) (input_keys=None), Example({'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not standing on top of a ladder that is leaned against a not so tall tree.', 'question': 'Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?', 'rationale': \"produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.', 'question': 'Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'rationale': 'produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The diver has not seen any fish on his dive that did not go too deep.', 'question': 'Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'rationale': 'produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.', 'answer': 'No'}) (input_keys=None), Example({'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'There is a boat nearby', 'question': 'Can we logically conclude for sure that there is a kayak nearby?', 'rationale': \"produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'the boy plays an instrument', 'question': 'Can we logically conclude for sure that the boy plays an flute?', 'answer': 'No', 'category': 'no_negation'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The men were outside speaking loudly so as to wake the bridesmaid indoors', 'question': 'Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?', 'rationale': 'produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'There is not a single person not walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single matriarch not walking in the city?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': \"There is a athlete walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a person walking in the city when it's not dark?\", 'rationale': 'produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The man is not listening to techno.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not holding anything in his hands.', 'question': 'Can we logically conclude for sure that a man is not holding tongues in his hands?', 'rationale': \"produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\", 'answer': 'Yes'}) (input_keys=None), Example({'context': 'To believe that a man walks confidently not dropping produce is to believe a falsity.', 'question': 'Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})]]} with Mean = 72.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 139 / 200 (69.5): 100%|██████████| 200/200 [00:00<00:00, 691.92it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UPDATING BEST SCORE WITH 69.5\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 128 / 200 (64.0): 100%|██████████| 200/200 [00:00<00:00, 643.31it/s]\n", - "[I 2024-06-21 01:06:02,325] Trial 10 finished with value: 76.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 9}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 18 / 25 (72.0): 100%|██████████| 25/25 [00:00<00:00, 1546.18it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 516.29it/s]\n", - "[I 2024-06-21 01:06:02,377] Trial 11 finished with value: 72.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 9}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 72.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 20 / 25 (80.0): 100%|██████████| 25/25 [00:00<00:00, 1216.49it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 739.61it/s]\n", - "[I 2024-06-21 01:06:02,442] Trial 12 finished with value: 80.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 80.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 15 / 25 (60.0): 100%|██████████| 25/25 [00:00<00:00, 1431.84it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 896.79it/s]\n", - "[I 2024-06-21 01:06:02,490] Trial 13 finished with value: 60.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 60.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 25 (56.0): 100%|██████████| 25/25 [00:00<00:00, 850.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 398.24it/s]\n", - "[I 2024-06-21 01:06:02,653] Trial 14 finished with value: 56.0 and parameters: {'0_predictor_instruction': 4, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 56.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 25 (56.0): 100%|██████████| 25/25 [00:00<00:00, 743.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 117.87it/s]\n", - "[I 2024-06-21 01:06:02,724] Trial 15 finished with value: 56.0 and parameters: {'0_predictor_instruction': 5, '0_predictor_demos': 0}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we already know that there is at least one person walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we already know that there is at least one person walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 56.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 17 / 25 (68.0): 100%|██████████| 25/25 [00:00<00:00, 967.53it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 608.22it/s]\n", - "[I 2024-06-21 01:06:02,793] Trial 16 finished with value: 68.0 and parameters: {'0_predictor_instruction': 7, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 68.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 17 / 25 (68.0): 100%|██████████| 25/25 [00:00<00:00, 1159.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 528.52it/s]\n", - "[I 2024-06-21 01:06:02,842] Trial 17 finished with value: 68.0 and parameters: {'0_predictor_instruction': 8, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 68.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 18 / 25 (72.0): 100%|██████████| 25/25 [00:00<00:00, 924.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 220.07it/s]\n", - "[I 2024-06-21 01:06:02,919] Trial 18 finished with value: 72.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 1}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: the boy, not girl, will play an horn, but not for another week\n", - "Question: Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?\n", - "Answer: Yes\n", - "\n", - "Context: The person did not like raspberries.\n", - "Question: Can we logically conclude for sure that the person did not like fruit?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. This means that there is at least one person walking in the city. However, we cannot logically conclude that there is not a single slaver walking in the city. We only know that there is at least one person, but we don't have any information about the presence of slavers.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: the boy, not girl, will play an horn, but not for another week\n", - "Question: Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?\n", - "Answer: Yes\n", - "\n", - "Context: The person did not like raspberries.\n", - "Question: Can we logically conclude for sure that the person did not like fruit?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. This means that there is at least one person walking in the city. However, we cannot logically conclude that there is not a single slaver walking in the city. We only know that there is at least one person, but we don't have any information about the presence of slavers.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 72.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 17 / 25 (68.0): 100%|██████████| 25/25 [00:00<00:00, 2253.45it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 285.21it/s]\n", - "[I 2024-06-21 01:06:03,017] Trial 19 finished with value: 68.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 8}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: The man is not listening to techno.\n", - "Question: Can we logically conclude for sure that the man is not listening to music?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a athlete walking in the city when it's not dark.\n", - "\n", - "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: The man is not listening to techno.\n", - "Question: Can we logically conclude for sure that the man is not listening to music?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a athlete walking in the city when it's not dark.\n", - "\n", - "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 68.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 18 / 25 (72.0): 100%|██████████| 25/25 [00:00<00:00, 1312.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 624.25it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 72.0\n", - "Best Combination: 0,{0: [[], [Example({'context': 'the boy, not girl, will play an horn, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The person did not like raspberries.', 'question': 'Can we logically conclude for sure that the person did not like fruit?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?', 'rationale': 'produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.', 'answer': 'No'}) (input_keys=None), Example({'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not standing on top of a ladder that is leaned against a not so tall tree.', 'question': 'Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?', 'rationale': \"produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.', 'question': 'Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'rationale': 'produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The diver has not seen any fish on his dive that did not go too deep.', 'question': 'Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'rationale': 'produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.', 'answer': 'No'}) (input_keys=None), Example({'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'There is a boat nearby', 'question': 'Can we logically conclude for sure that there is a kayak nearby?', 'rationale': \"produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'the boy plays an instrument', 'question': 'Can we logically conclude for sure that the boy plays an flute?', 'answer': 'No', 'category': 'no_negation'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The men were outside speaking loudly so as to wake the bridesmaid indoors', 'question': 'Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?', 'rationale': 'produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'There is not a single person not walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single matriarch not walking in the city?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': \"There is a athlete walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a person walking in the city when it's not dark?\", 'rationale': 'produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The man is not listening to techno.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not holding anything in his hands.', 'question': 'Can we logically conclude for sure that a man is not holding tongues in his hands?', 'rationale': \"produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\", 'answer': 'Yes'}) (input_keys=None), Example({'context': 'To believe that a man walks confidently not dropping produce is to believe a falsity.', 'question': 'Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})]]} with Mean = 72.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 142 / 200 (71.0): 100%|██████████| 200/200 [00:00<00:00, 725.50it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UPDATING BEST SCORE WITH 71.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 130 / 200 (65.0): 100%|██████████| 200/200 [00:00<00:00, 738.21it/s]\n", - "[I 2024-06-21 01:06:03,753] Trial 20 finished with value: 72.0 and parameters: {'0_predictor_instruction': 6, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 19 / 25 (76.0): 100%|██████████| 25/25 [00:00<00:00, 677.20it/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 525.14it/s]\n", - "[I 2024-06-21 01:06:03,818] Trial 21 finished with value: 76.0 and parameters: {'0_predictor_instruction': 5, '0_predictor_demos': 9}. Best is trial 9 with value: 80.0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 76.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the context and question, generate a logical answer based on the reasoning process.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 19 / 25 (76.0): 100%|██████████| 25/25 [00:00<00:00, 887.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 590.00it/s]\n", - "[I 2024-06-21 01:06:03,871] Trial 22 finished with value: 76.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 4}. Best is trial 9 with value: 80.0.\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "li3F9kMOqZHz" + }, + "source": [ + "\"DSPy7" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: The diver has not seen any fish on his dive that did not go too deep.\n", - "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city, because there is at least one person walking in the city, and that person could be a slaver.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "---\n", - "\n", - "Context: The diver has not seen any fish on his dive that did not go too deep.\n", - "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", - "\n", - "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city, because there is at least one person walking in the city, and that person could be a slaver.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 76.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: context, question -> answer\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "3wEDck3ZqZH0" + }, + "source": [ + "# Using __Multi-stage Instruction Proposal & Optimization (MIPROv2)__ in DSPy\n", + "[![colab-badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/nli/scone/scone_with_MIPRO.ipynb)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 22 / 25 (88.0): 100%|██████████| 25/25 [00:00<00:00, 1023.58it/s]\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "7DzBCQ0UqZH0" + }, + "source": [ + "### FAQ \ud83d\ude4b\n", + "#### 1) How does MIPRO work?\n", + "At a high level, the MIPRO program optimizer works by first __proposing__ candidate fewshot example sets and instructions for each prompt in your program, and then __optimizing__ over these fewshot example sets and instructions as hyperparameters for a specified number of batches. Each batch, the optimizer evaluates different combinations of prompts on a subset of training inputs, which allows it to learn which combinations yield the best performance.\n", + "\n", + "#### 2) How much will MIPRO cost me to run?\n", + "Note that __this notebook__ is free to run, because all LM calls have been cached. However, when using an optimizer on your own program, here is a breakdown of the upper bound of the number of calls to the task model and prompt model respectively:\n", + "\n", + "- **Task model calls**: MIPRO makes up to __O(TxPxM)__ task model calls if you run without minibatching, where T is the number of batches, P is the number of prompts in the program, and M is the size of the train set. This is because the model is evaluating the program on the train set each batch. If you run **with minibatching** you can reduce calls even further to __O(TxPxB)__ where **B** is the minibatch size. Note that every few steps (a parameter you set) MIPRO will also run a full eval over all **M** examples.\n", + "\n", + "- **Prompt model calls**: MIPRO makes up to N*P+10+(P+1) prompt model calls, where N is the number of instruction / fewshot example set candidates to generate for each prompt, and P is the number of prompts in the program. The extra 10 calls comes from generating a summary of the data in the training set, which we use in the meta prompt to create better instructions. The extra (P+1) comes from program summarization where the proposer LLM will look at the program code and try to describe what each module does and what the whole program does.\n", + "\n", + "#### 3) How should I configure the hyperparameters?\n", + "We have yet to run full hyperparameter sweeps with MIPRO, but based off of initial experimintation, we'd recommend the following:\n", + "- __Batch num__: Gains can be seen after about 20-30 batches. However, 100-200 batches can help with adding on additional marginal gains.\n", + "- __num candidates__: This hyperparameter controls the number of candidate prompts and fewshot example sets that are generated to optimize over. With more batches and less prompts to optimize, we can set n to be higher, as we have more batches to explore different combinations of prompts. If your program has between 2-3 modules and is the `num_batches=30`, we'd recommend ~`n=10`. If n is higher (say `n=100`), then we can go higher to ~`n=15`. If you have a program with only 1 module and are keeping the program 0-shot (ie. no fewshot examples), then `num_batches` should be set to equal `n`, because each batch can explore a new instruction.\n", + "- __Training set size__: Between 100 and 500 training examples are recommended, however MIPROv2 can still work well with fewer. Increasing the training set size can help prevent overfitting, and only slightly increases cost for the full evaluation runs.\n", + "\n", + "#### 4) What should I do if I want to reduce the cost?\n", + "You can always update hyperparameters accordingly, such as using a smaller train set, using less batches, or using a program with less modules. You should take advantage of minibatching.\n", + "Alternatively, one strategy would be to optimize using a cheaper task model (ie. locally hosted Llama-3), as initial experiments have shown that prompts optimized for a smaller model also transfer to working well on a larger model." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "SgTc-CutqZH1" + }, + "source": [ + "### 0] Setup" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 156.45it/s]\n", - "[I 2024-06-21 01:06:03,944] Trial 23 finished with value: 88.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 9}. Best is trial 23 with value: 88.0.\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "5Vo4Tb9srSow" + }, + "source": [ + "First, we will install __DSPy__ if it's not there already. We'll also __load in the cached requests__ for this tasks, so that we don't actually need to call any LMs for this notebook. We'll also load in our pre optimized program from hugging face to inspect later." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 88.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: context, question -> answer\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JpijP_d7qZH2", + "outputId": "c641a4b1-05f5-45cc-d715-4347c526b576" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + "\n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " !pip install dspy-ai==2.4.17\n", + " !pip install openai~=1.12\n", + " !pip install -e $repo_path\n", + " !pip install --upgrade cloudpickle==3.0.0\n", + "\n", + "from huggingface_hub import hf_hub_download\n", + "import zipfile\n", + "\n", + "repo_id = 'MichaelR207/MIPRO_notebook_cache_scone'\n", + "cache_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='MIPRO_notebook_cache.zip')\n", + "compiled_program_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='compiled_program.dspy')\n", + "trial_logs_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='trial_logs.pickle')\n", + "\n", + "with zipfile.ZipFile(cache_file_path, 'r') as zip_ref:\n", + " zip_ref.extractall(\".\")\n", + "\n", + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = f\"{os.getcwd()}/MIPRO_notebook_cache\"\n", + "\n", + "import dspy\n", + "import pandas as pd\n", + "import random\n", + "from dspy.evaluate import Evaluate" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 18 / 25 (72.0): 100%|██████████| 25/25 [00:00<00:00, 1211.71it/s]\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "U-DaFCBvqZH2" + }, + "source": [ + "We will also specify the __prompt LM model__ (in this case GPT 3.5), the __task LM model__ (Llama 3 8B)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "UHWzGRVgqZH2" + }, + "outputs": [], + "source": [ + "\n", + "### NOTE: if you'd like to run this code without a cache, you can remove these lines to configure your OPEN AI key ###\n", + "# os.environ['OPENAI_API_KEY'] = \"TODO: ADD YOUR OPEN AI KEY HERE\"\n", + "# openai.api_key = os.environ.get('OPENAI_API_KEY')\n", + "# openai.api_base = \"https://api.openai.com/v1\"\n", + "\n", + "prompt_model_name = \"gpt-3.5-turbo-1106\"\n", + "task_model_name = \"meta-llama/Meta-Llama-3-8B\"\n", + "\n", + "prompt_model = dspy.OpenAI(model=prompt_model_name, max_tokens=1000, stop=[\"\\n\\n\", \"\\n---\"])\n", + "task_model = dspy.HFClientVLLM(\n", + " model=task_model_name,\n", + " port=7410,\n", + " url=[\"http://future-hgx-2:7500\", \"http://future-hgx-2:7501\", \"http://future-hgx-2:7502\", \"http://future-hgx-2:7503\", \"http://future-hgx-2:7504\", \"http://future-hgx-2:7505\", \"http://future-hgx-2:7506\", \"http://future-hgx-2:7507\"],\n", + " max_tokens=1000,\n", + " stop=[\"\\n\\n\", \"\\n---\", \"assistant\"],\n", + ")\n", + "\n", + "dspy.settings.configure(lm=task_model)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 429.13it/s]\n", - "[I 2024-06-21 01:06:04,004] Trial 24 finished with value: 72.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 9}. Best is trial 23 with value: 88.0.\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "BFoPwDrUqZH2" + }, + "source": [ + "### 1] Define Task" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", - "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not holding anything in his hands.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 72.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "s4Cyb1JtqZH2" + }, + "source": [ + "Here, we'll define the program that we'd like to run, which is a multihop [...] (we can say that it was loosely inspired by a certain paper). We additionally load in the data, and define how we'd like to evaluate this task." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 18 / 25 (72.0): 100%|██████████| 25/25 [00:00<00:00, 1147.59it/s]\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: destination path 'ScoNe' already exists and is not an empty directory.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/selenashe/ScoNe.git" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class ScoNeSignature(dspy.Signature):\n", + " (\"\"\"context, question -> answer\"\"\")\n", + "\n", + " context = dspy.InputField()\n", + " question = dspy.InputField()\n", + " answer = dspy.OutputField(desc=\"Yes or No\")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 248.52it/s]\n", - "[I 2024-06-21 01:06:04,177] Trial 25 finished with value: 72.0 and parameters: {'0_predictor_instruction': 3, '0_predictor_demos': 7}. Best is trial 23 with value: 88.0.\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 340, + "referenced_widgets": [ + "e16044d880174c49af557bd12789493f", + "531c380db44a404ebb168648ea77c3f4", + "e8a46c6ac8a54fdbb44b2c8914552e64", + "81cbe1842465400cba02a46309d98064", + "f8ada809ecdb417ebc8214d860dd6552", + "74d58314b1c449e0b7dec3bda8b653c2", + "8b7b2b9489ae49049befae848d0fb1a1", + "2d47a6a66054438d8e9a3c5e5767c056", + "3b57d0a9768f4befb7509581289035ad", + "134fa83980e142bf82d5b97cfc10da69", + "35e4e99036894a2ba37d9ba23581a8ff", + "835a1e675186490692e336da943d635d", + "95bcef02eb794979b7873b81021e4b40", + "d84324c4c3dc40fea04cc0df1539b4d8", + "a6213bcdbcb24b1694204e6baeb763d8", + "bc423d0878154adf940062660a8315ad", + "0a1744c363f640b5b8939f32f6e72922", + "bbd2db64988147f1a4f8856d890bb4c7", + "ba2b4d7ecdbc4512acf180d8a0d602e8", + "1bb155669e2a441d8992030e8aaa84a9", + "43ed7af1d9c84ac8a6ec2195e48e60eb", + "ac6822762e6b46bd862d6f923aeb4437", + "348bb4fff900492cba8333156626a947", + "697eaeb1a004493a9260a3a89671a9ca", + "8a64950ea896468da3d10f46e9718ec8", + "7b0b45020d0f45288e2f0e4ceff22524", + "ad21e20d1f1b4b6ea74fdab03af8acdb", + "1aab1d48c6124526bee6c74892ecd953", + "598c6f37331e448889b175393a00deff", + "20d8df0ee0a4442582686846757bcc7f", + "fa4e50aaf53d4edfb9ca3046cbada2db", + "96ad4454539145aea6549995344160e3", + "acc3573e48f14dffb635f8632c6f6e74", + "6c0432c6cd8f4cae9aeb8c2870b39922", + "99b8f3882032404990f2b453111a63ba", + "6c21df0657ce486381ba2310c7fa0029", + "a20ac344a32340c785cd162fd0eb55b5", + "6ba50c3ec9e542d5a8d2f900fbcb8689", + "a298045042014c88939a9fe785fccda8", + "84bed63c6597486ca6c39b9c0c4d20bd", + "2a75d45acfb44463af974acb7a1b0d8e", + "ae814b8e55454e0aadc48f7e595575ee", + "ff4bcec9c18e4f04b1f7898daaffeb1a", + "e9c64a790d684c9eaf7f49eea003f43c", + "52f1714412df4ec78f6ff7d0f8a69862", + "988055b44cf9411e8d45d8a4185fef07", + "59be08d44c824d76b8525df14191d569", + "6fde64f36dd84d8eae670efe95e2313a", + "66e6502a463145daa440e967d8bdfdca", + "29cab51fec0b4dacaa8f829ff217c839", + "6ecbf23579324112981d4bce0c0ce369", + "a105ef4f1f754c44b7bc0ec8edf0c0cc", + "1c7d71e42c8c494f867b30d781beb681", + "2f1e652cfa514054abfda794bdfa61a5", + "b17f4731e8e44858889114c52b8f3dd4", + "d50f5eafe90c4684bbdd96569b8ff247", + "9f07fa213de247dabcd3f4213d35a97e", + "782af098d37d4543a4a01ecfed4e5da2", + "037b10adf9724e058c6468f4ca74ed86", + "c7d10443100e49d28266aef9f644ed47", + "d6a78aabcca74314a5b4bb98f350693a", + "954f3cfcb5d44dddbf1d4db9e99c0d70", + "62d8196169bb4a76a94c16d6f1ca61a6", + "0a16c7f37b9a4bbe9bfe7e737ea62801", + "942897aa22214745adee56d2c54447e9", + "0b49910850f3445abe63b6bc7ac18412", + "12a9763cd97742f4ab80c0494a398ca6", + "9d4f14862b704d9bb53d9e74365d14ac", + "56989c76e1534cfd8c9b0da93b3b8bf8", + "f120c447645446e4a04791b97ce9ae93", + "6a5d5ea44c0d4e4384b8956b48c34f14", + "e3dd508496f44171b0d91aca0e8b16a5", + "dc8077859eec4261a28d708ab06a1008", + "9a733957f8fc446fbde053ba87289c71", + "287c9f993cd44039923fdc122cd9e040", + "5cfebfd6308349038f9cd7ad5bc00fe5", + "56b80fc45dd247deadceffe17557f0f9", + "7d80528c4b1c48318756230b0174923c", + "398d7d36bbd041fe81ec633e973fc504", + "d01ca0dd46ee4a4daeeee92214bc07d1", + "6028a5e08f8641f5b8e8182e50f55419", + "567646442eb940b09456328d49248945", + "41961130caaf4537a4c1793574c785d7", + "e9935b60c48d46469904492e20f9c2e8", + "f048b50740e94bd8805c142eac1673b6", + "a5effa5d67534fb4be2e3320c1fb2b9f", + "09b3f2c456af41cba9264dbb9a724027", + "e2f3a3377ad64a4cb9f3a42c1ac97344", + "8e396041a4fd4e6db02410a09b7556c7", + "4cd1dc71c80b401da19ed52ef5148d60", + "1f23a95e292249a19055f70cda2622a3", + "69412c7605ec48859baef6f31c91c520", + "ee64a46b61454949ade4177c059bcf61", + "de6507e9f45741218bf31a9af728b2bf", + "38abb8f460d24c27a66c54bb0417f8ce", + "33267de625db44c2a63d7c7d412a7e61", + "19fe27a0df5a4d39873dd1031904405c", + "dba7ce7c756d468b94d0bc8f4815ab6f", + "926cbb119ea745cf9e344c0e50b75f62" + ] + }, + "id": "hiVgd3N7qZH3", + "outputId": "81b97d83-7c5f-4763-d104-3ad320425a2a" + }, + "outputs": [], + "source": [ + "def load_scone(dirname):\n", + " dfs = []\n", + " filenames = ['one_not_scoped.csv', 'one_scoped.csv', 'no_negation.csv', 'one_scoped_one_not_scoped.csv', 'two_scoped.csv', 'two_not_scoped.csv']\n", + "\n", + " for filename in filenames:\n", + " filename = os.path.join(dirname, filename)\n", + " df = pd.read_csv(filename, index_col=0)\n", + " df['category'] = os.path.basename(filename).replace(\".csv\", \"\")\n", + " dfs.append(df)\n", + " data_df = pd.concat(dfs)\n", + "\n", + " def as_example(row):\n", + " # The 'one_scoped' file is from an earlier dataset, MoNLI, and\n", + " # so is formatted a bit differently:\n", + " suffix = '' if row['category'] == 'one_scoped' else '_edited'\n", + " # Reformat the hypothesis to be an embedded clause in a question:\n", + " hkey = 'sentence2' + suffix\n", + " question = row[hkey][0].lower() + row[hkey][1: ].strip(\".\")\n", + " question = f\"Can we logically conclude for sure that {question}?\"\n", + " # Binary task formulation:\n", + " label = \"Yes\" if row['gold_label' + suffix] == 'entailment' else \"No\"\n", + " return dspy.Example({\n", + " \"context\": row['sentence1' + suffix],\n", + " \"question\": question,\n", + " \"answer\": label,\n", + " \"category\": row['category'],\n", + " }).with_inputs(\"context\", \"question\")\n", + " return list(data_df.apply(as_example, axis=1).values)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", - "\n", - "---\n", - "\n", - "Context: There is not a single person not walking in the city.\n", - "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", - "\n", - "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", - "\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 72.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: context, question -> answer\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Load and configure the datasets.\n", + "all_train = load_scone(\"ScoNe/scone_nli/train\")\n", + "\n", + "random.seed(1)\n", + "random.shuffle(all_train)\n", + "\n", + "# 1000 random train, 500 random dev:\n", + "trainset, valset, testset = all_train[: 200], all_train[200: 400], all_train[400: 600]" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 17 / 25 (68.0): 100%|██████████| 25/25 [00:00<00:00, 1022.33it/s]\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up metrics\n", + "NUM_THREADS = 10\n", + "metric = dspy.evaluate.answer_exact_match\n", + "\n", + "kwargs = dict(num_threads=NUM_THREADS, display_progress=True)\n", + "evaluate = Evaluate(devset=valset, metric=metric, **kwargs)\n", + "\n", + "program = ScoNeCoT()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "hRGld1C1qZH3" + }, + "source": [ + "### 2] Baseline Evaluation\n", + "Now, we'll quickly evaluate our baseline program so that we can see how the performance using the Prompt Optimizer compares. We should see performance of about __58.0%__ on our trainset, __49.5%__ on our valset, and __55.0%__ on our testset." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 216.73it/s]\n", - "[I 2024-06-21 01:06:04,245] Trial 26 finished with value: 68.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 3}. Best is trial 23 with value: 88.0.\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MU2aHQBTqZH3", + "outputId": "786ba2f2-ae0a-4c68-b602-d601fb5a5aa5" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 116 / 200 (58.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 907.05it/s]\n", + "Average Metric: 99 / 200 (49.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 862.68it/s]\n", + "Average Metric: 110 / 200 (55.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 647.97it/s]\n" + ] + } + ], + "source": [ + "baseline_train_score = evaluate(program,devset=trainset)\n", + "baseline_eval_score = evaluate(program, devset=valset)\n", + "baseline_test_score = evaluate(program, devset=testset)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", - "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. We can't conclude that the person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", - "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", - "Answer: Yes\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", - "\n", - "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. We can't conclude that the person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 68.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: context, question -> answer\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "cjCoL27yqZH3" + }, + "source": [ + "### 3] Optimizing with MIPRO" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 9 / 25 (36.0): 100%|██████████| 25/25 [00:00<00:00, 1336.67it/s]\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "eEGOKjXprc7z" + }, + "source": [ + "Now let's get into the key method in this notebook - optimizing our program with MIPRO!" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "G04L5j9iqZH3" + }, + "source": [ + "#### 3a] Compile Program\n", + "First, we'll get our optimized program. By default, we set `LOAD_PRECOMPILED_PROGRAM` to `True`, so that you can quickly access a program we've precompiled for you. However, if you wish to optimize yourself, `LOAD_PRECOMPILED_PROGRAM` can be set to `False` (though please note that this will require adding in your own LM API keys in the __Setup__ section above).\n", + "\n", + "MIPRO only needs a metric, DSPy module, and training set to see huge gains on your task! You can instantiate a MIPRO Optimizer and compile in just two lines:\n", + "```python\n", + "teleprompter = MIPROv2(prompt_model=prompt_model, task_model=task_model, metric=metric, num_candidates=N, init_temperature=temperature)\n", + "compiled_program = teleprompter.compile(program, trainset=trainset, valset=valset, num_batches=batches, max_bootstrapped_demos=1,max_labeled_demos=2, eval_kwargs=eval_kwargs)\n", + "```" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 53.79it/s]\n", - "[I 2024-06-21 01:06:04,322] Trial 27 finished with value: 36.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 6}. Best is trial 23 with value: 88.0.\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "NVfMJ_FpBlSI" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/michaelryan/Documents/School/Stanford/Research/dspy_official/dspy/examples/nli/scone/MIPRO_notebook_cache/compiler\n", + "[Example({'context': 'The cowboy did not tell other people that he fall off a horse at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a bronco at the competition?', 'answer': 'No', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The woman not crying is not wearing rings.', 'question': 'Can we logically conclude for sure that the woman not crying is not wearing jewelry?', 'answer': 'No', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The people were not trying to keep their voices down so they woke a woman who is not indoors.', 'question': 'Can we logically conclude for sure that the people were not trying to keep their voices down so they woke a lady who is not indoors?', 'answer': 'No', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'It is a lie that the man is not listening to bluegrass.', 'question': 'Can we logically conclude for sure that it is a lie that the man is not listening to music?', 'answer': 'Yes', 'category': 'two_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'There is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single African walking in the city?', 'answer': 'Yes', 'category': 'one_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The man does not dress up when listening to music.', 'question': 'Can we logically conclude for sure that the man does not dress up when listening to bluegrass?', 'answer': 'No', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The man is not listening to opera.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'}), Example({'context': \"There is a person walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a volunteer walking in the city when it's not dark?\", 'answer': 'No', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The girl who is not here is not wearing any jewelry at all.', 'question': 'Can we logically conclude for sure that the girl who is not here is not wearing any rings at all?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'the boy, not girl, will play an piccolo, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'})]\n", + "\u001b[93m\u001b[1mWARNING: Projected Language Model (LM) Calls\u001b[0m\n", + "\n", + "Please be advised that based on the parameters you have set, the maximum number of LM calls is projected as follows:\n", + "\n", + "\n", + "\u001b[93m- Prompt Model: \u001b[94m\u001b[1m10\u001b[0m\u001b[93m data summarizer calls + \u001b[94m\u001b[1m10\u001b[0m\u001b[93m * \u001b[94m\u001b[1m1\u001b[0m\u001b[93m lm calls in program + (\u001b[94m\u001b[1m2\u001b[0m\u001b[93m) lm calls in program aware proposer = \u001b[94m\u001b[1m22\u001b[0m\u001b[93m prompt model calls\u001b[0m\n", + "\u001b[93m- Task Model: \u001b[94m\u001b[1m25\u001b[0m\u001b[93m examples in minibatch * \u001b[94m\u001b[1m30\u001b[0m\u001b[93m batches + \u001b[94m\u001b[1m200\u001b[0m\u001b[93m examples in train set * \u001b[94m\u001b[1m3\u001b[0m\u001b[93m full evals = \u001b[94m\u001b[1m1350\u001b[0m\u001b[93m task model calls\u001b[0m\n", + "\n", + "\u001b[93m\u001b[1mEstimated Cost Calculation:\u001b[0m\n", + "\n", + "\u001b[93mTotal Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token) \n", + " + (Number of calls to prompt model * (Avg Input Token Length per Call * Task Prompt Price per Input Token + Avg Output Token Length per Call * Prompt Model Price per Output Token).\u001b[0m\n", + "\n", + "For a preliminary estimate of potential costs, we recommend you perform your own calculations based on the task\n", + "and prompt models you intend to use. If the projected costs exceed your budget or expectations, you may consider:\n", + "\n", + "\u001b[93m- Reducing the number of trials (`num_batches`), the size of the trainset, or the number of LM calls in your program.\u001b[0m\n", + "\u001b[93m- Using a cheaper task model to optimize the prompt.\u001b[0m\n", + "To proceed with the execution of this program, please confirm by typing \u001b[94m'y'\u001b[0m for yes or \u001b[94m'n'\u001b[0m for no.\n", + "\n", + "If you would like to bypass this confirmation step in future executions, set the \u001b[93m`requires_permission_to_run`\u001b[0m flag to \u001b[93m`False` when calling compile.\u001b[0m\n", + "\n", + "\u001b[93mAwaiting your input...\u001b[0m\n", + "\n", + "SOURCE CODE: ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "b: 10\n", + "e 'NoneType' object has no attribute 'write'. using observations from past round for a summary.\n", + "summary: Prediction(\n", + " summary='The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.'\n", + ")\n", + "DATA SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 1/200 [00:00<00:00, 650.99it/s]\n", + " 2%|\u258f | 3/200 [00:00<00:00, 1005.35it/s]\n", + " 0%| | 1/200 [00:00<00:00, 750.19it/s]\n", + " 0%| | 1/200 [00:00<00:00, 686.69it/s]\n", + " 0%| | 1/200 [00:00<00:00, 488.96it/s]\n", + " 1%| | 2/200 [00:00<00:00, 738.24it/s]\n", + " 0%| | 1/200 [00:00<00:00, 762.74it/s]\n", + " 0%| | 1/200 [00:00<00:00, 409.00it/s]\n", + "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/optuna/samplers/_tpe/sampler.py:295: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future.\n", + " warnings.warn(\n", + "[I 2024-06-21 01:05:59,906] A new study created in memory with name: no-name-361c2f94-d354-463c-bbbe-c93d529a0192\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using a randomly generated configuration for our grounded proposer.\n", + "Selected tip: description\n", + "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", + "task_demos \n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S): \n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", + "task_demos \n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: The program appears to be used to generate answers (Yes or No) based on a given context and question.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S): \n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "PROGRAM DESCRIPTION: This program is designed to solve logical reasoning tasks by using language models. It takes in a context and a question as input, and then generates an answer based on the logical reasoning of the given context and question. The program uses a Chain of Thought model to process the input and produce the answer. The example provided uses the program to determine the logical conclusion of a given statement about people walking in the city, demonstrating its ability to reason and generate responses based on the input context and question.\n", + "task_demos Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "Answer: No\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: This program is designed to solve logical reasoning tasks by using language models. It takes in a context and a question as input, and then generates an answer based on the logical reasoning of the given context and question. The program uses a Chain of Thought model to process the input and produce the answer. The example provided uses the program to determine the logical conclusion of a given statement about people walking in the city, demonstrating its ability to reason and generate responses based on the input context and question.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "Answer: No\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\"\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of logical reasoning and inference based on a given context and question. It uses a language model to generate an answer to the question based on the provided context. The program takes the context and question as input and returns a \"yes\" or \"no\" answer. In the example provided, the program uses the given context and question to produce a \"no\" answer, indicating that a logical conclusion cannot be made. The program seems to work by passing the context and question through a language model to generate the answer.\n", + "task_demos Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", + "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", + "Answer: No\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of logical reasoning and inference based on a given context and question. It uses a language model to generate an answer to the question based on the provided context. The program takes the context and question as input and returns a \"yes\" or \"no\" answer. In the example provided, the program uses the given context and question to produce a \"no\" answer, indicating that a logical conclusion cannot be made. The program seems to work by passing the context and question through a language model to generate the answer.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", + "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", + "Answer: No\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on a given context and question. The program takes a context and a question as input and uses a language model to generate an answer, which is either \"Yes\" or \"No\" based on logical inference. The program uses a Chain of Thought model to process the input and generate the answer. In the example provided, the program successfully processes the context and question to infer a logical conclusion and generate the answer \"Yes.\" Overall, the program is designed to use language models for logical reasoning tasks and inference.\n", + "task_demos Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", + "Answer: Yes\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on a given context and question. The program takes a context and a question as input and uses a language model to generate an answer, which is either \"Yes\" or \"No\" based on logical inference. The program uses a Chain of Thought model to process the input and generate the answer. In the example provided, the program successfully processes the context and question to infer a logical conclusion and generate the answer \"Yes.\" Overall, the program is designed to use language models for logical reasoning tasks and inference.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", + "Answer: Yes\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", + "PROGRAM DESCRIPTION: The program is designed to solve a task of answering yes or no questions based on a given context. It appears to work by taking in a context and a question as inputs, and then using a language model to generate an answer to the question based on the provided context. In the example given, the program uses the provided context and question to generate a logical step-by-step reasoning and ultimately provide an answer of \"No\" to the question. This suggests that the program is designed to solve tasks that require logical reasoning and inference based on textual information.\n", + "task_demos Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", + "Answer: No\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: The program is designed to solve a task of answering yes or no questions based on a given context. It appears to work by taking in a context and a question as inputs, and then using a language model to generate an answer to the question based on the provided context. In the example given, the program uses the provided context and question to generate a logical step-by-step reasoning and ultimately provide an answer of \"No\" to the question. This suggests that the program is designed to solve tasks that require logical reasoning and inference based on textual information.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", + "Answer: No\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve contextual reasoning tasks by using language models. It takes in a context and a question, and uses a language model to generate a yes or no answer based on the given context and question. In the provided example, the program is used to determine if it can be logically concluded that there is a kayak nearby based on the given context. The program uses a chain of thought to process the input and generate the answer, taking into account the context and question provided. It uses the language model to reason through the context and question in order to provide the appropriate yes or no answer.\n", + "task_demos Context: There is a boat nearby\n", + "Question: Can we logically conclude for sure that there is a kayak nearby?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", + "Answer: No\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve contextual reasoning tasks by using language models. It takes in a context and a question, and uses a language model to generate a yes or no answer based on the given context and question. In the provided example, the program is used to determine if it can be logically concluded that there is a kayak nearby based on the given context. The program uses a chain of thought to process the input and generate the answer, taking into account the context and question provided. It uses the language model to reason through the context and question in order to provide the appropriate yes or no answer.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: There is a boat nearby\n", + "Question: Can we logically conclude for sure that there is a kayak nearby?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", + "Answer: No\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "PROGRAM DESCRIPTION: This program is designed to solve tasks related to logical reasoning and inference based on a given context and question. It appears to work by taking in a context and a question as input, and then using a language model to generate an answer based on logical inference from the context. The `ScoNeSignature` function takes in the context and question as input, and returns an answer (either \"Yes\" or \"No) based on the logical reasoning process. The `ScoNeCoT` class sets up a chain of thought using the `ScoNeSignature` function to generate the answer based on the input context and question. The example provided demonstrates how the program uses the given context and question to generate a logical answer, \"Yes\".\n", + "task_demos Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "Answer: Yes\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: This program is designed to solve tasks related to logical reasoning and inference based on a given context and question. It appears to work by taking in a context and a question as input, and then using a language model to generate an answer based on logical inference from the context. The `ScoNeSignature` function takes in the context and question as input, and returns an answer (either \"Yes\" or \"No) based on the logical reasoning process. The `ScoNeCoT` class sets up a chain of thought using the `ScoNeSignature` function to generate the answer based on the input context and question. The example provided demonstrates how the program uses the given context and question to generate a logical answer, \"Yes\".\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "Answer: Yes\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of answering yes or no questions based on a given context and question. The program takes in a context and a question as input and uses a language model to generate an answer. It seems to work by taking the input context and question, passing it through the ScoNeSignature function, and using the ChainOfThought method to generate the answer based on the input context and question. The example provided shows how the program can be used to answer a yes or no question based on a given context and question.\n", + "task_demos Context: There is a athlete walking in the city when it's not dark.\n", + "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", + "Answer: Yes\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve a task of answering yes or no questions based on a given context and question. The program takes in a context and a question as input and uses a language model to generate an answer. It seems to work by taking the input context and question, passing it through the ScoNeSignature function, and using the ChainOfThought method to generate the answer based on the input context and question. The example provided shows how the program can be used to answer a yes or no question based on a given context and question.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: There is a athlete walking in the city when it's not dark.\n", + "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", + "Answer: Yes\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on given context and question. The program takes in a context and a question as input, and the goal is to produce a logical conclusion or answer based on the given context and question. It uses a language model to generate the answer by reasoning through the given context and question. The example provided demonstrates how the program works by taking in a context and a question and generating a logical answer based on the reasoning process.\n", + "task_demos Context: A man is not holding anything in his hands.\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "Answer: Yes\n", + "\n", + "\n", + "\n", + "\n", + "Use the information below to learn about a task that we are trying to solve using calls to an LM, then generate a new instruction that will be used to prompt a Language Model to better solve the task.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "DATASET SUMMARY: A description of the dataset that we are using.\n", + "\n", + "PROGRAM CODE: Language model program designed to solve a particular task.\n", + "\n", + "PROGRAM DESCRIPTION: Summary of the task the program is designed to solve, and how it goes about solving it.\n", + "\n", + "MODULE: The module to create an instruction for.\n", + "\n", + "TASK DEMO(S): Example inputs/outputs of our module.\n", + "\n", + "BASIC INSTRUCTION: Basic instruction.\n", + "\n", + "TIP: A suggestion for how to go about generating the new instruction.\n", + "\n", + "PROPOSED INSTRUCTION: Propose an instruction that will be used to prompt a Language Model to perform this task.\n", + "\n", + "---\n", + "\n", + "DATASET SUMMARY: The dataset consists of examples that involve logical tests and negation related to language understanding and logical reasoning. Each example includes a context, a question, an answer, and a category label, and encompasses both single-scoped and two-scoped logical statements. The dataset seems to be focused on training models for handling negation in logical reasoning and has a broad range of potential applications.\n", + "\n", + "PROGRAM CODE:\n", + "ScoNeSignature(context, question -> answer\n", + " instructions='context, question -> answer'\n", + " context = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Context:', 'desc': '${context}'})\n", + " question = Field(annotation=str required=True json_schema_extra={'__dspy_field_type': 'input', 'prefix': 'Question:', 'desc': '${question}'})\n", + " answer = Field(annotation=str required=True json_schema_extra={'desc': 'Yes or No', '__dspy_field_type': 'output', 'prefix': 'Answer:'})\n", + ")\n", + "\n", + "class ScoNeCoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + " def forward(self, context, question):\n", + " return self.generate_answer(context=context, question=question)\n", + "\n", + "\n", + "PROGRAM DESCRIPTION: This program appears to be designed to solve tasks related to logical reasoning and inference based on given context and question. The program takes in a context and a question as input, and the goal is to produce a logical conclusion or answer based on the given context and question. It uses a language model to generate the answer by reasoning through the given context and question. The example provided demonstrates how the program works by taking in a context and a question and generating a logical answer based on the reasoning process.\n", + "\n", + "MODULE: self.generate_answer = dspy.ChainOfThought(ScoNeSignature)\n", + "\n", + "TASK DEMO(S):\n", + "Context: A man is not holding anything in his hands.\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "Answer: Yes\n", + "\n", + "\n", + "BASIC INSTRUCTION: context, question -> answer\n", + "\n", + "TIP: Make sure your instruction is very informative and descriptive.\n", + "\n", + "PROPOSED INSTRUCTION:\u001b[32m Given the context and question, generate a logical answer based on the reasoning process.\u001b[0m\n", + "\n", + "\n", + "\n", + "PROPOSED INSTRUCTION: Given the context and question, generate a logical answer based on the reasoning process.\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 13 / 25 (52.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 654.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 1175.20it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 52.0\n", + "Best Combination: 1,{0: [[], [Example({'context': 'the boy, not girl, will play an horn, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The person did not like raspberries.', 'question': 'Can we logically conclude for sure that the person did not like fruit?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?', 'rationale': 'produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.', 'answer': 'No'}) (input_keys=None), Example({'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not standing on top of a ladder that is leaned against a not so tall tree.', 'question': 'Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?', 'rationale': \"produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.', 'question': 'Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'rationale': 'produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The diver has not seen any fish on his dive that did not go too deep.', 'question': 'Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'rationale': 'produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.', 'answer': 'No'}) (input_keys=None), Example({'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'There is a boat nearby', 'question': 'Can we logically conclude for sure that there is a kayak nearby?', 'rationale': \"produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'the boy plays an instrument', 'question': 'Can we logically conclude for sure that the boy plays an flute?', 'answer': 'No', 'category': 'no_negation'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The men were outside speaking loudly so as to wake the bridesmaid indoors', 'question': 'Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?', 'rationale': 'produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'There is not a single person not walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single matriarch not walking in the city?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': \"There is a athlete walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a person walking in the city when it's not dark?\", 'rationale': 'produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The man is not listening to techno.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not holding anything in his hands.', 'question': 'Can we logically conclude for sure that a man is not holding tongues in his hands?', 'rationale': \"produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\", 'answer': 'Yes'}) (input_keys=None), Example({'context': 'To believe that a man walks confidently not dropping produce is to believe a falsity.', 'question': 'Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})]]} with Mean = 52.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Average Metric: 113 / 200 (56.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 587.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UPDATING BEST SCORE WITH 56.5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 98 / 200 (49.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 660.76it/s]\n", + "[I 2024-06-21 01:06:00,667] Trial 0 finished with value: 52.0 and parameters: {'0_predictor_instruction': 1, '0_predictor_demos': 2}. Best is trial 0 with value: 52.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 11 / 25 (44.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 712.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 251.76it/s]\n", + "[I 2024-06-21 01:06:00,762] Trial 1 finished with value: 44.0 and parameters: {'0_predictor_instruction': 6, '0_predictor_demos': 2}. Best is trial 0 with value: 52.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 44.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 14 / 25 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 500.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 76.44it/s]\n", + "[I 2024-06-21 01:06:00,877] Trial 2 finished with value: 56.0 and parameters: {'0_predictor_instruction': 8, '0_predictor_demos': 6}. Best is trial 2 with value: 56.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", + "\n", + "---\n", + "\n", + "Context: the boy plays an instrument\n", + "Question: Can we logically conclude for sure that the boy plays an flute?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a boat nearby\n", + "\n", + "Question: Can we logically conclude for sure that there is a kayak nearby?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city, but we don't know anything about the type of person. It could be a slaver, but it could also be a tourist, a local, or a homeless person. We can't logically conclude that it is a slaver for sure.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", + "\n", + "---\n", + "\n", + "Context: the boy plays an instrument\n", + "Question: Can we logically conclude for sure that the boy plays an flute?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a boat nearby\n", + "\n", + "Question: Can we logically conclude for sure that there is a kayak nearby?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city, but we don't know anything about the type of person. It could be a slaver, but it could also be a tourist, a local, or a homeless person. We can't logically conclude that it is a slaver for sure.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 56.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 17 / 25 (68.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1802.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 71.35it/s]\n", + "[I 2024-06-21 01:06:01,019] Trial 3 finished with value: 68.0 and parameters: {'0_predictor_instruction': 4, '0_predictor_demos': 5}. Best is trial 3 with value: 68.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there would be a slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there would be a slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 68.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 14 / 25 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1270.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 280.50it/s]\n", + "[I 2024-06-21 01:06:01,124] Trial 4 finished with value: 56.0 and parameters: {'0_predictor_instruction': 3, '0_predictor_demos': 8}. Best is trial 3 with value: 68.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", + "\n", + "---\n", + "\n", + "Context: The man is not listening to techno.\n", + "Question: Can we logically conclude for sure that the man is not listening to music?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a athlete walking in the city when it's not dark.\n", + "\n", + "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", + "\n", + "---\n", + "\n", + "Context: The man is not listening to techno.\n", + "Question: Can we logically conclude for sure that the man is not listening to music?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a athlete walking in the city when it's not dark.\n", + "\n", + "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 56.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 18 / 25 (72.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 903.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 139.84it/s]\n", + "[I 2024-06-21 01:06:01,205] Trial 5 finished with value: 72.0 and parameters: {'0_predictor_instruction': 2, '0_predictor_demos': 3}. Best is trial 5 with value: 72.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", + "\n", + "---\n", + "\n", + "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", + "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We can't conclude that this person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "LogicalNegation(context: str, question: str -> answer: str) \"Given a context and a question involving negation, determine the logical conclusion and provide a yes or no answer based on the reasoning.\n", + "\n", + "---\n", + "\n", + "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", + "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We can't conclude that this person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 72.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 14 / 25 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 938.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 282.88it/s]\n", + "[I 2024-06-21 01:06:01,283] Trial 6 finished with value: 56.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 5}. Best is trial 5 with value: 72.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there is a slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a slaver walking in the city. There could be a non-slaver person walking in the city. Additionally, slavery is illegal in most places, so it is highly unlikely that there is a slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 56.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 14 / 25 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1269.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 326.99it/s]\n", + "[I 2024-06-21 01:06:01,363] Trial 7 finished with value: 56.0 and parameters: {'0_predictor_instruction': 7, '0_predictor_demos': 4}. Best is trial 5 with value: 72.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Context: The diver has not seen any fish on his dive that did not go too deep.\n", + "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Slaver is a type of person, so it is a type of person that is walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Context: The diver has not seen any fish on his dive that did not go too deep.\n", + "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Slaver is a type of person, so it is a type of person that is walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 56.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: context, question -> answer\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 18 / 25 (72.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 907.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 448.83it/s]\n", + "[I 2024-06-21 01:06:01,431] Trial 8 finished with value: 72.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 7}. Best is trial 5 with value: 72.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 72.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 20 / 25 (80.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 846.48it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 116.36it/s]\n", + "[I 2024-06-21 01:06:01,509] Trial 9 finished with value: 80.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 80.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 19 / 25 (76.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 202.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 315.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 76.0\n", + "Best Combination: 2,{0: [[], [Example({'context': 'the boy, not girl, will play an horn, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The person did not like raspberries.', 'question': 'Can we logically conclude for sure that the person did not like fruit?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?', 'rationale': 'produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.', 'answer': 'No'}) (input_keys=None), Example({'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not standing on top of a ladder that is leaned against a not so tall tree.', 'question': 'Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?', 'rationale': \"produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.', 'question': 'Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'rationale': 'produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The diver has not seen any fish on his dive that did not go too deep.', 'question': 'Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'rationale': 'produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.', 'answer': 'No'}) (input_keys=None), Example({'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'There is a boat nearby', 'question': 'Can we logically conclude for sure that there is a kayak nearby?', 'rationale': \"produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'the boy plays an instrument', 'question': 'Can we logically conclude for sure that the boy plays an flute?', 'answer': 'No', 'category': 'no_negation'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The men were outside speaking loudly so as to wake the bridesmaid indoors', 'question': 'Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?', 'rationale': 'produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'There is not a single person not walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single matriarch not walking in the city?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': \"There is a athlete walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a person walking in the city when it's not dark?\", 'rationale': 'produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The man is not listening to techno.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not holding anything in his hands.', 'question': 'Can we logically conclude for sure that a man is not holding tongues in his hands?', 'rationale': \"produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\", 'answer': 'Yes'}) (input_keys=None), Example({'context': 'To believe that a man walks confidently not dropping produce is to believe a falsity.', 'question': 'Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})]]} with Mean = 72.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 139 / 200 (69.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 691.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UPDATING BEST SCORE WITH 69.5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 128 / 200 (64.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 643.31it/s]\n", + "[I 2024-06-21 01:06:02,325] Trial 10 finished with value: 76.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 9}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 18 / 25 (72.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1546.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 516.29it/s]\n", + "[I 2024-06-21 01:06:02,377] Trial 11 finished with value: 72.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 9}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 72.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 20 / 25 (80.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1216.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 739.61it/s]\n", + "[I 2024-06-21 01:06:02,442] Trial 12 finished with value: 80.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 80.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 15 / 25 (60.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1431.84it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 896.79it/s]\n", + "[I 2024-06-21 01:06:02,490] Trial 13 finished with value: 60.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 60.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 14 / 25 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 850.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 398.24it/s]\n", + "[I 2024-06-21 01:06:02,653] Trial 14 finished with value: 56.0 and parameters: {'0_predictor_instruction': 4, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Please use the provided context and question to generate a logical inference for the answer using the language model. The answer should be \"Yes\" or \"No\" based on the logical reasoning and inference from the given context and question.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 56.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 14 / 25 (56.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 743.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 117.87it/s]\n", + "[I 2024-06-21 01:06:02,724] Trial 15 finished with value: 56.0 and parameters: {'0_predictor_instruction': 5, '0_predictor_demos': 0}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we already know that there is at least one person walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we already know that there is at least one person walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 56.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 17 / 25 (68.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 967.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 608.22it/s]\n", + "[I 2024-06-21 01:06:02,793] Trial 16 finished with value: 68.0 and parameters: {'0_predictor_instruction': 7, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Please generate a logical answer (either \"Yes\" or \"No\") based on the given context and question using the `ScoNeSignature` function to perform logical reasoning and inference. Take the context and question as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 68.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 17 / 25 (68.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1159.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 528.52it/s]\n", + "[I 2024-06-21 01:06:02,842] Trial 17 finished with value: 68.0 and parameters: {'0_predictor_instruction': 8, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given a context and a question, use the provided context and question to determine if the logical conclusion is \"Yes\" or \"No\" and generate the corresponding answer.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 68.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 18 / 25 (72.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 924.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 220.07it/s]\n", + "[I 2024-06-21 01:06:02,919] Trial 18 finished with value: 72.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 1}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: the boy, not girl, will play an horn, but not for another week\n", + "Question: Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?\n", + "Answer: Yes\n", + "\n", + "Context: The person did not like raspberries.\n", + "Question: Can we logically conclude for sure that the person did not like fruit?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. This means that there is at least one person walking in the city. However, we cannot logically conclude that there is not a single slaver walking in the city. We only know that there is at least one person, but we don't have any information about the presence of slavers.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: the boy, not girl, will play an horn, but not for another week\n", + "Question: Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?\n", + "Answer: Yes\n", + "\n", + "Context: The person did not like raspberries.\n", + "Question: Can we logically conclude for sure that the person did not like fruit?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. This means that there is at least one person walking in the city. However, we cannot logically conclude that there is not a single slaver walking in the city. We only know that there is at least one person, but we don't have any information about the presence of slavers.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 72.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 17 / 25 (68.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 2253.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 285.21it/s]\n", + "[I 2024-06-21 01:06:03,017] Trial 19 finished with value: 68.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 8}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: The man is not listening to techno.\n", + "Question: Can we logically conclude for sure that the man is not listening to music?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a athlete walking in the city when it's not dark.\n", + "\n", + "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: The man is not listening to techno.\n", + "Question: Can we logically conclude for sure that the man is not listening to music?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a athlete walking in the city when it's not dark.\n", + "\n", + "Question: Can we logically conclude for sure that there is a person walking in the city when it's not dark?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we cannot logically conclude that it is not true that there is not a single slaver walking in the city, because we have no information about the presence of slavers in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 68.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 18 / 25 (72.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1312.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 624.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 72.0\n", + "Best Combination: 0,{0: [[], [Example({'context': 'the boy, not girl, will play an horn, but not for another week', 'question': 'Can we logically conclude for sure that the boy, not girl, will play an instrument, but not for another week?', 'answer': 'Yes', 'category': 'two_not_scoped'}) (input_keys={'context', 'question'}), Example({'context': 'The person did not like raspberries.', 'question': 'Can we logically conclude for sure that the person did not like fruit?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?', 'rationale': 'produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.', 'answer': 'No'}) (input_keys=None), Example({'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not standing on top of a ladder that is leaned against a not so tall tree.', 'question': 'Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?', 'rationale': \"produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.', 'question': 'Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'rationale': 'produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The diver has not seen any fish on his dive that did not go too deep.', 'question': 'Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'It is not true that there is not a single person walking in the city.', 'question': 'Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?', 'rationale': 'produce the answer. We know that it is not true that there is not a single person walking in the city. This does not necessarily mean that there is a celebrity walking in the city. There could be a non-celebrity person walking in the city.', 'answer': 'No'}) (input_keys=None), Example({'context': 'The cowboy did not tell other people that he fall off a bronco at the competition.', 'question': 'Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?', 'answer': 'Yes', 'category': 'one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'There is a boat nearby', 'question': 'Can we logically conclude for sure that there is a kayak nearby?', 'rationale': \"produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\", 'answer': 'No'}) (input_keys=None), Example({'context': 'the boy plays an instrument', 'question': 'Can we logically conclude for sure that the boy plays an flute?', 'answer': 'No', 'category': 'no_negation'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'The men were outside speaking loudly so as to wake the bridesmaid indoors', 'question': 'Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?', 'rationale': 'produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'There is not a single person not walking in the city.', 'question': 'Can we logically conclude for sure that there is not a single matriarch not walking in the city?', 'answer': 'Yes', 'category': 'one_scoped_one_not_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': \"There is a athlete walking in the city when it's not dark.\", 'question': \"Can we logically conclude for sure that there is a person walking in the city when it's not dark?\", 'rationale': 'produce the answer. We know that the athlete is walking in the city, and we know that the athlete is a person. Therefore, we can logically conclude that there is a person walking in the city.', 'answer': 'Yes'}) (input_keys=None), Example({'context': 'The man is not listening to techno.', 'question': 'Can we logically conclude for sure that the man is not listening to music?', 'answer': 'No', 'category': 'one_scoped'}) (input_keys={'context', 'question'})], [Example({'augmented': True, 'context': 'A man is not holding anything in his hands.', 'question': 'Can we logically conclude for sure that a man is not holding tongues in his hands?', 'rationale': \"produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\", 'answer': 'Yes'}) (input_keys=None), Example({'context': 'To believe that a man walks confidently not dropping produce is to believe a falsity.', 'question': 'Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?', 'answer': 'No', 'category': 'two_scoped'}) (input_keys={'context', 'question'})]]} with Mean = 72.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 142 / 200 (71.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 725.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UPDATING BEST SCORE WITH 71.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 130 / 200 (65.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 738.21it/s]\n", + "[I 2024-06-21 01:06:03,753] Trial 20 finished with value: 72.0 and parameters: {'0_predictor_instruction': 6, '0_predictor_demos': 7}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 19 / 25 (76.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 677.20it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 525.14it/s]\n", + "[I 2024-06-21 01:06:03,818] Trial 21 finished with value: 76.0 and parameters: {'0_predictor_instruction': 5, '0_predictor_demos': 9}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and a question, use logical reasoning and inference to determine and provide a yes or no answer based on the information provided.\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Since the term \"person\" is a broader category that includes \"slaver\", we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 76.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given the context and question, generate a logical answer based on the reasoning process.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 19 / 25 (76.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 887.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 590.00it/s]\n", + "[I 2024-06-21 01:06:03,871] Trial 22 finished with value: 76.0 and parameters: {'0_predictor_instruction': 9, '0_predictor_demos': 4}. Best is trial 9 with value: 80.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: The diver has not seen any fish on his dive that did not go too deep.\n", + "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city, because there is at least one person walking in the city, and that person could be a slaver.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "---\n", + "\n", + "Context: The diver has not seen any fish on his dive that did not go too deep.\n", + "Question: Can we logically conclude for sure that the diver has not seen any tuna on his dive that did not go too deep?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The cowboy did not tell other people that he fall off a bronco at the competition.\n", + "\n", + "Question: Can we logically conclude for sure that the cowboy did not tell other people that he fall off a horse at the competition?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the cowboy did not tell other people that he fall off a bronco at the competition. Bronco is a type of horse, so it is a type of horse that the cowboy fell off. Therefore, we can logically conclude that the cowboy did not tell other people that he fall off a horse at the competition.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city, because there is at least one person walking in the city, and that person could be a slaver.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 76.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: context, question -> answer\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 22 / 25 (88.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1023.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 156.45it/s]\n", + "[I 2024-06-21 01:06:03,944] Trial 23 finished with value: 88.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 9}. Best is trial 23 with value: 88.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 88.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: context, question -> answer\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 18 / 25 (72.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1211.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 429.13it/s]\n", + "[I 2024-06-21 01:06:04,004] Trial 24 finished with value: 72.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 9}. Best is trial 23 with value: 88.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: To believe that a man walks confidently not dropping produce is to believe a falsity.\n", + "Question: Can we logically conclude for sure that to believe that a man walks confidently not dropping pears is to believe a falsity?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not holding anything in his hands.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not holding tongues in his hands?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that a man is not holding anything in his hands. This means that he is not holding any physical objects, including tongues. Tongues are not physical objects that can be held in one's hands. Therefore, we can logically conclude that a man is not holding tongues in his hands.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there is at least one person walking in the city. We cannot logically conclude that there is at least one slaver walking in the city, as the original statement does not mention anything about slavery. Therefore, we cannot logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 72.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 18 / 25 (72.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1147.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 248.52it/s]\n", + "[I 2024-06-21 01:06:04,177] Trial 25 finished with value: 72.0 and parameters: {'0_predictor_instruction': 3, '0_predictor_demos': 7}. Best is trial 23 with value: 88.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Given a context and a question, use logical reasoning to determine if a logical conclusion can be made and generate a \"yes\" or \"no\" answer based on the provided information.\n", + "\n", + "---\n", + "\n", + "Context: There is not a single person not walking in the city.\n", + "Question: Can we logically conclude for sure that there is not a single matriarch not walking in the city?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: The men were outside speaking loudly so as to wake the bridesmaid indoors\n", + "\n", + "Question: Can we logically conclude for sure that the men were outside speaking loudly so as to wake the woman indoors?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the men were outside speaking loudly, and we also know that the bridesmaid is a woman. Therefore, we can logically conclude that the men were outside speaking loudly so as to wake the woman indoors.\n", + "\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. Therefore, we can logically conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 72.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: context, question -> answer\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 17 / 25 (68.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1022.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 216.73it/s]\n", + "[I 2024-06-21 01:06:04,245] Trial 26 finished with value: 68.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 3}. Best is trial 23 with value: 88.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", + "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. We can't conclude that the person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: The people were outside not even trying to keep their voices down, and they woke up a mistress indoors.\n", + "Question: Can we logically conclude for sure that the people were outside not even trying to keep their voices down, and they woke up a woman indoors?\n", + "Answer: Yes\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: A man is not standing on top of a ladder that is leaned against a not so tall tree.\n", + "\n", + "Question: Can we logically conclude for sure that a man is not standing on top of a ladder that is leaned against a not so tall pine?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the man is not standing on top of a ladder that is leaned against a not so tall tree. We can't conclude that the ladder is leaned against a pine tree, so we can't conclude that the man is not standing on top of a ladder that is leaned against a not so tall pine.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. We can't conclude that the person is a slaver, so we can't conclude that it is not true that there is not a single slaver walking in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 68.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: context, question -> answer\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 9 / 25 (36.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1336.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 53.79it/s]\n", + "[I 2024-06-21 01:06:04,322] Trial 27 finished with value: 36.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 6}. Best is trial 23 with value: 88.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: the boy plays an instrument\n", + "Question: Can we logically conclude for sure that the boy plays an flute?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a boat nearby\n", + "\n", + "Question: Can we logically conclude for sure that there is a kayak nearby?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we can't logically conclude that it is not true that there is not a single slaver walking in the city. We don't have any information about the presence of slavers in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: the boy plays an instrument\n", + "Question: Can we logically conclude for sure that the boy plays an flute?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: There is a boat nearby\n", + "\n", + "Question: Can we logically conclude for sure that there is a kayak nearby?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we can't logically conclude that it is not true that there is not a single slaver walking in the city. We don't have any information about the presence of slavers in the city.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 36.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 16 / 25 (64.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1755.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 0 / 1 (0.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 164.77it/s]\n", + "[I 2024-06-21 01:06:04,385] Trial 28 finished with value: 64.0 and parameters: {'0_predictor_instruction': 1, '0_predictor_demos': 0}. Best is trial 23 with value: 88.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we know that there is at least one person walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we know that there is at least one person walking in the city.\n", + "\n", + "Answer:\u001b[32m Yes\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 64.0\n", + "CANDIDATE PROGRAM:\n", + "Predictor 0\n", + "i: context, question -> answer\n", + "p: Answer:\n", + "\n", + "\n", + "...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 11 / 25 (44.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 1164.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FULL TRACE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 1 / 1 (100.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 499.80it/s]\n", + "[I 2024-06-21 01:06:04,442] Trial 29 finished with value: 44.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 2}. Best is trial 23 with value: 88.0.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "context, question -> answer\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: Yes or No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer: No\n", + "\n", + "---\n", + "\n", + "Context: It is not true that there is not a single person walking in the city.\n", + "\n", + "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", + "\n", + "Answer:\u001b[32m No\u001b[0m\n", + "\n", + "\n", + "\n", + "...\n", + "Score 44.0\n" + ] + } + ], + "source": [ + "import cloudpickle as pickle\n", + "from dspy.teleprompt import MIPROv2\n", + "\n", + "LOAD_PRECOMPILED_PROGRAM = False\n", + "compiled_program = program.deepcopy()\n", + "\n", + "# We can load the precompiled program, but since scone is quick to compile, we can also compile it from scratch\n", + "if LOAD_PRECOMPILED_PROGRAM:\n", + " # Load the data from the file\n", + " compiled_program.load(compiled_program_file_path)\n", + " with open(trial_logs_file_path, \"rb\") as f:\n", + " trial_logs = pickle.load(f)\n", + " compiled_program.trial_logs = trial_logs\n", + "# Otherwise, if desired, the program can be compiled from scratch\n", + "else:\n", + " # Define hyperparameters:\n", + " N = 10 # The number of instructions and fewshot examples that we will generate and optimize over\n", + " batches = 30 # The number of optimization batches to be run (we will test out a new combination of instructions and fewshot examples in each trial)\n", + " temperature = 1.0 # The temperature configured for generating new instructions\n", + "\n", + " # Compile\n", + " eval_kwargs = dict(num_threads=16, display_progress=True, display_table=0)\n", + " teleprompter = MIPROv2(prompt_model=prompt_model, task_model=task_model, metric=metric, num_candidates=N, init_temperature=temperature, verbose=True)\n", + " print(trainset[:10])\n", + " compiled_program = teleprompter.compile(program, trainset=trainset, valset=valset, num_batches=batches, max_bootstrapped_demos=1,max_labeled_demos=2, eval_kwargs=eval_kwargs)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: the boy plays an instrument\n", - "Question: Can we logically conclude for sure that the boy plays an flute?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a boat nearby\n", - "\n", - "Question: Can we logically conclude for sure that there is a kayak nearby?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we can't logically conclude that it is not true that there is not a single slaver walking in the city. We don't have any information about the presence of slavers in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: the boy plays an instrument\n", - "Question: Can we logically conclude for sure that the boy plays an flute?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: There is a boat nearby\n", - "\n", - "Question: Can we logically conclude for sure that there is a kayak nearby?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that there is a boat nearby, but we don't know what type of boat it is. It could be a kayak, but it could also be a sailboat, a speedboat, or even a rowboat. We can't logically conclude that it is a kayak for sure.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. However, we can't logically conclude that it is not true that there is not a single slaver walking in the city. We don't have any information about the presence of slavers in the city.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 36.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "uqVVnaEBqZH3" + }, + "source": [ + "#### 3b] Evaluate optimized program\n", + "Now, we evaluate our program that has been optimized with MIPRO. We see that performance on train and dev have improved by __+13.0pt__, __+15.5pt__, and __+15.5pt__ respectively!" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 16 / 25 (64.0): 100%|██████████| 25/25 [00:00<00:00, 1755.88it/s]\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "VvnBp7huqZH3" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 142 / 200 (71.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 792.69it/s]\n", + "Average Metric: 130 / 200 (65.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 665.92it/s]\n", + "Average Metric: 141 / 200 (70.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 818.67it/s]\n" + ] + } + ], + "source": [ + "bayesian_train_score = evaluate(compiled_program, devset=trainset)\n", + "bayesian_val_score = evaluate(compiled_program, devset=valset)\n", + "bayesian_test_score = evaluate(compiled_program, devset=testset)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "j3UWn_UnqZH4" + }, + "source": [ + "#### 3c] Visualizing scores & prompts over trials" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 164.77it/s]\n", - "[I 2024-06-21 01:06:04,385] Trial 28 finished with value: 64.0 and parameters: {'0_predictor_instruction': 1, '0_predictor_demos': 0}. Best is trial 23 with value: 88.0.\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "pBYTLTwWqZH4" + }, + "source": [ + "Now, let's take a look at how this optimization looked over the course of each trial." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we know that there is at least one person walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that it is not true that there is not a single person walking in the city. This means that there must be at least one person walking in the city. Now, we are asked if we can logically conclude for sure that it is not true that there is not a single slaver walking in the city. Since we know that there is at least one person walking in the city, we can conclude that it is not true that there is not a single slaver walking in the city. This is because a slaver is a type of person, and we know that there is at least one person walking in the city.\n", - "\n", - "Answer:\u001b[32m Yes\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 64.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: context, question -> answer\n", - "p: Answer:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "rtMUNeicqZH4" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "trial_logs = compiled_program.trial_logs\n", + "\n", + "# Extracting trial numbers, scores, and pruning status\n", + "trial_numbers = list(trial_logs.keys())\n", + "scores = [trial_logs[trial]['score'] for trial in trial_numbers]\n", + "pruning_status = [trial_logs[trial]['pruned'] for trial in trial_numbers]\n", + "\n", + "# Plot setup\n", + "plt.figure(figsize=(5, 3))\n", + "\n", + "# Plotting each point\n", + "for trial_number, score, pruned in zip(trial_numbers, scores, pruning_status):\n", + " if pruned:\n", + " plt.scatter(trial_number, score, color='grey', label='Pruned Batch' if 'Pruned Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", + " else:\n", + " plt.scatter(trial_number, score, color='green', label='Successful Batch' if 'Successful Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", + "\n", + "plt.xlabel('Batch Number')\n", + "plt.ylabel('Score')\n", + "plt.title('Batch Scores')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 11 / 25 (44.0): 100%|██████████| 25/25 [00:00<00:00, 1164.42it/s]\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "5_Rwxfa1qZH4" + }, + "source": [ + "We can also __visualize the best prompts__ discovered by MIPRO as our trials progress... (though note that score increases are also due to the selected fewshot examples, which are not shown here for conciseness)." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "NnARfPRHqZH4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline program | Score: 0:\n", + "Prompt 1 Instruction: context, question -> answer\n", + "\n", + "----------------\n", + "Best program after 0 batches | Score: 56.5:\n", + "Prompt 1 Instruction: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "\n", + "Best program after 5 batches | Score: 56.5:\n", + "Prompt 1 Instruction: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", + "\n", + "Best program after 10 batches | Score: 69.5:\n", + "Prompt 1 Instruction: Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "Best program after 15 batches | Score: 69.5:\n", + "Prompt 1 Instruction: Given the context and question, generate a logical answer based on the reasoning process.\n", + "\n", + "Best program after 20 batches | Score: 71.0:\n", + "Prompt 1 Instruction: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "\n", + "Best program after 25 batches | Score: 71.0:\n", + "Prompt 1 Instruction: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", + "\n" + ] + } + ], + "source": [ + "best_score = 0\n", + "\n", + "def get_signature(predictor):\n", + " if (hasattr(predictor, 'extended_signature')):\n", + " return predictor.extended_signature\n", + " elif (hasattr(predictor, 'signature')):\n", + " return predictor.signature\n", + "\n", + "print(f\"Baseline program | Score: {best_score}:\")\n", + "for i,predictor in enumerate(program.predictors()):\n", + " print(f\"Prompt {i+1} Instruction: {get_signature(predictor).instructions}\")\n", + "print()\n", + "\n", + "print(\"----------------\")\n", + "\n", + "for trial_num in compiled_program.trial_logs:\n", + " program_score = compiled_program.trial_logs[trial_num][\"score\"]\n", + " program_pruned = compiled_program.trial_logs[trial_num][\"pruned\"]\n", + " if program_score > best_score and not program_pruned and compiled_program.trial_logs[trial_num][\"full_eval\"]:\n", + " best_score = program_score\n", + " best_program_so_far = compiled_program.trial_logs[trial_num][\"program\"]\n", + " if trial_num % 5 == 0:\n", + " print(f\"Best program after {trial_num} batches | Score: {best_score}:\")\n", + " for i,predictor in enumerate(best_program_so_far.predictors()):\n", + " print(f\"Prompt {i+1} Instruction: {get_signature(predictor).instructions}\")\n", + " print()" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 1 / 1 (100.0): 100%|██████████| 1/1 [00:00<00:00, 499.80it/s]\n", - "[I 2024-06-21 01:06:04,442] Trial 29 finished with value: 44.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 2}. Best is trial 23 with value: 88.0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3d] Saving your program for later use\n", + "\n", + "Now that we've gone through all this work of compiling a program it would be a shame to throw it away. Fortunately we don't have to. We can save your compiled program to disk with .save()!\n", + "\n", + "This file is also human interpretable, so it's worth taking a look at the optimized program. You can load it later with .load() on a program with the same modules." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "context, question -> answer\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single celebrity walking in the city?\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: Yes or No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer: No\n", - "\n", - "---\n", - "\n", - "Context: It is not true that there is not a single person walking in the city.\n", - "\n", - "Question: Can we logically conclude for sure that it is not true that there is not a single slaver walking in the city?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the answer. We know that the statement \"there is not a single person walking in the city\" is false. However, we cannot logically conclude that the statement \"there is not a single slaver walking in the city\" is also false. There could be a single person walking in the city who is not a slaver.\n", - "\n", - "Answer:\u001b[32m No\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 44.0\n" - ] - } - ], - "source": [ - "import cloudpickle as pickle\n", - "from dspy.teleprompt import MIPROv2\n", - "\n", - "LOAD_PRECOMPILED_PROGRAM = False\n", - "compiled_program = program.deepcopy()\n", - "\n", - "# We can load the precompiled program, but since scone is quick to compile, we can also compile it from scratch\n", - "if LOAD_PRECOMPILED_PROGRAM:\n", - " # Load the data from the file\n", - " compiled_program.load(compiled_program_file_path)\n", - " with open(trial_logs_file_path, \"rb\") as f:\n", - " trial_logs = pickle.load(f)\n", - " compiled_program.trial_logs = trial_logs\n", - "# Otherwise, if desired, the program can be compiled from scratch\n", - "else:\n", - " # Define hyperparameters:\n", - " N = 10 # The number of instructions and fewshot examples that we will generate and optimize over\n", - " batches = 30 # The number of optimization batches to be run (we will test out a new combination of instructions and fewshot examples in each trial)\n", - " temperature = 1.0 # The temperature configured for generating new instructions\n", - "\n", - " # Compile\n", - " eval_kwargs = dict(num_threads=16, display_progress=True, display_table=0)\n", - " teleprompter = MIPROv2(prompt_model=prompt_model, task_model=task_model, metric=metric, num_candidates=N, init_temperature=temperature, verbose=True)\n", - " print(trainset[:10])\n", - " compiled_program = teleprompter.compile(program, trainset=trainset, valset=valset, num_batches=batches, max_bootstrapped_demos=1,max_labeled_demos=2, eval_kwargs=eval_kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uqVVnaEBqZH3" - }, - "source": [ - "#### 3b] Evaluate optimized program\n", - "Now, we evaluate our program that has been optimized with MIPRO. We see that performance on train and dev have improved by __+13.0pt__, __+15.5pt__, and __+15.5pt__ respectively!" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "VvnBp7huqZH3" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 142 / 200 (71.0): 100%|██████████| 200/200 [00:00<00:00, 792.69it/s]\n", - "Average Metric: 130 / 200 (65.0): 100%|██████████| 200/200 [00:00<00:00, 665.92it/s]\n", - "Average Metric: 141 / 200 (70.5): 100%|██████████| 200/200 [00:00<00:00, 818.67it/s]\n" - ] - } - ], - "source": [ - "bayesian_train_score = evaluate(compiled_program, devset=trainset)\n", - "bayesian_val_score = evaluate(compiled_program, devset=valset)\n", - "bayesian_test_score = evaluate(compiled_program, devset=testset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j3UWn_UnqZH4" - }, - "source": [ - "#### 3c] Visualizing scores & prompts over trials" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pBYTLTwWqZH4" - }, - "source": [ - "Now, let's take a look at how this optimization looked over the course of each trial." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "rtMUNeicqZH4" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "compiled_program.save(\"compiled_program.dspy\")" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "trial_logs = compiled_program.trial_logs\n", - "\n", - "# Extracting trial numbers, scores, and pruning status\n", - "trial_numbers = list(trial_logs.keys())\n", - "scores = [trial_logs[trial]['score'] for trial in trial_numbers]\n", - "pruning_status = [trial_logs[trial]['pruned'] for trial in trial_numbers]\n", - "\n", - "# Plot setup\n", - "plt.figure(figsize=(5, 3))\n", - "\n", - "# Plotting each point\n", - "for trial_number, score, pruned in zip(trial_numbers, scores, pruning_status):\n", - " if pruned:\n", - " plt.scatter(trial_number, score, color='grey', label='Pruned Batch' if 'Pruned Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", - " else:\n", - " plt.scatter(trial_number, score, color='green', label='Successful Batch' if 'Successful Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", - "\n", - "plt.xlabel('Batch Number')\n", - "plt.ylabel('Score')\n", - "plt.title('Batch Scores')\n", - "plt.grid(True)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5_Rwxfa1qZH4" - }, - "source": [ - "We can also __visualize the best prompts__ discovered by MIPRO as our trials progress... (though note that score increases are also due to the selected fewshot examples, which are not shown here for conciseness)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "NnARfPRHqZH4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Baseline program | Score: 0:\n", - "Prompt 1 Instruction: context, question -> answer\n", - "\n", - "----------------\n", - "Best program after 0 batches | Score: 56.5:\n", - "Prompt 1 Instruction: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "\n", - "Best program after 5 batches | Score: 56.5:\n", - "Prompt 1 Instruction: Provide a yes or no answer based on the given context and question. The context and question are used as input to generate the answer.\n", - "\n", - "Best program after 10 batches | Score: 69.5:\n", - "Prompt 1 Instruction: Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "Best program after 15 batches | Score: 69.5:\n", - "Prompt 1 Instruction: Given the context and question, generate a logical answer based on the reasoning process.\n", - "\n", - "Best program after 20 batches | Score: 71.0:\n", - "Prompt 1 Instruction: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "\n", - "Best program after 25 batches | Score: 71.0:\n", - "Prompt 1 Instruction: Determine if it can be logically concluded that a kayak is nearby based on the given context and question, and generate a yes or no answer using the language model.\n", - "\n" - ] + ], + "metadata": { + "colab": { + "provenance": [] 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"cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "li3F9kMOqZHz" - }, - "source": [ - "\"DSPy7" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3wEDck3ZqZH0" - }, - "source": [ - "# Using __Multi-stage Instruction Proposal & Optimization (MIPROv2)__ in DSPy\n", - "[![colab-badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/qa/hotpot/hotpotqa_with_MIPRO.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7DzBCQ0UqZH0" - }, - "source": [ - "### FAQ 🙋\n", - "#### 1) How does MIPRO work?\n", - "At a high level, the MIPRO program optimizer works by first __proposing__ candidate fewshot example sets and instructions for each prompt in your program, and then __optimizing__ over these fewshot example sets and instructions as hyperparameters for a specified number of batches. Each batch, the optimizer evaluates different combinations of prompts on a subset of training inputs, which allows it to learn which combinations yield the best performance.\n", - "\n", - "#### 2) How much will MIPRO cost me to run?\n", - "Note that __this notebook__ is free to run, because all LM calls have been cached. However, when using an optimizer on your own program, here is a breakdown of the upper bound of the number of calls to the task model and prompt model respectively:\n", - "\n", - "- **Task model calls**: MIPRO makes up to __O(TxPxM)__ task model calls if you run without minibatching, where T is the number of batches, P is the number of prompts in the program, and M is the size of the train set. This is because the model is evaluating the program on the train set each batch. If you run **with minibatching** you can reduce calls even further to __O(TxPxB)__ where **B** is the minibatch size. Note that every few steps (a parameter you set) MIPRO will also run a full eval over all **M** examples.\n", - "\n", - "- **Prompt model calls**: MIPRO makes up to N*P+10+(P+1) prompt model calls, where N is the number of instruction / fewshot example set candidates to generate for each prompt, and P is the number of prompts in the program. The extra 10 calls comes from generating a summary of the data in the training set, which we use in the meta prompt to create better instructions. The extra (P+1) comes from program summarization where the proposer LLM will look at the program code and try to describe what each module does and what the whole program does.\n", - "\n", - "#### 3) How should I configure the hyperparameters?\n", - "We have yet to run full hyperparameter sweeps with MIPRO, but based off of initial experimintation, we'd recommend the following:\n", - "- __Batch num__: Gains can be seen after about 20-30 batches. However, 100-200 batches can help with adding on additional marginal gains.\n", - "- __num candidates__: This hyperparameter controls the number of candidate prompts and fewshot example sets that are generated to optimize over. With more batches and less prompts to optimize, we can set n to be higher, as we have more batches to explore different combinations of prompts. If your program has between 2-3 modules and is the `num_batches=30`, we'd recommend ~`n=10`. If n is higher (say `n=100`), then we can go higher to ~`n=15`. If you have a program with only 1 module and are keeping the program 0-shot (ie. no fewshot examples), then `num_batches` should be set to equal `n`, because each batch can explore a new instruction.\n", - "- __Training set size__: Between 100 and 500 training examples are recommended, however MIPROv2 can still work well with fewer. Increasing the training set size can help prevent overfitting, and only slightly increases cost for the full evaluation runs.\n", - "\n", - "#### 4) What should I do if I want to reduce the cost?\n", - "You can always update hyperparameters accordingly, such as using a smaller train set, using less batches, or using a program with less modules. You should take advantage of minibatching.\n", - "Alternatively, one strategy would be to optimize using a cheaper task model (ie. locally hosted Llama-3), as initial experiments have shown that prompts optimized for a smaller model also transfer to working well on a larger model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SgTc-CutqZH1" - }, - "source": [ - "### 0] Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5Vo4Tb9srSow" - }, - "source": [ - "First, we will install __DSPy__ if it's not there already. We'll also __load in the cached requests__ for this tasks, so that we don't actually need to call any LMs for this notebook. We'll also load in our pre optimized program from hugging face to inspect later." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JpijP_d7qZH2", - "outputId": "c641a4b1-05f5-45cc-d715-4347c526b576" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - "\n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " !pip install dspy-ai==2.4.17\n", - " !pip install openai~=1.12\n", - " !pip install -e $repo_path\n", - " !pip install --upgrade cloudpickle==3.0.0\n", - "\n", - "from huggingface_hub import hf_hub_download\n", - "import zipfile\n", - "\n", - "repo_id = 'MichaelR207/MIPRO_notebook_cache_hotpotqa'\n", - "cache_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='MIPRO_notebook_cache.zip')\n", - "compiled_program_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='compiled_program.dspy')\n", - "trial_logs_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='trial_logs.pickle')\n", - "with zipfile.ZipFile(cache_file_path, 'r') as zip_ref:\n", - " zip_ref.extractall(\".\")\n", - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = f\"{os.getcwd()}/MIPRO_notebook_cache\"\n", - "\n", - "import dspy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "U-DaFCBvqZH2" - }, - "source": [ - "We will also specify the __prompt LM model__ (in this case GPT 3.5), the __task LM model__ (Llama 3 8B) and the retrieval model we'll be using for our task (a HotPotQA multihop retrieval task)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "UHWzGRVgqZH2" - }, - "outputs": [], - "source": [ - "\n", - "### NOTE: if you'd like to run this code without a cache, you can remove these lines to configure your OPEN AI key ###\n", - "# os.environ['OPENAI_API_KEY'] = \"TODO: ADD YOUR OPEN AI KEY HERE\"\n", - "# openai.api_key = os.environ.get('OPENAI_API_KEY')\n", - "# openai.api_base = \"https://api.openai.com/v1\"\n", - "\n", - "prompt_model_name = \"gpt-3.5-turbo-1106\"\n", - "task_model_name = \"meta-llama/Meta-Llama-3-8B\"\n", - "colbert_v2_endpoint = \"http://20.102.90.50:2017/wiki17_abstracts\"\n", - "\n", - "prompt_model = dspy.OpenAI(model=prompt_model_name, max_tokens=1000, stop=[\"\\n\\n\", \"\\n---\"])\n", - "task_model = dspy.HFClientVLLM(\n", - " model=task_model_name,\n", - " port=7410,\n", - " url=[\"http://future-hgx-2:7500\", \"http://future-hgx-2:7501\", \"http://future-hgx-2:7502\", \"http://future-hgx-2:7503\", \"http://future-hgx-2:7504\", \"http://future-hgx-2:7505\", \"http://future-hgx-2:7506\", \"http://future-hgx-2:7507\"],\n", - " max_tokens=1000,\n", - " stop=[\"\\n\\n\", \"\\n---\", \"assistant\"],\n", - ")\n", - "\n", - "colbertv2 = dspy.ColBERTv2(url=colbert_v2_endpoint)\n", - "\n", - "dspy.settings.configure(rm=colbertv2, lm=task_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BFoPwDrUqZH2" - }, - "source": [ - "### 1] Define Task" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s4Cyb1JtqZH2" - }, - "source": [ - "Here, we'll define the program that we'd like to run, which is a multihop [...] (we can say that it was loosely inspired by a certain paper). We additionally load in the data, and define how we'd like to evaluate this task." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 340, - "referenced_widgets": [ - "e16044d880174c49af557bd12789493f", - "531c380db44a404ebb168648ea77c3f4", - "e8a46c6ac8a54fdbb44b2c8914552e64", - "81cbe1842465400cba02a46309d98064", - "f8ada809ecdb417ebc8214d860dd6552", - "74d58314b1c449e0b7dec3bda8b653c2", - "8b7b2b9489ae49049befae848d0fb1a1", - "2d47a6a66054438d8e9a3c5e5767c056", - "3b57d0a9768f4befb7509581289035ad", - "134fa83980e142bf82d5b97cfc10da69", - "35e4e99036894a2ba37d9ba23581a8ff", - "835a1e675186490692e336da943d635d", - "95bcef02eb794979b7873b81021e4b40", - "d84324c4c3dc40fea04cc0df1539b4d8", - "a6213bcdbcb24b1694204e6baeb763d8", - "bc423d0878154adf940062660a8315ad", - "0a1744c363f640b5b8939f32f6e72922", - "bbd2db64988147f1a4f8856d890bb4c7", - "ba2b4d7ecdbc4512acf180d8a0d602e8", - "1bb155669e2a441d8992030e8aaa84a9", - "43ed7af1d9c84ac8a6ec2195e48e60eb", - "ac6822762e6b46bd862d6f923aeb4437", - "348bb4fff900492cba8333156626a947", - "697eaeb1a004493a9260a3a89671a9ca", - "8a64950ea896468da3d10f46e9718ec8", - "7b0b45020d0f45288e2f0e4ceff22524", - "ad21e20d1f1b4b6ea74fdab03af8acdb", - "1aab1d48c6124526bee6c74892ecd953", - "598c6f37331e448889b175393a00deff", - "20d8df0ee0a4442582686846757bcc7f", - "fa4e50aaf53d4edfb9ca3046cbada2db", - "96ad4454539145aea6549995344160e3", - "acc3573e48f14dffb635f8632c6f6e74", - "6c0432c6cd8f4cae9aeb8c2870b39922", - "99b8f3882032404990f2b453111a63ba", - "6c21df0657ce486381ba2310c7fa0029", - "a20ac344a32340c785cd162fd0eb55b5", - "6ba50c3ec9e542d5a8d2f900fbcb8689", - "a298045042014c88939a9fe785fccda8", - "84bed63c6597486ca6c39b9c0c4d20bd", - "2a75d45acfb44463af974acb7a1b0d8e", - "ae814b8e55454e0aadc48f7e595575ee", - "ff4bcec9c18e4f04b1f7898daaffeb1a", - "e9c64a790d684c9eaf7f49eea003f43c", - "52f1714412df4ec78f6ff7d0f8a69862", - "988055b44cf9411e8d45d8a4185fef07", - "59be08d44c824d76b8525df14191d569", - "6fde64f36dd84d8eae670efe95e2313a", - "66e6502a463145daa440e967d8bdfdca", - "29cab51fec0b4dacaa8f829ff217c839", - "6ecbf23579324112981d4bce0c0ce369", - "a105ef4f1f754c44b7bc0ec8edf0c0cc", - "1c7d71e42c8c494f867b30d781beb681", - "2f1e652cfa514054abfda794bdfa61a5", - "b17f4731e8e44858889114c52b8f3dd4", - "d50f5eafe90c4684bbdd96569b8ff247", - "9f07fa213de247dabcd3f4213d35a97e", - "782af098d37d4543a4a01ecfed4e5da2", - "037b10adf9724e058c6468f4ca74ed86", - "c7d10443100e49d28266aef9f644ed47", - "d6a78aabcca74314a5b4bb98f350693a", - "954f3cfcb5d44dddbf1d4db9e99c0d70", - "62d8196169bb4a76a94c16d6f1ca61a6", - "0a16c7f37b9a4bbe9bfe7e737ea62801", - "942897aa22214745adee56d2c54447e9", - "0b49910850f3445abe63b6bc7ac18412", - "12a9763cd97742f4ab80c0494a398ca6", - "9d4f14862b704d9bb53d9e74365d14ac", - "56989c76e1534cfd8c9b0da93b3b8bf8", - "f120c447645446e4a04791b97ce9ae93", - "6a5d5ea44c0d4e4384b8956b48c34f14", - "e3dd508496f44171b0d91aca0e8b16a5", - "dc8077859eec4261a28d708ab06a1008", - "9a733957f8fc446fbde053ba87289c71", - "287c9f993cd44039923fdc122cd9e040", - "5cfebfd6308349038f9cd7ad5bc00fe5", - "56b80fc45dd247deadceffe17557f0f9", - "7d80528c4b1c48318756230b0174923c", - "398d7d36bbd041fe81ec633e973fc504", - "d01ca0dd46ee4a4daeeee92214bc07d1", - "6028a5e08f8641f5b8e8182e50f55419", - "567646442eb940b09456328d49248945", - "41961130caaf4537a4c1793574c785d7", - "e9935b60c48d46469904492e20f9c2e8", - "f048b50740e94bd8805c142eac1673b6", - "a5effa5d67534fb4be2e3320c1fb2b9f", - "09b3f2c456af41cba9264dbb9a724027", - "e2f3a3377ad64a4cb9f3a42c1ac97344", - "8e396041a4fd4e6db02410a09b7556c7", - "4cd1dc71c80b401da19ed52ef5148d60", - "1f23a95e292249a19055f70cda2622a3", - "69412c7605ec48859baef6f31c91c520", - "ee64a46b61454949ade4177c059bcf61", - "de6507e9f45741218bf31a9af728b2bf", - "38abb8f460d24c27a66c54bb0417f8ce", - "33267de625db44c2a63d7c7d412a7e61", - "19fe27a0df5a4d39873dd1031904405c", - "dba7ce7c756d468b94d0bc8f4815ab6f", - "926cbb119ea745cf9e344c0e50b75f62" - ] - }, - "id": "hiVgd3N7qZH3", - "outputId": "81b97d83-7c5f-4763-d104-3ad320425a2a" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by promote_options='default'.\n", - " table = cls._concat_blocks(blocks, axis=0)\n" - ] - } - ], - "source": [ - "import re\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.datasets import HotPotQA\n", - "\n", - "class ReturnRankedDocuments(dspy.Signature):\n", - " \"\"\"Given a question we are trying to answer and a list of passages, return a comma separated list of the numbers associated with each passage. These numbers should be ordered by helpfulness in answering the question, with most helpful passage number first, and the least helpful last.\"\"\"\n", - " question = dspy.InputField(desc=\"The question we're trying to answer.\")\n", - " context = dspy.InputField(desc=\"List of potentially related passages.\")\n", - " ranking = dspy.OutputField(desc=\"A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\")\n", - "\n", - "class RankingMultiHop(dspy.Module):\n", - " def __init__(self, hops, num_passages_to_retrieve, max_passages_in_context):\n", - " super().__init__()\n", - " self.hops = hops\n", - " self.num_passages_to_retrieve = num_passages_to_retrieve\n", - " self.max_passages_in_context = max_passages_in_context\n", - " self.retrieve = dspy.Retrieve(k = self.num_passages_to_retrieve)\n", - " self.generate_query = dspy.ChainOfThought(\"context ,question->search_query\")\n", - " self.generate_answer = dspy.ChainOfThought(\"context ,question->answer\")\n", - " self.generate_ranking = dspy.ChainOfThought(ReturnRankedDocuments)\n", - "\n", - " def forward(self,question):\n", - " context = []\n", - " full_context = []\n", - " top_context = []\n", - " max_passage_num = self.max_passages_in_context\n", - " for hop in range(self.hops):\n", - " # Get a new query\n", - " query = self.generate_query(context = context, question = question).search_query\n", - " # Get new passages\n", - " context = self.retrieve(query).passages\n", - " # Add these new passages to the previous top context\n", - " full_context = top_context + context\n", - " # Get the most important indices, ranked\n", - " most_important_indices = self.generate_ranking(question=question, context=full_context).ranking\n", - " indices = [int(num) for num in re.findall(r'\\d+', most_important_indices)]\n", - "\n", - " if len(indices) < max_passage_num:\n", - " indices = range(1,max_passage_num+1)\n", - "\n", - " valid_indices = [index-1 for index in indices if index-1 < len(context)]\n", - " top_indices = sorted(valid_indices, key=lambda x: x)[:max_passage_num+1]\n", - " most_important_context_list = [context[idx] for idx in top_indices]\n", - " # Save the top context\n", - " top_context = most_important_context_list\n", - "\n", - " return dspy.Prediction(context=context, answer=self.generate_answer(context = top_context , question = question).answer)\n", - "\n", - "program = RankingMultiHop(hops=4, num_passages_to_retrieve=5, max_passages_in_context=5)\n", - "\n", - "# Load and configure the datasets.\n", - "TRAIN_SIZE = 500\n", - "EVAL_SIZE = 500\n", - "\n", - "hotpot_dataset = HotPotQA(train_seed=1, eval_seed=2023, test_size=0)\n", - "trainset = [x.with_inputs('question') for x in hotpot_dataset.train][:TRAIN_SIZE]\n", - "valset = [x.with_inputs('question') for x in hotpot_dataset.dev][:EVAL_SIZE]\n", - "\n", - "# Set up metrics\n", - "NUM_THREADS = 10\n", - "\n", - "metric = dspy.evaluate.answer_exact_match\n", - "\n", - "kwargs = dict(num_threads=NUM_THREADS, display_progress=True)\n", - "evaluate = Evaluate(devset=valset, metric=metric, **kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hRGld1C1qZH3" - }, - "source": [ - "### 2] Baseline Evaluation\n", - "Now, we'll quickly evaluate our baseline program so that we can see how the performance using the Prompt Optimizer compares. We should see performance of about __35.4%__ on our trainset, and __38.2%__ on our devset." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MU2aHQBTqZH3", - "outputId": "786ba2f2-ae0a-4c68-b602-d601fb5a5aa5" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/joblib/memory.py:655: JobLibCollisionWarning: Possible name collisions between functions 'send_hfvllm_request_v01_wrapped' (/Users/michaelryan/Documents/School/Stanford/Research/dspy_official/dspy/dsp/modules/hf_client.py:-1) and 'send_hfvllm_request_v01_wrapped' (/Users/michaelryan/Documents/School/Stanford/Research/dspy_official/dspy/dsp/modules/hf_client.py:248)\n", - " return self._cached_call(args, kwargs)[0]\n", - " 0%| | 0/500 [00:00" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Rank the passages based on their relevance to the question. The most helpful passage should be listed first, followed by a comma and then the next most helpful passage, and so on, until the least helpful passage is listed last. Use the numbers associated with each passage to indicate their relevance in answering the question.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "[6] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[7] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[8] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[9] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[10] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\"\n", - "\n", - "Ranking:\u001b[32m 4, 2, 5, 1, 6, 7, 3, 8, 9, 10\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the information provided in the `context` field and the question in the `question` field, please provide the accurate answer in the `answer` field, ensuring that the response is concise and directly addresses the question being asked.\n", - "\n", - "---\n", - "\n", - "Question: Which cricketer debuted during the 2008-09 season and was suspended in 2017 for spot-fixing?\n", - "Answer: Mohammad Shahzaib Hasan Khan\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Audi Ur-S4 / Ur-S6 | Audi Ur-S4, and Audi Ur-S6 were unofficial names for the original models of the Audi S4 and S6 automobiles.»\n", - "[2] «Audi S4 | The Audi S4 is the high performance variant of Audi's compact executive car A4. The original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. All subsequent S4s since 1997 have been based on the Audi A4; and as the A4 has evolved from one generation to the next, so has the S4.»\n", - "[3] «Audi RS 4 | The Audi RS4 quattro is the top tier and highest performing version of some specific generations of the Audi A4 range of automobiles. It is a sports-focused compact executive car (often called sport compact in some countries ), produced by Audi's high-performance private subsidiary quattro GmbH, in limited numbers, for German car manufacturer AUDI AG, part of the larger Volkswagen Group. It slots distinctly above the Audi S4, as the fastest, most sports-focused model based on the A4's \"B\" automobile platform. The RS 4 made a comeback in 2012, in Avant form only based on the Audi A4 Avant.»\n", - "[4] «Audi S5 | The Audi S5 is the high-performance variant of Audi's A5. It is also the coupé, cabriolet, and five-door fastback versions of the fourth-generation (B8) Audi S4 saloon and estate models.»\n", - "[5] «Audi A4 | The Audi A4 is a line of compact executive cars produced since late 1994 by the German car manufacturer Audi, a subsidiary of the Volkswagen Group.»\n", - "\n", - "Question: What was a previous unoffical name for the high performance variant of Audi's compact executive car?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that the Audi S4 is the high performance variant of Audi's compact executive car A4. We also know that the original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. Therefore, the previous unofficial name for the high performance variant of Audi's compact executive car was the Audi Ur-S4.\n", - "\n", - "Answer: Audi Ur-S4\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We also know that he is a journalist. Therefore, Mikael Blomkvist's profession in \"The Girl in the Spider's Web\" is a journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the information provided in the `context` field and the question in the `question` field, please provide the accurate answer in the `answer` field, ensuring that the response is concise and directly addresses the question being asked.\n", - "\n", - "---\n", - "\n", - "Question: Which cricketer debuted during the 2008-09 season and was suspended in 2017 for spot-fixing?\n", - "Answer: Mohammad Shahzaib Hasan Khan\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Audi Ur-S4 / Ur-S6 | Audi Ur-S4, and Audi Ur-S6 were unofficial names for the original models of the Audi S4 and S6 automobiles.»\n", - "[2] «Audi S4 | The Audi S4 is the high performance variant of Audi's compact executive car A4. The original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. All subsequent S4s since 1997 have been based on the Audi A4; and as the A4 has evolved from one generation to the next, so has the S4.»\n", - "[3] «Audi RS 4 | The Audi RS4 quattro is the top tier and highest performing version of some specific generations of the Audi A4 range of automobiles. It is a sports-focused compact executive car (often called sport compact in some countries ), produced by Audi's high-performance private subsidiary quattro GmbH, in limited numbers, for German car manufacturer AUDI AG, part of the larger Volkswagen Group. It slots distinctly above the Audi S4, as the fastest, most sports-focused model based on the A4's \"B\" automobile platform. The RS 4 made a comeback in 2012, in Avant form only based on the Audi A4 Avant.»\n", - "[4] «Audi S5 | The Audi S5 is the high-performance variant of Audi's A5. It is also the coupé, cabriolet, and five-door fastback versions of the fourth-generation (B8) Audi S4 saloon and estate models.»\n", - "[5] «Audi A4 | The Audi A4 is a line of compact executive cars produced since late 1994 by the German car manufacturer Audi, a subsidiary of the Volkswagen Group.»\n", - "\n", - "Question: What was a previous unoffical name for the high performance variant of Audi's compact executive car?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that the Audi S4 is the high performance variant of Audi's compact executive car A4. We also know that the original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. Therefore, the previous unofficial name for the high performance variant of Audi's compact executive car was the Audi Ur-S4.\n", - "\n", - "Answer: Audi Ur-S4\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We also know that he is a journalist. Therefore, Mikael Blomkvist's profession in \"The Girl in the Spider's Web\" is a journalist.\n", - "\n", - "Answer:\u001b[32m Journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Rank the passages based on their relevance to the question. The most helpful passage should be listed first, followed by a comma and then the next most helpful passage, and so on, until the least helpful passage is listed last. Use the numbers associated with each passage to indicate their relevance in answering the question.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "[6] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[7] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[8] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[9] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[10] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\"\n", - "\n", - "Ranking:\u001b[32m 4, 2, 5, 1, 6, 7, 3, 8, 9, 10\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the information provided in the `context` field and the question in the `question` field, please provide the accurate answer in the `answer` field, ensuring that the response is concise and directly addresses the question being asked.\n", - "\n", - "---\n", - "\n", - "Question: Which cricketer debuted during the 2008-09 season and was suspended in 2017 for spot-fixing?\n", - "Answer: Mohammad Shahzaib Hasan Khan\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Audi Ur-S4 / Ur-S6 | Audi Ur-S4, and Audi Ur-S6 were unofficial names for the original models of the Audi S4 and S6 automobiles.»\n", - "[2] «Audi S4 | The Audi S4 is the high performance variant of Audi's compact executive car A4. The original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. All subsequent S4s since 1997 have been based on the Audi A4; and as the A4 has evolved from one generation to the next, so has the S4.»\n", - "[3] «Audi RS 4 | The Audi RS4 quattro is the top tier and highest performing version of some specific generations of the Audi A4 range of automobiles. It is a sports-focused compact executive car (often called sport compact in some countries ), produced by Audi's high-performance private subsidiary quattro GmbH, in limited numbers, for German car manufacturer AUDI AG, part of the larger Volkswagen Group. It slots distinctly above the Audi S4, as the fastest, most sports-focused model based on the A4's \"B\" automobile platform. The RS 4 made a comeback in 2012, in Avant form only based on the Audi A4 Avant.»\n", - "[4] «Audi S5 | The Audi S5 is the high-performance variant of Audi's A5. It is also the coupé, cabriolet, and five-door fastback versions of the fourth-generation (B8) Audi S4 saloon and estate models.»\n", - "[5] «Audi A4 | The Audi A4 is a line of compact executive cars produced since late 1994 by the German car manufacturer Audi, a subsidiary of the Volkswagen Group.»\n", - "\n", - "Question: What was a previous unoffical name for the high performance variant of Audi's compact executive car?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that the Audi S4 is the high performance variant of Audi's compact executive car A4. We also know that the original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. Therefore, the previous unofficial name for the high performance variant of Audi's compact executive car was the Audi Ur-S4.\n", - "\n", - "Answer: Audi Ur-S4\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We also know that he is a journalist. Therefore, Mikael Blomkvist's profession in \"The Girl in the Spider's Web\" is a journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given the information provided in the `context` field and the question in the `question` field, please provide the accurate answer in the `answer` field, ensuring that the response is concise and directly addresses the question being asked.\n", - "\n", - "---\n", - "\n", - "Question: Which cricketer debuted during the 2008-09 season and was suspended in 2017 for spot-fixing?\n", - "Answer: Mohammad Shahzaib Hasan Khan\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Audi Ur-S4 / Ur-S6 | Audi Ur-S4, and Audi Ur-S6 were unofficial names for the original models of the Audi S4 and S6 automobiles.»\n", - "[2] «Audi S4 | The Audi S4 is the high performance variant of Audi's compact executive car A4. The original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. All subsequent S4s since 1997 have been based on the Audi A4; and as the A4 has evolved from one generation to the next, so has the S4.»\n", - "[3] «Audi RS 4 | The Audi RS4 quattro is the top tier and highest performing version of some specific generations of the Audi A4 range of automobiles. It is a sports-focused compact executive car (often called sport compact in some countries ), produced by Audi's high-performance private subsidiary quattro GmbH, in limited numbers, for German car manufacturer AUDI AG, part of the larger Volkswagen Group. It slots distinctly above the Audi S4, as the fastest, most sports-focused model based on the A4's \"B\" automobile platform. The RS 4 made a comeback in 2012, in Avant form only based on the Audi A4 Avant.»\n", - "[4] «Audi S5 | The Audi S5 is the high-performance variant of Audi's A5. It is also the coupé, cabriolet, and five-door fastback versions of the fourth-generation (B8) Audi S4 saloon and estate models.»\n", - "[5] «Audi A4 | The Audi A4 is a line of compact executive cars produced since late 1994 by the German car manufacturer Audi, a subsidiary of the Volkswagen Group.»\n", - "\n", - "Question: What was a previous unoffical name for the high performance variant of Audi's compact executive car?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that the Audi S4 is the high performance variant of Audi's compact executive car A4. We also know that the original Audi S4, built from 1991 until 1994, was a performance-oriented version of Audi's 100 saloon/sedan. Therefore, the previous unofficial name for the high performance variant of Audi's compact executive car was the Audi Ur-S4.\n", - "\n", - "Answer: Audi Ur-S4\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We also know that he is a journalist. Therefore, Mikael Blomkvist's profession in \"The Girl in the Spider's Web\" is a journalist.\n", - "\n", - "Answer:\u001b[32m Journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 52.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the fields `context`, `question`, produce the fields `search_query`.\n", - "p: Search Query:\n", - "\n", - "\n", - "Predictor 1\n", - "i: Provide a concise and accurate answer to the given question within the provided context.\n", - "p: Answer:\n", - "\n", - "\n", - "Predictor 2\n", - "i: Given a question we are trying to answer and a list of passages, return a comma separated list of the numbers associated with each passage. These numbers should be ordered by helpfulness in answering the question, with most helpful passage number first, and the least helpful last.\n", - "p: Ranking:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "3wEDck3ZqZH0" + }, + "source": [ + "# Using __Multi-stage Instruction Proposal & Optimization (MIPROv2)__ in DSPy\n", + "[![colab-badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/qa/hotpot/hotpotqa_with_MIPRO.ipynb)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 25 (20.0): 100%|██████████| 25/25 [00:00<00:00, 28.04it/s]\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "7DzBCQ0UqZH0" + }, + "source": [ + "### FAQ \ud83d\ude4b\n", + "#### 1) How does MIPRO work?\n", + "At a high level, the MIPRO program optimizer works by first __proposing__ candidate fewshot example sets and instructions for each prompt in your program, and then __optimizing__ over these fewshot example sets and instructions as hyperparameters for a specified number of batches. Each batch, the optimizer evaluates different combinations of prompts on a subset of training inputs, which allows it to learn which combinations yield the best performance.\n", + "\n", + "#### 2) How much will MIPRO cost me to run?\n", + "Note that __this notebook__ is free to run, because all LM calls have been cached. However, when using an optimizer on your own program, here is a breakdown of the upper bound of the number of calls to the task model and prompt model respectively:\n", + "\n", + "- **Task model calls**: MIPRO makes up to __O(TxPxM)__ task model calls if you run without minibatching, where T is the number of batches, P is the number of prompts in the program, and M is the size of the train set. This is because the model is evaluating the program on the train set each batch. If you run **with minibatching** you can reduce calls even further to __O(TxPxB)__ where **B** is the minibatch size. Note that every few steps (a parameter you set) MIPRO will also run a full eval over all **M** examples.\n", + "\n", + "- **Prompt model calls**: MIPRO makes up to N*P+10+(P+1) prompt model calls, where N is the number of instruction / fewshot example set candidates to generate for each prompt, and P is the number of prompts in the program. The extra 10 calls comes from generating a summary of the data in the training set, which we use in the meta prompt to create better instructions. The extra (P+1) comes from program summarization where the proposer LLM will look at the program code and try to describe what each module does and what the whole program does.\n", + "\n", + "#### 3) How should I configure the hyperparameters?\n", + "We have yet to run full hyperparameter sweeps with MIPRO, but based off of initial experimintation, we'd recommend the following:\n", + "- __Batch num__: Gains can be seen after about 20-30 batches. However, 100-200 batches can help with adding on additional marginal gains.\n", + "- __num candidates__: This hyperparameter controls the number of candidate prompts and fewshot example sets that are generated to optimize over. With more batches and less prompts to optimize, we can set n to be higher, as we have more batches to explore different combinations of prompts. If your program has between 2-3 modules and is the `num_batches=30`, we'd recommend ~`n=10`. If n is higher (say `n=100`), then we can go higher to ~`n=15`. If you have a program with only 1 module and are keeping the program 0-shot (ie. no fewshot examples), then `num_batches` should be set to equal `n`, because each batch can explore a new instruction.\n", + "- __Training set size__: Between 100 and 500 training examples are recommended, however MIPROv2 can still work well with fewer. Increasing the training set size can help prevent overfitting, and only slightly increases cost for the full evaluation runs.\n", + "\n", + "#### 4) What should I do if I want to reduce the cost?\n", + "You can always update hyperparameters accordingly, such as using a smaller train set, using less batches, or using a program with less modules. You should take advantage of minibatching.\n", + "Alternatively, one strategy would be to optimize using a cheaper task model (ie. locally hosted Llama-3), as initial experiments have shown that prompts optimized for a smaller model also transfer to working well on a larger model." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "SgTc-CutqZH1" + }, + "source": [ + "### 0] Setup" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 32.76it/s]\n", - "[I 2024-06-20 22:45:50,514] Trial 24 finished with value: 20.0 and parameters: {'0_predictor_instruction': 0, '0_predictor_demos': 3, '1_predictor_instruction': 1, '1_predictor_demos': 2, '2_predictor_instruction': 0, '2_predictor_demos': 6}. Best is trial 22 with value: 60.0.\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "5Vo4Tb9srSow" + }, + "source": [ + "First, we will install __DSPy__ if it's not there already. We'll also __load in the cached requests__ for this tasks, so that we don't actually need to call any LMs for this notebook. We'll also load in our pre optimized program from hugging face to inspect later." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given a question we are trying to answer and a list of passages, return a comma separated list of the numbers associated with each passage. These numbers should be ordered by helpfulness in answering the question, with most helpful passage number first, and the least helpful last.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "[6] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[7] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[8] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[9] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[10] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\"\n", - "\n", - "Ranking:\u001b[32m 4, 5, 2, 1, 6, 7, 3, 8, 9, 10\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a concise and accurate answer to the given question within the provided context.\n", - "\n", - "---\n", - "\n", - "Question: What highest civilian award was the actress that starred in Yaraana, along side Rishi Kapooy, Raj Babbar, Kader Khan, and Shakti Kapoor, awarded by the Government of India in 2008?\n", - "Answer: Padma Shri\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Uwe Seeler | Uwe Seeler (born 5 November 1936) is a former German footballer and football official. He played for Hamburger SV and also made 72 appearances for the West German national team. Usually regarded as one of the greatest players in German football history, in 2004, he was named as one of FIFA's 125 greatest living players, by Pelé. His grandson, Levin Öztunalı, is also a professional footballer.»\n", - "[2] «Uwe Ehlers | Uwe Ehlers (born 8 March 1975) is a German former footballer who last managed Hansa Rostock.»\n", - "[3] «Uwe Behrens | Uwe Behrens (born 10 April 1959) is a former German footballer.»\n", - "[4] «Uwe Kirchner | Uwe Kirchner (born December 31, 1965) is a German former footballer.»\n", - "[5] «Uwe Zimmermann | Uwe 'Zimbo' Zimmermann (born February 11, 1962 in Kronau) is a German football coach and a former player. His son Simon Zimmermann is a professional footballer in the lower level German leagues.»\n", - "\n", - "Question: Uwe Seeler, professional footballer, appeared in a comedy called \"Willi Manages The Whole Thing\", his grandson plays football. What is his name?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Uwe Seeler is a professional footballer and his grandson is also a professional footballer. We can find the grandson's name in the text. It is Levin Öztunalı.\n", - "\n", - "Answer: Levin Öztunalı\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We can find his profession in the text. It is a journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a concise and accurate answer to the given question within the provided context.\n", - "\n", - "---\n", - "\n", - "Question: What highest civilian award was the actress that starred in Yaraana, along side Rishi Kapooy, Raj Babbar, Kader Khan, and Shakti Kapoor, awarded by the Government of India in 2008?\n", - "Answer: Padma Shri\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Uwe Seeler | Uwe Seeler (born 5 November 1936) is a former German footballer and football official. He played for Hamburger SV and also made 72 appearances for the West German national team. Usually regarded as one of the greatest players in German football history, in 2004, he was named as one of FIFA's 125 greatest living players, by Pelé. His grandson, Levin Öztunalı, is also a professional footballer.»\n", - "[2] «Uwe Ehlers | Uwe Ehlers (born 8 March 1975) is a German former footballer who last managed Hansa Rostock.»\n", - "[3] «Uwe Behrens | Uwe Behrens (born 10 April 1959) is a former German footballer.»\n", - "[4] «Uwe Kirchner | Uwe Kirchner (born December 31, 1965) is a German former footballer.»\n", - "[5] «Uwe Zimmermann | Uwe 'Zimbo' Zimmermann (born February 11, 1962 in Kronau) is a German football coach and a former player. His son Simon Zimmermann is a professional footballer in the lower level German leagues.»\n", - "\n", - "Question: Uwe Seeler, professional footballer, appeared in a comedy called \"Willi Manages The Whole Thing\", his grandson plays football. What is his name?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Uwe Seeler is a professional footballer and his grandson is also a professional footballer. We can find the grandson's name in the text. It is Levin Öztunalı.\n", - "\n", - "Answer: Levin Öztunalı\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We can find his profession in the text. It is a journalist.\n", - "\n", - "Answer:\u001b[32m Journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given a question we are trying to answer and a list of passages, return a comma separated list of the numbers associated with each passage. These numbers should be ordered by helpfulness in answering the question, with most helpful passage number first, and the least helpful last.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "[6] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[7] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[8] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[9] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[10] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\"\n", - "\n", - "Ranking:\u001b[32m 4, 5, 2, 1, 6, 7, 3, 8, 9, 10\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a concise and accurate answer to the given question within the provided context.\n", - "\n", - "---\n", - "\n", - "Question: What highest civilian award was the actress that starred in Yaraana, along side Rishi Kapooy, Raj Babbar, Kader Khan, and Shakti Kapoor, awarded by the Government of India in 2008?\n", - "Answer: Padma Shri\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Uwe Seeler | Uwe Seeler (born 5 November 1936) is a former German footballer and football official. He played for Hamburger SV and also made 72 appearances for the West German national team. Usually regarded as one of the greatest players in German football history, in 2004, he was named as one of FIFA's 125 greatest living players, by Pelé. His grandson, Levin Öztunalı, is also a professional footballer.»\n", - "[2] «Uwe Ehlers | Uwe Ehlers (born 8 March 1975) is a German former footballer who last managed Hansa Rostock.»\n", - "[3] «Uwe Behrens | Uwe Behrens (born 10 April 1959) is a former German footballer.»\n", - "[4] «Uwe Kirchner | Uwe Kirchner (born December 31, 1965) is a German former footballer.»\n", - "[5] «Uwe Zimmermann | Uwe 'Zimbo' Zimmermann (born February 11, 1962 in Kronau) is a German football coach and a former player. His son Simon Zimmermann is a professional footballer in the lower level German leagues.»\n", - "\n", - "Question: Uwe Seeler, professional footballer, appeared in a comedy called \"Willi Manages The Whole Thing\", his grandson plays football. What is his name?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Uwe Seeler is a professional footballer and his grandson is also a professional footballer. We can find the grandson's name in the text. It is Levin Öztunalı.\n", - "\n", - "Answer: Levin Öztunalı\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We can find his profession in the text. It is a journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a concise and accurate answer to the given question within the provided context.\n", - "\n", - "---\n", - "\n", - "Question: What highest civilian award was the actress that starred in Yaraana, along side Rishi Kapooy, Raj Babbar, Kader Khan, and Shakti Kapoor, awarded by the Government of India in 2008?\n", - "Answer: Padma Shri\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Uwe Seeler | Uwe Seeler (born 5 November 1936) is a former German footballer and football official. He played for Hamburger SV and also made 72 appearances for the West German national team. Usually regarded as one of the greatest players in German football history, in 2004, he was named as one of FIFA's 125 greatest living players, by Pelé. His grandson, Levin Öztunalı, is also a professional footballer.»\n", - "[2] «Uwe Ehlers | Uwe Ehlers (born 8 March 1975) is a German former footballer who last managed Hansa Rostock.»\n", - "[3] «Uwe Behrens | Uwe Behrens (born 10 April 1959) is a former German footballer.»\n", - "[4] «Uwe Kirchner | Uwe Kirchner (born December 31, 1965) is a German former footballer.»\n", - "[5] «Uwe Zimmermann | Uwe 'Zimbo' Zimmermann (born February 11, 1962 in Kronau) is a German football coach and a former player. His son Simon Zimmermann is a professional footballer in the lower level German leagues.»\n", - "\n", - "Question: Uwe Seeler, professional footballer, appeared in a comedy called \"Willi Manages The Whole Thing\", his grandson plays football. What is his name?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Uwe Seeler is a professional footballer and his grandson is also a professional footballer. We can find the grandson's name in the text. It is Levin Öztunalı.\n", - "\n", - "Answer: Levin Öztunalı\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[2] «Michael Nyqvist | Rolf Åke Mikael Nyqvist (] ; 8 November 1960 – 27 June 2017), better known as Michael Nyqvist, was a Swedish actor. Educated at the School of Drama in Malmö, he became well known for playing police officer Banck in the first series of Martin Beck films made in 1997, and later for his leading role in the film \"Grabben i graven bredvid\" in 2002. He was most recognized internationally for his role in the acclaimed \"Millennium\" series as Mikael Blomkvist, as well as the lead villains in \"\" (as Kurt Hendricks) and \"John Wick\" (as Viggo Tarasov). In 2004, he played the leading role in the Academy Award-nominated Best Foreign Film \"As It Is in Heaven\".»\n", - "[3] «Olov Blomkvist | Bror Olov Blomkvist (born 29 June 1922 in Stockholm) is a Swedish architect. He is the son of Bror Blomkvist and Elsa Gustavson. He graduated in 1942 later studied at Kungliga Tekniska högskolan and graduated from there in 1952. He was hired at architect Nils Lönnroth in 1953, later he worked for Stockholm tramways architect office in 1958. He was project manager for Kooperative förbundets arkitektkontor from 1965 to his retirement.»\n", - "[4] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[5] «The Girl with the Dragon Tattoo (2011 film) | The Girl with the Dragon Tattoo is a 2011 Swedish-American psychological thriller film based on the novel of the same name by Stieg Larsson. This film adaptation was directed by David Fincher and written by Steven Zaillian. Starring Daniel Craig and Rooney Mara, it tells the story of journalist Mikael Blomkvist (Daniel Craig)'s investigation to find out what happened to a woman from a wealthy family who disappeared 40 years prior. He recruits the help of computer hacker Lisbeth Salander (Rooney Mara).»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series. We can find his profession in the text. It is a journalist.\n", - "\n", - "Answer:\u001b[32m Journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 20.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the provided context and question, generate a search query that will yield the specific information needed to answer the question accurately and directly.\n", - "p: Search Query:\n", - "\n", - "\n", - "Predictor 1\n", - "i: Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "p: Answer:\n", - "\n", - "\n", - "Predictor 2\n", - "i: Given a specific question and a list of passages containing relevant information, rank the passages based on their level of helpfulness in answering the question, with the most helpful passage being ranked first and the least helpful passage being ranked last. Return a comma-separated list of the numbers associated with each passage, indicating their ranking in descending order of helpfulness.\n", - "p: Ranking:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JpijP_d7qZH2", + "outputId": "c641a4b1-05f5-45cc-d715-4347c526b576" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + "\n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " !pip install dspy-ai==2.4.17\n", + " !pip install openai~=1.12\n", + " !pip install -e $repo_path\n", + " !pip install --upgrade cloudpickle==3.0.0\n", + "\n", + "from huggingface_hub import hf_hub_download\n", + "import zipfile\n", + "\n", + "repo_id = 'MichaelR207/MIPRO_notebook_cache_hotpotqa'\n", + "cache_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='MIPRO_notebook_cache.zip')\n", + "compiled_program_file_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='compiled_program.dspy')\n", + "trial_logs_path = hf_hub_download(repo_id=repo_id, repo_type='dataset', filename='trial_logs.pickle')\n", + "with zipfile.ZipFile(cache_file_path, 'r') as zip_ref:\n", + " zip_ref.extractall(\".\")\n", + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = f\"{os.getcwd()}/MIPRO_notebook_cache\"\n", + "\n", + "import dspy" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 15 / 25 (60.0): 100%|██████████| 25/25 [00:01<00:00, 21.81it/s]\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "U-DaFCBvqZH2" + }, + "source": [ + "We will also specify the __prompt LM model__ (in this case GPT 3.5), the __task LM model__ (Llama 3 8B) and the retrieval model we'll be using for our task (a HotPotQA multihop retrieval task)." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "UHWzGRVgqZH2" + }, + "outputs": [], + "source": [ + "\n", + "### NOTE: if you'd like to run this code without a cache, you can remove these lines to configure your OPEN AI key ###\n", + "# os.environ['OPENAI_API_KEY'] = \"TODO: ADD YOUR OPEN AI KEY HERE\"\n", + "# openai.api_key = os.environ.get('OPENAI_API_KEY')\n", + "# openai.api_base = \"https://api.openai.com/v1\"\n", + "\n", + "prompt_model_name = \"gpt-3.5-turbo-1106\"\n", + "task_model_name = \"meta-llama/Meta-Llama-3-8B\"\n", + "colbert_v2_endpoint = \"http://20.102.90.50:2017/wiki17_abstracts\"\n", + "\n", + "prompt_model = dspy.OpenAI(model=prompt_model_name, max_tokens=1000, stop=[\"\\n\\n\", \"\\n---\"])\n", + "task_model = dspy.HFClientVLLM(\n", + " model=task_model_name,\n", + " port=7410,\n", + " url=[\"http://future-hgx-2:7500\", \"http://future-hgx-2:7501\", \"http://future-hgx-2:7502\", \"http://future-hgx-2:7503\", \"http://future-hgx-2:7504\", \"http://future-hgx-2:7505\", \"http://future-hgx-2:7506\", \"http://future-hgx-2:7507\"],\n", + " max_tokens=1000,\n", + " stop=[\"\\n\\n\", \"\\n---\", \"assistant\"],\n", + ")\n", + "\n", + "colbertv2 = dspy.ColBERTv2(url=colbert_v2_endpoint)\n", + "\n", + "dspy.settings.configure(rm=colbertv2, lm=task_model)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 18.21it/s]\n", - "[I 2024-06-20 22:45:51,775] Trial 25 finished with value: 60.0 and parameters: {'0_predictor_instruction': 6, '0_predictor_demos': 3, '1_predictor_instruction': 4, '1_predictor_demos': 6, '2_predictor_instruction': 4, '2_predictor_demos': 6}. Best is trial 22 with value: 60.0.\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "BFoPwDrUqZH2" + }, + "source": [ + "### 1] Define Task" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Given a specific question and a list of passages containing relevant information, rank the passages based on their level of helpfulness in answering the question, with the most helpful passage being ranked first and the least helpful passage being ranked last. Return a comma-separated list of the numbers associated with each passage, indicating their ranking in descending order of helpfulness.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[6] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[7] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[8] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[9] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[10] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\".\n", - "\n", - "Ranking:\u001b[32m 1, 5, 4, 3, 2, 6, 7, 8, 9, 10\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\n", - "\n", - "Answer:\u001b[32m journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Given a specific question and a list of passages containing relevant information, rank the passages based on their level of helpfulness in answering the question, with the most helpful passage being ranked first and the least helpful passage being ranked last. Return a comma-separated list of the numbers associated with each passage, indicating their ranking in descending order of helpfulness.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "[6] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[7] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[8] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[9] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[10] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\".\n", - "\n", - "Ranking:\u001b[32m 1, 5, 4, 3, 2, 6, 7, 8, 9, 10\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\n", - "\n", - "Answer:\u001b[32m journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 60.0\n", - "CANDIDATE PROGRAM:\n", - "Predictor 0\n", - "i: Given the fields `context`, which contains information related to the question being asked, and `question`, which is the specific query or inquiry, produce the field `search_query`, which should consist of a well-crafted search query that combines relevant keywords and search terms to efficiently retrieve the desired information. This search query should aim to be specific, accurate, and tailored to the context and question at hand.\n", - "p: Search Query:\n", - "\n", - "\n", - "Predictor 1\n", - "i: Given the information provided in the `context` field and the question in the `question` field, please provide the accurate answer in the `answer` field, ensuring that the response is concise and directly addresses the question being asked.\n", - "p: Answer:\n", - "\n", - "\n", - "Predictor 2\n", - "i: Given a specific question and a list of passages containing relevant information, rank the passages based on their level of helpfulness in answering the question, with the most helpful passage being ranked first and the least helpful passage being ranked last. Return a comma-separated list of the numbers associated with each passage, indicating their ranking in descending order of helpfulness.\n", - "p: Ranking:\n", - "\n", - "\n", - "...\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "s4Cyb1JtqZH2" + }, + "source": [ + "Here, we'll define the program that we'd like to run, which is a multihop [...] (we can say that it was loosely inspired by a certain paper). We additionally load in the data, and define how we'd like to evaluate this task." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 14 / 25 (56.0): 100%|██████████| 25/25 [00:00<00:00, 25.28it/s]\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 340, + "referenced_widgets": [ + "e16044d880174c49af557bd12789493f", + "531c380db44a404ebb168648ea77c3f4", + "e8a46c6ac8a54fdbb44b2c8914552e64", + "81cbe1842465400cba02a46309d98064", + "f8ada809ecdb417ebc8214d860dd6552", + "74d58314b1c449e0b7dec3bda8b653c2", + "8b7b2b9489ae49049befae848d0fb1a1", + "2d47a6a66054438d8e9a3c5e5767c056", + "3b57d0a9768f4befb7509581289035ad", + "134fa83980e142bf82d5b97cfc10da69", + "35e4e99036894a2ba37d9ba23581a8ff", + "835a1e675186490692e336da943d635d", + "95bcef02eb794979b7873b81021e4b40", + "d84324c4c3dc40fea04cc0df1539b4d8", + "a6213bcdbcb24b1694204e6baeb763d8", + "bc423d0878154adf940062660a8315ad", + "0a1744c363f640b5b8939f32f6e72922", + "bbd2db64988147f1a4f8856d890bb4c7", + "ba2b4d7ecdbc4512acf180d8a0d602e8", + "1bb155669e2a441d8992030e8aaa84a9", + "43ed7af1d9c84ac8a6ec2195e48e60eb", + "ac6822762e6b46bd862d6f923aeb4437", + "348bb4fff900492cba8333156626a947", + "697eaeb1a004493a9260a3a89671a9ca", + "8a64950ea896468da3d10f46e9718ec8", + "7b0b45020d0f45288e2f0e4ceff22524", + "ad21e20d1f1b4b6ea74fdab03af8acdb", + "1aab1d48c6124526bee6c74892ecd953", + "598c6f37331e448889b175393a00deff", + "20d8df0ee0a4442582686846757bcc7f", + "fa4e50aaf53d4edfb9ca3046cbada2db", + "96ad4454539145aea6549995344160e3", + "acc3573e48f14dffb635f8632c6f6e74", + "6c0432c6cd8f4cae9aeb8c2870b39922", + "99b8f3882032404990f2b453111a63ba", + "6c21df0657ce486381ba2310c7fa0029", + "a20ac344a32340c785cd162fd0eb55b5", + "6ba50c3ec9e542d5a8d2f900fbcb8689", + "a298045042014c88939a9fe785fccda8", + "84bed63c6597486ca6c39b9c0c4d20bd", + "2a75d45acfb44463af974acb7a1b0d8e", + "ae814b8e55454e0aadc48f7e595575ee", + "ff4bcec9c18e4f04b1f7898daaffeb1a", + "e9c64a790d684c9eaf7f49eea003f43c", + "52f1714412df4ec78f6ff7d0f8a69862", + "988055b44cf9411e8d45d8a4185fef07", + "59be08d44c824d76b8525df14191d569", + "6fde64f36dd84d8eae670efe95e2313a", + "66e6502a463145daa440e967d8bdfdca", + "29cab51fec0b4dacaa8f829ff217c839", + "6ecbf23579324112981d4bce0c0ce369", + "a105ef4f1f754c44b7bc0ec8edf0c0cc", + "1c7d71e42c8c494f867b30d781beb681", + "2f1e652cfa514054abfda794bdfa61a5", + "b17f4731e8e44858889114c52b8f3dd4", + "d50f5eafe90c4684bbdd96569b8ff247", + "9f07fa213de247dabcd3f4213d35a97e", + "782af098d37d4543a4a01ecfed4e5da2", + "037b10adf9724e058c6468f4ca74ed86", + "c7d10443100e49d28266aef9f644ed47", + "d6a78aabcca74314a5b4bb98f350693a", + "954f3cfcb5d44dddbf1d4db9e99c0d70", + "62d8196169bb4a76a94c16d6f1ca61a6", + "0a16c7f37b9a4bbe9bfe7e737ea62801", + "942897aa22214745adee56d2c54447e9", + "0b49910850f3445abe63b6bc7ac18412", + "12a9763cd97742f4ab80c0494a398ca6", + "9d4f14862b704d9bb53d9e74365d14ac", + "56989c76e1534cfd8c9b0da93b3b8bf8", + "f120c447645446e4a04791b97ce9ae93", + "6a5d5ea44c0d4e4384b8956b48c34f14", + "e3dd508496f44171b0d91aca0e8b16a5", + "dc8077859eec4261a28d708ab06a1008", + "9a733957f8fc446fbde053ba87289c71", + "287c9f993cd44039923fdc122cd9e040", + "5cfebfd6308349038f9cd7ad5bc00fe5", + "56b80fc45dd247deadceffe17557f0f9", + "7d80528c4b1c48318756230b0174923c", + "398d7d36bbd041fe81ec633e973fc504", + "d01ca0dd46ee4a4daeeee92214bc07d1", + "6028a5e08f8641f5b8e8182e50f55419", + "567646442eb940b09456328d49248945", + "41961130caaf4537a4c1793574c785d7", + "e9935b60c48d46469904492e20f9c2e8", + "f048b50740e94bd8805c142eac1673b6", + "a5effa5d67534fb4be2e3320c1fb2b9f", + "09b3f2c456af41cba9264dbb9a724027", + "e2f3a3377ad64a4cb9f3a42c1ac97344", + "8e396041a4fd4e6db02410a09b7556c7", + "4cd1dc71c80b401da19ed52ef5148d60", + "1f23a95e292249a19055f70cda2622a3", + "69412c7605ec48859baef6f31c91c520", + "ee64a46b61454949ade4177c059bcf61", + "de6507e9f45741218bf31a9af728b2bf", + "38abb8f460d24c27a66c54bb0417f8ce", + "33267de625db44c2a63d7c7d412a7e61", + "19fe27a0df5a4d39873dd1031904405c", + "dba7ce7c756d468b94d0bc8f4815ab6f", + "926cbb119ea745cf9e344c0e50b75f62" + ] + }, + "id": "hiVgd3N7qZH3", + "outputId": "81b97d83-7c5f-4763-d104-3ad320425a2a" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by promote_options='default'.\n", + " table = cls._concat_blocks(blocks, axis=0)\n" + ] + } + ], + "source": [ + "import re\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.datasets import HotPotQA\n", + "\n", + "class ReturnRankedDocuments(dspy.Signature):\n", + " \"\"\"Given a question we are trying to answer and a list of passages, return a comma separated list of the numbers associated with each passage. These numbers should be ordered by helpfulness in answering the question, with most helpful passage number first, and the least helpful last.\"\"\"\n", + " question = dspy.InputField(desc=\"The question we're trying to answer.\")\n", + " context = dspy.InputField(desc=\"List of potentially related passages.\")\n", + " ranking = dspy.OutputField(desc=\"A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\")\n", + "\n", + "class RankingMultiHop(dspy.Module):\n", + " def __init__(self, hops, num_passages_to_retrieve, max_passages_in_context):\n", + " super().__init__()\n", + " self.hops = hops\n", + " self.num_passages_to_retrieve = num_passages_to_retrieve\n", + " self.max_passages_in_context = max_passages_in_context\n", + " self.retrieve = dspy.Retrieve(k = self.num_passages_to_retrieve)\n", + " self.generate_query = dspy.ChainOfThought(\"context ,question->search_query\")\n", + " self.generate_answer = dspy.ChainOfThought(\"context ,question->answer\")\n", + " self.generate_ranking = dspy.ChainOfThought(ReturnRankedDocuments)\n", + "\n", + " def forward(self,question):\n", + " context = []\n", + " full_context = []\n", + " top_context = []\n", + " max_passage_num = self.max_passages_in_context\n", + " for hop in range(self.hops):\n", + " # Get a new query\n", + " query = self.generate_query(context = context, question = question).search_query\n", + " # Get new passages\n", + " context = self.retrieve(query).passages\n", + " # Add these new passages to the previous top context\n", + " full_context = top_context + context\n", + " # Get the most important indices, ranked\n", + " most_important_indices = self.generate_ranking(question=question, context=full_context).ranking\n", + " indices = [int(num) for num in re.findall(r'\\d+', most_important_indices)]\n", + "\n", + " if len(indices) < max_passage_num:\n", + " indices = range(1,max_passage_num+1)\n", + "\n", + " valid_indices = [index-1 for index in indices if index-1 < len(context)]\n", + " top_indices = sorted(valid_indices, key=lambda x: x)[:max_passage_num+1]\n", + " most_important_context_list = [context[idx] for idx in top_indices]\n", + " # Save the top context\n", + " top_context = most_important_context_list\n", + "\n", + " return dspy.Prediction(context=context, answer=self.generate_answer(context = top_context , question = question).answer)\n", + "\n", + "program = RankingMultiHop(hops=4, num_passages_to_retrieve=5, max_passages_in_context=5)\n", + "\n", + "# Load and configure the datasets.\n", + "TRAIN_SIZE = 500\n", + "EVAL_SIZE = 500\n", + "\n", + "hotpot_dataset = HotPotQA(train_seed=1, eval_seed=2023, test_size=0)\n", + "trainset = [x.with_inputs('question') for x in hotpot_dataset.train][:TRAIN_SIZE]\n", + "valset = [x.with_inputs('question') for x in hotpot_dataset.dev][:EVAL_SIZE]\n", + "\n", + "# Set up metrics\n", + "NUM_THREADS = 10\n", + "\n", + "metric = dspy.evaluate.answer_exact_match\n", + "\n", + "kwargs = dict(num_threads=NUM_THREADS, display_progress=True)\n", + "evaluate = Evaluate(devset=valset, metric=metric, **kwargs)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "hRGld1C1qZH3" + }, + "source": [ + "### 2] Baseline Evaluation\n", + "Now, we'll quickly evaluate our baseline program so that we can see how the performance using the Prompt Optimizer compares. We should see performance of about __35.4%__ on our trainset, and __38.2%__ on our devset." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 33.85it/s]\n", - "[I 2024-06-20 22:45:52,840] Trial 26 finished with value: 56.0 and parameters: {'0_predictor_instruction': 2, '0_predictor_demos': 3, '1_predictor_instruction': 9, '1_predictor_demos': 7, '2_predictor_instruction': 4, '2_predictor_demos': 2}. Best is trial 22 with value: 60.0.\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MU2aHQBTqZH3", + "outputId": "786ba2f2-ae0a-4c68-b602-d601fb5a5aa5" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/opt-prompt/lib/python3.11/site-packages/joblib/memory.py:655: JobLibCollisionWarning: Possible name collisions between functions 'send_hfvllm_request_v01_wrapped' (/Users/michaelryan/Documents/School/Stanford/Research/dspy_official/dspy/dsp/modules/hf_client.py:-1) and 'send_hfvllm_request_v01_wrapped' (/Users/michaelryan/Documents/School/Stanford/Research/dspy_official/dspy/dsp/modules/hf_client.py:248)\n", + " return self._cached_call(args, kwargs)[0]\n", + " 0%| | 0/500 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "trial_logs = compiled_program.trial_logs\n", + "\n", + "# Extracting trial numbers, scores, and pruning status\n", + "trial_numbers = list(trial_logs.keys())\n", + "scores = [trial_logs[trial]['score'] for trial in trial_numbers]\n", + "pruning_status = [trial_logs[trial]['pruned'] for trial in trial_numbers]\n", + "\n", + "# Plot setup\n", + "plt.figure(figsize=(5, 3))\n", + "\n", + "# Plotting each point\n", + "for trial_number, score, pruned in zip(trial_numbers, scores, pruning_status):\n", + " if pruned:\n", + " plt.scatter(trial_number, score, color='grey', label='Pruned Batch' if 'Pruned Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", + " else:\n", + " plt.scatter(trial_number, score, color='green', label='Successful Batch' if 'Successful Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", + "\n", + "plt.xlabel('Batch Number')\n", + "plt.ylabel('Score')\n", + "plt.title('Batch Scores')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.show()" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 9 / 25 (36.0): 100%|██████████| 25/25 [00:00<00:00, 29.18it/s]\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "5_Rwxfa1qZH4" + }, + "source": [ + "We can also __visualize the best prompts__ discovered by MIPRO as our trials progress... (though note that score increases are also due to the selected fewshot examples, which are not shown here for conciseness)." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "FULL TRACE\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "NnARfPRHqZH4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline program | Score: 0:\n", + "Prompt 1 Instruction: Given the fields `context`, `question`, produce the fields `search_query`.\n", + "Prompt 2 Instruction: Given the fields `context`, `question`, produce the fields `answer`.\n", + "Prompt 3 Instruction: Given a question we are trying to answer and a list of passages, return a comma separated list of the numbers associated with each passage. These numbers should be ordered by helpfulness in answering the question, with most helpful passage number first, and the least helpful last.\n", + "\n", + "----------------\n", + "Best program after 0 batches | Score: 43.8:\n", + "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", + "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", + "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", + "\n", + "Best program after 5 batches | Score: 43.8:\n", + "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", + "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", + "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", + "\n", + "Best program after 10 batches | Score: 43.8:\n", + "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", + "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", + "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", + "\n", + "Best program after 15 batches | Score: 43.8:\n", + "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", + "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", + "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", + "\n", + "Best program after 20 batches | Score: 46.4:\n", + "Prompt 1 Instruction: Given the provided context and question, generate a search query that will yield the specific information needed to answer the question accurately and directly.\n", + "Prompt 2 Instruction: Given the provided context about various film directors and their nominations for the Academy Award Oscar, and the specific question asking which director received more nominations, please generate the correct answer based on the information provided.\n", + "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", + "\n", + "Best program after 25 batches | Score: 46.4:\n", + "Prompt 1 Instruction: Given the provided context and question, generate a search query that will yield the specific information needed to answer the question accurately and directly.\n", + "Prompt 2 Instruction: Given the provided context about various film directors and their nominations for the Academy Award Oscar, and the specific question asking which director received more nominations, please generate the correct answer based on the information provided.\n", + "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", + "\n" + ] + } + ], + "source": [ + "best_score = 0\n", + "\n", + "def get_signature(predictor):\n", + " if (hasattr(predictor, 'extended_signature')):\n", + " return predictor.extended_signature\n", + " elif (hasattr(predictor, 'signature')):\n", + " return predictor.signature\n", + "\n", + "print(f\"Baseline program | Score: {best_score}:\")\n", + "for i,predictor in enumerate(program.predictors()):\n", + " print(f\"Prompt {i+1} Instruction: {get_signature(predictor).instructions}\")\n", + "print()\n", + "\n", + "print(\"----------------\")\n", + "\n", + "for trial_num in compiled_program.trial_logs:\n", + " program_score = compiled_program.trial_logs[trial_num][\"score\"]\n", + " program_pruned = compiled_program.trial_logs[trial_num][\"pruned\"]\n", + " if program_score > best_score and not program_pruned and compiled_program.trial_logs[trial_num][\"full_eval\"]:\n", + " best_score = program_score\n", + " best_program_so_far = compiled_program.trial_logs[trial_num][\"program\"]\n", + " if trial_num % 5 == 0:\n", + " print(f\"Best program after {trial_num} batches | Score: {best_score}:\")\n", + " for i,predictor in enumerate(best_program_so_far.predictors()):\n", + " print(f\"Prompt {i+1} Instruction: {get_signature(predictor).instructions}\")\n", + " print()" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 0 / 1 (0.0): 100%|██████████| 1/1 [00:00<00:00, 34.22it/s]\n", - "[I 2024-06-20 22:45:56,208] Trial 29 finished with value: 36.0 and parameters: {'0_predictor_instruction': 6, '0_predictor_demos': 3, '1_predictor_instruction': 4, '1_predictor_demos': 6, '2_predictor_instruction': 5, '2_predictor_demos': 6}. Best is trial 22 with value: 60.0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3d] Saving your program for later use\n", + "\n", + "Now that we've gone through all this work of compiling a program it would be a shame to throw it away. Fortunately we don't have to. We can save your compiled program to disk with .save()!\n", + "\n", + "This file is also human interpretable, so it's worth taking a look at the optimized program. You can load it later with .load() on a program with the same modules." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "Rank the passages in order of helpfulness in answering the given question, with the most helpful passage number first, and the least helpful last. Return a comma-separated list of the passage numbers.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[6] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[7] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[8] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[9] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\".\n", - "\n", - "Ranking:\u001b[32m 1, 9, 3, 5, 2, 4, 6, 7, 8\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\n", - "\n", - "Answer:\u001b[32m journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Rank the passages in order of helpfulness in answering the given question, with the most helpful passage number first, and the least helpful last. Return a comma-separated list of the passage numbers.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: The question we're trying to answer.\n", - "\n", - "Context: List of potentially related passages.\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the ranking}. We ...\n", - "\n", - "Ranking: A comma separated list of numbers corresponding to passage indices, ranked in descending order by their helpfulness in answering our question.\n", - "\n", - "---\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Context:\n", - "[1] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[2] «XYLO | XYLO (stylised as XYLØ) is an American electronic music duo consisting of lead vocalist and songwriter Paige Duddy and songwriter and musician Chase Duddy. They are best known for their debut single, which was self-released through NoiseTrade, entitled \"America\", released in February 2015. This single eventually became the lead single of their debut extended play under the same name, which was released in February 2016.»\n", - "[3] «Soundbwoy | \"Soundbwoy\" is a song by British singer Stylo G. It was produced by Dutch born producer Diztortion. The song was released in the United Kingdom as a digital download on 26 May 2013 on iTunes. The song entered the UK Singles Chart at number 18 and at number 29 in Scotland.»\n", - "[4] «Stylin' Up | Stylin' Up is the debut album by Torres Strait Islander singer Christine Anu, released on 1 May 1995 by Mushroom Records. A deluxe edition was later released with six live tracks. The album was certified platinum in 2000.»\n", - "[5] «ReStylin' Up 20 Years | ReStylin' Up 20 Years is a live album recorded by Australian singer Christine Anu. it is a live recording of her 1995 debut album \"Stylin' Up\" in celebration of its 20th anniversary. The album was recorded in one day and released on 19 June 2015. The tracks are recorded in a jazz/soul genre.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"Who released the album that 'Stylo' was a single on in the United States?\".\n", - "\n", - "Ranking: 1, 3, 5, 2, 4\n", - "\n", - "---\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[6] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[7] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[8] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[9] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Reasoning: Let's think step by step in order to produce the ranking. We are trying to answer the question \"What is Mikael Blomkvist's profession in 'The Girl in the Spider's Web'?\".\n", - "\n", - "Ranking:\u001b[32m 1, 9, 3, 5, 2, 4, 6, 7, 8\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to\u001b[32m find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\u001b[0m\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Provide a detailed and informative response to the given question using the information provided in the `context` and `question` fields.\n", - "\n", - "---\n", - "\n", - "Question: Which airport's main aircraft and maintainence base was a public airport six miles west of Downtown Tampa?\n", - "Answer: North American Airlines, Inc.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Plastic Beach | Plastic Beach is the third studio album by the British virtual band Gorillaz, released on 3 March 2010 by Parlophone Records internationally and by Virgin Records in the United States. Conceived from an unfinished Gorillaz project called \"Carousel\", the album was recorded from June 2008 to November 2009, and was produced primarily by group co-creator Damon Albarn. It features guest appearances by several artists including Snoop Dogg, Gruff Rhys, De La Soul, Bobby Womack, Mos Def, Lou Reed, Mick Jones, Mark E. Smith, Paul Simonon, Bashy, Kano, Little Dragon and the Hypnotic Brass Ensemble.»\n", - "[2] «Escape to Plastic Beach Tour | The Escape to Plastic Beach Tour was a concert tour by the British alternative rock virtual band Gorillaz. The tour was in support of their third studio album \"Plastic Beach\". It was notably the band's first world tour in its decade-spanning history. During the tour, Damon Albarn recorded \"The Fall\", described by Albarn as a \"diary of [his] experience\" during the American leg of the tour. The album was released in late December 2010 to fan club members, and physically in April 2011.»\n", - "[3] «Stylo (song) | \"Stylo\" is the first single from British virtual band Gorillaz's third studio album \"Plastic Beach\". The song features guest vocals from Bobby Womack and Mos Def. The single was released on 26 January 2010.»\n", - "[4] «Superfast Jellyfish | \"Superfast Jellyfish\" is the second single released from British alternative band Gorillaz' third studio album, \"Plastic Beach\". The single was released on May 9, 2010.»\n", - "[5] «On Melancholy Hill | \"On Melancholy Hill\" is the third single from British virtual band Gorillaz's third studio album, \"Plastic Beach\". The single was released on June 12, 2010. Pitchfork media ranked it at 152 on the top 200 songs of the decade so far. It was Number 9 on pongsocket.com's list of best songs of 2010, 92 on jongmusic.com list of 100 best songs of 2010, and Number 5 on BBC America's list of best British songs of 2010.»\n", - "\n", - "Question: Who released the album that \"Stylo\" was a single on in the United States?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that \"Stylo\" was a single from the album \"Plastic Beach\", which was released by Virgin Records in the United States. Therefore, the answer is Virgin Records.\n", - "\n", - "Answer: Virgin Records\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Girl in the Spider's Web | The Girl in the Spider's Web (original title in Swedish: \"Det som inte dödar oss\" , literally \"That which does not kill us\") is the fourth novel in the \"Millennium\" series, focuses on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the first novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 27 August 2015, except in the United States, where it was released on 1 September 2015.»\n", - "[2] «The Girl Who Takes an Eye for an Eye | The Girl Who Takes an Eye for an Eye (original title in Swedish: \"Mannen Som Sökte Sin Skugga\" , literally \"The Man Who Chased His Shadow\") is the fifth novel in the \"Millennium\" series, focusing on the characters Lisbeth Salander and Mikael Blomkvist. Written by David Lagercrantz, this is the second novel in the series not authored by the series' creator and author of the first three \"Millennium\" books, Stieg Larsson, who died of a heart attack in 2004. The novel was released worldwide on 7 September 2017.»\n", - "[3] «David Lagercrantz | David Lagercrantz (born 4 September 1962) is a Swedish journalist and best-selling author, internationally known as the author of \"I am Zlatan Ibrahimović\", \"The Girl in the Spider’s Web\" and \"The Girl Who Takes an Eye for an Eye\", the latter two being the the fourth and fifth instalments respectively in the \"Millennium\" series originated by \"Stieg Larsson.\" He is a board member of Swedish PEN.»\n", - "[4] «Lisbeth Salander | Lisbeth Salander is a fictional character created by Swedish author and journalist Stieg Larsson. She is the lead character in Larsson's award-winning \"Millennium\" series, along with the journalist Mikael Blomkvist.»\n", - "[5] «Mikael Blomkvist | Mikael Blomkvist is a fictional character created by Swedish author and journalist Stieg Larsson. He is a main character of Larsson's award-winning \"Millennium\" series, along with Lisbeth Salander.»\n", - "\n", - "Question: What is Mikael Blomkvist's profession in \"The Girl in the Spider's Web\"?\n", - "\n", - "Reasoning: Let's think step by step in order to find the answer. We know that Mikael Blomkvist is a main character in the \"Millennium\" series, and we also know that he is a journalist. Therefore, the answer is journalist.\n", - "\n", - "Answer:\u001b[32m journalist\u001b[0m\n", - "\n", - "\n", - "\n", - "...\n", - "Score 36.0\n" - ] - } - ], - "source": [ - "import cloudpickle as pickle\n", - "from dspy.teleprompt import MIPROv2\n", - "\n", - "LOAD_PRECOMPILED_PROGRAM = True\n", - "compiled_program = program.deepcopy()\n", - "\n", - "# By default, we will load the precompiled program\n", - "if LOAD_PRECOMPILED_PROGRAM:\n", - " # Load the data from the file\n", - " compiled_program.load(compiled_program_file_path)\n", - " with open(trial_logs_path, \"rb\") as f:\n", - " trial_logs = pickle.load(f)\n", - " compiled_program.trial_logs = trial_logs\n", - "# Otherwise, if desired, the program can be compiled from scratch\n", - "else:\n", - " # Define hyperparameters:\n", - " N = 10 # The number of instructions and fewshot examples that we will generate and optimize over\n", - " batches = 30 # The number of optimization trials to be run (we will test out a new combination of instructions and fewshot examples in each trial)\n", - " temperature = 1.0 # The temperature configured for generating new instructions\n", - "\n", - " # Compile\n", - " eval_kwargs = dict(num_threads=16, display_progress=True, display_table=0)\n", - " teleprompter = MIPROv2(prompt_model=prompt_model, task_model=task_model, metric=metric, num_candidates=N, init_temperature=temperature, verbose=True)\n", - " compiled_program = teleprompter.compile(program, trainset=trainset, valset=valset, num_batches=batches, max_bootstrapped_demos=1,max_labeled_demos=2, eval_kwargs=eval_kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uqVVnaEBqZH3" - }, - "source": [ - "#### 3b] Evaluate optimized program\n", - "Now, we evaluate our program that has been optimized with MIPRO. We see that performance on train and dev have improved by __+11pt__ and __+6.8pt__ respectively!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "VvnBp7huqZH3" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 232 / 500 (46.4): 100%|██████████| 500/500 [00:05<00:00, 97.66it/s] \n", - "Average Metric: 225 / 500 (45.0): 100%|██████████| 500/500 [00:05<00:00, 99.59it/s] \n" - ] - } - ], - "source": [ - "bayesian_train_score = evaluate(compiled_program, devset=trainset)\n", - "bayesian_eval_score = evaluate(compiled_program, devset=valset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j3UWn_UnqZH4" - }, - "source": [ - "#### 3c] Visualizing scores & prompts over trials" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pBYTLTwWqZH4" - }, - "source": [ - "Now, let's take a look at how this optimization looked over the course of each trial. We see that, in general, __performance increases overtime__, until it saturates after ~trial #21." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "rtMUNeicqZH4" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "compiled_program.save(\"compiled_program.dspy\")" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "trial_logs = compiled_program.trial_logs\n", - "\n", - "# Extracting trial numbers, scores, and pruning status\n", - "trial_numbers = list(trial_logs.keys())\n", - "scores = [trial_logs[trial]['score'] for trial in trial_numbers]\n", - "pruning_status = [trial_logs[trial]['pruned'] for trial in trial_numbers]\n", - "\n", - "# Plot setup\n", - "plt.figure(figsize=(5, 3))\n", - "\n", - "# Plotting each point\n", - "for trial_number, score, pruned in zip(trial_numbers, scores, pruning_status):\n", - " if pruned:\n", - " plt.scatter(trial_number, score, color='grey', label='Pruned Batch' if 'Pruned Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", - " else:\n", - " plt.scatter(trial_number, score, color='green', label='Successful Batch' if 'Successful Batch' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", - "\n", - "plt.xlabel('Batch Number')\n", - "plt.ylabel('Score')\n", - "plt.title('Batch Scores')\n", - "plt.grid(True)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5_Rwxfa1qZH4" - }, - "source": [ - "We can also __visualize the best prompts__ discovered by MIPRO as our trials progress... (though note that score increases are also due to the selected fewshot examples, which are not shown here for conciseness)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "NnARfPRHqZH4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Baseline program | Score: 0:\n", - "Prompt 1 Instruction: Given the fields `context`, `question`, produce the fields `search_query`.\n", - "Prompt 2 Instruction: Given the fields `context`, `question`, produce the fields `answer`.\n", - "Prompt 3 Instruction: Given a question we are trying to answer and a list of passages, return a comma separated list of the numbers associated with each passage. These numbers should be ordered by helpfulness in answering the question, with most helpful passage number first, and the least helpful last.\n", - "\n", - "----------------\n", - "Best program after 0 batches | Score: 43.8:\n", - "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", - "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", - "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", - "\n", - "Best program after 5 batches | Score: 43.8:\n", - "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", - "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", - "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", - "\n", - "Best program after 10 batches | Score: 43.8:\n", - "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", - "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", - "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", - "\n", - "Best program after 15 batches | Score: 43.8:\n", - "Prompt 1 Instruction: Given a set of context and question pairs, prompt the model to generate a search query that accurately captures the key information needed to answer the question within the given context.\n", - "Prompt 2 Instruction: Given the provided context about the album \"Plastic Beach\" and the specific question, \"Who released the album that 'Stylo' was a single on in the United States?\", generate the accurate and precise answer to the question.\n", - "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. Start by identifying the mentions of the mallow family in each passage and determine if the genus is part of the mallow family. Then, assign a number to each passage in the order of helpfulness, with the most helpful passage having the lowest number and the least helpful passage having the highest number. Separate the numbers with commas.\n", - "\n", - "Best program after 20 batches | Score: 46.4:\n", - "Prompt 1 Instruction: Given the provided context and question, generate a search query that will yield the specific information needed to answer the question accurately and directly.\n", - "Prompt 2 Instruction: Given the provided context about various film directors and their nominations for the Academy Award Oscar, and the specific question asking which director received more nominations, please generate the correct answer based on the information provided.\n", - "Prompt 3 Instruction: Rank the passages in order of helpfulness to answer the question \"Which genus is part of the mallow family; Ferraria or Abelmoschus?\" based on the information provided in the passages. 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a/examples/outdated_v2.4_examples/qa/hotpot/hotpotqa_with_assertions.ipynb +++ b/examples/outdated_v2.4_examples/qa/hotpot/hotpotqa_with_assertions.ipynb @@ -1,348 +1,348 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from dsp.utils import deduplicate\n", - "from dspy.datasets import HotPotQA\n", - "from dspy.predict.retry import Retry\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", - "from dspy.evaluate.evaluate import Evaluate\n", - "\n", - "from dspy.primitives.assertions import assert_transform_module, backtrack_handler" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dsp.utils import deduplicate\n", + "from dspy.datasets import HotPotQA\n", + "from dspy.predict.retry import Retry\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "from dspy.evaluate.evaluate import Evaluate\n", + "\n", + "from dspy.primitives.assertions import assert_transform_module, backtrack_handler" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import openai\n", + "openai.api_key = os.getenv('OPENAI_API_KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", + "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", + "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", + "trainset = [x.with_inputs('question') for x in dataset.train]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Suggestion helper functions and Teleprompter metric\n", + "\n", + "def validate_query_distinction_local(previous_queries, query):\n", + " \"\"\"check if query is distinct from previous queries\"\"\"\n", + " if previous_queries == []:\n", + " return True\n", + " if dspy.evaluate.answer_exact_match_str(query, previous_queries, frac=0.8):\n", + " return False\n", + " return True\n", + "\n", + "\n", + "def validate_context_and_answer_and_hops(example, pred, trace=None):\n", + " if not dspy.evaluate.answer_exact_match(example, pred):\n", + " return False\n", + "\n", + " if not dspy.evaluate.answer_passage_match(example, pred):\n", + " return False\n", + "\n", + " return True\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Extrinsic metrics\n", + "\n", + "def gold_passages_retrieved(example, pred, trace=None):\n", + " gold_titles = set(map(dspy.evaluate.normalize_text, example['gold_titles']))\n", + " found_titles = set(map(dspy.evaluate.normalize_text, [c.split(' | ')[0] for c in pred.context]))\n", + "\n", + " return gold_titles.issubset(found_titles)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# signatures of dspy modules\n", + "class GenerateAnswer(dspy.Signature):\n", + " \"\"\"Answer questions with short factoid answers.\"\"\"\n", + "\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " question = dspy.InputField()\n", + " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")\n", + "\n", + "\n", + "class GenerateSearchQuery(dspy.Signature):\n", + " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", + "\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " question = dspy.InputField()\n", + " query = dspy.OutputField()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def all_queries_distinct(prev_queries):\n", + " query_distinct = True\n", + " for i, query in enumerate(prev_queries):\n", + " if validate_query_distinction_local(prev_queries[:i], query) == False:\n", + " query_distinct = False\n", + " break\n", + " return query_distinct" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class SimplifiedBaleen(dspy.Module):\n", + " def __init__(self, passages_per_hop=2, max_hops=2):\n", + " super().__init__()\n", + "\n", + " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", + " self.max_hops = max_hops\n", + "\n", + " # for evaluating assertions only\n", + " self.passed_suggestions = 0\n", + "\n", + " def forward(self, question):\n", + " context = []\n", + " prev_queries = [question]\n", + "\n", + " for hop in range(self.max_hops):\n", + " query = self.generate_query[hop](context=context, question=question).query\n", + " prev_queries.append(query)\n", + " passages = self.retrieve(query).passages\n", + " context = deduplicate(context + passages)\n", + " \n", + " if all_queries_distinct(prev_queries):\n", + " self.passed_suggestions += 1\n", + " \n", + " pred = self.generate_answer(context=context, question=question)\n", + " pred = dspy.Prediction(context=context, answer=pred.answer)\n", + " return pred" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "class SimplifiedBaleenAssertions(dspy.Module):\n", + " def __init__(self, passages_per_hop=2, max_hops=2):\n", + " super().__init__()\n", + " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", + " self.max_hops = max_hops\n", + "\n", + " # for evaluating assertions only\n", + " self.passed_suggestions = 0\n", + "\n", + " def forward(self, question):\n", + " context = []\n", + " prev_queries = [question]\n", + "\n", + " for hop in range(self.max_hops):\n", + " query = self.generate_query[hop](context=context, question=question).query\n", + "\n", + " dspy.Suggest(\n", + " len(query) <= 100,\n", + " \"Query should be short and less than 100 characters\",\n", + " target_module=self.generate_query\n", + " )\n", + "\n", + " dspy.Suggest(\n", + " validate_query_distinction_local(prev_queries, query),\n", + " \"Query should be distinct from: \"\n", + " + \"; \".join(f\"{i+1}) {q}\" for i, q in enumerate(prev_queries)),\n", + " target_module=self.generate_query\n", + " )\n", + "\n", + " prev_queries.append(query)\n", + " passages = self.retrieve(query).passages\n", + " context = deduplicate(context + passages)\n", + " \n", + " if all_queries_distinct(prev_queries):\n", + " self.passed_suggestions += 1\n", + "\n", + " pred = self.generate_answer(context=context, question=question)\n", + " pred = dspy.Prediction(context=context, answer=pred.answer)\n", + " return pred\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=10, display_progress=True, display_table=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate(module):\n", + " module.passed_suggestions = 0\n", + "\n", + " retrieval_score = evaluate_on_hotpotqa(\n", + " module, metric=gold_passages_retrieved\n", + " )\n", + " \n", + " suggestions_score = module.passed_suggestions / len(devset) * 100\n", + "\n", + " accuracy_score = evaluate_on_hotpotqa(\n", + " module, metric=dspy.evaluate.answer_exact_match\n", + " )\n", + "\n", + " print(f\"## Suggestions Score: {suggestions_score}\")\n", + "\n", + " print(f\"## Retrieval Score: {retrieval_score}\")\n", + " print(f\"## Accuracy Score: {accuracy_score}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# No Compilation + No Assertion\n", + "baleen = SimplifiedBaleen()\n", + "evaluate(baleen)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# No Compilation + Yes Assertion\n", + "baleen_with_assertions = assert_transform_module(SimplifiedBaleenAssertions().map_named_predictors(Retry), backtrack_handler) \n", + "evaluate(baleen_with_assertions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "max_bootstrapped_demos = 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Yes Compilation + No Assertion\n", + "baleen = SimplifiedBaleen()\n", + "teleprompter = BootstrapFewShotWithRandomSearch(\n", + " metric=validate_context_and_answer_and_hops,\n", + " max_bootstrapped_demos=max_bootstrapped_demos,\n", + " num_candidate_programs=6,\n", + ")\n", + "\n", + "compiled_baleen = teleprompter.compile(student = SimplifiedBaleen(), teacher = SimplifiedBaleen(), trainset = trainset, valset = devset)\n", + "evaluate(compiled_baleen)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Yes Compilation + Yes Assertion\n", + "baleen = SimplifiedBaleen()\n", + "teleprompter = BootstrapFewShotWithRandomSearch(\n", + " metric=validate_context_and_answer_and_hops,\n", + " max_bootstrapped_demos=max_bootstrapped_demos,\n", + " num_candidate_programs=6,\n", + ")\n", + "compiled_baleen = teleprompter.compile(\n", + " student=assert_transform_module(\n", + " SimplifiedBaleenAssertions().map_named_predictors(Retry),\n", + " backtrack_handler,\n", + " ),\n", + " teacher=baleen,\n", + " trainset=trainset,\n", + " valset=devset\n", + ")\n", + "evaluate(compiled_baleen)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import openai\n", - "openai.api_key = os.getenv('OPENAI_API_KEY')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", - "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", - "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", - "trainset = [x.with_inputs('question') for x in dataset.train]\n", - "devset = [x.with_inputs('question') for x in dataset.dev]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Suggestion helper functions and Teleprompter metric\n", - "\n", - "def validate_query_distinction_local(previous_queries, query):\n", - " \"\"\"check if query is distinct from previous queries\"\"\"\n", - " if previous_queries == []:\n", - " return True\n", - " if dspy.evaluate.answer_exact_match_str(query, previous_queries, frac=0.8):\n", - " return False\n", - " return True\n", - "\n", - "\n", - "def validate_context_and_answer_and_hops(example, pred, trace=None):\n", - " if not dspy.evaluate.answer_exact_match(example, pred):\n", - " return False\n", - "\n", - " if not dspy.evaluate.answer_passage_match(example, pred):\n", - " return False\n", - "\n", - " return True\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Extrinsic metrics\n", - "\n", - "def gold_passages_retrieved(example, pred, trace=None):\n", - " gold_titles = set(map(dspy.evaluate.normalize_text, example['gold_titles']))\n", - " found_titles = set(map(dspy.evaluate.normalize_text, [c.split(' | ')[0] for c in pred.context]))\n", - "\n", - " return gold_titles.issubset(found_titles)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# signatures of dspy modules\n", - "class GenerateAnswer(dspy.Signature):\n", - " \"\"\"Answer questions with short factoid answers.\"\"\"\n", - "\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " question = dspy.InputField()\n", - " answer = dspy.OutputField(desc=\"often between 1 and 5 words\")\n", - "\n", - "\n", - "class GenerateSearchQuery(dspy.Signature):\n", - " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", - "\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " question = dspy.InputField()\n", - " query = dspy.OutputField()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def all_queries_distinct(prev_queries):\n", - " query_distinct = True\n", - " for i, query in enumerate(prev_queries):\n", - " if validate_query_distinction_local(prev_queries[:i], query) == False:\n", - " query_distinct = False\n", - " break\n", - " return query_distinct" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "class SimplifiedBaleen(dspy.Module):\n", - " def __init__(self, passages_per_hop=2, max_hops=2):\n", - " super().__init__()\n", - "\n", - " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", - " self.max_hops = max_hops\n", - "\n", - " # for evaluating assertions only\n", - " self.passed_suggestions = 0\n", - "\n", - " def forward(self, question):\n", - " context = []\n", - " prev_queries = [question]\n", - "\n", - " for hop in range(self.max_hops):\n", - " query = self.generate_query[hop](context=context, question=question).query\n", - " prev_queries.append(query)\n", - " passages = self.retrieve(query).passages\n", - " context = deduplicate(context + passages)\n", - " \n", - " if all_queries_distinct(prev_queries):\n", - " self.passed_suggestions += 1\n", - " \n", - " pred = self.generate_answer(context=context, question=question)\n", - " pred = dspy.Prediction(context=context, answer=pred.answer)\n", - " return pred" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "class SimplifiedBaleenAssertions(dspy.Module):\n", - " def __init__(self, passages_per_hop=2, max_hops=2):\n", - " super().__init__()\n", - " self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_answer = dspy.ChainOfThought(GenerateAnswer)\n", - " self.max_hops = max_hops\n", - "\n", - " # for evaluating assertions only\n", - " self.passed_suggestions = 0\n", - "\n", - " def forward(self, question):\n", - " context = []\n", - " prev_queries = [question]\n", - "\n", - " for hop in range(self.max_hops):\n", - " query = self.generate_query[hop](context=context, question=question).query\n", - "\n", - " dspy.Suggest(\n", - " len(query) <= 100,\n", - " \"Query should be short and less than 100 characters\",\n", - " target_module=self.generate_query\n", - " )\n", - "\n", - " dspy.Suggest(\n", - " validate_query_distinction_local(prev_queries, query),\n", - " \"Query should be distinct from: \"\n", - " + \"; \".join(f\"{i+1}) {q}\" for i, q in enumerate(prev_queries)),\n", - " target_module=self.generate_query\n", - " )\n", - "\n", - " prev_queries.append(query)\n", - " passages = self.retrieve(query).passages\n", - " context = deduplicate(context + passages)\n", - " \n", - " if all_queries_distinct(prev_queries):\n", - " self.passed_suggestions += 1\n", - "\n", - " pred = self.generate_answer(context=context, question=question)\n", - " pred = dspy.Prediction(context=context, answer=pred.answer)\n", - " return pred\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=10, display_progress=True, display_table=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(module):\n", - " module.passed_suggestions = 0\n", - "\n", - " retrieval_score = evaluate_on_hotpotqa(\n", - " module, metric=gold_passages_retrieved\n", - " )\n", - " \n", - " suggestions_score = module.passed_suggestions / len(devset) * 100\n", - "\n", - " accuracy_score = evaluate_on_hotpotqa(\n", - " module, metric=dspy.evaluate.answer_exact_match\n", - " )\n", - "\n", - " print(f\"## Suggestions Score: {suggestions_score}\")\n", - "\n", - " print(f\"## Retrieval Score: {retrieval_score}\")\n", - " print(f\"## Accuracy Score: {accuracy_score}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# No Compilation + No Assertion\n", - "baleen = SimplifiedBaleen()\n", - "evaluate(baleen)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# No Compilation + Yes Assertion\n", - "baleen_with_assertions = assert_transform_module(SimplifiedBaleenAssertions().map_named_predictors(Retry), backtrack_handler) \n", - "evaluate(baleen_with_assertions)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_bootstrapped_demos = 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Yes Compilation + No Assertion\n", - "baleen = SimplifiedBaleen()\n", - "teleprompter = BootstrapFewShotWithRandomSearch(\n", - " metric=validate_context_and_answer_and_hops,\n", - " max_bootstrapped_demos=max_bootstrapped_demos,\n", - " num_candidate_programs=6,\n", - ")\n", - "\n", - "compiled_baleen = teleprompter.compile(student = SimplifiedBaleen(), teacher = SimplifiedBaleen(), trainset = trainset, valset = devset)\n", - "evaluate(compiled_baleen)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Yes Compilation + Yes Assertion\n", - "baleen = SimplifiedBaleen()\n", - "teleprompter = BootstrapFewShotWithRandomSearch(\n", - " metric=validate_context_and_answer_and_hops,\n", - " max_bootstrapped_demos=max_bootstrapped_demos,\n", - " num_candidate_programs=6,\n", - ")\n", - "compiled_baleen = teleprompter.compile(\n", - " student=assert_transform_module(\n", - " SimplifiedBaleenAssertions().map_named_predictors(Retry),\n", - " backtrack_handler,\n", - " ),\n", - " teacher=baleen,\n", - " trainset=trainset,\n", - " valset=devset\n", - ")\n", - "evaluate(compiled_baleen)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/qa/hotpot/multihop_finetune.ipynb b/examples/outdated_v2.4_examples/qa/hotpot/multihop_finetune.ipynb index ec67735e6f..be3357f9a8 100644 --- a/examples/outdated_v2.4_examples/qa/hotpot/multihop_finetune.ipynb +++ b/examples/outdated_v2.4_examples/qa/hotpot/multihop_finetune.ipynb @@ -1,365 +1,365 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "# %set_env CUDA_VISIBLE_DEVICES=7\n", - "# import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy')\n", - "\n", - "import dspy\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.datasets.hotpotqa import HotPotQA\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch, BootstrapFinetune" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Configure the default LM and retriever" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ports = [7140, 7141, 7142, 7143, 7144, 7145]\n", - "llamaChat = dspy.HFClientTGI(model=\"meta-llama/Llama-2-13b-chat-hf\", port=ports, max_tokens=150)\n", - "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "\n", - "dspy.settings.configure(rm=colbertv2, lm=llamaChat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Load a small sample of HotPotQA data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "(200, 1000, 0)" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# %set_env CUDA_VISIBLE_DEVICES=7\n", + "# import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy')\n", + "\n", + "import dspy\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.datasets.hotpotqa import HotPotQA\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch, BootstrapFinetune" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=1000, test_size=0)\n", - "trainset = [x.with_inputs('question') for x in dataset.train]\n", - "devset = [x.with_inputs('question') for x in dataset.dev]\n", - "testset = [x.with_inputs('question') for x in dataset.test]\n", - "\n", - "len(trainset), len(devset), len(testset)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Configure the default LM and retriever" ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trainset[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) Define a simple multi-hop program" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from dsp.utils.utils import deduplicate\n", - "\n", - "class BasicMH(dspy.Module):\n", - " def __init__(self, passages_per_hop=3):\n", - " super().__init__()\n", - "\n", - " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " self.generate_query = [dspy.ChainOfThought(\"context, question -> search_query\") for _ in range(2)]\n", - " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " context = []\n", - " \n", - " for hop in range(2):\n", - " search_query = self.generate_query[hop](context=context, question=question).search_query\n", - " passages = self.retrieve(search_query).passages\n", - " context = deduplicate(context + passages)\n", - "\n", - " return self.generate_answer(context=context, question=question).copy(context=context)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Compile the program with `Llama2-13b-chat`" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "RECOMPILE_INTO_LLAMA_FROM_SCRATCH = False\n", - "NUM_THREADS = 24\n", - "\n", - "metric_EM = dspy.evaluate.answer_exact_match" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "if RECOMPILE_INTO_LLAMA_FROM_SCRATCH:\n", - " tp = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_threads=NUM_THREADS)\n", - " basicmh_bs = tp.compile(BasicMH(), trainset=trainset[:50], valset=trainset[50:200])\n", - "\n", - " ensemble = [prog for *_, prog in basicmh_bs.candidate_programs[:4]]\n", - "\n", - " for idx, prog in enumerate(ensemble):\n", - " # prog.save(f'checkpoints/multihop_llama213b_{idx}.json')\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "if not RECOMPILE_INTO_LLAMA_FROM_SCRATCH:\n", - " ensemble = []\n", - "\n", - " for idx in range(4):\n", - " prog = BasicMH()\n", - " prog.load(f'checkpoints/multihop_llama213b_{idx}.json')\n", - " ensemble.append(prog)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 424 / 1000 (42.4): 100%|██████████| 1000/1000 [00:14<00:00, 70.51it/s]\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ports = [7140, 7141, 7142, 7143, 7144, 7145]\n", + "llamaChat = dspy.HFClientTGI(model=\"meta-llama/Llama-2-13b-chat-hf\", port=ports, max_tokens=150)\n", + "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "\n", + "dspy.settings.configure(rm=colbertv2, lm=llamaChat)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 424 / 1000 (42.4%)\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Load a small sample of HotPotQA data" + ] }, { - "data": { - "text/plain": [ - "42.4" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200, 1000, 0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=1000, test_size=0)\n", + "trainset = [x.with_inputs('question') for x in dataset.train]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]\n", + "testset = [x.with_inputs('question') for x in dataset.test]\n", + "\n", + "len(trainset), len(devset), len(testset)" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "llama_program = ensemble[0]\n", - "\n", - "evaluate_hotpot = Evaluate(devset=devset[:1000], metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=0)\n", - "evaluate_hotpot(llama_program)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "llama_program(question=\"How many storeys are in the castle that David Gregory inherited?\")\n", - "\n", - "llamaChat.inspect_history(n=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6) Compile into `T5-Large` (770M parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Define a simple multi-hop program" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from dsp.utils.utils import deduplicate\n", + "\n", + "class BasicMH(dspy.Module):\n", + " def __init__(self, passages_per_hop=3):\n", + " super().__init__()\n", + "\n", + " self.retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " self.generate_query = [dspy.ChainOfThought(\"context, question -> search_query\") for _ in range(2)]\n", + " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " context = []\n", + " \n", + " for hop in range(2):\n", + " search_query = self.generate_query[hop](context=context, question=question).search_query\n", + " passages = self.retrieve(search_query).passages\n", + " context = deduplicate(context + passages)\n", + "\n", + " return self.generate_answer(context=context, question=question).copy(context=context)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Compile the program with `Llama2-13b-chat`" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "RECOMPILE_INTO_LLAMA_FROM_SCRATCH = False\n", + "NUM_THREADS = 24\n", + "\n", + "metric_EM = dspy.evaluate.answer_exact_match" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "if RECOMPILE_INTO_LLAMA_FROM_SCRATCH:\n", + " tp = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_threads=NUM_THREADS)\n", + " basicmh_bs = tp.compile(BasicMH(), trainset=trainset[:50], valset=trainset[50:200])\n", + "\n", + " ensemble = [prog for *_, prog in basicmh_bs.candidate_programs[:4]]\n", + "\n", + " for idx, prog in enumerate(ensemble):\n", + " # prog.save(f'checkpoints/multihop_llama213b_{idx}.json')\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "if not RECOMPILE_INTO_LLAMA_FROM_SCRATCH:\n", + " ensemble = []\n", + "\n", + " for idx in range(4):\n", + " prog = BasicMH()\n", + " prog.load(f'checkpoints/multihop_llama213b_{idx}.json')\n", + " ensemble.append(prog)" + ] + }, { - "data": { - "text/plain": [ - "3000" + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 424 / 1000 (42.4): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:14<00:00, 70.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 424 / 1000 (42.4%)\n" + ] + }, + { + "data": { + "text/plain": [ + "42.4" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llama_program = ensemble[0]\n", + "\n", + "evaluate_hotpot = Evaluate(devset=devset[:1000], metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=0)\n", + "evaluate_hotpot(llama_program)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llama_program(question=\"How many storeys are in the castle that David Gregory inherited?\")\n", + "\n", + "llamaChat.inspect_history(n=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6) Compile into `T5-Large` (770M parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3000" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unlabeled_train = HotPotQA(train_seed=1, train_size=3000, eval_seed=2023, dev_size=0, test_size=0).train\n", + "unlabeled_train = [dspy.Example(question=x.question).with_inputs('question') for x in unlabeled_train]\n", + "len(unlabeled_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optional step: pre-compute the ensemble on the unlabeled training set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "always_true = lambda g, p, trace=None: True\n", + "\n", + "for prog_ in ensemble:\n", + " evaluate_hotpot(prog_, devset=unlabeled_train[:3000], metric=always_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now compile into T5!" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "RECOMPILE_INTO_T5_FROM_SCRATCH = False\n", + "\n", + "if RECOMPILE_INTO_T5_FROM_SCRATCH:\n", + " config = dict(target='t5-large', epochs=2, bf16=True, bsize=6, accumsteps=2, lr=5e-5)\n", + "\n", + " tp = BootstrapFinetune(metric=None)\n", + " t5_program = tp.compile(BasicMH(), teacher=ensemble, trainset=unlabeled_train[:3000], **config)\n", + "\n", + " # Deactivate chain of thought prompting. Let's use T5 to directly predict outputs. (Faster and similar quality.)\n", + " for p in t5_program.predictors(): p.activated = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if not RECOMPILE_INTO_T5_FROM_SCRATCH:\n", + " t5_program = BasicMH()\n", + "\n", + " # ckpt_path = '../finetuning_ckpts/LMWEP0WZ5IKWM.all/checkpoint-5400'\n", + " ckpt_path = \"colbert-ir/dspy-Oct11-T5-Large-MH-3k-v1\"\n", + " LM = dspy.HFModel(checkpoint=ckpt_path, model='t5-large')\n", + "\n", + " for p in t5_program.predictors():\n", + " p.lm = LM\n", + " p.activated = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7) Evaluate the T5-Large `multihop` program" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "score = evaluate_hotpot(t5_program, num_threads=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t5_program.predictors()[0].lm.inspect_history(n=3)" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "unlabeled_train = HotPotQA(train_seed=1, train_size=3000, eval_seed=2023, dev_size=0, test_size=0).train\n", - "unlabeled_train = [dspy.Example(question=x.question).with_inputs('question') for x in unlabeled_train]\n", - "len(unlabeled_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optional step: pre-compute the ensemble on the unlabeled training set" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "always_true = lambda g, p, trace=None: True\n", - "\n", - "for prog_ in ensemble:\n", - " evaluate_hotpot(prog_, devset=unlabeled_train[:3000], metric=always_true)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now compile into T5!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "RECOMPILE_INTO_T5_FROM_SCRATCH = False\n", - "\n", - "if RECOMPILE_INTO_T5_FROM_SCRATCH:\n", - " config = dict(target='t5-large', epochs=2, bf16=True, bsize=6, accumsteps=2, lr=5e-5)\n", - "\n", - " tp = BootstrapFinetune(metric=None)\n", - " t5_program = tp.compile(BasicMH(), teacher=ensemble, trainset=unlabeled_train[:3000], **config)\n", - "\n", - " # Deactivate chain of thought prompting. Let's use T5 to directly predict outputs. (Faster and similar quality.)\n", - " for p in t5_program.predictors(): p.activated = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if not RECOMPILE_INTO_T5_FROM_SCRATCH:\n", - " t5_program = BasicMH()\n", - "\n", - " # ckpt_path = '../finetuning_ckpts/LMWEP0WZ5IKWM.all/checkpoint-5400'\n", - " ckpt_path = \"colbert-ir/dspy-Oct11-T5-Large-MH-3k-v1\"\n", - " LM = dspy.HFModel(checkpoint=ckpt_path, model='t5-large')\n", - "\n", - " for p in t5_program.predictors():\n", - " p.lm = LM\n", - " p.activated = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7) Evaluate the T5-Large `multihop` program" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "score = evaluate_hotpot(t5_program, num_threads=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t5_program.predictors()[0].lm.inspect_history(n=3)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39_aug2023_dspy", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" + ], + "metadata": { + "kernelspec": { + "display_name": "py39_aug2023_dspy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/quiz/quiz_assertions.ipynb b/examples/outdated_v2.4_examples/quiz/quiz_assertions.ipynb index 5bdf6a8cbb..933bac45c8 100644 --- a/examples/outdated_v2.4_examples/quiz/quiz_assertions.ipynb +++ b/examples/outdated_v2.4_examples/quiz/quiz_assertions.ipynb @@ -1,487 +1,487 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "## **DSPy Assertions**: Asserting Computational Constraints on Foundation Models\n", - "\n", - "### **QuizGen**: Generating multiple choice quiz questions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/quiz/quiz_assertions.ipynb)\n", - "\n", - "\n", - "This notebook highlights an example of [**DSPy Assertions**](https://dspy.ai/docs/building-blocks/assertions), allowing for declaration of computational constraints within DSPy programs. \n", - "\n", - "\n", - "This notebook builds upon the foundational concepts of the **DSPy** framework. Prerequisites of following this notebook is having gone through the [DSPy tutorial](../../intro.ipynb), the [**DSPy Assertions documentation**](https://dspy.ai/docs/building-blocks/assertions) and the introductory DSPy Assertions [tutorial on LongFormQA](../longformqa/longformqa_assertions.ipynb).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "import json\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - " \n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " !pip install dspy-ai==2.4.17\n", - " !pip install openai~=0.28.1\n", - " !pip install -e $repo_path\n", - "\n", - "import dspy\n", - "from dspy.predict import Retry\n", - "from dspy.datasets import HotPotQA\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", - "from dspy.evaluate.evaluate import Evaluate\n", - "from dspy.primitives.assertions import assert_transform_module, backtrack_handler" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import openai\n", - "openai.api_key = os.getenv('OPENAI_API_KEY')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", - "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", - "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", - "trainset = [x.with_inputs('question', 'answer') for x in dataset.train]\n", - "devset = [x.with_inputs('question', 'answer') for x in dataset.dev]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3] QuizGen\n", - "\n", - "Let's introduce a new task: QuizGen. \n", - "\n", - "QuizGen takes HotPotQA data points and turns them into multiple choice quiz questions with the corresponding options. Each set of options for the question is produced in a JSON key-value pair format. For this case, we specify the generation of 4 choices." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this program, we aim to generate quiz choices that adhere to the following guidelines:\n", - "1. The generated choices are in a JSON format.\n", - "2. The generated choices include the correct answer.\n", - "3. The generated choices include plausible distractor options besides the correct answer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class GenerateAnswerChoices(dspy.Signature):\n", - " \"\"\"Generate answer choices in JSON format that include the correct answer and plausible distractors for the specified question.\"\"\"\n", - " question = dspy.InputField()\n", - " correct_answer = dspy.InputField()\n", - " number_of_choices = dspy.InputField()\n", - " answer_choices = dspy.OutputField(desc='JSON key-value pairs')\n", - "\n", - "class QuizAnswerGenerator(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_choices = dspy.ChainOfThought(GenerateAnswerChoices)\n", - "\n", - " def forward(self, question, answer):\n", - " choices = self.generate_choices(question=question, correct_answer=answer, number_of_choices=number_of_choices).answer_choices\n", - " return dspy.Prediction(choices = choices)\n", - "\n", - "number_of_choices = '4'\n", - "quiz_generator = QuizAnswerGenerator()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4] Evaluation - Intrinsic and Extrinsic\n", - "\n", - "#### Intrinsic Metrics: passing internal computational constraints is the goal \n", - "\n", - "**Valid Formatting** - The outputted answer choices should be in JSON format which is verified after parsing the key-value pairs.\n", - "\n", - "**Correct Answer Inclusion** - This is a general check to ensure the generated quiz choices actually include the correct answer to the question.\n", - "\n", - "**Plausible Distractors** - This validation is to check that the generated choices include distractor answer options that are reasonable options as answers to the question. We define and call another **DSPy** program: ``Predict`` on ``AssessQuizChoices``, relying on the same LM to answer the question: `\"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def format_checker(choice_string):\n", - " try:\n", - " choices = json.loads(choice_string)\n", - " if isinstance(choices, dict) and all(isinstance(key, str) and isinstance(value, str) for key, value in choices.items()):\n", - " return True\n", - " except json.JSONDecodeError:\n", - " return False\n", - "\n", - " return False\n", - "\n", - "def is_correct_answer_included(correct_answer, generated_choices):\n", - " try:\n", - " choices_dict = json.loads(generated_choices)\n", - " return correct_answer in choices_dict.values()\n", - " except json.JSONDecodeError:\n", - " return False\n", - "\n", - "def is_plausibility_yes(assessment_answer):\n", - " \"\"\"Check if the first word of the assessment answer is 'yes'.\"\"\"\n", - " return assessment_answer.split()[0].lower() == 'yes'\n", - " \n", - "class AssessQuizChoices(dspy.Signature):\n", - " \"\"\"Assess the quality of quiz answer choices along specified dimensions.\"\"\"\n", - " \n", - " question = dspy.InputField()\n", - " answer_choices = dspy.InputField()\n", - " assessment_question = dspy.InputField()\n", - " assessment_answer = dspy.OutputField(desc=\"Yes or No\")\n", - " \n", - "def format_valid_metric(gold, pred, trace=None):\n", - " generated_choices = pred.choices\n", - " format_valid = format_checker(generated_choices)\n", - " score = format_valid\n", - " return score\n", - "\n", - "def is_correct_metric(gold, pred, trace=None):\n", - " correct_answer, generated_choices = gold.answer, pred.choices\n", - " correct_included = is_correct_answer_included(correct_answer, generated_choices)\n", - " score = correct_included\n", - " return score\n", - "\n", - "def plausibility_metric(gold, pred, trace=None):\n", - " question, generated_choices = gold.question, pred.choices\n", - " plausibility_question = \"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"\n", - " plausibility_assessment = dspy.Predict(AssessQuizChoices)(question=question, answer_choices=generated_choices, assessment_question=plausibility_question)\n", - " plausibility_result = plausibility_assessment.assessment_answer.split()[0].lower() == 'yes'\n", - " score = plausibility_result\n", - " return score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Extrinsic Metrics: Assess the overall quality and effectiveness of generated output on downstream task\n", - "\n", - "The extrinsic metric is defined as the overall quality of the generated quiz choices and is evaluated over a composite metric, accounting for these constraints.\n", - "\n", - "The composite metric maintains the core intrinsic metrics required for producing a valid set of quiz choices in validating valid formatting and correct answere icnlusion, and the overall composite metric returns an averaged score over the 3 intrinsic metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def overall_metric(gold, pred, trace=None):\n", - " question, correct_answer, generated_choices = gold.question, gold.answer, pred.choices\n", - " format_valid = format_checker(generated_choices)\n", - " correct_included = is_correct_answer_included(correct_answer, generated_choices)\n", - " plausibility_question = \"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"\n", - " plausibility_assessment = dspy.Predict(AssessQuizChoices)(question=question, answer_choices=generated_choices, assessment_question=plausibility_question)\n", - " plausibility_result = plausibility_assessment.assessment_answer.split()[0].lower() == 'yes'\n", - " score = (format_valid + correct_included + plausibility_result) / 3.0 if correct_included and format_valid else 0\n", - " return score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We hence define the evaluation as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics = [format_valid_metric, is_correct_metric, plausibility_metric, overall_metric]\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(quiz_generator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at an example quiz choice generation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example = devset[67]\n", - "quiz_choices = quiz_generator(question=example.question, answer = example.answer)\n", - "print('Generated Quiz Choices: ', quiz_choices.choices)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset[67:68], num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(quiz_generator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the generated quiz choices do not maintain valid JSON formatting, which violates the valid formatting and correctness check, even though the choices are noted as plausible. We also see that the correct answer is also labeled by \"(Correct Answer)\", which is not the intention of producing good quiz question answer choices. \n", - "\n", - "Let's take a look at how we can integrate DSPy Assertions and impose constraints to produce better answer choices." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5] Introducing Assertions: QuizAnswerGeneratorWithAssertions\n", - "Let's include assertions that simply reiterate our computational constraints within DSPy Assertion semantics. \n", - "\n", - "In the first **Assertion**, we check for if the generated quiz choices are in JSON format and if not, assert: **\"The format of the answer choices should be in JSON format. Please revise accordingly.\"**\n", - "\n", - "We also check for if the set of quiz choices includes the correct answer and ensure this if violated with the feedback message: **\"The answer choices do not include the correct answer to the question. Please revise accordingly.\"**\n", - "\n", - "Lastly, we assess if the plausible distractor choices are indeed good distractor options and if not, assert: **\"The answer choices are not plausible distractors or are too easily identifiable as incorrect. Please revise to provide more challenging and plausible distractors.\"**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class QuizAnswerGeneratorWithAssertions(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_choices = dspy.ChainOfThought(GenerateAnswerChoices)\n", - "\n", - " def forward(self, question, answer):\n", - " choice_string = self.generate_choices(question=question, correct_answer=answer, number_of_choices=number_of_choices).answer_choices\n", - " dspy.Suggest(format_checker(choice_string), \"The format of the answer choices should be in JSON format. Please revise accordingly.\", target_module=self.generate_choices)\n", - " dspy.Suggest(is_correct_answer_included(answer, choice_string), \"The answer choices do not include the correct answer to the question. Please revise accordingly.\", target_module=self.generate_choices)\n", - " plausibility_question = \"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"\n", - " plausibility_assessment = dspy.Predict(AssessQuizChoices)(question=question, answer_choices=choice_string, assessment_question=plausibility_question)\n", - " dspy.Suggest(is_plausibility_yes(plausibility_assessment.assessment_answer), \"The answer choices are not plausible distractors or are too easily identifiable as incorrect. Please revise to provide more challenging and plausible distractors.\", target_module=self.generate_choices)\n", - " return dspy.Prediction(choices = choice_string)\n", - "\n", - "number_of_choices = '4'\n", - "quiz_generator_with_assertions = assert_transform_module(QuizAnswerGeneratorWithAssertions().map_named_predictors(Retry), backtrack_handler) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's evaluate the `QuizAnswerGeneratorWithAssertions` now over the devset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics = [format_valid_metric, is_correct_metric, plausibility_metric, overall_metric]\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(quiz_generator_with_assertions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's take a look at how our generated set of quiz choices has improved with the addition of assertions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example = devset[67]\n", - "quiz_choices = quiz_generator_with_assertions(question=example.question, answer = example.answer)\n", - "print('Generated Quiz Choices: ', quiz_choices.choices)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset[67:68], num_threads=1, display_progress=True, display_table=30)\n", - " evaluate(quiz_generator_with_assertions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the quiz choices follow all of our constraints!\n", - "\n", - "Not only are the answer choices all plausible, and have removed any indicator of what the correct answer could be, but the answer choices now maintain valid JSON formatting with 4 possible answer choices to the question, which includes the correct answer." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6] Compilation With Assertions\n", - "\n", - "We can leverage **DSPy**'s`BootstrapFewShotWithRandomSearch` optimizer, to automatically generate few-shot demonstrations and conduct a random search over the candidates to output the best compiled program. We evaluate this over the `final_metric` composite metric. \n", - "\n", - "We can first evaluate this on `QuizAnswerGenerator` to see how compilation performs without the inclusion of assertions. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "compiled_quiz_generator = teleprompter.compile(student = quiz_generator, teacher = quiz_generator, trainset=trainset, valset=devset[:25])\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(compiled_quiz_generator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we test the compilation on 2 settings with assertions:\n", - "\n", - "**Compilation with Assertions**: assertion-driven example bootstrapping and counterexample bootstrapping during compilation. Teacher has assertions while the student does not as the student learns from the teacher's assertion-driven bootstrapped examples. \n", - "\n", - "**Compilation + Inference with Assertions**: assertion-driven optimizations for both the teacher and student to offer enhanced assertion-driven outputs during both compilation and inference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "compiled_with_assertions_quiz_generator = teleprompter.compile(student=quiz_generator, teacher = quiz_generator_with_assertions, trainset=trainset, valset=devset[:25])\n", - "\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(compiled_with_assertions_quiz_generator)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "compiled_quiz_generator_with_assertions = teleprompter.compile(student=quiz_generator_with_assertions, teacher = quiz_generator_with_assertions, trainset=trainset, valset=devset[:25])\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(compiled_quiz_generator_with_assertions)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dspy_dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "## **DSPy Assertions**: Asserting Computational Constraints on Foundation Models\n", + "\n", + "### **QuizGen**: Generating multiple choice quiz questions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/quiz/quiz_assertions.ipynb)\n", + "\n", + "\n", + "This notebook highlights an example of [**DSPy Assertions**](https://dspy.ai/docs/building-blocks/assertions), allowing for declaration of computational constraints within DSPy programs. \n", + "\n", + "\n", + "This notebook builds upon the foundational concepts of the **DSPy** framework. Prerequisites of following this notebook is having gone through the [DSPy tutorial](../../intro.ipynb), the [**DSPy Assertions documentation**](https://dspy.ai/docs/building-blocks/assertions) and the introductory DSPy Assertions [tutorial on LongFormQA](../longformqa/longformqa_assertions.ipynb).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "import json\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + " \n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " !pip install dspy-ai==2.4.17\n", + " !pip install openai~=0.28.1\n", + " !pip install -e $repo_path\n", + "\n", + "import dspy\n", + "from dspy.predict import Retry\n", + "from dspy.datasets import HotPotQA\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "from dspy.evaluate.evaluate import Evaluate\n", + "from dspy.primitives.assertions import assert_transform_module, backtrack_handler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "openai.api_key = os.getenv('OPENAI_API_KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", + "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", + "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", + "trainset = [x.with_inputs('question', 'answer') for x in dataset.train]\n", + "devset = [x.with_inputs('question', 'answer') for x in dataset.dev]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3] QuizGen\n", + "\n", + "Let's introduce a new task: QuizGen. \n", + "\n", + "QuizGen takes HotPotQA data points and turns them into multiple choice quiz questions with the corresponding options. Each set of options for the question is produced in a JSON key-value pair format. For this case, we specify the generation of 4 choices." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this program, we aim to generate quiz choices that adhere to the following guidelines:\n", + "1. The generated choices are in a JSON format.\n", + "2. The generated choices include the correct answer.\n", + "3. The generated choices include plausible distractor options besides the correct answer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class GenerateAnswerChoices(dspy.Signature):\n", + " \"\"\"Generate answer choices in JSON format that include the correct answer and plausible distractors for the specified question.\"\"\"\n", + " question = dspy.InputField()\n", + " correct_answer = dspy.InputField()\n", + " number_of_choices = dspy.InputField()\n", + " answer_choices = dspy.OutputField(desc='JSON key-value pairs')\n", + "\n", + "class QuizAnswerGenerator(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_choices = dspy.ChainOfThought(GenerateAnswerChoices)\n", + "\n", + " def forward(self, question, answer):\n", + " choices = self.generate_choices(question=question, correct_answer=answer, number_of_choices=number_of_choices).answer_choices\n", + " return dspy.Prediction(choices = choices)\n", + "\n", + "number_of_choices = '4'\n", + "quiz_generator = QuizAnswerGenerator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4] Evaluation - Intrinsic and Extrinsic\n", + "\n", + "#### Intrinsic Metrics: passing internal computational constraints is the goal \n", + "\n", + "**Valid Formatting** - The outputted answer choices should be in JSON format which is verified after parsing the key-value pairs.\n", + "\n", + "**Correct Answer Inclusion** - This is a general check to ensure the generated quiz choices actually include the correct answer to the question.\n", + "\n", + "**Plausible Distractors** - This validation is to check that the generated choices include distractor answer options that are reasonable options as answers to the question. We define and call another **DSPy** program: ``Predict`` on ``AssessQuizChoices``, relying on the same LM to answer the question: `\"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def format_checker(choice_string):\n", + " try:\n", + " choices = json.loads(choice_string)\n", + " if isinstance(choices, dict) and all(isinstance(key, str) and isinstance(value, str) for key, value in choices.items()):\n", + " return True\n", + " except json.JSONDecodeError:\n", + " return False\n", + "\n", + " return False\n", + "\n", + "def is_correct_answer_included(correct_answer, generated_choices):\n", + " try:\n", + " choices_dict = json.loads(generated_choices)\n", + " return correct_answer in choices_dict.values()\n", + " except json.JSONDecodeError:\n", + " return False\n", + "\n", + "def is_plausibility_yes(assessment_answer):\n", + " \"\"\"Check if the first word of the assessment answer is 'yes'.\"\"\"\n", + " return assessment_answer.split()[0].lower() == 'yes'\n", + " \n", + "class AssessQuizChoices(dspy.Signature):\n", + " \"\"\"Assess the quality of quiz answer choices along specified dimensions.\"\"\"\n", + " \n", + " question = dspy.InputField()\n", + " answer_choices = dspy.InputField()\n", + " assessment_question = dspy.InputField()\n", + " assessment_answer = dspy.OutputField(desc=\"Yes or No\")\n", + " \n", + "def format_valid_metric(gold, pred, trace=None):\n", + " generated_choices = pred.choices\n", + " format_valid = format_checker(generated_choices)\n", + " score = format_valid\n", + " return score\n", + "\n", + "def is_correct_metric(gold, pred, trace=None):\n", + " correct_answer, generated_choices = gold.answer, pred.choices\n", + " correct_included = is_correct_answer_included(correct_answer, generated_choices)\n", + " score = correct_included\n", + " return score\n", + "\n", + "def plausibility_metric(gold, pred, trace=None):\n", + " question, generated_choices = gold.question, pred.choices\n", + " plausibility_question = \"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"\n", + " plausibility_assessment = dspy.Predict(AssessQuizChoices)(question=question, answer_choices=generated_choices, assessment_question=plausibility_question)\n", + " plausibility_result = plausibility_assessment.assessment_answer.split()[0].lower() == 'yes'\n", + " score = plausibility_result\n", + " return score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extrinsic Metrics: Assess the overall quality and effectiveness of generated output on downstream task\n", + "\n", + "The extrinsic metric is defined as the overall quality of the generated quiz choices and is evaluated over a composite metric, accounting for these constraints.\n", + "\n", + "The composite metric maintains the core intrinsic metrics required for producing a valid set of quiz choices in validating valid formatting and correct answere icnlusion, and the overall composite metric returns an averaged score over the 3 intrinsic metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def overall_metric(gold, pred, trace=None):\n", + " question, correct_answer, generated_choices = gold.question, gold.answer, pred.choices\n", + " format_valid = format_checker(generated_choices)\n", + " correct_included = is_correct_answer_included(correct_answer, generated_choices)\n", + " plausibility_question = \"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"\n", + " plausibility_assessment = dspy.Predict(AssessQuizChoices)(question=question, answer_choices=generated_choices, assessment_question=plausibility_question)\n", + " plausibility_result = plausibility_assessment.assessment_answer.split()[0].lower() == 'yes'\n", + " score = (format_valid + correct_included + plausibility_result) / 3.0 if correct_included and format_valid else 0\n", + " return score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We hence define the evaluation as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = [format_valid_metric, is_correct_metric, plausibility_metric, overall_metric]\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(quiz_generator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at an example quiz choice generation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example = devset[67]\n", + "quiz_choices = quiz_generator(question=example.question, answer = example.answer)\n", + "print('Generated Quiz Choices: ', quiz_choices.choices)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset[67:68], num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(quiz_generator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the generated quiz choices do not maintain valid JSON formatting, which violates the valid formatting and correctness check, even though the choices are noted as plausible. We also see that the correct answer is also labeled by \"(Correct Answer)\", which is not the intention of producing good quiz question answer choices. \n", + "\n", + "Let's take a look at how we can integrate DSPy Assertions and impose constraints to produce better answer choices." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5] Introducing Assertions: QuizAnswerGeneratorWithAssertions\n", + "Let's include assertions that simply reiterate our computational constraints within DSPy Assertion semantics. \n", + "\n", + "In the first **Assertion**, we check for if the generated quiz choices are in JSON format and if not, assert: **\"The format of the answer choices should be in JSON format. Please revise accordingly.\"**\n", + "\n", + "We also check for if the set of quiz choices includes the correct answer and ensure this if violated with the feedback message: **\"The answer choices do not include the correct answer to the question. Please revise accordingly.\"**\n", + "\n", + "Lastly, we assess if the plausible distractor choices are indeed good distractor options and if not, assert: **\"The answer choices are not plausible distractors or are too easily identifiable as incorrect. Please revise to provide more challenging and plausible distractors.\"**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class QuizAnswerGeneratorWithAssertions(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_choices = dspy.ChainOfThought(GenerateAnswerChoices)\n", + "\n", + " def forward(self, question, answer):\n", + " choice_string = self.generate_choices(question=question, correct_answer=answer, number_of_choices=number_of_choices).answer_choices\n", + " dspy.Suggest(format_checker(choice_string), \"The format of the answer choices should be in JSON format. Please revise accordingly.\", target_module=self.generate_choices)\n", + " dspy.Suggest(is_correct_answer_included(answer, choice_string), \"The answer choices do not include the correct answer to the question. Please revise accordingly.\", target_module=self.generate_choices)\n", + " plausibility_question = \"Are the distractors in the answer choices plausible and not easily identifiable as incorrect?\"\n", + " plausibility_assessment = dspy.Predict(AssessQuizChoices)(question=question, answer_choices=choice_string, assessment_question=plausibility_question)\n", + " dspy.Suggest(is_plausibility_yes(plausibility_assessment.assessment_answer), \"The answer choices are not plausible distractors or are too easily identifiable as incorrect. Please revise to provide more challenging and plausible distractors.\", target_module=self.generate_choices)\n", + " return dspy.Prediction(choices = choice_string)\n", + "\n", + "number_of_choices = '4'\n", + "quiz_generator_with_assertions = assert_transform_module(QuizAnswerGeneratorWithAssertions().map_named_predictors(Retry), backtrack_handler) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate the `QuizAnswerGeneratorWithAssertions` now over the devset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = [format_valid_metric, is_correct_metric, plausibility_metric, overall_metric]\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(quiz_generator_with_assertions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's take a look at how our generated set of quiz choices has improved with the addition of assertions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example = devset[67]\n", + "quiz_choices = quiz_generator_with_assertions(question=example.question, answer = example.answer)\n", + "print('Generated Quiz Choices: ', quiz_choices.choices)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset[67:68], num_threads=1, display_progress=True, display_table=30)\n", + " evaluate(quiz_generator_with_assertions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the quiz choices follow all of our constraints!\n", + "\n", + "Not only are the answer choices all plausible, and have removed any indicator of what the correct answer could be, but the answer choices now maintain valid JSON formatting with 4 possible answer choices to the question, which includes the correct answer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6] Compilation With Assertions\n", + "\n", + "We can leverage **DSPy**'s`BootstrapFewShotWithRandomSearch` optimizer, to automatically generate few-shot demonstrations and conduct a random search over the candidates to output the best compiled program. We evaluate this over the `final_metric` composite metric. \n", + "\n", + "We can first evaluate this on `QuizAnswerGenerator` to see how compilation performs without the inclusion of assertions. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "compiled_quiz_generator = teleprompter.compile(student = quiz_generator, teacher = quiz_generator, trainset=trainset, valset=devset[:25])\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(compiled_quiz_generator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we test the compilation on 2 settings with assertions:\n", + "\n", + "**Compilation with Assertions**: assertion-driven example bootstrapping and counterexample bootstrapping during compilation. Teacher has assertions while the student does not as the student learns from the teacher's assertion-driven bootstrapped examples. \n", + "\n", + "**Compilation + Inference with Assertions**: assertion-driven optimizations for both the teacher and student to offer enhanced assertion-driven outputs during both compilation and inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "compiled_with_assertions_quiz_generator = teleprompter.compile(student=quiz_generator, teacher = quiz_generator_with_assertions, trainset=trainset, valset=devset[:25])\n", + "\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(compiled_with_assertions_quiz_generator)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "compiled_quiz_generator_with_assertions = teleprompter.compile(student=quiz_generator_with_assertions, teacher = quiz_generator_with_assertions, trainset=trainset, valset=devset[:25])\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(compiled_quiz_generator_with_assertions)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dspy_dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/skycamp2023.ipynb b/examples/outdated_v2.4_examples/skycamp2023.ipynb index 5ee0837e82..c6a6583e37 100644 --- a/examples/outdated_v2.4_examples/skycamp2023.ipynb +++ b/examples/outdated_v2.4_examples/skycamp2023.ipynb @@ -1,502 +1,502 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "# DSPy: Tutorial @ SkyCamp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook contains the **DSPy tutorial** for **SkyCamp 2023**.\n", - "\n", - "Let's begin by setting things up. The snippet below will also install **DSPy** if it's not there already." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "# Set up the cache for this notebook\n", - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " !pip install dspy-ai==2.1\n", - " # !pip install -e $repo_path\n", - "\n", - "!pip install transformers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Configure the default LM and retriever\n", - "\n", - "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models.\n", - "\n", - "In this notebook, we will use `Llama2-13b-chat` using the HuggingFace TGI serving software infrastructure. In principle you can run this on your own local GPUs, but for this tutorial all examples are pre-cached so you don't need to worry about cost.\n", - "\n", - "We will use the retriever `ColBERTv2`. To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", - "\n", - "**Note:** _If you run this notebook as instructed, you don't need an API key. All examples are already cached internally so you can inspect them!_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "llama = dspy.HFClientTGI(model=\"meta-llama/Llama-2-13b-chat-hf\", port=[7140, 7141, 7142, 7143], max_tokens=150)\n", - "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "\n", - "# # NOTE: After you finish this notebook, you can use GPT-3.5 like this if you like.\n", - "# turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct')\n", - "# # In that case, make sure to configure lm=turbo below if you choose to do that.\n", - "\n", - "dspy.settings.configure(rm=colbertv2, lm=llama)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Create a few question–answer pairs for our task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'),\n", - " ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'),\n", - " ('In what year was the star of To Hell and Back born?', '1925'),\n", - " ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'),\n", - " ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'),\n", - " ('Which author is English: John Braine or Studs Terkel?', 'John Braine'),\n", - " ('Who produced the album that included a re-recording of \"Lithium\"?', 'Butch Vig')]\n", - "\n", - "train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'),\n", - " ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'),\n", - " ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'),\n", - " ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'),\n", - " ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'),\n", - " ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'),\n", - " ('Kyle Moran was born in the town on what river?', 'Castletown River'),\n", - " (\"The actress who played the niece in the Priest film was born in what city, country?\", 'Surrey, England'),\n", - " ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'),\n", - " ('What year was the father of the Princes in the Tower born?', '1442'),\n", - " ('What river is near the Crichton Collegiate Church?', 'the River Tyne'),\n", - " ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'),\n", - " ('André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')]\n", - "\n", - "dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) Key Concepts: Signatures & Modules" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a dspy.Predict module with the signature `question -> answer` (i.e., takes a question and outputs an answer).\n", - "predict = dspy.Predict('question -> answer')\n", - "\n", - "# Use the module!\n", - "predict(question=\"What is the capital of Germany?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the example above, we used the `dspy.Predict` module **zero-shot**, i.e. without compiling it on any examples.\n", - "\n", - "Let's now build a slightly more advanced program. Our program will use the `dspy.ChainOfThought` module, which asks the LM to think step by step.\n", - "\n", - "We will call this program `CoT`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class CoT(dspy.Module): # let's define a new module\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " # here we declare the chain of thought sub-module, so we can later compile it (e.g., teach it a prompt)\n", - " self.generate_answer = dspy.ChainOfThought('question -> answer')\n", - " \n", - " def forward(self, question):\n", - " return self.generate_answer(question=question) # here we use the module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's compile this using our seven `train` examples. We will use the very simple `BootstrapFewShot` in DSPy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metric_EM = dspy.evaluate.answer_exact_match\n", - "\n", - "teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2)\n", - "cot_compiled = teleprompter.compile(CoT(), trainset=train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's ask a question to this new program." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cot_compiled(\"What is the capital of Germany?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might be curious what's happening under the hood. Let's inspect the last call to our Llama LM to see the prompt and the output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "llama.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the prompt ends with the question we asked (\"What is the capital of Germany?\"), but before that it includes few-shot examples.\n", - "\n", - "The final example in the prompt contains a rationale (step-by-step reasoning) self-generated from the LM for use as a demonstration, for the training question \"Which author is English: John Braine or Studs Terkel?\".\n", - "\n", - "Now, let's evaluate on our development set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NUM_THREADS = 32\n", - "evaluate_hotpot = Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's evaluate the compiled `CoT` program with Llama. Feel free to replace `cot_compiled` below with `CoT()` (notice the paranthesis) to test the zero-shot version of CoT." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluate_hotpot(cot_compiled)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Bonus 1: RAG with query generation\n", - "\n", - "As a bonus, let's define a more sophisticated program called `RAG`. This program will:\n", - "\n", - "- Use the LM to generate a search query based on the input question\n", - "- Retrieve three passages using our retriever\n", - "- Use the LM to generate a final answer using these passages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class RAG(dspy.Module):\n", - " def __init__(self, num_passages=3):\n", - " super().__init__()\n", - "\n", - " # declare three modules: the retriever, a query generator, and an answer generator\n", - " self.retrieve = dspy.Retrieve(k=num_passages)\n", - " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", - " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " # generate a search query from the question, and use it to retrieve passages\n", - " search_query = self.generate_query(question=question).search_query\n", - " passages = self.retrieve(search_query).passages\n", - "\n", - " # generate an answer from the passages and the question\n", - " return self.generate_answer(context=passages, question=question)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Out of curiosity, we can evaluate the **uncompiled** (or **zero-shot**) version of this program." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluate_hotpot(RAG(), display_table=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now compile this RAG program. We'll use a slightly more advanced teleprompter (automatic prompt optimizer) this time, which relies on random search." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter2 = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_candidate_programs=8, num_threads=NUM_THREADS)\n", - "rag_compiled = teleprompter2.compile(RAG(), trainset=train, valset=dev)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now evaluate this compiled version of RAG." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluate_hotpot(rag_compiled)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's inspect one of the LM calls for this. Focus in particular on the structure of the last few input/output examples in the prompt." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rag_compiled(\"What year was the party of the winner of the 1971 San Francisco mayoral election founded?\")\n", - "llama.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Bonus 2: Multi-Hop Retrieval and Reasoning" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now build a simple multi-hop program, which will interleave multiple calls to the LM and the retriever.\n", - "\n", - "Please follow the **TODO** instructions below to implement this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from dsp.utils.utils import deduplicate\n", - "\n", - "class MultiHop(dspy.Module):\n", - " def __init__(self, num_passages=3):\n", - " super().__init__()\n", - "\n", - " self.retrieve = dspy.Retrieve(k=num_passages)\n", - " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", - "\n", - " # TODO: Define a dspy.ChainOfThought module with the signature 'context, question -> search_query'.\n", - " self.generate_query_from_context = None\n", - "\n", - " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " passages = []\n", - " \n", - " search_query = self.generate_query(question=question).search_query\n", - " passages += self.retrieve(search_query).passages\n", - "\n", - " # TODO: Replace `None` with a call to self.generate_query_from_context to generate a search query.\n", - " # Note: In DSPy, always pass keyword arguments (e.g., context=..., question=...) to the modules to avoid ambiguity.\n", - " # Note 2: Don't forget to access the field .search_query to extract that from the output of the module.\n", - " # Note 3: Check the following notebook for a completed example: https://github.com/stanfordnlp/dspy/blob/main/skycamp2023_completed.ipynb.\n", - " search_query2 = None\n", - "\n", - " # TODO: Replace `None` with a call to self.retrieve to retrieve passages. Append them to the list `passages`.\n", - " passages += None\n", - "\n", - " return self.generate_answer(context=deduplicate(passages), question=question)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "multihop_compiled = teleprompter2.compile(MultiHop(), trainset=train, valset=dev)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluate_hotpot(multihop_compiled, devset=dev)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now inspect the prompt for the second-hop search query for one of the questions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "multihop_compiled(question=\"Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?\")\n", - "llama.inspect_history(n=1, skip=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39_aug2023_dspy", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "# DSPy: Tutorial @ SkyCamp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook contains the **DSPy tutorial** for **SkyCamp 2023**.\n", + "\n", + "Let's begin by setting things up. The snippet below will also install **DSPy** if it's not there already." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "# Set up the cache for this notebook\n", + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " !pip install dspy-ai==2.1\n", + " # !pip install -e $repo_path\n", + "\n", + "!pip install transformers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Configure the default LM and retriever\n", + "\n", + "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models.\n", + "\n", + "In this notebook, we will use `Llama2-13b-chat` using the HuggingFace TGI serving software infrastructure. In principle you can run this on your own local GPUs, but for this tutorial all examples are pre-cached so you don't need to worry about cost.\n", + "\n", + "We will use the retriever `ColBERTv2`. To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", + "\n", + "**Note:** _If you run this notebook as instructed, you don't need an API key. All examples are already cached internally so you can inspect them!_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llama = dspy.HFClientTGI(model=\"meta-llama/Llama-2-13b-chat-hf\", port=[7140, 7141, 7142, 7143], max_tokens=150)\n", + "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "\n", + "# # NOTE: After you finish this notebook, you can use GPT-3.5 like this if you like.\n", + "# turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct')\n", + "# # In that case, make sure to configure lm=turbo below if you choose to do that.\n", + "\n", + "dspy.settings.configure(rm=colbertv2, lm=llama)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Create a few question\u2013answer pairs for our task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'),\n", + " ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'),\n", + " ('In what year was the star of To Hell and Back born?', '1925'),\n", + " ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'),\n", + " ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'),\n", + " ('Which author is English: John Braine or Studs Terkel?', 'John Braine'),\n", + " ('Who produced the album that included a re-recording of \"Lithium\"?', 'Butch Vig')]\n", + "\n", + "train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'),\n", + " ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'),\n", + " ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'),\n", + " ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'),\n", + " ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'),\n", + " ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'),\n", + " ('Kyle Moran was born in the town on what river?', 'Castletown River'),\n", + " (\"The actress who played the niece in the Priest film was born in what city, country?\", 'Surrey, England'),\n", + " ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'),\n", + " ('What year was the father of the Princes in the Tower born?', '1442'),\n", + " ('What river is near the Crichton Collegiate Church?', 'the River Tyne'),\n", + " ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'),\n", + " ('Andr\u00e9 Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')]\n", + "\n", + "dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Key Concepts: Signatures & Modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a dspy.Predict module with the signature `question -> answer` (i.e., takes a question and outputs an answer).\n", + "predict = dspy.Predict('question -> answer')\n", + "\n", + "# Use the module!\n", + "predict(question=\"What is the capital of Germany?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the example above, we used the `dspy.Predict` module **zero-shot**, i.e. without compiling it on any examples.\n", + "\n", + "Let's now build a slightly more advanced program. Our program will use the `dspy.ChainOfThought` module, which asks the LM to think step by step.\n", + "\n", + "We will call this program `CoT`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class CoT(dspy.Module): # let's define a new module\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " # here we declare the chain of thought sub-module, so we can later compile it (e.g., teach it a prompt)\n", + " self.generate_answer = dspy.ChainOfThought('question -> answer')\n", + " \n", + " def forward(self, question):\n", + " return self.generate_answer(question=question) # here we use the module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compile this using our seven `train` examples. We will use the very simple `BootstrapFewShot` in DSPy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metric_EM = dspy.evaluate.answer_exact_match\n", + "\n", + "teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2)\n", + "cot_compiled = teleprompter.compile(CoT(), trainset=train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's ask a question to this new program." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cot_compiled(\"What is the capital of Germany?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might be curious what's happening under the hood. Let's inspect the last call to our Llama LM to see the prompt and the output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llama.inspect_history(n=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the prompt ends with the question we asked (\"What is the capital of Germany?\"), but before that it includes few-shot examples.\n", + "\n", + "The final example in the prompt contains a rationale (step-by-step reasoning) self-generated from the LM for use as a demonstration, for the training question \"Which author is English: John Braine or Studs Terkel?\".\n", + "\n", + "Now, let's evaluate on our development set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NUM_THREADS = 32\n", + "evaluate_hotpot = Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's evaluate the compiled `CoT` program with Llama. Feel free to replace `cot_compiled` below with `CoT()` (notice the paranthesis) to test the zero-shot version of CoT." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluate_hotpot(cot_compiled)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Bonus 1: RAG with query generation\n", + "\n", + "As a bonus, let's define a more sophisticated program called `RAG`. This program will:\n", + "\n", + "- Use the LM to generate a search query based on the input question\n", + "- Retrieve three passages using our retriever\n", + "- Use the LM to generate a final answer using these passages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class RAG(dspy.Module):\n", + " def __init__(self, num_passages=3):\n", + " super().__init__()\n", + "\n", + " # declare three modules: the retriever, a query generator, and an answer generator\n", + " self.retrieve = dspy.Retrieve(k=num_passages)\n", + " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", + " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " # generate a search query from the question, and use it to retrieve passages\n", + " search_query = self.generate_query(question=question).search_query\n", + " passages = self.retrieve(search_query).passages\n", + "\n", + " # generate an answer from the passages and the question\n", + " return self.generate_answer(context=passages, question=question)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Out of curiosity, we can evaluate the **uncompiled** (or **zero-shot**) version of this program." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluate_hotpot(RAG(), display_table=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now compile this RAG program. We'll use a slightly more advanced teleprompter (automatic prompt optimizer) this time, which relies on random search." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter2 = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_candidate_programs=8, num_threads=NUM_THREADS)\n", + "rag_compiled = teleprompter2.compile(RAG(), trainset=train, valset=dev)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now evaluate this compiled version of RAG." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluate_hotpot(rag_compiled)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's inspect one of the LM calls for this. Focus in particular on the structure of the last few input/output examples in the prompt." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rag_compiled(\"What year was the party of the winner of the 1971 San Francisco mayoral election founded?\")\n", + "llama.inspect_history(n=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Bonus 2: Multi-Hop Retrieval and Reasoning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now build a simple multi-hop program, which will interleave multiple calls to the LM and the retriever.\n", + "\n", + "Please follow the **TODO** instructions below to implement this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dsp.utils.utils import deduplicate\n", + "\n", + "class MultiHop(dspy.Module):\n", + " def __init__(self, num_passages=3):\n", + " super().__init__()\n", + "\n", + " self.retrieve = dspy.Retrieve(k=num_passages)\n", + " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", + "\n", + " # TODO: Define a dspy.ChainOfThought module with the signature 'context, question -> search_query'.\n", + " self.generate_query_from_context = None\n", + "\n", + " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " passages = []\n", + " \n", + " search_query = self.generate_query(question=question).search_query\n", + " passages += self.retrieve(search_query).passages\n", + "\n", + " # TODO: Replace `None` with a call to self.generate_query_from_context to generate a search query.\n", + " # Note: In DSPy, always pass keyword arguments (e.g., context=..., question=...) to the modules to avoid ambiguity.\n", + " # Note 2: Don't forget to access the field .search_query to extract that from the output of the module.\n", + " # Note 3: Check the following notebook for a completed example: https://github.com/stanfordnlp/dspy/blob/main/skycamp2023_completed.ipynb.\n", + " search_query2 = None\n", + "\n", + " # TODO: Replace `None` with a call to self.retrieve to retrieve passages. Append them to the list `passages`.\n", + " passages += None\n", + "\n", + " return self.generate_answer(context=deduplicate(passages), question=question)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "multihop_compiled = teleprompter2.compile(MultiHop(), trainset=train, valset=dev)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluate_hotpot(multihop_compiled, devset=dev)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now inspect the prompt for the second-hop search query for one of the questions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "multihop_compiled(question=\"Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?\")\n", + "llama.inspect_history(n=1, skip=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py39_aug2023_dspy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/skycamp2023_completed.ipynb b/examples/outdated_v2.4_examples/skycamp2023_completed.ipynb index e8bf60cdc4..a84aebd7e0 100644 --- a/examples/outdated_v2.4_examples/skycamp2023_completed.ipynb +++ b/examples/outdated_v2.4_examples/skycamp2023_completed.ipynb @@ -1,2067 +1,2067 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "# DSPy: Tutorial @ SkyCamp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook contains the **DSPy tutorial** for **SkyCamp 2023**.\n", - "\n", - "Let's begin by setting things up. The snippet below will also install **DSPy** if it's not there already." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "# Set up the cache for this notebook\n", - "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", - "\n", - "# import pkg_resources # Install the package if it's not installed\n", - "# if not \"dspy-ai\" in {pkg.key for pkg in pkg_resources.working_set}:\n", - "# !pip install -U pip\n", - "# # !pip install dspy-ai\n", - "# !pip install -e $repo_path\n", - "\n", - "import dspy" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", - "./cache/compiler\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy')\n", - "\n", - "from dspy.evaluate import Evaluate\n", - "from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Configure the default LM and retriever\n", - "\n", - "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models.\n", - "\n", - "In this notebook, we will use `Llama2-13b-chat` using the HuggingFace TGI serving software infrastructure. In principle you can run this on your own local GPUs, but for this tutorial all examples are pre-cached so you don't need to worry about cost.\n", - "\n", - "We will use the retriever `ColBERTv2`. To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", - "\n", - "**Note:** _If you run this notebook as instructed, you don't need an API key. All examples are already cached internally so you can inspect them!_" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "llama = dspy.HFClientTGI(model=\"meta-llama/Llama-2-13b-chat-hf\", port=[7140, 7141, 7142, 7143], max_tokens=150)\n", - "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "\n", - "# # NOTE: After you finish this notebook, you can use GPT-3.5 like this if you like.\n", - "# turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct')\n", - "# # In that case, make sure to configure lm=turbo below if you choose to do that.\n", - "\n", - "dspy.settings.configure(rm=colbertv2, lm=llama)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Create a few question–answer pairs for our task" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'),\n", - " ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'),\n", - " ('In what year was the star of To Hell and Back born?', '1925'),\n", - " ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'),\n", - " ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'),\n", - " ('Which author is English: John Braine or Studs Terkel?', 'John Braine'),\n", - " ('Who produced the album that included a re-recording of \"Lithium\"?', 'Butch Vig')]\n", - "\n", - "train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'),\n", - " ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'),\n", - " ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'),\n", - " ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'),\n", - " ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'),\n", - " ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'),\n", - " ('Kyle Moran was born in the town on what river?', 'Castletown River'),\n", - " (\"The actress who played the niece in the Priest film was born in what city, country?\", 'Surrey, England'),\n", - " ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'),\n", - " ('What year was the father of the Princes in the Tower born?', '1442'),\n", - " ('What river is near the Crichton Collegiate Church?', 'the River Tyne'),\n", - " ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'),\n", - " ('André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')]\n", - "\n", - "dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) Key Concepts: Signatures & Modules" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Prediction(\n", - " answer='Berlin'\n", - ")" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "# DSPy: Tutorial @ SkyCamp" ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Define a dspy.Predict module with the signature `question -> answer` (i.e., takes a question and outputs an answer).\n", - "predict = dspy.Predict('question -> answer')\n", - "\n", - "# Use the module!\n", - "predict(question=\"What is the capital of Germany?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the example above, we used the `dspy.Predict` module **zero-shot**, i.e. without compiling it on any examples.\n", - "\n", - "Let's now build a slightly more advanced program. Our program will use the `dspy.ChainOfThought` module, which asks the LM to think step by step.\n", - "\n", - "We will call this program `CoT`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "class CoT(dspy.Module): # let's define a new module\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " # here we declare the chain of thought sub-module, so we can later compile it (e.g., teach it a prompt)\n", - " self.generate_answer = dspy.ChainOfThought('question -> answer')\n", - " \n", - " def forward(self, question):\n", - " return self.generate_answer(question=question) # here we use the module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's compile this using our six `train` examples. We will us the very simple `BootstrapFewShot` in DSPy." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 7/7 [00:00<00:00, 29.36it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 7 examples in round 0.\n" - ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "metric_EM = dspy.evaluate.answer_exact_match\n", - "\n", - "teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2)\n", - "cot_compiled = teleprompter.compile(CoT(), trainset=train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's ask a question to this new program." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Prediction(\n", - " rationale='determine the capital of Germany. We know that the capital of Germany is Berlin, so the answer is Berlin.',\n", - " answer='Berlin'\n", - ")" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook contains the **DSPy tutorial** for **SkyCamp 2023**.\n", + "\n", + "Let's begin by setting things up. The snippet below will also install **DSPy** if it's not there already." ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cot_compiled(\"What is the capital of Germany?\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might be curious what's happening under the hood. Let's inspect the last call to our Llama LM to see the prompt and the output." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Given the fields `question`, produce the fields `answer`.\n", - "\n", - "---\n", - "\n", - "Question: Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?\n", - "Answer: Kevin Greutert\n", - "\n", - "Question: Which award did the first book of Gary Zukav receive?\n", - "Answer: U.S. National Book Award\n", - "\n", - "Question: What documentary about the Gilgo Beach Killer debuted on A&E?\n", - "Answer: The Killing Season\n", - "\n", - "Question: In what year was the star of To Hell and Back born?\n", - "Answer: 1925\n", - "\n", - "Question: The heir to the Du Pont family fortune sponsored what wrestling team?\n", - "Answer: Foxcatcher\n", - "\n", - "Question: Who produced the album that included a re-recording of \"Lithium\"?\n", - "Answer: Butch Vig\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: ${question}\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Question: Which author is English: John Braine or Studs Terkel?\n", - "Reasoning: Let's think step by step in order to determine which author is English. We know that John Braine is English, so we need to determine if Studs Terkel is English. After researching, we found that Studs Terkel was an American author, so the answer is John Braine.\n", - "Answer: John Braine\n", - "\n", - "---\n", - "\n", - "Question: What is the capital of Germany?\n", - "Reasoning: Let's think step by step in order to determine the capital of Germany. We know that the capital of Germany is Berlin, so the answer is Berlin.\n", - "Answer:\u001b[32m Berlin\n", - "\u001b[0m\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "llama.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the prompt ends with the question we asked (\"What is the capital of Germany?\"), but before that it includes few-shot examples.\n", - "\n", - "The final example in the prompt contains a rationale (step-by-step reasoning) self-generated from the LM for use as a demonstration, for the training question \"Which author is English: John Braine or Studs Terkel?\".\n", - "\n", - "Now, let's evaluate on our development set." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "NUM_THREADS = 32\n", - "evaluate_hotpot = Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's evaluate the compiled `CoT` program with Llama. Feel free to replace `cot_compiled` below with `CoT()` (notice the paranthesis) to test the zero-shot version of CoT." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1): 100%|██████████| 13/13 [00:00<00:00, 117.05it/s]\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1%)\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "# Set up the cache for this notebook\n", + "os.environ[\"DSP_NOTEBOOK_CACHEDIR\"] = os.path.join(repo_path, 'cache')\n", + "\n", + "# import pkg_resources # Install the package if it's not installed\n", + "# if not \"dspy-ai\" in {pkg.key for pkg in pkg_resources.working_set}:\n", + "# !pip install -U pip\n", + "# # !pip install dspy-ai\n", + "# !pip install -e $repo_path\n", + "\n", + "import dspy" + ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answerrationalepred_answeranswer_exact_match
0Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?E. L. Doctorowdetermine who has a broader scope of profession. We know that E. L. Doctorow was a novelist, but Julia Peterkin was a journalist and a...Julia Peterkin❌ [False]
1Right Back At It Again contains lyrics co-written by the singer born in what city?Gainesville, Floridadetermine the answer. We know that the singer was born in Minneapolis, so the answer is Minneapolis.Minneapolis❌ [False]
2What year was the party of the winner of the 1971 San Francisco mayoral election founded?1828determine the year the party of the winner of the 1971 San Francisco mayoral election was founded. We know that the party was founded in...1971❌ [False]
3Anthony Dirrell is the brother of which super middleweight title holder?Andre Dirrelldetermine which super middleweight title holder Anthony Dirrell is the brother of. We know that Anthony Dirrell is a professional boxer, and after researching, we...Andre Dirrell✔️ [True]
4The sports nutrition business established by Oliver Cookson is based in which county in the UK?Cheshiredetermine the county in the UK where Oliver Cookson's sports nutrition business is based. We know that Oliver Cookson is a British entrepreneur, so we...Surrey❌ [False]
5Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.February 13, 1980determine the birth date of the actor. We know that the actor was born in the 1950s, so we need to narrow down the possible...August 12, 1955❌ [False]
6Kyle Moran was born in the town on what river?Castletown Riverdetermine where Kyle Moran was born. We know that Kyle Moran was born in the town on the Delaware River.Delaware River❌ [False]
7The actress who played the niece in the Priest film was born in what city, country?Surrey, Englanddetermine the answer. We know that the actress was born in a city, so we need to determine the country. After researching, we found that...Los Angeles, California❌ [False]
8Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.Portrait of a Marriagedetermine the name of the movie. We know that the daughter of Noel Harrison plays Violet Trefusis, so we need to determine the name of...The Remains of the Day❌ [False]
9What year was the father of the Princes in the Tower born?1442determine the answer. We know that the father of the Princes in the Tower was born before 1483, so we need to find the correct...1452❌ [False]
10What river is near the Crichton Collegiate Church?the River Tynedetermine what river is near the Crichton Collegiate Church. We know that the church is located in Scotland, so we need to determine which river...River Tyne✔️ [True]
11Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?Renaultdetermine who purchased the team. We know that Michael Schumacher raced for the Benetton team in the 1995 Monaco Grand Prix, so we need to...Renault✔️ [True]
12André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?the Wehrmachtdetermine which Nazi organization André Zucca worked with. We know that he worked with a German propaganda magazine, so we need to determine which Nazi...SS❌ [False]
\n" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", + "./cache/compiler\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy')\n", + "\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "23.08" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Configure the default LM and retriever\n", + "\n", + "We'll start by setting up the language model (LM) and retrieval model (RM). **DSPy** supports multiple API and local models.\n", + "\n", + "In this notebook, we will use `Llama2-13b-chat` using the HuggingFace TGI serving software infrastructure. In principle you can run this on your own local GPUs, but for this tutorial all examples are pre-cached so you don't need to worry about cost.\n", + "\n", + "We will use the retriever `ColBERTv2`. To make things easy, we've set up a ColBERTv2 server hosting a Wikipedia 2017 \"abstracts\" search index (i.e., containing first paragraph of each article from this [2017 dump](https://hotpotqa.github.io/wiki-readme.html)), so you don't need to worry about setting one up! It's free.\n", + "\n", + "**Note:** _If you run this notebook as instructed, you don't need an API key. All examples are already cached internally so you can inspect them!_" ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate_hotpot(cot_compiled)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Bonus 1: RAG with query generation\n", - "\n", - "As a bonus, let's define a more sophisticated program called `RAG`. This program will:\n", - "\n", - "- Use the LM to generate a search query based on the input question\n", - "- Retrieve three passages using our retriever\n", - "- Use the LM to generate a final answer using these passages" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "class RAG(dspy.Module):\n", - " def __init__(self, num_passages=3):\n", - " super().__init__()\n", - "\n", - " # declare three modules: the retriever, a query generator, and an answer generator\n", - " self.retrieve = dspy.Retrieve(k=num_passages)\n", - " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", - " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " # generate a search query from the question, and use it to retrieve passages\n", - " search_query = self.generate_query(question=question).search_query\n", - " passages = self.retrieve(search_query).passages\n", - "\n", - " # generate an answer from the passages and the question\n", - " return self.generate_answer(context=passages, question=question)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Out of curiosity, we can evaluate the **uncompiled** (or **zero-shot**) version of this program." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1): 100%|██████████| 13/13 [00:00<00:00, 45.09it/s]" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1%)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/plain": [ - "23.08" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "llama = dspy.HFClientTGI(model=\"meta-llama/Llama-2-13b-chat-hf\", port=[7140, 7141, 7142, 7143], max_tokens=150)\n", + "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "\n", + "# # NOTE: After you finish this notebook, you can use GPT-3.5 like this if you like.\n", + "# turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct')\n", + "# # In that case, make sure to configure lm=turbo below if you choose to do that.\n", + "\n", + "dspy.settings.configure(rm=colbertv2, lm=llama)" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate_hotpot(RAG(), display_table=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now compile this RAG program. We'll use a slightly more advnaced teleprompter (automatic prompt optimizer) this time, which relies on random search." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Going to sample between 1 and 2 traces per predictor.\n", - "Will attempt to train 8 candidate sets.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1): 100%|██████████| 13/13 [00:00<00:00, 155.65it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1%)\n", - "Score: 23.08 for set: [0, 0]\n", - "New best score: 23.08 for seed -3\n", - "Scores so far: [23.08]\n", - "Best score: 23.08\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1): 100%|██████████| 13/13 [00:00<00:00, 72.77it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1%)\n", - "Score: 23.08 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08]\n", - "Best score: 23.08\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 86%|████████▌ | 6/7 [00:00<00:00, 13.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 7 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5): 100%|██████████| 13/13 [00:00<00:00, 45.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5%)\n", - "Score: 38.46 for set: [7, 7]\n", - "New best score: 38.46 for seed -1\n", - "Scores so far: [23.08, 23.08, 38.46]\n", - "Best score: 38.46\n", - "Average of max per entry across top 1 scores: 0.38461538461538464\n", - "Average of max per entry across top 2 scores: 0.46153846153846156\n", - "Average of max per entry across top 3 scores: 0.46153846153846156\n", - "Average of max per entry across top 5 scores: 0.46153846153846156\n", - "Average of max per entry across top 8 scores: 0.46153846153846156\n", - "Average of max per entry across top 9999 scores: 0.46153846153846156\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 7/7 [00:00<00:00, 19.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 7 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 6 / 13 (46.2): 100%|██████████| 13/13 [00:00<00:00, 42.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 6 / 13 (46.2%)\n", - "Score: 46.15 for set: [7, 7]\n", - "New best score: 46.15 for seed 0\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 14%|█▍ | 1/7 [00:00<00:00, 21.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8): 100%|██████████| 13/13 [00:00<00:00, 68.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8%)\n", - "Score: 30.77 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 29%|██▊ | 2/7 [00:00<00:00, 21.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 3 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8): 100%|██████████| 13/13 [00:00<00:00, 67.99it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8%)\n", - "Score: 30.77 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 43%|████▎ | 3/7 [00:00<00:00, 21.61it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 4 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1): 100%|██████████| 13/13 [00:00<00:00, 61.97it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1%)\n", - "Score: 23.08 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 14%|█▍ | 1/7 [00:00<00:00, 21.15it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5): 100%|██████████| 13/13 [00:00<00:00, 44.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5%)\n", - "Score: 38.46 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 7/7 [00:00<00:00, 22.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 7 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8): 100%|██████████| 13/13 [00:00<00:00, 66.59it/s]\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8%)\n", - "Score: 30.77 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46, 30.77]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:00<00:00, 23.46it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 5 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8): 100%|██████████| 13/13 [00:00<00:00, 68.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8%)\n", - "Score: 30.77 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46, 30.77, 30.77]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 7/7 [00:00<00:00, 20.87it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 7 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8): 100%|██████████| 13/13 [00:00<00:00, 70.76it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 4 / 13 (30.8%)\n", - "Score: 30.77 for set: [7, 7]\n", - "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46, 30.77, 30.77, 30.77]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.5384615384615384\n", - "Average of max per entry across top 3 scores: 0.5384615384615384\n", - "Average of max per entry across top 5 scores: 0.5384615384615384\n", - "Average of max per entry across top 8 scores: 0.5384615384615384\n", - "Average of max per entry across top 9999 scores: 0.5384615384615384\n", - "11 candidate programs found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Create a few question\u2013answer pairs for our task" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "teleprompter2 = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_candidate_programs=8, num_threads=NUM_THREADS)\n", - "rag_compiled = teleprompter2.compile(RAG(), trainset=train, valset=dev)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now evaluate this compiled version of RAG." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 6 / 13 (46.2): 100%|██████████| 13/13 [00:00<00:00, 137.18it/s]" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'),\n", + " ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'),\n", + " ('In what year was the star of To Hell and Back born?', '1925'),\n", + " ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'),\n", + " ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'),\n", + " ('Which author is English: John Braine or Studs Terkel?', 'John Braine'),\n", + " ('Who produced the album that included a re-recording of \"Lithium\"?', 'Butch Vig')]\n", + "\n", + "train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train]" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 6 / 13 (46.2%)\n" - ] + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'),\n", + " ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'),\n", + " ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'),\n", + " ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'),\n", + " ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'),\n", + " ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'),\n", + " ('Kyle Moran was born in the town on what river?', 'Castletown River'),\n", + " (\"The actress who played the niece in the Priest film was born in what city, country?\", 'Surrey, England'),\n", + " ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'),\n", + " ('What year was the father of the Princes in the Tower born?', '1442'),\n", + " ('What river is near the Crichton Collegiate Church?', 'the River Tyne'),\n", + " ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'),\n", + " ('Andr\u00e9 Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')]\n", + "\n", + "dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev]" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Key Concepts: Signatures & Modules" + ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answerrationalepred_answeranswer_exact_match
0Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?E. L. Doctorowanswer this question. We know that E. L. Doctorow and Julia Peterkin are both authors, but we also know that E. L. Doctorow is known...E. L. Doctorow.✔️ [True]
1Right Back At It Again contains lyrics co-written by the singer born in what city?Gainesville, Floridaanswer this question. We know that Beyoncé is the singer who co-wrote the lyrics of \"Right Back At It Again\". We also know that Beyoncé...Houston.❌ [False]
2What year was the party of the winner of the 1971 San Francisco mayoral election founded?1828answer this question. We know that the winner of the 1971 San Francisco mayoral election was a member of the Democratic Party. We also know...1828.✔️ [True]
3Anthony Dirrell is the brother of which super middleweight title holder?Andre Dirrellanswer this question. We know that Anthony Dirrell is a professional boxer. We also know that he held the WBC super middleweight title from 2014...Andre Dirrell.✔️ [True]
4The sports nutrition business established by Oliver Cookson is based in which county in the UK?Cheshireanswer this question. We know that Oliver Cookson established Myprotein, a sports nutrition business. We also know that Myprotein was sold for a reported £58...Cheshire.✔️ [True]
5Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.February 13, 1980answer this question. We know that the actor played roles in \"First Wives Club\" and \"Searching for the Elephant\". We also know that the actor's...October 17, 1976.❌ [False]
6Kyle Moran was born in the town on what river?Castletown Riveranswer this question. We know that Kyle Moran is an actor who was born in Livingston. We also know that Livingston is a town in...River Forth.❌ [False]
7The actress who played the niece in the Priest film was born in what city, country?Surrey, Englandanswer this question. We know that Lily Collins is an actress and the daughter of Phil Collins. We also know that she was born in...Surrey, England.✔️ [True]
8Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.Portrait of a Marriageanswer this question. We know that Noel Harrison is the father of Dhani Harrison. We also know that Dhani Harrison is a member of the...The daughter of Noel Harrison plays Violet Trefusis in the movie \"The Killing Season\".❌ [False]
9What year was the father of the Princes in the Tower born?1442answer this question. We know that the father of the Princes in the Tower was King Richard III of England. We also know that he...1452.❌ [False]
10What river is near the Crichton Collegiate Church?the River Tyneanswer this question. We know that Crichton Collegiate Church is situated in Midlothian, Scotland. We also know that the church is near the hamlet of...River Tyne.✔️ [True]
11Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?Renaultanswer this question. We know that Michael Schumacher raced for the Benetton team in the 1995 Monaco Grand Prix. We also know that Gilberto Benetton...Gilberto Benetton.❌ [False]
12André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?the Wehrmachtanswer this question. We know that André Zucca was a French photographer who worked with a German propaganda magazine. We also know that the magazine...Nazi organization.❌ [False]
\n" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " answer='Berlin'\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "# Define a dspy.Predict module with the signature `question -> answer` (i.e., takes a question and outputs an answer).\n", + "predict = dspy.Predict('question -> answer')\n", + "\n", + "# Use the module!\n", + "predict(question=\"What is the capital of Germany?\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "46.15" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the example above, we used the `dspy.Predict` module **zero-shot**, i.e. without compiling it on any examples.\n", + "\n", + "Let's now build a slightly more advanced program. Our program will use the `dspy.ChainOfThought` module, which asks the LM to think step by step.\n", + "\n", + "We will call this program `CoT`." ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate_hotpot(rag_compiled)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's inspect one of the LM calls for this. Focus in particular on the structure of the last few input/output examples in the prompt." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Given the fields `context`, `question`, produce the fields `answer`.\n", - "\n", - "---\n", - "\n", - "Question: Which author is English: John Braine or Studs Terkel?\n", - "Answer: John Braine\n", - "\n", - "Question: The heir to the Du Pont family fortune sponsored what wrestling team?\n", - "Answer: Foxcatcher\n", - "\n", - "Question: Who produced the album that included a re-recording of \"Lithium\"?\n", - "Answer: Butch Vig\n", - "\n", - "Question: In what year was the star of To Hell and Back born?\n", - "Answer: 1925\n", - "\n", - "Question: What documentary about the Gilgo Beach Killer debuted on A&E?\n", - "Answer: The Killing Season\n", - "\n", - "Question: Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?\n", - "Answer: Kevin Greutert\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", - "\n", - "Answer: ${answer}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Dancing Wu Li Masters | The Dancing Wu Li Masters is a 1979 book by Gary Zukav, a popular science work exploring modern physics, and quantum phenomena in particular. It was awarded a 1980 U.S. National Book Award in category of Science. Although it explores empirical topics in modern physics research, \"The Dancing Wu Li Masters\" gained attention for leveraging metaphors taken from eastern spiritual movements, in particular the Huayen school of Buddhism with the monk Fazang's treatise on The Golden Lion, to explain quantum phenomena and has been regarded by some reviewers as a New Age work, although the book is mostly concerned with the work of pioneers in western physics down through the ages.»\n", - "[2] «Gary Zukav | Gary Zukav (born October 17, 1942) is an American spiritual teacher and the author of four consecutive New York Times Best Sellers. Beginning in 1998, he appeared more than 30 times on \"The Oprah Winfrey Show\" to discuss transformation in human consciousness concepts presented in his book \"The Seat of the Soul\". His first book, \"The Dancing Wu Li Masters\" (1979), won a U.S. National Book Award.»\n", - "[3] «Li Junfeng | Master Li Junfeng (born October 13, 1938 in Gaocheng, Hebei) is a qigong master, the founder of Sheng Zhen Qigong, and a world-renowned wushu coach. He has also starred-in and choreographed several Chinese martial arts films.»\n", - "\n", - "Question: Which award did the first book of Gary Zukav receive?\n", - "\n", - "Reasoning: Let's think step by step in order to answer this question. We know that Gary Zukav is the author of \"The Dancing Wu Li Masters\". We also know that this book was awarded a U.S. National Book Award in category of Science. Therefore, the answer is the U.S. National Book Award.\n", - "\n", - "Answer: U.S. National Book Award.\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Democratic Party (United States) | The Democratic Party is one of the two major contemporary political parties in the United States, along with the Republican Party. Tracing its heritage back to Thomas Jefferson and James Madison's Democratic-Republican Party, the modern-day Democratic Party was founded around 1828 by supporters of Andrew Jackson, making it the world's oldest political party.»\n", - "[2] «Democratic Party (South Korea, 2008) | The Democratic Party (Hangul: 민주당 hanja: 民主黨 ) was a liberal political party in South Korea. Since its foundation in 2008, it was the main opposition party in the 18th Assembly. In late 2011, it merged into the Democratic United Party.»\n", - "[3] «Democrat Party (Turkey, current) | The Democratic Party (Turkish: \"Demokrat Parti\" ), abbreviated to DP, is a centre-right, conservative Turkish political party, established by Süleyman Demirel in 1983 as the True Path Party (Turkish: \"Doğru Yol Partisi\" or DYP). It succeeded the historical Democratic Party and the Justice Party, two parties with similar ideologies.»\n", - "\n", - "Question: What year was the party of the winner of the 1971 San Francisco mayoral election founded?\n", - "\n", - "Reasoning: Let's think step by step in order to answer this question. We know that the winner of the 1971 San Francisco mayoral election was a member of the Democratic Party. We also know that the Democratic Party was founded around 1828. Therefore, the answer is 1828.\n", - "\n", - "Answer:\u001b[32m 1828.\n", - "\u001b[0m\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "rag_compiled(\"What year was the party of the winner of the 1971 San Francisco mayoral election founded?\")\n", - "llama.inspect_history(n=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Bonus 2: Multi-Hop Retrieval and Reasoning" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now build a simple multi-hop program, which will interleave multiple calls to the LM and the retriever.\n", - "\n", - "Please follow the **TODO** instructions below to implement this." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from dsp.utils.utils import deduplicate\n", - "\n", - "class MultiHop(dspy.Module):\n", - " def __init__(self, num_passages=3):\n", - " super().__init__()\n", - "\n", - " self.retrieve = dspy.Retrieve(k=num_passages)\n", - " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", - "\n", - " # TODO: Define a dspy.ChainOfThought module with the signature 'context, question -> search_query'.\n", - " self.generate_query_from_context = dspy.ChainOfThought(\"context, question -> search_query\")\n", - "\n", - " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", - " \n", - " def forward(self, question):\n", - " passages = []\n", - " \n", - " search_query = self.generate_query(question=question).search_query\n", - " passages += self.retrieve(search_query).passages\n", - "\n", - " # TODO: Use self.generate_query_from_context to generate a search query.\n", - " # Note: Modules require named keyword arguments (e.g., context=..., question=...).\n", - " search_query = self.generate_query_from_context(context=passages, question=question).search_query\n", - "\n", - " # TODO: Use self.retrieve to retrieve passages. Append them to the list `passages`.\n", - " passages += self.retrieve(search_query).passages\n", - "\n", - " return self.generate_answer(context=deduplicate(passages), question=question)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1): 100%|██████████| 13/13 [00:00<00:00, 40.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1%)\n", - "Score: 23.08 for set: [0, 0, 0]\n", - "New best score: 23.08 for seed -3\n", - "Scores so far: [23.08]\n", - "Best score: 23.08\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1): 100%|██████████| 13/13 [00:00<00:00, 53.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 3 / 13 (23.1%)\n", - "Score: 23.08 for set: [7, 7, 7]\n", - "Scores so far: [23.08, 23.08]\n", - "Best score: 23.08\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 57%|█████▋ | 4/7 [00:00<00:00, 11.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 5 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 6 / 13 (46.2): 100%|██████████| 13/13 [00:00<00:00, 27.22it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 6 / 13 (46.2%)\n", - "Score: 46.15 for set: [7, 7, 7]\n", - "New best score: 46.15 for seed -1\n", - "Scores so far: [23.08, 23.08, 46.15]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.46153846153846156\n", - "Average of max per entry across top 3 scores: 0.46153846153846156\n", - "Average of max per entry across top 5 scores: 0.46153846153846156\n", - "Average of max per entry across top 8 scores: 0.46153846153846156\n", - "Average of max per entry across top 9999 scores: 0.46153846153846156\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 43%|████▎ | 3/7 [00:00<00:00, 15.41it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 4 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Average Metric: 5 / 13 (38.5): 100%|██████████| 13/13 [00:00<00:00, 27.45it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5%)\n", - "Score: 38.46 for set: [7, 7, 7]\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46]\n", - "Best score: 46.15\n", - "Average of max per entry across top 1 scores: 0.46153846153846156\n", - "Average of max per entry across top 2 scores: 0.46153846153846156\n", - "Average of max per entry across top 3 scores: 0.46153846153846156\n", - "Average of max per entry across top 5 scores: 0.46153846153846156\n", - "Average of max per entry across top 8 scores: 0.46153846153846156\n", - "Average of max per entry across top 9999 scores: 0.46153846153846156\n" - ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 14%|█▍ | 1/7 [00:00<00:00, 16.50it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 7 / 13 (53.8): 100%|██████████| 13/13 [00:00<00:00, 37.79it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 7 / 13 (53.8%)\n", - "Score: 53.85 for set: [7, 7, 7]\n", - "New best score: 53.85 for seed 1\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85]\n", - "Best score: 53.85\n", - "Average of max per entry across top 1 scores: 0.5384615384615384\n", - "Average of max per entry across top 2 scores: 0.6153846153846154\n", - "Average of max per entry across top 3 scores: 0.6153846153846154\n", - "Average of max per entry across top 5 scores: 0.6153846153846154\n", - "Average of max per entry across top 8 scores: 0.6153846153846154\n", - "Average of max per entry across top 9999 scores: 0.6153846153846154\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 29%|██▊ | 2/7 [00:00<00:00, 17.20it/s]\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class CoT(dspy.Module): # let's define a new module\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " # here we declare the chain of thought sub-module, so we can later compile it (e.g., teach it a prompt)\n", + " self.generate_answer = dspy.ChainOfThought('question -> answer')\n", + " \n", + " def forward(self, question):\n", + " return self.generate_answer(question=question) # here we use the module" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 3 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compile this using our six `train` examples. We will us the very simple `BootstrapFewShot` in DSPy." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 8 / 13 (61.5): 100%|██████████| 13/13 [00:00<00:00, 55.08it/s]\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:00<00:00, 29.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 7 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "metric_EM = dspy.evaluate.answer_exact_match\n", + "\n", + "teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2)\n", + "cot_compiled = teleprompter.compile(CoT(), trainset=train)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 8 / 13 (61.5%)\n", - "Score: 61.54 for set: [7, 7, 7]\n", - "New best score: 61.54 for seed 2\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54]\n", - "Best score: 61.54\n", - "Average of max per entry across top 1 scores: 0.6153846153846154\n", - "Average of max per entry across top 2 scores: 0.6153846153846154\n", - "Average of max per entry across top 3 scores: 0.6923076923076923\n", - "Average of max per entry across top 5 scores: 0.6923076923076923\n", - "Average of max per entry across top 8 scores: 0.6923076923076923\n", - "Average of max per entry across top 9999 scores: 0.6923076923076923\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's ask a question to this new program." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 43%|████▎ | 3/7 [00:00<00:00, 17.15it/s]\n" - ] + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Prediction(\n", + " rationale='determine the capital of Germany. We know that the capital of Germany is Berlin, so the answer is Berlin.',\n", + " answer='Berlin'\n", + ")" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cot_compiled(\"What is the capital of Germany?\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 4 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might be curious what's happening under the hood. Let's inspect the last call to our Llama LM to see the prompt and the output." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 8 / 13 (61.5): 100%|██████████| 13/13 [00:00<00:00, 50.97it/s]\n" - ] + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Given the fields `question`, produce the fields `answer`.\n", + "\n", + "---\n", + "\n", + "Question: Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?\n", + "Answer: Kevin Greutert\n", + "\n", + "Question: Which award did the first book of Gary Zukav receive?\n", + "Answer: U.S. National Book Award\n", + "\n", + "Question: What documentary about the Gilgo Beach Killer debuted on A&E?\n", + "Answer: The Killing Season\n", + "\n", + "Question: In what year was the star of To Hell and Back born?\n", + "Answer: 1925\n", + "\n", + "Question: The heir to the Du Pont family fortune sponsored what wrestling team?\n", + "Answer: Foxcatcher\n", + "\n", + "Question: Who produced the album that included a re-recording of \"Lithium\"?\n", + "Answer: Butch Vig\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Question: ${question}\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "Answer: ${answer}\n", + "\n", + "---\n", + "\n", + "Question: Which author is English: John Braine or Studs Terkel?\n", + "Reasoning: Let's think step by step in order to determine which author is English. We know that John Braine is English, so we need to determine if Studs Terkel is English. After researching, we found that Studs Terkel was an American author, so the answer is John Braine.\n", + "Answer: John Braine\n", + "\n", + "---\n", + "\n", + "Question: What is the capital of Germany?\n", + "Reasoning: Let's think step by step in order to determine the capital of Germany. We know that the capital of Germany is Berlin, so the answer is Berlin.\n", + "Answer:\u001b[32m Berlin\n", + "\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "llama.inspect_history(n=1)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 8 / 13 (61.5%)\n", - "Score: 61.54 for set: [7, 7, 7]\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54]\n", - "Best score: 61.54\n", - "Average of max per entry across top 1 scores: 0.6153846153846154\n", - "Average of max per entry across top 2 scores: 0.6153846153846154\n", - "Average of max per entry across top 3 scores: 0.6153846153846154\n", - "Average of max per entry across top 5 scores: 0.6923076923076923\n", - "Average of max per entry across top 8 scores: 0.6923076923076923\n", - "Average of max per entry across top 9999 scores: 0.6923076923076923\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the prompt ends with the question we asked (\"What is the capital of Germany?\"), but before that it includes few-shot examples.\n", + "\n", + "The final example in the prompt contains a rationale (step-by-step reasoning) self-generated from the LM for use as a demonstration, for the training question \"Which author is English: John Braine or Studs Terkel?\".\n", + "\n", + "Now, let's evaluate on our development set." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 14%|█▍ | 1/7 [00:00<00:00, 11.73it/s]\n" - ] + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "NUM_THREADS = 32\n", + "evaluate_hotpot = Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's evaluate the compiled `CoT` program with Llama. Feel free to replace `cot_compiled` below with `CoT()` (notice the paranthesis) to test the zero-shot version of CoT." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5): 100%|██████████| 13/13 [00:00<00:00, 38.16it/s]\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 117.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1%)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answerrationalepred_answeranswer_exact_match
0Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?E. L. Doctorowdetermine who has a broader scope of profession. We know that E. L. Doctorow was a novelist, but Julia Peterkin was a journalist and a...Julia Peterkin\u274c [False]
1Right Back At It Again contains lyrics co-written by the singer born in what city?Gainesville, Floridadetermine the answer. We know that the singer was born in Minneapolis, so the answer is Minneapolis.Minneapolis\u274c [False]
2What year was the party of the winner of the 1971 San Francisco mayoral election founded?1828determine the year the party of the winner of the 1971 San Francisco mayoral election was founded. We know that the party was founded in...1971\u274c [False]
3Anthony Dirrell is the brother of which super middleweight title holder?Andre Dirrelldetermine which super middleweight title holder Anthony Dirrell is the brother of. We know that Anthony Dirrell is a professional boxer, and after researching, we...Andre Dirrell\u2714\ufe0f [True]
4The sports nutrition business established by Oliver Cookson is based in which county in the UK?Cheshiredetermine the county in the UK where Oliver Cookson's sports nutrition business is based. We know that Oliver Cookson is a British entrepreneur, so we...Surrey\u274c [False]
5Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.February 13, 1980determine the birth date of the actor. We know that the actor was born in the 1950s, so we need to narrow down the possible...August 12, 1955\u274c [False]
6Kyle Moran was born in the town on what river?Castletown Riverdetermine where Kyle Moran was born. We know that Kyle Moran was born in the town on the Delaware River.Delaware River\u274c [False]
7The actress who played the niece in the Priest film was born in what city, country?Surrey, Englanddetermine the answer. We know that the actress was born in a city, so we need to determine the country. After researching, we found that...Los Angeles, California\u274c [False]
8Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.Portrait of a Marriagedetermine the name of the movie. We know that the daughter of Noel Harrison plays Violet Trefusis, so we need to determine the name of...The Remains of the Day\u274c [False]
9What year was the father of the Princes in the Tower born?1442determine the answer. We know that the father of the Princes in the Tower was born before 1483, so we need to find the correct...1452\u274c [False]
10What river is near the Crichton Collegiate Church?the River Tynedetermine what river is near the Crichton Collegiate Church. We know that the church is located in Scotland, so we need to determine which river...River Tyne\u2714\ufe0f [True]
11Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?Renaultdetermine who purchased the team. We know that Michael Schumacher raced for the Benetton team in the 1995 Monaco Grand Prix, so we need to...Renault\u2714\ufe0f [True]
12Andr\u00e9 Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?the Wehrmachtdetermine which Nazi organization Andr\u00e9 Zucca worked with. We know that he worked with a German propaganda magazine, so we need to determine which Nazi...SS\u274c [False]
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "23.08" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate_hotpot(cot_compiled)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5%)\n", - "Score: 38.46 for set: [7, 7, 7]\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46]\n", - "Best score: 61.54\n", - "Average of max per entry across top 1 scores: 0.6153846153846154\n", - "Average of max per entry across top 2 scores: 0.6153846153846154\n", - "Average of max per entry across top 3 scores: 0.6153846153846154\n", - "Average of max per entry across top 5 scores: 0.6923076923076923\n", - "Average of max per entry across top 8 scores: 0.6923076923076923\n", - "Average of max per entry across top 9999 scores: 0.6923076923076923\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Bonus 1: RAG with query generation\n", + "\n", + "As a bonus, let's define a more sophisticated program called `RAG`. This program will:\n", + "\n", + "- Use the LM to generate a search query based on the input question\n", + "- Retrieve three passages using our retriever\n", + "- Use the LM to generate a final answer using these passages" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 71%|███████▏ | 5/7 [00:00<00:00, 17.44it/s]\n" - ] + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "class RAG(dspy.Module):\n", + " def __init__(self, num_passages=3):\n", + " super().__init__()\n", + "\n", + " # declare three modules: the retriever, a query generator, and an answer generator\n", + " self.retrieve = dspy.Retrieve(k=num_passages)\n", + " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", + " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " # generate a search query from the question, and use it to retrieve passages\n", + " search_query = self.generate_query(question=question).search_query\n", + " passages = self.retrieve(search_query).passages\n", + "\n", + " # generate an answer from the passages and the question\n", + " return self.generate_answer(context=passages, question=question)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 6 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Out of curiosity, we can evaluate the **uncompiled** (or **zero-shot**) version of this program." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5): 100%|██████████| 13/13 [00:00<00:00, 34.45it/s]\n" - ] + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 45.09it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "23.08" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate_hotpot(RAG(), display_table=0)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5%)\n", - "Score: 38.46 for set: [7, 7, 7]\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46, 38.46]\n", - "Best score: 61.54\n", - "Average of max per entry across top 1 scores: 0.6153846153846154\n", - "Average of max per entry across top 2 scores: 0.6153846153846154\n", - "Average of max per entry across top 3 scores: 0.6153846153846154\n", - "Average of max per entry across top 5 scores: 0.6923076923076923\n", - "Average of max per entry across top 8 scores: 0.6923076923076923\n", - "Average of max per entry across top 9999 scores: 0.6923076923076923\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now compile this RAG program. We'll use a slightly more advnaced teleprompter (automatic prompt optimizer) this time, which relies on random search." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 29%|██▊ | 2/7 [00:00<00:00, 20.30it/s]\n" - ] + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Going to sample between 1 and 2 traces per predictor.\n", + "Will attempt to train 8 candidate sets.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 155.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1%)\n", + "Score: 23.08 for set: [0, 0]\n", + "New best score: 23.08 for seed -3\n", + "Scores so far: [23.08]\n", + "Best score: 23.08\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 72.77it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1%)\n", + "Score: 23.08 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08]\n", + "Best score: 23.08\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 6/7 [00:00<00:00, 13.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 2 full traces after 7 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 45.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5%)\n", + "Score: 38.46 for set: [7, 7]\n", + "New best score: 38.46 for seed -1\n", + "Scores so far: [23.08, 23.08, 38.46]\n", + "Best score: 38.46\n", + "Average of max per entry across top 1 scores: 0.38461538461538464\n", + "Average of max per entry across top 2 scores: 0.46153846153846156\n", + "Average of max per entry across top 3 scores: 0.46153846153846156\n", + "Average of max per entry across top 5 scores: 0.46153846153846156\n", + "Average of max per entry across top 8 scores: 0.46153846153846156\n", + "Average of max per entry across top 9999 scores: 0.46153846153846156\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:00<00:00, 19.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 7 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 6 / 13 (46.2): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 42.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 6 / 13 (46.2%)\n", + "Score: 46.15 for set: [7, 7]\n", + "New best score: 46.15 for seed 0\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 14%|\u2588\u258d | 1/7 [00:00<00:00, 21.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 68.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8%)\n", + "Score: 30.77 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 29%|\u2588\u2588\u258a | 2/7 [00:00<00:00, 21.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 3 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 67.99it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8%)\n", + "Score: 30.77 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 43%|\u2588\u2588\u2588\u2588\u258e | 3/7 [00:00<00:00, 21.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 4 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 61.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1%)\n", + "Score: 23.08 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 14%|\u2588\u258d | 1/7 [00:00<00:00, 21.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 44.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5%)\n", + "Score: 38.46 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:00<00:00, 22.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 7 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 66.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8%)\n", + "Score: 30.77 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46, 30.77]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:00<00:00, 23.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 68.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8%)\n", + "Score: 30.77 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46, 30.77, 30.77]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:00<00:00, 20.87it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 7 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 70.76it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 4 / 13 (30.8%)\n", + "Score: 30.77 for set: [7, 7]\n", + "Scores so far: [23.08, 23.08, 38.46, 46.15, 30.77, 30.77, 23.08, 38.46, 30.77, 30.77, 30.77]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.5384615384615384\n", + "Average of max per entry across top 3 scores: 0.5384615384615384\n", + "Average of max per entry across top 5 scores: 0.5384615384615384\n", + "Average of max per entry across top 8 scores: 0.5384615384615384\n", + "Average of max per entry across top 9999 scores: 0.5384615384615384\n", + "11 candidate programs found.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "teleprompter2 = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_candidate_programs=8, num_threads=NUM_THREADS)\n", + "rag_compiled = teleprompter2.compile(RAG(), trainset=train, valset=dev)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 3 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now evaluate this compiled version of RAG." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5): 100%|██████████| 13/13 [00:00<00:00, 56.06it/s]\n" - ] + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 6 / 13 (46.2): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 137.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 6 / 13 (46.2%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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 questionexample_answerrationalepred_answeranswer_exact_match
0Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?E. L. Doctorowanswer this question. We know that E. L. Doctorow and Julia Peterkin are both authors, but we also know that E. L. Doctorow is known...E. L. Doctorow.\u2714\ufe0f [True]
1Right Back At It Again contains lyrics co-written by the singer born in what city?Gainesville, Floridaanswer this question. We know that Beyonc\u00e9 is the singer who co-wrote the lyrics of \"Right Back At It Again\". We also know that Beyonc\u00e9...Houston.\u274c [False]
2What year was the party of the winner of the 1971 San Francisco mayoral election founded?1828answer this question. We know that the winner of the 1971 San Francisco mayoral election was a member of the Democratic Party. We also know...1828.\u2714\ufe0f [True]
3Anthony Dirrell is the brother of which super middleweight title holder?Andre Dirrellanswer this question. We know that Anthony Dirrell is a professional boxer. We also know that he held the WBC super middleweight title from 2014...Andre Dirrell.\u2714\ufe0f [True]
4The sports nutrition business established by Oliver Cookson is based in which county in the UK?Cheshireanswer this question. We know that Oliver Cookson established Myprotein, a sports nutrition business. We also know that Myprotein was sold for a reported \u00a358...Cheshire.\u2714\ufe0f [True]
5Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.February 13, 1980answer this question. We know that the actor played roles in \"First Wives Club\" and \"Searching for the Elephant\". We also know that the actor's...October 17, 1976.\u274c [False]
6Kyle Moran was born in the town on what river?Castletown Riveranswer this question. We know that Kyle Moran is an actor who was born in Livingston. We also know that Livingston is a town in...River Forth.\u274c [False]
7The actress who played the niece in the Priest film was born in what city, country?Surrey, Englandanswer this question. We know that Lily Collins is an actress and the daughter of Phil Collins. We also know that she was born in...Surrey, England.\u2714\ufe0f [True]
8Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.Portrait of a Marriageanswer this question. We know that Noel Harrison is the father of Dhani Harrison. We also know that Dhani Harrison is a member of the...The daughter of Noel Harrison plays Violet Trefusis in the movie \"The Killing Season\".\u274c [False]
9What year was the father of the Princes in the Tower born?1442answer this question. We know that the father of the Princes in the Tower was King Richard III of England. We also know that he...1452.\u274c [False]
10What river is near the Crichton Collegiate Church?the River Tyneanswer this question. We know that Crichton Collegiate Church is situated in Midlothian, Scotland. We also know that the church is near the hamlet of...River Tyne.\u2714\ufe0f [True]
11Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?Renaultanswer this question. We know that Michael Schumacher raced for the Benetton team in the 1995 Monaco Grand Prix. We also know that Gilberto Benetton...Gilberto Benetton.\u274c [False]
12Andr\u00e9 Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?the Wehrmachtanswer this question. We know that Andr\u00e9 Zucca was a French photographer who worked with a German propaganda magazine. We also know that the magazine...Nazi organization.\u274c [False]
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "46.15" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate_hotpot(rag_compiled)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 5 / 13 (38.5%)\n", - "Score: 38.46 for set: [7, 7, 7]\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46, 38.46, 38.46]\n", - "Best score: 61.54\n", - "Average of max per entry across top 1 scores: 0.6153846153846154\n", - "Average of max per entry across top 2 scores: 0.6153846153846154\n", - "Average of max per entry across top 3 scores: 0.6153846153846154\n", - "Average of max per entry across top 5 scores: 0.6923076923076923\n", - "Average of max per entry across top 8 scores: 0.6923076923076923\n", - "Average of max per entry across top 9999 scores: 0.6923076923076923\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's inspect one of the LM calls for this. Focus in particular on the structure of the last few input/output examples in the prompt." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 43%|████▎ | 3/7 [00:00<00:00, 20.01it/s]\n" - ] + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Given the fields `context`, `question`, produce the fields `answer`.\n", + "\n", + "---\n", + "\n", + "Question: Which author is English: John Braine or Studs Terkel?\n", + "Answer: John Braine\n", + "\n", + "Question: The heir to the Du Pont family fortune sponsored what wrestling team?\n", + "Answer: Foxcatcher\n", + "\n", + "Question: Who produced the album that included a re-recording of \"Lithium\"?\n", + "Answer: Butch Vig\n", + "\n", + "Question: In what year was the star of To Hell and Back born?\n", + "Answer: 1925\n", + "\n", + "Question: What documentary about the Gilgo Beach Killer debuted on A&E?\n", + "Answer: The Killing Season\n", + "\n", + "Question: Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?\n", + "Answer: Kevin Greutert\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the answer}. We ...\n", + "\n", + "Answer: ${answer}\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abThe Dancing Wu Li Masters | The Dancing Wu Li Masters is a 1979 book by Gary Zukav, a popular science work exploring modern physics, and quantum phenomena in particular. It was awarded a 1980 U.S. National Book Award in category of Science. Although it explores empirical topics in modern physics research, \"The Dancing Wu Li Masters\" gained attention for leveraging metaphors taken from eastern spiritual movements, in particular the Huayen school of Buddhism with the monk Fazang's treatise on The Golden Lion, to explain quantum phenomena and has been regarded by some reviewers as a New Age work, although the book is mostly concerned with the work of pioneers in western physics down through the ages.\u00bb\n", + "[2] \u00abGary Zukav | Gary Zukav (born October 17, 1942) is an American spiritual teacher and the author of four consecutive New York Times Best Sellers. Beginning in 1998, he appeared more than 30 times on \"The Oprah Winfrey Show\" to discuss transformation in human consciousness concepts presented in his book \"The Seat of the Soul\". His first book, \"The Dancing Wu Li Masters\" (1979), won a U.S. National Book Award.\u00bb\n", + "[3] \u00abLi Junfeng | Master Li Junfeng (born October 13, 1938 in Gaocheng, Hebei) is a qigong master, the founder of Sheng Zhen Qigong, and a world-renowned wushu coach. He has also starred-in and choreographed several Chinese martial arts films.\u00bb\n", + "\n", + "Question: Which award did the first book of Gary Zukav receive?\n", + "\n", + "Reasoning: Let's think step by step in order to answer this question. We know that Gary Zukav is the author of \"The Dancing Wu Li Masters\". We also know that this book was awarded a U.S. National Book Award in category of Science. Therefore, the answer is the U.S. National Book Award.\n", + "\n", + "Answer: U.S. National Book Award.\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abDemocratic Party (United States) | The Democratic Party is one of the two major contemporary political parties in the United States, along with the Republican Party. Tracing its heritage back to Thomas Jefferson and James Madison's Democratic-Republican Party, the modern-day Democratic Party was founded around 1828 by supporters of Andrew Jackson, making it the world's oldest political party.\u00bb\n", + "[2] \u00abDemocratic Party (South Korea, 2008) | The Democratic Party (Hangul: \ubbfc\uc8fc\ub2f9 hanja: \u6c11\u4e3b\u9ee8 ) was a liberal political party in South Korea. Since its foundation in 2008, it was the main opposition party in the 18th Assembly. In late 2011, it merged into the Democratic United Party.\u00bb\n", + "[3] \u00abDemocrat Party (Turkey, current) | The Democratic Party (Turkish: \"Demokrat Parti\" ), abbreviated to DP, is a centre-right, conservative Turkish political party, established by S\u00fcleyman Demirel in 1983 as the True Path Party (Turkish: \"Do\u011fru Yol Partisi\" or DYP). It succeeded the historical Democratic Party and the Justice Party, two parties with similar ideologies.\u00bb\n", + "\n", + "Question: What year was the party of the winner of the 1971 San Francisco mayoral election founded?\n", + "\n", + "Reasoning: Let's think step by step in order to answer this question. We know that the winner of the 1971 San Francisco mayoral election was a member of the Democratic Party. We also know that the Democratic Party was founded around 1828. Therefore, the answer is 1828.\n", + "\n", + "Answer:\u001b[32m 1828.\n", + "\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "rag_compiled(\"What year was the party of the winner of the 1971 San Francisco mayoral election founded?\")\n", + "llama.inspect_history(n=1)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 4 examples in round 0.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Bonus 2: Multi-Hop Retrieval and Reasoning" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 7 / 13 (53.8): 100%|██████████| 13/13 [00:00<00:00, 26.05it/s]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now build a simple multi-hop program, which will interleave multiple calls to the LM and the retriever.\n", + "\n", + "Please follow the **TODO** instructions below to implement this." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 7 / 13 (53.8%)\n", - "Score: 53.85 for set: [7, 7, 7]\n", - "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46, 38.46, 38.46, 53.85]\n", - "Best score: 61.54\n", - "Average of max per entry across top 1 scores: 0.6153846153846154\n", - "Average of max per entry across top 2 scores: 0.6153846153846154\n", - "Average of max per entry across top 3 scores: 0.6153846153846154\n", - "Average of max per entry across top 5 scores: 0.8461538461538461\n", - "Average of max per entry across top 8 scores: 0.8461538461538461\n", - "Average of max per entry across top 9999 scores: 0.8461538461538461\n", - "11 candidate programs found.\n" - ] + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from dsp.utils.utils import deduplicate\n", + "\n", + "class MultiHop(dspy.Module):\n", + " def __init__(self, num_passages=3):\n", + " super().__init__()\n", + "\n", + " self.retrieve = dspy.Retrieve(k=num_passages)\n", + " self.generate_query = dspy.ChainOfThought(\"question -> search_query\")\n", + "\n", + " # TODO: Define a dspy.ChainOfThought module with the signature 'context, question -> search_query'.\n", + " self.generate_query_from_context = dspy.ChainOfThought(\"context, question -> search_query\")\n", + "\n", + " self.generate_answer = dspy.ChainOfThought(\"context, question -> answer\")\n", + " \n", + " def forward(self, question):\n", + " passages = []\n", + " \n", + " search_query = self.generate_query(question=question).search_query\n", + " passages += self.retrieve(search_query).passages\n", + "\n", + " # TODO: Use self.generate_query_from_context to generate a search query.\n", + " # Note: Modules require named keyword arguments (e.g., context=..., question=...).\n", + " search_query = self.generate_query_from_context(context=passages, question=question).search_query\n", + "\n", + " # TODO: Use self.retrieve to retrieve passages. Append them to the list `passages`.\n", + " passages += self.retrieve(search_query).passages\n", + "\n", + " return self.generate_answer(context=deduplicate(passages), question=question)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "multihop_compiled = teleprompter2.compile(MultiHop(), trainset=train, valset=dev)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 8 / 13 (61.5): 100%|██████████| 13/13 [00:00<00:00, 92.27it/s]" - ] + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 40.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1%)\n", + "Score: 23.08 for set: [0, 0, 0]\n", + "New best score: 23.08 for seed -3\n", + "Scores so far: [23.08]\n", + "Best score: 23.08\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 53.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 3 / 13 (23.1%)\n", + "Score: 23.08 for set: [7, 7, 7]\n", + "Scores so far: [23.08, 23.08]\n", + "Best score: 23.08\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 57%|\u2588\u2588\u2588\u2588\u2588\u258b | 4/7 [00:00<00:00, 11.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 2 full traces after 5 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 6 / 13 (46.2): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 27.22it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 6 / 13 (46.2%)\n", + "Score: 46.15 for set: [7, 7, 7]\n", + "New best score: 46.15 for seed -1\n", + "Scores so far: [23.08, 23.08, 46.15]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.46153846153846156\n", + "Average of max per entry across top 3 scores: 0.46153846153846156\n", + "Average of max per entry across top 5 scores: 0.46153846153846156\n", + "Average of max per entry across top 8 scores: 0.46153846153846156\n", + "Average of max per entry across top 9999 scores: 0.46153846153846156\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 43%|\u2588\u2588\u2588\u2588\u258e | 3/7 [00:00<00:00, 15.41it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 2 full traces after 4 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Average Metric: 5 / 13 (38.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 27.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5%)\n", + "Score: 38.46 for set: [7, 7, 7]\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46]\n", + "Best score: 46.15\n", + "Average of max per entry across top 1 scores: 0.46153846153846156\n", + "Average of max per entry across top 2 scores: 0.46153846153846156\n", + "Average of max per entry across top 3 scores: 0.46153846153846156\n", + "Average of max per entry across top 5 scores: 0.46153846153846156\n", + "Average of max per entry across top 8 scores: 0.46153846153846156\n", + "Average of max per entry across top 9999 scores: 0.46153846153846156\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 14%|\u2588\u258d | 1/7 [00:00<00:00, 16.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 7 / 13 (53.8): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 37.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 7 / 13 (53.8%)\n", + "Score: 53.85 for set: [7, 7, 7]\n", + "New best score: 53.85 for seed 1\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85]\n", + "Best score: 53.85\n", + "Average of max per entry across top 1 scores: 0.5384615384615384\n", + "Average of max per entry across top 2 scores: 0.6153846153846154\n", + "Average of max per entry across top 3 scores: 0.6153846153846154\n", + "Average of max per entry across top 5 scores: 0.6153846153846154\n", + "Average of max per entry across top 8 scores: 0.6153846153846154\n", + "Average of max per entry across top 9999 scores: 0.6153846153846154\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 29%|\u2588\u2588\u258a | 2/7 [00:00<00:00, 17.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 3 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 8 / 13 (61.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 55.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 8 / 13 (61.5%)\n", + "Score: 61.54 for set: [7, 7, 7]\n", + "New best score: 61.54 for seed 2\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54]\n", + "Best score: 61.54\n", + "Average of max per entry across top 1 scores: 0.6153846153846154\n", + "Average of max per entry across top 2 scores: 0.6153846153846154\n", + "Average of max per entry across top 3 scores: 0.6923076923076923\n", + "Average of max per entry across top 5 scores: 0.6923076923076923\n", + "Average of max per entry across top 8 scores: 0.6923076923076923\n", + "Average of max per entry across top 9999 scores: 0.6923076923076923\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 43%|\u2588\u2588\u2588\u2588\u258e | 3/7 [00:00<00:00, 17.15it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 4 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 8 / 13 (61.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 50.97it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 8 / 13 (61.5%)\n", + "Score: 61.54 for set: [7, 7, 7]\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54]\n", + "Best score: 61.54\n", + "Average of max per entry across top 1 scores: 0.6153846153846154\n", + "Average of max per entry across top 2 scores: 0.6153846153846154\n", + "Average of max per entry across top 3 scores: 0.6153846153846154\n", + "Average of max per entry across top 5 scores: 0.6923076923076923\n", + "Average of max per entry across top 8 scores: 0.6923076923076923\n", + "Average of max per entry across top 9999 scores: 0.6923076923076923\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 14%|\u2588\u258d | 1/7 [00:00<00:00, 11.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 38.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5%)\n", + "Score: 38.46 for set: [7, 7, 7]\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46]\n", + "Best score: 61.54\n", + "Average of max per entry across top 1 scores: 0.6153846153846154\n", + "Average of max per entry across top 2 scores: 0.6153846153846154\n", + "Average of max per entry across top 3 scores: 0.6153846153846154\n", + "Average of max per entry across top 5 scores: 0.6923076923076923\n", + "Average of max per entry across top 8 scores: 0.6923076923076923\n", + "Average of max per entry across top 9999 scores: 0.6923076923076923\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 5/7 [00:00<00:00, 17.44it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 2 full traces after 6 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 34.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5%)\n", + "Score: 38.46 for set: [7, 7, 7]\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46, 38.46]\n", + "Best score: 61.54\n", + "Average of max per entry across top 1 scores: 0.6153846153846154\n", + "Average of max per entry across top 2 scores: 0.6153846153846154\n", + "Average of max per entry across top 3 scores: 0.6153846153846154\n", + "Average of max per entry across top 5 scores: 0.6923076923076923\n", + "Average of max per entry across top 8 scores: 0.6923076923076923\n", + "Average of max per entry across top 9999 scores: 0.6923076923076923\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 29%|\u2588\u2588\u258a | 2/7 [00:00<00:00, 20.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 3 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 56.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 5 / 13 (38.5%)\n", + "Score: 38.46 for set: [7, 7, 7]\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46, 38.46, 38.46]\n", + "Best score: 61.54\n", + "Average of max per entry across top 1 scores: 0.6153846153846154\n", + "Average of max per entry across top 2 scores: 0.6153846153846154\n", + "Average of max per entry across top 3 scores: 0.6153846153846154\n", + "Average of max per entry across top 5 scores: 0.6923076923076923\n", + "Average of max per entry across top 8 scores: 0.6923076923076923\n", + "Average of max per entry across top 9999 scores: 0.6923076923076923\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 43%|\u2588\u2588\u2588\u2588\u258e | 3/7 [00:00<00:00, 20.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 2 full traces after 4 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 7 / 13 (53.8): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 26.05it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 7 / 13 (53.8%)\n", + "Score: 53.85 for set: [7, 7, 7]\n", + "Scores so far: [23.08, 23.08, 46.15, 38.46, 53.85, 61.54, 61.54, 38.46, 38.46, 38.46, 53.85]\n", + "Best score: 61.54\n", + "Average of max per entry across top 1 scores: 0.6153846153846154\n", + "Average of max per entry across top 2 scores: 0.6153846153846154\n", + "Average of max per entry across top 3 scores: 0.6153846153846154\n", + "Average of max per entry across top 5 scores: 0.8461538461538461\n", + "Average of max per entry across top 8 scores: 0.8461538461538461\n", + "Average of max per entry across top 9999 scores: 0.8461538461538461\n", + "11 candidate programs found.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "multihop_compiled = teleprompter2.compile(MultiHop(), trainset=train, valset=dev)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 8 / 13 (61.5%)\n" - ] + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 8 / 13 (61.5): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 92.27it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 8 / 13 (61.5%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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 questionexample_answerrationalepred_answeranswer_exact_match
0Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?E. L. Doctorowanswer this question. We know that E. L. Doctorow is an American novelist, editor, and professor, and he has been described as one of the...E. L. Doctorow.\u2714\ufe0f [True]
1Right Back At It Again contains lyrics co-written by the singer born in what city?Gainesville, Floridaanswer this question. We know that Beyonc\u00e9 is an American singer, songwriter, dancer, and actress, and she was born in Houston, Texas. Her album \"Beyonc\u00e9\"...Houston.\u274c [False]
2What year was the party of the winner of the 1971 San Francisco mayoral election founded?1828answer this question. We know that the Democratic Party is one of the two major contemporary political parties in the United States, and it was...1828.\u2714\ufe0f [True]
3Anthony Dirrell is the brother of which super middleweight title holder?Andre Dirrellanswer this question. We know that Anthony Dirrell is a professional boxer who held the WBC super middleweight title from 2014 to 2015. We also...Andre Dirrell.\u2714\ufe0f [True]
4The sports nutrition business established by Oliver Cookson is based in which county in the UK?Cheshireanswer this question. We know that Oliver Cookson is a UK entrepreneur who established the sports nutrition business Myprotein. We also know that Myprotein was...Cheshire.\u2714\ufe0f [True]
5Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.February 13, 1980answer this question. We know that the actor's name is Jo Dong-hyuk, and he was born on December 11, 1977. Therefore, the answer is December...December 11, 1977.\u274c [False]
6Kyle Moran was born in the town on what river?Castletown Riveranswer this question. We know that Kyle Moran is an Irish footballer who plays as a forward for Perth SC in the NPL Western Australia....River Dundalk.\u274c [False]
7The actress who played the niece in the Priest film was born in what city, country?Surrey, Englandanswer this question. We know that Lily Collins is an actress, and she was born in Surrey, England. Therefore, the answer is Surrey, England.Surrey, England.\u2714\ufe0f [True]
8Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.Portrait of a Marriageanswer this question. We know that Cathryn Harrison is the daughter of Noel Harrison, and she is an English actress. One of her roles was...First Daughter.\u274c [False]
9What year was the father of the Princes in the Tower born?1442answer this question. We know that the father of the Princes in the Tower was King Richard III of England, and he was born on...1452.\u274c [False]
10What river is near the Crichton Collegiate Church?the River Tyneanswer this question. We know that Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. We...River Tyne.\u2714\ufe0f [True]
11Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?Renaultanswer this question. We know that Michael Schumacher raced for the Benetton team in the 1995 Monaco Grand Prix. In 2000, the Benetton team was...Renault.\u2714\ufe0f [True]
12Andr\u00e9 Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?the Wehrmachtanswer this question. We know that Andr\u00e9 Zucca was a French photographer who worked with a German propaganda magazine called \"Signal\". Therefore, the answer is...Wehrmacht.\u2714\ufe0f [True]
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "61.54" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate_hotpot(multihop_compiled, devset=dev)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now inspect the prompt for the second-hop search query for one of the questions." + ] }, { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionexample_answerrationalepred_answeranswer_exact_match
0Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?E. L. Doctorowanswer this question. We know that E. L. Doctorow is an American novelist, editor, and professor, and he has been described as one of the...E. L. Doctorow.✔️ [True]
1Right Back At It Again contains lyrics co-written by the singer born in what city?Gainesville, Floridaanswer this question. We know that Beyoncé is an American singer, songwriter, dancer, and actress, and she was born in Houston, Texas. Her album \"Beyoncé\"...Houston.❌ [False]
2What year was the party of the winner of the 1971 San Francisco mayoral election founded?1828answer this question. We know that the Democratic Party is one of the two major contemporary political parties in the United States, and it was...1828.✔️ [True]
3Anthony Dirrell is the brother of which super middleweight title holder?Andre Dirrellanswer this question. We know that Anthony Dirrell is a professional boxer who held the WBC super middleweight title from 2014 to 2015. We also...Andre Dirrell.✔️ [True]
4The sports nutrition business established by Oliver Cookson is based in which county in the UK?Cheshireanswer this question. We know that Oliver Cookson is a UK entrepreneur who established the sports nutrition business Myprotein. We also know that Myprotein was...Cheshire.✔️ [True]
5Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.February 13, 1980answer this question. We know that the actor's name is Jo Dong-hyuk, and he was born on December 11, 1977. Therefore, the answer is December...December 11, 1977.❌ [False]
6Kyle Moran was born in the town on what river?Castletown Riveranswer this question. We know that Kyle Moran is an Irish footballer who plays as a forward for Perth SC in the NPL Western Australia....River Dundalk.❌ [False]
7The actress who played the niece in the Priest film was born in what city, country?Surrey, Englandanswer this question. We know that Lily Collins is an actress, and she was born in Surrey, England. Therefore, the answer is Surrey, England.Surrey, England.✔️ [True]
8Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.Portrait of a Marriageanswer this question. We know that Cathryn Harrison is the daughter of Noel Harrison, and she is an English actress. One of her roles was...First Daughter.❌ [False]
9What year was the father of the Princes in the Tower born?1442answer this question. We know that the father of the Princes in the Tower was King Richard III of England, and he was born on...1452.❌ [False]
10What river is near the Crichton Collegiate Church?the River Tyneanswer this question. We know that Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. We...River Tyne.✔️ [True]
11Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?Renaultanswer this question. We know that Michael Schumacher raced for the Benetton team in the 1995 Monaco Grand Prix. In 2000, the Benetton team was...Renault.✔️ [True]
12André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?the Wehrmachtanswer this question. We know that André Zucca was a French photographer who worked with a German propaganda magazine called \"Signal\". Therefore, the answer is...Wehrmacht.✔️ [True]
\n" + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "Given the fields `context`, `question`, produce the fields `search_query`.\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "\n", + "Question: ${question}\n", + "\n", + "Reasoning: Let's think step by step in order to ${produce the search_query}. We ...\n", + "\n", + "Search Query: ${search_query}\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abThe Dancing Wu Li Masters | The Dancing Wu Li Masters is a 1979 book by Gary Zukav, a popular science work exploring modern physics, and quantum phenomena in particular. It was awarded a 1980 U.S. National Book Award in category of Science. Although it explores empirical topics in modern physics research, \"The Dancing Wu Li Masters\" gained attention for leveraging metaphors taken from eastern spiritual movements, in particular the Huayen school of Buddhism with the monk Fazang's treatise on The Golden Lion, to explain quantum phenomena and has been regarded by some reviewers as a New Age work, although the book is mostly concerned with the work of pioneers in western physics down through the ages.\u00bb\n", + "[2] \u00abGary Zukav | Gary Zukav (born October 17, 1942) is an American spiritual teacher and the author of four consecutive New York Times Best Sellers. Beginning in 1998, he appeared more than 30 times on \"The Oprah Winfrey Show\" to discuss transformation in human consciousness concepts presented in his book \"The Seat of the Soul\". His first book, \"The Dancing Wu Li Masters\" (1979), won a U.S. National Book Award.\u00bb\n", + "[3] \u00abLi Junfeng | Master Li Junfeng (born October 13, 1938 in Gaocheng, Hebei) is a qigong master, the founder of Sheng Zhen Qigong, and a world-renowned wushu coach. He has also starred-in and choreographed several Chinese martial arts films.\u00bb\n", + "[4] \u00abThe Dancing Wu Li Masters | The Dancing Wu Li Masters is a 1979 book by Gary Zukav, a popular science work exploring modern physics, and quantum phenomena in particular. It was awarded a 1980 U.S. National Book Award in category of Science. Although it explores empirical topics in modern physics research, \"The Dancing Wu Li Masters\" gained attention for leveraging metaphors taken from eastern spiritual movements, in particular the Huayen school of Buddhism with the monk Fazang's treatise on The Golden Lion, to explain quantum phenomena and has been regarded by some reviewers as a New Age work, although the book is mostly concerned with the work of pioneers in western physics down through the ages.\u00bb\n", + "[5] \u00abGary Zukav | Gary Zukav (born October 17, 1942) is an American spiritual teacher and the author of four consecutive New York Times Best Sellers. Beginning in 1998, he appeared more than 30 times on \"The Oprah Winfrey Show\" to discuss transformation in human consciousness concepts presented in his book \"The Seat of the Soul\". His first book, \"The Dancing Wu Li Masters\" (1979), won a U.S. National Book Award.\u00bb\n", + "[6] \u00abWu Pao-chun | Wu Pao-chun (, born 5 September 1970), is a Taiwanese baker best known for winning the title of Master Baker in the bread category of the 2010 Bakery Masters competition held in Paris. Wu is also known for a rose-lychee bread he created which includes Taiwanese ingredients such as millet wine, rose petals and dried lychees.\u00bb\n", + "\n", + "Question: Which award did the first book of Gary Zukav receive?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the search query. We know that the first book of Gary Zukav is \"The Dancing Wu Li Masters\". We also know that this book received an award. Therefore, we can start by searching for the name of the award that the book received.\n", + "\n", + "Search Query: \"The Dancing Wu Li Masters\" award\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abBenetton Group | Benetton Group S.r.l. (correct ] ; often mispronounced ] or ] ) is a global fashion brand, based in Ponzano Veneto, Italy. The name comes from the Benetton family who founded the company in 1965.\u00bb\n", + "[2] \u00abBenetton Rugby | Benetton Rugby (] or ] ) are an Italian professional rugby union team based in Treviso, Veneto competing in the Pro14 and the European Rugby Champions Cup.\u00bb\n", + "[3] \u00abGilberto Benetton | Gilberto Benetton (born 19 June 1941) is an Italian billionaire businessman, one of the co-founders of Benetton Group, the Italian fashion brand.\u00bb\n", + "\n", + "Question: Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?\n", + "\n", + "Reasoning: Let's think step by step in order to produce the search query. We know that Michael Schumacher raced for a team in the 1995 Monaco Grand Prix. We also know that the team was purchased by someone in 2000. Therefore, we can start by searching for the name of the team that Michael Schumacher raced for in the 1995 Monaco Grand Prix.\n", + "\n", + "Search Query:\u001b[32m Michael Schumacher team Monaco Grand Prix 1995\n", + "\u001b[0m\n", + "\n", + "\n", + "\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "multihop_compiled(question=\"Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?\")\n", + "llama.inspect_history(n=1, skip=2)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "61.54" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "evaluate_hotpot(multihop_compiled, devset=dev)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now inspect the prompt for the second-hop search query for one of the questions." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "Given the fields `context`, `question`, produce the fields `search_query`.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "\n", - "Question: ${question}\n", - "\n", - "Reasoning: Let's think step by step in order to ${produce the search_query}. We ...\n", - "\n", - "Search Query: ${search_query}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Dancing Wu Li Masters | The Dancing Wu Li Masters is a 1979 book by Gary Zukav, a popular science work exploring modern physics, and quantum phenomena in particular. It was awarded a 1980 U.S. National Book Award in category of Science. Although it explores empirical topics in modern physics research, \"The Dancing Wu Li Masters\" gained attention for leveraging metaphors taken from eastern spiritual movements, in particular the Huayen school of Buddhism with the monk Fazang's treatise on The Golden Lion, to explain quantum phenomena and has been regarded by some reviewers as a New Age work, although the book is mostly concerned with the work of pioneers in western physics down through the ages.»\n", - "[2] «Gary Zukav | Gary Zukav (born October 17, 1942) is an American spiritual teacher and the author of four consecutive New York Times Best Sellers. Beginning in 1998, he appeared more than 30 times on \"The Oprah Winfrey Show\" to discuss transformation in human consciousness concepts presented in his book \"The Seat of the Soul\". His first book, \"The Dancing Wu Li Masters\" (1979), won a U.S. National Book Award.»\n", - "[3] «Li Junfeng | Master Li Junfeng (born October 13, 1938 in Gaocheng, Hebei) is a qigong master, the founder of Sheng Zhen Qigong, and a world-renowned wushu coach. He has also starred-in and choreographed several Chinese martial arts films.»\n", - "[4] «The Dancing Wu Li Masters | The Dancing Wu Li Masters is a 1979 book by Gary Zukav, a popular science work exploring modern physics, and quantum phenomena in particular. It was awarded a 1980 U.S. National Book Award in category of Science. Although it explores empirical topics in modern physics research, \"The Dancing Wu Li Masters\" gained attention for leveraging metaphors taken from eastern spiritual movements, in particular the Huayen school of Buddhism with the monk Fazang's treatise on The Golden Lion, to explain quantum phenomena and has been regarded by some reviewers as a New Age work, although the book is mostly concerned with the work of pioneers in western physics down through the ages.»\n", - "[5] «Gary Zukav | Gary Zukav (born October 17, 1942) is an American spiritual teacher and the author of four consecutive New York Times Best Sellers. Beginning in 1998, he appeared more than 30 times on \"The Oprah Winfrey Show\" to discuss transformation in human consciousness concepts presented in his book \"The Seat of the Soul\". His first book, \"The Dancing Wu Li Masters\" (1979), won a U.S. National Book Award.»\n", - "[6] «Wu Pao-chun | Wu Pao-chun (, born 5 September 1970), is a Taiwanese baker best known for winning the title of Master Baker in the bread category of the 2010 Bakery Masters competition held in Paris. Wu is also known for a rose-lychee bread he created which includes Taiwanese ingredients such as millet wine, rose petals and dried lychees.»\n", - "\n", - "Question: Which award did the first book of Gary Zukav receive?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the search query. We know that the first book of Gary Zukav is \"The Dancing Wu Li Masters\". We also know that this book received an award. Therefore, we can start by searching for the name of the award that the book received.\n", - "\n", - "Search Query: \"The Dancing Wu Li Masters\" award\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Benetton Group | Benetton Group S.r.l. (correct ] ; often mispronounced ] or ] ) is a global fashion brand, based in Ponzano Veneto, Italy. The name comes from the Benetton family who founded the company in 1965.»\n", - "[2] «Benetton Rugby | Benetton Rugby (] or ] ) are an Italian professional rugby union team based in Treviso, Veneto competing in the Pro14 and the European Rugby Champions Cup.»\n", - "[3] «Gilberto Benetton | Gilberto Benetton (born 19 June 1941) is an Italian billionaire businessman, one of the co-founders of Benetton Group, the Italian fashion brand.»\n", - "\n", - "Question: Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?\n", - "\n", - "Reasoning: Let's think step by step in order to produce the search query. We know that Michael Schumacher raced for a team in the 1995 Monaco Grand Prix. We also know that the team was purchased by someone in 2000. Therefore, we can start by searching for the name of the team that Michael Schumacher raced for in the 1995 Monaco Grand Prix.\n", - "\n", - "Search Query:\u001b[32m Michael Schumacher team Monaco Grand Prix 1995\n", - "\u001b[0m\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "multihop_compiled(question=\"Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?\")\n", - "llama.inspect_history(n=1, skip=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39_aug2023_dspy", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" + ], + "metadata": { + "kernelspec": { + "display_name": "py39_aug2023_dspy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + }, + "orig_nbformat": 4 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/text_to_sql/financial_data_text_to_sql.ipynb b/examples/outdated_v2.4_examples/text_to_sql/financial_data_text_to_sql.ipynb index d25522fbfa..ecc0a77280 100644 --- a/examples/outdated_v2.4_examples/text_to_sql/financial_data_text_to_sql.ipynb +++ b/examples/outdated_v2.4_examples/text_to_sql/financial_data_text_to_sql.ipynb @@ -1,2464 +1,2464 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Dependencies\n", - "!pip install dspy-ai[chromadb] -Uqq\n", - "!pip install termcolor -Uqq\n", - "!pip install sqlalchemy -Uqq" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## AIM OF THE TUTORIAL\n", - "\n", - "* Build an end-to-end Text-to-SQL pipeline inspired from this [video](https://www.youtube.com/watch?v=L1o1VPVfbb0&pp=ygUYYWR2YW5jZWQgUkFHIGxsYW1hIGluZGV4) from Llama Index. In Llama Index, they used llama index Query Pipeline to build a Text-to-SQL pipeline. Here, we will build a Text-to-SQL pipeline based on our own dataset and from scratch. We will go from the dataset scraping, to building SQLlite database and using DSPy signatures to implementa a text-to-SQL pipeline\n", - "\n", - "## ABOUT THE DATASET\n", - "* You can find the dataset [here](https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datacurrent.html). The dataset has different industry based different financial metrics like WACC, tax rates, EBITDA, etc. There are multiple regions data `['US', 'Europe', 'Japan', 'AUS_NZ_CANADA', 'Emerging', 'China', 'India', 'Global']` where we have multiple tables for each region. There are nearly 250 tables with multiple columns in each table. We will build a text-to-SQL pipeline based on our own dataset and from scratch, starting from embedding tables schema and rows using ChromDB vector database." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "#imports\n", - "from bs4 import BeautifulSoup \n", - "import urllib.request\n", - "import ssl\n", - "from dotenv import load_dotenv\n", - "import openai\n", - "import os\n", - "import requests\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "from tqdm import tqdm\n", - "import pandas as pd\n", - "import json\n", - "from sqlalchemy import (\n", - " create_engine,\n", - " MetaData,\n", - " Table,\n", - " Column,\n", - " String,\n", - " Integer,\n", - ")\n", - "import re\n", - "from sqlalchemy import inspect\n", - "import sqlalchemy\n", - "from sqlalchemy import text \n", - "import dspy\n", - "from termcolor import colored\n", - "import chromadb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dsp.modules.cache_utils import cache_turn_on\n", - "\n", - "cache_turn_on" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SCRAPING THE LINKS OF THE EXCEL FILES FROM THE WEBSITE" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "ssl._create_default_https_context = ssl._create_stdlib_context\n", - "html_link = \"https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datacurrent.html\"\n", - "\n", - "with urllib.request.urlopen(html_link) as url:\n", - " s = url.read()\n", - " # I'm guessing this would output the html source code ?\n", - " soup = BeautifulSoup(s,\"lxml\")\n", - "\n", - "html_table = soup.find_all(\"table\")\n", - "req_table = html_table[1]\n", - "hrefs_list = req_table.find_all('a')" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "req_href = {\"US\":[],\"Europe\":[],\"Japan\":[],\"AUS_NZ_CANADA\":[],\"Emerging\":[],\"China\":[],\"India\":[],\"Global\":[]}\n", - "\n", - "for i in hrefs_list:\n", - " name = i.get_text().strip()\n", - " try:\n", - " href_attr = i['href']\n", - " # Only get the excel files\n", - " if href_attr.endswith('.xls'):\n", - " if \"US\" in name:\n", - " req_href[\"US\"].append(href_attr)\n", - " elif \"Europe\" in name:\n", - " req_href[\"Europe\"].append(href_attr)\n", - " elif \"Japan\" in name:\n", - " req_href[\"Japan\"].append(href_attr)\n", - " elif \"Aus\" in name:\n", - " req_href['AUS_NZ_CANADA'].append(href_attr)\n", - " elif \"Emerging\" in name:\n", - " req_href['Emerging'].append(href_attr)\n", - " elif \"China\" in name:\n", - " req_href['China'].append(href_attr)\n", - " elif \"India\" in name:\n", - " req_href['India'].append(href_attr)\n", - " elif \"Global\" in name: \n", - " req_href['Global'].append(href_attr)\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [], - "source": [ - "#Download the excel files from the website and store it in a folder named DATA\n", - "ssl._create_default_https_context = ssl._create_stdlib_context\n", - "\n", - "os.makedirs(\"DATA\",exist_ok=True)\n", - "for country,excel_files in req_href.items():\n", - " country_path = os.path.join(\"DATA\",country) \n", - " os.makedirs(country_path,exist_ok=True)\n", - " for file in excel_files:\n", - " file_name = file.split(\"/\")[-1].split(\".\")[0]\n", - " full_file_name = os.path.join(country_path,f\"{file_name}.xls\")\n", - " resp = requests.get(file,verify=False)\n", - " output = open(full_file_name, 'wb')\n", - " output.write(resp.content)\n", - " output.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FOR Emerging WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", - "FOR Europe WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", - "FOR Global WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", - "FOR AUS_NZ_CANADA WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", - "FOR China WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", - "FOR Japan WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", - "FOR US WE HAVE DIRECTORY LEN = 24 and ACTUAL LEN = 24\n", - "FOR India WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n" - ] - } - ], - "source": [ - "# Sanity check\n", - "for country in os.listdir(\"DATA\"):\n", - " dir_len = len(os.listdir(os.path.join(\"DATA\",country)))\n", - " country_len = len(req_href[country])\n", - " print(f'FOR {country} WE HAVE DIRECTORY LEN = {dir_len} and ACTUAL LEN = {country_len}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## CLEANING THE DATASET" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "sample_excel = pd.ExcelFile(\"DATA2/US/capex.xls\")" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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End GameTo measure how much companies are reinvesting back into their long term assets, as a prelude to forecasting expected growth.Unnamed: 2Unnamed: 3
0NaNNaNNaNNaN
1NaNNaNNaNNaN
2VariableHow it is measuredWhat it measuresUnits
3Capital ExpendituresSum of the capital expenditures reported on st...Gross investment in long term assets, at least...$ millions
4DepreciationSum of the depreciation reported on statement ...Loss in value of assets, from use, as measured...$ millions
5Net Cap ExSum of capital expenditures on statement of ca...Net investment in long term assets, at least a...$ millions
6Net R&DSum of R&D reported as expense in most recent ...Net investment in long term assets, expanded t...$ millions
7AcquisitionsSum of acquisitions reported on statement of c...Augments investment to include acquisition.$ millions
\n", - "
" + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Dependencies\n", + "!pip install dspy-ai[chromadb] -Uqq\n", + "!pip install termcolor -Uqq\n", + "!pip install sqlalchemy -Uqq" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AIM OF THE TUTORIAL\n", + "\n", + "* Build an end-to-end Text-to-SQL pipeline inspired from this [video](https://www.youtube.com/watch?v=L1o1VPVfbb0&pp=ygUYYWR2YW5jZWQgUkFHIGxsYW1hIGluZGV4) from Llama Index. In Llama Index, they used llama index Query Pipeline to build a Text-to-SQL pipeline. Here, we will build a Text-to-SQL pipeline based on our own dataset and from scratch. We will go from the dataset scraping, to building SQLlite database and using DSPy signatures to implementa a text-to-SQL pipeline\n", + "\n", + "## ABOUT THE DATASET\n", + "* You can find the dataset [here](https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datacurrent.html). The dataset has different industry based different financial metrics like WACC, tax rates, EBITDA, etc. There are multiple regions data `['US', 'Europe', 'Japan', 'AUS_NZ_CANADA', 'Emerging', 'China', 'India', 'Global']` where we have multiple tables for each region. There are nearly 250 tables with multiple columns in each table. We will build a text-to-SQL pipeline based on our own dataset and from scratch, starting from embedding tables schema and rows using ChromDB vector database." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#imports\n", + "from bs4 import BeautifulSoup \n", + "import urllib.request\n", + "import ssl\n", + "from dotenv import load_dotenv\n", + "import openai\n", + "import os\n", + "import requests\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "import json\n", + "from sqlalchemy import (\n", + " create_engine,\n", + " MetaData,\n", + " Table,\n", + " Column,\n", + " String,\n", + " Integer,\n", + ")\n", + "import re\n", + "from sqlalchemy import inspect\n", + "import sqlalchemy\n", + "from sqlalchemy import text \n", + "import dspy\n", + "from termcolor import colored\n", + "import chromadb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " End Game \\\n", - "0 NaN \n", - "1 NaN \n", - "2 Variable \n", - "3 Capital Expenditures \n", - "4 Depreciation \n", - "5 Net Cap Ex \n", - "6 Net R&D \n", - "7 Acquisitions \n", - "\n", - " To measure how much companies are reinvesting back into their long term assets, as a prelude to forecasting expected growth. \\\n", - "0 NaN \n", - "1 NaN \n", - "2 How it is measured \n", - "3 Sum of the capital expenditures reported on st... \n", - "4 Sum of the depreciation reported on statement ... \n", - "5 Sum of capital expenditures on statement of ca... \n", - "6 Sum of R&D reported as expense in most recent ... \n", - "7 Sum of acquisitions reported on statement of c... \n", - "\n", - " Unnamed: 2 Unnamed: 3 \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 What it measures Units \n", - "3 Gross investment in long term assets, at least... $ millions \n", - "4 Loss in value of assets, from use, as measured... $ millions \n", - "5 Net investment in long term assets, at least a... $ millions \n", - "6 Net investment in long term assets, expanded t... $ millions \n", - "7 Augments investment to include acquisition. $ millions " - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sn = 'Variables & FAQ'\n", - "sample_excel.parse(sn).head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Date updated:2024-01-05 00:00:00Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0Created by:Aswath Damodaran, adamodar@stern.nyu.eduNaNNaNNaNNaNNaNNaNNaNNaN
1What is this data?Capital Expenditures, Acquisitions and R&D and...NaNNaNNaNUS companiesNaNNaNNaNNaN
2Home Page:http://www.damodaran.comNaNNaNNaNNaNNaNNaNNaNNaN
3Data website:https://pages.stern.nyu.edu/~adamodar/New_Home...NaNNaNNaNNaNNaNNaNNaNNaN
4Companies in each industry:https://pages.stern.nyu.edu/~adamodar/pc/datas...NaNNaNNaNNaNNaNNaNNaNNaN
5Variable definitions:https://pages.stern.nyu.edu/~adamodar/New_Home...NaNNaNNaNNaNNaNNaNNaNNaN
6Industry NameNumber of FirmsCapital Expenditures (US $ millions)Depreciation & Amort ((US $ millions)Cap Ex/DeprecnAcquisitions (US $ millions)Net R&D (US $ millions)Net Cap Ex/SalesNet Cap Ex/ EBIT (1-t)Sales/ Invested Capital (LTM)
7Advertising57775.7291887.3380.411018322.55975.08-0.016886-0.218123.283403
8Aerospace/Defense7010982.12813311.5980.82500410344.75830.46340.0228290.3180771.98434
9Air Transport2525559.72510609.9482.409034368.2173.54860.0672031.3551731.77732
\n", - "
" + "source": [ + "from dsp.modules.cache_utils import cache_turn_on\n", + "\n", + "cache_turn_on" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SCRAPING THE LINKS OF THE EXCEL FILES FROM THE WEBSITE" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ssl._create_default_https_context = ssl._create_stdlib_context\n", + "html_link = \"https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datacurrent.html\"\n", + "\n", + "with urllib.request.urlopen(html_link) as url:\n", + " s = url.read()\n", + " # I'm guessing this would output the html source code ?\n", + " soup = BeautifulSoup(s,\"lxml\")\n", + "\n", + "html_table = soup.find_all(\"table\")\n", + "req_table = html_table[1]\n", + "hrefs_list = req_table.find_all('a')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "req_href = {\"US\":[],\"Europe\":[],\"Japan\":[],\"AUS_NZ_CANADA\":[],\"Emerging\":[],\"China\":[],\"India\":[],\"Global\":[]}\n", + "\n", + "for i in hrefs_list:\n", + " name = i.get_text().strip()\n", + " try:\n", + " href_attr = i['href']\n", + " # Only get the excel files\n", + " if href_attr.endswith('.xls'):\n", + " if \"US\" in name:\n", + " req_href[\"US\"].append(href_attr)\n", + " elif \"Europe\" in name:\n", + " req_href[\"Europe\"].append(href_attr)\n", + " elif \"Japan\" in name:\n", + " req_href[\"Japan\"].append(href_attr)\n", + " elif \"Aus\" in name:\n", + " req_href['AUS_NZ_CANADA'].append(href_attr)\n", + " elif \"Emerging\" in name:\n", + " req_href['Emerging'].append(href_attr)\n", + " elif \"China\" in name:\n", + " req_href['China'].append(href_attr)\n", + " elif \"India\" in name:\n", + " req_href['India'].append(href_attr)\n", + " elif \"Global\" in name: \n", + " req_href['Global'].append(href_attr)\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "#Download the excel files from the website and store it in a folder named DATA\n", + "ssl._create_default_https_context = ssl._create_stdlib_context\n", + "\n", + "os.makedirs(\"DATA\",exist_ok=True)\n", + "for country,excel_files in req_href.items():\n", + " country_path = os.path.join(\"DATA\",country) \n", + " os.makedirs(country_path,exist_ok=True)\n", + " for file in excel_files:\n", + " file_name = file.split(\"/\")[-1].split(\".\")[0]\n", + " full_file_name = os.path.join(country_path,f\"{file_name}.xls\")\n", + " resp = requests.get(file,verify=False)\n", + " output = open(full_file_name, 'wb')\n", + " output.write(resp.content)\n", + " output.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FOR Emerging WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", + "FOR Europe WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", + "FOR Global WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", + "FOR AUS_NZ_CANADA WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", + "FOR China WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", + "FOR Japan WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n", + "FOR US WE HAVE DIRECTORY LEN = 24 and ACTUAL LEN = 24\n", + "FOR India WE HAVE DIRECTORY LEN = 29 and ACTUAL LEN = 29\n" + ] + } ], - "text/plain": [ - " Date updated: \\\n", - "0 Created by: \n", - "1 What is this data? \n", - "2 Home Page: \n", - "3 Data website: \n", - "4 Companies in each industry: \n", - "5 Variable definitions: \n", - "6 Industry Name \n", - "7 Advertising \n", - "8 Aerospace/Defense \n", - "9 Air Transport \n", - "\n", - " 2024-01-05 00:00:00 \\\n", - "0 Aswath Damodaran, adamodar@stern.nyu.edu \n", - "1 Capital Expenditures, Acquisitions and R&D and... \n", - "2 http://www.damodaran.com \n", - "3 https://pages.stern.nyu.edu/~adamodar/New_Home... \n", - "4 https://pages.stern.nyu.edu/~adamodar/pc/datas... \n", - "5 https://pages.stern.nyu.edu/~adamodar/New_Home... \n", - "6 Number of Firms \n", - "7 57 \n", - "8 70 \n", - "9 25 \n", - "\n", - " Unnamed: 2 \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 Capital Expenditures (US $ millions) \n", - "7 775.729 \n", - "8 10982.128 \n", - "9 25559.725 \n", - "\n", - " Unnamed: 3 Unnamed: 4 \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 Depreciation & Amort ((US $ millions) Cap Ex/Deprecn \n", - "7 1887.338 0.411018 \n", - "8 13311.598 0.825004 \n", - "9 10609.948 2.409034 \n", - "\n", - " Unnamed: 5 Unnamed: 6 Unnamed: 7 \\\n", - "0 NaN NaN NaN \n", - "1 US companies NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 Acquisitions (US $ millions) Net R&D (US $ millions) Net Cap Ex/Sales \n", - "7 322.559 75.08 -0.016886 \n", - "8 10344.75 830.4634 0.022829 \n", - "9 368.21 73.5486 0.067203 \n", - "\n", - " Unnamed: 8 Unnamed: 9 \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 Net Cap Ex/ EBIT (1-t) Sales/ Invested Capital (LTM) \n", - "7 -0.21812 3.283403 \n", - "8 0.318077 1.98434 \n", - "9 1.355173 1.77732 " - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_excel.parse(sample_excel.sheet_names[1]).head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* In the dataset above, there are two sheets. The first sheet is the variables and summary, and the second sheet is the table with the data. \n", - "* We will clean the first sheet to get the table name and summary" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ + "source": [ + "# Sanity check\n", + "for country in os.listdir(\"DATA\"):\n", + " dir_len = len(os.listdir(os.path.join(\"DATA\",country)))\n", + " country_len = len(req_href[country])\n", + " print(f'FOR {country} WE HAVE DIRECTORY LEN = {dir_len} and ACTUAL LEN = {country_len}')" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Emerging\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CLEANING THE DATASET" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 29/29 [00:00<00:00, 86.52it/s]\n" - ] + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "sample_excel = pd.ExcelFile(\"DATA2/US/capex.xls\")" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Europe\n" - ] + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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End GameTo measure how much companies are reinvesting back into their long term assets, as a prelude to forecasting expected growth.Unnamed: 2Unnamed: 3
0NaNNaNNaNNaN
1NaNNaNNaNNaN
2VariableHow it is measuredWhat it measuresUnits
3Capital ExpendituresSum of the capital expenditures reported on st...Gross investment in long term assets, at least...$ millions
4DepreciationSum of the depreciation reported on statement ...Loss in value of assets, from use, as measured...$ millions
5Net Cap ExSum of capital expenditures on statement of ca...Net investment in long term assets, at least a...$ millions
6Net R&DSum of R&D reported as expense in most recent ...Net investment in long term assets, expanded t...$ millions
7AcquisitionsSum of acquisitions reported on statement of c...Augments investment to include acquisition.$ millions
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Date updated:2024-01-05 00:00:00Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0Created by:Aswath Damodaran, adamodar@stern.nyu.eduNaNNaNNaNNaNNaNNaNNaNNaN
1What is this data?Capital Expenditures, Acquisitions and R&D and...NaNNaNNaNUS companiesNaNNaNNaNNaN
2Home Page:http://www.damodaran.comNaNNaNNaNNaNNaNNaNNaNNaN
3Data website:https://pages.stern.nyu.edu/~adamodar/New_Home...NaNNaNNaNNaNNaNNaNNaNNaN
4Companies in each industry:https://pages.stern.nyu.edu/~adamodar/pc/datas...NaNNaNNaNNaNNaNNaNNaNNaN
5Variable definitions:https://pages.stern.nyu.edu/~adamodar/New_Home...NaNNaNNaNNaNNaNNaNNaNNaN
6Industry NameNumber of FirmsCapital Expenditures (US $ millions)Depreciation & Amort ((US $ millions)Cap Ex/DeprecnAcquisitions (US $ millions)Net R&D (US $ millions)Net Cap Ex/SalesNet Cap Ex/ EBIT (1-t)Sales/ Invested Capital (LTM)
7Advertising57775.7291887.3380.411018322.55975.08-0.016886-0.218123.283403
8Aerospace/Defense7010982.12813311.5980.82500410344.75830.46340.0228290.3180771.98434
9Air Transport2525559.72510609.9482.409034368.2173.54860.0672031.3551731.77732
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" + ], + "text/plain": [ + " Date updated: \\\n", + "0 Created by: \n", + "1 What is this data? \n", + "2 Home Page: \n", + "3 Data website: \n", + "4 Companies in each industry: \n", + "5 Variable definitions: \n", + "6 Industry Name \n", + "7 Advertising \n", + "8 Aerospace/Defense \n", + "9 Air Transport \n", + "\n", + " 2024-01-05 00:00:00 \\\n", + "0 Aswath Damodaran, adamodar@stern.nyu.edu \n", + "1 Capital Expenditures, Acquisitions and R&D and... \n", + "2 http://www.damodaran.com \n", + "3 https://pages.stern.nyu.edu/~adamodar/New_Home... \n", + "4 https://pages.stern.nyu.edu/~adamodar/pc/datas... \n", + "5 https://pages.stern.nyu.edu/~adamodar/New_Home... \n", + "6 Number of Firms \n", + "7 57 \n", + "8 70 \n", + "9 25 \n", + "\n", + " Unnamed: 2 \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 Capital Expenditures (US $ millions) \n", + "7 775.729 \n", + "8 10982.128 \n", + "9 25559.725 \n", + "\n", + " Unnamed: 3 Unnamed: 4 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 Depreciation & Amort ((US $ millions) Cap Ex/Deprecn \n", + "7 1887.338 0.411018 \n", + "8 13311.598 0.825004 \n", + "9 10609.948 2.409034 \n", + "\n", + " Unnamed: 5 Unnamed: 6 Unnamed: 7 \\\n", + "0 NaN NaN NaN \n", + "1 US companies NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 Acquisitions (US $ millions) Net R&D (US $ millions) Net Cap Ex/Sales \n", + "7 322.559 75.08 -0.016886 \n", + "8 10344.75 830.4634 0.022829 \n", + "9 368.21 73.5486 0.067203 \n", + "\n", + " Unnamed: 8 Unnamed: 9 \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 Net Cap Ex/ EBIT (1-t) Sales/ Invested Capital (LTM) \n", + "7 -0.21812 3.283403 \n", + "8 0.318077 1.98434 \n", + "9 1.355173 1.77732 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_excel.parse(sample_excel.sheet_names[1]).head(10)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Global\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* In the dataset above, there are two sheets. The first sheet is the variables and summary, and the second sheet is the table with the data. \n", + "* We will clean the first sheet to get the table name and summary" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 29/29 [00:00<00:00, 102.75it/s]\n" - ] + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Emerging\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 86.52it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Europe\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 94.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Global\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 102.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUS_NZ_CANADA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 104.23it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "China\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 103.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Japan\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 66.34it/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "US\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 24/24 [00:00<00:00, 102.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "India\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 100.35it/s]\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "pd.set_option('display.max_rows', 50)\n", + "\n", + "def sanitize_column_name(col_name):\n", + " # Remove special characters and replace spaces with underscores\n", + " return re.sub(r\"\\W+\", \"_\", col_name)\n", + "\n", + "dir = \"DATA\"\n", + "processed_dir = \"Processed Data\"\n", + "all_infos_dict = []\n", + "os.makedirs(processed_dir,exist_ok=True)\n", + "for country in os.listdir(dir):\n", + " print(country)\n", + " file_name = os.path.join(dir,country)\n", + " os.makedirs(os.path.join(processed_dir,country),exist_ok=True)\n", + " os.makedirs(file_name,exist_ok=True)\n", + " for excel_file in tqdm(os.listdir(file_name)):\n", + " full_file_name = os.path.join(file_name,excel_file)\n", + " xls = pd.ExcelFile(full_file_name)\n", + " sns = xls.sheet_names\n", + " for sheet_name in sns:\n", + " if \"Var\" in sheet_name or \"var\" in sheet_name:\n", + " info_df = xls.parse(sheet_name)\n", + " info_df.dropna(how=\"all\",inplace=True)\n", + " info_dict = {}\n", + " for cols in info_df.columns:\n", + " if \"End\" not in cols and 'Unnamed' not in cols:\n", + " info_dict['Summary'] = cols\n", + " info_dict['Vars'] = info_df.values[1:].tolist()\n", + " all_infos_dict.append(info_dict)\n", + " elif \"Industry\" in sheet_name or \"industry\" in sheet_name:\n", + " data_df = xls.parse(sheet_name)\n", + " try:\n", + " data_df.dropna(axis=1,thresh=5,inplace=True)\n", + " data_df.dropna(inplace=True)\n", + " new_header = data_df.iloc[0] #grab the first row for the header\n", + " except:\n", + " print(full_file_name)\n", + " print(data_df)\n", + " data_df = data_df[1:] #take the data less the header row\n", + " data_df.reset_index(inplace=True,drop=True)\n", + " new_header = [sanitize_column_name(str(col)) for col in new_header]\n", + " data_df.columns = new_header #set the header row as the df header\n", + " save_name = full_file_name.split(\".\")[0].split(\"/\")[-1]\n", + " save_file_path = os.path.join(os.path.join(processed_dir,country),save_name)\n", + " data_df.to_csv(save_file_path+\".csv\",index=False)\n", + " with open(save_file_path+\".json\", \"w\") as outfile: \n", + " json.dump(info_dict, outfile)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUS_NZ_CANADA\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A SAMPLE METADATA JSON" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 29/29 [00:00<00:00, 104.23it/s]\n" - ] + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Summary': 'Measures of accounting returns, to all claim holders (and from operations)',\n", + " 'Vars': [['Number of firms',\n", + " 'Number of firms in the industry grouping.',\n", + " 'Law of large numbers?'],\n", + " ['R&D Capitalized',\n", + " 'My estimate of R&D capitalization, based upon a 5-year straight line amortization period, aggregated across firms in the group',\n", + " 'Capitalized value of R&D gets added on to book equity and to invested capital'],\n", + " ['Capitalized R&D as percent of invested capital',\n", + " 'My R&D capitalization estimate, as a percent of invested capital including that number, based upon aggregated values across firms',\n", + " 'Magnitude of investment in R&D, relative to investment in more traditional capital expenditures.'],\n", + " ['R&D -LTM',\n", + " 'Aggregated R&D expenses across the last twelve months, across companies in the group.',\n", + " 'Spending on R&D in most recent year'],\n", + " ['R&D: Years minus 1 to minus 5',\n", + " 'Aggregated R&D expenses for each of the previous five years, across companies in the group.',\n", + " 'Sepnding on R&D over last five years']]}" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_infos_dict[0]" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "China\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DATAFRAME AFTER PREPROCESSING" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 29/29 [00:00<00:00, 103.50it/s]\n" - ] + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Industry_NameNumber_of_FirmsCapital_Expenditures_(US_$_millions)Depreciation_&_Amort_((US_$_millions)Cap_Ex/DeprecnAcquisitions_(US_$_millions)Net_R&D_(US_$_millions)Net_Cap_Ex/SalesNet_Cap_Ex/_EBIT_(1-t)Sales/_Invested_Capital_(LTM)
0Advertising57775.7291887.3380.411018322.55975.0800-0.016886-0.2181203.283403
1Aerospace/Defense7010982.12813311.5980.82500410344.750830.46340.0228290.3180771.984340
2Air Transport2525559.72510609.9482.409034368.21073.54860.0672031.3551731.777320
3Apparel381730.9811386.4801.24847238.7390.94740.0053650.0725571.773076
4Auto & Truck3429899.48618677.6681.600815193.220983.06960.0265770.6759181.048732
\n", + "
" + ], + "text/plain": [ + " Industry_Name Number_of_Firms Capital_Expenditures_(US_$_millions) \\\n", + "0 Advertising 57 775.729 \n", + "1 Aerospace/Defense 70 10982.128 \n", + "2 Air Transport 25 25559.725 \n", + "3 Apparel 38 1730.981 \n", + "4 Auto & Truck 34 29899.486 \n", + "\n", + " Depreciation_&_Amort_((US_$_millions) Cap_Ex/Deprecn \\\n", + "0 1887.338 0.411018 \n", + "1 13311.598 0.825004 \n", + "2 10609.948 2.409034 \n", + "3 1386.480 1.248472 \n", + "4 18677.668 1.600815 \n", + "\n", + " Acquisitions_(US_$_millions) Net_R&D_(US_$_millions) Net_Cap_Ex/Sales \\\n", + "0 322.559 75.0800 -0.016886 \n", + "1 10344.750 830.4634 0.022829 \n", + "2 368.210 73.5486 0.067203 \n", + "3 38.739 0.9474 0.005365 \n", + "4 193.220 983.0696 0.026577 \n", + "\n", + " Net_Cap_Ex/_EBIT_(1-t) Sales/_Invested_Capital_(LTM) \n", + "0 -0.218120 3.283403 \n", + "1 0.318077 1.984340 \n", + "2 1.355173 1.777320 \n", + "3 0.072557 1.773076 \n", + "4 0.675918 1.048732 " + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"Processed Data/US/capex.csv\")\n", + "df.head()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Japan\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BUILD TABLE NAMES AND METADATA\n", + "\n", + "* Here we use a DSPy signature given the first 10 rows of the dataframe, we generate the table name and table explanation. It will help us to dynamically select the correct table based on the query." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 29/29 [00:00<00:00, 66.34it/s] \n" - ] + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "',Industry_Name,Number_of_Firms,Capital_Expenditures_(US_$_millions),Depreciation_&_Amort_((US_$_millions),Cap_Ex/Deprecn,Acquisitions_(US_$_millions),Net_R&D_(US_$_millions),Net_Cap_Ex/Sales,Net_Cap_Ex/_EBIT_(1-t),Sales/_Invested_Capital_(LTM)\\n0,Advertising,57,775.729,1887.338,0.4110175283918408,322.559,75.07999999999993,-0.0168860862168837,-0.2181203933865563,3.283403041333076\\n1,Aerospace/Defense,70,10982.128,13311.597999999998,0.8250044810547916,10344.749999999998,830.4633999999933,0.0228292321038578,0.318077061754813,1.9843395804666923\\n2,Air Transport,25,25559.725,10609.948,2.4090339556800844,368.21,73.5486000000019,0.0672027546276203,1.3551729988499104,1.7773199625336142\\n3,Apparel,38,1730.9809999999998,1386.4799999999996,1.2484716692631703,38.739,0.9473999999991064,0.0053650838666078,0.0725567415076527,1.7730762352050575\\n4,Auto & Truck,34,29899.486,18677.668,1.6008147269776931,193.22,983.0695999999988,0.0265766732899557,0.6759175844836917,1.048731678622114\\n5,Auto Parts,39,3565.4210000000007,2034.471,1.7525051966825778,1061.7600000000002,454.9352000000008,0.0316936036463534,0.8398185079860373,2.004790963758861\\n6,Beverage (Alcoholic),19,2244.8160000000007,864.0629999999999,2.59797723082692,1370.0,0.0,0.0939030584900288,0.6367443614850201,0.8968231153352131\\n7,Beverage (Soft),29,7926.687000000001,4543.389,1.7446639501922463,674.6669999999999,7.705600000001141,0.0238699281532688,0.1517545513863501,1.6931990505110932\\n8,Broadcasting,22,1837.548,3387.6400000000003,0.5424271764414165,43.856,0.2399999999997817,-0.0206121524427381,-0.2104150803584717,1.1159893806472496\\n9,Brokerage & Investment Banking,27,7783.945999999999,4646.382,1.675270350134793,876.0999999999999,-189.38079999999945,0.0167661627913481,3.4298253908840737,0.2794992243703733\\n'" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(10).to_csv()" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "US\n" - ] + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "load_dotenv(override=True)\n", + "openai.api_key = os.environ['OPENAI_API_KEY']" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 24/24 [00:00<00:00, 102.91it/s]\n" - ] + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct', max_tokens=250)\n", + "dspy.settings.configure(lm=turbo)\n", + "\n", + "class SQLTableMetadata(dspy.Signature):\n", + " \"\"\"Give a suitable table name and description about the given table\"\"\"\n", + " pandas_dataframe_str = dspy.InputField(desc=\"First 10 rows of a pandas dataframe delimited by newline character\")\n", + " table_name = dspy.OutputField(desc=\"suitable table name\")\n", + " table_summary = dspy.OutputField(desc=\"a summary about the table\")\n", + "\n", + "class CoT(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.prog = dspy.ChainOfThought(SQLTableMetadata)\n", + " \n", + " def forward(self, pandas_dataframe_str):\n", + " return self.prog(pandas_dataframe_str=pandas_dataframe_str)\n", + "\n", + "cot = CoT()\n", + "\n", + "# cot(pandas_dataframe_str = df.head(10).to_csv())\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "India\n" - ] + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "processed_dir = \"Processed Data\"\n", + "dfs_str = []\n", + "for country in os.listdir(processed_dir):\n", + " country_folder = os.path.join(processed_dir,country)\n", + " # print(f\"{country}\")\n", + " for files in tqdm(os.listdir(country_folder),desc=f\"Building the summary and name for {country}\"):\n", + " if files.endswith(\".csv\"):\n", + " file_name = files.split(\".\")[0]\n", + " csv_file_path = os.path.join(country_folder,files)\n", + " df = pd.read_csv(csv_file_path,index_col=False)\n", + " json_file_path = os.path.join(country_folder,f\"{file_name}.json\")\n", + " with open(json_file_path,'r') as f:\n", + " data = json.loads(f.read())\n", + " if 'table_name' in data and 'table_summary' in data:\n", + " # if data['table_name'] == \"\" or data['table_summary'] == \"\":\n", + " if data['table_summary'] == \"\":\n", + " pass\n", + " else:\n", + " continue\n", + " dfs_str.append(df.head(10).to_csv())\n", + " table_preds = cot(pandas_dataframe_str = df.head(10).to_csv())\n", + " data['table_name'] = table_preds.table_name\n", + " data['table_summary'] = table_preds.table_summary\n", + " with open(json_file_path,'w') as f:\n", + " json.dump(data, f)" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 29/29 [00:00<00:00, 100.35it/s]\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "pd.set_option('display.max_rows', 50)\n", - "\n", - "def sanitize_column_name(col_name):\n", - " # Remove special characters and replace spaces with underscores\n", - " return re.sub(r\"\\W+\", \"_\", col_name)\n", - "\n", - "dir = \"DATA\"\n", - "processed_dir = \"Processed Data\"\n", - "all_infos_dict = []\n", - "os.makedirs(processed_dir,exist_ok=True)\n", - "for country in os.listdir(dir):\n", - " print(country)\n", - " file_name = os.path.join(dir,country)\n", - " os.makedirs(os.path.join(processed_dir,country),exist_ok=True)\n", - " os.makedirs(file_name,exist_ok=True)\n", - " for excel_file in tqdm(os.listdir(file_name)):\n", - " full_file_name = os.path.join(file_name,excel_file)\n", - " xls = pd.ExcelFile(full_file_name)\n", - " sns = xls.sheet_names\n", - " for sheet_name in sns:\n", - " if \"Var\" in sheet_name or \"var\" in sheet_name:\n", - " info_df = xls.parse(sheet_name)\n", - " info_df.dropna(how=\"all\",inplace=True)\n", - " info_dict = {}\n", - " for cols in info_df.columns:\n", - " if \"End\" not in cols and 'Unnamed' not in cols:\n", - " info_dict['Summary'] = cols\n", - " info_dict['Vars'] = info_df.values[1:].tolist()\n", - " all_infos_dict.append(info_dict)\n", - " elif \"Industry\" in sheet_name or \"industry\" in sheet_name:\n", - " data_df = xls.parse(sheet_name)\n", - " try:\n", - " data_df.dropna(axis=1,thresh=5,inplace=True)\n", - " data_df.dropna(inplace=True)\n", - " new_header = data_df.iloc[0] #grab the first row for the header\n", - " except:\n", - " print(full_file_name)\n", - " print(data_df)\n", - " data_df = data_df[1:] #take the data less the header row\n", - " data_df.reset_index(inplace=True,drop=True)\n", - " new_header = [sanitize_column_name(str(col)) for col in new_header]\n", - " data_df.columns = new_header #set the header row as the df header\n", - " save_name = full_file_name.split(\".\")[0].split(\"/\")[-1]\n", - " save_file_path = os.path.join(os.path.join(processed_dir,country),save_name)\n", - " data_df.to_csv(save_file_path+\".csv\",index=False)\n", - " with open(save_file_path+\".json\", \"w\") as outfile: \n", - " json.dump(info_dict, outfile)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A SAMPLE METADATA JSON" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Summary': 'Measures of accounting returns, to all claim holders (and from operations)',\n", - " 'Vars': [['Number of firms',\n", - " 'Number of firms in the industry grouping.',\n", - " 'Law of large numbers?'],\n", - " ['R&D Capitalized',\n", - " 'My estimate of R&D capitalization, based upon a 5-year straight line amortization period, aggregated across firms in the group',\n", - " 'Capitalized value of R&D gets added on to book equity and to invested capital'],\n", - " ['Capitalized R&D as percent of invested capital',\n", - " 'My R&D capitalization estimate, as a percent of invested capital including that number, based upon aggregated values across firms',\n", - " 'Magnitude of investment in R&D, relative to investment in more traditional capital expenditures.'],\n", - " ['R&D -LTM',\n", - " 'Aggregated R&D expenses across the last twelve months, across companies in the group.',\n", - " 'Spending on R&D in most recent year'],\n", - " ['R&D: Years minus 1 to minus 5',\n", - " 'Aggregated R&D expenses for each of the previous five years, across companies in the group.',\n", - " 'Sepnding on R&D over last five years']]}" - ] - }, - "execution_count": 196, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_infos_dict[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DATAFRAME AFTER PREPROCESSING" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Industry_NameNumber_of_FirmsCapital_Expenditures_(US_$_millions)Depreciation_&_Amort_((US_$_millions)Cap_Ex/DeprecnAcquisitions_(US_$_millions)Net_R&D_(US_$_millions)Net_Cap_Ex/SalesNet_Cap_Ex/_EBIT_(1-t)Sales/_Invested_Capital_(LTM)
0Advertising57775.7291887.3380.411018322.55975.0800-0.016886-0.2181203.283403
1Aerospace/Defense7010982.12813311.5980.82500410344.750830.46340.0228290.3180771.984340
2Air Transport2525559.72510609.9482.409034368.21073.54860.0672031.3551731.777320
3Apparel381730.9811386.4801.24847238.7390.94740.0053650.0725571.773076
4Auto & Truck3429899.48618677.6681.600815193.220983.06960.0265770.6759181.048732
\n", - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NEXT TASKS\n", + "1. Build database with each region for each table\n", + "2. Embed the table summary and table SCHEMA. Also, embed the table rows\n", + "3. Retrieval at table level and embed the rows to retrieve relevant rows from the retrieved schema of table\n", + "4. Text-to-SQL pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BUILD THE SQLITE DATABASE FROM THE CSV FILES\n", + "\n", + "It was taken from the [tutorial](https://docs.llamaindex.ai/en/stable/examples/pipeline/query_pipeline_sql/)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to create a sanitized column name\n", + "def sanitize_column_name(col_name):\n", + " # Remove special characters and replace spaces with underscores\n", + " return re.sub(r\"\\W+\", \"_\", col_name)\n", + "\n", + "\n", + "# Function to create a table from a DataFrame using SQLAlchemy\n", + "def create_table_from_dataframe(\n", + " df: pd.DataFrame, table_name: str, engine, metadata_obj\n", + "):\n", + " # Sanitize column names\n", + " sanitized_columns = {col: sanitize_column_name(col) for col in df.columns}\n", + " df = df.rename(columns=sanitized_columns)\n", + "\n", + " # Dynamically create columns based on DataFrame columns and data types\n", + " columns = [\n", + " Column(col, String if dtype == \"object\" else Integer)\n", + " for col, dtype in zip(df.columns, df.dtypes)\n", + " ]\n", + "\n", + " # Create a table with the defined columns\n", + " table = Table(table_name, metadata_obj, *columns)\n", + "\n", + " # Create the table in the database\n", + " metadata_obj.create_all(engine)\n", + "\n", + " # Insert data from DataFrame into the table\n", + " with engine.connect() as conn:\n", + " for _, row in df.iterrows():\n", + " insert_stmt = table.insert().values(**row.to_dict())\n", + " conn.execute(insert_stmt)\n", + " conn.commit()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DATABASE CREATION" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "processed_dir = \"Processed Data\"\n", + "def sqlalchemy_engine(region:str):\n", + " \"\"\"Create a SQLAlchemy engine for the given region\"\"\"\n", + " assert region in os.listdir(processed_dir), f\"{region} is not a valid region from {os.listdir(processed_dir)}\"\n", + " # Create a SQLAlchemy database for each region\n", + " engine = create_engine(f\"sqlite:///{region}.db\")\n", + " metadata_obj = MetaData()\n", + " region_path = os.path.join(processed_dir,region)\n", + " dfs = []\n", + " for dataframes_path in os.listdir(region_path):\n", + " if dataframes_path.endswith(\".csv\"):\n", + " df = pd.read_csv(os.path.join(region_path,dataframes_path),index_col=False)\n", + " dfs.append((dataframes_path,df))\n", + " pbar = tqdm(total=len(dfs),desc=f\"Creating tables for {region}\")\n", + " for _, df_table_name in enumerate(dfs):\n", + " table_name = df_table_name[0]\n", + " table_name = table_name.split(\".\")[0]\n", + " df = df_table_name[1]\n", + " # print(f\"Creating table: {table_name}\")\n", + " create_table_from_dataframe(df,table_name, engine, metadata_obj)\n", + " # print(f\"Done creating table for: {table_name}\")\n", + " pbar.update(1)\n", + " return engine" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Creating tables for US: 0%| | 0/24 [00:00\n", - " \"Sublime's\n", - "

" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def get_table_results(table_collection_,question:str):\n", - " # question_emb = emb_fn.embed_with_retries(question)[0]\n", - " # Get the table results for the given question\n", - " table_results = table_collection_.query(\n", - " query_texts = question,\n", - " n_results = 5\n", - " )\n", - " # print(table_results['documents'][0])\n", - " return table_results\n", - "\n", - "def get_row_results(row_collection_,question,table_name:str):\n", - " # Get the row results for the given question\n", - " row_results = row_collection_.query(\n", - " query_texts = question,\n", - " where = {\"table_name\":table_name},\n", - " n_results = 5\n", - " )\n", - " print(row_results['documents'][0])\n", - " return row_results" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mTable name: margin \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", - "row 1 : Packaging & Container | 22 | 0.2171338247779519 | 0.0285269723092998 | 0.1016364547958028 | 0.0975946017284918 | 0.0799113339135634 | 0.0997355299061771 | 0.0816643450787498 | 0.0998025797146062 | 0.0817313948871789 | 0.1571078511407175 | 0.2522079768591083 | 0.1617111987330198 | 0.782866175222048 | 0.0046033475923023 | 0.0951001257183908 | 0.004041853067311 | 0.0111359826715\n", - "row 2 : Software (Entertainment) | 84 | 0.6343190737595645 | 0.203484453534525 | 0.3536120749157563 | 0.2680484859792075 | 0.2543465254185649 | 0.2650138705989933 | 0.2514670319003695 | 0.2816776939314963 | 0.2681308552328725 | 0.290337329951565 | 0.4724580252979131 | 0.4795894540185941 | 0.3656809262404354 | 0.1892521240670291 | 0.182120695346348 | 0.0855635889365488 | 0.0140141013588\n", - "row 3 : Software (Internet) | 35 | 0.591108552762756 | -0.1431970955040302 | 0.1239579652577063 | -0.0335948165704685 | -0.0327194780747064 | -0.0289602901492322 | -0.0282057077640929 | -0.0083175074368089 | -0.0075629250516695 | 0.0122636239274153 | 0.4161715858041718 | 0.2226547217998442 | 0.408891447237244 | 0.2103910978724289 | 0.4039079618767565 | 0.1575527818281749 | 0.02956631949802\n", - "row 4 : Software (System & Application) | 351 | 0.7152038033402293 | 0.1914080753155209 | 0.3405275162267305 | 0.2529865559329903 | 0.2423866072527709 | 0.2534208835158065 | 0.2428027368326486 | 0.2629596189730837 | 0.2523414722899257 | 0.2853446560160495 | 0.5664609609773408 | 0.4563813852186625 | 0.2847961966597707 | 0.171036729202613 | 0.2811163049612913 | 0.0875409602937401 | 0.01484216535122\n", - "row 5 : Electronics (Consumer & Office) | 13 | 0.323558031288744 | -0.0304513738086002 | 0.0289465210995791 | -0.0007643401586021 | -0.0007084824743491 | -0.0004205933789403 | -0.0003898565768825 | 0.0056724354829463 | 0.005703172285004 | 0.0359642158837002 | 0.2637004492123042 | 0.130762045590879 | 0.676441968711256 | 0.0947978297071788 | 0.2277362333286039 | 0.0297108612581812 | 0.00871672702736\n", - "*/\n", - "\n", - "Table name: EVA \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | ROE (INTEGER) | Cost_of_Equity (INTEGER) | _ROE_COE_ (INTEGER) | BV_of_Equity (INTEGER) | Equity_EVA_US_millions_ (INTEGER) | ROC (INTEGER) | Cost_of_Capital (INTEGER) | _ROC_WACC_ (INTEGER) | BV_of_Capital (INTEGER) | EVA_US_millions_ (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER)\n", - "row 1 : Packaging & Container | 22 | 1.1327220379604754 | 0.0854306569307375 | 0.0909052137461818 | -0.0054745568154443 | 49084.58099999999 | -268.71632744677845 | 0.1421364757846558 | 0.0708610226399319 | 0.0712754531447238 | 84525.26117167753 | 6024.57629218745 | 0.620115895714898 | 0.2624270896574677 | 0.050855 | 0.1811910443993945 | 0.03814125 | 0.3798841042851\n", - "row 2 : Software (Entertainment) | 84 | 1.1083305740238332 | 0.2325593855621183 | 0.0897832064050963 | 0.1427761791570219 | 403869.63 | 57662.96264896017 | 0.1994487340625551 | 0.088266127488506 | 0.1111826065740491 | 620525.0853364643 | 68991.59643229235 | 0.9694385050656324 | 0.5278932207822914 | 0.053524 | 0.0511174704478853 | 0.040143 | 0.03056149493436\n", - "row 3 : Software (Internet) | 35 | 1.6160843271745926 | -0.1454065112919735 | 0.1131398790500312 | -0.2585463903420048 | 28399.863 | -7342.682064857458 | -0.0048109532624457 | 0.1059203603347457 | -0.1107313135971915 | 45334.05312597519 | -5019.899253324099 | 0.8930134645020447 | 0.65221602599035 | 0.060879 | 0.0260557605345441 | 0.04565925 | 0.10698653549795\n", - "row 4 : Software (System & Application) | 351 | 1.2939647291542515 | 0.2444183538229951 | 0.0983223775410955 | 0.1460959762818996 | 397835.59000000014 | 58122.178920735554 | 0.2322438986671061 | 0.0949239234411956 | 0.1373199752259105 | 551977.6712097155 | 75797.5601357739 | 0.9415866198035656 | 0.5208648982480728 | 0.053524 | 0.0418992568246478 | 0.040143 | 0.05841338019643\n", - "row 5 : Electronics (Consumer & Office) | 13 | 1.29517877665579 | -0.0662680013872474 | 0.0983782237261663 | -0.1646462251134137 | 2969.9099999999994 | -488.9844704265786 | 0.009542635546224 | 0.0890493344743714 | -0.0795066989281474 | 3862.6772748588 | -307.1087191487957 | 0.845130180440282 | 0.3942415498208712 | 0.050855 | 0.0730796146510295 | 0.03814125 | 0.15486981955971\n", - "*/\n", - "\n", - "Table name: DollarUS \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Company_Age_years_ (INTEGER) | Market_Cap_millions_ (INTEGER) | Book_Equity_millions_ (INTEGER) | Enteprise_Value_millions_ (INTEGER) | Invested_Capital_millions_ (INTEGER) | Total_Debt_including_leases_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Gross_Profit_millions_ (INTEGER) | EBITDA_millions_ (INTEGER) | EBIT_Operating_Income_millions_ (INTEGER) | Net_Income_millions_ (INTEGER)\n", - "row 1 : Packaging & Container | 22 | 80.19047619047619 | 132627.167 | 49084.58099999999 | 205727.3441716776 | 86259.32917167754 | 81247.63917167754 | 146995.2 | 31917.63 | 23820.874 | 14660.644165664493 | 4193.3279999999\n", - "row 2 : Software (Entertainment) | 84 | 14.544117647058824 | 2773080.8990000025 | 403869.63 | 2781007.513051708 | 383348.20548245066 | 87421.2210517081 | 461576.65299999993 | 292786.8749999999 | 156210.96800000002 | 122324.21538965842 | 93923.673000000\n", - "row 3 : Software (Internet) | 35 | 14.413793103448276 | 220091.11299999992 | 28399.863 | 241745.7304766904 | 25653.71492597519 | 26367.783476690336 | 28838.050000000003 | 17046.417999999998 | 1980.7540000000004 | -835.1582953380669 | -4129.5\n", - "row 4 : Software (System & Application) | 351 | 20.125 | 5277974.335000003 | 397835.59000000014 | 5446717.280701609 | 326815.0326293967 | 327430.6527016032 | 508015.76600000006 | 363334.808 | 170251.41100000014 | 128741.80425967928 | 97238.320000000\n", - "row 5 : Electronics (Consumer & Office) | 13 | 35.61538461538461 | 5288.128000000001 | 2969.9099999999994 | 5468.249666410444 | 2031.147666410444 | 969.0476664104436 | 6463.090999999999 | 2091.185 | 195.229 | -2.7183332820887505 | -196.\n", - "*/\n", - "\n", - "Table name: dbtfund \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Book_Debt_to_Capital (INTEGER) | Market_Debt_to_Capital_Unadjusted_ (INTEGER) | Market_D_E_unadjusted_ (INTEGER) | Market_Debt_to_Capital_adjusted_for_leases_ (INTEGER) | Market_D_E_adjusted_for_leases_ (INTEGER) | Interest_Coverage_Ratio (INTEGER) | Debt_to_EBITDA (INTEGER) | Effective_tax_rate (INTEGER) | Institutional_Holdings (INTEGER) | Std_dev_in_Stock_Prices (INTEGER) | EBITDA_EV (INTEGER) | Net_PP_E_Total_Assets (INTEGER) | Capital_Spending_Total_Assets (INTEGER)\n", - "row 1 : Packaging & Container | 22 | 0.6103387810613522 | 0.3782286453204903 | 0.6083082510538735 | 0.379884104285102 | 0.6126017844570075 | 3.764310529409295 | 3.5181123824560183 | 0.1811910443993945 | 0.6783750000000001 | 0.2624270896574677 | 0.1157885651803374 | 0.3623114386831347 | 0.04952312081083\n", - "row 2 : Software (Entertainment) | 84 | 0.1668568483248547 | 0.0310823698127216 | 0.0320794759475208 | 0.0305614949343675 | 0.0315249443617865 | 82.70045339060373 | 0.6523342120398788 | 0.0511174704478853 | 0.380174716981132 | 0.5278932207822914 | 0.0561706386145586 | 0.3664219490280888 | 0.08632671853148\n", - "row 3 : Software (Internet) | 35 | 0.5188435528953421 | 0.1069721233683309 | 0.1197858724990863 | 0.1069865354979553 | 0.1198039444540877 | -1.2424451113163029 | 74.55708316963612 | 0.0260557605345441 | 0.503754090909091 | 0.65221602599035 | 0.0081935428439386 | 0.1393637519325522 | 0.02491814851918\n", - "row 4 : Software (System & Application) | 351 | 0.4037655070426813 | 0.0578505240039251 | 0.0614027025199621 | 0.0584133801964343 | 0.0620371816759891 | 12.091079193408472 | 2.258772022287284 | 0.0418992568246478 | 0.4265529149797569 | 0.5208648982480728 | 0.0312576185298293 | 0.1534323819146964 | 0.03979088603498\n", - "row 5 : Electronics (Consumer & Office) | 13 | 0.257103532061476 | 0.1538670284938941 | 0.1818473380372033 | 0.1548698195597179 | 0.1832496615835402 | -0.0653906229317238 | 4.169022829162121 | 0.0730796146510295 | 0.322611111111111 | 0.3942415498208712 | 0.0357022835294488 | 0.0758014027166977 | 0.01817671397788\n", - "*/\n", - "\n", - "Table name: finflows \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Dividends_in_millions (INTEGER) | Buybacks_in_millions (INTEGER) | Equity_Issuance_in_millions (INTEGER) | Net_Equity_Change_in_millions (INTEGER) | Net_Equity_Change_as_of_Book_Equity (INTEGER) | Debt_Repaid_in_millions (INTEGER) | Debt_Raised_in_millions (INTEGER) | Net_Debt_Change_in_millions (INTEGER) | Net_Change_in_Debt_as_of_Total_Debt (INTEGER) | Change_in_Lease_Debt_in_millions (INTEGER)\n", - "row 1 : Packaging & Container | 22 | 2944.63 | 2233.409 | 88.02499999999999 | -2145.384 | -0.0437079008579089 | 21249.516000000007 | 23186.13 | 1936.6139999999905 | 0.0252378854855187 | 1078.3980000000\n", - "row 2 : Software (Entertainment) | 84 | 235.41 | 90230.608 | 492.176 | -89738.43199999999 | -0.2221965340647178 | 19934.036 | 26704.583 | 6770.546999999999 | 0.1247056048135642 | 6193.5280000000\n", - "row 3 : Software (Internet) | 35 | 0.256 | 5130.625999999999 | 532.7600000000001 | -4597.865999999999 | -0.1618974711251248 | 5242.0070000000005 | 5173.334999999999 | -68.67200000000139 | -0.0029087745986446 | 1487.\n", - "row 4 : Software (System & Application) | 351 | 26468.845 | 51669.898 | 11749.602 | -39920.296 | -0.1003437022816384 | 35002.797 | 51147.364 | 16144.567000000005 | 0.0548813000879868 | 3688.94599999997\n", - "row 5 : Electronics (Consumer & Office) | 13 | 0.0 | 193.63 | 36.461000000000006 | -157.16899999999998 | -0.0529204588691239 | 397.182 | 379.297 | -17.885000000000048 | -0.023463092038029 | 74.409999999999\n", - "*/\n", - "\n", - "\u001b[0m\n", - "Prediction(\n", - " rationale='produce the SQL. We need to find the EBITDA for the software industry, which includes Software (Entertainment), Software (Internet), and Software (System & Application).',\n", - " sql=\"```sql\\nSELECT DISTINCT DollarUS.Industry_Name AS Industry, DollarUS.EBITDA_millions_ AS EBITDA\\nFROM DollarUS\\nWHERE DollarUS.Industry_Name LIKE 'Software%'\\n```\"\n", - ")\n", - "Extracted rows: Industry = Software (Entertainment), EBITDA = 156210.96800000002\n", - " Industry = Software (Internet), EBITDA = 1980.7540000000004\n", - " Industry = Software (System & Application), EBITDA = 170251.41100000014\n", - "\n" - ] - } - ], - "source": [ - "from typing import Any\n", - "\n", - "class TextToSQLQueryModule(dspy.Module):\n", - " \"\"\"Text to SQL to final module\"\"\"\n", - " def __init__(self,region:str,use_cot:bool=True,max_retries:int=3):\n", - " \"\"\"Text to Answer init module\n", - "\n", - " Args:\n", - " region (str): Region for which the module will be used.\n", - " use_cot (bool, optional): Whether to use chain of thought for sql query generation. Defaults to True.\n", - " max_retries (int, optional): Number of max retries for SQLError. Defaults to 3.\n", - " \"\"\"\n", - " super().__init__()\n", - " self.region = region\n", - " db,table_collection,row_collection = db_collection_dict[region]\n", - " # print(db,table_collection,row_collection)\n", - " self.table_collection = table_collection\n", - " self.use_cot = use_cot\n", - " self.db = db\n", - " self.row_collection = row_collection\n", - " if self.use_cot == True:\n", - " self.sqlAnswer = dspy.ChainOfThought(TextToSQLAnswer)\n", - " else:\n", - " self.sqlAnswer = dspy.Predict(TextToSQLAnswer)\n", - " self.final_output = dspy.Predict(SQLReturnToAnswer)\n", - " self.max_tries = max_retries\n", - " # Initialize the sql rectifier with CoT reasoning\n", - " self.sql_rectifier = dspy.ChainOfThought(SQLRectifier,rationale_type=dspy.OutputField(\n", - " prefix=\"Reasoning: Let's think step by step in order to\",\n", - " desc=\"${produce the answer}. We ...\"\n", - " ))\n", - " \n", - " def __call__(self, *args: Any, **kwargs: Any) -> Any:\n", - " return self.forward(*args, **kwargs)\n", - " \n", - " def forward(self,question):\n", - " # Embed the question with embedding function\n", - " question_emb = emb_fn([question])[0]\n", - " # Retrieve the relevant tables from table schema and table summary\n", - " docs = self.table_collection.query(\n", - " query_embeddings = question_emb,\n", - " n_results = 5\n", - " )\n", - " # docs = get_table_results(db_collection_dict[self.region][1],question)\n", - " relevant_rows_schemas = \"\"\n", - " \n", - " existing_table_names = []\n", - "\n", - " for table_idx,metadata_name in enumerate(docs['metadatas'][0]):\n", - " table_metadata = metadata_name['table_metadata']\n", - " table_name = metadata_name['table_name']\n", - " # If the table name is already in the list of existing table names, skip it\n", - " # if table_name in existing_table_names: \n", - " # continue\n", - " existing_table_names.append(table_name)\n", - " # Retrieve the relevant rows from the current table\n", - " rows = self.row_collection.query(\n", - " query_embeddings = question_emb,\n", - " n_results = 5,\n", - " # where clause to filter the rows\n", - " where = {\"table_name\":table_name}\n", - " )\n", - " # Retrieve the relevant table with the schema and summary\n", - " relevant_rows_schemas += f'Table name: {table_name} \\n'\n", - " relevant_rows_schemas += \"/* \\n\"\n", - " for match in re.finditer(\"columns: \",table_metadata):\n", - " cols_end = match.end()\n", - " relevant_rows_schemas += \"col : \" + \" | \".join(table_metadata[cols_end:].split(\", \")) + \"\\n\"\n", - " for row_idx,row in enumerate(rows['metadatas'][0]):\n", - " # Get the relevant rows from the current table\n", - " # relevant_rows_schemas += f'\\tRow {row_idx+1} from table {table_name}: {row[\"full_rows\"]}\\n'\n", - " relevant_rows_schemas += f'row {row_idx+1} : {\" | \".join(row[\"full_rows\"].split(\", \"))}\\n'\n", - " relevant_rows_schemas += \"*/\" + '\\n\\n'\n", - " print(colored(relevant_rows_schemas,\"yellow\"))\n", - " # return \n", - " sql_query = self.sqlAnswer(question=question,relevant_table_schemas_rows=relevant_rows_schemas)\n", - "\n", - " num_tries = 0\n", - " print(sql_query)\n", - " while num_tries <= self.max_tries:\n", - " with self.db.connect() as conn:\n", - " try:\n", - " # Try executing the sql query for the database\n", - " result = conn.execute(text(process_sql_str(sql_query.sql)))\n", - " num_tries = self.max_tries + 1\n", - " except Exception as error:\n", - " # If there is an sql error, then try again with the sql rectifier\n", - " print(colored(str(error),'red'))\n", - " sql_query = self.sql_rectifier(input_sql=sql_query.sql,error_str=str(error),relevant_table_schemas_rows=relevant_rows_schemas)\n", - " print(colored(sql_query.rationale,'green'))\n", - " print()\n", - " print(colored(sql_query.sql,'green'))\n", - " # If all the num_retries are exhausted, then exit the program\n", - " num_tries += 1\n", - " if num_tries == self.max_tries+1:\n", - " return sql_query,error\n", - " # With the retrieved rows from the database, then try to answer the question with dspy context\n", - " with dspy.context(lm=sql_to_answer):\n", - " row_str = \"\"\n", - " key = tuple(result.keys())\n", - " for row in result.fetchall():\n", - " for r,k in zip(row,key):\n", - " row_str += f\" {k} = {r},\"\n", - " row_str = row_str[:-1]\n", - " row_str += \"\\n\"\n", - " print(f\"Extracted rows: {row_str}\")\n", - " final_answer = self.final_output(question=question,sql=sql_query.sql,relevant_rows=row_str)\n", - " return final_answer\n", - "tsql_ = TextToSQLQueryModule(\"US\")\n", - "question = \"What is the ebitda of software and packaging industry?\"\n", - "sq = tsql_(question = question)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction(\n", - " answer='The EBITDA of the software industry is as follows:\\n- Software (Entertainment): $156,210.97 million\\n- Software (Internet): $1,980.75 million\\n- Software (System & Application): $170,251.41 million'\n", - ")\n" - ] - } - ], - "source": [ - "print(sq)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mTable name: EVA \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | ROE (INTEGER) | Cost_of_Equity (INTEGER) | _ROE_COE_ (INTEGER) | BV_of_Equity (INTEGER) | Equity_EVA_US_millions_ (INTEGER) | ROC (INTEGER) | Cost_of_Capital (INTEGER) | _ROC_WACC_ (INTEGER) | BV_of_Capital (INTEGER) | EVA_US_millions_ (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER)\n", - "row 1 : Aerospace/Defense | 70 | 1.0764050153356324 | 0.1319148365195281 | 0.088314630705439 | 0.043600205814089 | 145775.301 | 6355.833126210783 | 0.1616771766434065 | 0.0781335598681736 | 0.0835436167752329 | 195266.1836785369 | 16313.243218401924 | 0.7970822237983712 | 0.3640214967123067 | 0.050855 | 0.0727529390842114 | 0.03814125 | 0.20291777620162\n", - "row 2 : Total Market (without financials) | 5214 | 1.097522642247185 | 0.1659255247192018 | 0.0892860415433705 | 0.0766394831758313 | 8566043.05699999 | 656497.1127503973 | 0.1466884216172786 | 0.0798580827010761 | 0.0668303389162024 | 14577190.305961732 | 974198.568593403 | 0.815661408370401 | 0.4700345950094993 | 0.050855 | 0.0680489965490159 | 0.03814125 | 0.1843385916295\n", - "row 3 : Software (Entertainment) | 84 | 1.1083305740238332 | 0.2325593855621183 | 0.0897832064050963 | 0.1427761791570219 | 403869.63 | 57662.96264896017 | 0.1994487340625551 | 0.088266127488506 | 0.1111826065740491 | 620525.0853364643 | 68991.59643229235 | 0.9694385050656324 | 0.5278932207822914 | 0.053524 | 0.0511174704478853 | 0.040143 | 0.03056149493436\n", - "row 4 : Electronics (General) | 129 | 0.9339545990672654 | 0.0879766010692752 | 0.0817619115570942 | 0.006214689512181 | 72980.08699999998 | 453.548581276959 | 0.1511438395305996 | 0.0753610851527285 | 0.0757827543778711 | 81592.19108678924 | 6183.280976282475 | 0.8532615926517355 | 0.4294264362055603 | 0.050855 | 0.0817457737542162 | 0.03814125 | 0.14673840734826\n", - "row 5 : Semiconductor Equip | 30 | 1.5279822074255314 | 0.3289066823779071 | 0.1090871815415744 | 0.2198195008363326 | 44503.16999999999 | 9782.664615034451 | 0.2568363246651193 | 0.1039673141944747 | 0.1528690104706445 | 70041.38797094872 | 10707.15767110944 | 0.9278342360745604 | 0.3704014298865129 | 0.050855 | 0.1214195116015062 | 0.03814125 | 0.07216576392543\n", - "*/\n", - "\n", - "Table name: margin \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", - "row 1 : Aerospace/Defense | 70 | 0.1727498953821253 | 0.0496288894345249 | 0.0970899067124733 | 0.0853938467891159 | 0.0791811934555008 | 0.085557881411237 | 0.0793332940767511 | 0.0877011542663323 | 0.0814765669318464 | 0.1213211709923032 | 0.1854721753152544 | 0.163301436750081 | 0.8272501046178746 | 0.0419802657577777 | 0.0641510043229512 | 0.0116960599233574 | 0.00737353454744\n", - "row 2 : Total Market (without financials) | 5214 | 0.3341124303745417 | 0.0759000807387551 | 0.1349854587183475 | 0.1203243079012881 | 0.1121363594881506 | 0.1193029155861628 | 0.1111844718951524 | 0.122305912015432 | 0.1141874683244217 | 0.1649863910103977 | 0.308807292459029 | 0.2037377626528898 | 0.6658875696254583 | 0.038751371642492 | 0.1438209014486313 | 0.0146611508170593 | 0.01534583398930\n", - "row 3 : Software (Entertainment) | 84 | 0.6343190737595645 | 0.203484453534525 | 0.3536120749157563 | 0.2680484859792075 | 0.2543465254185649 | 0.2650138705989933 | 0.2514670319003695 | 0.2816776939314963 | 0.2681308552328725 | 0.290337329951565 | 0.4724580252979131 | 0.4795894540185941 | 0.3656809262404354 | 0.1892521240670291 | 0.182120695346348 | 0.0855635889365488 | 0.0140141013588\n", - "row 4 : Electronics (General) | 129 | 0.2736441477972378 | 0.0469265820090722 | 0.1081365384368007 | 0.0948166055844074 | 0.0870657487961617 | 0.095866096633002 | 0.0880294483869408 | 0.0979701850709138 | 0.0901335368248526 | 0.1483155870270167 | 0.274700126354909 | 0.1987893448489177 | 0.7263558522027622 | 0.0504737578219009 | 0.1263845393278922 | 0.0133199328523933 | 0.00864521049991\n", - "row 5 : Semiconductor Equip | 30 | 0.4427439580396038 | 0.1793844069139284 | 0.2615904123507109 | 0.2418675138617241 | 0.212500078456363 | 0.243380403940693 | 0.2138292741608367 | 0.2500123642381983 | 0.220461234458342 | 0.3032502002655829 | 0.3930952875683865 | 0.4064476540724488 | 0.5572560419603962 | 0.1031974538068659 | 0.0898450873028035 | 0.0197228984889867 | 0.00906011117075\n", - "*/\n", - "\n", - "Table name: DollarUS \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Company_Age_years_ (INTEGER) | Market_Cap_millions_ (INTEGER) | Book_Equity_millions_ (INTEGER) | Enteprise_Value_millions_ (INTEGER) | Invested_Capital_millions_ (INTEGER) | Total_Debt_including_leases_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Gross_Profit_millions_ (INTEGER) | EBITDA_millions_ (INTEGER) | EBIT_Operating_Income_millions_ (INTEGER) | Net_Income_millions_ (INTEGER)\n", - "row 1 : Aerospace/Defense | 70 | 53.82539682539682 | 788306.4130000002 | 145775.301 | 960283.3679021292 | 164231.158307403 | 200683.66790212895 | 387474.417 | 66936.16500000001 | 48918.45400000002 | 33151.49021957421 | 19229.925000000\n", - "row 2 : Total Market (without financials) | 5214 | 34.79463487696991 | 43163609.00299983 | 8566043.05699999 | 51078189.49081551 | 13583012.82046062 | 9754928.713815568 | 18726267.155 | 6256678.631000005 | 3370108.721999998 | 2234098.269636899 | 1421325.1889999\n", - "row 3 : Software (Entertainment) | 84 | 14.544117647058824 | 2773080.8990000025 | 403869.63 | 2781007.513051708 | 383348.20548245066 | 87421.2210517081 | 461576.65299999993 | 292786.8749999999 | 156210.96800000002 | 122324.21538965842 | 93923.673000000\n", - "row 4 : Electronics (General) | 129 | 39.15573770491803 | 269499.1279999999 | 72980.08699999998 | 297860.8621360993 | 66341.67523831947 | 46346.71613609941 | 136820.96000000014 | 37440.255 | 20098.71 | 13116.491372780118 | 6420.5399999999\n", - "row 5 : Semiconductor Equip | 30 | 42.5 | 414640.35 | 44503.16999999999 | 427920.4277709488 | 48010.57777094871 | 32250.19777094872 | 81597.89499999999 | 36126.97499999999 | 23479.94899999999 | 19859.32864581025 | 14637.\n", - "*/\n", - "\n", - "Table name: wacc \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", - "row 1 : Aerospace/Defense | 70 | 1.0764050153356324 | 0.088314630705439 | 0.7970822237983712 | 0.3640214967123067 | 0.050855 | 0.0727529390842114 | 0.03814125 | 0.2029177762016287 | 0.0781335598681736 | 0.07813355986817\n", - "row 2 : Total Market (without financials) | 5214 | 1.097522642247185 | 0.0892860415433705 | 0.815661408370401 | 0.4700345950094993 | 0.050855 | 0.0680489965490159 | 0.03814125 | 0.1843385916295989 | 0.0798580827010761 | 0.07985808270107\n", - "row 3 : Software (Entertainment) | 84 | 1.1083305740238332 | 0.0897832064050963 | 0.9694385050656324 | 0.5278932207822914 | 0.053524 | 0.0511174704478853 | 0.040143 | 0.0305614949343675 | 0.088266127488506 | 0.08826612748850\n", - "row 4 : Electronics (General) | 129 | 0.9339545990672654 | 0.0817619115570942 | 0.8532615926517355 | 0.4294264362055603 | 0.050855 | 0.0817457737542162 | 0.03814125 | 0.1467384073482645 | 0.0753610851527285 | 0.07536108515272\n", - "row 5 : Semiconductor Equip | 30 | 1.5279822074255314 | 0.1090871815415744 | 0.9278342360745604 | 0.3704014298865129 | 0.050855 | 0.1214195116015062 | 0.03814125 | 0.0721657639254397 | 0.1039673141944747 | 0.10396731419447\n", - "*/\n", - "\n", - "Table name: dbtfund \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Book_Debt_to_Capital (INTEGER) | Market_Debt_to_Capital_Unadjusted_ (INTEGER) | Market_D_E_unadjusted_ (INTEGER) | Market_Debt_to_Capital_adjusted_for_leases_ (INTEGER) | Market_D_E_adjusted_for_leases_ (INTEGER) | Interest_Coverage_Ratio (INTEGER) | Debt_to_EBITDA (INTEGER) | Effective_tax_rate (INTEGER) | Institutional_Holdings (INTEGER) | Std_dev_in_Stock_Prices (INTEGER) | EBITDA_EV (INTEGER) | Net_PP_E_Total_Assets (INTEGER) | Capital_Spending_Total_Assets (INTEGER)\n", - "row 1 : Aerospace/Defense | 70 | 0.5598223230984364 | 0.2003176164886141 | 0.250496472873423 | 0.2029177762016287 | 0.2545757139516368 | 3.740440705503212 | 4.269061419331231 | 0.0727529390842114 | 0.485818947368421 | 0.3640214967123067 | 0.0509416862096332 | 0.1258713546270282 | 0.01798339388026\n", - "row 2 : Total Market (without financials) | 5214 | 0.5001187576177913 | 0.1827761005242874 | 0.2236548645255564 | 0.184338591629599 | 0.225998912953215 | 6.428195471118638 | 3.157364796897844 | 0.0680489965490159 | 0.4702478911478115 | 0.4700345950094993 | 0.0659794083462176 | 0.32642976153906 | 0.04335448376240\n", - "row 3 : Software (Entertainment) | 84 | 0.1668568483248547 | 0.0310823698127216 | 0.0320794759475208 | 0.0305614949343675 | 0.0315249443617865 | 82.70045339060373 | 0.6523342120398788 | 0.0511174704478853 | 0.380174716981132 | 0.5278932207822914 | 0.0561706386145586 | 0.3664219490280888 | 0.08632671853148\n", - "row 4 : Electronics (General) | 129 | 0.3587835842228601 | 0.1457673736295513 | 0.1706413090880205 | 0.1467384073482645 | 0.1719735291169455 | 6.641668616399566 | 2.2839129110687453 | 0.0817457737542162 | 0.4456912621359226 | 0.4294264362055603 | 0.0674768408842396 | 0.182597141990381 | 0.03108461726876\n", - "row 5 : Semiconductor Equip | 30 | 0.3856389110464103 | 0.0719131674240597 | 0.0774853870348122 | 0.0721657639254397 | 0.0777787250347167 | 12.620099818076602 | 1.3033238138451468 | 0.1214195116015062 | 0.6812737931034483 | 0.3704014298865129 | 0.0548698951398693 | 0.1426826921482972 | 0.03734252665127\n", - "*/\n", - "\n", - "\u001b[0m\n", - "Prediction(\n", - " rationale='produce the sql. We need to first join the tables based on the Industry_Name column and then filter out the rows for Software industries, semiconductor industry, and aerospace. Finally, we need to select the EBITDA value and count the number of firms.',\n", - " sql=\"```sql\\nSELECT DISTINCT DollarUS.Industry_Name AS Industry, DollarUS.EBITDA_millions_ AS EBITDA, DollarUS.Number_of_firms AS Number_of_Firms\\nFROM DollarUS\\nJOIN EVA ON DollarUS.Industry_Name = EVA.Industry_Name\\nJOIN margin ON DollarUS.Industry_Name = margin.Industry_Name\\nWHERE DollarUS.Industry_Name IN ('Software (Entertainment)', 'Semiconductor Equip', 'Aerospace/Defense');\\n```\"\n", - ")\n", - "Extracted rows: Industry = Aerospace/Defense, EBITDA = 48918.45400000002, Number_of_Firms = 70\n", - " Industry = Semiconductor Equip, EBITDA = 23479.94899999999, Number_of_Firms = 30\n", - " Industry = Software (Entertainment), EBITDA = 156210.96800000002, Number_of_Firms = 84\n", - "\n" - ] - } - ], - "source": [ - "# tsql = TextToSQLQueryModule(\"US\")\n", - "# sq = tsql(question=\"What is the effective tax rate of the healthcare industry?\")\n", - "sq = tsql_(question=\"What is the EBITDA value and number of firms for all the Software industries, semiconductor industry and aerospace?\")\n", - "# sq = tsql(\"What is the debt to EBITDA ratio for software industry?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The EBITDA value for the Software (Entertainment) industry is $156,210.97 million with 84 firms. For the Semiconductor Equip industry, the EBITDA value is $23,479.95 million with 30 firms. Lastly, for the Aerospace/Defense industry, the EBITDA value is $48,918.45 million with 70 firms.\n" - ] - } - ], - "source": [ - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mTable name: betaIndia \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Beta_ (INTEGER) | D_E_Ratio (INTEGER) | Effective_Tax_rate (INTEGER) | Unlevered_beta (INTEGER) | Cash_Firm_value (INTEGER) | Unlevered_beta_corrected_for_cash (INTEGER) | HiLo_Risk (INTEGER) | Standard_deviation_of_equity (INTEGER) | Standard_deviation_in_operating_income_last_10_years_ (INTEGER) | Beta_2020 (INTEGER) | Beta_2021 (INTEGER) | Beta_2022 (INTEGER) | Beta_2023 (INTEGER) | Average_Beta_2019_23 (INTEGER)\n", - "row 1 : Semiconductor | 8 | 1.611640106554315 | 0.0408116499112951 | 0.1361426723519776 | 1.566877312715955 | 0.0034062258080553 | 1.572232692288697 | 0.5628222021115665 | 0.5010518370389238 | 12.74567091312641 | 0.73 | 1.16 | 1.9836144211648223 | 1.9836144211648223 | 1.48589230692366\n", - "row 2 : Total Market (without financials) | 3850 | 0.8198477825445601 | 0.1608082521880037 | 0.1670198672216159 | 0.7368982579173127 | 0.0296361733703686 | 0.7594040891618811 | 0.384976556985476 | 0.3699643172605134 | 0.4325539868550488 | 0.62 | 0.88 | 0.8646047974287592 | 0.8646047974287592 | 0.79772273680387\n", - "row 3 : Software (Entertainment) | 5 | 0.7830360024669405 | 0.0075317267581366 | 0.2752644087938206 | 0.7789293244818127 | 0.0099775560297291 | 0.7867794606333218 | 0.2025157685180063 | 0.1867409361734939 | 0.698956301092383 | 0.59 | 1.16 | 1.219451180957866 | 1.219451180957866 | 0.99513636450981\n", - "row 4 : Software (Internet) | 6 | -0.0057433681694908 | 0.0336472713198297 | 0.0983609410926519 | -0.0056112069085913 | 0.0328328215881574 | -0.0058016928550091 | 0.512617043205324 | 0.504931470076568 | 10.948979663023987 | 1.04 | 0.52 | 0.4883816218840528 | 0.4883816218840528 | 0.50619231018261\n", - "row 5 : Software (System & Application) | 73 | 1.164011103524074 | 0.014057190040673 | 0.1609424721087531 | 1.1526688044177154 | 0.0427366294396399 | 1.2041292290782728 | 0.3973005076855119 | 0.4048728665269909 | 0.3296550990654787 | 0.83 | 1.2 | 1.2407987165306618 | 1.2407987165306618 | 1.14314533242791\n", - "*/\n", - "\n", - "Table name: totalbetaIndia \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Unlevered_Beta (INTEGER) | Average_Levered_Beta (INTEGER) | Average_correlation_with_the_market (INTEGER) | Total_Unlevered_Beta (INTEGER) | Total_Levered_Beta (INTEGER)\n", - "row 1 : Semiconductor | 8 | 1.572232692288697 | 1.611640106554315 | 0.1520316120175535 | 10.34148537547025 | 10.6006907719181\n", - "row 2 : Total Market (without financials) | 3850 | 0.7594040891618811 | 0.8198477825445601 | 0.138919459674997 | 5.466506211142138 | 5.9016050340434\n", - "row 3 : Software (Entertainment) | 5 | 0.7867794606333218 | 0.7830360024669405 | 0.2362678264192356 | 3.3300321612018964 | 3.3141880311605\n", - "row 4 : Software (Internet) | 6 | -0.0058016928550091 | -0.0057433681694908 | 0.0877398209734128 | -0.0661238282759565 | -0.06545908238439\n", - "row 5 : Software (System & Application) | 73 | 1.2041292290782728 | 1.164011103524074 | 0.1534528684231733 | 7.846899451613207 | 7.5854633118626\n", - "*/\n", - "\n", - "Table name: psIndia \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Price_Sales (INTEGER) | Net_Margin (INTEGER) | EV_Sales (INTEGER) | Pre_tax_Operating_Margin (INTEGER)\n", - "row 1 : Semiconductor | 8 | 6.380530138666162 | 0.0160833883595887 | 6.618309593434581 | 0.04773134609942\n", - "row 2 : Total Market (without financials) | 3850 | 2.210898698094238 | 0.0681170556023806 | 2.490370305272766 | 0.11262992702128\n", - "row 3 : Semiconductor Equip | 1 | 4.350600126342388 | 0.0243840808591282 | 4.593177511054959 | 0.03006948831332\n", - "row 4 : Software (Entertainment) | 5 | 17.543944376879494 | 0.078800724226794 | 17.499716487891103 | 0.20023775912029\n", - "row 5 : Software (Internet) | 6 | 11.275821937736398 | 0.1813354669770148 | 11.272548734361362 | 0.23581611870817\n", - "*/\n", - "\n", - "Table name: waccIndia \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", - "row 1 : Semiconductor | 8 | 1.611640106554315 | 0.164669092321892 | 0.9607886307625656 | 0.5010518370389238 | 0.077424 | 0.1361426723519776 | 0.0541968 | 0.0392113692374344 | 0.1603373224771525 | 0.18286814427282\n", - "row 2 : Total Market (without financials) | 3850 | 0.8198477825445601 | 0.1028301118167301 | 0.8614687207082679 | 0.3699643172605134 | 0.074755 | 0.1670198672216159 | 0.0523285 | 0.1385312792917321 | 0.095834058925464 | 0.11711239016673\n", - "row 3 : Software (Entertainment) | 5 | 0.7830360024669405 | 0.099955111792668 | 0.9925245760921386 | 0.1867409361734939 | 0.068943 | 0.2752644087938206 | 0.0482601 | 0.0074754239078613 | 0.0995686696655959 | 0.12091951762026\n", - "row 4 : Software (Internet) | 6 | -0.0057433681694908 | 0.0383514429459627 | 0.967448014179086 | 0.504931470076568 | 0.077424 | 0.0983609410926519 | 0.0541968 | 0.0325519858209141 | 0.0388672407841131 | 0.05903942021681\n", - "row 5 : Software (System & Application) | 73 | 1.164011103524074 | 0.1297092671852301 | 0.9861376752921508 | 0.4048728665269909 | 0.074755 | 0.1609424721087531 | 0.0523285 | 0.0138623247078493 | 0.128636589864366 | 0.15055186345396\n", - "*/\n", - "\n", - "Table name: pbvIndia \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | PBV (INTEGER) | ROE (INTEGER) | EV_Invested_Capital (INTEGER) | ROIC (INTEGER)\n", - "row 1 : Semiconductor | 8 | 8.627551020408164 | 0.0218198105963654 | 7.1386259299656505 | 0.04466875019704\n", - "row 2 : Total Market (without financials) | 3850 | 4.152459768346949 | 0.1526067560105244 | 3.119937106367408 | 0.12893652513884\n", - "row 3 : Semiconductor Equip | 1 | 5.957612456747405 | 0.0373307543520309 | 4.721005520284547 | 0.02701816743642\n", - "row 4 : Software (Entertainment) | 5 | 2.9646582024861243 | 0.0182539574699368 | 3.0371036678952725 | 0.03630006976547\n", - "row 5 : Software (Internet) | 6 | 10.946194040389774 | 0.2266569688153468 | 10.876201838725525 | 0.24757464461570\n", - "*/\n", - "\n", - "\u001b[0m\n", - "Prediction(\n", - " rationale='produce the SQL. We need to retrieve the beta value and number of firms for the Software industries and semiconductor industry. We have the relevant information in the tables betaIndia and totalbetaIndia.',\n", - " sql=\"```sql\\nSELECT DISTINCT betaIndia.Industry_Name AS Industry, betaIndia.Beta_ AS Beta_Value, betaIndia.Number_of_firms AS Number_of_Firms\\nFROM betaIndia\\nWHERE betaIndia.Industry_Name LIKE 'Software%' OR betaIndia.Industry_Name = 'Semiconductor'\\n\\nUNION\\n\\nSELECT DISTINCT totalbetaIndia.Industry_Name AS Industry, totalbetaIndia.Average_Unlevered_Beta AS Beta_Value, totalbetaIndia.Number_of_firms AS Number_of_Firms\\nFROM totalbetaIndia\\nWHERE totalbetaIndia.Industry_Name LIKE 'Software%' OR totalbetaIndia.Industry_Name = 'Semiconductor';\\n```\"\n", - ")\n", - "Extracted rows: Industry = Semiconductor, Beta_Value = 1.572232692288697, Number_of_Firms = 8\n", - " Industry = Semiconductor, Beta_Value = 1.611640106554315, Number_of_Firms = 8\n", - " Industry = Software (Entertainment), Beta_Value = 0.7830360024669405, Number_of_Firms = 5\n", - " Industry = Software (Entertainment), Beta_Value = 0.7867794606333218, Number_of_Firms = 5\n", - " Industry = Software (Internet), Beta_Value = -0.0058016928550091, Number_of_Firms = 6\n", - " Industry = Software (Internet), Beta_Value = -0.0057433681694908, Number_of_Firms = 6\n", - " Industry = Software (System & Application), Beta_Value = 1.164011103524074, Number_of_Firms = 73\n", - " Industry = Software (System & Application), Beta_Value = 1.2041292290782728, Number_of_Firms = 73\n", - "\n", - "The beta value and number of firms for the Software industries and semiconductor industry are as follows:\n", - "\n", - "- Industry: Semiconductor, Beta Value: 1.572232692288697, Number of Firms: 8\n", - "- Industry: Semiconductor, Beta Value: 1.611640106554315, Number of Firms: 8\n", - "- Industry: Software (Entertainment), Beta Value: 0.7830360024669405, Number of Firms: 5\n", - "- Industry: Software (Entertainment), Beta Value: 0.7867794606333218, Number of Firms: 5\n", - "- Industry: Software (Internet), Beta Value: -0.0058016928550091, Number of Firms: 6\n", - "- Industry: Software (Internet), Beta Value: -0.0057433681694908, Number of Firms: 6\n", - "- Industry: Software (System & Application), Beta Value: 1.164011103524074, Number of Firms: 73\n", - "- Industry: Software (System & Application), Beta Value: 1.2041292290782728, Number of Firms: 73\n" - ] - } - ], - "source": [ - "tsql = TextToSQLQueryModule(\"India\")\n", - "sq = tsql(question=\"What is the beta value and number of firms for all the Software industries and semiconductor industry?\")\n", - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The beta value and number of firms for the Software industries and semiconductor industry are as follows:\n", - "\n", - "- Industry: Semiconductor, Beta Value: 1.572232692288697, Number of Firms: 8\n", - "- Industry: Semiconductor, Beta Value: 1.611640106554315, Number of Firms: 8\n", - "- Industry: Software (Entertainment), Beta Value: 0.7830360024669405, Number of Firms: 5\n", - "- Industry: Software (Entertainment), Beta Value: 0.7867794606333218, Number of Firms: 5\n", - "- Industry: Software (Internet), Beta Value: -0.0058016928550091, Number of Firms: 6\n", - "- Industry: Software (Internet), Beta Value: -0.0057433681694908, Number of Firms: 6\n", - "- Industry: Software (System & Application), Beta Value: 1.164011103524074, Number of Firms: 73\n", - "- Industry: Software (System & Application), Beta Value: 1.2041292290782728, Number of Firms: 73\n" - ] - } - ], - "source": [ - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mTable name: betaChina \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Beta_ (INTEGER) | D_E_Ratio (INTEGER) | Effective_Tax_rate (INTEGER) | Unlevered_beta (INTEGER) | Cash_Firm_value (INTEGER) | Unlevered_beta_corrected_for_cash (INTEGER) | HiLo_Risk (INTEGER) | Standard_deviation_of_equity (INTEGER) | Standard_deviation_in_operating_income_last_10_years_ (INTEGER)\n", - "row 1 : Semiconductor | 173 | 1.3766066410015918 | 0.1214640196981001 | 0.0455831075871332 | 1.261670924483595 | 0.1353894923931989 | 1.4592361686371793 | 0.3256484142873909 | 0.3505300755906819 | 0.93670130463571\n", - "row 2 : Total Market (without financials) | 7161 | 1.0686360714269856 | 0.4705306537504954 | 0.0996397852701986 | 0.7898866574410776 | 0.1404090933852625 | 0.9189099737592956 | 0.3186107366027715 | 0.3211951486316037 | 0.26343082981471\n", - "row 3 : Semiconductor Equip | 59 | 1.1458070496329555 | 0.1005963129277871 | 0.072001108546688 | 1.0654237722383604 | 0.0896726175453628 | 1.170374299151055 | 0.3370962305052479 | 0.3780387083633861 | 1.05671095308164\n", - "row 4 : Software (Entertainment) | 33 | 1.9284684177392413 | 0.134930367400854 | 0.0960440405122507 | 1.7512461980545997 | 0.0533537078374321 | 1.849947770939832 | 0.5242121688667037 | 0.4696202053064469 | 0.48614502813710\n", - "row 5 : Software (Internet) | 19 | 0.9517387989546392 | 0.2511530032375074 | 0.0593218951020171 | 0.8008810401099454 | 0.0847676071125495 | 0.8750575769977502 | 0.3425492316782614 | 0.3399832570802881 | 0.42653552101587\n", - "*/\n", - "\n", - "Table name: totalbetaChina \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Unlevered_Beta (INTEGER) | Average_Levered_Beta (INTEGER) | Average_correlation_with_the_market (INTEGER) | Total_Unlevered_Beta (INTEGER) | Total_Levered_Beta (INTEGER)\n", - "row 1 : Semiconductor | 173 | 1.4592361686371793 | 1.3766066410015918 | 0.2051159812465771 | 7.114200267423239 | 6.7113573142149\n", - "row 2 : Total Market (without financials) | 7161 | 0.9189099737592956 | 1.0686360714269856 | 0.1807939202400685 | 5.082637582829746 | 5.9107965024929\n", - "row 3 : Semiconductor Equip | 59 | 1.170374299151055 | 1.1458070496329555 | 0.1422791350917505 | 8.225902542887432 | 8.053233166578\n", - "row 4 : Software (Entertainment) | 33 | 1.849947770939832 | 1.9284684177392413 | 0.189683512387109 | 9.75281271239026 | 10.1667688112164\n", - "row 5 : Software (Internet) | 19 | 0.8750575769977502 | 0.9517387989546392 | 0.1824645387000501 | 4.795767896776043 | 5.2160206346679\n", - "*/\n", - "\n", - "Table name: EVAChina \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | ROE (INTEGER) | Cost_of_Equity (INTEGER) | _ROE_COE_ (INTEGER) | BV_of_Equity (INTEGER) | Equity_EVA (INTEGER) | ROC (INTEGER) | Cost_of_Capital (INTEGER) | _ROC_WACC_ (INTEGER) | BV_of_Capital (INTEGER) | EVA (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER)\n", - "row 1 : Semiconductor | 173 | 1.3766066410015918 | 0.0962582854914372 | 0.1163029538883896 | -0.0200446683969523 | 126261.36999999998 | -2530.8672929949053 | 0.0928822041397873 | 0.1084628628354935 | -0.0155806586957062 | 138747.35577016464 | -2161.775195186669 | 0.8916915589224178 | 0.3505300755906819 | 0.0585549999999999 | 0.0455831075871332 | 0.04391625 | 0.10830844107758\n", - "row 2 : Total Market (without financials) | 7161 | 1.0686360714269856 | 0.0639810392810329 | 0.0989642108213393 | -0.0349831715403063 | 7039463.864000044 | -246262.7719061013 | 0.0650803131143393 | 0.0813503291067108 | -0.0162700159923715 | 10535415.2412372 | -171411.37446120442 | 0.6800266267483532 | 0.3211951486316037 | 0.0585549999999999 | 0.0996397852701986 | 0.04391625 | 0.31997337325164\n", - "row 3 : Semiconductor Equip | 59 | 1.1458070496329555 | 0.2052549059382613 | 0.1033089368943354 | 0.1019459690439259 | 47325.30000000001 | 4824.623568794508 | 0.1911922171025874 | 0.0978803476411596 | 0.0933118694614277 | 50881.7695261246 | 4747.873035988193 | 0.9085983555040426 | 0.3780387083633861 | 0.0585549999999999 | 0.072001108546688 | 0.04391625 | 0.09140164449595\n", - "row 4 : Software (Entertainment) | 33 | 1.9284684177392413 | 0.2294528801199007 | 0.1473727719187193 | 0.0820801082011814 | 117637.29 | 9655.68149169376 | 0.0906573095366518 | 0.1350729631256245 | -0.0444156535889726 | 176984.04958861455 | -7860.862237301461 | 0.8811113251733117 | 0.4696202053064469 | 0.0585549999999999 | 0.0960440405122507 | 0.04391625 | 0.11888867482668\n", - "row 5 : Software (Internet) | 19 | 0.9517387989546392 | 0.0250537522727571 | 0.0923828943811461 | -0.067329142108389 | 8376.390000000001 | -563.9751526652892 | 0.034746695140248 | 0.0826538338572364 | -0.0479071387169884 | 11768.570206159107 | -563.7985253670817 | 0.7992627579619607 | 0.3399832570802881 | 0.0585549999999999 | 0.0593218951020171 | 0.04391625 | 0.20073724203803\n", - "*/\n", - "\n", - "Table name: waccChina \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", - "row 1 : Semiconductor | 173 | 1.3766066410015918 | 0.1163029538883896 | 0.8916915589224178 | 0.3505300755906819 | 0.0585549999999999 | 0.0455831075871332 | 0.04391625 | 0.1083084410775822 | 0.1084628628354936 | 0.10846286283549\n", - "row 2 : Total Market (without financials) | 7161 | 1.0686360714269856 | 0.0989642108213393 | 0.6800266267483532 | 0.3211951486316037 | 0.0585549999999999 | 0.0996397852701986 | 0.04391625 | 0.3199733732516467 | 0.0813503291067108 | 0.08135032910671\n", - "row 3 : Semiconductor Equip | 59 | 1.1458070496329555 | 0.1033089368943354 | 0.9085983555040426 | 0.3780387083633861 | 0.0585549999999999 | 0.072001108546688 | 0.04391625 | 0.0914016444959574 | 0.0978803476411596 | 0.09788034764115\n", - "row 4 : Software (Entertainment) | 33 | 1.9284684177392413 | 0.1473727719187193 | 0.8811113251733117 | 0.4696202053064469 | 0.0585549999999999 | 0.0960440405122507 | 0.04391625 | 0.1188886748266883 | 0.1350729631256245 | 0.13507296312562\n", - "row 5 : Software (Internet) | 19 | 0.9517387989546392 | 0.0923828943811461 | 0.7992627579619607 | 0.3399832570802881 | 0.0585549999999999 | 0.0593218951020171 | 0.04391625 | 0.2007372420380393 | 0.0826538338572364 | 0.08265383385723\n", - "*/\n", - "\n", - "Table name: pbvChina \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | PBV (INTEGER) | ROE (INTEGER) | EV_Invested_Capital (INTEGER) | ROIC (INTEGER)\n", - "row 1 : Semiconductor | 173 | 2.73256217631175 | 0.0962582854914372 | 2.4500098154005925 | 0.09288220413978\n", - "row 2 : Total Market (without financials) | 7161 | 1.4084499744108725 | 0.0639810392810329 | 1.2430260813341163 | 0.06508031311433\n", - "row 3 : Semiconductor Equip | 59 | 2.954264696454902 | 0.2052549059382613 | 2.6326043462726965 | 0.19119221710258\n", - "row 4 : Software (Entertainment) | 33 | 3.1135265373627137 | 0.2294528801199007 | 2.248186099053892 | 0.09065730953665\n", - "row 5 : Software (Internet) | 19 | 2.6160739269602717 | 0.0250537522727571 | 2.044727771310165 | 0.0347466951402\n", - "*/\n", - "\n", - "\u001b[0m\n", - "Prediction(\n", - " rationale='produce the SQL. We need to retrieve the beta value and number of firms for the Software industries and semiconductor industry. We have tables that contain this information for each industry separately.',\n", - " sql=\"```sql\\nSELECT DISTINCT betaChina.Beta_ AS Beta_Value, betaChina.Number_of_firms AS Number_of_Firms\\nFROM betaChina\\nWHERE betaChina.Industry_Name IN ('Software (Entertainment)', 'Software (Internet)', 'Semiconductor')\\nUNION\\nSELECT DISTINCT EVAChina.Beta AS Beta_Value, EVAChina.Number_of_Firms AS Number_of_Firms\\nFROM EVAChina\\nWHERE EVAChina.Industry_Name IN ('Software (Entertainment)', 'Software (Internet)', 'Semiconductor')\\n```\"\n", - ")\n", - "Extracted rows: Beta_Value = 0.9517387989546392, Number_of_Firms = 19\n", - " Beta_Value = 1.3766066410015918, Number_of_Firms = 173\n", - " Beta_Value = 1.9284684177392413, Number_of_Firms = 33\n", - "\n", - "Beta value and number of firms for the Software (Entertainment) industry are Beta_Value = 0.9517387989546392, Number_of_Firms = 19. For the Software (Internet) industry, the values are Beta_Value = 1.3766066410015918, Number_of_Firms = 173. And for the Semiconductor industry, the values are Beta_Value = 1.9284684177392413, Number_of_Firms = 33.\n" - ] - } - ], - "source": [ - "tsql = TextToSQLQueryModule(\"China\")\n", - "sq = tsql(question=\"What is the beta value and number of firms for all the Software industries and semiconductor industry?\")\n", - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beta value and number of firms for the Software (Entertainment) industry are Beta_Value = 0.9517387989546392, Number_of_Firms = 19. For the Software (Internet) industry, the values are Beta_Value = 1.3766066410015918, Number_of_Firms = 173. And for the Semiconductor industry, the values are Beta_Value = 1.9284684177392413, Number_of_Firms = 33.\n" - ] - } - ], - "source": [ - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mTable name: taxrate \n", - "/* \n", - "col : Industry_name (VARCHAR) | Number_of_firms (INTEGER) | Total_Taxable_Income (INTEGER) | Total_Taxes_Paid_Accrual_ (INTEGER) | Total_Cash_Taxes_Paid (INTEGER) | Cash_Taxes_Accrual_Taxes (INTEGER) | Average_across_all_companies (INTEGER) | Average_across_only_money_making_companies (INTEGER) | Aggregate_tax_rate (INTEGER) | Average_across_only_money_making_companies2 (INTEGER) | Aggregate_tax_rate3 (INTEGER)\n", - "row 1 : Healthcare Products | 230 | 26657.20900000001 | 5486.0970000000025 | 7358.37 | 1.341275956294611 | 0.0481441302819232 | 0.2058016276197557 | 0.2473315283348905 | 0.2760367748926752 | 0.35300275681386\n", - "row 2 : Hospitals/Healthcare Facilities | 32 | 12803.409999999998 | 2598.52 | 2117.581 | 0.8149181072302696 | 0.0685657680171947 | 0.2029553064378943 | 0.2241515389301438 | 0.1653919541747081 | 0.18582584339767\n", - "row 3 : Healthcare Support Services | 119 | 76367.83500000002 | 17205.211 | 18119.770000000008 | 1.0531559304910594 | 0.0808427903603178 | 0.2252939473798097 | 0.2520069365480554 | 0.2372696567867873 | 0.2683567809591\n", - "row 4 : Heathcare Information and Technology | 128 | 21838.42000000001 | 2553.3450000000003 | 6092.359 | 2.386030481583961 | 0.0311109029990876 | 0.1169198595869114 | 0.2128897982354291 | 0.2789743488768875 | 0.5756510369422\n", - "row 5 : Total Market (without financials) | 5214 | 2091519.969999999 | 405892.603 | 448383.9540000016 | 1.1046861920762858 | 0.0680489965490159 | 0.1940658510661986 | 0.2166068386944213 | 0.2143818660263625 | 0.25088890170136\n", - "*/\n", - "\n", - "Table name: margin \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", - "row 1 : Healthcare Products | 230 | 0.5563922869375535 | 0.0818669682093114 | 0.1558110098462976 | 0.1329496754079793 | 0.1265489289141982 | 0.1339775386003306 | 0.1275273065271049 | 0.1383573960583481 | 0.1319071639851224 | 0.209192854485968 | 0.5282884406553701 | 0.294308887098031 | 0.4436077130624465 | 0.0851160326120629 | 0.3190955861694019 | 0.0228613344383182 | 0.00977793897038\n", - "row 2 : Hospitals/Healthcare Facilities | 32 | 0.35988660694384 | 0.0512378794350515 | 0.120654788689701 | 0.1156935415013266 | 0.107760924973659 | 0.1154810689328797 | 0.1075630207500502 | 0.1154805210386438 | 0.1075624728558143 | 0.1535618716018076 | 0.1846309639990737 | 0.1535712434768955 | 0.64011339305616 | 9.371875087928916e-06 | 0.031069092397266 | 0.0049612471883743 | 0.02033043025564\n", - "row 3 : Healthcare Support Services | 119 | 0.1436810802508208 | 0.0225465585118185 | 0.0418808661001017 | 0.0391834198903671 | 0.0360157228905698 | 0.0386507190050279 | 0.0355260870312289 | 0.0386351017430003 | 0.0355104697692013 | 0.0461573478838079 | 0.1419758713498951 | 0.0469153240204038 | 0.8563189197491792 | 0.0007579761365958 | 0.0958185234660871 | 0.0026974462097345 | 0.0040901341131\n", - "row 4 : Heathcare Information and Technology | 128 | 0.4766949090992926 | 0.0572239502258181 | 0.1655618579641873 | 0.1400806639108432 | 0.1357226279638652 | 0.1403673029983578 | 0.1360003494505323 | 0.1414922855264892 | 0.1371253319786638 | 0.2365968665611617 | 0.4859769860183378 | 0.3029234453206219 | 0.5233050909007073 | 0.0663265787594602 | 0.249380119457176 | 0.0254811940533441 | 0.01221262918932\n", - "row 5 : Total Market (without financials) | 5214 | 0.3341124303745417 | 0.0759000807387551 | 0.1349854587183475 | 0.1203243079012881 | 0.1121363594881506 | 0.1193029155861628 | 0.1111844718951524 | 0.122305912015432 | 0.1141874683244217 | 0.1649863910103977 | 0.308807292459029 | 0.2037377626528898 | 0.6658875696254583 | 0.038751371642492 | 0.1438209014486313 | 0.0146611508170593 | 0.01534583398930\n", - "*/\n", - "\n", - "Table name: wacc \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", - "row 1 : Healthcare Products | 230 | 1.062079895442844 | 0.0876556751903708 | 0.8876027024944009 | 0.5220553450431473 | 0.053524 | 0.0481441302819232 | 0.040143 | 0.1123972975055991 | 0.0823153789017118 | 0.08231537890171\n", - "row 2 : Hospitals/Healthcare Facilities | 32 | 0.879576324903561 | 0.0792605109455638 | 0.5563496280559845 | 0.4632806494431785 | 0.050855 | 0.0685657680171947 | 0.03814125 | 0.4436503719440154 | 0.0610179355330013 | 0.06101793553300\n", - "row 3 : Healthcare Support Services | 119 | 1.0326229061406076 | 0.0863006536824679 | 0.7882476948159491 | 0.495257334489641 | 0.050855 | 0.0808427903603178 | 0.03814125 | 0.2117523051840508 | 0.076102788936416 | 0.0761027889364\n", - "row 4 : Heathcare Information and Technology | 128 | 1.2747542568245518 | 0.0974386958139293 | 0.8615496523681622 | 0.5415316524692178 | 0.053524 | 0.0311109029990876 | 0.040143 | 0.1384503476318378 | 0.0895060868106828 | 0.08950608681068\n", - "row 5 : Total Market (without financials) | 5214 | 1.097522642247185 | 0.0892860415433705 | 0.815661408370401 | 0.4700345950094993 | 0.050855 | 0.0680489965490159 | 0.03814125 | 0.1843385916295989 | 0.0798580827010761 | 0.07985808270107\n", - "*/\n", - "\n", - "Table name: histgr \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | CAGR_in_Net_Income_Last_5_years (INTEGER) | CAGR_in_Revenues_Last_5_years (INTEGER) | Expected_Growth_in_Revenues_Next_2_years (INTEGER) | Expected_growth_in_Revenues_Next_5_years (INTEGER) | Expected_Growth_in_EPS_Next_5_years (INTEGER)\n", - "row 1 : Healthcare Products | 230 | 0.1271279069767442 | 0.1794840000000001 | 0.5909131578947368 | 0.2743437920966664 | 0.16686195121951\n", - "row 2 : Hospitals/Healthcare Facilities | 32 | 0.0409985714285714 | 0.181068947368421 | 0.0413333333333333 | 0.0408655475254498 | 0.10645555555555\n", - "row 3 : Healthcare Support Services | 119 | 0.0625710344827586 | 0.1242187719298245 | 0.0938797014925373 | 0.1106991073675205 | 0.11186538461538\n", - "row 4 : Heathcare Information and Technology | 128 | 0.2291124999999999 | 0.1966670689655173 | 0.1482884810126581 | 0.1667594853751504 | 0.10272187499999\n", - "row 5 : Total Market (without financials) | 5214 | 0.1008838061178445 | 0.1270981652178585 | 0.2757367288045685 | 0.2849241661766458 | 0.14538921052109\n", - "*/\n", - "\n", - "Table name: roe \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | ROE_unadjusted_ (INTEGER) | ROE_adjusted_for_R_D_ (INTEGER)\n", - "row 1 : Healthcare Products | 230 | 0.0830579792856188 | 0.06980113651478\n", - "row 2 : Hospitals/Healthcare Facilities | 32 | 0.6196567951123925 | 0.61942896417013\n", - "row 3 : Healthcare Support Services | 119 | 0.1562031003418411 | 0.1536389472830\n", - "row 4 : Heathcare Information and Technology | 128 | 0.0517765805634577 | 0.04477886360563\n", - "row 5 : Total Market (without financials) | 5214 | 0.1659255247192018 | 0.13839857100630\n", - "*/\n", - "\n", - "\u001b[0m\n", - "Prediction(\n", - " rationale='produce the SQL. We need to calculate the average tax rate of all healthcare industries. To do this, we need to join the taxrate table with the industry_name column as the common key.',\n", - " sql=\"```sql\\nSELECT AVG(taxrate.Aggregate_tax_rate) AS Average_Tax_Rate\\nFROM taxrate\\nWHERE taxrate.Industry_name LIKE '%Healthcare%'\\n```\"\n", - ")\n", - "Extracted rows: Average_Tax_Rate = 0.24116333460436337\n", - "\n", - "The average tax rate of all healthcare industries is 0.24116333460436337.\n" - ] - } - ], - "source": [ - "tsql = TextToSQLQueryModule(\"US\")\n", - "sq = tsql(question=\"What is the average tax rate of all healthcare industries?\")\n", - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The average tax rate of all healthcare industries is 0.24116333460436337.\n" - ] - } - ], - "source": [ - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mTable name: taxrate \n", - "/* \n", - "col : Industry_name (VARCHAR) | Number_of_firms (INTEGER) | Total_Taxable_Income (INTEGER) | Total_Taxes_Paid_Accrual_ (INTEGER) | Total_Cash_Taxes_Paid (INTEGER) | Cash_Taxes_Accrual_Taxes (INTEGER) | Average_across_all_companies (INTEGER) | Average_across_only_money_making_companies (INTEGER) | Aggregate_tax_rate (INTEGER) | Average_across_only_money_making_companies2 (INTEGER) | Aggregate_tax_rate3 (INTEGER)\n", - "row 1 : Hospitals/Healthcare Facilities | 32 | 12803.409999999998 | 2598.52 | 2117.581 | 0.8149181072302696 | 0.0685657680171947 | 0.2029553064378943 | 0.2241515389301438 | 0.1653919541747081 | 0.18582584339767\n", - "row 2 : Healthcare Products | 230 | 26657.20900000001 | 5486.0970000000025 | 7358.37 | 1.341275956294611 | 0.0481441302819232 | 0.2058016276197557 | 0.2473315283348905 | 0.2760367748926752 | 0.35300275681386\n", - "row 3 : Healthcare Support Services | 119 | 76367.83500000002 | 17205.211 | 18119.770000000008 | 1.0531559304910594 | 0.0808427903603178 | 0.2252939473798097 | 0.2520069365480554 | 0.2372696567867873 | 0.2683567809591\n", - "row 4 : Heathcare Information and Technology | 128 | 21838.42000000001 | 2553.3450000000003 | 6092.359 | 2.386030481583961 | 0.0311109029990876 | 0.1169198595869114 | 0.2128897982354291 | 0.2789743488768875 | 0.5756510369422\n", - "row 5 : Total Market (without financials) | 5214 | 2091519.969999999 | 405892.603 | 448383.9540000016 | 1.1046861920762858 | 0.0680489965490159 | 0.1940658510661986 | 0.2166068386944213 | 0.2143818660263625 | 0.25088890170136\n", - "*/\n", - "\n", - "Table name: Employee \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Number_of_Employees (INTEGER) | Market_Capitalization_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Mkt_Cap_per_Employee_ (INTEGER) | Revenues_per_Employee_ (INTEGER) | Stock_based_Compensation_millions_ (INTEGER) | Stock_based_Compensation_as_of_Revenue (INTEGER)\n", - "row 1 : Hospitals/Healthcare Facilities | 32 | 716231 | 6704.900000000001 | 7793.799999999999 | 9361.365257856754 | 10881.684819562402 | 688.1889999999999 | 0.00496124718837\n", - "row 2 : Healthcare Products | 230 | 692739 | 132639.79999999996 | 34790.487 | 191471.5354556333 | 50221.63758645031 | 5056.955000000004 | 0.02286133443831\n", - "row 3 : Healthcare Support Services | 119 | 1634485 | 55806.99999999999 | 277699.3 | 34143.47638552816 | 169900.18262633184 | 6016.255999999999 | 0.00269744620973\n", - "row 4 : Heathcare Information and Technology | 128 | 535393 | 104368.758 | 29258.17 | 194938.59277203845 | 54648.02490880531 | 3955.762000000001 | 0.02548119405334\n", - "row 5 : Total Market (without financials) | 5214 | 37972621 | 10850254.874000004 | 3275178.419000001 | 285738.8978759197 | 86251.04964442673 | 274548.627 | 0.01466115081705\n", - "*/\n", - "\n", - "Table name: margin \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", - "row 1 : Hospitals/Healthcare Facilities | 32 | 0.35988660694384 | 0.0512378794350515 | 0.120654788689701 | 0.1156935415013266 | 0.107760924973659 | 0.1154810689328797 | 0.1075630207500502 | 0.1154805210386438 | 0.1075624728558143 | 0.1535618716018076 | 0.1846309639990737 | 0.1535712434768955 | 0.64011339305616 | 9.371875087928916e-06 | 0.031069092397266 | 0.0049612471883743 | 0.02033043025564\n", - "row 2 : Healthcare Products | 230 | 0.5563922869375535 | 0.0818669682093114 | 0.1558110098462976 | 0.1329496754079793 | 0.1265489289141982 | 0.1339775386003306 | 0.1275273065271049 | 0.1383573960583481 | 0.1319071639851224 | 0.209192854485968 | 0.5282884406553701 | 0.294308887098031 | 0.4436077130624465 | 0.0851160326120629 | 0.3190955861694019 | 0.0228613344383182 | 0.00977793897038\n", - "row 3 : Healthcare Support Services | 119 | 0.1436810802508208 | 0.0225465585118185 | 0.0418808661001017 | 0.0391834198903671 | 0.0360157228905698 | 0.0386507190050279 | 0.0355260870312289 | 0.0386351017430003 | 0.0355104697692013 | 0.0461573478838079 | 0.1419758713498951 | 0.0469153240204038 | 0.8563189197491792 | 0.0007579761365958 | 0.0958185234660871 | 0.0026974462097345 | 0.0040901341131\n", - "row 4 : Heathcare Information and Technology | 128 | 0.4766949090992926 | 0.0572239502258181 | 0.1655618579641873 | 0.1400806639108432 | 0.1357226279638652 | 0.1403673029983578 | 0.1360003494505323 | 0.1414922855264892 | 0.1371253319786638 | 0.2365968665611617 | 0.4859769860183378 | 0.3029234453206219 | 0.5233050909007073 | 0.0663265787594602 | 0.249380119457176 | 0.0254811940533441 | 0.01221262918932\n", - "row 5 : Total Market (without financials) | 5214 | 0.3341124303745417 | 0.0759000807387551 | 0.1349854587183475 | 0.1203243079012881 | 0.1121363594881506 | 0.1193029155861628 | 0.1111844718951524 | 0.122305912015432 | 0.1141874683244217 | 0.1649863910103977 | 0.308807292459029 | 0.2037377626528898 | 0.6658875696254583 | 0.038751371642492 | 0.1438209014486313 | 0.0146611508170593 | 0.01534583398930\n", - "*/\n", - "\n", - "Table name: DollarUS \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Company_Age_years_ (INTEGER) | Market_Cap_millions_ (INTEGER) | Book_Equity_millions_ (INTEGER) | Enteprise_Value_millions_ (INTEGER) | Invested_Capital_millions_ (INTEGER) | Total_Debt_including_leases_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Gross_Profit_millions_ (INTEGER) | EBITDA_millions_ (INTEGER) | EBIT_Operating_Income_millions_ (INTEGER) | Net_Income_millions_ (INTEGER)\n", - "row 1 : Hospitals/Healthcare Facilities | 32 | 25.23333333333333 | 122470.973 | 11469.825000000004 | 216360.12943388568 | 87395.24268778635 | 97662.13543388566 | 138712.903 | 49920.916000000005 | 24233.965 | 16018.714313222865 | 7107.3549999999\n", - "row 2 : Healthcare Products | 230 | 25.382075471698112 | 1050360.2549999994 | 218029.299 | 1141634.0060547118 | 175743.51313973693 | 133007.31705471186 | 221201.21699999992 | 123074.651 | 47150.65200000003 | 29635.99458905761 | 18109.072999999\n", - "row 3 : Healthcare Support Services | 119 | 28.918367346938776 | 1172283.046 | 321931.98400000005 | 1359335.1278580544 | 189878.4492234088 | 314918.3168580551 | 2230352.538 | 320459.4619999998 | 114747.873 | 86204.72922838898 | 50286.774000000\n", - "row 4 : Heathcare Information and Technology | 128 | 22.776785714285715 | 738408.3160000001 | 171575.33200000005 | 821060.925284837 | 123028.59595086362 | 118661.63228483696 | 155242.41099999996 | 74003.267 | 34169.073000000004 | 21790.95854303259 | 8883.5840000000\n", - "row 5 : Total Market (without financials) | 5214 | 34.79463487696991 | 43163609.00299983 | 8566043.05699999 | 51078189.49081551 | 13583012.82046062 | 9754928.713815568 | 18726267.155 | 6256678.631000005 | 3370108.721999998 | 2234098.269636899 | 1421325.1889999\n", - "*/\n", - "\n", - "Table name: fundgrEB \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | ROC (INTEGER) | Reinvestment_Rate (INTEGER) | Expected_Growth_in_EBIT (INTEGER)\n", - "row 1 : Hospitals/Healthcare Facilities | 32 | 0.2015664022610945 | 0.3491979818825059 | 0.07038658088489\n", - "row 2 : Healthcare Products | 230 | 0.1308118160288834 | 0.4951245525175228 | 0.06476814187530\n", - "row 3 : Healthcare Support Services | 119 | 0.4574102830620488 | 0.6275372045055494 | 0.28704197034485\n", - "row 4 : Heathcare Information and Technology | 128 | 0.1428121724863794 | 0.3133945551322462 | 0.04475655726383\n", - "row 5 : Total Market (without financials) | 5214 | 0.1466884216172786 | 0.4437349493666002 | 0.06509077933900\n", - "*/\n", - "\n", - "\u001b[0m\n", - "Prediction(\n", - " rationale='produce the SQL. We need to first filter out the healthcare industries where revenues per employee is more than 1 million, then calculate the average tax rate for those industries.',\n", - " sql='```sql\\nSELECT AVG(taxrate.Aggregate_tax_rate) AS Average_Tax_Rate\\nFROM taxrate\\nJOIN Employee ON taxrate.Industry_name = Employee.Industry_Name\\nWHERE Employee.Revenues_per_Employee_ > 1000000\\n```'\n", - ")\n", - "Extracted rows: Average_Tax_Rate = 0.18875589135103757\n", - "\n", - "The average tax rate of all healthcare industries where revenues per employee is more than 1 million is approximately 18.88%.\n" - ] + "source": [ + "us_engine = sqlalchemy_engine(\"US\")\n", + "india_engine = sqlalchemy_engine(\"India\")\n", + "china_engine = sqlalchemy_engine(\"China\")\n", + "europe_engine = sqlalchemy_engine(\"Europe\")\n", + "global_engine = sqlalchemy_engine(\"Global\")\n", + "aus_nz_canada_engine = sqlalchemy_engine(\"AUS_NZ_CANADA\")\n", + "japan_engine = sqlalchemy_engine(\"Japan\")\n", + "emerging_engine = sqlalchemy_engine(\"Emerging\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_table_infos(sql_engine:sqlalchemy.engine.base.Engine,region:str):\n", + " \"\"\"Get all the tables info in the database based on the given region\"\"\"\n", + " inspector = inspect(sql_engine)\n", + " table_names = inspector.get_table_names()\n", + " table_infos_dict = {tb: [] for tb in table_names}\n", + " for tb in table_names:\n", + " column_dict = inspector.get_columns(tb)\n", + " schema_str = \"\"\n", + " primary_keys = []\n", + " for col in column_dict:\n", + " schema_str += f\"{col['name']} ({col['type']}), \"\n", + " if col[\"primary_key\"] not in primary_keys:\n", + " primary_keys.append(col[\"name\"])\n", + " with open(os.path.join(processed_dir,region,f\"{tb}.json\")) as f:\n", + " table_info = json.loads(f.read())\n", + " table_infos_dict[tb] = [\n", + " {\n", + " \"table_info\": f\"Table {tb} has columns: {schema_str[:-2]}\",\n", + " \"table_summary\": f'{table_info[\"Summary\"]}. {table_info[\"table_summary\"]}. ',\n", + " }\n", + " ]\n", + " return table_infos_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "us_tb_dict = get_table_infos(us_engine,\"US\")\n", + "india_tb_dict = get_table_infos(india_engine,\"India\")\n", + "china_tb_dict = get_table_infos(china_engine,\"China\")\n", + "europe_tb_dict = get_table_infos(europe_engine,\"Europe\")\n", + "global_tb_dict = get_table_infos(global_engine,\"Global\")\n", + "aus_nz_canada_tb_dict = get_table_infos(aus_nz_canada_engine,\"AUS_NZ_CANADA\")\n", + "japan_tb_dict = get_table_infos(japan_engine,\"Japan\")\n", + "emerging_tb_dict = get_table_infos(emerging_engine,\"Emerging\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'table_info': 'Table DollarUS has columns: Industry_Name (VARCHAR), Number_of_firms (INTEGER), Average_Company_Age_years_ (INTEGER), Market_Cap_millions_ (INTEGER), Book_Equity_millions_ (INTEGER), Enteprise_Value_millions_ (INTEGER), Invested_Capital_millions_ (INTEGER), Total_Debt_including_leases_millions_ (INTEGER), Revenues_millions_ (INTEGER), Gross_Profit_millions_ (INTEGER), EBITDA_millions_ (INTEGER), EBIT_Operating_Income_millions_ (INTEGER), Net_Income_millions_ (INTEGER)',\n", + " 'table_summary': 'To report aggregated dollar value of key operating and marker numbers, by industry group, in millions of US $.. This table contains financial information about various industries such as their names, number of firms, average company age, market cap, book equity, enterprise value, invested capital, total debt, revenues, gross profit, EBITDA, EBIT, and net income. The data is delimited by a newline character and can be used for comparison and analysis of the financial performance of different industries.. '}]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "us_tb_dict['DollarUS']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EMBEDDINGS\n", + "\n", + "1. Embed the table summary and table SCHEMA to get the table that the user is looking for\n", + "2. Embed the table rows for each table, so as to get relevant rows from the retrieved table " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EMBED THE TABLE SUMMARY AND TABLE SCHEMA" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import chromadb.utils.embedding_functions as embedding_functions\n", + "from chromadb.utils.batch_utils import create_batches\n", + "\n", + "load_dotenv(override=True)\n", + "emb_fn = embedding_functions.OpenAIEmbeddingFunction(\n", + " api_key=os.environ['OPENAI_API_KEY'],\n", + " model_name=\"text-embedding-3-small\")\n", + "# EMBEDDING_MODEL = \"mixedbread-ai/mxbai-embed-large-v1\"\n", + "# emb_fn = embedding_functions.HuggingFaceEmbeddingFunction(model_name=EMBEDDING_MODEL,api_key=os.environ[\"HF_API_KEY\"])\n", + "def embed_table_info(region:str,tb_dict):\n", + " \"\"\"Embed the table summary and table SCHEMA to get the table that the user is looking for\"\"\"\n", + " client = chromadb.PersistentClient(path=f\"{region}_TABLE\")\n", + "\n", + " table_collection = client.create_collection(name=\"table\",embedding_function=emb_fn)\n", + "\n", + " table_docs = []\n", + " table_metadata = []\n", + "\n", + "\n", + " for table_name,table_data in tb_dict.items():\n", + " table_docs.append(table_data[0]['table_info'] + \". \" + table_data[0]['table_summary'])\n", + " table_metadata.append({\"table_name\":table_name,'table_metadata':table_data[0]['table_info']})\n", + " table_ids = [f\"id{i}\" for i in range(len(table_docs))]\n", + " assert len(table_docs) == len(table_metadata)\n", + " print(len(table_docs),len(table_metadata))\n", + " # Create a batch of data to be sent to OpenAI Embedding API\n", + " batches = create_batches(api=client,ids=table_ids, documents=table_docs, metadatas=table_metadata)\n", + " for batch in tqdm(batches,desc=\"Embedding table info\"):\n", + " table_collection.add(ids=batch[0],\n", + " documents=batch[3],\n", + " metadatas=batch[2])\n", + "\n", + "# embed_table_info(\"US\",us_tb_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## For some strange reason, the `create_batches` was not batching the below documents, hence I had to do it manually" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def embed_rows(region:str,batch_size:int=24):\n", + " client = chromadb.PersistentClient(path=f\"{region}_TABLE\")\n", + " # client.delete_collection(name=\"rows\")\n", + " rows_collection = client.create_collection(name=\"rows\",embedding_function=emb_fn)\n", + "\n", + " rows_docs = []\n", + " rows_metadata = []\n", + " region_path = os.path.join(processed_dir,region)\n", + " for df_path in os.listdir(region_path):\n", + " df_full_path = os.path.join(region_path,df_path)\n", + " df = pd.read_csv(df_full_path,index_col=False)\n", + " for idx,row in df.iterrows():\n", + " row_str = \"\"\n", + " full_rows = []\n", + " for rv in row.values:\n", + " if isinstance(rv,str):\n", + " row_str+= rv + \", \"\n", + " full_rows.append(str(rv))\n", + " row_str = row_str.replace('\"',\"\")\n", + " # row_str = row_str.replace(\"'\",'\"')\n", + " full_rows_str = \", \".join(full_rows)[:-2]\n", + " full_rows_str = full_rows_str.replace('\"',\"\")\n", + " rows_docs.append(row_str[:-2])\n", + " rows_metadata.append({\"table_name\":df_path.split(\".\")[0],\"region\":region,\"index\":idx,\"full_rows\":full_rows_str})\n", + " row_ids = [f\"id{i}\" for i in range(len(rows_docs))]\n", + " # print(len(rows_docs),len(rows_metadata))\n", + " assert len(rows_docs) == len(rows_metadata) == len(row_ids)\n", + " # return rows_docs,rows_metadata,row_ids\n", + " for start in tqdm(range(0,len(rows_docs),batch_size),desc=\"Embedding rows\"):\n", + " end = min(start+batch_size,len(rows_docs))\n", + " batch_ids = row_ids[start:end]\n", + " batch_rows = rows_docs[start:end]\n", + " batch_metadatas = rows_metadata[start:end]\n", + " rows_collection.add(ids=batch_ids,\n", + " documents=batch_rows,\n", + " metadatas=batch_metadatas)\n", + " # return batches" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:04<00:00, 2.44s/it]\n" + ] + } + ], + "source": [ + "region = \"US\"\n", + "embed_table_info(region,us_tb_dict)\n", + "embed_rows(region,2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:05<00:00, 2.61s/it]\n" + ] + } + ], + "source": [ + "region = \"India\"\n", + "embed_table_info(region,india_tb_dict)\n", + "embed_rows(region,2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:25<00:00, 12.86s/it]\n" + ] + } + ], + "source": [ + "region = \"China\"\n", + "embed_table_info(region,china_tb_dict)\n", + "embed_rows(region,2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3/3 [00:26<00:00, 8.89s/it]\n" + ] + } + ], + "source": [ + "region = \"Europe\"\n", + "embed_table_info(region,europe_tb_dict)\n", + "embed_rows(region,1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:25<00:00, 12.69s/it]\n" + ] + } + ], + "source": [ + "region = \"Global\"\n", + "embed_table_info(region,global_tb_dict)\n", + "embed_rows(region,2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:25<00:00, 12.61s/it]\n" + ] + } + ], + "source": [ + "region = \"Emerging\"\n", + "embed_table_info(region,emerging_tb_dict)\n", + "embed_rows(region,2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:45<00:00, 22.99s/it]\n" + ] + } + ], + "source": [ + "region = \"Japan\"\n", + "embed_table_info(region,japan_tb_dict)\n", + "embed_rows(region,2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding rows: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:25<00:00, 12.68s/it]\n" + ] + } + ], + "source": [ + "region = \"AUS_NZ_CANADA\"\n", + "embed_table_info(region,aus_nz_canada_tb_dict)\n", + "embed_rows(region,2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TEXT-TO-SQL PIPELINE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LOAD DATABASE" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "db_dict = {\n", + " \"US\":us_engine,\n", + " \"India\":india_engine,\n", + " \"China\":china_engine,\n", + " \"Europe\":europe_engine,\n", + " \"Global\":global_engine,\n", + " \"AUS_NZ_CANADA\":aus_nz_canada_engine,\n", + " \"Japan\":japan_engine,\n", + " \"Emerging\":emerging_engine,\n", + "}\n", + "\n", + "def get_collections_db(region:str):\n", + " # Get the database for the given region, table collection and row collection\n", + " client = chromadb.PersistentClient(path=f\"{region}_TABLE\")\n", + " table_collection = client.get_collection(name=\"table\",embedding_function=emb_fn)\n", + " row_collection = client.get_collection(name=\"rows\",embedding_function=emb_fn)\n", + " return [db_dict[region],table_collection,row_collection]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "db_collection_dict = {\n", + " \"US\":get_collections_db(\"US\"),\n", + " \"India\":get_collections_db(\"India\"),\n", + " \"China\":get_collections_db(\"China\"),\n", + " \"Europe\":get_collections_db(\"Europe\"),\n", + " \"Global\":get_collections_db(\"Global\"),\n", + " \"AUS_NZ_CANADA\":get_collections_db(\"AUS_NZ_CANADA\"),\n", + " \"Japan\":get_collections_db(\"Japan\"),\n", + " \"Emerging\":get_collections_db(\"Emerging\"),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "load_dotenv(override=True)\n", + "text_to_sql = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=1024)\n", + "sql_to_answer = dspy.OpenAI(model='gpt-3.5-turbo',max_tokens=1024)\n", + "\n", + "# DSPy signature for converting text to SQL query\n", + "class TextToSQLAnswer(dspy.Signature):\n", + " \"\"\"Convert natural language text to SQL using suitable schema(s) from multiple schema choices\"\"\"\n", + "\n", + " question:str = dspy.InputField(desc=\"natural language input which will be converted to SQL\")\n", + " relevant_table_schemas_rows:str = dspy.InputField(desc=\"Multiple possible tables which has table name and corresponding columns, along with relevant rows from the table (values in the same order as columns above)\")\n", + " sql:str = dspy.OutputField(desc=\"Generate syntactically correct sqlite query with correct column names using suitable tables(s) and its rows.\\n Don't forget to add distinct.\\n Please rename the returned columns into suitable names.\\n DON'T OUTPUT anything else other than the sqlite query\")\n", + "\n", + "# DSPy signature for converting SQL query and question to natural language text\n", + "class SQLReturnToAnswer(dspy.Signature):\n", + " \"\"\"Answer the question using the rows from the SQL query\"\"\"\n", + " question:str = dspy.InputField()\n", + " sql:str = dspy.InputField(desc=\"sqlite query that generated the rows\")\n", + " relevant_rows:str = dspy.InputField(desc=\"relevant rows to answer the question\")\n", + " answer:str = dspy.OutputField(desc=\"answer to the question using relevant rows and the sql query\")\n", + "\n", + "# If there is an SQLError, then rectify the error by trying again\n", + "class SQLRectifier(dspy.Signature):\n", + " \"\"\"Correct the SQL query to resolve the error using the proper table names, columns and rows\"\"\" \n", + " input_sql:str = dspy.InputField(desc=\"sqlite query that needs to be fixed\")\n", + " error_str: str = dspy.InputField(desc=\"error that needs to be resolved\")\n", + " relevant_table_schemas_rows:str = dspy.InputField(desc=\"Multiple possible tables which has table name and corresponding columns, along with relevant rows from the table (values in the same order as columns above)\")\n", + " sql:str = dspy.OutputField(desc=\"corrected sqlite query to resolve the error and remove and any invalid syntax in the query.\\n Don't output anything else other than the sqlite query\")\n", + "\n", + "dspy.settings.configure(lm=text_to_sql)\n", + "\n", + "# Filter out the SQL Query\n", + "def process_sql_str(sql_str:str):\n", + " sql_str = sql_str.replace(\"```\",\"\")\n", + " sql_str = sql_str.replace(\"sql\",\"\")\n", + " sql_str = sql_str.strip()\n", + " return sql_str" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

\n", + " \"Sublime's\n", + "

" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_table_results(table_collection_,question:str):\n", + " # question_emb = emb_fn.embed_with_retries(question)[0]\n", + " # Get the table results for the given question\n", + " table_results = table_collection_.query(\n", + " query_texts = question,\n", + " n_results = 5\n", + " )\n", + " # print(table_results['documents'][0])\n", + " return table_results\n", + "\n", + "def get_row_results(row_collection_,question,table_name:str):\n", + " # Get the row results for the given question\n", + " row_results = row_collection_.query(\n", + " query_texts = question,\n", + " where = {\"table_name\":table_name},\n", + " n_results = 5\n", + " )\n", + " print(row_results['documents'][0])\n", + " return row_results" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mTable name: margin \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", + "row 1 : Packaging & Container | 22 | 0.2171338247779519 | 0.0285269723092998 | 0.1016364547958028 | 0.0975946017284918 | 0.0799113339135634 | 0.0997355299061771 | 0.0816643450787498 | 0.0998025797146062 | 0.0817313948871789 | 0.1571078511407175 | 0.2522079768591083 | 0.1617111987330198 | 0.782866175222048 | 0.0046033475923023 | 0.0951001257183908 | 0.004041853067311 | 0.0111359826715\n", + "row 2 : Software (Entertainment) | 84 | 0.6343190737595645 | 0.203484453534525 | 0.3536120749157563 | 0.2680484859792075 | 0.2543465254185649 | 0.2650138705989933 | 0.2514670319003695 | 0.2816776939314963 | 0.2681308552328725 | 0.290337329951565 | 0.4724580252979131 | 0.4795894540185941 | 0.3656809262404354 | 0.1892521240670291 | 0.182120695346348 | 0.0855635889365488 | 0.0140141013588\n", + "row 3 : Software (Internet) | 35 | 0.591108552762756 | -0.1431970955040302 | 0.1239579652577063 | -0.0335948165704685 | -0.0327194780747064 | -0.0289602901492322 | -0.0282057077640929 | -0.0083175074368089 | -0.0075629250516695 | 0.0122636239274153 | 0.4161715858041718 | 0.2226547217998442 | 0.408891447237244 | 0.2103910978724289 | 0.4039079618767565 | 0.1575527818281749 | 0.02956631949802\n", + "row 4 : Software (System & Application) | 351 | 0.7152038033402293 | 0.1914080753155209 | 0.3405275162267305 | 0.2529865559329903 | 0.2423866072527709 | 0.2534208835158065 | 0.2428027368326486 | 0.2629596189730837 | 0.2523414722899257 | 0.2853446560160495 | 0.5664609609773408 | 0.4563813852186625 | 0.2847961966597707 | 0.171036729202613 | 0.2811163049612913 | 0.0875409602937401 | 0.01484216535122\n", + "row 5 : Electronics (Consumer & Office) | 13 | 0.323558031288744 | -0.0304513738086002 | 0.0289465210995791 | -0.0007643401586021 | -0.0007084824743491 | -0.0004205933789403 | -0.0003898565768825 | 0.0056724354829463 | 0.005703172285004 | 0.0359642158837002 | 0.2637004492123042 | 0.130762045590879 | 0.676441968711256 | 0.0947978297071788 | 0.2277362333286039 | 0.0297108612581812 | 0.00871672702736\n", + "*/\n", + "\n", + "Table name: EVA \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | ROE (INTEGER) | Cost_of_Equity (INTEGER) | _ROE_COE_ (INTEGER) | BV_of_Equity (INTEGER) | Equity_EVA_US_millions_ (INTEGER) | ROC (INTEGER) | Cost_of_Capital (INTEGER) | _ROC_WACC_ (INTEGER) | BV_of_Capital (INTEGER) | EVA_US_millions_ (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER)\n", + "row 1 : Packaging & Container | 22 | 1.1327220379604754 | 0.0854306569307375 | 0.0909052137461818 | -0.0054745568154443 | 49084.58099999999 | -268.71632744677845 | 0.1421364757846558 | 0.0708610226399319 | 0.0712754531447238 | 84525.26117167753 | 6024.57629218745 | 0.620115895714898 | 0.2624270896574677 | 0.050855 | 0.1811910443993945 | 0.03814125 | 0.3798841042851\n", + "row 2 : Software (Entertainment) | 84 | 1.1083305740238332 | 0.2325593855621183 | 0.0897832064050963 | 0.1427761791570219 | 403869.63 | 57662.96264896017 | 0.1994487340625551 | 0.088266127488506 | 0.1111826065740491 | 620525.0853364643 | 68991.59643229235 | 0.9694385050656324 | 0.5278932207822914 | 0.053524 | 0.0511174704478853 | 0.040143 | 0.03056149493436\n", + "row 3 : Software (Internet) | 35 | 1.6160843271745926 | -0.1454065112919735 | 0.1131398790500312 | -0.2585463903420048 | 28399.863 | -7342.682064857458 | -0.0048109532624457 | 0.1059203603347457 | -0.1107313135971915 | 45334.05312597519 | -5019.899253324099 | 0.8930134645020447 | 0.65221602599035 | 0.060879 | 0.0260557605345441 | 0.04565925 | 0.10698653549795\n", + "row 4 : Software (System & Application) | 351 | 1.2939647291542515 | 0.2444183538229951 | 0.0983223775410955 | 0.1460959762818996 | 397835.59000000014 | 58122.178920735554 | 0.2322438986671061 | 0.0949239234411956 | 0.1373199752259105 | 551977.6712097155 | 75797.5601357739 | 0.9415866198035656 | 0.5208648982480728 | 0.053524 | 0.0418992568246478 | 0.040143 | 0.05841338019643\n", + "row 5 : Electronics (Consumer & Office) | 13 | 1.29517877665579 | -0.0662680013872474 | 0.0983782237261663 | -0.1646462251134137 | 2969.9099999999994 | -488.9844704265786 | 0.009542635546224 | 0.0890493344743714 | -0.0795066989281474 | 3862.6772748588 | -307.1087191487957 | 0.845130180440282 | 0.3942415498208712 | 0.050855 | 0.0730796146510295 | 0.03814125 | 0.15486981955971\n", + "*/\n", + "\n", + "Table name: DollarUS \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Company_Age_years_ (INTEGER) | Market_Cap_millions_ (INTEGER) | Book_Equity_millions_ (INTEGER) | Enteprise_Value_millions_ (INTEGER) | Invested_Capital_millions_ (INTEGER) | Total_Debt_including_leases_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Gross_Profit_millions_ (INTEGER) | EBITDA_millions_ (INTEGER) | EBIT_Operating_Income_millions_ (INTEGER) | Net_Income_millions_ (INTEGER)\n", + "row 1 : Packaging & Container | 22 | 80.19047619047619 | 132627.167 | 49084.58099999999 | 205727.3441716776 | 86259.32917167754 | 81247.63917167754 | 146995.2 | 31917.63 | 23820.874 | 14660.644165664493 | 4193.3279999999\n", + "row 2 : Software (Entertainment) | 84 | 14.544117647058824 | 2773080.8990000025 | 403869.63 | 2781007.513051708 | 383348.20548245066 | 87421.2210517081 | 461576.65299999993 | 292786.8749999999 | 156210.96800000002 | 122324.21538965842 | 93923.673000000\n", + "row 3 : Software (Internet) | 35 | 14.413793103448276 | 220091.11299999992 | 28399.863 | 241745.7304766904 | 25653.71492597519 | 26367.783476690336 | 28838.050000000003 | 17046.417999999998 | 1980.7540000000004 | -835.1582953380669 | -4129.5\n", + "row 4 : Software (System & Application) | 351 | 20.125 | 5277974.335000003 | 397835.59000000014 | 5446717.280701609 | 326815.0326293967 | 327430.6527016032 | 508015.76600000006 | 363334.808 | 170251.41100000014 | 128741.80425967928 | 97238.320000000\n", + "row 5 : Electronics (Consumer & Office) | 13 | 35.61538461538461 | 5288.128000000001 | 2969.9099999999994 | 5468.249666410444 | 2031.147666410444 | 969.0476664104436 | 6463.090999999999 | 2091.185 | 195.229 | -2.7183332820887505 | -196.\n", + "*/\n", + "\n", + "Table name: dbtfund \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Book_Debt_to_Capital (INTEGER) | Market_Debt_to_Capital_Unadjusted_ (INTEGER) | Market_D_E_unadjusted_ (INTEGER) | Market_Debt_to_Capital_adjusted_for_leases_ (INTEGER) | Market_D_E_adjusted_for_leases_ (INTEGER) | Interest_Coverage_Ratio (INTEGER) | Debt_to_EBITDA (INTEGER) | Effective_tax_rate (INTEGER) | Institutional_Holdings (INTEGER) | Std_dev_in_Stock_Prices (INTEGER) | EBITDA_EV (INTEGER) | Net_PP_E_Total_Assets (INTEGER) | Capital_Spending_Total_Assets (INTEGER)\n", + "row 1 : Packaging & Container | 22 | 0.6103387810613522 | 0.3782286453204903 | 0.6083082510538735 | 0.379884104285102 | 0.6126017844570075 | 3.764310529409295 | 3.5181123824560183 | 0.1811910443993945 | 0.6783750000000001 | 0.2624270896574677 | 0.1157885651803374 | 0.3623114386831347 | 0.04952312081083\n", + "row 2 : Software (Entertainment) | 84 | 0.1668568483248547 | 0.0310823698127216 | 0.0320794759475208 | 0.0305614949343675 | 0.0315249443617865 | 82.70045339060373 | 0.6523342120398788 | 0.0511174704478853 | 0.380174716981132 | 0.5278932207822914 | 0.0561706386145586 | 0.3664219490280888 | 0.08632671853148\n", + "row 3 : Software (Internet) | 35 | 0.5188435528953421 | 0.1069721233683309 | 0.1197858724990863 | 0.1069865354979553 | 0.1198039444540877 | -1.2424451113163029 | 74.55708316963612 | 0.0260557605345441 | 0.503754090909091 | 0.65221602599035 | 0.0081935428439386 | 0.1393637519325522 | 0.02491814851918\n", + "row 4 : Software (System & Application) | 351 | 0.4037655070426813 | 0.0578505240039251 | 0.0614027025199621 | 0.0584133801964343 | 0.0620371816759891 | 12.091079193408472 | 2.258772022287284 | 0.0418992568246478 | 0.4265529149797569 | 0.5208648982480728 | 0.0312576185298293 | 0.1534323819146964 | 0.03979088603498\n", + "row 5 : Electronics (Consumer & Office) | 13 | 0.257103532061476 | 0.1538670284938941 | 0.1818473380372033 | 0.1548698195597179 | 0.1832496615835402 | -0.0653906229317238 | 4.169022829162121 | 0.0730796146510295 | 0.322611111111111 | 0.3942415498208712 | 0.0357022835294488 | 0.0758014027166977 | 0.01817671397788\n", + "*/\n", + "\n", + "Table name: finflows \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Dividends_in_millions (INTEGER) | Buybacks_in_millions (INTEGER) | Equity_Issuance_in_millions (INTEGER) | Net_Equity_Change_in_millions (INTEGER) | Net_Equity_Change_as_of_Book_Equity (INTEGER) | Debt_Repaid_in_millions (INTEGER) | Debt_Raised_in_millions (INTEGER) | Net_Debt_Change_in_millions (INTEGER) | Net_Change_in_Debt_as_of_Total_Debt (INTEGER) | Change_in_Lease_Debt_in_millions (INTEGER)\n", + "row 1 : Packaging & Container | 22 | 2944.63 | 2233.409 | 88.02499999999999 | -2145.384 | -0.0437079008579089 | 21249.516000000007 | 23186.13 | 1936.6139999999905 | 0.0252378854855187 | 1078.3980000000\n", + "row 2 : Software (Entertainment) | 84 | 235.41 | 90230.608 | 492.176 | -89738.43199999999 | -0.2221965340647178 | 19934.036 | 26704.583 | 6770.546999999999 | 0.1247056048135642 | 6193.5280000000\n", + "row 3 : Software (Internet) | 35 | 0.256 | 5130.625999999999 | 532.7600000000001 | -4597.865999999999 | -0.1618974711251248 | 5242.0070000000005 | 5173.334999999999 | -68.67200000000139 | -0.0029087745986446 | 1487.\n", + "row 4 : Software (System & Application) | 351 | 26468.845 | 51669.898 | 11749.602 | -39920.296 | -0.1003437022816384 | 35002.797 | 51147.364 | 16144.567000000005 | 0.0548813000879868 | 3688.94599999997\n", + "row 5 : Electronics (Consumer & Office) | 13 | 0.0 | 193.63 | 36.461000000000006 | -157.16899999999998 | -0.0529204588691239 | 397.182 | 379.297 | -17.885000000000048 | -0.023463092038029 | 74.409999999999\n", + "*/\n", + "\n", + "\u001b[0m\n", + "Prediction(\n", + " rationale='produce the SQL. We need to find the EBITDA for the software industry, which includes Software (Entertainment), Software (Internet), and Software (System & Application).',\n", + " sql=\"```sql\\nSELECT DISTINCT DollarUS.Industry_Name AS Industry, DollarUS.EBITDA_millions_ AS EBITDA\\nFROM DollarUS\\nWHERE DollarUS.Industry_Name LIKE 'Software%'\\n```\"\n", + ")\n", + "Extracted rows: Industry = Software (Entertainment), EBITDA = 156210.96800000002\n", + " Industry = Software (Internet), EBITDA = 1980.7540000000004\n", + " Industry = Software (System & Application), EBITDA = 170251.41100000014\n", + "\n" + ] + } + ], + "source": [ + "from typing import Any\n", + "\n", + "class TextToSQLQueryModule(dspy.Module):\n", + " \"\"\"Text to SQL to final module\"\"\"\n", + " def __init__(self,region:str,use_cot:bool=True,max_retries:int=3):\n", + " \"\"\"Text to Answer init module\n", + "\n", + " Args:\n", + " region (str): Region for which the module will be used.\n", + " use_cot (bool, optional): Whether to use chain of thought for sql query generation. Defaults to True.\n", + " max_retries (int, optional): Number of max retries for SQLError. Defaults to 3.\n", + " \"\"\"\n", + " super().__init__()\n", + " self.region = region\n", + " db,table_collection,row_collection = db_collection_dict[region]\n", + " # print(db,table_collection,row_collection)\n", + " self.table_collection = table_collection\n", + " self.use_cot = use_cot\n", + " self.db = db\n", + " self.row_collection = row_collection\n", + " if self.use_cot == True:\n", + " self.sqlAnswer = dspy.ChainOfThought(TextToSQLAnswer)\n", + " else:\n", + " self.sqlAnswer = dspy.Predict(TextToSQLAnswer)\n", + " self.final_output = dspy.Predict(SQLReturnToAnswer)\n", + " self.max_tries = max_retries\n", + " # Initialize the sql rectifier with CoT reasoning\n", + " self.sql_rectifier = dspy.ChainOfThought(SQLRectifier,rationale_type=dspy.OutputField(\n", + " prefix=\"Reasoning: Let's think step by step in order to\",\n", + " desc=\"${produce the answer}. We ...\"\n", + " ))\n", + " \n", + " def __call__(self, *args: Any, **kwargs: Any) -> Any:\n", + " return self.forward(*args, **kwargs)\n", + " \n", + " def forward(self,question):\n", + " # Embed the question with embedding function\n", + " question_emb = emb_fn([question])[0]\n", + " # Retrieve the relevant tables from table schema and table summary\n", + " docs = self.table_collection.query(\n", + " query_embeddings = question_emb,\n", + " n_results = 5\n", + " )\n", + " # docs = get_table_results(db_collection_dict[self.region][1],question)\n", + " relevant_rows_schemas = \"\"\n", + " \n", + " existing_table_names = []\n", + "\n", + " for table_idx,metadata_name in enumerate(docs['metadatas'][0]):\n", + " table_metadata = metadata_name['table_metadata']\n", + " table_name = metadata_name['table_name']\n", + " # If the table name is already in the list of existing table names, skip it\n", + " # if table_name in existing_table_names: \n", + " # continue\n", + " existing_table_names.append(table_name)\n", + " # Retrieve the relevant rows from the current table\n", + " rows = self.row_collection.query(\n", + " query_embeddings = question_emb,\n", + " n_results = 5,\n", + " # where clause to filter the rows\n", + " where = {\"table_name\":table_name}\n", + " )\n", + " # Retrieve the relevant table with the schema and summary\n", + " relevant_rows_schemas += f'Table name: {table_name} \\n'\n", + " relevant_rows_schemas += \"/* \\n\"\n", + " for match in re.finditer(\"columns: \",table_metadata):\n", + " cols_end = match.end()\n", + " relevant_rows_schemas += \"col : \" + \" | \".join(table_metadata[cols_end:].split(\", \")) + \"\\n\"\n", + " for row_idx,row in enumerate(rows['metadatas'][0]):\n", + " # Get the relevant rows from the current table\n", + " # relevant_rows_schemas += f'\\tRow {row_idx+1} from table {table_name}: {row[\"full_rows\"]}\\n'\n", + " relevant_rows_schemas += f'row {row_idx+1} : {\" | \".join(row[\"full_rows\"].split(\", \"))}\\n'\n", + " relevant_rows_schemas += \"*/\" + '\\n\\n'\n", + " print(colored(relevant_rows_schemas,\"yellow\"))\n", + " # return \n", + " sql_query = self.sqlAnswer(question=question,relevant_table_schemas_rows=relevant_rows_schemas)\n", + "\n", + " num_tries = 0\n", + " print(sql_query)\n", + " while num_tries <= self.max_tries:\n", + " with self.db.connect() as conn:\n", + " try:\n", + " # Try executing the sql query for the database\n", + " result = conn.execute(text(process_sql_str(sql_query.sql)))\n", + " num_tries = self.max_tries + 1\n", + " except Exception as error:\n", + " # If there is an sql error, then try again with the sql rectifier\n", + " print(colored(str(error),'red'))\n", + " sql_query = self.sql_rectifier(input_sql=sql_query.sql,error_str=str(error),relevant_table_schemas_rows=relevant_rows_schemas)\n", + " print(colored(sql_query.rationale,'green'))\n", + " print()\n", + " print(colored(sql_query.sql,'green'))\n", + " # If all the num_retries are exhausted, then exit the program\n", + " num_tries += 1\n", + " if num_tries == self.max_tries+1:\n", + " return sql_query,error\n", + " # With the retrieved rows from the database, then try to answer the question with dspy context\n", + " with dspy.context(lm=sql_to_answer):\n", + " row_str = \"\"\n", + " key = tuple(result.keys())\n", + " for row in result.fetchall():\n", + " for r,k in zip(row,key):\n", + " row_str += f\" {k} = {r},\"\n", + " row_str = row_str[:-1]\n", + " row_str += \"\\n\"\n", + " print(f\"Extracted rows: {row_str}\")\n", + " final_answer = self.final_output(question=question,sql=sql_query.sql,relevant_rows=row_str)\n", + " return final_answer\n", + "tsql_ = TextToSQLQueryModule(\"US\")\n", + "question = \"What is the ebitda of software and packaging industry?\"\n", + "sq = tsql_(question = question)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction(\n", + " answer='The EBITDA of the software industry is as follows:\\n- Software (Entertainment): $156,210.97 million\\n- Software (Internet): $1,980.75 million\\n- Software (System & Application): $170,251.41 million'\n", + ")\n" + ] + } + ], + "source": [ + "print(sq)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mTable name: EVA \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | ROE (INTEGER) | Cost_of_Equity (INTEGER) | _ROE_COE_ (INTEGER) | BV_of_Equity (INTEGER) | Equity_EVA_US_millions_ (INTEGER) | ROC (INTEGER) | Cost_of_Capital (INTEGER) | _ROC_WACC_ (INTEGER) | BV_of_Capital (INTEGER) | EVA_US_millions_ (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER)\n", + "row 1 : Aerospace/Defense | 70 | 1.0764050153356324 | 0.1319148365195281 | 0.088314630705439 | 0.043600205814089 | 145775.301 | 6355.833126210783 | 0.1616771766434065 | 0.0781335598681736 | 0.0835436167752329 | 195266.1836785369 | 16313.243218401924 | 0.7970822237983712 | 0.3640214967123067 | 0.050855 | 0.0727529390842114 | 0.03814125 | 0.20291777620162\n", + "row 2 : Total Market (without financials) | 5214 | 1.097522642247185 | 0.1659255247192018 | 0.0892860415433705 | 0.0766394831758313 | 8566043.05699999 | 656497.1127503973 | 0.1466884216172786 | 0.0798580827010761 | 0.0668303389162024 | 14577190.305961732 | 974198.568593403 | 0.815661408370401 | 0.4700345950094993 | 0.050855 | 0.0680489965490159 | 0.03814125 | 0.1843385916295\n", + "row 3 : Software (Entertainment) | 84 | 1.1083305740238332 | 0.2325593855621183 | 0.0897832064050963 | 0.1427761791570219 | 403869.63 | 57662.96264896017 | 0.1994487340625551 | 0.088266127488506 | 0.1111826065740491 | 620525.0853364643 | 68991.59643229235 | 0.9694385050656324 | 0.5278932207822914 | 0.053524 | 0.0511174704478853 | 0.040143 | 0.03056149493436\n", + "row 4 : Electronics (General) | 129 | 0.9339545990672654 | 0.0879766010692752 | 0.0817619115570942 | 0.006214689512181 | 72980.08699999998 | 453.548581276959 | 0.1511438395305996 | 0.0753610851527285 | 0.0757827543778711 | 81592.19108678924 | 6183.280976282475 | 0.8532615926517355 | 0.4294264362055603 | 0.050855 | 0.0817457737542162 | 0.03814125 | 0.14673840734826\n", + "row 5 : Semiconductor Equip | 30 | 1.5279822074255314 | 0.3289066823779071 | 0.1090871815415744 | 0.2198195008363326 | 44503.16999999999 | 9782.664615034451 | 0.2568363246651193 | 0.1039673141944747 | 0.1528690104706445 | 70041.38797094872 | 10707.15767110944 | 0.9278342360745604 | 0.3704014298865129 | 0.050855 | 0.1214195116015062 | 0.03814125 | 0.07216576392543\n", + "*/\n", + "\n", + "Table name: margin \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", + "row 1 : Aerospace/Defense | 70 | 0.1727498953821253 | 0.0496288894345249 | 0.0970899067124733 | 0.0853938467891159 | 0.0791811934555008 | 0.085557881411237 | 0.0793332940767511 | 0.0877011542663323 | 0.0814765669318464 | 0.1213211709923032 | 0.1854721753152544 | 0.163301436750081 | 0.8272501046178746 | 0.0419802657577777 | 0.0641510043229512 | 0.0116960599233574 | 0.00737353454744\n", + "row 2 : Total Market (without financials) | 5214 | 0.3341124303745417 | 0.0759000807387551 | 0.1349854587183475 | 0.1203243079012881 | 0.1121363594881506 | 0.1193029155861628 | 0.1111844718951524 | 0.122305912015432 | 0.1141874683244217 | 0.1649863910103977 | 0.308807292459029 | 0.2037377626528898 | 0.6658875696254583 | 0.038751371642492 | 0.1438209014486313 | 0.0146611508170593 | 0.01534583398930\n", + "row 3 : Software (Entertainment) | 84 | 0.6343190737595645 | 0.203484453534525 | 0.3536120749157563 | 0.2680484859792075 | 0.2543465254185649 | 0.2650138705989933 | 0.2514670319003695 | 0.2816776939314963 | 0.2681308552328725 | 0.290337329951565 | 0.4724580252979131 | 0.4795894540185941 | 0.3656809262404354 | 0.1892521240670291 | 0.182120695346348 | 0.0855635889365488 | 0.0140141013588\n", + "row 4 : Electronics (General) | 129 | 0.2736441477972378 | 0.0469265820090722 | 0.1081365384368007 | 0.0948166055844074 | 0.0870657487961617 | 0.095866096633002 | 0.0880294483869408 | 0.0979701850709138 | 0.0901335368248526 | 0.1483155870270167 | 0.274700126354909 | 0.1987893448489177 | 0.7263558522027622 | 0.0504737578219009 | 0.1263845393278922 | 0.0133199328523933 | 0.00864521049991\n", + "row 5 : Semiconductor Equip | 30 | 0.4427439580396038 | 0.1793844069139284 | 0.2615904123507109 | 0.2418675138617241 | 0.212500078456363 | 0.243380403940693 | 0.2138292741608367 | 0.2500123642381983 | 0.220461234458342 | 0.3032502002655829 | 0.3930952875683865 | 0.4064476540724488 | 0.5572560419603962 | 0.1031974538068659 | 0.0898450873028035 | 0.0197228984889867 | 0.00906011117075\n", + "*/\n", + "\n", + "Table name: DollarUS \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Company_Age_years_ (INTEGER) | Market_Cap_millions_ (INTEGER) | Book_Equity_millions_ (INTEGER) | Enteprise_Value_millions_ (INTEGER) | Invested_Capital_millions_ (INTEGER) | Total_Debt_including_leases_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Gross_Profit_millions_ (INTEGER) | EBITDA_millions_ (INTEGER) | EBIT_Operating_Income_millions_ (INTEGER) | Net_Income_millions_ (INTEGER)\n", + "row 1 : Aerospace/Defense | 70 | 53.82539682539682 | 788306.4130000002 | 145775.301 | 960283.3679021292 | 164231.158307403 | 200683.66790212895 | 387474.417 | 66936.16500000001 | 48918.45400000002 | 33151.49021957421 | 19229.925000000\n", + "row 2 : Total Market (without financials) | 5214 | 34.79463487696991 | 43163609.00299983 | 8566043.05699999 | 51078189.49081551 | 13583012.82046062 | 9754928.713815568 | 18726267.155 | 6256678.631000005 | 3370108.721999998 | 2234098.269636899 | 1421325.1889999\n", + "row 3 : Software (Entertainment) | 84 | 14.544117647058824 | 2773080.8990000025 | 403869.63 | 2781007.513051708 | 383348.20548245066 | 87421.2210517081 | 461576.65299999993 | 292786.8749999999 | 156210.96800000002 | 122324.21538965842 | 93923.673000000\n", + "row 4 : Electronics (General) | 129 | 39.15573770491803 | 269499.1279999999 | 72980.08699999998 | 297860.8621360993 | 66341.67523831947 | 46346.71613609941 | 136820.96000000014 | 37440.255 | 20098.71 | 13116.491372780118 | 6420.5399999999\n", + "row 5 : Semiconductor Equip | 30 | 42.5 | 414640.35 | 44503.16999999999 | 427920.4277709488 | 48010.57777094871 | 32250.19777094872 | 81597.89499999999 | 36126.97499999999 | 23479.94899999999 | 19859.32864581025 | 14637.\n", + "*/\n", + "\n", + "Table name: wacc \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", + "row 1 : Aerospace/Defense | 70 | 1.0764050153356324 | 0.088314630705439 | 0.7970822237983712 | 0.3640214967123067 | 0.050855 | 0.0727529390842114 | 0.03814125 | 0.2029177762016287 | 0.0781335598681736 | 0.07813355986817\n", + "row 2 : Total Market (without financials) | 5214 | 1.097522642247185 | 0.0892860415433705 | 0.815661408370401 | 0.4700345950094993 | 0.050855 | 0.0680489965490159 | 0.03814125 | 0.1843385916295989 | 0.0798580827010761 | 0.07985808270107\n", + "row 3 : Software (Entertainment) | 84 | 1.1083305740238332 | 0.0897832064050963 | 0.9694385050656324 | 0.5278932207822914 | 0.053524 | 0.0511174704478853 | 0.040143 | 0.0305614949343675 | 0.088266127488506 | 0.08826612748850\n", + "row 4 : Electronics (General) | 129 | 0.9339545990672654 | 0.0817619115570942 | 0.8532615926517355 | 0.4294264362055603 | 0.050855 | 0.0817457737542162 | 0.03814125 | 0.1467384073482645 | 0.0753610851527285 | 0.07536108515272\n", + "row 5 : Semiconductor Equip | 30 | 1.5279822074255314 | 0.1090871815415744 | 0.9278342360745604 | 0.3704014298865129 | 0.050855 | 0.1214195116015062 | 0.03814125 | 0.0721657639254397 | 0.1039673141944747 | 0.10396731419447\n", + "*/\n", + "\n", + "Table name: dbtfund \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Book_Debt_to_Capital (INTEGER) | Market_Debt_to_Capital_Unadjusted_ (INTEGER) | Market_D_E_unadjusted_ (INTEGER) | Market_Debt_to_Capital_adjusted_for_leases_ (INTEGER) | Market_D_E_adjusted_for_leases_ (INTEGER) | Interest_Coverage_Ratio (INTEGER) | Debt_to_EBITDA (INTEGER) | Effective_tax_rate (INTEGER) | Institutional_Holdings (INTEGER) | Std_dev_in_Stock_Prices (INTEGER) | EBITDA_EV (INTEGER) | Net_PP_E_Total_Assets (INTEGER) | Capital_Spending_Total_Assets (INTEGER)\n", + "row 1 : Aerospace/Defense | 70 | 0.5598223230984364 | 0.2003176164886141 | 0.250496472873423 | 0.2029177762016287 | 0.2545757139516368 | 3.740440705503212 | 4.269061419331231 | 0.0727529390842114 | 0.485818947368421 | 0.3640214967123067 | 0.0509416862096332 | 0.1258713546270282 | 0.01798339388026\n", + "row 2 : Total Market (without financials) | 5214 | 0.5001187576177913 | 0.1827761005242874 | 0.2236548645255564 | 0.184338591629599 | 0.225998912953215 | 6.428195471118638 | 3.157364796897844 | 0.0680489965490159 | 0.4702478911478115 | 0.4700345950094993 | 0.0659794083462176 | 0.32642976153906 | 0.04335448376240\n", + "row 3 : Software (Entertainment) | 84 | 0.1668568483248547 | 0.0310823698127216 | 0.0320794759475208 | 0.0305614949343675 | 0.0315249443617865 | 82.70045339060373 | 0.6523342120398788 | 0.0511174704478853 | 0.380174716981132 | 0.5278932207822914 | 0.0561706386145586 | 0.3664219490280888 | 0.08632671853148\n", + "row 4 : Electronics (General) | 129 | 0.3587835842228601 | 0.1457673736295513 | 0.1706413090880205 | 0.1467384073482645 | 0.1719735291169455 | 6.641668616399566 | 2.2839129110687453 | 0.0817457737542162 | 0.4456912621359226 | 0.4294264362055603 | 0.0674768408842396 | 0.182597141990381 | 0.03108461726876\n", + "row 5 : Semiconductor Equip | 30 | 0.3856389110464103 | 0.0719131674240597 | 0.0774853870348122 | 0.0721657639254397 | 0.0777787250347167 | 12.620099818076602 | 1.3033238138451468 | 0.1214195116015062 | 0.6812737931034483 | 0.3704014298865129 | 0.0548698951398693 | 0.1426826921482972 | 0.03734252665127\n", + "*/\n", + "\n", + "\u001b[0m\n", + "Prediction(\n", + " rationale='produce the sql. We need to first join the tables based on the Industry_Name column and then filter out the rows for Software industries, semiconductor industry, and aerospace. Finally, we need to select the EBITDA value and count the number of firms.',\n", + " sql=\"```sql\\nSELECT DISTINCT DollarUS.Industry_Name AS Industry, DollarUS.EBITDA_millions_ AS EBITDA, DollarUS.Number_of_firms AS Number_of_Firms\\nFROM DollarUS\\nJOIN EVA ON DollarUS.Industry_Name = EVA.Industry_Name\\nJOIN margin ON DollarUS.Industry_Name = margin.Industry_Name\\nWHERE DollarUS.Industry_Name IN ('Software (Entertainment)', 'Semiconductor Equip', 'Aerospace/Defense');\\n```\"\n", + ")\n", + "Extracted rows: Industry = Aerospace/Defense, EBITDA = 48918.45400000002, Number_of_Firms = 70\n", + " Industry = Semiconductor Equip, EBITDA = 23479.94899999999, Number_of_Firms = 30\n", + " Industry = Software (Entertainment), EBITDA = 156210.96800000002, Number_of_Firms = 84\n", + "\n" + ] + } + ], + "source": [ + "# tsql = TextToSQLQueryModule(\"US\")\n", + "# sq = tsql(question=\"What is the effective tax rate of the healthcare industry?\")\n", + "sq = tsql_(question=\"What is the EBITDA value and number of firms for all the Software industries, semiconductor industry and aerospace?\")\n", + "# sq = tsql(\"What is the debt to EBITDA ratio for software industry?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The EBITDA value for the Software (Entertainment) industry is $156,210.97 million with 84 firms. For the Semiconductor Equip industry, the EBITDA value is $23,479.95 million with 30 firms. Lastly, for the Aerospace/Defense industry, the EBITDA value is $48,918.45 million with 70 firms.\n" + ] + } + ], + "source": [ + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mTable name: betaIndia \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Beta_ (INTEGER) | D_E_Ratio (INTEGER) | Effective_Tax_rate (INTEGER) | Unlevered_beta (INTEGER) | Cash_Firm_value (INTEGER) | Unlevered_beta_corrected_for_cash (INTEGER) | HiLo_Risk (INTEGER) | Standard_deviation_of_equity (INTEGER) | Standard_deviation_in_operating_income_last_10_years_ (INTEGER) | Beta_2020 (INTEGER) | Beta_2021 (INTEGER) | Beta_2022 (INTEGER) | Beta_2023 (INTEGER) | Average_Beta_2019_23 (INTEGER)\n", + "row 1 : Semiconductor | 8 | 1.611640106554315 | 0.0408116499112951 | 0.1361426723519776 | 1.566877312715955 | 0.0034062258080553 | 1.572232692288697 | 0.5628222021115665 | 0.5010518370389238 | 12.74567091312641 | 0.73 | 1.16 | 1.9836144211648223 | 1.9836144211648223 | 1.48589230692366\n", + "row 2 : Total Market (without financials) | 3850 | 0.8198477825445601 | 0.1608082521880037 | 0.1670198672216159 | 0.7368982579173127 | 0.0296361733703686 | 0.7594040891618811 | 0.384976556985476 | 0.3699643172605134 | 0.4325539868550488 | 0.62 | 0.88 | 0.8646047974287592 | 0.8646047974287592 | 0.79772273680387\n", + "row 3 : Software (Entertainment) | 5 | 0.7830360024669405 | 0.0075317267581366 | 0.2752644087938206 | 0.7789293244818127 | 0.0099775560297291 | 0.7867794606333218 | 0.2025157685180063 | 0.1867409361734939 | 0.698956301092383 | 0.59 | 1.16 | 1.219451180957866 | 1.219451180957866 | 0.99513636450981\n", + "row 4 : Software (Internet) | 6 | -0.0057433681694908 | 0.0336472713198297 | 0.0983609410926519 | -0.0056112069085913 | 0.0328328215881574 | -0.0058016928550091 | 0.512617043205324 | 0.504931470076568 | 10.948979663023987 | 1.04 | 0.52 | 0.4883816218840528 | 0.4883816218840528 | 0.50619231018261\n", + "row 5 : Software (System & Application) | 73 | 1.164011103524074 | 0.014057190040673 | 0.1609424721087531 | 1.1526688044177154 | 0.0427366294396399 | 1.2041292290782728 | 0.3973005076855119 | 0.4048728665269909 | 0.3296550990654787 | 0.83 | 1.2 | 1.2407987165306618 | 1.2407987165306618 | 1.14314533242791\n", + "*/\n", + "\n", + "Table name: totalbetaIndia \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Unlevered_Beta (INTEGER) | Average_Levered_Beta (INTEGER) | Average_correlation_with_the_market (INTEGER) | Total_Unlevered_Beta (INTEGER) | Total_Levered_Beta (INTEGER)\n", + "row 1 : Semiconductor | 8 | 1.572232692288697 | 1.611640106554315 | 0.1520316120175535 | 10.34148537547025 | 10.6006907719181\n", + "row 2 : Total Market (without financials) | 3850 | 0.7594040891618811 | 0.8198477825445601 | 0.138919459674997 | 5.466506211142138 | 5.9016050340434\n", + "row 3 : Software (Entertainment) | 5 | 0.7867794606333218 | 0.7830360024669405 | 0.2362678264192356 | 3.3300321612018964 | 3.3141880311605\n", + "row 4 : Software (Internet) | 6 | -0.0058016928550091 | -0.0057433681694908 | 0.0877398209734128 | -0.0661238282759565 | -0.06545908238439\n", + "row 5 : Software (System & Application) | 73 | 1.2041292290782728 | 1.164011103524074 | 0.1534528684231733 | 7.846899451613207 | 7.5854633118626\n", + "*/\n", + "\n", + "Table name: psIndia \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Price_Sales (INTEGER) | Net_Margin (INTEGER) | EV_Sales (INTEGER) | Pre_tax_Operating_Margin (INTEGER)\n", + "row 1 : Semiconductor | 8 | 6.380530138666162 | 0.0160833883595887 | 6.618309593434581 | 0.04773134609942\n", + "row 2 : Total Market (without financials) | 3850 | 2.210898698094238 | 0.0681170556023806 | 2.490370305272766 | 0.11262992702128\n", + "row 3 : Semiconductor Equip | 1 | 4.350600126342388 | 0.0243840808591282 | 4.593177511054959 | 0.03006948831332\n", + "row 4 : Software (Entertainment) | 5 | 17.543944376879494 | 0.078800724226794 | 17.499716487891103 | 0.20023775912029\n", + "row 5 : Software (Internet) | 6 | 11.275821937736398 | 0.1813354669770148 | 11.272548734361362 | 0.23581611870817\n", + "*/\n", + "\n", + "Table name: waccIndia \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", + "row 1 : Semiconductor | 8 | 1.611640106554315 | 0.164669092321892 | 0.9607886307625656 | 0.5010518370389238 | 0.077424 | 0.1361426723519776 | 0.0541968 | 0.0392113692374344 | 0.1603373224771525 | 0.18286814427282\n", + "row 2 : Total Market (without financials) | 3850 | 0.8198477825445601 | 0.1028301118167301 | 0.8614687207082679 | 0.3699643172605134 | 0.074755 | 0.1670198672216159 | 0.0523285 | 0.1385312792917321 | 0.095834058925464 | 0.11711239016673\n", + "row 3 : Software (Entertainment) | 5 | 0.7830360024669405 | 0.099955111792668 | 0.9925245760921386 | 0.1867409361734939 | 0.068943 | 0.2752644087938206 | 0.0482601 | 0.0074754239078613 | 0.0995686696655959 | 0.12091951762026\n", + "row 4 : Software (Internet) | 6 | -0.0057433681694908 | 0.0383514429459627 | 0.967448014179086 | 0.504931470076568 | 0.077424 | 0.0983609410926519 | 0.0541968 | 0.0325519858209141 | 0.0388672407841131 | 0.05903942021681\n", + "row 5 : Software (System & Application) | 73 | 1.164011103524074 | 0.1297092671852301 | 0.9861376752921508 | 0.4048728665269909 | 0.074755 | 0.1609424721087531 | 0.0523285 | 0.0138623247078493 | 0.128636589864366 | 0.15055186345396\n", + "*/\n", + "\n", + "Table name: pbvIndia \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | PBV (INTEGER) | ROE (INTEGER) | EV_Invested_Capital (INTEGER) | ROIC (INTEGER)\n", + "row 1 : Semiconductor | 8 | 8.627551020408164 | 0.0218198105963654 | 7.1386259299656505 | 0.04466875019704\n", + "row 2 : Total Market (without financials) | 3850 | 4.152459768346949 | 0.1526067560105244 | 3.119937106367408 | 0.12893652513884\n", + "row 3 : Semiconductor Equip | 1 | 5.957612456747405 | 0.0373307543520309 | 4.721005520284547 | 0.02701816743642\n", + "row 4 : Software (Entertainment) | 5 | 2.9646582024861243 | 0.0182539574699368 | 3.0371036678952725 | 0.03630006976547\n", + "row 5 : Software (Internet) | 6 | 10.946194040389774 | 0.2266569688153468 | 10.876201838725525 | 0.24757464461570\n", + "*/\n", + "\n", + "\u001b[0m\n", + "Prediction(\n", + " rationale='produce the SQL. We need to retrieve the beta value and number of firms for the Software industries and semiconductor industry. We have the relevant information in the tables betaIndia and totalbetaIndia.',\n", + " sql=\"```sql\\nSELECT DISTINCT betaIndia.Industry_Name AS Industry, betaIndia.Beta_ AS Beta_Value, betaIndia.Number_of_firms AS Number_of_Firms\\nFROM betaIndia\\nWHERE betaIndia.Industry_Name LIKE 'Software%' OR betaIndia.Industry_Name = 'Semiconductor'\\n\\nUNION\\n\\nSELECT DISTINCT totalbetaIndia.Industry_Name AS Industry, totalbetaIndia.Average_Unlevered_Beta AS Beta_Value, totalbetaIndia.Number_of_firms AS Number_of_Firms\\nFROM totalbetaIndia\\nWHERE totalbetaIndia.Industry_Name LIKE 'Software%' OR totalbetaIndia.Industry_Name = 'Semiconductor';\\n```\"\n", + ")\n", + "Extracted rows: Industry = Semiconductor, Beta_Value = 1.572232692288697, Number_of_Firms = 8\n", + " Industry = Semiconductor, Beta_Value = 1.611640106554315, Number_of_Firms = 8\n", + " Industry = Software (Entertainment), Beta_Value = 0.7830360024669405, Number_of_Firms = 5\n", + " Industry = Software (Entertainment), Beta_Value = 0.7867794606333218, Number_of_Firms = 5\n", + " Industry = Software (Internet), Beta_Value = -0.0058016928550091, Number_of_Firms = 6\n", + " Industry = Software (Internet), Beta_Value = -0.0057433681694908, Number_of_Firms = 6\n", + " Industry = Software (System & Application), Beta_Value = 1.164011103524074, Number_of_Firms = 73\n", + " Industry = Software (System & Application), Beta_Value = 1.2041292290782728, Number_of_Firms = 73\n", + "\n", + "The beta value and number of firms for the Software industries and semiconductor industry are as follows:\n", + "\n", + "- Industry: Semiconductor, Beta Value: 1.572232692288697, Number of Firms: 8\n", + "- Industry: Semiconductor, Beta Value: 1.611640106554315, Number of Firms: 8\n", + "- Industry: Software (Entertainment), Beta Value: 0.7830360024669405, Number of Firms: 5\n", + "- Industry: Software (Entertainment), Beta Value: 0.7867794606333218, Number of Firms: 5\n", + "- Industry: Software (Internet), Beta Value: -0.0058016928550091, Number of Firms: 6\n", + "- Industry: Software (Internet), Beta Value: -0.0057433681694908, Number of Firms: 6\n", + "- Industry: Software (System & Application), Beta Value: 1.164011103524074, Number of Firms: 73\n", + "- Industry: Software (System & Application), Beta Value: 1.2041292290782728, Number of Firms: 73\n" + ] + } + ], + "source": [ + "tsql = TextToSQLQueryModule(\"India\")\n", + "sq = tsql(question=\"What is the beta value and number of firms for all the Software industries and semiconductor industry?\")\n", + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The beta value and number of firms for the Software industries and semiconductor industry are as follows:\n", + "\n", + "- Industry: Semiconductor, Beta Value: 1.572232692288697, Number of Firms: 8\n", + "- Industry: Semiconductor, Beta Value: 1.611640106554315, Number of Firms: 8\n", + "- Industry: Software (Entertainment), Beta Value: 0.7830360024669405, Number of Firms: 5\n", + "- Industry: Software (Entertainment), Beta Value: 0.7867794606333218, Number of Firms: 5\n", + "- Industry: Software (Internet), Beta Value: -0.0058016928550091, Number of Firms: 6\n", + "- Industry: Software (Internet), Beta Value: -0.0057433681694908, Number of Firms: 6\n", + "- Industry: Software (System & Application), Beta Value: 1.164011103524074, Number of Firms: 73\n", + "- Industry: Software (System & Application), Beta Value: 1.2041292290782728, Number of Firms: 73\n" + ] + } + ], + "source": [ + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mTable name: betaChina \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Beta_ (INTEGER) | D_E_Ratio (INTEGER) | Effective_Tax_rate (INTEGER) | Unlevered_beta (INTEGER) | Cash_Firm_value (INTEGER) | Unlevered_beta_corrected_for_cash (INTEGER) | HiLo_Risk (INTEGER) | Standard_deviation_of_equity (INTEGER) | Standard_deviation_in_operating_income_last_10_years_ (INTEGER)\n", + "row 1 : Semiconductor | 173 | 1.3766066410015918 | 0.1214640196981001 | 0.0455831075871332 | 1.261670924483595 | 0.1353894923931989 | 1.4592361686371793 | 0.3256484142873909 | 0.3505300755906819 | 0.93670130463571\n", + "row 2 : Total Market (without financials) | 7161 | 1.0686360714269856 | 0.4705306537504954 | 0.0996397852701986 | 0.7898866574410776 | 0.1404090933852625 | 0.9189099737592956 | 0.3186107366027715 | 0.3211951486316037 | 0.26343082981471\n", + "row 3 : Semiconductor Equip | 59 | 1.1458070496329555 | 0.1005963129277871 | 0.072001108546688 | 1.0654237722383604 | 0.0896726175453628 | 1.170374299151055 | 0.3370962305052479 | 0.3780387083633861 | 1.05671095308164\n", + "row 4 : Software (Entertainment) | 33 | 1.9284684177392413 | 0.134930367400854 | 0.0960440405122507 | 1.7512461980545997 | 0.0533537078374321 | 1.849947770939832 | 0.5242121688667037 | 0.4696202053064469 | 0.48614502813710\n", + "row 5 : Software (Internet) | 19 | 0.9517387989546392 | 0.2511530032375074 | 0.0593218951020171 | 0.8008810401099454 | 0.0847676071125495 | 0.8750575769977502 | 0.3425492316782614 | 0.3399832570802881 | 0.42653552101587\n", + "*/\n", + "\n", + "Table name: totalbetaChina \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Unlevered_Beta (INTEGER) | Average_Levered_Beta (INTEGER) | Average_correlation_with_the_market (INTEGER) | Total_Unlevered_Beta (INTEGER) | Total_Levered_Beta (INTEGER)\n", + "row 1 : Semiconductor | 173 | 1.4592361686371793 | 1.3766066410015918 | 0.2051159812465771 | 7.114200267423239 | 6.7113573142149\n", + "row 2 : Total Market (without financials) | 7161 | 0.9189099737592956 | 1.0686360714269856 | 0.1807939202400685 | 5.082637582829746 | 5.9107965024929\n", + "row 3 : Semiconductor Equip | 59 | 1.170374299151055 | 1.1458070496329555 | 0.1422791350917505 | 8.225902542887432 | 8.053233166578\n", + "row 4 : Software (Entertainment) | 33 | 1.849947770939832 | 1.9284684177392413 | 0.189683512387109 | 9.75281271239026 | 10.1667688112164\n", + "row 5 : Software (Internet) | 19 | 0.8750575769977502 | 0.9517387989546392 | 0.1824645387000501 | 4.795767896776043 | 5.2160206346679\n", + "*/\n", + "\n", + "Table name: EVAChina \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | ROE (INTEGER) | Cost_of_Equity (INTEGER) | _ROE_COE_ (INTEGER) | BV_of_Equity (INTEGER) | Equity_EVA (INTEGER) | ROC (INTEGER) | Cost_of_Capital (INTEGER) | _ROC_WACC_ (INTEGER) | BV_of_Capital (INTEGER) | EVA (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER)\n", + "row 1 : Semiconductor | 173 | 1.3766066410015918 | 0.0962582854914372 | 0.1163029538883896 | -0.0200446683969523 | 126261.36999999998 | -2530.8672929949053 | 0.0928822041397873 | 0.1084628628354935 | -0.0155806586957062 | 138747.35577016464 | -2161.775195186669 | 0.8916915589224178 | 0.3505300755906819 | 0.0585549999999999 | 0.0455831075871332 | 0.04391625 | 0.10830844107758\n", + "row 2 : Total Market (without financials) | 7161 | 1.0686360714269856 | 0.0639810392810329 | 0.0989642108213393 | -0.0349831715403063 | 7039463.864000044 | -246262.7719061013 | 0.0650803131143393 | 0.0813503291067108 | -0.0162700159923715 | 10535415.2412372 | -171411.37446120442 | 0.6800266267483532 | 0.3211951486316037 | 0.0585549999999999 | 0.0996397852701986 | 0.04391625 | 0.31997337325164\n", + "row 3 : Semiconductor Equip | 59 | 1.1458070496329555 | 0.2052549059382613 | 0.1033089368943354 | 0.1019459690439259 | 47325.30000000001 | 4824.623568794508 | 0.1911922171025874 | 0.0978803476411596 | 0.0933118694614277 | 50881.7695261246 | 4747.873035988193 | 0.9085983555040426 | 0.3780387083633861 | 0.0585549999999999 | 0.072001108546688 | 0.04391625 | 0.09140164449595\n", + "row 4 : Software (Entertainment) | 33 | 1.9284684177392413 | 0.2294528801199007 | 0.1473727719187193 | 0.0820801082011814 | 117637.29 | 9655.68149169376 | 0.0906573095366518 | 0.1350729631256245 | -0.0444156535889726 | 176984.04958861455 | -7860.862237301461 | 0.8811113251733117 | 0.4696202053064469 | 0.0585549999999999 | 0.0960440405122507 | 0.04391625 | 0.11888867482668\n", + "row 5 : Software (Internet) | 19 | 0.9517387989546392 | 0.0250537522727571 | 0.0923828943811461 | -0.067329142108389 | 8376.390000000001 | -563.9751526652892 | 0.034746695140248 | 0.0826538338572364 | -0.0479071387169884 | 11768.570206159107 | -563.7985253670817 | 0.7992627579619607 | 0.3399832570802881 | 0.0585549999999999 | 0.0593218951020171 | 0.04391625 | 0.20073724203803\n", + "*/\n", + "\n", + "Table name: waccChina \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", + "row 1 : Semiconductor | 173 | 1.3766066410015918 | 0.1163029538883896 | 0.8916915589224178 | 0.3505300755906819 | 0.0585549999999999 | 0.0455831075871332 | 0.04391625 | 0.1083084410775822 | 0.1084628628354936 | 0.10846286283549\n", + "row 2 : Total Market (without financials) | 7161 | 1.0686360714269856 | 0.0989642108213393 | 0.6800266267483532 | 0.3211951486316037 | 0.0585549999999999 | 0.0996397852701986 | 0.04391625 | 0.3199733732516467 | 0.0813503291067108 | 0.08135032910671\n", + "row 3 : Semiconductor Equip | 59 | 1.1458070496329555 | 0.1033089368943354 | 0.9085983555040426 | 0.3780387083633861 | 0.0585549999999999 | 0.072001108546688 | 0.04391625 | 0.0914016444959574 | 0.0978803476411596 | 0.09788034764115\n", + "row 4 : Software (Entertainment) | 33 | 1.9284684177392413 | 0.1473727719187193 | 0.8811113251733117 | 0.4696202053064469 | 0.0585549999999999 | 0.0960440405122507 | 0.04391625 | 0.1188886748266883 | 0.1350729631256245 | 0.13507296312562\n", + "row 5 : Software (Internet) | 19 | 0.9517387989546392 | 0.0923828943811461 | 0.7992627579619607 | 0.3399832570802881 | 0.0585549999999999 | 0.0593218951020171 | 0.04391625 | 0.2007372420380393 | 0.0826538338572364 | 0.08265383385723\n", + "*/\n", + "\n", + "Table name: pbvChina \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | PBV (INTEGER) | ROE (INTEGER) | EV_Invested_Capital (INTEGER) | ROIC (INTEGER)\n", + "row 1 : Semiconductor | 173 | 2.73256217631175 | 0.0962582854914372 | 2.4500098154005925 | 0.09288220413978\n", + "row 2 : Total Market (without financials) | 7161 | 1.4084499744108725 | 0.0639810392810329 | 1.2430260813341163 | 0.06508031311433\n", + "row 3 : Semiconductor Equip | 59 | 2.954264696454902 | 0.2052549059382613 | 2.6326043462726965 | 0.19119221710258\n", + "row 4 : Software (Entertainment) | 33 | 3.1135265373627137 | 0.2294528801199007 | 2.248186099053892 | 0.09065730953665\n", + "row 5 : Software (Internet) | 19 | 2.6160739269602717 | 0.0250537522727571 | 2.044727771310165 | 0.0347466951402\n", + "*/\n", + "\n", + "\u001b[0m\n", + "Prediction(\n", + " rationale='produce the SQL. We need to retrieve the beta value and number of firms for the Software industries and semiconductor industry. We have tables that contain this information for each industry separately.',\n", + " sql=\"```sql\\nSELECT DISTINCT betaChina.Beta_ AS Beta_Value, betaChina.Number_of_firms AS Number_of_Firms\\nFROM betaChina\\nWHERE betaChina.Industry_Name IN ('Software (Entertainment)', 'Software (Internet)', 'Semiconductor')\\nUNION\\nSELECT DISTINCT EVAChina.Beta AS Beta_Value, EVAChina.Number_of_Firms AS Number_of_Firms\\nFROM EVAChina\\nWHERE EVAChina.Industry_Name IN ('Software (Entertainment)', 'Software (Internet)', 'Semiconductor')\\n```\"\n", + ")\n", + "Extracted rows: Beta_Value = 0.9517387989546392, Number_of_Firms = 19\n", + " Beta_Value = 1.3766066410015918, Number_of_Firms = 173\n", + " Beta_Value = 1.9284684177392413, Number_of_Firms = 33\n", + "\n", + "Beta value and number of firms for the Software (Entertainment) industry are Beta_Value = 0.9517387989546392, Number_of_Firms = 19. For the Software (Internet) industry, the values are Beta_Value = 1.3766066410015918, Number_of_Firms = 173. And for the Semiconductor industry, the values are Beta_Value = 1.9284684177392413, Number_of_Firms = 33.\n" + ] + } + ], + "source": [ + "tsql = TextToSQLQueryModule(\"China\")\n", + "sq = tsql(question=\"What is the beta value and number of firms for all the Software industries and semiconductor industry?\")\n", + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta value and number of firms for the Software (Entertainment) industry are Beta_Value = 0.9517387989546392, Number_of_Firms = 19. For the Software (Internet) industry, the values are Beta_Value = 1.3766066410015918, Number_of_Firms = 173. And for the Semiconductor industry, the values are Beta_Value = 1.9284684177392413, Number_of_Firms = 33.\n" + ] + } + ], + "source": [ + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mTable name: taxrate \n", + "/* \n", + "col : Industry_name (VARCHAR) | Number_of_firms (INTEGER) | Total_Taxable_Income (INTEGER) | Total_Taxes_Paid_Accrual_ (INTEGER) | Total_Cash_Taxes_Paid (INTEGER) | Cash_Taxes_Accrual_Taxes (INTEGER) | Average_across_all_companies (INTEGER) | Average_across_only_money_making_companies (INTEGER) | Aggregate_tax_rate (INTEGER) | Average_across_only_money_making_companies2 (INTEGER) | Aggregate_tax_rate3 (INTEGER)\n", + "row 1 : Healthcare Products | 230 | 26657.20900000001 | 5486.0970000000025 | 7358.37 | 1.341275956294611 | 0.0481441302819232 | 0.2058016276197557 | 0.2473315283348905 | 0.2760367748926752 | 0.35300275681386\n", + "row 2 : Hospitals/Healthcare Facilities | 32 | 12803.409999999998 | 2598.52 | 2117.581 | 0.8149181072302696 | 0.0685657680171947 | 0.2029553064378943 | 0.2241515389301438 | 0.1653919541747081 | 0.18582584339767\n", + "row 3 : Healthcare Support Services | 119 | 76367.83500000002 | 17205.211 | 18119.770000000008 | 1.0531559304910594 | 0.0808427903603178 | 0.2252939473798097 | 0.2520069365480554 | 0.2372696567867873 | 0.2683567809591\n", + "row 4 : Heathcare Information and Technology | 128 | 21838.42000000001 | 2553.3450000000003 | 6092.359 | 2.386030481583961 | 0.0311109029990876 | 0.1169198595869114 | 0.2128897982354291 | 0.2789743488768875 | 0.5756510369422\n", + "row 5 : Total Market (without financials) | 5214 | 2091519.969999999 | 405892.603 | 448383.9540000016 | 1.1046861920762858 | 0.0680489965490159 | 0.1940658510661986 | 0.2166068386944213 | 0.2143818660263625 | 0.25088890170136\n", + "*/\n", + "\n", + "Table name: margin \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", + "row 1 : Healthcare Products | 230 | 0.5563922869375535 | 0.0818669682093114 | 0.1558110098462976 | 0.1329496754079793 | 0.1265489289141982 | 0.1339775386003306 | 0.1275273065271049 | 0.1383573960583481 | 0.1319071639851224 | 0.209192854485968 | 0.5282884406553701 | 0.294308887098031 | 0.4436077130624465 | 0.0851160326120629 | 0.3190955861694019 | 0.0228613344383182 | 0.00977793897038\n", + "row 2 : Hospitals/Healthcare Facilities | 32 | 0.35988660694384 | 0.0512378794350515 | 0.120654788689701 | 0.1156935415013266 | 0.107760924973659 | 0.1154810689328797 | 0.1075630207500502 | 0.1154805210386438 | 0.1075624728558143 | 0.1535618716018076 | 0.1846309639990737 | 0.1535712434768955 | 0.64011339305616 | 9.371875087928916e-06 | 0.031069092397266 | 0.0049612471883743 | 0.02033043025564\n", + "row 3 : Healthcare Support Services | 119 | 0.1436810802508208 | 0.0225465585118185 | 0.0418808661001017 | 0.0391834198903671 | 0.0360157228905698 | 0.0386507190050279 | 0.0355260870312289 | 0.0386351017430003 | 0.0355104697692013 | 0.0461573478838079 | 0.1419758713498951 | 0.0469153240204038 | 0.8563189197491792 | 0.0007579761365958 | 0.0958185234660871 | 0.0026974462097345 | 0.0040901341131\n", + "row 4 : Heathcare Information and Technology | 128 | 0.4766949090992926 | 0.0572239502258181 | 0.1655618579641873 | 0.1400806639108432 | 0.1357226279638652 | 0.1403673029983578 | 0.1360003494505323 | 0.1414922855264892 | 0.1371253319786638 | 0.2365968665611617 | 0.4859769860183378 | 0.3029234453206219 | 0.5233050909007073 | 0.0663265787594602 | 0.249380119457176 | 0.0254811940533441 | 0.01221262918932\n", + "row 5 : Total Market (without financials) | 5214 | 0.3341124303745417 | 0.0759000807387551 | 0.1349854587183475 | 0.1203243079012881 | 0.1121363594881506 | 0.1193029155861628 | 0.1111844718951524 | 0.122305912015432 | 0.1141874683244217 | 0.1649863910103977 | 0.308807292459029 | 0.2037377626528898 | 0.6658875696254583 | 0.038751371642492 | 0.1438209014486313 | 0.0146611508170593 | 0.01534583398930\n", + "*/\n", + "\n", + "Table name: wacc \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Local_Currency_ (INTEGER)\n", + "row 1 : Healthcare Products | 230 | 1.062079895442844 | 0.0876556751903708 | 0.8876027024944009 | 0.5220553450431473 | 0.053524 | 0.0481441302819232 | 0.040143 | 0.1123972975055991 | 0.0823153789017118 | 0.08231537890171\n", + "row 2 : Hospitals/Healthcare Facilities | 32 | 0.879576324903561 | 0.0792605109455638 | 0.5563496280559845 | 0.4632806494431785 | 0.050855 | 0.0685657680171947 | 0.03814125 | 0.4436503719440154 | 0.0610179355330013 | 0.06101793553300\n", + "row 3 : Healthcare Support Services | 119 | 1.0326229061406076 | 0.0863006536824679 | 0.7882476948159491 | 0.495257334489641 | 0.050855 | 0.0808427903603178 | 0.03814125 | 0.2117523051840508 | 0.076102788936416 | 0.0761027889364\n", + "row 4 : Heathcare Information and Technology | 128 | 1.2747542568245518 | 0.0974386958139293 | 0.8615496523681622 | 0.5415316524692178 | 0.053524 | 0.0311109029990876 | 0.040143 | 0.1384503476318378 | 0.0895060868106828 | 0.08950608681068\n", + "row 5 : Total Market (without financials) | 5214 | 1.097522642247185 | 0.0892860415433705 | 0.815661408370401 | 0.4700345950094993 | 0.050855 | 0.0680489965490159 | 0.03814125 | 0.1843385916295989 | 0.0798580827010761 | 0.07985808270107\n", + "*/\n", + "\n", + "Table name: histgr \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | CAGR_in_Net_Income_Last_5_years (INTEGER) | CAGR_in_Revenues_Last_5_years (INTEGER) | Expected_Growth_in_Revenues_Next_2_years (INTEGER) | Expected_growth_in_Revenues_Next_5_years (INTEGER) | Expected_Growth_in_EPS_Next_5_years (INTEGER)\n", + "row 1 : Healthcare Products | 230 | 0.1271279069767442 | 0.1794840000000001 | 0.5909131578947368 | 0.2743437920966664 | 0.16686195121951\n", + "row 2 : Hospitals/Healthcare Facilities | 32 | 0.0409985714285714 | 0.181068947368421 | 0.0413333333333333 | 0.0408655475254498 | 0.10645555555555\n", + "row 3 : Healthcare Support Services | 119 | 0.0625710344827586 | 0.1242187719298245 | 0.0938797014925373 | 0.1106991073675205 | 0.11186538461538\n", + "row 4 : Heathcare Information and Technology | 128 | 0.2291124999999999 | 0.1966670689655173 | 0.1482884810126581 | 0.1667594853751504 | 0.10272187499999\n", + "row 5 : Total Market (without financials) | 5214 | 0.1008838061178445 | 0.1270981652178585 | 0.2757367288045685 | 0.2849241661766458 | 0.14538921052109\n", + "*/\n", + "\n", + "Table name: roe \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | ROE_unadjusted_ (INTEGER) | ROE_adjusted_for_R_D_ (INTEGER)\n", + "row 1 : Healthcare Products | 230 | 0.0830579792856188 | 0.06980113651478\n", + "row 2 : Hospitals/Healthcare Facilities | 32 | 0.6196567951123925 | 0.61942896417013\n", + "row 3 : Healthcare Support Services | 119 | 0.1562031003418411 | 0.1536389472830\n", + "row 4 : Heathcare Information and Technology | 128 | 0.0517765805634577 | 0.04477886360563\n", + "row 5 : Total Market (without financials) | 5214 | 0.1659255247192018 | 0.13839857100630\n", + "*/\n", + "\n", + "\u001b[0m\n", + "Prediction(\n", + " rationale='produce the SQL. We need to calculate the average tax rate of all healthcare industries. To do this, we need to join the taxrate table with the industry_name column as the common key.',\n", + " sql=\"```sql\\nSELECT AVG(taxrate.Aggregate_tax_rate) AS Average_Tax_Rate\\nFROM taxrate\\nWHERE taxrate.Industry_name LIKE '%Healthcare%'\\n```\"\n", + ")\n", + "Extracted rows: Average_Tax_Rate = 0.24116333460436337\n", + "\n", + "The average tax rate of all healthcare industries is 0.24116333460436337.\n" + ] + } + ], + "source": [ + "tsql = TextToSQLQueryModule(\"US\")\n", + "sq = tsql(question=\"What is the average tax rate of all healthcare industries?\")\n", + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average tax rate of all healthcare industries is 0.24116333460436337.\n" + ] + } + ], + "source": [ + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mTable name: taxrate \n", + "/* \n", + "col : Industry_name (VARCHAR) | Number_of_firms (INTEGER) | Total_Taxable_Income (INTEGER) | Total_Taxes_Paid_Accrual_ (INTEGER) | Total_Cash_Taxes_Paid (INTEGER) | Cash_Taxes_Accrual_Taxes (INTEGER) | Average_across_all_companies (INTEGER) | Average_across_only_money_making_companies (INTEGER) | Aggregate_tax_rate (INTEGER) | Average_across_only_money_making_companies2 (INTEGER) | Aggregate_tax_rate3 (INTEGER)\n", + "row 1 : Hospitals/Healthcare Facilities | 32 | 12803.409999999998 | 2598.52 | 2117.581 | 0.8149181072302696 | 0.0685657680171947 | 0.2029553064378943 | 0.2241515389301438 | 0.1653919541747081 | 0.18582584339767\n", + "row 2 : Healthcare Products | 230 | 26657.20900000001 | 5486.0970000000025 | 7358.37 | 1.341275956294611 | 0.0481441302819232 | 0.2058016276197557 | 0.2473315283348905 | 0.2760367748926752 | 0.35300275681386\n", + "row 3 : Healthcare Support Services | 119 | 76367.83500000002 | 17205.211 | 18119.770000000008 | 1.0531559304910594 | 0.0808427903603178 | 0.2252939473798097 | 0.2520069365480554 | 0.2372696567867873 | 0.2683567809591\n", + "row 4 : Heathcare Information and Technology | 128 | 21838.42000000001 | 2553.3450000000003 | 6092.359 | 2.386030481583961 | 0.0311109029990876 | 0.1169198595869114 | 0.2128897982354291 | 0.2789743488768875 | 0.5756510369422\n", + "row 5 : Total Market (without financials) | 5214 | 2091519.969999999 | 405892.603 | 448383.9540000016 | 1.1046861920762858 | 0.0680489965490159 | 0.1940658510661986 | 0.2166068386944213 | 0.2143818660263625 | 0.25088890170136\n", + "*/\n", + "\n", + "Table name: Employee \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Number_of_Employees (INTEGER) | Market_Capitalization_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Mkt_Cap_per_Employee_ (INTEGER) | Revenues_per_Employee_ (INTEGER) | Stock_based_Compensation_millions_ (INTEGER) | Stock_based_Compensation_as_of_Revenue (INTEGER)\n", + "row 1 : Hospitals/Healthcare Facilities | 32 | 716231 | 6704.900000000001 | 7793.799999999999 | 9361.365257856754 | 10881.684819562402 | 688.1889999999999 | 0.00496124718837\n", + "row 2 : Healthcare Products | 230 | 692739 | 132639.79999999996 | 34790.487 | 191471.5354556333 | 50221.63758645031 | 5056.955000000004 | 0.02286133443831\n", + "row 3 : Healthcare Support Services | 119 | 1634485 | 55806.99999999999 | 277699.3 | 34143.47638552816 | 169900.18262633184 | 6016.255999999999 | 0.00269744620973\n", + "row 4 : Heathcare Information and Technology | 128 | 535393 | 104368.758 | 29258.17 | 194938.59277203845 | 54648.02490880531 | 3955.762000000001 | 0.02548119405334\n", + "row 5 : Total Market (without financials) | 5214 | 37972621 | 10850254.874000004 | 3275178.419000001 | 285738.8978759197 | 86251.04964442673 | 274548.627 | 0.01466115081705\n", + "*/\n", + "\n", + "Table name: margin \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Gross_Margin (INTEGER) | Net_Margin (INTEGER) | Pre_tax_Pre_stock_compensation_Operating_Margin (INTEGER) | Pre_tax_Unadjusted_Operating_Margin (INTEGER) | After_tax_Unadjusted_Operating_Margin (INTEGER) | Pre_tax_Lease_adjusted_Margin (INTEGER) | After_tax_Lease_Adjusted_Margin (INTEGER) | Pre_tax_Lease_R_D_adj_Margin (INTEGER) | After_tax_Lease_R_D_adj_Margin (INTEGER) | EBITDA_Sales (INTEGER) | EBITDASG_A_Sales (INTEGER) | EBITDAR_D_Sales (INTEGER) | COGS_Sales (INTEGER) | R_D_Sales (INTEGER) | SG_A_Sales (INTEGER) | Stock_Based_Compensation_Sales (INTEGER) | Lease_Expense_Sales (INTEGER)\n", + "row 1 : Hospitals/Healthcare Facilities | 32 | 0.35988660694384 | 0.0512378794350515 | 0.120654788689701 | 0.1156935415013266 | 0.107760924973659 | 0.1154810689328797 | 0.1075630207500502 | 0.1154805210386438 | 0.1075624728558143 | 0.1535618716018076 | 0.1846309639990737 | 0.1535712434768955 | 0.64011339305616 | 9.371875087928916e-06 | 0.031069092397266 | 0.0049612471883743 | 0.02033043025564\n", + "row 2 : Healthcare Products | 230 | 0.5563922869375535 | 0.0818669682093114 | 0.1558110098462976 | 0.1329496754079793 | 0.1265489289141982 | 0.1339775386003306 | 0.1275273065271049 | 0.1383573960583481 | 0.1319071639851224 | 0.209192854485968 | 0.5282884406553701 | 0.294308887098031 | 0.4436077130624465 | 0.0851160326120629 | 0.3190955861694019 | 0.0228613344383182 | 0.00977793897038\n", + "row 3 : Healthcare Support Services | 119 | 0.1436810802508208 | 0.0225465585118185 | 0.0418808661001017 | 0.0391834198903671 | 0.0360157228905698 | 0.0386507190050279 | 0.0355260870312289 | 0.0386351017430003 | 0.0355104697692013 | 0.0461573478838079 | 0.1419758713498951 | 0.0469153240204038 | 0.8563189197491792 | 0.0007579761365958 | 0.0958185234660871 | 0.0026974462097345 | 0.0040901341131\n", + "row 4 : Heathcare Information and Technology | 128 | 0.4766949090992926 | 0.0572239502258181 | 0.1655618579641873 | 0.1400806639108432 | 0.1357226279638652 | 0.1403673029983578 | 0.1360003494505323 | 0.1414922855264892 | 0.1371253319786638 | 0.2365968665611617 | 0.4859769860183378 | 0.3029234453206219 | 0.5233050909007073 | 0.0663265787594602 | 0.249380119457176 | 0.0254811940533441 | 0.01221262918932\n", + "row 5 : Total Market (without financials) | 5214 | 0.3341124303745417 | 0.0759000807387551 | 0.1349854587183475 | 0.1203243079012881 | 0.1121363594881506 | 0.1193029155861628 | 0.1111844718951524 | 0.122305912015432 | 0.1141874683244217 | 0.1649863910103977 | 0.308807292459029 | 0.2037377626528898 | 0.6658875696254583 | 0.038751371642492 | 0.1438209014486313 | 0.0146611508170593 | 0.01534583398930\n", + "*/\n", + "\n", + "Table name: DollarUS \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Average_Company_Age_years_ (INTEGER) | Market_Cap_millions_ (INTEGER) | Book_Equity_millions_ (INTEGER) | Enteprise_Value_millions_ (INTEGER) | Invested_Capital_millions_ (INTEGER) | Total_Debt_including_leases_millions_ (INTEGER) | Revenues_millions_ (INTEGER) | Gross_Profit_millions_ (INTEGER) | EBITDA_millions_ (INTEGER) | EBIT_Operating_Income_millions_ (INTEGER) | Net_Income_millions_ (INTEGER)\n", + "row 1 : Hospitals/Healthcare Facilities | 32 | 25.23333333333333 | 122470.973 | 11469.825000000004 | 216360.12943388568 | 87395.24268778635 | 97662.13543388566 | 138712.903 | 49920.916000000005 | 24233.965 | 16018.714313222865 | 7107.3549999999\n", + "row 2 : Healthcare Products | 230 | 25.382075471698112 | 1050360.2549999994 | 218029.299 | 1141634.0060547118 | 175743.51313973693 | 133007.31705471186 | 221201.21699999992 | 123074.651 | 47150.65200000003 | 29635.99458905761 | 18109.072999999\n", + "row 3 : Healthcare Support Services | 119 | 28.918367346938776 | 1172283.046 | 321931.98400000005 | 1359335.1278580544 | 189878.4492234088 | 314918.3168580551 | 2230352.538 | 320459.4619999998 | 114747.873 | 86204.72922838898 | 50286.774000000\n", + "row 4 : Heathcare Information and Technology | 128 | 22.776785714285715 | 738408.3160000001 | 171575.33200000005 | 821060.925284837 | 123028.59595086362 | 118661.63228483696 | 155242.41099999996 | 74003.267 | 34169.073000000004 | 21790.95854303259 | 8883.5840000000\n", + "row 5 : Total Market (without financials) | 5214 | 34.79463487696991 | 43163609.00299983 | 8566043.05699999 | 51078189.49081551 | 13583012.82046062 | 9754928.713815568 | 18726267.155 | 6256678.631000005 | 3370108.721999998 | 2234098.269636899 | 1421325.1889999\n", + "*/\n", + "\n", + "Table name: fundgrEB \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | ROC (INTEGER) | Reinvestment_Rate (INTEGER) | Expected_Growth_in_EBIT (INTEGER)\n", + "row 1 : Hospitals/Healthcare Facilities | 32 | 0.2015664022610945 | 0.3491979818825059 | 0.07038658088489\n", + "row 2 : Healthcare Products | 230 | 0.1308118160288834 | 0.4951245525175228 | 0.06476814187530\n", + "row 3 : Healthcare Support Services | 119 | 0.4574102830620488 | 0.6275372045055494 | 0.28704197034485\n", + "row 4 : Heathcare Information and Technology | 128 | 0.1428121724863794 | 0.3133945551322462 | 0.04475655726383\n", + "row 5 : Total Market (without financials) | 5214 | 0.1466884216172786 | 0.4437349493666002 | 0.06509077933900\n", + "*/\n", + "\n", + "\u001b[0m\n", + "Prediction(\n", + " rationale='produce the SQL. We need to first filter out the healthcare industries where revenues per employee is more than 1 million, then calculate the average tax rate for those industries.',\n", + " sql='```sql\\nSELECT AVG(taxrate.Aggregate_tax_rate) AS Average_Tax_Rate\\nFROM taxrate\\nJOIN Employee ON taxrate.Industry_name = Employee.Industry_Name\\nWHERE Employee.Revenues_per_Employee_ > 1000000\\n```'\n", + ")\n", + "Extracted rows: Average_Tax_Rate = 0.18875589135103757\n", + "\n", + "The average tax rate of all healthcare industries where revenues per employee is more than 1 million is approximately 18.88%.\n" + ] + } + ], + "source": [ + "sq = tsql(question=\"Give me the average tax rate of all healthcare industries where revenues per employee is more than 1 million?\")\n", + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mTable name: taxrateEurope \n", + "/* \n", + "col : Industry_name (VARCHAR) | Number_of_firms (INTEGER) | Total_Taxable_Income (INTEGER) | Total_Taxes_Paid_Accrual_ (INTEGER) | Total_Cash_Taxes_Paid (INTEGER) | Cash_Taxes_Accrual_Taxes (INTEGER) | Average_across_all_companies (INTEGER) | Average_across_only_money_making_companies (INTEGER) | Aggregate_tax_rate (INTEGER) | Average_across_only_money_making_companies_1 (INTEGER) | Aggregate_tax_rate_1 (INTEGER)\n", + "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 67531.72600000002 | 5859.747000000001 | 4199.801999999999 | 0.716720704835891 | 0.133380349433826 | 0.0867702833480074 | 0.0851491015690155 | 0.0621900586399938 | 0.0637353624270\n", + "row 2 : Banks (Regional) | 72 | 7968.15 | 1353.5790000000002 | 675.7199999999998 | 0.499209872493589 | 0.1876823623247109 | 0.169873684606841 | 0.1693503510852582 | 0.0848026204325972 | 0.08480262043259\n", + "row 3 : Total Market (without financials) | 6149 | 1358894.4370000027 | 346515.07499999995 | 347363.15499999904 | 1.0024474548473803 | 0.114021587002736 | 0.2549977875875278 | 0.2805680522510425 | 0.2556218831588302 | 0.29150994341615\n", + "row 4 : Brokerage & Investment Banking | 74 | 3648.079999999999 | 709.6220000000001 | 633.1540000000001 | 0.8922412213826517 | 0.1518944442193559 | 0.194519308787088 | 0.195143715024721 | 0.1735581456547006 | 0.1756212164289\n", + "row 5 : Bank (Money Center) | 112 | 300482.43000000005 | 70812.87999999998 | 18826.331 | 0.2658602644038769 | 0.2354411572737627 | 0.2356639621158547 | 0.2359508773550057 | 0.0626536832785863 | 0.06272274286469\n", + "*/\n", + "\n", + "Table name: betaEurope \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Beta_ (INTEGER) | D_E_Ratio (INTEGER) | Effective_Tax_rate (INTEGER) | Unlevered_beta (INTEGER) | Cash_Firm_value (INTEGER) | Unlevered_beta_corrected_for_cash (INTEGER) | HiLo_Risk (INTEGER) | Standard_deviation_of_equity (INTEGER) | Standard_deviation_in_operating_income_last_10_years_ (INTEGER) | 2020 (INTEGER) | 2021_0 (INTEGER) | 2022_0 (INTEGER) | 2022_0_1 (INTEGER) | Average_2020_24 (INTEGER)\n", + "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 1.0216367356825768 | 4.109932277910462 | 0.133380349433826 | 0.2495224495401071 | 0.2851940249654809 | 0.3490771737436264 | 0.3644714846498691 | 0.355513616932455 | 0.4724600661983447 | 0.26 | 0.27 | 0.33 | 0.2491729799474152 | 0.29165003073820\n", + "row 2 : Total Market (without financials) | 6149 | 1.0163277560226116 | 0.3625130044987187 | 0.114021587002736 | 0.7984122714874734 | 0.0654744498128502 | 0.8543503934457243 | 0.3678598807686726 | 0.3394865864311263 | 0.2520005799939234 | 0.85 | 0.78 | 0.87 | 0.8817115657835057 | 0.8472123918458\n", + "row 3 : Brokerage & Investment Banking | 74 | 0.8707315899058546 | 0.6876389626893673 | 0.1518944442193559 | 0.573709019867202 | 0.2412741526659604 | 0.7561479839958829 | 0.3177625193382241 | 0.2926244614542751 | 0.8202608076286918 | 0.95 | 0.73 | 0.9 | 0.6959319603512203 | 0.80641598886942\n", + "row 4 : Bank (Money Center) | 112 | 1.2153935486343157 | 5.615675883136155 | 0.2354411572737627 | 0.2324758412532342 | 0.3625662794009818 | 0.3647059039091448 | 0.2449799817999482 | 0.2535837672757894 | 1.5390713693641629 | 0.32 | 0.28 | 0.42 | 0.3686115410275469 | 0.35066348898733\n", + "row 5 : Retail (REITs) | 33 | 0.960746998690458 | 1.4416481088663338 | 0.0421971427092918 | 0.4606978185423953 | 0.0706780075754195 | 0.4957354095757971 | 0.1860296862232068 | 0.2120972268727357 | 0.2453273616689489 | 1.23 | 1.12 | 1.25 | 1.4672850705706748 | 1.11260409602929\n", + "*/\n", + "\n", + "Table name: dbtfundEurope \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Book_Debt_to_Capital (INTEGER) | Market_Debt_to_Capital_Unadjusted_ (INTEGER) | Market_D_E_unadjusted_ (INTEGER) | Market_Debt_to_Capital_adjusted_for_leases_ (INTEGER) | Market_D_E_adjusted_for_leases_ (INTEGER) | Interest_Coverage_Ratio (INTEGER) | Debt_to_EBITDA (INTEGER) | Effective_tax_rate (INTEGER) | Institutional_Holdings (INTEGER) | Std_dev_in_Stock_Prices (INTEGER) | EBITDA_EV (INTEGER) | Net_PP_E_Total_Assets (INTEGER) | Capital_Spending_Total_Assets (INTEGER)\n", + "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 0.8064776202399659 | 0.8042998495207718 | 4.109858104611632 | 0.8043026902092493 | 4.109932277910462 | 5.282640237538722 | 134.02896372206712 | 0.133380349433826 | 0.2892209174311926 | 0.355513616932455 | 0.0106491195890414 | 0.0051764605692062 | 0.00082237262437\n", + "row 2 : Total Market (without financials) | 6149 | 0.4398362396046586 | 0.2654959269193548 | 0.3614628381920554 | 0.2660620510055906 | 0.3625130044987187 | 7.095364720701234 | 2.8548850784071864 | 0.114021587002736 | 0.2515864670455792 | 0.3395466680004999 | 0.1015165247503355 | 0.2846296929541041 | 0.03637768238513\n", + "row 3 : Brokerage & Investment Banking | 74 | 0.6232840556024103 | 0.4073341226467672 | 0.6872913359984001 | 0.4074562023583337 | 0.6876389626893673 | 2.863783019167806 | 60.495499496601965 | 0.1518944442193559 | 0.2122024561403509 | 0.2926244614542751 | 0.0079760754842969 | 0.0115649007081163 | 0.00158586948421\n", + "row 4 : Bank (Money Center) | 112 | 0.8088976777082401 | 0.8488435545278178 | 5.615662315134511 | 0.8488438645325607 | 5.615675883136155 | -0.1518602885345482 | -965121.437387172 | 0.2354411572737627 | 0.2839601801801802 | 0.2535837672757894 | 7.41793318300574e-07 | 0.0097046332187522 | 0.00033102263072\n", + "row 5 : Retail (REITs) | 33 | 0.4885361986828798 | 0.5904405308196344 | 1.441647856418162 | 0.5904405731650234 | 1.4416481088663338 | 2.737197146373365 | 12.669698049396471 | 0.0421971427092918 | 0.3597851724137931 | 0.2120972268727357 | 0.0484124926581707 | 0.7223189363320469 | 0.00068097022051\n", + "*/\n", + "\n", + "Table name: fundgrEBEurope \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | ROC (INTEGER) | Reinvestment_Rate (INTEGER) | Expected_Growth_in_EBIT (INTEGER)\n", + "row 1 : Financial Svcs. (Non-bank & Insurance) | 158 | 0.0090540861074978 | -12.65841678108797 | -0.11461039552056\n", + "row 2 : Banks (Regional) | 72 | 2.806938756766444e-06 | 1149.8288982874033 | 0.00322749929825\n", + "row 3 : Total Market (without financials) | 6134 | 0.1446756721246595 | 0.3207162519218786 | 0.04639983930809\n", + "row 4 : Brokerage & Investment Banking | 74 | 0.0115528682067912 | 1.892566684166849 | 0.02186457347474\n", + "row 5 : Retail (REITs) | 33 | 0.0428673616720445 | 0.0452603514308576 | 0.00194019185419\n", + "*/\n", + "\n", + "Table name: waccEurope \n", + "/* \n", + "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Euros_ (INTEGER)\n", + "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 1.0216367356825768 | 0.0989744037317037 | 0.1956973097907507 | 0.355513616932455 | 0.0604549999999999 | 0.133380349433826 | 0.0455165695 | 0.8043026902092493 | 0.0559781238463843 | 0.04059980165444\n", + "row 2 : Banks (Regional) | 72 | 0.4682886846720631 | 0.0663822035271845 | 0.2580269829998616 | 0.132835433574351 | 0.054643 | 0.1876823623247109 | 0.0411407147 | 0.7419730170001384 | 0.0476536999085031 | 0.03239660719138\n", + "row 3 : Total Market (without financials) | 6149 | 1.0163277560226116 | 0.0986617048297318 | 0.7339379489944093 | 0.3395466680004999 | 0.0604549999999999 | 0.114021587002736 | 0.0455165695 | 0.2660620510055906 | 0.0845218011229337 | 0.06872779431046\n", + "row 4 : Brokerage & Investment Banking | 74 | 0.8707315899058546 | 0.0900860906454548 | 0.5925437976416663 | 0.2926244614542751 | 0.0604549999999999 | 0.1518944442193559 | 0.0455165695 | 0.4074562023583337 | 0.0719259628185983 | 0.0563153905445\n", + "row 5 : Bank (Money Center) | 112 | 1.2153935486343157 | 0.1103866800145611 | 0.1511561354674393 | 0.2535837672757894 | 0.0604549999999999 | 0.2354411572737627 | 0.0455165695 | 0.8488438645325607 | 0.0553220847127267 | 0.03995331648875\n", + "*/\n", + "\n", + "\u001b[0m\n", + "Prediction(\n", + " rationale='produce the SQL. We need to find the average tax rate of all banking industries where the number of firms is more than 500. To do this, we need to join the taxrateEurope table with the betaEurope table on the Industry_Name column and filter the results based on the Number_of_firms column.',\n", + " sql=\"```sql\\nSELECT AVG(te.Aggregate_tax_rate) AS Average_Tax_Rate\\nFROM taxrateEurope te\\nJOIN betaEurope be ON te.Industry_name = be.Industry_Name\\nWHERE te.Industry_name LIKE '%Bank%'\\nAND te.Number_of_firms > 500\\n```\"\n", + ")\n", + "Extracted rows: Average_Tax_Rate = None\n", + "\n", + "There are no relevant rows that match the criteria of banking industries with more than 500 firms in the database.\n" + ] + } + ], + "source": [ + "tsql = TextToSQLQueryModule(\"Europe\")\n", + "sq = tsql(question=\"Give me the average tax rate of all banking industries where number of firms is more than 500?\")\n", + "print(sq.answer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ], - "source": [ - "sq = tsql(question=\"Give me the average tax rate of all healthcare industries where revenues per employee is more than 1 million?\")\n", - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mTable name: taxrateEurope \n", - "/* \n", - "col : Industry_name (VARCHAR) | Number_of_firms (INTEGER) | Total_Taxable_Income (INTEGER) | Total_Taxes_Paid_Accrual_ (INTEGER) | Total_Cash_Taxes_Paid (INTEGER) | Cash_Taxes_Accrual_Taxes (INTEGER) | Average_across_all_companies (INTEGER) | Average_across_only_money_making_companies (INTEGER) | Aggregate_tax_rate (INTEGER) | Average_across_only_money_making_companies_1 (INTEGER) | Aggregate_tax_rate_1 (INTEGER)\n", - "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 67531.72600000002 | 5859.747000000001 | 4199.801999999999 | 0.716720704835891 | 0.133380349433826 | 0.0867702833480074 | 0.0851491015690155 | 0.0621900586399938 | 0.0637353624270\n", - "row 2 : Banks (Regional) | 72 | 7968.15 | 1353.5790000000002 | 675.7199999999998 | 0.499209872493589 | 0.1876823623247109 | 0.169873684606841 | 0.1693503510852582 | 0.0848026204325972 | 0.08480262043259\n", - "row 3 : Total Market (without financials) | 6149 | 1358894.4370000027 | 346515.07499999995 | 347363.15499999904 | 1.0024474548473803 | 0.114021587002736 | 0.2549977875875278 | 0.2805680522510425 | 0.2556218831588302 | 0.29150994341615\n", - "row 4 : Brokerage & Investment Banking | 74 | 3648.079999999999 | 709.6220000000001 | 633.1540000000001 | 0.8922412213826517 | 0.1518944442193559 | 0.194519308787088 | 0.195143715024721 | 0.1735581456547006 | 0.1756212164289\n", - "row 5 : Bank (Money Center) | 112 | 300482.43000000005 | 70812.87999999998 | 18826.331 | 0.2658602644038769 | 0.2354411572737627 | 0.2356639621158547 | 0.2359508773550057 | 0.0626536832785863 | 0.06272274286469\n", - "*/\n", - "\n", - "Table name: betaEurope \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Beta_ (INTEGER) | D_E_Ratio (INTEGER) | Effective_Tax_rate (INTEGER) | Unlevered_beta (INTEGER) | Cash_Firm_value (INTEGER) | Unlevered_beta_corrected_for_cash (INTEGER) | HiLo_Risk (INTEGER) | Standard_deviation_of_equity (INTEGER) | Standard_deviation_in_operating_income_last_10_years_ (INTEGER) | 2020 (INTEGER) | 2021_0 (INTEGER) | 2022_0 (INTEGER) | 2022_0_1 (INTEGER) | Average_2020_24 (INTEGER)\n", - "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 1.0216367356825768 | 4.109932277910462 | 0.133380349433826 | 0.2495224495401071 | 0.2851940249654809 | 0.3490771737436264 | 0.3644714846498691 | 0.355513616932455 | 0.4724600661983447 | 0.26 | 0.27 | 0.33 | 0.2491729799474152 | 0.29165003073820\n", - "row 2 : Total Market (without financials) | 6149 | 1.0163277560226116 | 0.3625130044987187 | 0.114021587002736 | 0.7984122714874734 | 0.0654744498128502 | 0.8543503934457243 | 0.3678598807686726 | 0.3394865864311263 | 0.2520005799939234 | 0.85 | 0.78 | 0.87 | 0.8817115657835057 | 0.8472123918458\n", - "row 3 : Brokerage & Investment Banking | 74 | 0.8707315899058546 | 0.6876389626893673 | 0.1518944442193559 | 0.573709019867202 | 0.2412741526659604 | 0.7561479839958829 | 0.3177625193382241 | 0.2926244614542751 | 0.8202608076286918 | 0.95 | 0.73 | 0.9 | 0.6959319603512203 | 0.80641598886942\n", - "row 4 : Bank (Money Center) | 112 | 1.2153935486343157 | 5.615675883136155 | 0.2354411572737627 | 0.2324758412532342 | 0.3625662794009818 | 0.3647059039091448 | 0.2449799817999482 | 0.2535837672757894 | 1.5390713693641629 | 0.32 | 0.28 | 0.42 | 0.3686115410275469 | 0.35066348898733\n", - "row 5 : Retail (REITs) | 33 | 0.960746998690458 | 1.4416481088663338 | 0.0421971427092918 | 0.4606978185423953 | 0.0706780075754195 | 0.4957354095757971 | 0.1860296862232068 | 0.2120972268727357 | 0.2453273616689489 | 1.23 | 1.12 | 1.25 | 1.4672850705706748 | 1.11260409602929\n", - "*/\n", - "\n", - "Table name: dbtfundEurope \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_firms (INTEGER) | Book_Debt_to_Capital (INTEGER) | Market_Debt_to_Capital_Unadjusted_ (INTEGER) | Market_D_E_unadjusted_ (INTEGER) | Market_Debt_to_Capital_adjusted_for_leases_ (INTEGER) | Market_D_E_adjusted_for_leases_ (INTEGER) | Interest_Coverage_Ratio (INTEGER) | Debt_to_EBITDA (INTEGER) | Effective_tax_rate (INTEGER) | Institutional_Holdings (INTEGER) | Std_dev_in_Stock_Prices (INTEGER) | EBITDA_EV (INTEGER) | Net_PP_E_Total_Assets (INTEGER) | Capital_Spending_Total_Assets (INTEGER)\n", - "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 0.8064776202399659 | 0.8042998495207718 | 4.109858104611632 | 0.8043026902092493 | 4.109932277910462 | 5.282640237538722 | 134.02896372206712 | 0.133380349433826 | 0.2892209174311926 | 0.355513616932455 | 0.0106491195890414 | 0.0051764605692062 | 0.00082237262437\n", - "row 2 : Total Market (without financials) | 6149 | 0.4398362396046586 | 0.2654959269193548 | 0.3614628381920554 | 0.2660620510055906 | 0.3625130044987187 | 7.095364720701234 | 2.8548850784071864 | 0.114021587002736 | 0.2515864670455792 | 0.3395466680004999 | 0.1015165247503355 | 0.2846296929541041 | 0.03637768238513\n", - "row 3 : Brokerage & Investment Banking | 74 | 0.6232840556024103 | 0.4073341226467672 | 0.6872913359984001 | 0.4074562023583337 | 0.6876389626893673 | 2.863783019167806 | 60.495499496601965 | 0.1518944442193559 | 0.2122024561403509 | 0.2926244614542751 | 0.0079760754842969 | 0.0115649007081163 | 0.00158586948421\n", - "row 4 : Bank (Money Center) | 112 | 0.8088976777082401 | 0.8488435545278178 | 5.615662315134511 | 0.8488438645325607 | 5.615675883136155 | -0.1518602885345482 | -965121.437387172 | 0.2354411572737627 | 0.2839601801801802 | 0.2535837672757894 | 7.41793318300574e-07 | 0.0097046332187522 | 0.00033102263072\n", - "row 5 : Retail (REITs) | 33 | 0.4885361986828798 | 0.5904405308196344 | 1.441647856418162 | 0.5904405731650234 | 1.4416481088663338 | 2.737197146373365 | 12.669698049396471 | 0.0421971427092918 | 0.3597851724137931 | 0.2120972268727357 | 0.0484124926581707 | 0.7223189363320469 | 0.00068097022051\n", - "*/\n", - "\n", - "Table name: fundgrEBEurope \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | ROC (INTEGER) | Reinvestment_Rate (INTEGER) | Expected_Growth_in_EBIT (INTEGER)\n", - "row 1 : Financial Svcs. (Non-bank & Insurance) | 158 | 0.0090540861074978 | -12.65841678108797 | -0.11461039552056\n", - "row 2 : Banks (Regional) | 72 | 2.806938756766444e-06 | 1149.8288982874033 | 0.00322749929825\n", - "row 3 : Total Market (without financials) | 6134 | 0.1446756721246595 | 0.3207162519218786 | 0.04639983930809\n", - "row 4 : Brokerage & Investment Banking | 74 | 0.0115528682067912 | 1.892566684166849 | 0.02186457347474\n", - "row 5 : Retail (REITs) | 33 | 0.0428673616720445 | 0.0452603514308576 | 0.00194019185419\n", - "*/\n", - "\n", - "Table name: waccEurope \n", - "/* \n", - "col : Industry_Name (VARCHAR) | Number_of_Firms (INTEGER) | Beta (INTEGER) | Cost_of_Equity (INTEGER) | E_D_E_ (INTEGER) | Std_Dev_in_Stock (INTEGER) | Cost_of_Debt (INTEGER) | Tax_Rate (INTEGER) | After_tax_Cost_of_Debt (INTEGER) | D_D_E_ (INTEGER) | Cost_of_Capital (INTEGER) | Cost_of_Capital_Euros_ (INTEGER)\n", - "row 1 : Financial Svcs. (Non-bank & Insurance) | 143 | 1.0216367356825768 | 0.0989744037317037 | 0.1956973097907507 | 0.355513616932455 | 0.0604549999999999 | 0.133380349433826 | 0.0455165695 | 0.8043026902092493 | 0.0559781238463843 | 0.04059980165444\n", - "row 2 : Banks (Regional) | 72 | 0.4682886846720631 | 0.0663822035271845 | 0.2580269829998616 | 0.132835433574351 | 0.054643 | 0.1876823623247109 | 0.0411407147 | 0.7419730170001384 | 0.0476536999085031 | 0.03239660719138\n", - "row 3 : Total Market (without financials) | 6149 | 1.0163277560226116 | 0.0986617048297318 | 0.7339379489944093 | 0.3395466680004999 | 0.0604549999999999 | 0.114021587002736 | 0.0455165695 | 0.2660620510055906 | 0.0845218011229337 | 0.06872779431046\n", - "row 4 : Brokerage & Investment Banking | 74 | 0.8707315899058546 | 0.0900860906454548 | 0.5925437976416663 | 0.2926244614542751 | 0.0604549999999999 | 0.1518944442193559 | 0.0455165695 | 0.4074562023583337 | 0.0719259628185983 | 0.0563153905445\n", - "row 5 : Bank (Money Center) | 112 | 1.2153935486343157 | 0.1103866800145611 | 0.1511561354674393 | 0.2535837672757894 | 0.0604549999999999 | 0.2354411572737627 | 0.0455165695 | 0.8488438645325607 | 0.0553220847127267 | 0.03995331648875\n", - "*/\n", - "\n", - "\u001b[0m\n", - "Prediction(\n", - " rationale='produce the SQL. We need to find the average tax rate of all banking industries where the number of firms is more than 500. To do this, we need to join the taxrateEurope table with the betaEurope table on the Industry_Name column and filter the results based on the Number_of_firms column.',\n", - " sql=\"```sql\\nSELECT AVG(te.Aggregate_tax_rate) AS Average_Tax_Rate\\nFROM taxrateEurope te\\nJOIN betaEurope be ON te.Industry_name = be.Industry_Name\\nWHERE te.Industry_name LIKE '%Bank%'\\nAND te.Number_of_firms > 500\\n```\"\n", - ")\n", - "Extracted rows: Average_Tax_Rate = None\n", - "\n", - "There are no relevant rows that match the criteria of banking industries with more than 500 firms in the database.\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } - ], - "source": [ - "tsql = TextToSQLQueryModule(\"Europe\")\n", - "sq = tsql(question=\"Give me the average tax rate of all banking industries where number of firms is more than 500?\")\n", - "print(sq.answer)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/tweets/compiling_langchain.ipynb b/examples/outdated_v2.4_examples/tweets/compiling_langchain.ipynb index 7139fd8a8c..bc9a7b3cf9 100644 --- a/examples/outdated_v2.4_examples/tweets/compiling_langchain.ipynb +++ b/examples/outdated_v2.4_examples/tweets/compiling_langchain.ipynb @@ -1,992 +1,992 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DEPRECATION WARNING\n", - "\n", - "This integration with LangChain is no longer supported.\n", - "\n", - "\n", - "----" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "## DSPy: Compiling chains from `LangChain`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the most powerful features in **DSPy** is optimizers. **DSPy optimizers** can take any LM system and tune the prompts (or the LM weights) to maximize any objective.\n", - "\n", - "Optimizers can improve the quality of your LM systems and make your code adaptive to new LMs or new data. This is meant to bring structure and modularity in place of hacky things like (i) manual prompt engineering, (ii) designing complex pipelines for generating synthetic data, (iii) or designing complex pipelines for finetuning." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Install the dependencies if needed.\n", - "# %pip install -U dspy-ai\n", - "# %pip install -U openai jinja2\n", - "# %pip install -U langchain langchain-community langchain-openai langchain-core" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, we use DSPy optimizers with DSPy modules. But here, we've worked with [Harrison Chase](https://twitter.com/hwchase17) to make sure DSPy can also optimize chains built with the `LangChain` library.\n", - "\n", - "This short tutorial demonstrates how this proof-of-concept feature works. _This will **not** give you the full power of DSPy or LangChain yet, but we will expand it if there's high demand._\n", - "\n", - "If we convert this into a fuller integration, all users stand to benefit. LangChain users will gain the ability to optimize any chain with any DSPy optimizer. DSPy users will gain the ability to _export_ any DSPy program into an LCEL that supports streaming and tracing, and other rich production-targeted features in LangChain." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Setting Up\n", - "\n", - "First, let's import `dspy` and configure the default language model and retrieval model in it." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import dspy\n", - "\n", - "from dspy.evaluate.evaluate import Evaluate\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", - "\n", - "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "\n", - "dspy.configure(rm=colbertv2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's import `langchain` and the DSPy modules for interacting with LangChain runnables, namely, `LangChainPredict` and `LangChainModule`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_openai import OpenAI\n", - "from langchain.globals import set_llm_cache\n", - "from langchain.cache import SQLiteCache\n", - "\n", - "set_llm_cache(SQLiteCache(database_path=\"cache.db\"))\n", - "\n", - "llm = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0)\n", - "retrieve = lambda x: dspy.Retrieve(k=5)(x[\"question\"]).passages" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If it's useful, we can set up some caches so you can run this whole notebook in Google Colab without any API keys. Let us know." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Defining a chain as a `LangChain` expression\n", - "\n", - "For illustration, let's tackle the following task.\n", - "\n", - "**Task:** Build a RAG system for generating informative tweets.\n", - "- **Input:** A factual **question**, which may be fairly complex.\n", - "- **Output:** An engaging **tweet** that correctly answers the question from the retrieved info.\n", - "\n", - "Let's use LangChain's expression language (LCEL) to illustrate this. Any prompt here will do, we will optimize the final prompt with DSPy.\n", - "\n", - "Considering that, let's just keep it to the barebones: **Given {context}, answer the question {question} as a tweet.**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# From LangChain, import standard modules for prompting.\n", - "from langchain_core.prompts import PromptTemplate\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "from langchain_core.runnables import RunnablePassthrough\n", - "\n", - "# Just a simple prompt for this task. It's fine if it's complex too.\n", - "prompt = PromptTemplate.from_template(\"Given {context}, answer the question `{question}` as a tweet.\")\n", - "\n", - "# This is how you'd normally build a chain with LCEL. This chain does retrieval then generation (RAG).\n", - "vanilla_chain = RunnablePassthrough.assign(context=retrieve) | prompt | llm | StrOutputParser()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) Converting the chain into a **DSPy module**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our goal is to optimize this prompt so we have a better tweet generator. DSPy optimizers can help, but they only work with DSPy modules!\n", - "\n", - "For this reason, we created two new modules in DSPy: `LangChainPredict` and `LangChainModule`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# From DSPy, import the modules that know how to interact with LangChain LCEL.\n", - "from dspy.predict.langchain import LangChainPredict, LangChainModule\n", - "\n", - "# This is how to wrap it so it behaves like a DSPy program.\n", - "# Just Replace every pattern like `prompt | llm` with `LangChainPredict(prompt, llm)`.\n", - "zeroshot_chain = RunnablePassthrough.assign(context=retrieve) | LangChainPredict(prompt, llm) | StrOutputParser()\n", - "zeroshot_chain = LangChainModule(zeroshot_chain) # then wrap the chain in a DSPy module." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4) Trying the module\n", - "\n", - "How good is our `LangChainModule` at this task? Well, we can ask it to generate a tweet for the following question." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "' Eddy Mazzoleni, Italian professional cyclist, was born in Bergamo, Italy on July 29, 1973. #cyclist #Italy #Bergamo'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "question = \"In what region was Eddy Mazzoleni born?\"\n", - "\n", - "zeroshot_chain.invoke({\"question\": question})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ah that sounds about right! (It's technically not perfect: we asked for the _region_ not the city. We can do better below.)\n", - "\n", - "Inspecting questions and answers manually is very important to get a sense of your system. However, a good system designer always looks to iteratively **benchmark** their work to quantify progress!\n", - "\n", - "To do this, we need two things: the **metric** we want to maximize and a (tiny) **dataset** of examples for our system.\n", - "\n", - "Are there pre-defined metrics for good tweets? Should I label 100,000 tweets by hand? Probably not. We can easily do something reasonable, though, until you start getting data in production!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5) Evaluating the module\n", - "\n", - "To get started, we'll define our own simple metric and we'll borrow a bunch of questions from a QA dataset and use them here for tuning.\n", - "\n", - "**What makes a good tweet?** I don't know, but in the spirit of iterative development, let's start simple!\n", - "\n", - "Define a good tweet to be have three properties: it should be (1) factually correct, (2) based on real sources, and (3) engaging for people." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.\n", - " table = cls._concat_blocks(blocks, axis=0)\n" - ] - }, - { - "data": { - "text/plain": [ - "(200, 50, 150)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We took the liberty to define this metric and load a few examples from a standard QA dataset.\n", - "# Let's impore them from `tweet_metric.py` in the same directory that contains this notebook.\n", - "from tweet_metric import metric, trainset, valset, devset\n", - "\n", - "# We loaded 200, 50, and 150 examples for training, validation (tuning), and development (evaluation), respectively.\n", - "# You could load less (or more) and, chances are, the right DSPy optimizers will work well for many problems.\n", - "len(trainset), len(valset), len(devset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Is this the right metric or the most representative set of questions? Not necessarily. But they get us started in a way we can iterate on systematically!\n", - "\n", - "**Note:** Notice that our dataset doesn't actually include any tweets! It only has questions and answers. That's OK, our metric will take care of evaluating outputs in tweet form.\n", - "\n", - "Okay, let's evaluate the unoptimized \"zero-shot\" version of our chain, converted from our `LangChain` LCEL object." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 63.999999999999986 / 150 (42.7): 100%|██████████| 150/150 [00:02<00:00, 66.08it/s]\n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 63.999999999999986 / 150 (42.7%)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 questionanswergold_titlesoutputtweet_responsemetric
0Who was a producer who produced albums for both rock bands Juke Karten and Thirty Seconds to Mars?Brian Virtue{'Thirty Seconds to Mars', 'Levolution (album)'}Brian Virtue, who has worked with bands like Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars, showcasing...Brian Virtue, who has worked with bands like Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars, showcasing...1.0
1Are both the University of Chicago and Syracuse University public universities? no{'Syracuse University', 'University of Chicago'} No, only Syracuse University is a public university. The University of Chicago is a private research university. #Syracuse #University #Chicago #Public #Private No, only Syracuse University is a public university. The University of Chicago is a private research university. #Syracuse #University #Chicago #Public #Private0.3333333333333333
2In what region was Eddy Mazzoleni born?Lombardy, northern Italy{'Eddy Mazzoleni', 'Bergamo'} Eddy Mazzoleni, Italian professional cyclist, was born in Bergamo, Italy on July 29, 1973. #cyclist #Italy #Bergamo Eddy Mazzoleni, Italian professional cyclist, was born in Bergamo, Italy on July 29, 1973. #cyclist #Italy #Bergamo0.0
3Who edited the 1990 American romantic comedy film directed by Garry Marshall?Raja Raymond Gosnell{'Raja Gosnell', 'Pretty Woman'} J. F. Lawton edited the 1990 American romantic comedy film directed by Garry Marshall. #PrettyWoman #GarryMarshall #JFLawton J. F. Lawton edited the 1990 American romantic comedy film directed by Garry Marshall. #PrettyWoman #GarryMarshall #JFLawton0.0
4Burrs Country Park railway station is what stop on the railway line that runs between Heywood and Rawtenstallseventh{'East Lancashire Railway', 'Burrs Country Park railway station'} Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line that runs between Heywood and Rawtenstall. Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line that runs between Heywood and Rawtenstall.1.0
\n" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DEPRECATION WARNING\n", + "\n", + "This integration with LangChain is no longer supported.\n", + "\n", + "\n", + "----" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "## DSPy: Compiling chains from `LangChain`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the most powerful features in **DSPy** is optimizers. **DSPy optimizers** can take any LM system and tune the prompts (or the LM weights) to maximize any objective.\n", + "\n", + "Optimizers can improve the quality of your LM systems and make your code adaptive to new LMs or new data. This is meant to bring structure and modularity in place of hacky things like (i) manual prompt engineering, (ii) designing complex pipelines for generating synthetic data, (iii) or designing complex pipelines for finetuning." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Install the dependencies if needed.\n", + "# %pip install -U dspy-ai\n", + "# %pip install -U openai jinja2\n", + "# %pip install -U langchain langchain-community langchain-openai langchain-core" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Typically, we use DSPy optimizers with DSPy modules. But here, we've worked with [Harrison Chase](https://twitter.com/hwchase17) to make sure DSPy can also optimize chains built with the `LangChain` library.\n", + "\n", + "This short tutorial demonstrates how this proof-of-concept feature works. _This will **not** give you the full power of DSPy or LangChain yet, but we will expand it if there's high demand._\n", + "\n", + "If we convert this into a fuller integration, all users stand to benefit. LangChain users will gain the ability to optimize any chain with any DSPy optimizer. DSPy users will gain the ability to _export_ any DSPy program into an LCEL that supports streaming and tracing, and other rich production-targeted features in LangChain." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Setting Up\n", + "\n", + "First, let's import `dspy` and configure the default language model and retrieval model in it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import dspy\n", + "\n", + "from dspy.evaluate.evaluate import Evaluate\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "\n", + "colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "\n", + "dspy.configure(rm=colbertv2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's import `langchain` and the DSPy modules for interacting with LangChain runnables, namely, `LangChainPredict` and `LangChainModule`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import OpenAI\n", + "from langchain.globals import set_llm_cache\n", + "from langchain.cache import SQLiteCache\n", + "\n", + "set_llm_cache(SQLiteCache(database_path=\"cache.db\"))\n", + "\n", + "llm = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0)\n", + "retrieve = lambda x: dspy.Retrieve(k=5)(x[\"question\"]).passages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If it's useful, we can set up some caches so you can run this whole notebook in Google Colab without any API keys. Let us know." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Defining a chain as a `LangChain` expression\n", + "\n", + "For illustration, let's tackle the following task.\n", + "\n", + "**Task:** Build a RAG system for generating informative tweets.\n", + "- **Input:** A factual **question**, which may be fairly complex.\n", + "- **Output:** An engaging **tweet** that correctly answers the question from the retrieved info.\n", + "\n", + "Let's use LangChain's expression language (LCEL) to illustrate this. Any prompt here will do, we will optimize the final prompt with DSPy.\n", + "\n", + "Considering that, let's just keep it to the barebones: **Given {context}, answer the question {question} as a tweet.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# From LangChain, import standard modules for prompting.\n", + "from langchain_core.prompts import PromptTemplate\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "\n", + "# Just a simple prompt for this task. It's fine if it's complex too.\n", + "prompt = PromptTemplate.from_template(\"Given {context}, answer the question `{question}` as a tweet.\")\n", + "\n", + "# This is how you'd normally build a chain with LCEL. This chain does retrieval then generation (RAG).\n", + "vanilla_chain = RunnablePassthrough.assign(context=retrieve) | prompt | llm | StrOutputParser()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Converting the chain into a **DSPy module**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our goal is to optimize this prompt so we have a better tweet generator. DSPy optimizers can help, but they only work with DSPy modules!\n", + "\n", + "For this reason, we created two new modules in DSPy: `LangChainPredict` and `LangChainModule`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# From DSPy, import the modules that know how to interact with LangChain LCEL.\n", + "from dspy.predict.langchain import LangChainPredict, LangChainModule\n", + "\n", + "# This is how to wrap it so it behaves like a DSPy program.\n", + "# Just Replace every pattern like `prompt | llm` with `LangChainPredict(prompt, llm)`.\n", + "zeroshot_chain = RunnablePassthrough.assign(context=retrieve) | LangChainPredict(prompt, llm) | StrOutputParser()\n", + "zeroshot_chain = LangChainModule(zeroshot_chain) # then wrap the chain in a DSPy module." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4) Trying the module\n", + "\n", + "How good is our `LangChainModule` at this task? Well, we can ask it to generate a tweet for the following question." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' Eddy Mazzoleni, Italian professional cyclist, was born in Bergamo, Italy on July 29, 1973. #cyclist #Italy #Bergamo'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " ... 145 more rows not displayed ...\n", - "
\n", - " " + "source": [ + "question = \"In what region was Eddy Mazzoleni born?\"\n", + "\n", + "zeroshot_chain.invoke({\"question\": question})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah that sounds about right! (It's technically not perfect: we asked for the _region_ not the city. We can do better below.)\n", + "\n", + "Inspecting questions and answers manually is very important to get a sense of your system. However, a good system designer always looks to iteratively **benchmark** their work to quantify progress!\n", + "\n", + "To do this, we need two things: the **metric** we want to maximize and a (tiny) **dataset** of examples for our system.\n", + "\n", + "Are there pre-defined metrics for good tweets? Should I label 100,000 tweets by hand? Probably not. We can easily do something reasonable, though, until you start getting data in production!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5) Evaluating the module\n", + "\n", + "To get started, we'll define our own simple metric and we'll borrow a bunch of questions from a QA dataset and use them here for tuning.\n", + "\n", + "**What makes a good tweet?** I don't know, but in the spirit of iterative development, let's start simple!\n", + "\n", + "Define a good tweet to be have three properties: it should be (1) factually correct, (2) based on real sources, and (3) engaging for people." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.\n", + " table = cls._concat_blocks(blocks, axis=0)\n" + ] + }, + { + "data": { + "text/plain": [ + "(200, 50, 150)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "# We took the liberty to define this metric and load a few examples from a standard QA dataset.\n", + "# Let's impore them from `tweet_metric.py` in the same directory that contains this notebook.\n", + "from tweet_metric import metric, trainset, valset, devset\n", + "\n", + "# We loaded 200, 50, and 150 examples for training, validation (tuning), and development (evaluation), respectively.\n", + "# You could load less (or more) and, chances are, the right DSPy optimizers will work well for many problems.\n", + "len(trainset), len(valset), len(devset)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "42.67" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is this the right metric or the most representative set of questions? Not necessarily. But they get us started in a way we can iterate on systematically!\n", + "\n", + "**Note:** Notice that our dataset doesn't actually include any tweets! It only has questions and answers. That's OK, our metric will take care of evaluating outputs in tweet form.\n", + "\n", + "Okay, let's evaluate the unoptimized \"zero-shot\" version of our chain, converted from our `LangChain` LCEL object." ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluate = Evaluate(metric=metric, devset=devset, num_threads=8, display_progress=True, display_table=5)\n", - "evaluate(zeroshot_chain)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Okay, cool. Our `zeroshot_chain` gets about **43%** on the 150 questions from the devset.\n", - "\n", - "The table above shows some examples. For instance:\n", - "\n", - "- **Question**: Who was a producer who produced albums for both rock bands Juke Karten and Thirty Seconds to Mars?\t\n", - "- **Tweet**: Brian Virtue, who has worked with bands like Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars, showcasing... [truncated]\n", - "- **Metric**: 1.0 (A tweet that is correct, faithful, and engaging!*)\n", - "\n", - "footnote: * At least according to our metric, which is just a DSPy program, so _it too_ can be optimized if you'd like! Topic for another notebook, though." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6) Optimizing the module\n", - "\n", - "DSPy has many optimizers, but the de-facto default one currently is `BootstrapFewShotWithRandomSearch`.\n", - "\n", - "**If you're curious how it works:** This optimizer works by running your program (in this case, `zeroshot_chain`) on `trainset` questions. Each time it runs, DSPy will remember the input and output of each LM call. These are called traces, and this particular optimizer will keep track of \"good\" traces (i.e., ones that the metric likes). Then, this optimizer will try to find good ways to leverage these traces as automatic few-shot examples. It will try them out, seeking to maximize the average metric on `valset`. There are many ways to self-generate (bootstrap) examples. There are also many ways to optimize their selection (here, with random search). That's why there are several other optimizers in DSPy." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Going to sample between 1 and 3 traces per predictor.\n", - "Will attempt to train 3 candidate sets.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 22.333333333333336 / 50 (44.7): 100%|██████████| 50/50 [00:00<00:00, 55.47it/s]\n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 22.333333333333336 / 50 (44.7%)\n", - "Score: 44.67 for set: [0]\n", - "New best score: 44.67 for seed -3\n", - "Scores so far: [44.67]\n", - "Best score: 44.67\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 22.333333333333336 / 50 (44.7): 100%|██████████| 50/50 [00:00<00:00, 166.70it/s]\n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 22.333333333333336 / 50 (44.7%)\n", - "Score: 44.67 for set: [16]\n", - "Scores so far: [44.67, 44.67]\n", - "Best score: 44.67\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 2%|▎ | 5/200 [00:00<00:07, 26.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 3 full traces after 6 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 27.000000000000004 / 50 (54.0): 100%|██████████| 50/50 [00:00<00:00, 72.21it/s]\n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 27.000000000000004 / 50 (54.0%)\n", - "Score: 54.0 for set: [16]\n", - "New best score: 54.0 for seed -1\n", - "Scores so far: [44.67, 44.67, 54.0]\n", - "Best score: 54.0\n", - "Average of max per entry across top 1 scores: 0.54\n", - "Average of max per entry across top 2 scores: 0.5933333333333334\n", - "Average of max per entry across top 3 scores: 0.5933333333333334\n", - "Average of max per entry across top 5 scores: 0.5933333333333334\n", - "Average of max per entry across top 8 scores: 0.5933333333333334\n", - "Average of max per entry across top 9999 scores: 0.5933333333333334\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 4%|▍ | 9/200 [00:00<00:06, 28.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 2 full traces after 10 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 25.000000000000007 / 50 (50.0): 100%|██████████| 50/50 [00:00<00:00, 70.71it/s]\n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 25.000000000000007 / 50 (50.0%)\n", - "Score: 50.0 for set: [16]\n", - "Scores so far: [44.67, 44.67, 54.0, 50.0]\n", - "Best score: 54.0\n", - "Average of max per entry across top 1 scores: 0.54\n", - "Average of max per entry across top 2 scores: 0.5933333333333334\n", - "Average of max per entry across top 3 scores: 0.6066666666666667\n", - "Average of max per entry across top 5 scores: 0.6066666666666667\n", - "Average of max per entry across top 8 scores: 0.6066666666666667\n", - "Average of max per entry across top 9999 scores: 0.6066666666666667\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 1/200 [00:00<00:07, 28.24it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 2 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 25.666666666666664 / 50 (51.3): 100%|██████████| 50/50 [00:00<00:00, 75.37it/s]\n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 25.666666666666664 / 50 (51.3%)\n", - "Score: 51.33 for set: [16]\n", - "Scores so far: [44.67, 44.67, 54.0, 50.0, 51.33]\n", - "Best score: 54.0\n", - "Average of max per entry across top 1 scores: 0.54\n", - "Average of max per entry across top 2 scores: 0.5800000000000001\n", - "Average of max per entry across top 3 scores: 0.6133333333333334\n", - "Average of max per entry across top 5 scores: 0.6266666666666667\n", - "Average of max per entry across top 8 scores: 0.6266666666666667\n", - "Average of max per entry across top 9999 scores: 0.6266666666666667\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 1%| | 2/200 [00:00<00:07, 27.81it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrapped 1 full traces after 3 examples in round 0.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 26.0 / 50 (52.0): 100%|██████████| 50/50 [00:00<00:00, 73.67it/s] \n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", - " df = df.applymap(truncate_cell)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 26.0 / 50 (52.0%)\n", - "Score: 52.0 for set: [16]\n", - "Scores so far: [44.67, 44.67, 54.0, 50.0, 51.33, 52.0]\n", - "Best score: 54.0\n", - "Average of max per entry across top 1 scores: 0.54\n", - "Average of max per entry across top 2 scores: 0.5733333333333335\n", - "Average of max per entry across top 3 scores: 0.6133333333333334\n", - "Average of max per entry across top 5 scores: 0.64\n", - "Average of max per entry across top 8 scores: 0.64\n", - "Average of max per entry across top 9999 scores: 0.64\n", - "6 candidate programs found.\n" - ] - } - ], - "source": [ - "# Set up the optimizer. We'll use very minimal hyperparameters for this example.\n", - "# Just do random search with ~3 attempts, and in each attempt, bootstrap <= 3 traces.\n", - "optimizer = BootstrapFewShotWithRandomSearch(metric=metric, max_bootstrapped_demos=3, num_candidate_programs=3)\n", - "\n", - "# Now use the optimizer to *compile* the chain. This could take 5-10 minutes, unless it's cached.\n", - "optimized_chain = optimizer.compile(zeroshot_chain, trainset=trainset, valset=valset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 7) Evaluating the optimized chain\n", - "\n", - "Well, how good is this? _Not every optimization run will magically result in improvement on unseen examples!_ So let's check!\n", - "\n", - "First let's ask that question from above." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "' Eddy Mazzoleni was born in Bergamo, a city in the Lombardy region of Italy. #EddyMazzoleni #Italy #Lombardy'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "question = \"In what region was Eddy Mazzoleni born?\"\n", - "\n", - "optimized_chain.invoke({\"question\": question})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nice, anecdotally, it appears a bit more precise than the answer with `zeroshot_chain`. But now let's do some proper evals!" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Average Metric: 78.66666666666667 / 150 (52.4): 100%|██████████| 150/150 [00:02<00:00, 72.64it/s] " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Metric: 78.66666666666667 / 150 (52.4%)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. 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 questionanswergold_titlesoutputtweet_responsemetric
0Who was a producer who produced albums for both rock bands Juke Karten and Thirty Seconds to Mars?Brian Virtue{'Thirty Seconds to Mars', 'Levolution (album)'}Brian Virtue is a producer who has worked with both Juke Kartel and Thirty Seconds to Mars, helping to create their unique sounds. #BrianVirtue #producer...Brian Virtue is a producer who has worked with both Juke Kartel and Thirty Seconds to Mars, helping to create their unique sounds. #BrianVirtue #producer...1.0
1Are both the University of Chicago and Syracuse University public universities? no{'Syracuse University', 'University of Chicago'} Yes, both Northeastern Illinois University and Syracuse University are public universities. #publicuniversity #Chicago #Syracuse Yes, both Northeastern Illinois University and Syracuse University are public universities. #publicuniversity #Chicago #Syracuse0.0
2In what region was Eddy Mazzoleni born?Lombardy, northern Italy{'Eddy Mazzoleni', 'Bergamo'} Eddy Mazzoleni was born in Bergamo, a city in the Lombardy region of Italy. #EddyMazzoleni #Italy #Lombardy Eddy Mazzoleni was born in Bergamo, a city in the Lombardy region of Italy. #EddyMazzoleni #Italy #Lombardy1.0
3Who edited the 1990 American romantic comedy film directed by Garry Marshall?Raja Raymond Gosnell{'Raja Gosnell', 'Pretty Woman'} Garry Marshall directed and edited the 1990 American romantic comedy film \"Pretty Woman\", starring Richard Gere and Julia Roberts. #PrettyWoman #GarryMarshall #RomanticComedy Garry Marshall directed and edited the 1990 American romantic comedy film \"Pretty Woman\", starring Richard Gere and Julia Roberts. #PrettyWoman #GarryMarshall #RomanticComedy0.0
4Burrs Country Park railway station is what stop on the railway line that runs between Heywood and Rawtenstallseventh{'East Lancashire Railway', 'Burrs Country Park railway station'} Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line, which runs between Heywood and Rawtenstall. #EastLancashireRailway #BurrsCountryPark #railwaystation Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line, which runs between Heywood and Rawtenstall. #EastLancashireRailway #BurrsCountryPark #railwaystation1.0
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 questionanswergold_titlesoutputtweet_responsemetric
0Who was a producer who produced albums for both rock bands Juke Karten and Thirty Seconds to Mars?Brian Virtue{'Thirty Seconds to Mars', 'Levolution (album)'}Brian Virtue, who has worked with bands like Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars, showcasing...Brian Virtue, who has worked with bands like Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars, showcasing...1.0
1Are both the University of Chicago and Syracuse University public universities? no{'Syracuse University', 'University of Chicago'} No, only Syracuse University is a public university. The University of Chicago is a private research university. #Syracuse #University #Chicago #Public #Private No, only Syracuse University is a public university. The University of Chicago is a private research university. #Syracuse #University #Chicago #Public #Private0.3333333333333333
2In what region was Eddy Mazzoleni born?Lombardy, northern Italy{'Eddy Mazzoleni', 'Bergamo'} Eddy Mazzoleni, Italian professional cyclist, was born in Bergamo, Italy on July 29, 1973. #cyclist #Italy #Bergamo Eddy Mazzoleni, Italian professional cyclist, was born in Bergamo, Italy on July 29, 1973. #cyclist #Italy #Bergamo0.0
3Who edited the 1990 American romantic comedy film directed by Garry Marshall?Raja Raymond Gosnell{'Raja Gosnell', 'Pretty Woman'} J. F. Lawton edited the 1990 American romantic comedy film directed by Garry Marshall. #PrettyWoman #GarryMarshall #JFLawton J. F. Lawton edited the 1990 American romantic comedy film directed by Garry Marshall. #PrettyWoman #GarryMarshall #JFLawton0.0
4Burrs Country Park railway station is what stop on the railway line that runs between Heywood and Rawtenstallseventh{'East Lancashire Railway', 'Burrs Country Park railway station'} Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line that runs between Heywood and Rawtenstall. Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line that runs between Heywood and Rawtenstall.1.0
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[truncated]\n", + "- **Metric**: 1.0 (A tweet that is correct, faithful, and engaging!*)\n", + "\n", + "footnote: * At least according to our metric, which is just a DSPy program, so _it too_ can be optimized if you'd like! Topic for another notebook, though." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6) Optimizing the module\n", + "\n", + "DSPy has many optimizers, but the de-facto default one currently is `BootstrapFewShotWithRandomSearch`.\n", + "\n", + "**If you're curious how it works:** This optimizer works by running your program (in this case, `zeroshot_chain`) on `trainset` questions. Each time it runs, DSPy will remember the input and output of each LM call. These are called traces, and this particular optimizer will keep track of \"good\" traces (i.e., ones that the metric likes). Then, this optimizer will try to find good ways to leverage these traces as automatic few-shot examples. It will try them out, seeking to maximize the average metric on `valset`. There are many ways to self-generate (bootstrap) examples. There are also many ways to optimize their selection (here, with random search). That's why there are several other optimizers in DSPy." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Going to sample between 1 and 3 traces per predictor.\n", + "Will attempt to train 3 candidate sets.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 22.333333333333336 / 50 (44.7): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 55.47it/s]\n", + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(truncate_cell)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 22.333333333333336 / 50 (44.7%)\n", + "Score: 44.67 for set: [0]\n", + "New best score: 44.67 for seed -3\n", + "Scores so far: [44.67]\n", + "Best score: 44.67\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 22.333333333333336 / 50 (44.7): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 166.70it/s]\n", + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(truncate_cell)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 22.333333333333336 / 50 (44.7%)\n", + "Score: 44.67 for set: [16]\n", + "Scores so far: [44.67, 44.67]\n", + "Best score: 44.67\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 2%|\u258e | 5/200 [00:00<00:07, 26.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 3 full traces after 6 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 27.000000000000004 / 50 (54.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 72.21it/s]\n", + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(truncate_cell)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 27.000000000000004 / 50 (54.0%)\n", + "Score: 54.0 for set: [16]\n", + "New best score: 54.0 for seed -1\n", + "Scores so far: [44.67, 44.67, 54.0]\n", + "Best score: 54.0\n", + "Average of max per entry across top 1 scores: 0.54\n", + "Average of max per entry across top 2 scores: 0.5933333333333334\n", + "Average of max per entry across top 3 scores: 0.5933333333333334\n", + "Average of max per entry across top 5 scores: 0.5933333333333334\n", + "Average of max per entry across top 8 scores: 0.5933333333333334\n", + "Average of max per entry across top 9999 scores: 0.5933333333333334\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 4%|\u258d | 9/200 [00:00<00:06, 28.04it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 2 full traces after 10 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 25.000000000000007 / 50 (50.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 70.71it/s]\n", + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(truncate_cell)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 25.000000000000007 / 50 (50.0%)\n", + "Score: 50.0 for set: [16]\n", + "Scores so far: [44.67, 44.67, 54.0, 50.0]\n", + "Best score: 54.0\n", + "Average of max per entry across top 1 scores: 0.54\n", + "Average of max per entry across top 2 scores: 0.5933333333333334\n", + "Average of max per entry across top 3 scores: 0.6066666666666667\n", + "Average of max per entry across top 5 scores: 0.6066666666666667\n", + "Average of max per entry across top 8 scores: 0.6066666666666667\n", + "Average of max per entry across top 9999 scores: 0.6066666666666667\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 1/200 [00:00<00:07, 28.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 2 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 25.666666666666664 / 50 (51.3): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 75.37it/s]\n", + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(truncate_cell)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 25.666666666666664 / 50 (51.3%)\n", + "Score: 51.33 for set: [16]\n", + "Scores so far: [44.67, 44.67, 54.0, 50.0, 51.33]\n", + "Best score: 54.0\n", + "Average of max per entry across top 1 scores: 0.54\n", + "Average of max per entry across top 2 scores: 0.5800000000000001\n", + "Average of max per entry across top 3 scores: 0.6133333333333334\n", + "Average of max per entry across top 5 scores: 0.6266666666666667\n", + "Average of max per entry across top 8 scores: 0.6266666666666667\n", + "Average of max per entry across top 9999 scores: 0.6266666666666667\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1%| | 2/200 [00:00<00:07, 27.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapped 1 full traces after 3 examples in round 0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 26.0 / 50 (52.0): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 73.67it/s] \n", + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(truncate_cell)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 26.0 / 50 (52.0%)\n", + "Score: 52.0 for set: [16]\n", + "Scores so far: [44.67, 44.67, 54.0, 50.0, 51.33, 52.0]\n", + "Best score: 54.0\n", + "Average of max per entry across top 1 scores: 0.54\n", + "Average of max per entry across top 2 scores: 0.5733333333333335\n", + "Average of max per entry across top 3 scores: 0.6133333333333334\n", + "Average of max per entry across top 5 scores: 0.64\n", + "Average of max per entry across top 8 scores: 0.64\n", + "Average of max per entry across top 9999 scores: 0.64\n", + "6 candidate programs found.\n" + ] + } ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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\n", - " " + "source": [ + "# Set up the optimizer. We'll use very minimal hyperparameters for this example.\n", + "# Just do random search with ~3 attempts, and in each attempt, bootstrap <= 3 traces.\n", + "optimizer = BootstrapFewShotWithRandomSearch(metric=metric, max_bootstrapped_demos=3, num_candidate_programs=3)\n", + "\n", + "# Now use the optimizer to *compile* the chain. This could take 5-10 minutes, unless it's cached.\n", + "optimized_chain = optimizer.compile(zeroshot_chain, trainset=trainset, valset=valset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7) Evaluating the optimized chain\n", + "\n", + "Well, how good is this? _Not every optimization run will magically result in improvement on unseen examples!_ So let's check!\n", + "\n", + "First let's ask that question from above." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' Eddy Mazzoleni was born in Bergamo, a city in the Lombardy region of Italy. #EddyMazzoleni #Italy #Lombardy'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "question = \"In what region was Eddy Mazzoleni born?\"\n", + "\n", + "optimized_chain.invoke({\"question\": question})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice, anecdotally, it appears a bit more precise than the answer with `zeroshot_chain`. But now let's do some proper evals!" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/plain": [ - "52.44" + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 78.66666666666667 / 150 (52.4): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 150/150 [00:02<00:00, 72.64it/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Metric: 78.66666666666667 / 150 (52.4%)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/scr-ssd/okhattab/miniconda3/envs/py39_jan2024_01/lib/python3.9/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(truncate_cell)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionanswergold_titlesoutputtweet_responsemetric
0Who was a producer who produced albums for both rock bands Juke Karten and Thirty Seconds to Mars?Brian Virtue{'Thirty Seconds to Mars', 'Levolution (album)'}Brian Virtue is a producer who has worked with both Juke Kartel and Thirty Seconds to Mars, helping to create their unique sounds. #BrianVirtue #producer...Brian Virtue is a producer who has worked with both Juke Kartel and Thirty Seconds to Mars, helping to create their unique sounds. #BrianVirtue #producer...1.0
1Are both the University of Chicago and Syracuse University public universities? no{'Syracuse University', 'University of Chicago'} Yes, both Northeastern Illinois University and Syracuse University are public universities. #publicuniversity #Chicago #Syracuse Yes, both Northeastern Illinois University and Syracuse University are public universities. #publicuniversity #Chicago #Syracuse0.0
2In what region was Eddy Mazzoleni born?Lombardy, northern Italy{'Eddy Mazzoleni', 'Bergamo'} Eddy Mazzoleni was born in Bergamo, a city in the Lombardy region of Italy. #EddyMazzoleni #Italy #Lombardy Eddy Mazzoleni was born in Bergamo, a city in the Lombardy region of Italy. #EddyMazzoleni #Italy #Lombardy1.0
3Who edited the 1990 American romantic comedy film directed by Garry Marshall?Raja Raymond Gosnell{'Raja Gosnell', 'Pretty Woman'} Garry Marshall directed and edited the 1990 American romantic comedy film \"Pretty Woman\", starring Richard Gere and Julia Roberts. #PrettyWoman #GarryMarshall #RomanticComedy Garry Marshall directed and edited the 1990 American romantic comedy film \"Pretty Woman\", starring Richard Gere and Julia Roberts. #PrettyWoman #GarryMarshall #RomanticComedy0.0
4Burrs Country Park railway station is what stop on the railway line that runs between Heywood and Rawtenstallseventh{'East Lancashire Railway', 'Burrs Country Park railway station'} Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line, which runs between Heywood and Rawtenstall. #EastLancashireRailway #BurrsCountryPark #railwaystation Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line, which runs between Heywood and Rawtenstall. #EastLancashireRailway #BurrsCountryPark #railwaystation1.0
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "52.44" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate(optimized_chain)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We started with `zeroshot_chain` at **43%** and now we have **52%**. That's a nice **21%** relative improvement. Not bad!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8) Inspecting the optimized chain in action" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PROMPT:\n", + "\n", + " Essential Instructions: Respond to the provided question based on the given context in the style of a tweet, which typically requires a concise and engaging answer within the character limit of a tweet (280 characters).\n", + "\n", + "---\n", + "\n", + "Follow the following format.\n", + "\n", + "Context: ${context}\n", + "Question: ${question}\n", + "Tweet Response: ${tweet_response}\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abCandace Kita | Kita's first role was as a news anchor in the 1991 movie \"Stealth Hunters\". Kita's first recurring television role was in Fox's \"Masked Rider\", from 1995 to 1996. She appeared as a series regular lead in all 40 episodes. Kita also portrayed a frantic stewardess in a music video directed by Mark Pellington for the British group, Catherine Wheel, titled, \"Waydown\" in 1995. In 1996, Kita also appeared in the film \"Barb Wire\" (1996) and guest starred on \"The Wayans Bros.\". She also guest starred in \"Miriam Teitelbaum: Homicide\" with \"Saturday Night Live\" alumni Nora Dunn, \"Wall To Wall Records\" with Jordan Bridges, \"Even Stevens\", \"Felicity\" with Keri Russell, \"V.I.P.\" with Pamela Anderson, \"Girlfriends\", \"The Sweet Spot\" with Bill Murray, and \"Movies at Our House\". She also had recurring roles on the FX spoof, \"Son of the Beach\" from 2001 to 2002, ABC-Family's \"Dance Fever\" and Oxygen Network's \"Running with Scissors\". Kita also appeared in the films \"Little Heroes\" (2002) and \"Rennie's Landing\" (2001).\u00bb\n", + "[2] \u00abJilly Kitzinger | Jilly Kitzinger is a fictional character in the science fiction series \"Torchwood\", portrayed by American actress Lauren Ambrose. The character was promoted as one of five new main characters to join \"Torchwood\" in its fourth series, \"\" (2011), as part of a new co-production between \"Torchwood\"' s British network, BBC One, and its American financiers on US premium television network Starz. Ambrose appears in seven of the ten episodes, and is credited as a \"special guest star\" throughout. Whilst reaction to the serial was mixed, Ambrose' portrayal was often singled out by critics for particular praise and in 2012 she received a Saturn Award nomination for Best Supporting Actress on Television.\u00bb\n", + "[3] \u00abCandace Brown | Candace June Brown (born June 15, 1980) is an American actress and comedian best known for her work on shows such as \"Grey's Anatomy\", \"Desperate Housewives\", \"Head Case\", The \"Wizards Of Waverly Place\". In 2011, she joined the guest cast for \"Torchwood\"' s fourth series' \"\", airing on BBC One in the United Kingdom and premium television network Starz.\u00bb\n", + "[4] \u00abCandace Elaine | Candace Elaine is a Canadian actress who has become a naturalized American citizen. Born 1972 in Edmonton, Alberta, Canada, Elaine is an accomplished dancer, fashionista, and stage and film actor. She most recently appeared opposite Stone Cold Steve Austin, Michael Shanks, and Michael Jai White in the action feature \"Tactical Force\", playing the role of Ilya Kalashnikova.\u00bb\n", + "[5] \u00abAmy Steel | Amy Steel (born Alice Amy Steel; May 3, 1960) is an American film and television actress. She is best known for her roles as Ginny Field in \"Friday the 13th Part 2\" (1981) and Kit Graham in \"April Fool's Day\" (1986). She has starred in films such as \"Exposed\" (1983), \"Walk Like a Man\" (1987), \"What Ever Happened to Baby Jane? \" (1991), and \"Tales of Poe\" (2014). Steel has had numerous guest appearances on several television series, such as \"Family Ties\" (1983), \"The A-Team\" (1983), \"Quantum Leap\" (1990), and \"China Beach\" (1991), as well as a starring role in \"The Powers of Matthew Star\" (1982\u201383).\u00bb\n", + "Question: which American actor was Candace Kita guest starred with\n", + "Tweet Response: Candace Kita has guest starred with many American actors, including Nora Dunn, Jordan Bridges, Keri Russell, Pamela Anderson, and Bill Murray. #CandaceKita #gueststar #Americanactors\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abThe Victorians | The Victorians - Their Story In Pictures is a 2009 British documentary series which focuses on Victorian art and culture. The four-part series is written and presented by Jeremy Paxman and debuted on BBC One at 9:00pm on Sunday 15 February 2009.\u00bb\n", + "[2] \u00abVictorian (comics) | The Victorian is a 25-issue comic book series published by Penny-Farthing Press and starting in 1999. The brainchild of creator Trainor Houghton, the series included a number of notable script writers and illustrators, including Len Wein, Glen Orbik and Howard Chaykin.\u00bb\n", + "[3] \u00abThe Great Victorian Collection | The Great Victorian Collection, published in 1975, is a novel by Northern Irish-Canadian writer Brian Moore. Set in Carmel, California, it tells the story of a man who dreams that the empty parking lot he can see from his hotel window has been transformed by the arrival of a collection of priceless Victoriana on display in a vast open-air market. When he awakes he finds that he can no longer distinguish the dream from reality.\u00bb\n", + "[4] \u00abVictorian People | Victorian People: A Reassessment of Persons and Themes, 1851-1867 is a book by the historian Asa Briggs originally published in 1955. It is part of a trilogy that also incorporates \"Victorian Cities\" and \"Victorian Things\".\u00bb\n", + "[5] \u00abThe Caxtons | The Caxtons: A Family Picture is an 1849 Victorian novel by Edward Bulwer-Lytton that was popular in its time.\u00bb\n", + "Question: The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?\n", + "Tweet Response: The Victorians - Their Story In Pictures is a 2009 British documentary series written and presented by Jeremy Paxman, who was born in 1950. #Victorian #documentary #JeremyPaxman\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abTae Kwon Do Times | Tae Kwon Do Times is a magazine devoted to the martial art of taekwondo, and is published in the United States of America. While the title suggests that it focuses on taekwondo exclusively, the magazine also covers other Korean martial arts. \"Tae Kwon Do Times\" has published articles by a wide range of authors, including He-Young Kimm, Thomas Kurz, Scott Shaw, and Mark Van Schuyver.\u00bb\n", + "[2] \u00abKwon Tae-man | Kwon Tae-man (born 1941) was an early Korean hapkido practitioner and a pioneer of the art, first in Korea and then in the United States. He formed one of the earliest dojang's for hapkido in the United States in Torrance, California, and has been featured in many magazine articles promoting the art.\u00bb\n", + "[3] \u00abHee Il Cho | Cho Hee Il (born October 13, 1940) is a prominent Korean-American master of taekwondo, holding the rank of 9th \"dan\" in the martial art. He has written 11 martial art books, produced 70 martial art training videos, and has appeared on more than 70 martial arts magazine covers. Cho won several national and international competitions as a taekwondo competitor, and has appeared in several films, including \"Fight to Win\", \"Best of the Best\", \"Bloodsport II\", and \"Bloodsport III\". He founded the Action International Martial Arts Association (AIMAA) in 1980, and is its President. Cho is a member of both \"Black Belt\" magazine's Hall of Fame and \"Tae Kwon Do Times\" magazine's Hall of Fame.\u00bb\n", + "[4] \u00abWest Coast Magazine | West Coast Magazine (1987\u20131998). was a three times a year Scottish literary publication consisting of poetry, short fiction, articles, essays and reviews. Founding editors were Gordon Giles, Kenny MacKenzie and Joe Murray. The proof issue appeared in October 1987 and contained some articles and poems that did not appear in official issues. West Coast Magazine (WCM) was initially funded by East Glasgow Gear Project and Glasgow City Council; ultimately funded by the Scottish Arts Council.\u00bb\n", + "[5] \u00abSouthwest Art | Southwest Art is a magazine published by F+W that specializes in fine art depicting artwork of the American Southwest.\u00bb\n", + "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", + "Tweet Response: Tae Kwon Do Times has published articles by Scott Shaw, along with other notable authors in the martial arts world. #TaeKwonDo #MartialArts #Magazine\n", + "\n", + "---\n", + "\n", + "Context:\n", + "[1] \u00abScott Lowell | Scott Lowell (born February 22, 1965 in Denver, Colorado) is an American actor best known for his role as Ted Schmidt on the Showtime drama \"Queer as Folk\".\u00bb\n", + "[2] \u00abTed Schmidt | Theodore \"Ted\" Schmidt is a fictional character from the American Showtime television drama series \"Queer as Folk\", played by Scott Lowell. Fellow show cast member Peter Paige, who plays Emmett Honeycutt originally auditioned for the role. Lowell was cast and he stated that he had an instant connection with the character. \"Queer as Folk\" is based on the British show of the same name and Ted is loosely based on the character Phil Delaney, played by Jason Merrells. Phil was killed off in that series, whereas show creator Daniel Lipman decided to develop the character into a full-time role for the US version.\u00bb\n", + "[3] \u00abChris Lowell | Christopher Lowell (born October 17, 1984) is an American television actor. He played the role of Stosh \"Piz\" Piznarski in the CW noir drama \"Veronica Mars\" and the character William \"Dell\" Parker in the ABC \"Grey's Anatomy\" spin-off \"Private Practice\".\u00bb\n", + "[4] \u00abKevin Schmidt | Kevin Gerard Schmidt (born August 16, 1988) is an American actor, known best for his role as Henry in \"Cheaper by the Dozen\" and its sequel and as Noah Newman in \"The Young and the Restless\". Schmidt also starred on Cartoon Network's first live-action scripted television series, \"Unnatural History\". Schmidt also co-created, starred in, produced, and directed a cult web-series, \"Poor Paul\". Schmidt continues to write, direct, and act, and has also participated in humanitarian organizations. Schmidt is president of the Conscious Human Initiative, a non-profit entity that intends to alleviate malnutrition worldwide. He played Ryan in .\u00bb\n", + "[5] \u00abFrederick Koehler | Frederick Koehler (born June 16, 1975) is an American actor best known for his role as Chip Lowell on \"Kate & Allie\" as well as Andrew Schillinger on the HBO drama \"Oz\". He is distinguished for appearing much younger than his chronological age (e.g., appearing about 20 years old when he was actually 38).\u00bb\n", + "Question: What show is an American-Canadian drama starring Scott Lowell playing Ted Schmidt?\n", + "Tweet Response:\n", + "\n", + "\n", + "OUTPUT:\n", + "\n", + " Prediction(\n", + " tweet_response=' Scott Lowell played Ted Schmidt on the American-Canadian drama \"Queer as Folk\". #ScottLowell #TedSchmidt #QueerAsFolk'\n", + ")\n" + ] + } + ], + "source": [ + "prompt, output = dspy.settings.langchain_history[-4]\n", + "\n", + "print('PROMPT:\\n\\n', prompt)\n", + "print('\\n\\nOUTPUT:\\n\\n', output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Acknowledgements:\n", + "\n", + "Thanks to [Harrison Chase](https://twitter.com/hwchase17) for co-leading this new integration. Thanks to our own [Arnav Singhvi](https://arnavsinghvi11.github.io/) for helping cook this tweet generation task and the insight about how to get data to use here." ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "evaluate(optimized_chain)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We started with `zeroshot_chain` at **43%** and now we have **52%**. That's a nice **21%** relative improvement. Not bad!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 8) Inspecting the optimized chain in action" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PROMPT:\n", - "\n", - " Essential Instructions: Respond to the provided question based on the given context in the style of a tweet, which typically requires a concise and engaging answer within the character limit of a tweet (280 characters).\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Context: ${context}\n", - "Question: ${question}\n", - "Tweet Response: ${tweet_response}\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Candace Kita | Kita's first role was as a news anchor in the 1991 movie \"Stealth Hunters\". Kita's first recurring television role was in Fox's \"Masked Rider\", from 1995 to 1996. She appeared as a series regular lead in all 40 episodes. Kita also portrayed a frantic stewardess in a music video directed by Mark Pellington for the British group, Catherine Wheel, titled, \"Waydown\" in 1995. In 1996, Kita also appeared in the film \"Barb Wire\" (1996) and guest starred on \"The Wayans Bros.\". She also guest starred in \"Miriam Teitelbaum: Homicide\" with \"Saturday Night Live\" alumni Nora Dunn, \"Wall To Wall Records\" with Jordan Bridges, \"Even Stevens\", \"Felicity\" with Keri Russell, \"V.I.P.\" with Pamela Anderson, \"Girlfriends\", \"The Sweet Spot\" with Bill Murray, and \"Movies at Our House\". She also had recurring roles on the FX spoof, \"Son of the Beach\" from 2001 to 2002, ABC-Family's \"Dance Fever\" and Oxygen Network's \"Running with Scissors\". Kita also appeared in the films \"Little Heroes\" (2002) and \"Rennie's Landing\" (2001).»\n", - "[2] «Jilly Kitzinger | Jilly Kitzinger is a fictional character in the science fiction series \"Torchwood\", portrayed by American actress Lauren Ambrose. The character was promoted as one of five new main characters to join \"Torchwood\" in its fourth series, \"\" (2011), as part of a new co-production between \"Torchwood\"' s British network, BBC One, and its American financiers on US premium television network Starz. Ambrose appears in seven of the ten episodes, and is credited as a \"special guest star\" throughout. Whilst reaction to the serial was mixed, Ambrose' portrayal was often singled out by critics for particular praise and in 2012 she received a Saturn Award nomination for Best Supporting Actress on Television.»\n", - "[3] «Candace Brown | Candace June Brown (born June 15, 1980) is an American actress and comedian best known for her work on shows such as \"Grey's Anatomy\", \"Desperate Housewives\", \"Head Case\", The \"Wizards Of Waverly Place\". In 2011, she joined the guest cast for \"Torchwood\"' s fourth series' \"\", airing on BBC One in the United Kingdom and premium television network Starz.»\n", - "[4] «Candace Elaine | Candace Elaine is a Canadian actress who has become a naturalized American citizen. Born 1972 in Edmonton, Alberta, Canada, Elaine is an accomplished dancer, fashionista, and stage and film actor. She most recently appeared opposite Stone Cold Steve Austin, Michael Shanks, and Michael Jai White in the action feature \"Tactical Force\", playing the role of Ilya Kalashnikova.»\n", - "[5] «Amy Steel | Amy Steel (born Alice Amy Steel; May 3, 1960) is an American film and television actress. She is best known for her roles as Ginny Field in \"Friday the 13th Part 2\" (1981) and Kit Graham in \"April Fool's Day\" (1986). She has starred in films such as \"Exposed\" (1983), \"Walk Like a Man\" (1987), \"What Ever Happened to Baby Jane? \" (1991), and \"Tales of Poe\" (2014). Steel has had numerous guest appearances on several television series, such as \"Family Ties\" (1983), \"The A-Team\" (1983), \"Quantum Leap\" (1990), and \"China Beach\" (1991), as well as a starring role in \"The Powers of Matthew Star\" (1982–83).»\n", - "Question: which American actor was Candace Kita guest starred with\n", - "Tweet Response: Candace Kita has guest starred with many American actors, including Nora Dunn, Jordan Bridges, Keri Russell, Pamela Anderson, and Bill Murray. #CandaceKita #gueststar #Americanactors\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «The Victorians | The Victorians - Their Story In Pictures is a 2009 British documentary series which focuses on Victorian art and culture. The four-part series is written and presented by Jeremy Paxman and debuted on BBC One at 9:00pm on Sunday 15 February 2009.»\n", - "[2] «Victorian (comics) | The Victorian is a 25-issue comic book series published by Penny-Farthing Press and starting in 1999. The brainchild of creator Trainor Houghton, the series included a number of notable script writers and illustrators, including Len Wein, Glen Orbik and Howard Chaykin.»\n", - "[3] «The Great Victorian Collection | The Great Victorian Collection, published in 1975, is a novel by Northern Irish-Canadian writer Brian Moore. Set in Carmel, California, it tells the story of a man who dreams that the empty parking lot he can see from his hotel window has been transformed by the arrival of a collection of priceless Victoriana on display in a vast open-air market. When he awakes he finds that he can no longer distinguish the dream from reality.»\n", - "[4] «Victorian People | Victorian People: A Reassessment of Persons and Themes, 1851-1867 is a book by the historian Asa Briggs originally published in 1955. It is part of a trilogy that also incorporates \"Victorian Cities\" and \"Victorian Things\".»\n", - "[5] «The Caxtons | The Caxtons: A Family Picture is an 1849 Victorian novel by Edward Bulwer-Lytton that was popular in its time.»\n", - "Question: The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?\n", - "Tweet Response: The Victorians - Their Story In Pictures is a 2009 British documentary series written and presented by Jeremy Paxman, who was born in 1950. #Victorian #documentary #JeremyPaxman\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Tae Kwon Do Times | Tae Kwon Do Times is a magazine devoted to the martial art of taekwondo, and is published in the United States of America. While the title suggests that it focuses on taekwondo exclusively, the magazine also covers other Korean martial arts. \"Tae Kwon Do Times\" has published articles by a wide range of authors, including He-Young Kimm, Thomas Kurz, Scott Shaw, and Mark Van Schuyver.»\n", - "[2] «Kwon Tae-man | Kwon Tae-man (born 1941) was an early Korean hapkido practitioner and a pioneer of the art, first in Korea and then in the United States. He formed one of the earliest dojang's for hapkido in the United States in Torrance, California, and has been featured in many magazine articles promoting the art.»\n", - "[3] «Hee Il Cho | Cho Hee Il (born October 13, 1940) is a prominent Korean-American master of taekwondo, holding the rank of 9th \"dan\" in the martial art. He has written 11 martial art books, produced 70 martial art training videos, and has appeared on more than 70 martial arts magazine covers. Cho won several national and international competitions as a taekwondo competitor, and has appeared in several films, including \"Fight to Win\", \"Best of the Best\", \"Bloodsport II\", and \"Bloodsport III\". He founded the Action International Martial Arts Association (AIMAA) in 1980, and is its President. Cho is a member of both \"Black Belt\" magazine's Hall of Fame and \"Tae Kwon Do Times\" magazine's Hall of Fame.»\n", - "[4] «West Coast Magazine | West Coast Magazine (1987–1998). was a three times a year Scottish literary publication consisting of poetry, short fiction, articles, essays and reviews. Founding editors were Gordon Giles, Kenny MacKenzie and Joe Murray. The proof issue appeared in October 1987 and contained some articles and poems that did not appear in official issues. West Coast Magazine (WCM) was initially funded by East Glasgow Gear Project and Glasgow City Council; ultimately funded by the Scottish Arts Council.»\n", - "[5] «Southwest Art | Southwest Art is a magazine published by F+W that specializes in fine art depicting artwork of the American Southwest.»\n", - "Question: Which magazine has published articles by Scott Shaw, Tae Kwon Do Times or Southwest Art?\n", - "Tweet Response: Tae Kwon Do Times has published articles by Scott Shaw, along with other notable authors in the martial arts world. #TaeKwonDo #MartialArts #Magazine\n", - "\n", - "---\n", - "\n", - "Context:\n", - "[1] «Scott Lowell | Scott Lowell (born February 22, 1965 in Denver, Colorado) is an American actor best known for his role as Ted Schmidt on the Showtime drama \"Queer as Folk\".»\n", - "[2] «Ted Schmidt | Theodore \"Ted\" Schmidt is a fictional character from the American Showtime television drama series \"Queer as Folk\", played by Scott Lowell. Fellow show cast member Peter Paige, who plays Emmett Honeycutt originally auditioned for the role. Lowell was cast and he stated that he had an instant connection with the character. \"Queer as Folk\" is based on the British show of the same name and Ted is loosely based on the character Phil Delaney, played by Jason Merrells. Phil was killed off in that series, whereas show creator Daniel Lipman decided to develop the character into a full-time role for the US version.»\n", - "[3] «Chris Lowell | Christopher Lowell (born October 17, 1984) is an American television actor. He played the role of Stosh \"Piz\" Piznarski in the CW noir drama \"Veronica Mars\" and the character William \"Dell\" Parker in the ABC \"Grey's Anatomy\" spin-off \"Private Practice\".»\n", - "[4] «Kevin Schmidt | Kevin Gerard Schmidt (born August 16, 1988) is an American actor, known best for his role as Henry in \"Cheaper by the Dozen\" and its sequel and as Noah Newman in \"The Young and the Restless\". Schmidt also starred on Cartoon Network's first live-action scripted television series, \"Unnatural History\". Schmidt also co-created, starred in, produced, and directed a cult web-series, \"Poor Paul\". Schmidt continues to write, direct, and act, and has also participated in humanitarian organizations. Schmidt is president of the Conscious Human Initiative, a non-profit entity that intends to alleviate malnutrition worldwide. He played Ryan in .»\n", - "[5] «Frederick Koehler | Frederick Koehler (born June 16, 1975) is an American actor best known for his role as Chip Lowell on \"Kate & Allie\" as well as Andrew Schillinger on the HBO drama \"Oz\". He is distinguished for appearing much younger than his chronological age (e.g., appearing about 20 years old when he was actually 38).»\n", - "Question: What show is an American-Canadian drama starring Scott Lowell playing Ted Schmidt?\n", - "Tweet Response:\n", - "\n", - "\n", - "OUTPUT:\n", - "\n", - " Prediction(\n", - " tweet_response=' Scott Lowell played Ted Schmidt on the American-Canadian drama \"Queer as Folk\". #ScottLowell #TedSchmidt #QueerAsFolk'\n", - ")\n" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "py39_dec_2023", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" } - ], - "source": [ - "prompt, output = dspy.settings.langchain_history[-4]\n", - "\n", - "print('PROMPT:\\n\\n', prompt)\n", - "print('\\n\\nOUTPUT:\\n\\n', output)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Acknowledgements:\n", - "\n", - "Thanks to [Harrison Chase](https://twitter.com/hwchase17) for co-leading this new integration. Thanks to our own [Arnav Singhvi](https://arnavsinghvi11.github.io/) for helping cook this tweet generation task and the insight about how to get data to use here." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39_dec_2023", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/tweets/tweets_assertions.ipynb b/examples/outdated_v2.4_examples/tweets/tweets_assertions.ipynb index 2d96cff3c1..beb4d36fd3 100644 --- a/examples/outdated_v2.4_examples/tweets/tweets_assertions.ipynb +++ b/examples/outdated_v2.4_examples/tweets/tweets_assertions.ipynb @@ -1,536 +1,536 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"DSPy7\n", - "\n", - "## **DSPy Assertions**: Asserting Computational Constraints on Foundation Models\n", - "\n", - "### **TweetGen**: Generating tweets to answer questions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/tweets/tweets_assertions.ipynb)\n", - "\n", - "\n", - "This notebook highlights an example of [**DSPy Assertions**](https://dspy-docs.vercel.app/docs/building-blocks/assertions), allowing for declaration of computational constraints within DSPy programs. \n", - "\n", - "\n", - "This notebook builds upon the foundational concepts of the **DSPy** framework. Prerequisites of following this notebook is having gone through the [DSPy tutorial](../../intro.ipynb), the [**DSPy Assertions documentation**](https://dspy-docs.vercel.app/docs/building-blocks/assertions) and the introductory DSPy Assertions [tutorial on LongFormQA](../longformqa/longformqa_assertions.ipynb).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import sys\n", - "import os\n", - "import regex as re\n", - "\n", - "try: # When on google Colab, let's clone the notebook so we download the cache.\n", - " import google.colab # noqa: F401\n", - " repo_path = 'dspy'\n", - " \n", - " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", - "except:\n", - " repo_path = '.'\n", - "\n", - "if repo_path not in sys.path:\n", - " sys.path.append(repo_path)\n", - "\n", - "\n", - "import pkg_resources # Install the package if it's not installed\n", - "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", - " !pip install -U pip\n", - " !pip install dspy-ai==2.4.17\n", - " !pip install openai~=0.28.1\n", - " !pip install -e $repo_path\n", - "\n", - "import dspy\n", - "from dspy.predict import Retry\n", - "from dspy.datasets import HotPotQA\n", - "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", - "from dsp.utils import deduplicate\n", - "from dspy.evaluate.evaluate import Evaluate\n", - "from dspy.primitives.assertions import assert_transform_module, backtrack_handler" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import openai\n", - "openai.api_key = os.getenv('OPENAI_API_KEY')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", - "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", - "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", - "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", - "trainset = [x.with_inputs('question', 'answer') for x in dataset.train]\n", - "devset = [x.with_inputs('question', 'answer') for x in dataset.dev]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3] TweetGen\n", - "\n", - "Let's introduce a new task: TweetGen. We extend the `Multi-Hop QA` program, but now aim to present the answer generation in the form of a tweet. \n", - "\n", - "The `Tweeter` module captures the iterative multi-hop generation process from `Multi-Hop QA` in query generation, passage retrieval, and context assembly. The `GenerateTweet` layer now utilizes the context alongside the question to generate a tweet that effectively answers the question." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this program, we aim to generate tweets that adhere to the following guidelines:\n", - "1. The tweet has no hashtags. \n", - "2. The tweet includes the correct answer\n", - "3. The tweet is within a character limit. \n", - "4. The tweet is engaging\n", - "5. The tweet is faithful" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class GenerateSearchQuery(dspy.Signature):\n", - " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " question = dspy.InputField()\n", - " query = dspy.OutputField()\n", - "\n", - "class GenerateTweet(dspy.Signature):\n", - " \"\"\"Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.\"\"\"\n", - " question = dspy.InputField()\n", - " context = dspy.InputField(desc=\"may contain relevant facts\")\n", - " tweet = dspy.OutputField()\n", - "\n", - "class Tweeter(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_tweet = dspy.ChainOfThought(GenerateTweet)\n", - "\n", - " def forward(self, question, answer):\n", - " context = []\n", - " max_hops=2\n", - " passages_per_hop=3\n", - " generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", - " retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " for hop in range(max_hops):\n", - " query = generate_query[hop](context=context, question=question).query\n", - " passages = retrieve(query).passages\n", - " context = deduplicate(context + passages)\n", - " generated_tweet = self.generate_tweet(question=question, context=context).tweet\n", - " return dspy.Prediction(generated_tweet=generated_tweet, context=context)\n", - " \n", - "tweeter = Tweeter()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4] Evaluation - Intrinsic and Extrinsic\n", - "\n", - "#### Intrinsic Metrics: passing internal computational constraints is the goal \n", - "\n", - "**No Hashtags** - This is a user-personalized constraint to test how well the model can follow a specific, yet simple guideline of not including any hashtags within the generated tweet.\n", - "\n", - "**Correct Answer Inclusion** - This is a general check to ensure the tweet indeed has the correct answer to the question.\n", - "\n", - "**Within Length** - This check follows Twitter platform guidelines of 280 character limits per tweet.\n", - "\n", - "**Engagement** - To verify the engagement quality of the tweet, we define and call another **DSPy** program: ``Predict`` on ``AssessTweet``, relying on the same LM to answer the question: `\"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"`\n", - "\n", - "**Faithfulness** - To verify the faithfulness of the tweet to its referenced context, we similarly use `AssessTweet` as above but prompt it with the question: `\"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\"`\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def has_no_hashtags(text):\n", - " return len(re.findall(r\"#\\w+\", text)) == 0\n", - "\n", - "def is_within_length_limit(text, length_limit=280):\n", - " return len(text) <= length_limit\n", - "\n", - "def is_assessment_yes(assessment_answer):\n", - " \"\"\"Check if the first word of the assessment answer is 'yes'.\"\"\"\n", - " return assessment_answer.split()[0].lower() == 'yes'\n", - "\n", - "def has_correct_answer(text, answer):\n", - " return answer in text\n", - "\n", - "\n", - "class AssessTweet(dspy.Signature):\n", - " \"\"\"Assess the quality of a tweet along the specified dimension.\"\"\"\n", - "\n", - " context = dspy.InputField(desc='ignore if N/A')\n", - " assessed_text = dspy.InputField()\n", - " assessment_question = dspy.InputField()\n", - " assessment_answer = dspy.OutputField(desc=\"Yes or No\")\n", - "\n", - "def no_hashtags_metric(gold, pred, trace=None):\n", - " tweet = pred.generated_tweet\n", - " no_hashtags = has_no_hashtags(tweet)\n", - " score = no_hashtags\n", - " return score\n", - "\n", - "def is_correct_metric(gold, pred, trace=None):\n", - " answer, tweet = gold.answer, pred.generated_tweet\n", - " correct = has_correct_answer(tweet, answer)\n", - " score = correct\n", - " return score\n", - "\n", - "def within_length_metric(gold, pred, trace=None):\n", - " tweet = pred.generated_tweet\n", - " within_length_limit = is_within_length_limit(tweet, 280)\n", - " score = within_length_limit\n", - " return score\n", - "\n", - "def engaging_metric(gold, pred, trace=None):\n", - " tweet = pred.generated_tweet\n", - " engaging = \"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"\n", - " engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging)\n", - " engaging = engaging.assessment_answer.split()[0].lower() == 'yes'\n", - " score = engaging\n", - " return score\n", - "\n", - "def faithful_metric(gold, pred, trace=None):\n", - " context, tweet = pred.context, pred.generated_tweet\n", - " faithful = \"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\" \n", - " faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful)\n", - " faithful = faithful.assessment_answer.split()[0].lower() == 'yes'\n", - " score = faithful\n", - " return score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Extrinsic Metrics: Assess the overall quality and effectiveness of generated output on downstream task\n", - "\n", - "The extrinsic metric is defined as the overall quality of the generated tweet in following the mentioned constraints, and this is evaluated over a composite metric.\n", - "\n", - "While maintaining the most relevant intrinsic metrics of forming a valid tweet in the correctness and within_length constraints, the overall composite metric returns an averaged score over the 5 intrinsic metrics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def overall_metric(gold, pred, trace=None):\n", - " answer, context, tweet = gold.answer, pred.context, pred.generated_tweet\n", - " no_hashtags = has_no_hashtags(tweet)\n", - " within_length_limit = is_within_length_limit(tweet, 280)\n", - " correct = has_correct_answer(tweet, answer)\n", - " engaging = \"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"\n", - " faithful = \"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\" \n", - " faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful)\n", - " engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging)\n", - " engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]]\n", - " score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0\n", - " return score / 5.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We hence define the evaluation as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric]\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(tweeter)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at an example tweet generation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example = devset[118]\n", - "tweet = tweeter(question=example.question, answer = example.answer)\n", - "print('Generated Tweet: ', tweet.generated_tweet)\n", - "tweet.context" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset[118:119], num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(tweeter)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we see that the generated tweet is within the length of 280 characters at 151 characters. It does in fact include the correct answer `Hooke`.\n", - "\n", - "However, it fails to not include hashtags as we see `#knowledge` at the end of the tweet. Additionally, the tweet has been determined to not be engaging, which makes sense from an eye-test as it simply states the answer and nothing more. \n", - "\n", - "Let's try to fix this and produce tweets using DSPy Assertions. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5] Introducing Assertions: TweeterWithAssertions\n", - "\n", - "To correct these various errors, let's include assertions that simply reiterate our computational constraints within DSPy Assertion semantics. \n", - "\n", - "In the first **Assertion**, we check for if the generated tweet has any hashtags through regex and if violated, assert: **\"Please revise the tweet to remove hashtag phrases following it.\"**\n", - "\n", - "Similarly, we check for the tweet length and if it is not within 280 characters, we send the feedback message: **\"Please ensure the tweet is within {280} characters.\"**\n", - "\n", - "We check for if the generated tweet has the answer and if not, we assert: **\"The tweet does not include the correct answer to the question. Please revise accordingly.\"**\n", - "\n", - "For the engagement and faithfulness checks, we make use of the setup from above, checking for if the respective assessment is determined as `Yes` or `No`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class TweeterWithAssertions(dspy.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.generate_tweet = dspy.ChainOfThought(GenerateTweet)\n", - "\n", - " def forward(self, question, answer):\n", - " context = []\n", - " max_hops=2\n", - " passages_per_hop=3\n", - " generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", - " retrieve = dspy.Retrieve(k=passages_per_hop)\n", - " for hop in range(max_hops):\n", - " query = generate_query[hop](context=context, question=question).query\n", - " passages = retrieve(query).passages\n", - " context = deduplicate(context + passages)\n", - " generated_tweet = self.generate_tweet(question=question, context=context).tweet\n", - " dspy.Suggest(has_no_hashtags(generated_tweet), \"Please revise the tweet to remove hashtag phrases following it.\", target_module=self.generate_tweet)\n", - " dspy.Suggest(is_within_length_limit(generated_tweet, 280), f\"Please ensure the tweet is within {280} characters.\", target_module=self.generate_tweet)\n", - " dspy.Suggest(has_correct_answer(generated_tweet, answer), \"The tweet does not include the correct answer to the question. Please revise accordingly.\", target_module=self.generate_tweet)\n", - " engaging_question = \"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"\n", - " engaging_assessment = dspy.Predict(AssessTweet)(context=context, assessed_text=generated_tweet, assessment_question=engaging_question)\n", - " dspy.Suggest(is_assessment_yes(engaging_assessment.assessment_answer), \"The text is not engaging enough. Please revise to make it more captivating.\", target_module=self.generate_tweet)\n", - " faithful_question = \"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\"\n", - " faithful_assessment = dspy.Predict(AssessTweet)(context='N/A', assessed_text=generated_tweet, assessment_question=faithful_question)\n", - " dspy.Suggest(is_assessment_yes(faithful_assessment.assessment_answer), \"The text contains unfaithful elements or significant facts not in the context. Please revise for accuracy.\", target_module=self.generate_tweet)\n", - " return dspy.Prediction(generated_tweet=generated_tweet, context=context)\n", - "\n", - "tweeter_with_assertions = assert_transform_module(TweeterWithAssertions().map_named_predictors(Retry), backtrack_handler) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's evaluate the `TweeterWithAssertions` now over the devset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric]\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(tweeter_with_assertions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's take a look at how our generated tweet has improved with the addition of assertions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example = devset[118]\n", - "tweet = tweeter_with_assertions(question=example.question, answer = example.answer)\n", - "print('Generated Tweet: ', tweet.generated_tweet)\n", - "tweet.context" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset[118:119], num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(tweeter_with_assertions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the tweet has improved significantly, following all of our set constraints! \n", - "\n", - "It no longer has hashtags, and is both engaging and faithful, while maintaining the inclusion of the correct answer within 280 characters. Exciting!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6] Compilation With Assertions\n", - "\n", - "We can leverage **DSPy**'s`BootstrapFewShotWithRandomSearch` optimizer, to automatically generate few-shot demonstrations and conduct a random search over the candidates to output the best compiled program. We evaluate this over the `overall_metric` composite metric. \n", - "\n", - "We can first evaluate this on `Tweeter` to see how compilation performs without the inclusion of assertions. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "compiled_tweeter = teleprompter.compile(student = tweeter, teacher = tweeter, trainset=trainset, valset=devset[:25])\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(compiled_tweeter)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we test the compilation on 2 settings with assertions:\n", - "\n", - "**Compilation with Assertions**: assertion-driven example bootstrapping and counterexample bootstrapping during compilation. Teacher has assertions while the student does not as the student learns from the teacher's assertion-driven bootstrapped examples. \n", - "\n", - "**Compilation + Inference with Assertions**: assertion-driven optimizations for both the teacher and student to offer enhanced assertion-driven outputs during both compilation and inference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", - "compiled_with_assertions_tweeter = teleprompter.compile(student=tweeter, teacher = tweeter_with_assertions, trainset=trainset, valset=devset[:25])\n", - "\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(compiled_with_assertions_tweeter)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6, num_threads=1)\n", - "compiled_tweeter_with_assertions = teleprompter.compile(student=tweeter_with_assertions, teacher = tweeter_with_assertions, trainset=trainset, valset=devset[:25])\n", - "\n", - "for metric in metrics:\n", - " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", - " evaluate(compiled_tweeter_with_assertions)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dspy_dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "## **DSPy Assertions**: Asserting Computational Constraints on Foundation Models\n", + "\n", + "### **TweetGen**: Generating tweets to answer questions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/tweets/tweets_assertions.ipynb)\n", + "\n", + "\n", + "This notebook highlights an example of [**DSPy Assertions**](https://dspy-docs.vercel.app/docs/building-blocks/assertions), allowing for declaration of computational constraints within DSPy programs. \n", + "\n", + "\n", + "This notebook builds upon the foundational concepts of the **DSPy** framework. Prerequisites of following this notebook is having gone through the [DSPy tutorial](../../intro.ipynb), the [**DSPy Assertions documentation**](https://dspy-docs.vercel.app/docs/building-blocks/assertions) and the introductory DSPy Assertions [tutorial on LongFormQA](../longformqa/longformqa_assertions.ipynb).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "import os\n", + "import regex as re\n", + "\n", + "try: # When on google Colab, let's clone the notebook so we download the cache.\n", + " import google.colab # noqa: F401\n", + " repo_path = 'dspy'\n", + " \n", + " !git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path\n", + "except:\n", + " repo_path = '.'\n", + "\n", + "if repo_path not in sys.path:\n", + " sys.path.append(repo_path)\n", + "\n", + "\n", + "import pkg_resources # Install the package if it's not installed\n", + "if \"dspy-ai\" not in {pkg.key for pkg in pkg_resources.working_set}:\n", + " !pip install -U pip\n", + " !pip install dspy-ai==2.4.17\n", + " !pip install openai~=0.28.1\n", + " !pip install -e $repo_path\n", + "\n", + "import dspy\n", + "from dspy.predict import Retry\n", + "from dspy.datasets import HotPotQA\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "from dsp.utils import deduplicate\n", + "from dspy.evaluate.evaluate import Evaluate\n", + "from dspy.primitives.assertions import assert_transform_module, backtrack_handler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "openai.api_key = os.getenv('OPENAI_API_KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "dspy.settings.configure(rm=colbertv2_wiki17_abstracts)\n", + "turbo = dspy.OpenAI(model='gpt-4o-mini', max_tokens=500)\n", + "dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0, keep_details=True)\n", + "trainset = [x.with_inputs('question', 'answer') for x in dataset.train]\n", + "devset = [x.with_inputs('question', 'answer') for x in dataset.dev]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3] TweetGen\n", + "\n", + "Let's introduce a new task: TweetGen. We extend the `Multi-Hop QA` program, but now aim to present the answer generation in the form of a tweet. \n", + "\n", + "The `Tweeter` module captures the iterative multi-hop generation process from `Multi-Hop QA` in query generation, passage retrieval, and context assembly. The `GenerateTweet` layer now utilizes the context alongside the question to generate a tweet that effectively answers the question." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this program, we aim to generate tweets that adhere to the following guidelines:\n", + "1. The tweet has no hashtags. \n", + "2. The tweet includes the correct answer\n", + "3. The tweet is within a character limit. \n", + "4. The tweet is engaging\n", + "5. The tweet is faithful" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class GenerateSearchQuery(dspy.Signature):\n", + " \"\"\"Write a simple search query that will help answer a complex question.\"\"\"\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " question = dspy.InputField()\n", + " query = dspy.OutputField()\n", + "\n", + "class GenerateTweet(dspy.Signature):\n", + " \"\"\"Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.\"\"\"\n", + " question = dspy.InputField()\n", + " context = dspy.InputField(desc=\"may contain relevant facts\")\n", + " tweet = dspy.OutputField()\n", + "\n", + "class Tweeter(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_tweet = dspy.ChainOfThought(GenerateTweet)\n", + "\n", + " def forward(self, question, answer):\n", + " context = []\n", + " max_hops=2\n", + " passages_per_hop=3\n", + " generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", + " retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " for hop in range(max_hops):\n", + " query = generate_query[hop](context=context, question=question).query\n", + " passages = retrieve(query).passages\n", + " context = deduplicate(context + passages)\n", + " generated_tweet = self.generate_tweet(question=question, context=context).tweet\n", + " return dspy.Prediction(generated_tweet=generated_tweet, context=context)\n", + " \n", + "tweeter = Tweeter()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4] Evaluation - Intrinsic and Extrinsic\n", + "\n", + "#### Intrinsic Metrics: passing internal computational constraints is the goal \n", + "\n", + "**No Hashtags** - This is a user-personalized constraint to test how well the model can follow a specific, yet simple guideline of not including any hashtags within the generated tweet.\n", + "\n", + "**Correct Answer Inclusion** - This is a general check to ensure the tweet indeed has the correct answer to the question.\n", + "\n", + "**Within Length** - This check follows Twitter platform guidelines of 280 character limits per tweet.\n", + "\n", + "**Engagement** - To verify the engagement quality of the tweet, we define and call another **DSPy** program: ``Predict`` on ``AssessTweet``, relying on the same LM to answer the question: `\"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"`\n", + "\n", + "**Faithfulness** - To verify the faithfulness of the tweet to its referenced context, we similarly use `AssessTweet` as above but prompt it with the question: `\"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\"`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def has_no_hashtags(text):\n", + " return len(re.findall(r\"#\\w+\", text)) == 0\n", + "\n", + "def is_within_length_limit(text, length_limit=280):\n", + " return len(text) <= length_limit\n", + "\n", + "def is_assessment_yes(assessment_answer):\n", + " \"\"\"Check if the first word of the assessment answer is 'yes'.\"\"\"\n", + " return assessment_answer.split()[0].lower() == 'yes'\n", + "\n", + "def has_correct_answer(text, answer):\n", + " return answer in text\n", + "\n", + "\n", + "class AssessTweet(dspy.Signature):\n", + " \"\"\"Assess the quality of a tweet along the specified dimension.\"\"\"\n", + "\n", + " context = dspy.InputField(desc='ignore if N/A')\n", + " assessed_text = dspy.InputField()\n", + " assessment_question = dspy.InputField()\n", + " assessment_answer = dspy.OutputField(desc=\"Yes or No\")\n", + "\n", + "def no_hashtags_metric(gold, pred, trace=None):\n", + " tweet = pred.generated_tweet\n", + " no_hashtags = has_no_hashtags(tweet)\n", + " score = no_hashtags\n", + " return score\n", + "\n", + "def is_correct_metric(gold, pred, trace=None):\n", + " answer, tweet = gold.answer, pred.generated_tweet\n", + " correct = has_correct_answer(tweet, answer)\n", + " score = correct\n", + " return score\n", + "\n", + "def within_length_metric(gold, pred, trace=None):\n", + " tweet = pred.generated_tweet\n", + " within_length_limit = is_within_length_limit(tweet, 280)\n", + " score = within_length_limit\n", + " return score\n", + "\n", + "def engaging_metric(gold, pred, trace=None):\n", + " tweet = pred.generated_tweet\n", + " engaging = \"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"\n", + " engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging)\n", + " engaging = engaging.assessment_answer.split()[0].lower() == 'yes'\n", + " score = engaging\n", + " return score\n", + "\n", + "def faithful_metric(gold, pred, trace=None):\n", + " context, tweet = pred.context, pred.generated_tweet\n", + " faithful = \"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\" \n", + " faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful)\n", + " faithful = faithful.assessment_answer.split()[0].lower() == 'yes'\n", + " score = faithful\n", + " return score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extrinsic Metrics: Assess the overall quality and effectiveness of generated output on downstream task\n", + "\n", + "The extrinsic metric is defined as the overall quality of the generated tweet in following the mentioned constraints, and this is evaluated over a composite metric.\n", + "\n", + "While maintaining the most relevant intrinsic metrics of forming a valid tweet in the correctness and within_length constraints, the overall composite metric returns an averaged score over the 5 intrinsic metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def overall_metric(gold, pred, trace=None):\n", + " answer, context, tweet = gold.answer, pred.context, pred.generated_tweet\n", + " no_hashtags = has_no_hashtags(tweet)\n", + " within_length_limit = is_within_length_limit(tweet, 280)\n", + " correct = has_correct_answer(tweet, answer)\n", + " engaging = \"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"\n", + " faithful = \"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\" \n", + " faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful)\n", + " engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging)\n", + " engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]]\n", + " score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0\n", + " return score / 5.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We hence define the evaluation as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric]\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(tweeter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at an example tweet generation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example = devset[118]\n", + "tweet = tweeter(question=example.question, answer = example.answer)\n", + "print('Generated Tweet: ', tweet.generated_tweet)\n", + "tweet.context" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset[118:119], num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(tweeter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we see that the generated tweet is within the length of 280 characters at 151 characters. It does in fact include the correct answer `Hooke`.\n", + "\n", + "However, it fails to not include hashtags as we see `#knowledge` at the end of the tweet. Additionally, the tweet has been determined to not be engaging, which makes sense from an eye-test as it simply states the answer and nothing more. \n", + "\n", + "Let's try to fix this and produce tweets using DSPy Assertions. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5] Introducing Assertions: TweeterWithAssertions\n", + "\n", + "To correct these various errors, let's include assertions that simply reiterate our computational constraints within DSPy Assertion semantics. \n", + "\n", + "In the first **Assertion**, we check for if the generated tweet has any hashtags through regex and if violated, assert: **\"Please revise the tweet to remove hashtag phrases following it.\"**\n", + "\n", + "Similarly, we check for the tweet length and if it is not within 280 characters, we send the feedback message: **\"Please ensure the tweet is within {280} characters.\"**\n", + "\n", + "We check for if the generated tweet has the answer and if not, we assert: **\"The tweet does not include the correct answer to the question. Please revise accordingly.\"**\n", + "\n", + "For the engagement and faithfulness checks, we make use of the setup from above, checking for if the respective assessment is determined as `Yes` or `No`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class TweeterWithAssertions(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.generate_tweet = dspy.ChainOfThought(GenerateTweet)\n", + "\n", + " def forward(self, question, answer):\n", + " context = []\n", + " max_hops=2\n", + " passages_per_hop=3\n", + " generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)]\n", + " retrieve = dspy.Retrieve(k=passages_per_hop)\n", + " for hop in range(max_hops):\n", + " query = generate_query[hop](context=context, question=question).query\n", + " passages = retrieve(query).passages\n", + " context = deduplicate(context + passages)\n", + " generated_tweet = self.generate_tweet(question=question, context=context).tweet\n", + " dspy.Suggest(has_no_hashtags(generated_tweet), \"Please revise the tweet to remove hashtag phrases following it.\", target_module=self.generate_tweet)\n", + " dspy.Suggest(is_within_length_limit(generated_tweet, 280), f\"Please ensure the tweet is within {280} characters.\", target_module=self.generate_tweet)\n", + " dspy.Suggest(has_correct_answer(generated_tweet, answer), \"The tweet does not include the correct answer to the question. Please revise accordingly.\", target_module=self.generate_tweet)\n", + " engaging_question = \"Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging.\"\n", + " engaging_assessment = dspy.Predict(AssessTweet)(context=context, assessed_text=generated_tweet, assessment_question=engaging_question)\n", + " dspy.Suggest(is_assessment_yes(engaging_assessment.assessment_answer), \"The text is not engaging enough. Please revise to make it more captivating.\", target_module=self.generate_tweet)\n", + " faithful_question = \"Is the assessed text grounded in the context? Say no if it includes significant facts not in the context.\"\n", + " faithful_assessment = dspy.Predict(AssessTweet)(context='N/A', assessed_text=generated_tweet, assessment_question=faithful_question)\n", + " dspy.Suggest(is_assessment_yes(faithful_assessment.assessment_answer), \"The text contains unfaithful elements or significant facts not in the context. Please revise for accuracy.\", target_module=self.generate_tweet)\n", + " return dspy.Prediction(generated_tweet=generated_tweet, context=context)\n", + "\n", + "tweeter_with_assertions = assert_transform_module(TweeterWithAssertions().map_named_predictors(Retry), backtrack_handler) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate the `TweeterWithAssertions` now over the devset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric]\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(tweeter_with_assertions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's take a look at how our generated tweet has improved with the addition of assertions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example = devset[118]\n", + "tweet = tweeter_with_assertions(question=example.question, answer = example.answer)\n", + "print('Generated Tweet: ', tweet.generated_tweet)\n", + "tweet.context" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset[118:119], num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(tweeter_with_assertions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the tweet has improved significantly, following all of our set constraints! \n", + "\n", + "It no longer has hashtags, and is both engaging and faithful, while maintaining the inclusion of the correct answer within 280 characters. Exciting!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6] Compilation With Assertions\n", + "\n", + "We can leverage **DSPy**'s`BootstrapFewShotWithRandomSearch` optimizer, to automatically generate few-shot demonstrations and conduct a random search over the candidates to output the best compiled program. We evaluate this over the `overall_metric` composite metric. \n", + "\n", + "We can first evaluate this on `Tweeter` to see how compilation performs without the inclusion of assertions. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "compiled_tweeter = teleprompter.compile(student = tweeter, teacher = tweeter, trainset=trainset, valset=devset[:25])\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(compiled_tweeter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we test the compilation on 2 settings with assertions:\n", + "\n", + "**Compilation with Assertions**: assertion-driven example bootstrapping and counterexample bootstrapping during compilation. Teacher has assertions while the student does not as the student learns from the teacher's assertion-driven bootstrapped examples. \n", + "\n", + "**Compilation + Inference with Assertions**: assertion-driven optimizations for both the teacher and student to offer enhanced assertion-driven outputs during both compilation and inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6)\n", + "compiled_with_assertions_tweeter = teleprompter.compile(student=tweeter, teacher = tweeter_with_assertions, trainset=trainset, valset=devset[:25])\n", + "\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(compiled_with_assertions_tweeter)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6, num_threads=1)\n", + "compiled_tweeter_with_assertions = teleprompter.compile(student=tweeter_with_assertions, teacher = tweeter_with_assertions, trainset=trainset, valset=devset[:25])\n", + "\n", + "for metric in metrics:\n", + " evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)\n", + " evaluate(compiled_tweeter_with_assertions)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dspy_dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/outdated_v2.4_examples/vlm/mmmu.ipynb b/examples/outdated_v2.4_examples/vlm/mmmu.ipynb index f529069b98..e827d027f2 100644 --- a/examples/outdated_v2.4_examples/vlm/mmmu.ipynb +++ b/examples/outdated_v2.4_examples/vlm/mmmu.ipynb @@ -1,793 +1,793 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NOTE: This is not a cookbook\n", - "\n", - "This is a testing notebook in order to make sure that multimodal works.\n", - "\n", - "Cookbook is on the way, but if you have particular ideas, message @isaac on discord" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from dspy.datasets import DataLoader\n", - "from dspy.evaluate.metrics import answer_exact_match\n", - "from typing import List\n", - "from dspy.evaluate import Evaluate\n", - "\n", - "import dotenv\n", - "import litellm\n", - "\n", - "litellm.suppress_debug_info = True\n", - "\n", - "dotenv.load_dotenv()\n", - "\n", - "def debug_exact_match(example, pred, trace=None, frac=1.0):\n", - " print(example.inputs())\n", - " print(example.answer)\n", - " print(pred)\n", - " # print(trace)\n", - " # print(frac)\n", - " return answer_exact_match(example, pred, trace, frac)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# vllm serve Qwen/Qwen2-VL-7B-Instruct --trust-remote-code --limit-mm-per-prompt image=16 --seed 42 --pipeline-parallel-size 2\n", - "qwen_lm = dspy.LM(model=\"openai/Qwen/Qwen2-VL-7B-Instruct\", api_base=\"http://localhost:8000/v1\", api_key=\"sk-fake-key\", max_tokens=5000, temperature=0.3)\n", - "haiku_lm = dspy.LM(model=\"anthropic/claude-3-haiku-20240307\", max_tokens=4096)\n", - "# vllm serve meta-llama/Llama-3.2-11B-Vision-Instruct --trust-remote-code --limit-mm-per-prompt image=16 --seed 42 --enforce-eager --max-num-seqs 48\n", - "llama_lm = dspy.LM(model=\"openai/meta-llama/Llama-3.2-11B-Vision-Instruct\", api_base=\"http://localhost:8000/v1\", api_key=\"sk-fake-key\", max_tokens=5000)\n", - "internlm_lm = dspy.LM(model=\"openai/OpenGVLab/InternVL2-8B\", api_base=\"http://localhost:8000/v1\", api_key=\"sk-fake-key\", max_tokens=5000)\n", - "gpt_lm = dspy.LM(model=\"openai/gpt-4o-mini\", max_tokens=5000)\n", - "all_lms = [qwen_lm, haiku_lm, llama_lm, gpt_lm]\n", - "\n", - "dspy.settings.configure(lm=qwen_lm)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class DogPictureSignature(dspy.Signature):\n", - " \"\"\"Answer the question based on the image.\"\"\"\n", - " image: dspy.Image = dspy.InputField()\n", - " question: str = dspy.InputField()\n", - " answer: str = dspy.OutputField()\n", - "\n", - "class DogPicture(dspy.Module):\n", - " def __init__(self) -> None:\n", - " self.predictor = dspy.ChainOfThought(DogPictureSignature)\n", - " \n", - " def __call__(self, **kwargs):\n", - " return self.predictor(**kwargs)\n", - "\n", - "dog_picture = DogPicture()\n", - "\n", - "example = dspy.Example(image=dspy.Image.from_url(\"https://i.pinimg.com/564x/78/f9/6d/78f96d0314d39a1b8a849005123e166d.jpg\"), question=\"What is the breed of the dog in the image?\").with_inputs(\"image\", \"question\")\n", - "print(dog_picture(**example.inputs()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# qwen_lm.inspect_history()\n", - "import rich\n", - "rich.print(qwen_lm.history)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = dspy.Predict(\"question -> answer\")\n", - "p(question=\"What is the capital of France?\")\n", - "qwen_lm.inspect_history()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "from concurrent.futures import ThreadPoolExecutor\n", - "\n", - "input_keys = tuple([f\"image_{i}\" for i in range(1, 3)] + [\"question\", \"options\"])\n", - "subsets = ['Accounting', 'Agriculture', 'Architecture_and_Engineering', 'Art', 'Art_Theory', 'Basic_Medical_Science', 'Biology', 'Chemistry', 'Clinical_Medicine', 'Computer_Science', 'Design', 'Diagnostics_and_Laboratory_Medicine', 'Economics', 'Electronics', 'Energy_and_Power', 'Finance', 'Geography', 'History', 'Literature', 'Manage', 'Marketing', 'Materials', 'Math', 'Mechanical_Engineering', 'Music', 'Pharmacy', 'Physics', 'Psychology', 'Public_Health', 'Sociology']\n", - "\n", - "devset = []\n", - "valset = []\n", - "with ThreadPoolExecutor(max_workers=len(subsets)) as executor:\n", - " def load_dataset(subset_index_subset):\n", - " subset_index, subset = subset_index_subset\n", - " dataset = DataLoader().from_huggingface(\"MMMU/MMMU\", subset, split=[\"dev\", \"validation\"], input_keys=input_keys)\n", - " return subset_index, dataset[\"dev\"], dataset[\"validation\"]\n", - " \n", - " results = list(executor.map(load_dataset, enumerate(subsets)))\n", - " \n", - " results.sort(key=lambda x: x[0])\n", - " \n", - " for _, dev, val in results:\n", - " devset.extend(dev)\n", - " valset.extend(val)\n", - "\n", - "print(len(devset))\n", - "print(len(valset))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ast\n", - "\n", - "def count_images(dataset):\n", - " image_counts = {i: 0 for i in range(6)} # Initialize counts for 0 to 2 images\n", - " for example in dataset:\n", - " count = sum(1 for key in example.inputs().keys() if key.startswith('image_') and example.inputs()[key] is not None)\n", - " image_counts[count] += 1\n", - " return image_counts\n", - "\n", - "def count_multiple_choice_questions(dataset):\n", - " return sum(1 for example in dataset if example[\"question_type\"] == \"multiple-choice\")\n", - "max_images = 5\n", - "\n", - "num_images = 1\n", - "\n", - "devset_filtered = [example for example in devset if sum(1 for key in example.inputs().keys() if key.startswith('image_') and example.inputs()[key] is not None) == num_images]\n", - "valset_filtered = [example for example in valset if sum(1 for key in example.inputs().keys() if key.startswith('image_') and example.inputs()[key] is not None) == num_images]\n", - "\n", - "devset_filtered = [example for example in devset_filtered if example[\"question_type\"] == \"multiple-choice\"]\n", - "valset_filtered = [example for example in valset_filtered if example[\"question_type\"] == \"multiple-choice\"]\n", - "\n", - "def update_example_image_key(example):\n", - " example_copy = example.copy()\n", - " example_copy[\"image\"] = dspy.Image.from_PIL(example_copy[\"image_1\"])\n", - " return example_copy.with_inputs(*example.inputs().keys(), \"image\")\n", - "\n", - "\n", - "\n", - "devset_filtered = list(map(update_example_image_key, devset_filtered))\n", - "valset_filtered = list(map(update_example_image_key, valset_filtered))\n", - "\n", - "devset_image_counts = count_images(devset_filtered)\n", - "valset_image_counts = count_images(valset_filtered)\n", - "\n", - "devset_multiple_choice_questions = count_multiple_choice_questions(devset_filtered)\n", - "valset_multiple_choice_questions = count_multiple_choice_questions(valset_filtered)\n", - "\n", - "print(\"Image counts in devset:\")\n", - "for count, num_examples in devset_image_counts.items():\n", - " print(f\"{count} image(s): {num_examples} examples\")\n", - "\n", - "print(\"\\nImage counts in valset:\")\n", - "for count, num_examples in valset_image_counts.items():\n", - " print(f\"{count} image(s): {num_examples} examples\")\n", - "\n", - "print(\"\\nMultiple choice questions in devset:\")\n", - "print(devset_multiple_choice_questions, \"out of\", len(devset_filtered))\n", - "print(\"\\nMultiple choice questions in valset:\")\n", - "print(valset_multiple_choice_questions, \"out of\", len(valset_filtered))\n", - "\n", - "def convert_multiple_choice_to_letter(dataset):\n", - " new_dataset = []\n", - " for example in dataset:\n", - " if example[\"question_type\"] == \"multiple-choice\":\n", - " # print(example[\"options\"])\n", - " options = ast.literal_eval(example[\"options\"])\n", - " example[\"choices\"] = str([chr(65 + i) + \". \" + option for i, option in enumerate(options)])\n", - " else:\n", - " example[\"choices\"] = str(ast.literal_eval(example[\"options\"]))\n", - " if example[\"choices\"] == []:\n", - " example[\"choices\"] = \"Free response\"\n", - "\n", - " updated_example = example.with_inputs(*example.inputs().keys(), \"choices\")\n", - " new_dataset.append(updated_example)\n", - " return new_dataset\n", - "\n", - "print(devset_filtered[0])\n", - "updated_devset = convert_multiple_choice_to_letter(devset_filtered)\n", - "print(updated_devset[0])\n", - "updated_valset = convert_multiple_choice_to_letter(valset_filtered)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "from typing import Literal\n", - "class MMMUSignature(dspy.Signature):\n", - " \"\"\"Answer with the letter of the correct answer.\"\"\"\n", - "\n", - " question: str = dspy.InputField()\n", - " image: dspy.Image = dspy.InputField()\n", - " choices: List[str] = dspy.InputField()\n", - " answer: Literal[\"A\", \"B\", \"C\", \"D\", \"E\"] = dspy.OutputField()\n", - "\n", - "class MMMUModule(dspy.Module):\n", - " def __init__(self, cot=True):\n", - " super().__init__()\n", - " if cot:\n", - " self.predictor = dspy.ChainOfThought(MMMUSignature)\n", - " else:\n", - " self.predictor = dspy.Predict(MMMUSignature)\n", - "\n", - " def __call__(self, **kwargs):\n", - " # Clean up predictions\n", - " prediction = self.predictor(**kwargs)\n", - " # Multiple choice case\n", - " if \"A.\" in kwargs[\"choices\"]:\n", - " # regex to extract A, B, C, or D, or E\n", - " answer = re.search(r'[A-E]', prediction[\"answer\"])\n", - " if not answer:\n", - " answer = prediction[\"answer\"]\n", - " else:\n", - " answer = answer.group(0)\n", - " prediction[\"answer\"] = answer\n", - " # Free response case\n", - " return prediction\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "sample_input = updated_devset[0]\n", - "# print(sample_input.inputs())\n", - "# print(encode_image(sample_input.inputs()[\"image_1\"]))\n", - "mmmu = MMMUModule()\n", - "print(sample_input.inputs())\n", - "print(mmmu(**sample_input.inputs()))\n", - "print(sample_input.answer)\n", - "\n", - "evaluate_mmmu = Evaluate(metric=answer_exact_match, num_threads=50, devset=updated_valset, display_progress=True, max_errors=500, return_outputs=True)\n", - "qwen_lm.inspect_history()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def test_lm(lm, cot=False):\n", - " if lm.model == \"openai/gpt-4o-mini\":\n", - " num_threads = 10\n", - " else:\n", - " num_threads = 30\n", - " evaluate_mmmu = Evaluate(metric=answer_exact_match, num_threads=num_threads, devset=updated_valset, display_progress=True, max_errors=500, return_outputs=True)\n", - " mmmu = MMMUModule(cot=cot)\n", - " with dspy.context(lm=lm):\n", - " scores, outputs = evaluate_mmmu(mmmu)\n", - " num_bad_format = sum(1 for example in outputs if example[1].get(\"answer\", None) is None)\n", - " return scores, num_bad_format\n", - "\n", - "res1 = test_lm(qwen_lm)\n", - "res1_cot = test_lm(qwen_lm, cot=True)\n", - "# test_lm(haiku_lm)\n", - "# test_lm(llama_lm)\n", - "# res1 = test_lm(internlm_lm)\n", - "# res2 = test_lm(gpt_lm)\n", - "# res2_cot = test_lm(gpt_lm, cot=True)\n", - "# Results:\n", - "# MMMU Val(single image only, multiple choice only), N=805\n", - "# Temp 0, max_tokens=5k\n", - "\n", - "# 4o-mini:\n", - "# Reported: 59.4\n", - "\n", - "# Measured (cot, predict): 60.0, 56.4\n", - "# Num bad format (cot, predict): 0, 1\n", - "\n", - "# qwen-7b\n", - "# Reported: 54.1\n", - "# Measured (cot, predict): 49.0, 49.69\n", - "# Num bad format (cot, predict): 17, 0\n", - "print(\"MMMU Validation Set (single image only, multiple choice only), N=805\")\n", - "print(\"Temp 0, max_tokens=5k\")\n", - "print(\"qwen-7b\")\n", - "print(\"Reported:\", 54.1)\n", - "print(\"Measured (cot, predict):\", f\"{res1_cot[0]:.1f}, {res1[0]:.2f}\")\n", - "print(\"Num bad format (cot, predict):\", f\"{res1_cot[1]}, {res1[1]}\")\n", - "# print()\n", - "# print(\"gpt-4o-mini\")\n", - "# print(\"Reported:\", 59.4)\n", - "# print(\"Measured (cot, predict):\", f\"{res2_cot[0]:.1f}, {res2[0]:.2f}\") \n", - "# print(\"Num bad format (cot, predict):\", f\"{res2_cot[1]}, {res2[1]}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "qwen_lm.inspect_history()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scores, outputs = evaluate_mmmu(mmmu)\n", - "# lm.inspect_history()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import Counter\n", - "c = Counter([outputs[i][1].get(\"answer\", \"nothing returned\") for i in range(len(outputs))])\n", - "non_letters = sum([1 for output in outputs if output[1].get(\"answer\", \"nothing returned\") not in [\"A\", \"B\", \"C\", \"D\"]])\n", - "print(c)\n", - "print(non_letters)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mc_correct = sum(outputs[i][2] for i in range(len(outputs)) if outputs[i][0][\"question_type\"] == \"multiple-choice\")\n", - "total_mc = sum(1 for example in outputs if example[0][\"question_type\"] == \"multiple-choice\")\n", - "print(mc_correct, total_mc)\n", - "print(mc_correct / total_mc)\n", - "print(sum(outputs[i][1].get(\"answer\", None) is None for i in range(len(outputs))))\n", - "\n", - "# Note: Run above here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make sure that multiple images work" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## No examples" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import PIL\n", - "def set_image_to_black_square(example, key):\n", - " example_copy = example.copy()\n", - " example_copy[key] = PIL.Image.open(\"black_image_300x300.png\")\n", - " return example_copy.with_inputs(*example.inputs().keys())\n", - "\n", - "print(updated_devset[0][\"image_1\"])\n", - "print(updated_devset[0][\"image_2\"])\n", - "examples_no_image_1 = list(map(lambda x: set_image_to_black_square(x, \"image_1\"), updated_valset))\n", - "print(examples_no_image_1[0][\"image_1\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", - "print(examples_no_image_1[0][\"image_2\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", - "examples_no_image_2 = list(map(lambda x: set_image_to_black_square(x, \"image_2\"), updated_valset))\n", - "print(examples_no_image_2[0][\"image_1\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", - "print(examples_no_image_2[0][\"image_2\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", - "\n", - "examples_no_actual_image = list(map(lambda x: set_image_to_black_square(x, \"image_1\"), updated_valset))\n", - "examples_no_actual_image = list(map(lambda x: set_image_to_black_square(x, \"image_2\"), examples_no_actual_image))\n", - "print(examples_no_actual_image[0][\"image_1\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", - "print(examples_no_actual_image[0][\"image_2\"] == PIL.Image.open(\"black_image_300x300.png\"))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mmmu = MMMUModule()\n", - "print(examples_no_image_1[0].inputs())\n", - "print(mmmu(**examples_no_image_1[0].inputs()))\n", - "\n", - "print(examples_no_image_2[0].inputs())\n", - "print(mmmu(**examples_no_image_2[0].inputs()))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal = evaluate_mmmu(mmmu, devset=updated_valset)\n", - "no_image_1 = evaluate_mmmu(mmmu, devset=examples_no_image_1)\n", - "no_image_2 = evaluate_mmmu(mmmu, devset=examples_no_image_2)\n", - "no_actual_image = evaluate_mmmu(mmmu, devset=examples_no_actual_image)\n", - "print(\"Testing on MMMU validation set (N=\", len(updated_valset), \")\")\n", - "print(\"Score with both images:\", normal)\n", - "print(\"Score with image_1 set to black square:\", no_image_1)\n", - "print(\"Score with image_2 set to black square:\", no_image_2)\n", - "print(\"Score with both images set to black squares:\", no_actual_image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TODO: Test with bootstrapped examples\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make sure that JPGs work" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert images to JPGs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import io\n", - "from PIL import Image\n", - "\n", - "def convert_to_jpg(example):\n", - " example_copy = example.copy()\n", - " for key in ['image_1', 'image_2']:\n", - " if key in example_copy and isinstance(example_copy[key], Image.Image):\n", - " # Convert to RGB mode (in case it's not already)\n", - " img = example[key].convert('RGB')\n", - " \n", - " # Save as JPG in memory\n", - " buffer = io.BytesIO()\n", - " img.save(buffer, format='JPEG')\n", - " buffer.seek(0)\n", - " \n", - " # Load the JPG back as a PIL Image\n", - " example_copy[key] = Image.open(buffer)\n", - " \n", - " return example_copy.with_inputs(*example.inputs().keys())\n", - "\n", - "# Convert all images in the dataset to JPG\n", - "examples_jpg = list(map(convert_to_jpg, updated_valset))\n", - "\n", - "# Verify conversion\n", - "print(\"Original image format:\", updated_valset[0]['image_1'].format)\n", - "print(\"Converted image format:\", examples_jpg[0]['image_1'].format)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "examples_jpg = list(map(convert_to_jpg, updated_valset))\n", - "examples_no_image_1_jpg = list(map(lambda x: convert_to_jpg(x), examples_no_image_1))\n", - "examples_no_image_2_jpg = list(map(lambda x: convert_to_jpg(x), examples_no_image_2))\n", - "examples_no_actual_image_jpg = list(map(lambda x: convert_to_jpg(x), examples_no_actual_image))\n", - "\n", - "mmmu = MMMUModule()\n", - "print(examples_no_image_1_jpg[0].inputs())\n", - "print(mmmu(**examples_no_image_1_jpg[0].inputs()))\n", - "print(examples_no_image_1_jpg[0][\"image_1\"].format)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "normal = evaluate_mmmu(mmmu, devset=examples_jpg)\n", - "no_image_1 = evaluate_mmmu(mmmu, devset=examples_no_image_1_jpg)\n", - "no_image_2 = evaluate_mmmu(mmmu, devset=examples_no_image_2_jpg)\n", - "no_actual_image = evaluate_mmmu(mmmu, devset=examples_no_actual_image_jpg)\n", - "print(\"Testing on MMMU validation set (N=\", len(updated_valset), \")\")\n", - "print(\"Score with both images:\", normal)\n", - "print(\"Score with image_1 set to black square:\", no_image_1)\n", - "print(\"Score with image_2 set to black square:\", no_image_2)\n", - "print(\"Score with both images set to black squares:\", no_actual_image)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lm.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing that URLs work" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "colors = {\n", - " \"White\": \"FFFFFF\",\n", - " \"Red\": \"FF0000\",\n", - " \"Green\": \"00FF00\",\n", - " \"Blue\": \"0000FF\",\n", - " \"Yellow\": \"FFFF00\",\n", - " \"Cyan\": \"00FFFF\",\n", - " \"Magenta\": \"FF00FF\",\n", - " \"Gray\": \"808080\",\n", - " \"Orange\": \"FFA500\",\n", - " \"Purple\": \"800080\"\n", - "}\n", - "def get_color_image_url(color, file_extension=\"png\"):\n", - " return f\"https://placehold.co/300/{colors[color]}/{colors[color]}.{file_extension}\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import dspy\n", - "def generate_random_2_color_image_examples(n):\n", - " examples = []\n", - " for _ in range(n):\n", - " color_1, color_2 = random.sample(list(colors.keys()), 2)\n", - " chosen_color = color_1 if random.random() < 0.5 else color_2\n", - " example_kwargs = {\n", - " \"images\": [dspy.Image.from_url(get_color_image_url(color_1)), dspy.Image.from_url(get_color_image_url(color_2))],\n", - " \"question\": f\"What color is the {'first' if chosen_color == color_1 else 'second'} image?\",\n", - " \"answer\": chosen_color\n", - " }\n", - " examples.append(dspy.Example(**example_kwargs).with_inputs(\"images\", \"question\"))\n", - " return examples\n", - "\n", - "examples = generate_random_2_color_image_examples(100)\n", - "print(examples[0])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import dspy\n", - "from typing import List\n", - "class ColorSignature(dspy.Signature):\n", - " \"\"\"Output the color of the designated image.\"\"\"\n", - " images: List[dspy.Image] = dspy.InputField(desc=\"An image\")\n", - " question: str = dspy.InputField(desc=\"A question about the image\")\n", - " answer: str = dspy.OutputField(desc=\"The color of the designated image\")\n", - "\n", - "class ColorModule(dspy.Module):\n", - " def __init__(self):\n", - " self.predictor = dspy.Predict(ColorSignature)\n", - "\n", - " def forward(self, **kwargs):\n", - " return self.predictor(**kwargs)\n", - "\n", - "color_program = ColorModule()\n", - "optimizer = dspy.LabeledFewShot(k=2)\n", - "compiled_color_program = optimizer.compile(color_program, trainset=examples)\n", - "compiled_color_program.save(\"color_program.json\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(examples[0])\n", - "print(color_program(**examples[0].inputs()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "few_shot_optimizer = dspy.BootstrapFewShot(metric=answer_exact_match, max_bootstrapped_demos=3, max_labeled_demos=10)\n", - "smaller_few_shot_optimizer = dspy.BootstrapFewShot(metric=answer_exact_match, max_bootstrapped_demos=1, max_labeled_demos=1)\n", - "dataset = generate_random_2_color_image_examples(1000)\n", - "trainset = dataset[:200]\n", - "validationset = dataset[200:400]\n", - "evaluate_colors = Evaluate(metric=answer_exact_match, num_threads=300, devset=validationset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compiled_color_program = few_shot_optimizer.compile(color_program, trainset=trainset)\n", - "compiled_smaller_color_program = smaller_few_shot_optimizer.compile(color_program, trainset=trainset)\n", - "print(evaluate_colors(color_program))\n", - "print(evaluate_colors(compiled_color_program))\n", - "print(evaluate_colors(compiled_smaller_color_program))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(compiled_color_program(**validationset[0].inputs()))\n", - "lm.inspect_history()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TODO(Isaac): Delete; Archive of old experiments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = DataLoader().from_huggingface(\"Alanox/stanford-dogs\", split=\"full\", input_keys=(\"image\",), trust_remote_code=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# rename the field from \"image\" to \"image_1\"\n", - "def rename_field(example, old_name, new_name):\n", - " try:\n", - " example[new_name] = example[old_name]\n", - " del example[old_name]\n", - " except Exception:\n", - " pass\n", - " return example\n", - " \n", - "dog_dataset = list(map(rename_field, dataset, [\"image\"]*len(dataset), [\"image_1\"]*len(dataset)))\n", - "dog_dataset2 = list(map(rename_field, dog_dataset, [\"target\"]*len(dog_dataset), [\"answer\"]*len(dog_dataset)))\n", - "dog_dataset3 = list(map(lambda x: x.with_inputs(\"image_1\"), dog_dataset2))\n", - "dog_dataset = dog_dataset3\n", - "random.shuffle(dog_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class DogPictureSignature(dspy.Signature):\n", - " \"\"\"Output the dog breed of the dog in the image.\"\"\"\n", - " image: dspy.Image = dspy.InputField(desc=\"An image of a dog\")\n", - " answer: str = dspy.OutputField(desc=\"The dog breed of the dog in the image\")\n", - "\n", - "class DogPicture(dspy.Module):\n", - " def __init__(self) -> None:\n", - " self.predictor = dspy.ChainOfThought(DogPictureSignature)\n", - " \n", - " def __call__(self, **kwargs):\n", - " return self.predictor(**kwargs)\n", - "\n", - "dog_picture = DogPicture()\n", - "\n", - "example = dspy.Example(image=dspy.Image.from_url(\"https://i.pinimg.com/564x/78/f9/6d/78f96d0314d39a1b8a849005123e166d.jpg\"))\n", - "print(dog_picture(**example.inputs()))\n", - "# print(dog_picture(**dog_dataset[0].inputs()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Test inline signature\n", - "# TODO: Test json adapter" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NOTE: This is not a cookbook\n", + "\n", + "This is a testing notebook in order to make sure that multimodal works.\n", + "\n", + "Cookbook is on the way, but if you have particular ideas, message @isaac on discord" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.datasets import DataLoader\n", + "from dspy.evaluate.metrics import answer_exact_match\n", + "from typing import List\n", + "from dspy.evaluate import Evaluate\n", + "\n", + "import dotenv\n", + "import litellm\n", + "\n", + "litellm.suppress_debug_info = True\n", + "\n", + "dotenv.load_dotenv()\n", + "\n", + "def debug_exact_match(example, pred, trace=None, frac=1.0):\n", + " print(example.inputs())\n", + " print(example.answer)\n", + " print(pred)\n", + " # print(trace)\n", + " # print(frac)\n", + " return answer_exact_match(example, pred, trace, frac)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# vllm serve Qwen/Qwen2-VL-7B-Instruct --trust-remote-code --limit-mm-per-prompt image=16 --seed 42 --pipeline-parallel-size 2\n", + "qwen_lm = dspy.LM(model=\"openai/Qwen/Qwen2-VL-7B-Instruct\", api_base=\"http://localhost:8000/v1\", api_key=\"sk-fake-key\", max_tokens=5000, temperature=0.3)\n", + "haiku_lm = dspy.LM(model=\"anthropic/claude-3-haiku-20240307\", max_tokens=4096)\n", + "# vllm serve meta-llama/Llama-3.2-11B-Vision-Instruct --trust-remote-code --limit-mm-per-prompt image=16 --seed 42 --enforce-eager --max-num-seqs 48\n", + "llama_lm = dspy.LM(model=\"openai/meta-llama/Llama-3.2-11B-Vision-Instruct\", api_base=\"http://localhost:8000/v1\", api_key=\"sk-fake-key\", max_tokens=5000)\n", + "internlm_lm = dspy.LM(model=\"openai/OpenGVLab/InternVL2-8B\", api_base=\"http://localhost:8000/v1\", api_key=\"sk-fake-key\", max_tokens=5000)\n", + "gpt_lm = dspy.LM(model=\"openai/gpt-4o-mini\", max_tokens=5000)\n", + "all_lms = [qwen_lm, haiku_lm, llama_lm, gpt_lm]\n", + "\n", + "dspy.settings.configure(lm=qwen_lm)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class DogPictureSignature(dspy.Signature):\n", + " \"\"\"Answer the question based on the image.\"\"\"\n", + " image: dspy.Image = dspy.InputField()\n", + " question: str = dspy.InputField()\n", + " answer: str = dspy.OutputField()\n", + "\n", + "class DogPicture(dspy.Module):\n", + " def __init__(self) -> None:\n", + " self.predictor = dspy.ChainOfThought(DogPictureSignature)\n", + " \n", + " def __call__(self, **kwargs):\n", + " return self.predictor(**kwargs)\n", + "\n", + "dog_picture = DogPicture()\n", + "\n", + "example = dspy.Example(image=dspy.Image.from_url(\"https://i.pinimg.com/564x/78/f9/6d/78f96d0314d39a1b8a849005123e166d.jpg\"), question=\"What is the breed of the dog in the image?\").with_inputs(\"image\", \"question\")\n", + "print(dog_picture(**example.inputs()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# qwen_lm.inspect_history()\n", + "import rich\n", + "rich.print(qwen_lm.history)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p = dspy.Predict(\"question -> answer\")\n", + "p(question=\"What is the capital of France?\")\n", + "qwen_lm.inspect_history()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "input_keys = tuple([f\"image_{i}\" for i in range(1, 3)] + [\"question\", \"options\"])\n", + "subsets = ['Accounting', 'Agriculture', 'Architecture_and_Engineering', 'Art', 'Art_Theory', 'Basic_Medical_Science', 'Biology', 'Chemistry', 'Clinical_Medicine', 'Computer_Science', 'Design', 'Diagnostics_and_Laboratory_Medicine', 'Economics', 'Electronics', 'Energy_and_Power', 'Finance', 'Geography', 'History', 'Literature', 'Manage', 'Marketing', 'Materials', 'Math', 'Mechanical_Engineering', 'Music', 'Pharmacy', 'Physics', 'Psychology', 'Public_Health', 'Sociology']\n", + "\n", + "devset = []\n", + "valset = []\n", + "with ThreadPoolExecutor(max_workers=len(subsets)) as executor:\n", + " def load_dataset(subset_index_subset):\n", + " subset_index, subset = subset_index_subset\n", + " dataset = DataLoader().from_huggingface(\"MMMU/MMMU\", subset, split=[\"dev\", \"validation\"], input_keys=input_keys)\n", + " return subset_index, dataset[\"dev\"], dataset[\"validation\"]\n", + " \n", + " results = list(executor.map(load_dataset, enumerate(subsets)))\n", + " \n", + " results.sort(key=lambda x: x[0])\n", + " \n", + " for _, dev, val in results:\n", + " devset.extend(dev)\n", + " valset.extend(val)\n", + "\n", + "print(len(devset))\n", + "print(len(valset))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ast\n", + "\n", + "def count_images(dataset):\n", + " image_counts = {i: 0 for i in range(6)} # Initialize counts for 0 to 2 images\n", + " for example in dataset:\n", + " count = sum(1 for key in example.inputs().keys() if key.startswith('image_') and example.inputs()[key] is not None)\n", + " image_counts[count] += 1\n", + " return image_counts\n", + "\n", + "def count_multiple_choice_questions(dataset):\n", + " return sum(1 for example in dataset if example[\"question_type\"] == \"multiple-choice\")\n", + "max_images = 5\n", + "\n", + "num_images = 1\n", + "\n", + "devset_filtered = [example for example in devset if sum(1 for key in example.inputs().keys() if key.startswith('image_') and example.inputs()[key] is not None) == num_images]\n", + "valset_filtered = [example for example in valset if sum(1 for key in example.inputs().keys() if key.startswith('image_') and example.inputs()[key] is not None) == num_images]\n", + "\n", + "devset_filtered = [example for example in devset_filtered if example[\"question_type\"] == \"multiple-choice\"]\n", + "valset_filtered = [example for example in valset_filtered if example[\"question_type\"] == \"multiple-choice\"]\n", + "\n", + "def update_example_image_key(example):\n", + " example_copy = example.copy()\n", + " example_copy[\"image\"] = dspy.Image.from_PIL(example_copy[\"image_1\"])\n", + " return example_copy.with_inputs(*example.inputs().keys(), \"image\")\n", + "\n", + "\n", + "\n", + "devset_filtered = list(map(update_example_image_key, devset_filtered))\n", + "valset_filtered = list(map(update_example_image_key, valset_filtered))\n", + "\n", + "devset_image_counts = count_images(devset_filtered)\n", + "valset_image_counts = count_images(valset_filtered)\n", + "\n", + "devset_multiple_choice_questions = count_multiple_choice_questions(devset_filtered)\n", + "valset_multiple_choice_questions = count_multiple_choice_questions(valset_filtered)\n", + "\n", + "print(\"Image counts in devset:\")\n", + "for count, num_examples in devset_image_counts.items():\n", + " print(f\"{count} image(s): {num_examples} examples\")\n", + "\n", + "print(\"\\nImage counts in valset:\")\n", + "for count, num_examples in valset_image_counts.items():\n", + " print(f\"{count} image(s): {num_examples} examples\")\n", + "\n", + "print(\"\\nMultiple choice questions in devset:\")\n", + "print(devset_multiple_choice_questions, \"out of\", len(devset_filtered))\n", + "print(\"\\nMultiple choice questions in valset:\")\n", + "print(valset_multiple_choice_questions, \"out of\", len(valset_filtered))\n", + "\n", + "def convert_multiple_choice_to_letter(dataset):\n", + " new_dataset = []\n", + " for example in dataset:\n", + " if example[\"question_type\"] == \"multiple-choice\":\n", + " # print(example[\"options\"])\n", + " options = ast.literal_eval(example[\"options\"])\n", + " example[\"choices\"] = str([chr(65 + i) + \". \" + option for i, option in enumerate(options)])\n", + " else:\n", + " example[\"choices\"] = str(ast.literal_eval(example[\"options\"]))\n", + " if example[\"choices\"] == []:\n", + " example[\"choices\"] = \"Free response\"\n", + "\n", + " updated_example = example.with_inputs(*example.inputs().keys(), \"choices\")\n", + " new_dataset.append(updated_example)\n", + " return new_dataset\n", + "\n", + "print(devset_filtered[0])\n", + "updated_devset = convert_multiple_choice_to_letter(devset_filtered)\n", + "print(updated_devset[0])\n", + "updated_valset = convert_multiple_choice_to_letter(valset_filtered)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "from typing import Literal\n", + "class MMMUSignature(dspy.Signature):\n", + " \"\"\"Answer with the letter of the correct answer.\"\"\"\n", + "\n", + " question: str = dspy.InputField()\n", + " image: dspy.Image = dspy.InputField()\n", + " choices: List[str] = dspy.InputField()\n", + " answer: Literal[\"A\", \"B\", \"C\", \"D\", \"E\"] = dspy.OutputField()\n", + "\n", + "class MMMUModule(dspy.Module):\n", + " def __init__(self, cot=True):\n", + " super().__init__()\n", + " if cot:\n", + " self.predictor = dspy.ChainOfThought(MMMUSignature)\n", + " else:\n", + " self.predictor = dspy.Predict(MMMUSignature)\n", + "\n", + " def __call__(self, **kwargs):\n", + " # Clean up predictions\n", + " prediction = self.predictor(**kwargs)\n", + " # Multiple choice case\n", + " if \"A.\" in kwargs[\"choices\"]:\n", + " # regex to extract A, B, C, or D, or E\n", + " answer = re.search(r'[A-E]', prediction[\"answer\"])\n", + " if not answer:\n", + " answer = prediction[\"answer\"]\n", + " else:\n", + " answer = answer.group(0)\n", + " prediction[\"answer\"] = answer\n", + " # Free response case\n", + " return prediction\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "sample_input = updated_devset[0]\n", + "# print(sample_input.inputs())\n", + "# print(encode_image(sample_input.inputs()[\"image_1\"]))\n", + "mmmu = MMMUModule()\n", + "print(sample_input.inputs())\n", + "print(mmmu(**sample_input.inputs()))\n", + "print(sample_input.answer)\n", + "\n", + "evaluate_mmmu = Evaluate(metric=answer_exact_match, num_threads=50, devset=updated_valset, display_progress=True, max_errors=500, return_outputs=True)\n", + "qwen_lm.inspect_history()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def test_lm(lm, cot=False):\n", + " if lm.model == \"openai/gpt-4o-mini\":\n", + " num_threads = 10\n", + " else:\n", + " num_threads = 30\n", + " evaluate_mmmu = Evaluate(metric=answer_exact_match, num_threads=num_threads, devset=updated_valset, display_progress=True, max_errors=500, return_outputs=True)\n", + " mmmu = MMMUModule(cot=cot)\n", + " with dspy.context(lm=lm):\n", + " scores, outputs = evaluate_mmmu(mmmu)\n", + " num_bad_format = sum(1 for example in outputs if example[1].get(\"answer\", None) is None)\n", + " return scores, num_bad_format\n", + "\n", + "res1 = test_lm(qwen_lm)\n", + "res1_cot = test_lm(qwen_lm, cot=True)\n", + "# test_lm(haiku_lm)\n", + "# test_lm(llama_lm)\n", + "# res1 = test_lm(internlm_lm)\n", + "# res2 = test_lm(gpt_lm)\n", + "# res2_cot = test_lm(gpt_lm, cot=True)\n", + "# Results:\n", + "# MMMU Val(single image only, multiple choice only), N=805\n", + "# Temp 0, max_tokens=5k\n", + "\n", + "# 4o-mini:\n", + "# Reported: 59.4\n", + "\n", + "# Measured (cot, predict): 60.0, 56.4\n", + "# Num bad format (cot, predict): 0, 1\n", + "\n", + "# qwen-7b\n", + "# Reported: 54.1\n", + "# Measured (cot, predict): 49.0, 49.69\n", + "# Num bad format (cot, predict): 17, 0\n", + "print(\"MMMU Validation Set (single image only, multiple choice only), N=805\")\n", + "print(\"Temp 0, max_tokens=5k\")\n", + "print(\"qwen-7b\")\n", + "print(\"Reported:\", 54.1)\n", + "print(\"Measured (cot, predict):\", f\"{res1_cot[0]:.1f}, {res1[0]:.2f}\")\n", + "print(\"Num bad format (cot, predict):\", f\"{res1_cot[1]}, {res1[1]}\")\n", + "# print()\n", + "# print(\"gpt-4o-mini\")\n", + "# print(\"Reported:\", 59.4)\n", + "# print(\"Measured (cot, predict):\", f\"{res2_cot[0]:.1f}, {res2[0]:.2f}\") \n", + "# print(\"Num bad format (cot, predict):\", f\"{res2_cot[1]}, {res2[1]}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qwen_lm.inspect_history()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scores, outputs = evaluate_mmmu(mmmu)\n", + "# lm.inspect_history()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "c = Counter([outputs[i][1].get(\"answer\", \"nothing returned\") for i in range(len(outputs))])\n", + "non_letters = sum([1 for output in outputs if output[1].get(\"answer\", \"nothing returned\") not in [\"A\", \"B\", \"C\", \"D\"]])\n", + "print(c)\n", + "print(non_letters)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mc_correct = sum(outputs[i][2] for i in range(len(outputs)) if outputs[i][0][\"question_type\"] == \"multiple-choice\")\n", + "total_mc = sum(1 for example in outputs if example[0][\"question_type\"] == \"multiple-choice\")\n", + "print(mc_correct, total_mc)\n", + "print(mc_correct / total_mc)\n", + "print(sum(outputs[i][1].get(\"answer\", None) is None for i in range(len(outputs))))\n", + "\n", + "# Note: Run above here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Make sure that multiple images work" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## No examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import PIL\n", + "def set_image_to_black_square(example, key):\n", + " example_copy = example.copy()\n", + " example_copy[key] = PIL.Image.open(\"black_image_300x300.png\")\n", + " return example_copy.with_inputs(*example.inputs().keys())\n", + "\n", + "print(updated_devset[0][\"image_1\"])\n", + "print(updated_devset[0][\"image_2\"])\n", + "examples_no_image_1 = list(map(lambda x: set_image_to_black_square(x, \"image_1\"), updated_valset))\n", + "print(examples_no_image_1[0][\"image_1\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", + "print(examples_no_image_1[0][\"image_2\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", + "examples_no_image_2 = list(map(lambda x: set_image_to_black_square(x, \"image_2\"), updated_valset))\n", + "print(examples_no_image_2[0][\"image_1\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", + "print(examples_no_image_2[0][\"image_2\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", + "\n", + "examples_no_actual_image = list(map(lambda x: set_image_to_black_square(x, \"image_1\"), updated_valset))\n", + "examples_no_actual_image = list(map(lambda x: set_image_to_black_square(x, \"image_2\"), examples_no_actual_image))\n", + "print(examples_no_actual_image[0][\"image_1\"] == PIL.Image.open(\"black_image_300x300.png\"))\n", + "print(examples_no_actual_image[0][\"image_2\"] == PIL.Image.open(\"black_image_300x300.png\"))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mmmu = MMMUModule()\n", + "print(examples_no_image_1[0].inputs())\n", + "print(mmmu(**examples_no_image_1[0].inputs()))\n", + "\n", + "print(examples_no_image_2[0].inputs())\n", + "print(mmmu(**examples_no_image_2[0].inputs()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "normal = evaluate_mmmu(mmmu, devset=updated_valset)\n", + "no_image_1 = evaluate_mmmu(mmmu, devset=examples_no_image_1)\n", + "no_image_2 = evaluate_mmmu(mmmu, devset=examples_no_image_2)\n", + "no_actual_image = evaluate_mmmu(mmmu, devset=examples_no_actual_image)\n", + "print(\"Testing on MMMU validation set (N=\", len(updated_valset), \")\")\n", + "print(\"Score with both images:\", normal)\n", + "print(\"Score with image_1 set to black square:\", no_image_1)\n", + "print(\"Score with image_2 set to black square:\", no_image_2)\n", + "print(\"Score with both images set to black squares:\", no_actual_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TODO: Test with bootstrapped examples\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Make sure that JPGs work" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert images to JPGs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "from PIL import Image\n", + "\n", + "def convert_to_jpg(example):\n", + " example_copy = example.copy()\n", + " for key in ['image_1', 'image_2']:\n", + " if key in example_copy and isinstance(example_copy[key], Image.Image):\n", + " # Convert to RGB mode (in case it's not already)\n", + " img = example[key].convert('RGB')\n", + " \n", + " # Save as JPG in memory\n", + " buffer = io.BytesIO()\n", + " img.save(buffer, format='JPEG')\n", + " buffer.seek(0)\n", + " \n", + " # Load the JPG back as a PIL Image\n", + " example_copy[key] = Image.open(buffer)\n", + " \n", + " return example_copy.with_inputs(*example.inputs().keys())\n", + "\n", + "# Convert all images in the dataset to JPG\n", + "examples_jpg = list(map(convert_to_jpg, updated_valset))\n", + "\n", + "# Verify conversion\n", + "print(\"Original image format:\", updated_valset[0]['image_1'].format)\n", + "print(\"Converted image format:\", examples_jpg[0]['image_1'].format)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "examples_jpg = list(map(convert_to_jpg, updated_valset))\n", + "examples_no_image_1_jpg = list(map(lambda x: convert_to_jpg(x), examples_no_image_1))\n", + "examples_no_image_2_jpg = list(map(lambda x: convert_to_jpg(x), examples_no_image_2))\n", + "examples_no_actual_image_jpg = list(map(lambda x: convert_to_jpg(x), examples_no_actual_image))\n", + "\n", + "mmmu = MMMUModule()\n", + "print(examples_no_image_1_jpg[0].inputs())\n", + "print(mmmu(**examples_no_image_1_jpg[0].inputs()))\n", + "print(examples_no_image_1_jpg[0][\"image_1\"].format)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "normal = evaluate_mmmu(mmmu, devset=examples_jpg)\n", + "no_image_1 = evaluate_mmmu(mmmu, devset=examples_no_image_1_jpg)\n", + "no_image_2 = evaluate_mmmu(mmmu, devset=examples_no_image_2_jpg)\n", + "no_actual_image = evaluate_mmmu(mmmu, devset=examples_no_actual_image_jpg)\n", + "print(\"Testing on MMMU validation set (N=\", len(updated_valset), \")\")\n", + "print(\"Score with both images:\", normal)\n", + "print(\"Score with image_1 set to black square:\", no_image_1)\n", + "print(\"Score with image_2 set to black square:\", no_image_2)\n", + "print(\"Score with both images set to black squares:\", no_actual_image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lm.inspect_history()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing that URLs work" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "colors = {\n", + " \"White\": \"FFFFFF\",\n", + " \"Red\": \"FF0000\",\n", + " \"Green\": \"00FF00\",\n", + " \"Blue\": \"0000FF\",\n", + " \"Yellow\": \"FFFF00\",\n", + " \"Cyan\": \"00FFFF\",\n", + " \"Magenta\": \"FF00FF\",\n", + " \"Gray\": \"808080\",\n", + " \"Orange\": \"FFA500\",\n", + " \"Purple\": \"800080\"\n", + "}\n", + "def get_color_image_url(color, file_extension=\"png\"):\n", + " return f\"https://placehold.co/300/{colors[color]}/{colors[color]}.{file_extension}\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import dspy\n", + "def generate_random_2_color_image_examples(n):\n", + " examples = []\n", + " for _ in range(n):\n", + " color_1, color_2 = random.sample(list(colors.keys()), 2)\n", + " chosen_color = color_1 if random.random() < 0.5 else color_2\n", + " example_kwargs = {\n", + " \"images\": [dspy.Image.from_url(get_color_image_url(color_1)), dspy.Image.from_url(get_color_image_url(color_2))],\n", + " \"question\": f\"What color is the {'first' if chosen_color == color_1 else 'second'} image?\",\n", + " \"answer\": chosen_color\n", + " }\n", + " examples.append(dspy.Example(**example_kwargs).with_inputs(\"images\", \"question\"))\n", + " return examples\n", + "\n", + "examples = generate_random_2_color_image_examples(100)\n", + "print(examples[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from typing import List\n", + "class ColorSignature(dspy.Signature):\n", + " \"\"\"Output the color of the designated image.\"\"\"\n", + " images: List[dspy.Image] = dspy.InputField(desc=\"An image\")\n", + " question: str = dspy.InputField(desc=\"A question about the image\")\n", + " answer: str = dspy.OutputField(desc=\"The color of the designated image\")\n", + "\n", + "class ColorModule(dspy.Module):\n", + " def __init__(self):\n", + " self.predictor = dspy.Predict(ColorSignature)\n", + "\n", + " def forward(self, **kwargs):\n", + " return self.predictor(**kwargs)\n", + "\n", + "color_program = ColorModule()\n", + "optimizer = dspy.LabeledFewShot(k=2)\n", + "compiled_color_program = optimizer.compile(color_program, trainset=examples)\n", + "compiled_color_program.save(\"color_program.json\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(examples[0])\n", + "print(color_program(**examples[0].inputs()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "few_shot_optimizer = dspy.BootstrapFewShot(metric=answer_exact_match, max_bootstrapped_demos=3, max_labeled_demos=10)\n", + "smaller_few_shot_optimizer = dspy.BootstrapFewShot(metric=answer_exact_match, max_bootstrapped_demos=1, max_labeled_demos=1)\n", + "dataset = generate_random_2_color_image_examples(1000)\n", + "trainset = dataset[:200]\n", + "validationset = dataset[200:400]\n", + "evaluate_colors = Evaluate(metric=answer_exact_match, num_threads=300, devset=validationset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "compiled_color_program = few_shot_optimizer.compile(color_program, trainset=trainset)\n", + "compiled_smaller_color_program = smaller_few_shot_optimizer.compile(color_program, trainset=trainset)\n", + "print(evaluate_colors(color_program))\n", + "print(evaluate_colors(compiled_color_program))\n", + "print(evaluate_colors(compiled_smaller_color_program))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(compiled_color_program(**validationset[0].inputs()))\n", + "lm.inspect_history()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TODO(Isaac): Delete; Archive of old experiments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = DataLoader().from_huggingface(\"Alanox/stanford-dogs\", split=\"full\", input_keys=(\"image\",), trust_remote_code=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# rename the field from \"image\" to \"image_1\"\n", + "def rename_field(example, old_name, new_name):\n", + " try:\n", + " example[new_name] = example[old_name]\n", + " del example[old_name]\n", + " except Exception:\n", + " pass\n", + " return example\n", + " \n", + "dog_dataset = list(map(rename_field, dataset, [\"image\"]*len(dataset), [\"image_1\"]*len(dataset)))\n", + "dog_dataset2 = list(map(rename_field, dog_dataset, [\"target\"]*len(dog_dataset), [\"answer\"]*len(dog_dataset)))\n", + "dog_dataset3 = list(map(lambda x: x.with_inputs(\"image_1\"), dog_dataset2))\n", + "dog_dataset = dog_dataset3\n", + "random.shuffle(dog_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class DogPictureSignature(dspy.Signature):\n", + " \"\"\"Output the dog breed of the dog in the image.\"\"\"\n", + " image: dspy.Image = dspy.InputField(desc=\"An image of a dog\")\n", + " answer: str = dspy.OutputField(desc=\"The dog breed of the dog in the image\")\n", + "\n", + "class DogPicture(dspy.Module):\n", + " def __init__(self) -> None:\n", + " self.predictor = dspy.ChainOfThought(DogPictureSignature)\n", + " \n", + " def __call__(self, **kwargs):\n", + " return self.predictor(**kwargs)\n", + "\n", + "dog_picture = DogPicture()\n", + "\n", + "example = dspy.Example(image=dspy.Image.from_url(\"https://i.pinimg.com/564x/78/f9/6d/78f96d0314d39a1b8a849005123e166d.jpg\"))\n", + "print(dog_picture(**example.inputs()))\n", + "# print(dog_picture(**dog_dataset[0].inputs()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Test inline signature\n", + "# TODO: Test json adapter" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/tests/multihop_llama213b_0.json b/tests/multihop_llama213b_0.json index f65a695bec..44b008825b 100644 --- a/tests/multihop_llama213b_0.json +++ b/tests/multihop_llama213b_0.json @@ -1,347 +1,347 @@ { - "retrieve": { - "k": 3 - }, - "generate_query[0]": { + "generate_answer": { + "demos": [ + { + "answer": "2005", + "augmented": true, + "context": [ + "Twilight (novel series) | Twilight is a series of four vampire-themed fantasy romance novels by American author Stephenie Meyer. Released annually from 2005 through 2008, the four books chart the later teen years of Isabella \"Bella\" Swan, a girl who moves to Forks, Washington, and falls in love with a 104-year-old vampire named Edward Cullen. The series is told primarily from Bella's point of view, with the epilogue of \"Eclipse\" and Part II of \"Breaking Dawn\" being told from the viewpoint of character Jacob Black, a werewolf. The unpublished \"Midnight Sun\" is a retelling of the first book, \"Twilight\", from Edward Cullen's point of view. The novella \"The Short Second Life of Bree Tanner\", which tells the story of a newborn vampire who appeared in \"Eclipse\", was published on June 5, 2010, as a hardcover book and on June 7 as a free online ebook. \"\" , a definitive encyclopedic reference with nearly 100 full color illustrations, was released in bookstores on April 12, 2011.", + "Harper Connelly Mysteries | The Harper Connelly Mysteries is a series of fantasy mystery novels written by Charlaine Harris, and first published in 2005. Harris is known best for penning The Southern Vampire Mysteries (also referred to as the True Blood Series), a series rich in supernatural characters such as vampires, telepaths, werewolves, shapeshifters and fairies; she has also written more traditional (non-paranormal) mysteries. The Harper Connelly Mysteries is also centered on a character with supernatural abilities, however these abilities are more subtle than in the Southern Vampire series.", + "The Dark Heroine | The Dark Heroine is a series of vampire-themed fantasy romance novels written by English author Abigail Gibbs, published by HarperCollins in 2012. The first novel in the series, \"Dinner with a Vampire,\" revolves around London-born Violet Lee, who is kidnapped and held hostage by a Royal Family of vampires known as the Varns. The series is told from both Violet Lee and Kaspar Varn's perspective, the latter being heir to the Vamperic Throne in the novel.", + "Night Huntress | Night Huntress is a series of \"New York Times\" bestselling urban fantasy romance novels by author Jeaniene Frost. The first novel was published in 2007 by Avon and takes place in a world where supernatural creatures exist but are not known to the general public at large. The series initially focused around the character of half-vampire Catherine \"Cat\" Crawfield and her full-vampire lover Bones, but eventually shifted focus to other characters such as Vlad Tepesh, a character that Frost had initially not planned to include.", + "John William Polidori | John William Polidori (7 September 1795 \u2013 24 August 1821) was an English writer and physician. He is known for his associations with the Romantic movement and credited by some as the creator of the vampire genre of fantasy fiction. His most successful work was the short story \"The Vampyre\" (1819), the first published modern vampire story. Although originally and erroneously accredited to Lord Byron, both Byron and Polidori affirmed that the story is Polidori's." + ], + "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", + "question": "In which year was the first of the vampire-themed fantasy romance novels for which The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book first published?", + "rationale": "determine the answer. We know that The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book for the Twilight series of novels, which was written by Stephenie Meyer. The first novel in the series, Twilight, was published in 2005. Therefore, the first year in which a vampire-themed fantasy romance novel was published was 2005." + }, + { + "answer": "1950", + "augmented": true, + "context": [ + "The Victorians | The Victorians - Their Story In Pictures is a 2009 British documentary series which focuses on Victorian art and culture. The four-part series is written and presented by Jeremy Paxman and debuted on BBC One at 9:00pm on Sunday 15 February 2009.", + "The Caxtons | The Caxtons: A Family Picture is an 1849 Victorian novel by Edward Bulwer-Lytton that was popular in its time.", + "Victorian (comics) | The Victorian is a 25-issue comic book series published by Penny-Farthing Press and starting in 1999. The brainchild of creator Trainor Houghton, the series included a number of notable script writers and illustrators, including Len Wein, Glen Orbik and Howard Chaykin.", + "Jeremy Paxman | Jeremy Dickson Paxman (born 11 May 1950) is an English broadcaster, journalist, and author. He is the question master of \"University Challenge\", having succeeded Bamber Gascoigne when the programme was revived in 1994.", + "Jeremy I | Jeremy I was king of the Miskito nation, who came to power following the death of his father, Oldman, in 1686 or 1687. according to an English visitor, W. M., in 1699, he was about 60 years old at that time, making his birth year about 1639.", + "Jeremy Cheeseman | Jeremy Cheeseman (born June 6, 1990 in Manorville, New York) is a former American professional soccer player. Playing two seasons for the Dayton Dutch Lions in the USL Professional Division before retiring due to injury" + ], + "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", + "rationale": "answer this question. We know that the author of The Victorians - Their Story In Pictures is Jeremy Paxman, and we can find his year of birth in one of the given contexts. After searching, we found that Jeremy Paxman was born in 1950. Therefore, the answer to the question is 1950." + }, + { + "answer": "Clydesdales", + "dspy_split": "train", + "dspy_uuid": "543e5564-9a2f-4cf2-b729-8b946fe9f57a", + "question": "Which type of horse has been used by both the British Household Cavalry and Anheuser-Busch Brewing Company for commercials?" + }, + { + "answer": "iron and steel", + "dspy_split": "train", + "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", + "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? " + }, + { + "answer": "Brussels", + "dspy_split": "train", + "dspy_uuid": "4b5f93e8-3f2a-464e-864c-76e9ca7e45a1", + "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?" + }, + { + "answer": "Bruno Senna Lalli", + "dspy_split": "train", + "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" + }, + { + "answer": "no", + "dspy_split": "train", + "dspy_uuid": "823305fd-37a1-4052-b1d7-5b7eb502d184", + "question": "Are Baltasar Korm\u00e1kur and John G. 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" + }, + { + "answer": "Bill Paxton", + "dspy_split": "train", + "dspy_uuid": "86fca9c5-3c75-4aae-bc16-cb6c62335f22", + "question": "Tombstone stared an actor born May 17, 1955 known as who?" + } + ], "lm": null, "traces": [], - "train": [], + "train": [] + }, + "generate_query[0]": { "demos": [ { "augmented": true, - "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", "context": [], + "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", "question": "In which year was the first of the vampire-themed fantasy romance novels for which The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book first published?", "rationale": "find the answer to this question. We know that the first book in The Twilight Saga was published in 2005, so we can start by searching for books published in that year. 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" }, { - "question": "Both Brian Warren and Jake Shields are considered what?", "answer": "mixed martial arts fighter", + "dspy_split": "train", "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" + "question": "Both Brian Warren and Jake Shields are considered what?" }, { - "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?", "answer": "Brussels", + "dspy_split": "train", "dspy_uuid": "4b5f93e8-3f2a-464e-864c-76e9ca7e45a1", - "dspy_split": "train" + "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?" }, { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", "answer": "Bruno Senna Lalli", + "dspy_split": "train", "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" }, { - "question": "Which is taller, the Empire State Building or the Bank of America Tower?", "answer": "The Empire State Building", + "dspy_split": "train", "dspy_uuid": "30d0143b-8f61-4591-9506-06d5144bc084", - "dspy_split": "train" + "question": "Which is taller, the Empire State Building or the Bank of America Tower?" }, { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? 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Released annually from 2005 through 2008, the four books chart the later teen years of Isabella \"Bella\" Swan, a girl who moves to Forks, Washington, and falls in love with a 104-year-old vampire named Edward Cullen. The series is told primarily from Bella's point of view, with the epilogue of \"Eclipse\" and Part II of \"Breaking Dawn\" being told from the viewpoint of character Jacob Black, a werewolf. The unpublished \"Midnight Sun\" is a retelling of the first book, \"Twilight\", from Edward Cullen's point of view. 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The series is told primarily from Bella's point of view, with the epilogue of \"Eclipse\" and Part II of \"Breaking Dawn\" being told from the viewpoint of character Jacob Black, a werewolf. The unpublished \"Midnight Sun\" is a retelling of the first book, \"Twilight\", from Edward Cullen's point of view. The novella \"The Short Second Life of Bree Tanner\", which tells the story of a newborn vampire who appeared in \"Eclipse\", was published on June 5, 2010, as a hardcover book and on June 7 as a free online ebook. \"\" , a definitive encyclopedic reference with nearly 100 full color illustrations, was released in bookstores on April 12, 2011.", - "Harper Connelly Mysteries | The Harper Connelly Mysteries is a series of fantasy mystery novels written by Charlaine Harris, and first published in 2005. Harris is known best for penning The Southern Vampire Mysteries (also referred to as the True Blood Series), a series rich in supernatural characters such as vampires, telepaths, werewolves, shapeshifters and fairies; she has also written more traditional (non-paranormal) mysteries. The Harper Connelly Mysteries is also centered on a character with supernatural abilities, however these abilities are more subtle than in the Southern Vampire series.", - "The Dark Heroine | The Dark Heroine is a series of vampire-themed fantasy romance novels written by English author Abigail Gibbs, published by HarperCollins in 2012. The first novel in the series, \"Dinner with a Vampire,\" revolves around London-born Violet Lee, who is kidnapped and held hostage by a Royal Family of vampires known as the Varns. The series is told from both Violet Lee and Kaspar Varn's perspective, the latter being heir to the Vamperic Throne in the novel.", - "Night Huntress | Night Huntress is a series of \"New York Times\" bestselling urban fantasy romance novels by author Jeaniene Frost. The first novel was published in 2007 by Avon and takes place in a world where supernatural creatures exist but are not known to the general public at large. The series initially focused around the character of half-vampire Catherine \"Cat\" Crawfield and her full-vampire lover Bones, but eventually shifted focus to other characters such as Vlad Tepesh, a character that Frost had initially not planned to include.", - "John William Polidori | John William Polidori (7 September 1795 \u2013 24 August 1821) was an English writer and physician. He is known for his associations with the Romantic movement and credited by some as the creator of the vampire genre of fantasy fiction. His most successful work was the short story \"The Vampyre\" (1819), the first published modern vampire story. Although originally and erroneously accredited to Lord Byron, both Byron and Polidori affirmed that the story is Polidori's." - ], - "question": "In which year was the first of the vampire-themed fantasy romance novels for which The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book first published?", - "rationale": "determine the answer. We know that The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book for the Twilight series of novels, which was written by Stephenie Meyer. The first novel in the series, Twilight, was published in 2005. Therefore, the first year in which a vampire-themed fantasy romance novel was published was 2005.", - "answer": "2005" - }, - { - "augmented": true, - "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "context": [ - "The Victorians | The Victorians - Their Story In Pictures is a 2009 British documentary series which focuses on Victorian art and culture. The four-part series is written and presented by Jeremy Paxman and debuted on BBC One at 9:00pm on Sunday 15 February 2009.", - "The Caxtons | The Caxtons: A Family Picture is an 1849 Victorian novel by Edward Bulwer-Lytton that was popular in its time.", - "Victorian (comics) | The Victorian is a 25-issue comic book series published by Penny-Farthing Press and starting in 1999. The brainchild of creator Trainor Houghton, the series included a number of notable script writers and illustrators, including Len Wein, Glen Orbik and Howard Chaykin.", - "Jeremy Paxman | Jeremy Dickson Paxman (born 11 May 1950) is an English broadcaster, journalist, and author. He is the question master of \"University Challenge\", having succeeded Bamber Gascoigne when the programme was revived in 1994.", - "Jeremy I | Jeremy I was king of the Miskito nation, who came to power following the death of his father, Oldman, in 1686 or 1687. according to an English visitor, W. M., in 1699, he was about 60 years old at that time, making his birth year about 1639.", - "Jeremy Cheeseman | Jeremy Cheeseman (born June 6, 1990 in Manorville, New York) is a former American professional soccer player. Playing two seasons for the Dayton Dutch Lions in the USL Professional Division before retiring due to injury" - ], - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", - "rationale": "answer this question. We know that the author of The Victorians - Their Story In Pictures is Jeremy Paxman, and we can find his year of birth in one of the given contexts. After searching, we found that Jeremy Paxman was born in 1950. Therefore, the answer to the question is 1950.", - "answer": "1950" - }, - { - "question": "Which type of horse has been used by both the British Household Cavalry and Anheuser-Busch Brewing Company for commercials?", - "answer": "Clydesdales", - "dspy_uuid": "543e5564-9a2f-4cf2-b729-8b946fe9f57a", - "dspy_split": "train" - }, - { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? ", - "answer": "iron and steel", - "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", - "dspy_split": "train" - }, - { - "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?", - "answer": "Brussels", - "dspy_uuid": "4b5f93e8-3f2a-464e-864c-76e9ca7e45a1", - "dspy_split": "train" - }, - { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", - "answer": "Bruno Senna Lalli", - "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" - }, - { - "question": "Are Baltasar Korm\u00e1kur and John G. Avildsen both film producers?", - "answer": "no", - "dspy_uuid": "823305fd-37a1-4052-b1d7-5b7eb502d184", - "dspy_split": "train" - }, - { - "question": "What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?", - "answer": "Operation Citadel", - "dspy_uuid": "d7fb5c24-6ca2-4bf8-b9f1-0a6e0060ebea", - "dspy_split": "train" - }, - { - "question": "Which is taller, the Empire State Building or the Bank of America Tower?", - "answer": "The Empire State Building", - "dspy_uuid": "30d0143b-8f61-4591-9506-06d5144bc084", - "dspy_split": "train" - }, - { - "question": "Who was the Tennis Masters Cup champion in 2000, Gustavo Kuerten or Stan Wawrinka?", - "answer": "Gustavo Kuerten", - "dspy_uuid": "a8e15619-7c04-467a-8580-4dcf10f48bed", - "dspy_split": "train" - }, - { - "question": "What is the nickname for this United States drag racer who drove Brutus?", - "answer": "Jungle Jim", - "dspy_uuid": "e2421205-d692-42e8-aa14-7a477e516ae8", - "dspy_split": "train" - }, - { - "question": "What type of film was it that featured the actress who is regarded as the greatest actress of all time?", - "answer": "anti-McCarthyism", - "dspy_uuid": "96c92062-7e1c-4712-b3c8-30f35f8b6c65", - "dspy_split": "train" - }, - { - "question": "Both Brian Warren and Jake Shields are considered what?", - "answer": "mixed martial arts fighter", - "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" - }, - { - "question": "Where was the singer who released the single \"I Never Made Love (Till I Made It with You)\" originally from ?", - "answer": "Lubbock, Texas", - "dspy_uuid": "1729ebde-5804-4157-b43b-6422cce75d4f", - "dspy_split": "train" - }, - { - "question": "Who was coach of the No. 9-ranked team that was upset in the NCAA Tournament by the 2014-15 UAB Blazers men's basketball team? ", - "answer": "Fred Hoiberg", - "dspy_uuid": "089c6c36-6317-4dd1-8de5-5cd72185f0ab", - "dspy_split": "train" - }, - { - "question": "Tombstone stared an actor born May 17, 1955 known as who?", - "answer": "Bill Paxton", - "dspy_uuid": "86fca9c5-3c75-4aae-bc16-cb6c62335f22", - "dspy_split": "train" - } - ] + "train": [] + }, + "retrieve": { + "k": 3 } } diff --git a/tests/multihop_llama213b_1.json b/tests/multihop_llama213b_1.json index 0cc2adbe7d..8f906ddc58 100644 --- a/tests/multihop_llama213b_1.json +++ b/tests/multihop_llama213b_1.json @@ -1,345 +1,345 @@ { - "retrieve": { - "k": 3 - }, - "generate_query[0]": { + "generate_answer": { + "demos": [ + { + "answer": "Self", + "augmented": true, + "context": [ + "Self (disambiguation) | The self is an individual person as the object of his or her own reflective consciousness.", + "Self | Self is a reference by an individual to the same individual person. This reference is necessarily subjective, thus self is a reference by a subject to the same subject. The sense of having a self \u2013 or self-hood \u2013 should, however, not be confused with subjectivity itself. Ostensibly, there is a directedness outward from the subject that refers inward, back to its \"self\" (or itself). Examples of psychiatric conditions where such 'sameness' is broken include depersonalization, which sometimes occur in schizophrenia: the self appears different to the subject.", + "Self (programming language) | Self is an object-oriented programming language based on the concept of \"prototypes\". Self was a dialect of Smalltalk, being dynamically typed and using just-in-time compilation (JIT) as well as the prototype-based approach to objects: it was first used as an experimental test system for language design in the 1980s and 1990s. In 2006, Self was still being developed as part of the Klein project, which was a Self virtual machine written fully in Self. The latest version is 2017.1 released in May 2017." + ], + "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", + "question": "Which of these publications was most recently published, Who Put the Bomp or Self?", + "rationale": "determine which of these publications was most recently published. We know that Self is a programming language based on the concept of \"prototypes\" and was first used as an experimental test system in the 1980s and 1990s. Therefore, it is likely that Self was published before Who Put the Bomp, which is a book about the history of rock and roll. Additionally, the latest version of Self was released in 2017, while Who Put the Bomp was published in 2001. Therefore, the answer is Self." + }, + { + "answer": "1950", + "augmented": true, + "context": [ + "The Victorians | The Victorians - Their Story In Pictures is a 2009 British documentary series which focuses on Victorian art and culture. The four-part series is written and presented by Jeremy Paxman and debuted on BBC One at 9:00pm on Sunday 15 February 2009.", + "The Caxtons | The Caxtons: A Family Picture is an 1849 Victorian novel by Edward Bulwer-Lytton that was popular in its time.", + "Victorian (comics) | The Victorian is a 25-issue comic book series published by Penny-Farthing Press and starting in 1999. The brainchild of creator Trainor Houghton, the series included a number of notable script writers and illustrators, including Len Wein, Glen Orbik and Howard Chaykin.", + "Jeremy Paxman | Jeremy Dickson Paxman (born 11 May 1950) is an English broadcaster, journalist, and author. He is the question master of \"University Challenge\", having succeeded Bamber Gascoigne when the programme was revived in 1994.", + "Jeremy I | Jeremy I was king of the Miskito nation, who came to power following the death of his father, Oldman, in 1686 or 1687. according to an English visitor, W. M., in 1699, he was about 60 years old at that time, making his birth year about 1639.", + "Jeremy Cheeseman | Jeremy Cheeseman (born June 6, 1990 in Manorville, New York) is a former American professional soccer player. Playing two seasons for the Dayton Dutch Lions in the USL Professional Division before retiring due to injury" + ], + "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", + "rationale": "answer this question. We know that the author of The Victorians - Their Story In Pictures is Jeremy Paxman, and we can find his year of birth in one of the given contexts. After searching, we found that Jeremy Paxman was born in 1950. Therefore, the answer to the question is 1950." + }, + { + "answer": "mixed martial arts fighter", + "dspy_split": "train", + "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", + "question": "Both Brian Warren and Jake Shields are considered what?" + }, + { + "answer": "iron and steel", + "dspy_split": "train", + "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", + "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? " + }, + { + "answer": "Bill Paxton", + "dspy_split": "train", + "dspy_uuid": "86fca9c5-3c75-4aae-bc16-cb6c62335f22", + "question": "Tombstone stared an actor born May 17, 1955 known as who?" + }, + { + "answer": "Davy Crockett, King of the Wild Frontier", + "dspy_split": "train", + "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", + "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?" + }, + { + "answer": "Buena Vista Distribution", + "dspy_split": "train", + "dspy_uuid": "f1bad71d-550f-4716-b29d-311fdb0fcfbd", + "question": "Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?" + }, + { + "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", + "dspy_split": "train", + "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", + "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?" + }, + { + "answer": "space", + "dspy_split": "train", + "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", + "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? " + }, + { + "answer": "Rosario Dawson", + "dspy_split": "train", + "dspy_uuid": "a3d5fe2b-f4ad-44f6-8a25-a2e25e40db9b", + "question": "Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?" + }, + { + "answer": "Operation Citadel", + "dspy_split": "train", + "dspy_uuid": "d7fb5c24-6ca2-4bf8-b9f1-0a6e0060ebea", + "question": "What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?" + }, + { + "answer": "7", + "dspy_split": "train", + "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", + "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?" + }, + { + "answer": "Brussels", + "dspy_split": "train", + "dspy_uuid": "4b5f93e8-3f2a-464e-864c-76e9ca7e45a1", + "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?" + }, + { + "answer": "the Shadows", + "dspy_split": "train", + "dspy_uuid": "8710b3a8-da56-453d-bd91-431f264cb704", + "question": "\"The Day I Met Marie\" is a song written by an English songwriter best known as the lead guitarist for what band?" + }, + { + "answer": "President of the United States", + "dspy_split": "train", + "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", + "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?" + }, + { + "answer": "Bruno Senna Lalli", + "dspy_split": "train", + "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" + } + ], "lm": null, "traces": [], - "train": [], + "train": [] + }, + "generate_query[0]": { "demos": [ { "augmented": true, - "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", "context": [], + "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", "question": "Which of these publications was most recently published, Who Put the Bomp or Self?", "rationale": "determine the most recent publication. We know that Who Put the Bomp was published in 1961 and Self was published in 1970. Therefore, the most recent publication is Self.", "search_query": "Self" }, { "augmented": true, - "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", "context": [], + "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", "rationale": "find the correct search query. We know that the author was born in the 19th century, but we don't know the exact year. We can start by searching for the author's name and the title of the documentary series.", "search_query": "\"The Victorians - Their Story In Pictures\" author:${author_name}" }, { - "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?", "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", + "dspy_split": "train", "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", - "dspy_split": "train" + "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?" }, { - "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?", "answer": "7", + "dspy_split": "train", "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", - "dspy_split": "train" + "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?" }, { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", "answer": "Bruno Senna Lalli", + "dspy_split": "train", "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" }, { - "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?", "answer": "President of the United States", + "dspy_split": "train", "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", - "dspy_split": "train" + "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?" }, { - "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?", "answer": "Brussels", + "dspy_split": "train", "dspy_uuid": "4b5f93e8-3f2a-464e-864c-76e9ca7e45a1", - "dspy_split": "train" + "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?" }, { - "question": "Tombstone stared an actor born May 17, 1955 known as who?", "answer": "Bill Paxton", + "dspy_split": "train", "dspy_uuid": "86fca9c5-3c75-4aae-bc16-cb6c62335f22", - "dspy_split": "train" + "question": "Tombstone stared an actor born May 17, 1955 known as who?" }, { - "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?", "answer": "Davy Crockett, King of the Wild Frontier", + "dspy_split": "train", "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", - "dspy_split": "train" + "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?" }, { - "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? ", "answer": "space", + "dspy_split": "train", "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", - "dspy_split": "train" + "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? " }, { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? ", "answer": "iron and steel", + "dspy_split": "train", "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", - "dspy_split": "train" + "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? " }, { - "question": "Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?", "answer": "Buena Vista Distribution", + "dspy_split": "train", "dspy_uuid": "f1bad71d-550f-4716-b29d-311fdb0fcfbd", - "dspy_split": "train" + "question": "Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?" }, { - "question": "\"The Day I Met Marie\" is a song written by an English songwriter best known as the lead guitarist for what band?", "answer": "the Shadows", + "dspy_split": "train", "dspy_uuid": "8710b3a8-da56-453d-bd91-431f264cb704", - "dspy_split": "train" + "question": "\"The Day I Met Marie\" is a song written by an English songwriter best known as the lead guitarist for what band?" }, { - "question": "Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?", "answer": "Rosario Dawson", + "dspy_split": "train", "dspy_uuid": "a3d5fe2b-f4ad-44f6-8a25-a2e25e40db9b", - "dspy_split": "train" + "question": "Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?" }, { - "question": "Both Brian Warren and Jake Shields are considered what?", "answer": "mixed martial arts fighter", + "dspy_split": "train", "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" + "question": "Both Brian Warren and Jake Shields are considered what?" }, { - "question": "What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?", "answer": "Operation Citadel", + "dspy_split": "train", "dspy_uuid": "d7fb5c24-6ca2-4bf8-b9f1-0a6e0060ebea", - "dspy_split": "train" + "question": "What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?" } - ] - }, - "generate_query[1]": { + ], "lm": null, "traces": [], - "train": [], + "train": [] + }, + "generate_query[1]": { "demos": [ { "augmented": true, - "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", "context": [ "Self (disambiguation) | The self is an individual person as the object of his or her own reflective consciousness.", "Self | Self is a reference by an individual to the same individual person. This reference is necessarily subjective, thus self is a reference by a subject to the same subject. The sense of having a self \u2013 or self-hood \u2013 should, however, not be confused with subjectivity itself. Ostensibly, there is a directedness outward from the subject that refers inward, back to its \"self\" (or itself). Examples of psychiatric conditions where such 'sameness' is broken include depersonalization, which sometimes occur in schizophrenia: the self appears different to the subject.", "Self (programming language) | Self is an object-oriented programming language based on the concept of \"prototypes\". Self was a dialect of Smalltalk, being dynamically typed and using just-in-time compilation (JIT) as well as the prototype-based approach to objects: it was first used as an experimental test system for language design in the 1980s and 1990s. In 2006, Self was still being developed as part of the Klein project, which was a Self virtual machine written fully in Self. The latest version is 2017.1 released in May 2017." ], + "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", "question": "Which of these publications was most recently published, Who Put the Bomp or Self?", "rationale": "produce the search query. We know that the most recently published publication is the one that should be searched. We can see that the publication \"Who Put the Bomp\" is not listed as a recent publication, so we can eliminate it from our search. Therefore, the search query should be \"Self\".", "search_query": "Self" }, { "augmented": true, - "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", "context": [ "The Victorians | The Victorians - Their Story In Pictures is a 2009 British documentary series which focuses on Victorian art and culture. The four-part series is written and presented by Jeremy Paxman and debuted on BBC One at 9:00pm on Sunday 15 February 2009.", "The Caxtons | The Caxtons: A Family Picture is an 1849 Victorian novel by Edward Bulwer-Lytton that was popular in its time.", "Victorian (comics) | The Victorian is a 25-issue comic book series published by Penny-Farthing Press and starting in 1999. The brainchild of creator Trainor Houghton, the series included a number of notable script writers and illustrators, including Len Wein, Glen Orbik and Howard Chaykin." ], + "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", "rationale": "produce the search query. We know that the documentary series is about Victorian art and culture, and it was written and presented by Jeremy Paxman. Therefore, we need to find the year in which Jeremy Paxman was born.", "search_query": "Jeremy Paxman birth year" }, { - "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?", "answer": "Brussels", + "dspy_split": "train", "dspy_uuid": "4b5f93e8-3f2a-464e-864c-76e9ca7e45a1", - "dspy_split": "train" + "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?" }, { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", "answer": "Bruno Senna Lalli", + "dspy_split": "train", "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" }, { - "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?", "answer": "7", + "dspy_split": "train", "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", - "dspy_split": "train" + "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?" }, { - "question": "Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?", "answer": "Buena Vista Distribution", + "dspy_split": "train", "dspy_uuid": "f1bad71d-550f-4716-b29d-311fdb0fcfbd", - "dspy_split": "train" + "question": "Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?" }, { - "question": "What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?", "answer": "Operation Citadel", + "dspy_split": "train", "dspy_uuid": "d7fb5c24-6ca2-4bf8-b9f1-0a6e0060ebea", - "dspy_split": "train" + "question": "What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?" }, { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? ", "answer": "iron and steel", + "dspy_split": "train", "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", - "dspy_split": "train" + "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? " }, { - "question": "Both Brian Warren and Jake Shields are considered what?", "answer": "mixed martial arts fighter", + "dspy_split": "train", "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" + "question": "Both Brian Warren and Jake Shields are considered what?" }, { - "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? ", "answer": "space", + "dspy_split": "train", "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", - "dspy_split": "train" + "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? " }, { - "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?", "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", + "dspy_split": "train", "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", - "dspy_split": "train" + "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?" }, { - "question": "Tombstone stared an actor born May 17, 1955 known as who?", "answer": "Bill Paxton", + "dspy_split": "train", "dspy_uuid": "86fca9c5-3c75-4aae-bc16-cb6c62335f22", - "dspy_split": "train" + "question": "Tombstone stared an actor born May 17, 1955 known as who?" }, { - "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?", "answer": "Davy Crockett, King of the Wild Frontier", + "dspy_split": "train", "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", - "dspy_split": "train" + "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?" }, { - "question": "Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?", "answer": "Rosario Dawson", + "dspy_split": "train", "dspy_uuid": "a3d5fe2b-f4ad-44f6-8a25-a2e25e40db9b", - "dspy_split": "train" + "question": "Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?" }, { - "question": "\"The Day I Met Marie\" is a song written by an English songwriter best known as the lead guitarist for what band?", "answer": "the Shadows", + "dspy_split": "train", "dspy_uuid": "8710b3a8-da56-453d-bd91-431f264cb704", - "dspy_split": "train" + "question": "\"The Day I Met Marie\" is a song written by an English songwriter best known as the lead guitarist for what band?" }, { - "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?", "answer": "President of the United States", + "dspy_split": "train", "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", - "dspy_split": "train" + "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?" } - ] - }, - "generate_answer": { + ], "lm": null, "traces": [], - "train": [], - "demos": [ - { - "augmented": true, - "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", - "context": [ - "Self (disambiguation) | The self is an individual person as the object of his or her own reflective consciousness.", - "Self | Self is a reference by an individual to the same individual person. This reference is necessarily subjective, thus self is a reference by a subject to the same subject. The sense of having a self \u2013 or self-hood \u2013 should, however, not be confused with subjectivity itself. Ostensibly, there is a directedness outward from the subject that refers inward, back to its \"self\" (or itself). Examples of psychiatric conditions where such 'sameness' is broken include depersonalization, which sometimes occur in schizophrenia: the self appears different to the subject.", - "Self (programming language) | Self is an object-oriented programming language based on the concept of \"prototypes\". Self was a dialect of Smalltalk, being dynamically typed and using just-in-time compilation (JIT) as well as the prototype-based approach to objects: it was first used as an experimental test system for language design in the 1980s and 1990s. In 2006, Self was still being developed as part of the Klein project, which was a Self virtual machine written fully in Self. The latest version is 2017.1 released in May 2017." - ], - "question": "Which of these publications was most recently published, Who Put the Bomp or Self?", - "rationale": "determine which of these publications was most recently published. We know that Self is a programming language based on the concept of \"prototypes\" and was first used as an experimental test system in the 1980s and 1990s. Therefore, it is likely that Self was published before Who Put the Bomp, which is a book about the history of rock and roll. Additionally, the latest version of Self was released in 2017, while Who Put the Bomp was published in 2001. Therefore, the answer is Self.", - "answer": "Self" - }, - { - "augmented": true, - "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "context": [ - "The Victorians | The Victorians - Their Story In Pictures is a 2009 British documentary series which focuses on Victorian art and culture. The four-part series is written and presented by Jeremy Paxman and debuted on BBC One at 9:00pm on Sunday 15 February 2009.", - "The Caxtons | The Caxtons: A Family Picture is an 1849 Victorian novel by Edward Bulwer-Lytton that was popular in its time.", - "Victorian (comics) | The Victorian is a 25-issue comic book series published by Penny-Farthing Press and starting in 1999. The brainchild of creator Trainor Houghton, the series included a number of notable script writers and illustrators, including Len Wein, Glen Orbik and Howard Chaykin.", - "Jeremy Paxman | Jeremy Dickson Paxman (born 11 May 1950) is an English broadcaster, journalist, and author. He is the question master of \"University Challenge\", having succeeded Bamber Gascoigne when the programme was revived in 1994.", - "Jeremy I | Jeremy I was king of the Miskito nation, who came to power following the death of his father, Oldman, in 1686 or 1687. according to an English visitor, W. M., in 1699, he was about 60 years old at that time, making his birth year about 1639.", - "Jeremy Cheeseman | Jeremy Cheeseman (born June 6, 1990 in Manorville, New York) is a former American professional soccer player. Playing two seasons for the Dayton Dutch Lions in the USL Professional Division before retiring due to injury" - ], - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", - "rationale": "answer this question. We know that the author of The Victorians - Their Story In Pictures is Jeremy Paxman, and we can find his year of birth in one of the given contexts. After searching, we found that Jeremy Paxman was born in 1950. Therefore, the answer to the question is 1950.", - "answer": "1950" - }, - { - "question": "Both Brian Warren and Jake Shields are considered what?", - "answer": "mixed martial arts fighter", - "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" - }, - { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? ", - "answer": "iron and steel", - "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", - "dspy_split": "train" - }, - { - "question": "Tombstone stared an actor born May 17, 1955 known as who?", - "answer": "Bill Paxton", - "dspy_uuid": "86fca9c5-3c75-4aae-bc16-cb6c62335f22", - "dspy_split": "train" - }, - { - "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?", - "answer": "Davy Crockett, King of the Wild Frontier", - "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", - "dspy_split": "train" - }, - { - "question": "Which company distributed this 1977 American animated film produced by Walt Disney Productions for which Sherman Brothers wrote songs?", - "answer": "Buena Vista Distribution", - "dspy_uuid": "f1bad71d-550f-4716-b29d-311fdb0fcfbd", - "dspy_split": "train" - }, - { - "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?", - "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", - "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", - "dspy_split": "train" - }, - { - "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? ", - "answer": "space", - "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", - "dspy_split": "train" - }, - { - "question": "Which American actress who made their film debut in the 1995 teen drama \"Kids\" was the co-founder of Voto Latino?", - "answer": "Rosario Dawson", - "dspy_uuid": "a3d5fe2b-f4ad-44f6-8a25-a2e25e40db9b", - "dspy_split": "train" - }, - { - "question": "What is the code name for the German offensive that started this Second World War engagement on the Eastern Front (a few hundred kilometers from Moscow) between Soviet and German forces, which included 102nd Infantry Division?", - "answer": "Operation Citadel", - "dspy_uuid": "d7fb5c24-6ca2-4bf8-b9f1-0a6e0060ebea", - "dspy_split": "train" - }, - { - "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?", - "answer": "7", - "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", - "dspy_split": "train" - }, - { - "question": "What part of the world do the Viezenbeek and Lindemans Brewery hale from?", - "answer": "Brussels", - "dspy_uuid": "4b5f93e8-3f2a-464e-864c-76e9ca7e45a1", - "dspy_split": "train" - }, - { - "question": "\"The Day I Met Marie\" is a song written by an English songwriter best known as the lead guitarist for what band?", - "answer": "the Shadows", - "dspy_uuid": "8710b3a8-da56-453d-bd91-431f264cb704", - "dspy_split": "train" - }, - { - "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?", - "answer": "President of the United States", - "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", - "dspy_split": "train" - }, - { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", - "answer": "Bruno Senna Lalli", - "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" - } - ] + "train": [] + }, + "retrieve": { + "k": 3 } } diff --git a/tests/multihop_llama213b_2.json b/tests/multihop_llama213b_2.json index 22e059278c..6150747722 100644 --- a/tests/multihop_llama213b_2.json +++ b/tests/multihop_llama213b_2.json @@ -1,346 +1,346 @@ { - "retrieve": { - "k": 3 - }, - "generate_query[0]": { + "generate_answer": { + "demos": [ + { + "answer": "Gustavo Kuerten", + "augmented": true, + "context": [ + "Gustavo Kuerten | Gustavo Kuerten (] ; born 10 September 1976), nicknamed Guga, is a retired former World No. 1 tennis player from Brazil. He won the French Open singles title three times (1997, 2000, and 2001), and was the Tennis Masters Cup champion in 2000. Kuerten suffered many problems with injuries which led him to miss a number of tournaments between 2002 and 2005. After a few failed attempted comebacks, he retired from top-level tennis in May 2008. During his career he won 20 singles and 8 doubles titles.", + "Gustavo Kuerten career statistics | This is a list of the main career statistics of Brazilian tennis player, Gustavo Kuerten. Kuerten won a total of 28 ATP titles \u2014 20 in singles and 8 in doubles. He won 3 Grand Slam titles, 5 ATP Masters Series tournaments and a Tennis Masters Cup.", + "2004 Heineken Open \u2013 Singles | Gustavo Kuerten was the defending champion of the singles event at the Heineken Open tennis tournament, held in Auckland, New Zealand, but lost in the semifinals to Dominik Hrbat\u00fd.", + "2000 Tennis Masters Cup \u2013 Singles | Pete Sampras was the defending champion, but lost in the semifinals to Gustavo Kuerten.", + "2000 Monte Carlo Masters \u2013 Singles | Gustavo Kuerten was the defending champion, but lost in the first round this year." + ], + "dspy_uuid": "a8e15619-7c04-467a-8580-4dcf10f48bed", + "question": "Who was the Tennis Masters Cup champion in 2000, Gustavo Kuerten or Stan Wawrinka?", + "rationale": "answer this question. We know that Gustavo Kuerten was the Tennis Masters Cup champion in 2000, as stated in [2]. We also know that Stan Wawrinka did not win the Tennis Masters Cup in 2000, as he was not born yet (he was born in 1985). Therefore, the answer is Gustavo Kuerten." + }, + { + "answer": "2005", + "augmented": true, + "context": [ + "Twilight (novel series) | Twilight is a series of four vampire-themed fantasy romance novels by American author Stephenie Meyer. Released annually from 2005 through 2008, the four books chart the later teen years of Isabella \"Bella\" Swan, a girl who moves to Forks, Washington, and falls in love with a 104-year-old vampire named Edward Cullen. The series is told primarily from Bella's point of view, with the epilogue of \"Eclipse\" and Part II of \"Breaking Dawn\" being told from the viewpoint of character Jacob Black, a werewolf. The unpublished \"Midnight Sun\" is a retelling of the first book, \"Twilight\", from Edward Cullen's point of view. The novella \"The Short Second Life of Bree Tanner\", which tells the story of a newborn vampire who appeared in \"Eclipse\", was published on June 5, 2010, as a hardcover book and on June 7 as a free online ebook. \"\" , a definitive encyclopedic reference with nearly 100 full color illustrations, was released in bookstores on April 12, 2011.", + "Harper Connelly Mysteries | The Harper Connelly Mysteries is a series of fantasy mystery novels written by Charlaine Harris, and first published in 2005. Harris is known best for penning The Southern Vampire Mysteries (also referred to as the True Blood Series), a series rich in supernatural characters such as vampires, telepaths, werewolves, shapeshifters and fairies; she has also written more traditional (non-paranormal) mysteries. The Harper Connelly Mysteries is also centered on a character with supernatural abilities, however these abilities are more subtle than in the Southern Vampire series.", + "The Dark Heroine | The Dark Heroine is a series of vampire-themed fantasy romance novels written by English author Abigail Gibbs, published by HarperCollins in 2012. The first novel in the series, \"Dinner with a Vampire,\" revolves around London-born Violet Lee, who is kidnapped and held hostage by a Royal Family of vampires known as the Varns. The series is told from both Violet Lee and Kaspar Varn's perspective, the latter being heir to the Vamperic Throne in the novel.", + "Night Huntress | Night Huntress is a series of \"New York Times\" bestselling urban fantasy romance novels by author Jeaniene Frost. The first novel was published in 2007 by Avon and takes place in a world where supernatural creatures exist but are not known to the general public at large. The series initially focused around the character of half-vampire Catherine \"Cat\" Crawfield and her full-vampire lover Bones, but eventually shifted focus to other characters such as Vlad Tepesh, a character that Frost had initially not planned to include.", + "John William Polidori | John William Polidori (7 September 1795 \u2013 24 August 1821) was an English writer and physician. He is known for his associations with the Romantic movement and credited by some as the creator of the vampire genre of fantasy fiction. His most successful work was the short story \"The Vampyre\" (1819), the first published modern vampire story. Although originally and erroneously accredited to Lord Byron, both Byron and Polidori affirmed that the story is Polidori's." + ], + "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", + "question": "In which year was the first of the vampire-themed fantasy romance novels for which The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book first published?", + "rationale": "determine the correct answer. We know that The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book for the Twilight series, which was first published in 2005. However, the other options are also vampire-themed fantasy romance novels, and were first published in 2005, 2007, 2010, and 2012 respectively. Therefore, we need to examine each option carefully to determine which one is the correct answer." + }, + { + "answer": "Self", + "dspy_split": "train", + "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", + "question": "Which of these publications was most recently published, Who Put the Bomp or Self?" + }, + { + "answer": "John Townes Van Zandt", + "dspy_split": "train", + "dspy_uuid": "da05e5f3-7e82-4abe-a855-e26a85c958d6", + "question": "At My Window was released by which American singer-songwriter?" + }, + { + "answer": "The Waltz King", + "dspy_split": "train", + "dspy_uuid": "03b3104e-c9f4-4c17-83c7-1354563946df", + "question": "This American guitarist best known for her work with the Iron Maidens is an ancestor of a composer who was known as what?" + }, + { + "answer": "1979", + "dspy_split": "train", + "dspy_uuid": "7b759a0a-8c96-4735-8f6e-1e8b1eae4861", + "question": "Remember Me Ballin' is a CD single by Indo G that features an American rapper born in what year?" + }, + { + "answer": "Jungle Jim", + "dspy_split": "train", + "dspy_uuid": "e2421205-d692-42e8-aa14-7a477e516ae8", + "question": "What is the nickname for this United States drag racer who drove Brutus?" + }, + { + "answer": "no", + "dspy_split": "train", + "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", + "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?" + }, + { + "answer": "2010", + "dspy_split": "train", + "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", + "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?" + }, + { + "answer": "Nick at Nite", + "dspy_split": "train", + "dspy_uuid": "fc3163d5-be7d-4f18-bd21-138373f638b4", + "question": "What evening cable television station programming block has a show with Ashley Holliday as a cast member?" + }, + { + "answer": "Davy Crockett, King of the Wild Frontier", + "dspy_split": "train", + "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", + "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?" + }, + { + "answer": "River Thames", + "dspy_split": "train", + "dspy_uuid": "937a5676-7522-4e67-8e1e-d0b5651fad73", + "question": "Some performance of Take a Bow took place near the bank of which river?" + }, + { + "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", + "dspy_split": "train", + "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", + "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?" + }, + { + "answer": "Moravian and other Slavic folk music", + "dspy_split": "train", + "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", + "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?" + }, + { + "answer": "1950", + "dspy_split": "train", + "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?" + }, + { + "answer": "Bruno Senna Lalli", + "dspy_split": "train", + "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" + } + ], "lm": null, "traces": [], - "train": [], + "train": [] + }, + "generate_query[0]": { "demos": [ { "augmented": true, - "dspy_uuid": "a8e15619-7c04-467a-8580-4dcf10f48bed", "context": [], + "dspy_uuid": "a8e15619-7c04-467a-8580-4dcf10f48bed", "question": "Who was the Tennis Masters Cup champion in 2000, Gustavo Kuerten or Stan Wawrinka?", "rationale": "find the answer. We know that Gustavo Kuerten won the tournament in 2000, so we can start by searching for his name.", "search_query": "Gustavo Kuerten" }, { "augmented": true, - "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", "context": [], + "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", "question": "In which year was the first of the vampire-themed fantasy romance novels for which The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book first published?", "rationale": "find the answer to this question. We know that the first book in The Twilight Saga was published in 2005, so we can start by searching for books published in that year. We can also use keywords like \"vampire-themed\" and \"fantasy romance\" to narrow down our search results.", "search_query": "\"vampire-themed fantasy romance novels 2005\"" }, { - "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?", "answer": "no", + "dspy_split": "train", "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", - "dspy_split": "train" + "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?" }, { - "question": "Some performance of Take a Bow took place near the bank of which river?", "answer": "River Thames", + "dspy_split": "train", "dspy_uuid": "937a5676-7522-4e67-8e1e-d0b5651fad73", - "dspy_split": "train" + "question": "Some performance of Take a Bow took place near the bank of which river?" }, { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", "answer": "Bruno Senna Lalli", + "dspy_split": "train", "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" }, { - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", "answer": "1950", + "dspy_split": "train", "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "dspy_split": "train" + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?" }, { - "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?", "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", + "dspy_split": "train", "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", - "dspy_split": "train" + "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?" }, { - "question": "This American guitarist best known for her work with the Iron Maidens is an ancestor of a composer who was known as what?", "answer": "The Waltz King", + "dspy_split": "train", "dspy_uuid": "03b3104e-c9f4-4c17-83c7-1354563946df", - "dspy_split": "train" + "question": "This American guitarist best known for her work with the Iron Maidens is an ancestor of a composer who was known as what?" }, { - "question": "Remember Me Ballin' is a CD single by Indo G that features an American rapper born in what year?", "answer": "1979", + "dspy_split": "train", "dspy_uuid": "7b759a0a-8c96-4735-8f6e-1e8b1eae4861", - "dspy_split": "train" + "question": "Remember Me Ballin' is a CD single by Indo G that features an American rapper born in what year?" }, { - "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?", "answer": "2010", + "dspy_split": "train", "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", - "dspy_split": "train" + "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?" }, { - "question": "At My Window was released by which American singer-songwriter?", "answer": "John Townes Van Zandt", + "dspy_split": "train", "dspy_uuid": "da05e5f3-7e82-4abe-a855-e26a85c958d6", - "dspy_split": "train" + "question": "At My Window was released by which American singer-songwriter?" }, { - "question": "What is the nickname for this United States drag racer who drove Brutus?", "answer": "Jungle Jim", + "dspy_split": "train", "dspy_uuid": "e2421205-d692-42e8-aa14-7a477e516ae8", - "dspy_split": "train" + "question": "What is the nickname for this United States drag racer who drove Brutus?" }, { - "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?", "answer": "Moravian and other Slavic folk music", + "dspy_split": "train", "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", - "dspy_split": "train" + "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?" }, { - "question": "What evening cable television station programming block has a show with Ashley Holliday as a cast member?", "answer": "Nick at Nite", + "dspy_split": "train", "dspy_uuid": "fc3163d5-be7d-4f18-bd21-138373f638b4", - "dspy_split": "train" + "question": "What evening cable television station programming block has a show with Ashley Holliday as a cast member?" }, { - "question": "Which of these publications was most recently published, Who Put the Bomp or Self?", "answer": "Self", + "dspy_split": "train", "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", - "dspy_split": "train" + "question": "Which of these publications was most recently published, Who Put the Bomp or Self?" }, { - "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?", "answer": "Davy Crockett, King of the Wild Frontier", + "dspy_split": "train", "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", - "dspy_split": "train" + "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?" } - ] - }, - "generate_query[1]": { + ], "lm": null, "traces": [], - "train": [], + "train": [] + }, + "generate_query[1]": { "demos": [ { "augmented": true, - "dspy_uuid": "a8e15619-7c04-467a-8580-4dcf10f48bed", "context": [ "Gustavo Kuerten | Gustavo Kuerten (] ; born 10 September 1976), nicknamed Guga, is a retired former World No. 1 tennis player from Brazil. He won the French Open singles title three times (1997, 2000, and 2001), and was the Tennis Masters Cup champion in 2000. Kuerten suffered many problems with injuries which led him to miss a number of tournaments between 2002 and 2005. After a few failed attempted comebacks, he retired from top-level tennis in May 2008. During his career he won 20 singles and 8 doubles titles.", "Gustavo Kuerten career statistics | This is a list of the main career statistics of Brazilian tennis player, Gustavo Kuerten. Kuerten won a total of 28 ATP titles \u2014 20 in singles and 8 in doubles. He won 3 Grand Slam titles, 5 ATP Masters Series tournaments and a Tennis Masters Cup.", "2004 Heineken Open \u2013 Singles | Gustavo Kuerten was the defending champion of the singles event at the Heineken Open tennis tournament, held in Auckland, New Zealand, but lost in the semifinals to Dominik Hrbat\u00fd." ], + "dspy_uuid": "a8e15619-7c04-467a-8580-4dcf10f48bed", "question": "Who was the Tennis Masters Cup champion in 2000, Gustavo Kuerten or Stan Wawrinka?", "rationale": "produce the search query. We know that Gustavo Kuerten won the Tennis Masters Cup in 2000, so we can start by searching for [Gustavo Kuerten Tennis Masters Cup 2000]. This should give us the information we need.", "search_query": "Gustavo Kuerten Tennis Masters Cup 2000" }, { "augmented": true, - "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", "context": [ "Twilight (novel series) | Twilight is a series of four vampire-themed fantasy romance novels by American author Stephenie Meyer. Released annually from 2005 through 2008, the four books chart the later teen years of Isabella \"Bella\" Swan, a girl who moves to Forks, Washington, and falls in love with a 104-year-old vampire named Edward Cullen. The series is told primarily from Bella's point of view, with the epilogue of \"Eclipse\" and Part II of \"Breaking Dawn\" being told from the viewpoint of character Jacob Black, a werewolf. The unpublished \"Midnight Sun\" is a retelling of the first book, \"Twilight\", from Edward Cullen's point of view. The novella \"The Short Second Life of Bree Tanner\", which tells the story of a newborn vampire who appeared in \"Eclipse\", was published on June 5, 2010, as a hardcover book and on June 7 as a free online ebook. \"\" , a definitive encyclopedic reference with nearly 100 full color illustrations, was released in bookstores on April 12, 2011.", "Harper Connelly Mysteries | The Harper Connelly Mysteries is a series of fantasy mystery novels written by Charlaine Harris, and first published in 2005. Harris is known best for penning The Southern Vampire Mysteries (also referred to as the True Blood Series), a series rich in supernatural characters such as vampires, telepaths, werewolves, shapeshifters and fairies; she has also written more traditional (non-paranormal) mysteries. The Harper Connelly Mysteries is also centered on a character with supernatural abilities, however these abilities are more subtle than in the Southern Vampire series.", "The Dark Heroine | The Dark Heroine is a series of vampire-themed fantasy romance novels written by English author Abigail Gibbs, published by HarperCollins in 2012. The first novel in the series, \"Dinner with a Vampire,\" revolves around London-born Violet Lee, who is kidnapped and held hostage by a Royal Family of vampires known as the Varns. The series is told from both Violet Lee and Kaspar Varn's perspective, the latter being heir to the Vamperic Throne in the novel." ], + "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", "question": "In which year was the first of the vampire-themed fantasy romance novels for which The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book first published?", "rationale": "determine the year the first of the vampire-themed fantasy romance novels was first published. We know that The Twilight Saga: The Official Illustrated Guide was published in 2011, and it serves as a spin-off encyclopedic reference book for the Twilight series. Therefore, we can deduce that the first of the vampire-themed fantasy romance novels must have been published before 2011.", "search_query": "When was the first of the vampire-themed fantasy romance novels published?" }, { - "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?", "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", + "dspy_split": "train", "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", - "dspy_split": "train" + "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?" }, { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", "answer": "Bruno Senna Lalli", + "dspy_split": "train", "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" }, { - "question": "Some performance of Take a Bow took place near the bank of which river?", "answer": "River Thames", + "dspy_split": "train", "dspy_uuid": "937a5676-7522-4e67-8e1e-d0b5651fad73", - "dspy_split": "train" + "question": "Some performance of Take a Bow took place near the bank of which river?" }, { - "question": "What is the nickname for this United States drag racer who drove Brutus?", "answer": "Jungle Jim", + "dspy_split": "train", "dspy_uuid": "e2421205-d692-42e8-aa14-7a477e516ae8", - "dspy_split": "train" + "question": "What is the nickname for this United States drag racer who drove Brutus?" }, { - "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?", "answer": "Davy Crockett, King of the Wild Frontier", + "dspy_split": "train", "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", - "dspy_split": "train" + "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?" }, { - "question": "At My Window was released by which American singer-songwriter?", "answer": "John Townes Van Zandt", + "dspy_split": "train", "dspy_uuid": "da05e5f3-7e82-4abe-a855-e26a85c958d6", - "dspy_split": "train" + "question": "At My Window was released by which American singer-songwriter?" }, { - "question": "Which of these publications was most recently published, Who Put the Bomp or Self?", "answer": "Self", + "dspy_split": "train", "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", - "dspy_split": "train" + "question": "Which of these publications was most recently published, Who Put the Bomp or Self?" }, { - "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?", "answer": "2010", + "dspy_split": "train", "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", - "dspy_split": "train" + "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?" }, { - "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?", "answer": "no", + "dspy_split": "train", "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", - "dspy_split": "train" + "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?" }, { - "question": "This American guitarist best known for her work with the Iron Maidens is an ancestor of a composer who was known as what?", "answer": "The Waltz King", + "dspy_split": "train", "dspy_uuid": "03b3104e-c9f4-4c17-83c7-1354563946df", - "dspy_split": "train" + "question": "This American guitarist best known for her work with the Iron Maidens is an ancestor of a composer who was known as what?" }, { - "question": "Remember Me Ballin' is a CD single by Indo G that features an American rapper born in what year?", "answer": "1979", + "dspy_split": "train", "dspy_uuid": "7b759a0a-8c96-4735-8f6e-1e8b1eae4861", - "dspy_split": "train" + "question": "Remember Me Ballin' is a CD single by Indo G that features an American rapper born in what year?" }, { - "question": "What evening cable television station programming block has a show with Ashley Holliday as a cast member?", "answer": "Nick at Nite", + "dspy_split": "train", "dspy_uuid": "fc3163d5-be7d-4f18-bd21-138373f638b4", - "dspy_split": "train" + "question": "What evening cable television station programming block has a show with Ashley Holliday as a cast member?" }, { - "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?", "answer": "Moravian and other Slavic folk music", + "dspy_split": "train", "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", - "dspy_split": "train" + "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?" }, { - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", "answer": "1950", + "dspy_split": "train", "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "dspy_split": "train" + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?" } - ] - }, - "generate_answer": { + ], "lm": null, "traces": [], - "train": [], - "demos": [ - { - "augmented": true, - "dspy_uuid": "a8e15619-7c04-467a-8580-4dcf10f48bed", - "context": [ - "Gustavo Kuerten | Gustavo Kuerten (] ; born 10 September 1976), nicknamed Guga, is a retired former World No. 1 tennis player from Brazil. He won the French Open singles title three times (1997, 2000, and 2001), and was the Tennis Masters Cup champion in 2000. Kuerten suffered many problems with injuries which led him to miss a number of tournaments between 2002 and 2005. After a few failed attempted comebacks, he retired from top-level tennis in May 2008. During his career he won 20 singles and 8 doubles titles.", - "Gustavo Kuerten career statistics | This is a list of the main career statistics of Brazilian tennis player, Gustavo Kuerten. Kuerten won a total of 28 ATP titles \u2014 20 in singles and 8 in doubles. He won 3 Grand Slam titles, 5 ATP Masters Series tournaments and a Tennis Masters Cup.", - "2004 Heineken Open \u2013 Singles | Gustavo Kuerten was the defending champion of the singles event at the Heineken Open tennis tournament, held in Auckland, New Zealand, but lost in the semifinals to Dominik Hrbat\u00fd.", - "2000 Tennis Masters Cup \u2013 Singles | Pete Sampras was the defending champion, but lost in the semifinals to Gustavo Kuerten.", - "2000 Monte Carlo Masters \u2013 Singles | Gustavo Kuerten was the defending champion, but lost in the first round this year." - ], - "question": "Who was the Tennis Masters Cup champion in 2000, Gustavo Kuerten or Stan Wawrinka?", - "rationale": "answer this question. We know that Gustavo Kuerten was the Tennis Masters Cup champion in 2000, as stated in [2]. We also know that Stan Wawrinka did not win the Tennis Masters Cup in 2000, as he was not born yet (he was born in 1985). Therefore, the answer is Gustavo Kuerten.", - "answer": "Gustavo Kuerten" - }, - { - "augmented": true, - "dspy_uuid": "ef6efe73-7815-4e31-a72d-d4a1537c5380", - "context": [ - "Twilight (novel series) | Twilight is a series of four vampire-themed fantasy romance novels by American author Stephenie Meyer. Released annually from 2005 through 2008, the four books chart the later teen years of Isabella \"Bella\" Swan, a girl who moves to Forks, Washington, and falls in love with a 104-year-old vampire named Edward Cullen. The series is told primarily from Bella's point of view, with the epilogue of \"Eclipse\" and Part II of \"Breaking Dawn\" being told from the viewpoint of character Jacob Black, a werewolf. The unpublished \"Midnight Sun\" is a retelling of the first book, \"Twilight\", from Edward Cullen's point of view. The novella \"The Short Second Life of Bree Tanner\", which tells the story of a newborn vampire who appeared in \"Eclipse\", was published on June 5, 2010, as a hardcover book and on June 7 as a free online ebook. \"\" , a definitive encyclopedic reference with nearly 100 full color illustrations, was released in bookstores on April 12, 2011.", - "Harper Connelly Mysteries | The Harper Connelly Mysteries is a series of fantasy mystery novels written by Charlaine Harris, and first published in 2005. Harris is known best for penning The Southern Vampire Mysteries (also referred to as the True Blood Series), a series rich in supernatural characters such as vampires, telepaths, werewolves, shapeshifters and fairies; she has also written more traditional (non-paranormal) mysteries. The Harper Connelly Mysteries is also centered on a character with supernatural abilities, however these abilities are more subtle than in the Southern Vampire series.", - "The Dark Heroine | The Dark Heroine is a series of vampire-themed fantasy romance novels written by English author Abigail Gibbs, published by HarperCollins in 2012. The first novel in the series, \"Dinner with a Vampire,\" revolves around London-born Violet Lee, who is kidnapped and held hostage by a Royal Family of vampires known as the Varns. The series is told from both Violet Lee and Kaspar Varn's perspective, the latter being heir to the Vamperic Throne in the novel.", - "Night Huntress | Night Huntress is a series of \"New York Times\" bestselling urban fantasy romance novels by author Jeaniene Frost. The first novel was published in 2007 by Avon and takes place in a world where supernatural creatures exist but are not known to the general public at large. The series initially focused around the character of half-vampire Catherine \"Cat\" Crawfield and her full-vampire lover Bones, but eventually shifted focus to other characters such as Vlad Tepesh, a character that Frost had initially not planned to include.", - "John William Polidori | John William Polidori (7 September 1795 \u2013 24 August 1821) was an English writer and physician. He is known for his associations with the Romantic movement and credited by some as the creator of the vampire genre of fantasy fiction. His most successful work was the short story \"The Vampyre\" (1819), the first published modern vampire story. Although originally and erroneously accredited to Lord Byron, both Byron and Polidori affirmed that the story is Polidori's." - ], - "question": "In which year was the first of the vampire-themed fantasy romance novels for which The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book first published?", - "rationale": "determine the correct answer. We know that The Twilight Saga: The Official Illustrated Guide serves as a spin-off encyclopedic reference book for the Twilight series, which was first published in 2005. However, the other options are also vampire-themed fantasy romance novels, and were first published in 2005, 2007, 2010, and 2012 respectively. Therefore, we need to examine each option carefully to determine which one is the correct answer.", - "answer": "2005" - }, - { - "question": "Which of these publications was most recently published, Who Put the Bomp or Self?", - "answer": "Self", - "dspy_uuid": "c126c397-254f-455f-bd22-1df7228cf1bc", - "dspy_split": "train" - }, - { - "question": "At My Window was released by which American singer-songwriter?", - "answer": "John Townes Van Zandt", - "dspy_uuid": "da05e5f3-7e82-4abe-a855-e26a85c958d6", - "dspy_split": "train" - }, - { - "question": "This American guitarist best known for her work with the Iron Maidens is an ancestor of a composer who was known as what?", - "answer": "The Waltz King", - "dspy_uuid": "03b3104e-c9f4-4c17-83c7-1354563946df", - "dspy_split": "train" - }, - { - "question": "Remember Me Ballin' is a CD single by Indo G that features an American rapper born in what year?", - "answer": "1979", - "dspy_uuid": "7b759a0a-8c96-4735-8f6e-1e8b1eae4861", - "dspy_split": "train" - }, - { - "question": "What is the nickname for this United States drag racer who drove Brutus?", - "answer": "Jungle Jim", - "dspy_uuid": "e2421205-d692-42e8-aa14-7a477e516ae8", - "dspy_split": "train" - }, - { - "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?", - "answer": "no", - "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", - "dspy_split": "train" - }, - { - "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?", - "answer": "2010", - "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", - "dspy_split": "train" - }, - { - "question": "What evening cable television station programming block has a show with Ashley Holliday as a cast member?", - "answer": "Nick at Nite", - "dspy_uuid": "fc3163d5-be7d-4f18-bd21-138373f638b4", - "dspy_split": "train" - }, - { - "question": "Which movie was released first, Son of Flubber or Davy Crockett, King of the Wild Frontier?", - "answer": "Davy Crockett, King of the Wild Frontier", - "dspy_uuid": "4e2f2530-99ac-4a75-9010-ef54a90480b9", - "dspy_split": "train" - }, - { - "question": "Some performance of Take a Bow took place near the bank of which river?", - "answer": "River Thames", - "dspy_uuid": "937a5676-7522-4e67-8e1e-d0b5651fad73", - "dspy_split": "train" - }, - { - "question": "What Scottish nobleman was the subject of a 1934 British short documentary and was the first man to fly over Mount Everest?", - "answer": "Douglas Douglas-Hamilton, 14th Duke of Hamilton", - "dspy_uuid": "66c1198b-2f31-4f1c-a1f8-d10dcfaa0a66", - "dspy_split": "train" - }, - { - "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?", - "answer": "Moravian and other Slavic folk music", - "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", - "dspy_split": "train" - }, - { - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", - "answer": "1950", - "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "dspy_split": "train" - }, - { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", - "answer": "Bruno Senna Lalli", - "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" - } - ] + "train": [] + }, + "retrieve": { + "k": 3 } } diff --git a/tests/multihop_llama213b_3.json b/tests/multihop_llama213b_3.json index baa6b24113..95f2a60d61 100644 --- a/tests/multihop_llama213b_3.json +++ b/tests/multihop_llama213b_3.json @@ -1,348 +1,348 @@ { - "retrieve": { - "k": 3 - }, - "generate_query[0]": { + "generate_answer": { + "demos": [ + { + "answer": "No.", + "augmented": true, + "context": [ + "Iced Earth | Iced Earth is an American heavy metal band from Tampa, Florida. It was formed in 1985 under the name Purgatory by guitarist and main songwriter Jon Schaffer and original drummer Greg Seymour. Iced Earth released their debut album in 1990 and have since released eleven studio albums, four EPs, three compilations, three box sets, three live albums and one cover album.", + "Iced Earth (album) | Iced Earth is the eponymous debut studio album by American heavy metal band Iced Earth. It was released in November 1990 internationally and February 1991 in North America. The album has three different covers (a European/Japanese cover, an American cover and a remastered cover). This album was Mike McGill's only album with Iced Earth, as well as the last for singer Gene Adam.", + "Iced Earth discography | The discography of Iced Earth, an American heavy metal band, consists of twelve studio releases, two live albums, three compilations, five singles, three videos, and ten music videos. Originally formed as Purgatory by guitarist Jon Schaffer on January 20, 1985, the band spent five years playing live locally and went through several line-up changes. After changing their name to Iced Earth, the band recorded the demo \"Enter the Realm\" (1989), which landed them a deal with the German label, Century Media Records. One year later, the band recorded their self-titled album \"Iced Earth\", followed by 1991's \"Night of the Stormrider\", which peaked at number 60 on the Japanese Oricon charts. Three years later, Iced Earth released \"Burnt Offerings\", featuring new vocalist Matthew Barlow, followed by \"The Dark Saga\" (1996).", + "Metal | A metal (from Greek \u03bc\u03ad\u03c4\u03b1\u03bb\u03bb\u03bf\u03bd \"m\u00e9tallon\", \"mine, quarry, metal\") is a material (an element, compound, or alloy) that is typically hard, opaque, shiny, and has good electrical and thermal conductivity. Metals are generally malleable\u2014that is, they can be hammered or pressed permanently out of shape without breaking or cracking\u2014as well as fusible (able to be fused or melted) and ductile (able to be drawn out into a thin wire). About 91 of the 118 elements in the periodic table are metals; the others are nonmetals or metalloids. Some elements appear in both metallic and non-metallic forms.", + "Metal (disambiguation) | A metal is a material that is typically hard, opaque, shiny, and has good electrical and thermal conductivity.", + "Metal (Wu Xing) | Metal (), the fourth phase of the Chinese philosophy of Wu Xing, is the decline of the matter, or the matter's decline stage. Metal is yin in character, its motion is inwards and its energy is contracting. It is associated with the autumn, the west, old age, the planet Venus, the color white, dry weather, and the White Tiger (Bai Hu) in Four Symbols. The archetypal metals are silver and gold." + ], + "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", + "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?", + "rationale": "determine if Stu Block and Johnny Bonnel's bands play the same type of music. We know that Stu Block is the lead vocalist of Iced Earth, an American heavy metal band from Tampa, Florida, and Johnny Bonnel is the lead vocalist of the band Evergrey, a Swedish heavy metal band. Both bands are considered heavy metal bands, but there are some differences in their styles. Iced Earth's music is more power metal-oriented, while Evergrey's music is more progressive metal-oriented. Therefore, we can conclude that Stu Block and Johnny Bonnel's bands do not play the same type of music." + }, + { + "answer": "Moravian and other Slavic folk music.", + "augmented": true, + "context": [ + "Jen\u016ffa | Jen\u016ffa (Jej\u00ed pastorky\u0148a, \"Her Stepdaughter\" in Czech) is an opera in three acts by Leo\u0161 Jan\u00e1\u010dek to a Czech libretto by the composer, based on the play \"Jej\u00ed pastorky\u0148a\" by Gabriela Preissov\u00e1. It was first performed at the Brno Theater, Brno, 21 January 1904. It was written between 1896 and 1902, and counts among the first operas written in prose.", + "Jenkka | Jenkka ['je\u014bk\u02d0\u0251] is a fast Finnish partner dance originated in Finnish folk dance, the Finnish version of Schottische. It is danced to the music in 2/4 or 4/4 time signature of about 140 beats per minute.", + "Jeni Jol | Jeni Jol is a Rom folk dance from the region of Skopje, Macedonia.", + "Leo\u0161 Jan\u00e1\u010dek | Leo\u0161 Jan\u00e1\u010dek (] , baptised Leo Eugen Jan\u00e1\u010dek; 3 July 1854 \u2013 12 August 1928) was a Czech composer, musical theorist, folklorist, publicist and teacher. He was inspired by Moravian and other Slavic folk music to create an original, modern musical style.", + "Lachian Dances | The Lachian Dances was the first mature work by the Czech composer Leo\u0161 Jan\u00e1\u010dek. Originally titled Wallachian Dances after the Moravian Wallachia region, Jan\u00e1\u010dek later changed the title when the region's name also changed, since it reflects folk songs from that specific area.", + "The Cunning Little Vixen | The Cunning Little Vixen (Czech: \"P\u0159\u00edhody li\u0161ky Bystrou\u0161ky\" , lit. \"Adventures of the vixen known as Sharp-Ears\", and, until the 1970s, generally referred to in English as Adventures of Vixen Sharp-Ears) is a Czech language opera by Leo\u0161 Jan\u00e1\u010dek, composed 1921 to 1923, in which a clever fox and accompanying wildlife (as well as a few humans) have small adventures while traversing their lifecycles. Its libretto was adapted by the composer from a serialized novella (daily comic) by Rudolf T\u011bsnohl\u00eddek and Stanislav Lolek, which was first published in the newspaper \"Lidov\u00e9 noviny\". The opera incorporates Moravian folk music and rhythms. Described as a comic opera, it has nonetheless been noted to contain a serious theme. Interpretations of the work remain varied, ranging from children's entertainment to a tragedy." + ], + "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", + "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?", + "rationale": "answer the question. We know that the composer of Jen\u016ffa was Leo\u0161 Jan\u00e1\u010dek, and that the play was inspired by Moravian and other Slavic folk music. Milan Sachs conducted the premiere of the play, so we can infer that he was involved in the performance of the opera. Therefore, the answer is Moravian and other Slavic folk music." + }, + { + "answer": "7", + "dspy_split": "train", + "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", + "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?" + }, + { + "answer": "Bill Murray", + "dspy_split": "train", + "dspy_uuid": "f1a7ba94-ab97-4efe-a38e-e4b1596ce2af", + "question": "which American actor was Candace Kita guest starred with " + }, + { + "answer": "iron and steel", + "dspy_split": "train", + "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", + "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? " + }, + { + "answer": "mixed martial arts fighter", + "dspy_split": "train", + "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", + "question": "Both Brian Warren and Jake Shields are considered what?" + }, + { + "answer": "Bruno Senna Lalli", + "dspy_split": "train", + "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" + }, + { + "answer": "Scott Hayden", + "dspy_split": "train", + "dspy_uuid": "4ab462bd-4d9b-4b7f-9371-14ba20ccd0db", + "question": "Who composed \"Sunflower Slow Drag\" with the King of Ragtime?" + }, + { + "answer": "Lubbock, Texas", + "dspy_split": "train", + "dspy_uuid": "1729ebde-5804-4157-b43b-6422cce75d4f", + "question": "Where was the singer who released the single \"I Never Made Love (Till I Made It with You)\" originally from ?" + }, + { + "answer": "Einstein", + "dspy_split": "train", + "dspy_uuid": "6dae3c13-007a-459e-a1bc-16fc64a3d0f9", + "question": "What person does Wormholes in fiction and Nathan Rosen have in common?" + }, + { + "answer": "anti-McCarthyism", + "dspy_split": "train", + "dspy_uuid": "96c92062-7e1c-4712-b3c8-30f35f8b6c65", + "question": "What type of film was it that featured the actress who is regarded as the greatest actress of all time?" + }, + { + "answer": "2010", + "dspy_split": "train", + "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", + "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?" + }, + { + "answer": "President of the United States", + "dspy_split": "train", + "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", + "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?" + }, + { + "answer": "1950", + "dspy_split": "train", + "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?" + }, + { + "answer": "space", + "dspy_split": "train", + "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", + "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? " + }, + { + "answer": "Kerry Condon", + "dspy_split": "train", + "dspy_uuid": "1e87ebea-f541-4629-b995-498965fa1127", + "question": "Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?" + } + ], "lm": null, "traces": [], - "train": [], + "train": [] + }, + "generate_query[0]": { "demos": [ { "augmented": true, - "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", "context": [], + "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?", "rationale": "determine if Stu Block and Johnny Bonnel's bands play the same type of music. We know that Stu Block is the lead vocalist of Iced Earth and Johnny Bonnel is the lead vocalist of Bonelord. Both bands are from the metal genre, but they have different styles. Iced Earth is a power metal band, while Bonelord is a doom metal band. Therefore, we can conclude that Stu Block and Johnny Bonnel's bands do not play the same type of music.", "search_query": "\"Iced Earth\" OR \"Bonelord\"" }, { "augmented": true, - "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", "context": [], + "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?", "rationale": "find the search query. We know that Milan Sachs conducted the premiere of the play Jen\u016ffa, and that the composer was inspired by folk music. Therefore, we can start by searching for \"Jen\u016ffa\" and \"folk music\" to find relevant information.", "search_query": "Jen\u016ffa folk music" }, { - "question": "Who composed \"Sunflower Slow Drag\" with the King of Ragtime?", "answer": "Scott Hayden", + "dspy_split": "train", "dspy_uuid": "4ab462bd-4d9b-4b7f-9371-14ba20ccd0db", - "dspy_split": "train" + "question": "Who composed \"Sunflower Slow Drag\" with the King of Ragtime?" }, { - "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?", "answer": "2010", + "dspy_split": "train", "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", - "dspy_split": "train" + "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?" }, { - "question": "Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?", "answer": "Kerry Condon", + "dspy_split": "train", "dspy_uuid": "1e87ebea-f541-4629-b995-498965fa1127", - "dspy_split": "train" + "question": "Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?" }, { - "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? ", "answer": "space", + "dspy_split": "train", "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", - "dspy_split": "train" + "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? " }, { - "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?", "answer": "President of the United States", + "dspy_split": "train", "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", - "dspy_split": "train" + "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?" }, { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? ", "answer": "iron and steel", + "dspy_split": "train", "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", - "dspy_split": "train" + "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? " }, { - "question": "Both Brian Warren and Jake Shields are considered what?", "answer": "mixed martial arts fighter", + "dspy_split": "train", "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" + "question": "Both Brian Warren and Jake Shields are considered what?" }, { - "question": "Where was the singer who released the single \"I Never Made Love (Till I Made It with You)\" originally from ?", "answer": "Lubbock, Texas", + "dspy_split": "train", "dspy_uuid": "1729ebde-5804-4157-b43b-6422cce75d4f", - "dspy_split": "train" + "question": "Where was the singer who released the single \"I Never Made Love (Till I Made It with You)\" originally from ?" }, { - "question": "which American actor was Candace Kita guest starred with ", "answer": "Bill Murray", + "dspy_split": "train", "dspy_uuid": "f1a7ba94-ab97-4efe-a38e-e4b1596ce2af", - "dspy_split": "train" + "question": "which American actor was Candace Kita guest starred with " }, { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", "answer": "Bruno Senna Lalli", + "dspy_split": "train", "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" }, { - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", "answer": "1950", + "dspy_split": "train", "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "dspy_split": "train" + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?" }, { - "question": "What person does Wormholes in fiction and Nathan Rosen have in common?", "answer": "Einstein", + "dspy_split": "train", "dspy_uuid": "6dae3c13-007a-459e-a1bc-16fc64a3d0f9", - "dspy_split": "train" + "question": "What person does Wormholes in fiction and Nathan Rosen have in common?" }, { - "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?", "answer": "7", + "dspy_split": "train", "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", - "dspy_split": "train" + "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?" }, { - "question": "What type of film was it that featured the actress who is regarded as the greatest actress of all time?", "answer": "anti-McCarthyism", + "dspy_split": "train", "dspy_uuid": "96c92062-7e1c-4712-b3c8-30f35f8b6c65", - "dspy_split": "train" + "question": "What type of film was it that featured the actress who is regarded as the greatest actress of all time?" } - ] - }, - "generate_query[1]": { + ], "lm": null, "traces": [], - "train": [], + "train": [] + }, + "generate_query[1]": { "demos": [ { "augmented": true, - "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", "context": [ "Iced Earth | Iced Earth is an American heavy metal band from Tampa, Florida. It was formed in 1985 under the name Purgatory by guitarist and main songwriter Jon Schaffer and original drummer Greg Seymour. Iced Earth released their debut album in 1990 and have since released eleven studio albums, four EPs, three compilations, three box sets, three live albums and one cover album.", - "Iced Earth (album) | Iced Earth is the eponymous debut studio album by American heavy metal band Iced Earth. It was released in November 1990 internationally and February 1991 in North America. The album has three different covers (a European\/Japanese cover, an American cover and a remastered cover). This album was Mike McGill's only album with Iced Earth, as well as the last for singer Gene Adam.", + "Iced Earth (album) | Iced Earth is the eponymous debut studio album by American heavy metal band Iced Earth. It was released in November 1990 internationally and February 1991 in North America. The album has three different covers (a European/Japanese cover, an American cover and a remastered cover). This album was Mike McGill's only album with Iced Earth, as well as the last for singer Gene Adam.", "Iced Earth discography | The discography of Iced Earth, an American heavy metal band, consists of twelve studio releases, two live albums, three compilations, five singles, three videos, and ten music videos. Originally formed as Purgatory by guitarist Jon Schaffer on January 20, 1985, the band spent five years playing live locally and went through several line-up changes. After changing their name to Iced Earth, the band recorded the demo \"Enter the Realm\" (1989), which landed them a deal with the German label, Century Media Records. One year later, the band recorded their self-titled album \"Iced Earth\", followed by 1991's \"Night of the Stormrider\", which peaked at number 60 on the Japanese Oricon charts. Three years later, Iced Earth released \"Burnt Offerings\", featuring new vocalist Matthew Barlow, followed by \"The Dark Saga\" (1996)." ], + "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?", "rationale": "determine if Stu Block and Johnny Bonnel's bands play the same type of music. We know that Stu Block is the vocalist of In Flames, and Johnny Bonnel is the vocalist of The Gathering. We also know that both bands are from Europe and have been active in the metal scene for a long time. However, we need to consider the specific genre of music that each band plays.", "search_query": "metal" }, { "augmented": true, - "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", "context": [ "Jen\u016ffa | Jen\u016ffa (Jej\u00ed pastorky\u0148a, \"Her Stepdaughter\" in Czech) is an opera in three acts by Leo\u0161 Jan\u00e1\u010dek to a Czech libretto by the composer, based on the play \"Jej\u00ed pastorky\u0148a\" by Gabriela Preissov\u00e1. It was first performed at the Brno Theater, Brno, 21 January 1904. It was written between 1896 and 1902, and counts among the first operas written in prose.", - "Jenkka | Jenkka ['je\u014bk\u02d0\u0251] is a fast Finnish partner dance originated in Finnish folk dance, the Finnish version of Schottische. It is danced to the music in 2\/4 or 4\/4 time signature of about 140 beats per minute.", + "Jenkka | Jenkka ['je\u014bk\u02d0\u0251] is a fast Finnish partner dance originated in Finnish folk dance, the Finnish version of Schottische. It is danced to the music in 2/4 or 4/4 time signature of about 140 beats per minute.", "Jeni Jol | Jeni Jol is a Rom folk dance from the region of Skopje, Macedonia." ], + "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?", "rationale": "produce the search query. We know that the opera Jen\u016ffa was written by Leo\u0161 Jan\u00e1\u010dek, and it was inspired by Czech folk music. Therefore, we can start by searching for \"Leo\u0161 Jan\u00e1\u010dek\" and \"Czech folk music\" to find relevant information.", "search_query": "Leo\u0161 Jan\u00e1\u010dek Czech folk music" }, { - "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?", "answer": "President of the United States", + "dspy_split": "train", "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", - "dspy_split": "train" + "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?" }, { - "question": "Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?", "answer": "Kerry Condon", + "dspy_split": "train", "dspy_uuid": "1e87ebea-f541-4629-b995-498965fa1127", - "dspy_split": "train" + "question": "Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?" }, { - "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?", "answer": "2010", + "dspy_split": "train", "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", - "dspy_split": "train" + "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?" }, { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", "answer": "Bruno Senna Lalli", + "dspy_split": "train", "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" + "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?" }, { - "question": "What type of film was it that featured the actress who is regarded as the greatest actress of all time?", "answer": "anti-McCarthyism", + "dspy_split": "train", "dspy_uuid": "96c92062-7e1c-4712-b3c8-30f35f8b6c65", - "dspy_split": "train" + "question": "What type of film was it that featured the actress who is regarded as the greatest actress of all time?" }, { - "question": "which American actor was Candace Kita guest starred with ", "answer": "Bill Murray", + "dspy_split": "train", "dspy_uuid": "f1a7ba94-ab97-4efe-a38e-e4b1596ce2af", - "dspy_split": "train" + "question": "which American actor was Candace Kita guest starred with " }, { - "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?", "answer": "7", + "dspy_split": "train", "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", - "dspy_split": "train" + "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?" }, { - "question": "Where was the singer who released the single \"I Never Made Love (Till I Made It with You)\" originally from ?", "answer": "Lubbock, Texas", + "dspy_split": "train", "dspy_uuid": "1729ebde-5804-4157-b43b-6422cce75d4f", - "dspy_split": "train" + "question": "Where was the singer who released the single \"I Never Made Love (Till I Made It with You)\" originally from ?" }, { - "question": "Who composed \"Sunflower Slow Drag\" with the King of Ragtime?", "answer": "Scott Hayden", + "dspy_split": "train", "dspy_uuid": "4ab462bd-4d9b-4b7f-9371-14ba20ccd0db", - "dspy_split": "train" + "question": "Who composed \"Sunflower Slow Drag\" with the King of Ragtime?" }, { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? ", "answer": "iron and steel", + "dspy_split": "train", "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", - "dspy_split": "train" + "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? " }, { - "question": "Both Brian Warren and Jake Shields are considered what?", "answer": "mixed martial arts fighter", + "dspy_split": "train", "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" + "question": "Both Brian Warren and Jake Shields are considered what?" }, { - "question": "What person does Wormholes in fiction and Nathan Rosen have in common?", "answer": "Einstein", + "dspy_split": "train", "dspy_uuid": "6dae3c13-007a-459e-a1bc-16fc64a3d0f9", - "dspy_split": "train" + "question": "What person does Wormholes in fiction and Nathan Rosen have in common?" }, { - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", "answer": "1950", + "dspy_split": "train", "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "dspy_split": "train" + "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?" }, { - "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? ", "answer": "space", + "dspy_split": "train", "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", - "dspy_split": "train" + "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? " } - ] - }, - "generate_answer": { + ], "lm": null, "traces": [], - "train": [], - "demos": [ - { - "augmented": true, - "dspy_uuid": "7466462a-c7b5-4fee-a867-9324d39125e9", - "context": [ - "Iced Earth | Iced Earth is an American heavy metal band from Tampa, Florida. It was formed in 1985 under the name Purgatory by guitarist and main songwriter Jon Schaffer and original drummer Greg Seymour. Iced Earth released their debut album in 1990 and have since released eleven studio albums, four EPs, three compilations, three box sets, three live albums and one cover album.", - "Iced Earth (album) | Iced Earth is the eponymous debut studio album by American heavy metal band Iced Earth. It was released in November 1990 internationally and February 1991 in North America. The album has three different covers (a European\/Japanese cover, an American cover and a remastered cover). This album was Mike McGill's only album with Iced Earth, as well as the last for singer Gene Adam.", - "Iced Earth discography | The discography of Iced Earth, an American heavy metal band, consists of twelve studio releases, two live albums, three compilations, five singles, three videos, and ten music videos. Originally formed as Purgatory by guitarist Jon Schaffer on January 20, 1985, the band spent five years playing live locally and went through several line-up changes. After changing their name to Iced Earth, the band recorded the demo \"Enter the Realm\" (1989), which landed them a deal with the German label, Century Media Records. One year later, the band recorded their self-titled album \"Iced Earth\", followed by 1991's \"Night of the Stormrider\", which peaked at number 60 on the Japanese Oricon charts. Three years later, Iced Earth released \"Burnt Offerings\", featuring new vocalist Matthew Barlow, followed by \"The Dark Saga\" (1996).", - "Metal | A metal (from Greek \u03bc\u03ad\u03c4\u03b1\u03bb\u03bb\u03bf\u03bd \"m\u00e9tallon\", \"mine, quarry, metal\") is a material (an element, compound, or alloy) that is typically hard, opaque, shiny, and has good electrical and thermal conductivity. Metals are generally malleable\u2014that is, they can be hammered or pressed permanently out of shape without breaking or cracking\u2014as well as fusible (able to be fused or melted) and ductile (able to be drawn out into a thin wire). About 91 of the 118 elements in the periodic table are metals; the others are nonmetals or metalloids. Some elements appear in both metallic and non-metallic forms.", - "Metal (disambiguation) | A metal is a material that is typically hard, opaque, shiny, and has good electrical and thermal conductivity.", - "Metal (Wu Xing) | Metal (), the fourth phase of the Chinese philosophy of Wu Xing, is the decline of the matter, or the matter's decline stage. Metal is yin in character, its motion is inwards and its energy is contracting. It is associated with the autumn, the west, old age, the planet Venus, the color white, dry weather, and the White Tiger (Bai Hu) in Four Symbols. The archetypal metals are silver and gold." - ], - "question": "Do Stu Block and Johnny Bonnel's bands play the same type of music?", - "rationale": "determine if Stu Block and Johnny Bonnel's bands play the same type of music. We know that Stu Block is the lead vocalist of Iced Earth, an American heavy metal band from Tampa, Florida, and Johnny Bonnel is the lead vocalist of the band Evergrey, a Swedish heavy metal band. Both bands are considered heavy metal bands, but there are some differences in their styles. Iced Earth's music is more power metal-oriented, while Evergrey's music is more progressive metal-oriented. Therefore, we can conclude that Stu Block and Johnny Bonnel's bands do not play the same type of music.", - "answer": "No." - }, - { - "augmented": true, - "dspy_uuid": "0610d91f-d32e-4bcd-b0a7-d03fbe9538e3", - "context": [ - "Jen\u016ffa | Jen\u016ffa (Jej\u00ed pastorky\u0148a, \"Her Stepdaughter\" in Czech) is an opera in three acts by Leo\u0161 Jan\u00e1\u010dek to a Czech libretto by the composer, based on the play \"Jej\u00ed pastorky\u0148a\" by Gabriela Preissov\u00e1. It was first performed at the Brno Theater, Brno, 21 January 1904. It was written between 1896 and 1902, and counts among the first operas written in prose.", - "Jenkka | Jenkka ['je\u014bk\u02d0\u0251] is a fast Finnish partner dance originated in Finnish folk dance, the Finnish version of Schottische. It is danced to the music in 2\/4 or 4\/4 time signature of about 140 beats per minute.", - "Jeni Jol | Jeni Jol is a Rom folk dance from the region of Skopje, Macedonia.", - "Leo\u0161 Jan\u00e1\u010dek | Leo\u0161 Jan\u00e1\u010dek (] , baptised Leo Eugen Jan\u00e1\u010dek; 3 July 1854 \u2013 12 August 1928) was a Czech composer, musical theorist, folklorist, publicist and teacher. He was inspired by Moravian and other Slavic folk music to create an original, modern musical style.", - "Lachian Dances | The Lachian Dances was the first mature work by the Czech composer Leo\u0161 Jan\u00e1\u010dek. Originally titled Wallachian Dances after the Moravian Wallachia region, Jan\u00e1\u010dek later changed the title when the region's name also changed, since it reflects folk songs from that specific area.", - "The Cunning Little Vixen | The Cunning Little Vixen (Czech: \"P\u0159\u00edhody li\u0161ky Bystrou\u0161ky\" , lit. \"Adventures of the vixen known as Sharp-Ears\", and, until the 1970s, generally referred to in English as Adventures of Vixen Sharp-Ears) is a Czech language opera by Leo\u0161 Jan\u00e1\u010dek, composed 1921 to 1923, in which a clever fox and accompanying wildlife (as well as a few humans) have small adventures while traversing their lifecycles. Its libretto was adapted by the composer from a serialized novella (daily comic) by Rudolf T\u011bsnohl\u00eddek and Stanislav Lolek, which was first published in the newspaper \"Lidov\u00e9 noviny\". The opera incorporates Moravian folk music and rhythms. Described as a comic opera, it has nonetheless been noted to contain a serious theme. Interpretations of the work remain varied, ranging from children's entertainment to a tragedy." - ], - "question": "Milan Sachs conducted the premiere of the play Jen\u016ffa by the composer who was inspired by what types of music?", - "rationale": "answer the question. We know that the composer of Jen\u016ffa was Leo\u0161 Jan\u00e1\u010dek, and that the play was inspired by Moravian and other Slavic folk music. Milan Sachs conducted the premiere of the play, so we can infer that he was involved in the performance of the opera. Therefore, the answer is Moravian and other Slavic folk music.", - "answer": "Moravian and other Slavic folk music." - }, - { - "question": "Whats was the population in 2003 of Centralia, the least populated municipality in Pennsylvania, USA?", - "answer": "7", - "dspy_uuid": "92183f94-9c08-4915-a666-dbcc22aeb633", - "dspy_split": "train" - }, - { - "question": "which American actor was Candace Kita guest starred with ", - "answer": "Bill Murray", - "dspy_uuid": "f1a7ba94-ab97-4efe-a38e-e4b1596ce2af", - "dspy_split": "train" - }, - { - "question": "What metals were produced by the the steelworks company hit in the Barrow Blitz alongside VSEL? ", - "answer": "iron and steel", - "dspy_uuid": "a8ff9dee-f23d-4a2f-b10b-5be98114b8bf", - "dspy_split": "train" - }, - { - "question": "Both Brian Warren and Jake Shields are considered what?", - "answer": "mixed martial arts fighter", - "dspy_uuid": "d2af37d1-975f-4423-bbda-1be5bf9baa35", - "dspy_split": "train" - }, - { - "question": "What Brazilian professional racing driver who races for Rebellion Racing has a mother named Viviane Senna da Silva Lalli?", - "answer": "Bruno Senna Lalli", - "dspy_uuid": "e4e1c569-f4d6-4b5a-9b31-d4ce129395ac", - "dspy_split": "train" - }, - { - "question": "Who composed \"Sunflower Slow Drag\" with the King of Ragtime?", - "answer": "Scott Hayden", - "dspy_uuid": "4ab462bd-4d9b-4b7f-9371-14ba20ccd0db", - "dspy_split": "train" - }, - { - "question": "Where was the singer who released the single \"I Never Made Love (Till I Made It with You)\" originally from ?", - "answer": "Lubbock, Texas", - "dspy_uuid": "1729ebde-5804-4157-b43b-6422cce75d4f", - "dspy_split": "train" - }, - { - "question": "What person does Wormholes in fiction and Nathan Rosen have in common?", - "answer": "Einstein", - "dspy_uuid": "6dae3c13-007a-459e-a1bc-16fc64a3d0f9", - "dspy_split": "train" - }, - { - "question": "What type of film was it that featured the actress who is regarded as the greatest actress of all time?", - "answer": "anti-McCarthyism", - "dspy_uuid": "96c92062-7e1c-4712-b3c8-30f35f8b6c65", - "dspy_split": "train" - }, - { - "question": "The Organisation that allows a community to influence their operation or use and to enjoy the benefits arisingwas founded in what year?", - "answer": "2010", - "dspy_uuid": "821a4e4d-a146-44a7-bdaa-48b7efb4e7cc", - "dspy_split": "train" - }, - { - "question": "What head of state position was held by Harry S Truman when he gave Harold E Wilson the Medal of Honor?", - "answer": "President of the United States", - "dspy_uuid": "9465e8d9-277b-43c6-81f5-b3349923045c", - "dspy_split": "train" - }, - { - "question": "The Victorians - Their Story In Pictures is a documentary series written by an author born in what year?", - "answer": "1950", - "dspy_uuid": "7c56ed8c-85b9-4b4f-be70-7eb3a20539ec", - "dspy_split": "train" - }, - { - "question": "Samantha Cristoforetti and Mark Shuttleworth are both best known for being first in their field to go where? ", - "answer": "space", - "dspy_uuid": "47b1cbeb-dba8-4cab-a50c-97b81fd86eb3", - "dspy_split": "train" - }, - { - "question": "Who acted in the shot film The Shore and is also the youngest actress ever to play Ophelia in a Royal Shakespeare Company production of \"Hamlet.\" ?", - "answer": "Kerry Condon", - "dspy_uuid": "1e87ebea-f541-4629-b995-498965fa1127", - "dspy_split": "train" - } - ] + "train": [] + }, + "retrieve": { + "k": 3 } } diff --git a/tests/reliability/complex_types/generated/test_many_types_1/inputs/input1.json b/tests/reliability/complex_types/generated/test_many_types_1/inputs/input1.json index 63044a2510..d2cc0700bb 100644 --- a/tests/reliability/complex_types/generated/test_many_types_1/inputs/input1.json +++ b/tests/reliability/complex_types/generated/test_many_types_1/inputs/input1.json @@ -15,12 +15,18 @@ "datetimeField": "2023-10-12T07:20:50.52Z", "enumField": "option2", "literalField": "literalValue", - "tupleField": ["nestedString", 789] + "tupleField": [ + "nestedString", + 789 + ] }, "objectField": { "subField1": "example", "subField2": 456 }, - "tupleField": ["string1", 123] + "tupleField": [ + "string1", + 123 + ] } } diff --git a/tests/reliability/complex_types/generated/test_many_types_1/inputs/input2.json b/tests/reliability/complex_types/generated/test_many_types_1/inputs/input2.json index cae7c21a5a..90e9e8b600 100644 --- a/tests/reliability/complex_types/generated/test_many_types_1/inputs/input2.json +++ b/tests/reliability/complex_types/generated/test_many_types_1/inputs/input2.json @@ -15,12 +15,18 @@ "datetimeField": "2023-11-01T12:00:00Z", "enumField": "option2", "literalField": "literalValue", - "tupleField": ["nestedString", 789] + "tupleField": [ + "nestedString", + 789 + ] }, "objectField": { "subField1": "Patriotism is a feeling of love, devotion, and sense of attachment to one's country. This attachment can be a combination of many different feelings relating to one's homeland, including ethnic, cultural, political or historical aspects. It encompasses a set of concepts closely related to those of nationalism. In the context of patriotism, people may express their feelings in a variety of ways, including supporting their country's interests and policies, celebrating national holidays, and participating in civic activities. Patriotism often involves a sense of pride in one's country and a willingness to defend it against any threats. It can also include a commitment to improving the country and making it a better place for future generations. The concept of patriotism is often linked with the idea of national identity, which is the sense of a nation as a cohesive whole, as represented by distinctive traditions, culture, language, and politics. Patriots may feel a strong sense of loyalty and duty to their country, and they may take actions to support and protect it. However, it is important to note that patriotism can also be a complex and sometimes controversial concept. While it can inspire positive actions and a sense of community, it can also lead to exclusionary or aggressive behaviors if taken to an extreme. In some cases, excessive patriotism can result in nationalism, which can lead to conflicts with other nations or groups. Despite these potential issues, many people view patriotism as a positive force that can unite people and inspire them to work together for the common good. It can foster a sense of belonging and purpose, and it can motivate individuals to contribute to the well-being of their country. Overall, patriotism is a multifaceted and deeply personal sentiment that can manifest in many different ways, depending on an individual's experiences, beliefs, and values.", "subField2": 456 }, - "tupleField": ["exampleString", 123] + "tupleField": [ + "exampleString", + 123 + ] } } diff --git a/tests/reliability/complex_types/generated/test_many_types_1/schema.json b/tests/reliability/complex_types/generated/test_many_types_1/schema.json index 1e102dc8b5..6ce50fceea 100644 --- a/tests/reliability/complex_types/generated/test_many_types_1/schema.json +++ b/tests/reliability/complex_types/generated/test_many_types_1/schema.json @@ -8,12 +8,18 @@ "type": "string" }, "enumField": { - "enum": ["option1", "option2", "option3"], + "enum": [ + "option1", + "option2", + "option3" + ], "type": "string" }, "literalField": { "const": "literalValue", - "enum": ["literalValue"], + "enum": [ + "literalValue" + ], "type": "string" }, "nestedObjectField": { @@ -23,12 +29,18 @@ "type": "string" }, "enumField": { - "enum": ["option1", "option2", "option3"], + "enum": [ + "option1", + "option2", + "option3" + ], "type": "string" }, "literalField": { "const": "literalValue", - "enum": ["literalValue"], + "enum": [ + "literalValue" + ], "type": "string" }, "tupleField": { @@ -47,7 +59,12 @@ "type": "array" } }, - "required": ["tupleField", "enumField", "datetimeField", "literalField"], + "required": [ + "tupleField", + "enumField", + "datetimeField", + "literalField" + ], "type": "object" }, "objectField": { @@ -59,7 +76,10 @@ "type": "number" } }, - "required": ["subField1", "subField2"], + "required": [ + "subField1", + "subField2" + ], "type": "object" }, "processedDatetimeField": { @@ -69,12 +89,18 @@ "type": "string" }, "processedEnumField": { - "enum": ["option1", "option2", "option3"], + "enum": [ + "option1", + "option2", + "option3" + ], "type": "string" }, "processedLiteralField": { "const": "literalValue", - "enum": ["literalValue"], + "enum": [ + "literalValue" + ], "type": "string" }, "processedNestedObjectField": { @@ -87,12 +113,18 @@ "type": "string" }, "enumField": { - "enum": ["option1", "option2", "option3"], + "enum": [ + "option1", + "option2", + "option3" + ], "type": "string" }, "literalField": { "const": "literalValue", - "enum": ["literalValue"], + "enum": [ + "literalValue" + ], "type": "string" }, "tupleField": { @@ -132,7 +164,11 @@ "type": "number" } }, - "required": ["subField1", "subField2", "additionalField"], + "required": [ + "subField1", + "subField2", + "additionalField" + ], "type": "object" }, "processedTupleField": { diff --git a/tests/reliability/complex_types/generated/test_nesting_1/schema.json b/tests/reliability/complex_types/generated/test_nesting_1/schema.json index 7cac603901..cda2c125d2 100644 --- a/tests/reliability/complex_types/generated/test_nesting_1/schema.json +++ b/tests/reliability/complex_types/generated/test_nesting_1/schema.json @@ -20,23 +20,34 @@ "type": "number" } }, - "required": ["field1", "field2"], + "required": [ + "field1", + "field2" + ], "type": "object" } }, - "required": ["level5"], + "required": [ + "level5" + ], "type": "object" } }, - "required": ["level4"], + "required": [ + "level4" + ], "type": "object" } }, - "required": ["level3"], + "required": [ + "level3" + ], "type": "object" } }, - "required": ["level2"], + "required": [ + "level2" + ], "type": "object" }, "resultLevel1": { @@ -61,26 +72,40 @@ "type": "array" } }, - "required": ["outputField1", "outputField2"], + "required": [ + "outputField1", + "outputField2" + ], "type": "object" } }, - "required": ["resultLevel5"], + "required": [ + "resultLevel5" + ], "type": "object" } }, - "required": ["resultLevel4"], + "required": [ + "resultLevel4" + ], "type": "object" } }, - "required": ["resultLevel3"], + "required": [ + "resultLevel3" + ], "type": "object" } }, - "required": ["resultLevel2"], + "required": [ + "resultLevel2" + ], "type": "object" } }, - "required": ["level1", "resultLevel1"], + "required": [ + "level1", + "resultLevel1" + ], "type": "object" } diff --git a/tests/reliability/complex_types/generated/test_nesting_2/schema.json b/tests/reliability/complex_types/generated/test_nesting_2/schema.json index 1ff44d2651..e53706ea96 100644 --- a/tests/reliability/complex_types/generated/test_nesting_2/schema.json +++ b/tests/reliability/complex_types/generated/test_nesting_2/schema.json @@ -22,11 +22,18 @@ "type": "string" } }, - "required": ["value", "age"], + "required": [ + "value", + "age" + ], "type": "object" } }, - "required": ["customer_id", "customer_type", "details"], + "required": [ + "customer_id", + "customer_type", + "details" + ], "type": "object" }, "customer_summary": { @@ -46,7 +53,10 @@ "type": "boolean" } }, - "required": ["is_premium", "category"], + "required": [ + "is_premium", + "category" + ], "type": "object" }, "value": { @@ -54,7 +64,11 @@ "type": "string" } }, - "required": ["customer_id", "customer_type", "value"], + "required": [ + "customer_id", + "customer_type", + "value" + ], "type": "object" }, "transaction": { @@ -75,7 +89,10 @@ "type": "number" } }, - "required": ["value", "timestamp"], + "required": [ + "value", + "timestamp" + ], "type": "object" }, "transaction_id": { @@ -83,7 +100,11 @@ "type": "string" } }, - "required": ["transaction_id", "amount", "details"], + "required": [ + "transaction_id", + "amount", + "details" + ], "type": "object" }, "transaction_summary": { @@ -100,7 +121,10 @@ "type": "number" } }, - "required": ["value", "timestamp"], + "required": [ + "value", + "timestamp" + ], "type": "object" }, "total_amount": { @@ -112,7 +136,11 @@ "type": "string" } }, - "required": ["transaction_id", "total_amount", "details"], + "required": [ + "transaction_id", + "total_amount", + "details" + ], "type": "object" } }, diff --git a/tests/reliability/input_formats/generated/test_markdown_1/schema.json b/tests/reliability/input_formats/generated/test_markdown_1/schema.json index 56d9790f84..e5da5c7985 100644 --- a/tests/reliability/input_formats/generated/test_markdown_1/schema.json +++ b/tests/reliability/input_formats/generated/test_markdown_1/schema.json @@ -14,6 +14,9 @@ "type": "string" } }, - "required": ["markdown_content", "table_of_contents"], + "required": [ + "markdown_content", + "table_of_contents" + ], "type": "object" }