notebooks/experiment.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "from ragbench import RAGPipeline, RAGEval, RAGTools\n",
        "import json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8oGHLfl7yXv"
      },
      "source": [
        "# From YAML Config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2tHtDlu_7yKX"
      },
      "outputs": [],
      "source": [
        "!cat ../experiment_configs/sample.yaml"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "RAGPipeline.run_pipeline_from_yaml('../experiment_configs/sample.yaml')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a50pvYFQ7vXX"
      },
      "source": [
        "# Step by Step"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QrIL6z4wd6FF"
      },
      "source": [
        "### Loading Corpus & Queries and Creating RAG Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "azESxxMHg-8_"
      },
      "outputs": [],
      "source": [
        "system_prompt = '''Using the information contained in the context, give a comprehensive answer to the question.\n",
        "Respond only to the question asked, response should be concise and relevant to the question.\n",
        "If the answer cannot be deduced from the context, do not generate any response on your own and just say `answer not found`.\n",
        "'''\n",
        "\n",
        "context_prompt = '''Context:\n",
        "{CONTEXT}\n",
        "---\n",
        "Now here is the question you need to answer.\n",
        "{QUERY}\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FBxCmstLr-pV"
      },
      "outputs": [],
      "source": [
        "with open('../datasets/multihoprag.json', 'r') as file:\n",
        "    ds = json.load(file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "mGT3huj4PvjS"
      },
      "outputs": [],
      "source": [
        "rag_pipe = RAGPipeline('../caches/sample_experiment', system_prompt, context_prompt)\n",
        "ls_docs = ds['corpus']\n",
        "ls_queries = list(ds['gold_answers'].keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYP1N4TOTNDV"
      },
      "source": [
        "### Vectorize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYrgyZnqPvjS",
        "outputId": "1c77a7e4-b97a-42ee-ee04-53c562afaa36"
      },
      "outputs": [],
      "source": [
        "rag_pipe.load_embedding_model_from_hf('thenlper/gte-small')\n",
        "text_splitter = RAGTools.load_text_splitter_hf_tokenizer(rag_pipe.embedding_tokenizer, 256, 0.1)\n",
        "ls_chunks = rag_pipe.split_docs(ls_docs, text_splitter)\n",
        "rag_pipe.prepare_vector_db(ls_chunks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3ysSYT0TQPh"
      },
      "source": [
        "### Retrieve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXMqWAYmSQg_",
        "outputId": "8648ccff-7d7b-4d74-e25f-e8f3f7d4b53f"
      },
      "outputs": [],
      "source": [
        "ls_rets = rag_pipe.retrieve(ls_queries, 10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rf7GgGAjTSrd"
      },
      "source": [
        "### Rerank"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "YV3dzXM2TUSr"
      },
      "outputs": [],
      "source": [
        "rag_pipe.load_reranker_model_from_hf('colbert-ir/colbertv2.0')\n",
        "ls_reranked_rets = rag_pipe.rerank(ls_rets, 4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EFoFQgtWVKh"
      },
      "source": [
        "### Evaluate Retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "rets, golds = ls_reranked_rets, ds['gold_retrieves']\n",
        "RAGEval.retrieval_metrics(rets, golds)\n",
        "\n",
        "# or\n",
        "\n",
        "# rets, golds = ls_rets, ds['gold_retrieves']\n",
        "# hit10 = RAGEval.hits_at(10, rets, golds)\n",
        "# hit4 = RAGEval.hits_at(4, rets, golds)\n",
        "# map10 = RAGEval.map_at(10, rets, golds)\n",
        "# mrr10 = RAGEval.mrr_at(10, rets, golds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwKf6LUBesmW"
      },
      "source": [
        "### Generate Responses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCneBXYMfXGb",
        "outputId": "ff4d227d-203b-4762-a692-c218f3654bce"
      },
      "outputs": [],
      "source": [
        "ls_prompts = rag_pipe.create_prompts(ls_rets)\n",
        "\n",
        "# Sampling 10 for testing, turn this off for actual experiments\n",
        "random_queries = list(ls_prompts.keys())[:10]\n",
        "random_prompts = {k:ls_prompts[k] for k in random_queries}\n",
        "\n",
        "rag_pipe.load_generator_model_from_openai('gpt-4o-mini', API_KEY)\n",
        "ls_responses = rag_pipe.generate_responses(random_prompts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iw31kY8Lxv3Z"
      },
      "source": [
        "### Evaluate Answer Generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6FB5wQ4xvf_",
        "outputId": "f42e9b66-47b1-4749-858e-6c130194a08f"
      },
      "outputs": [],
      "source": [
        "metric_per_query = RAGEval.generation_metrics(ls_responses, ds['gold_answers'], rag_pipe.embedding_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G1QI5AkgYelv"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "po2jAYvyYeiT"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xCxfzBVsXqfG"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EMNm45aSXqb4"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "temp4",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
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