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Merge pull request #3 from explodinggradients/rag
chore: reorderd some files
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‎Langchain/chains.ipynb

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‎Langchain/langsmith-qa-evaluation.ipynb

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‎Langchain/QA-intro.ipynb renamed to ‎langchain/QA-intro.ipynb

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‎Langchain/README.md renamed to ‎langchain/README.md

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‎langchain/callback-manager.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4ac3c236",
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"metadata": {},
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"source": [
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"# CallbackHandlers and CallbackManagers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "0c11f0ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.callbacks.manager import (\n",
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" trace_as_chain_group, \n",
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" atrace_as_chain_group,\n",
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")\n",
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"\n",
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"# Example usage:\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import LLMChain\n",
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"llm = ChatOpenAI(temperature=0.9)\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"question\"],\n",
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" template=\"What is the answer to {question}?\",\n",
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")\n",
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"chain = LLMChain(llm=llm, prompt=prompt)\n",
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"with trace_as_chain_group(\"my_group\") as group_manager:\n",
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" chain.run(question=\"What is your name?\", callbacks=group_manager)\n",
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" with trace_as_chain_group(\"inside_my_group\", callback_manager=group_manager) as sub_group:\n",
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" chain.run(question=\"What is your quest?\", callbacks=sub_group)\n",
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" chain.run(question=\"What is your favorite color?\", callbacks=sub_group)\n",
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" llm.predict(prompt.format(question=\"how is president?\"))\n",
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" llm.predict(prompt.format(question=\"how is president?\"), callbacks=sub_group)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "bc8310b2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'The president is the head of state and the highest-ranking government official in a country.'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm.predict(prompt.format(question=\"how is president?\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8cb6fce",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎langchain/chains.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f46200c8",
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"metadata": {},
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"source": [
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"# Chains - Intro\n",
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"\n",
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"chains are a core concept in langchain. It enables you to group together complex LLM calls and other functionality together\n",
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"\n",
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"\n",
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"The base interface is \n",
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"```python\n",
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"class Chain(BaseModel, ABC):\n",
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" \"\"\"Base interface that all chains should implement.\"\"\"\n",
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"\n",
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" memory: BaseMemory\n",
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" callbacks: Callbacks\n",
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"\n",
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" def __call__(\n",
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" self,\n",
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" inputs: Any,\n",
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" return_only_outputs: bool = False,\n",
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" callbacks: Callbacks = None,\n",
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" ) -> Dict[str, Any]:\n",
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" ...\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b779141",
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"metadata": {},
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"source": [
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"## Getting Started\n",
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"\n",
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"`LLMChain` is the basic building block for chains."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "f9207cec",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"\n",
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"llm = OpenAI(temperature=0.9)\n",
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"chat_llm = ChatOpenAI()\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"product\"],\n",
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" template=\"What is a good name for a company that makes {product}?\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "6d81a9b8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'product': 'colorful vests', 'text': 'VibrantVests'}"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.chains import LLMChain\n",
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"chain = LLMChain(llm=chat_llm, prompt=prompt)\n",
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"\n",
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"# Run the chain only specifying the input variable.\n",
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"chain({\"product\": \"colorful vests\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a3908709",
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"metadata": {},
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"source": [
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"## Custom Chain\n",
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"\n",
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"You have to implement"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "528baa20",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains.base import Chain\n",
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"\n",
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"class MyCustomChain(Chain):\n",
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" @property\n",
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" def input_keys(self):\n",
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" return [\"question\", \"answer\", \"contexts\"]\n",
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" \n",
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" @property\n",
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" def output_keys(self):\n",
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" return [\"score\"]\n",
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" \n",
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" def _call(self, inputs, run_manager = None):\n",
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" print(inputs)\n",
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" return {\"score\": 1}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "391033b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = MyCustomChain()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "598474ff",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'question': 'q', 'answer': 'a', 'contexts': ['a']}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'question': 'q', 'answer': 'a', 'contexts': ['a'], 'score': 1}"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain({\n",
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" \"question\": \"q\",\n",
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" \"answer\": \"a\",\n",
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" \"contexts\": [\"a\"]\n",
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"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ab88ba75",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Langchain/evaluations.ipynb renamed to ‎langchain/evaluations.ipynb

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