Retriever_Pipeline_Evaluations.ipynb

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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FeyD5_ypOZRU"
      },
      "outputs": [],
      "source": [
        "# !pip install -U -q langchain openai ragas chromadb sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install pypdf"
      ],
      "metadata": {
        "id": "yeFXBCL1PgVf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install datasets"
      ],
      "metadata": {
        "id": "NPnmXSYrSW82"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain_openai import OpenAIEmbeddings\n",
        "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.embeddings import SentenceTransformerEmbeddings\n",
        "from langchain.callbacks import get_openai_callback"
      ],
      "metadata": {
        "id": "gwMSIiRYOq8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Em3f-AO3lhUR",
        "outputId": "be4b7611-dce6-490f-f2f4-0084631fcf31"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OpenAI API Key:··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5306d_yPGj-",
        "outputId": "9d72277e-4533-4e85-f744-59758c515ff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def pretty_print_docs(docs):\n",
        "    print(\n",
        "        f\"\\n{'-' * 100}\\n\".join(\n",
        "            [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
        "        )\n",
        "    )"
      ],
      "metadata": {
        "id": "kgkpMYE6emXd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b_docs = []\n",
        "\n",
        "loader = PyPDFLoader('/content/drive/My Drive/bert.pdf')\n",
        "b_docs.extend(loader.load())\n"
      ],
      "metadata": {
        "id": "pAibx8lNPDyR"
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      "execution_count": null,
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        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap = 100)\n",
        "\n",
        "docs = text_splitter.split_documents(b_docs)\n",
        "embeddings = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "vectorstore = Chroma.from_documents(docs, embeddings)"
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        },
        "id": "6uFUKeq_PZFq",
        "outputId": "b126758a-a531-4045-bce0-6185384b541a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b60f5eb674744958db31d2079637869"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d2f48c0f353d4f44826c47f1042695bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "569cab05d09a4eeda47a00d7c8805ded"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cffe10d6a5c24df1ab9a0998a71712e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c42861891a2949f98f5f06e51ece9be1"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c74e5a184d7d4ca4ba6144cc64411d62"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e510c21a4b664dc08859fa434283cfa7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcEl8V7lQJFd",
        "outputId": "7afbd619-4aac-4f40-bd26-4f6e5ff8aa98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Vector Store Backed Retriever\n",
        "A vector store retriever is a retriever that uses a vector store to retrieve documents.\n",
        "It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store."
      ],
      "metadata": {
        "id": "RWzrEL7uQd3a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_retriever = vectorstore.as_retriever(\n",
        "        search_type=\"mmr\", search_kwargs={\"k\": 4, \"fetch_k\": 10}\n",
        "    )"
      ],
      "metadata": {
        "id": "u2cKfaVnQPfw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(max([len(chunk.page_content) for chunk in docs]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLK-MFfDQXZl",
        "outputId": "6f7e733f-e90d-4d6d-a15b-9a0ce2ff8a26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "============================================================================================="
      ],
      "metadata": {
        "id": "XsKP4ocTRCzC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prompt template\n"
      ],
      "metadata": {
        "id": "HQ-fsaKLRH3z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.prompts import ChatPromptTemplate\n",
        "template = \"\"\"You are an assistant for question-answering tasks.\n",
        "Use the following pieces of retrieved context to answer the question.\n",
        "If you don't know the answer, just say that you don't know.\n",
        "Use three sentences maximum and keep the answer concise.\n",
        "Question: {question}\n",
        "Context: {context}\n",
        "Answer:\n",
        "\"\"\"\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "# print(prompt)\n",
        "prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUHmQiRaRD15",
        "outputId": "199ab0fe-cafa-4928-edd1-318690d72d4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ChatPromptTemplate(input_variables=['context', 'question'], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template=\"You are an assistant for question-answering tasks.\\nUse the following pieces of retrieved context to answer the question.\\nIf you don't know the answer, just say that you don't know.\\nUse three sentences maximum and keep the answer concise.\\nQuestion: {question}\\nContext: {context}\\nAnswer:\\n\"))])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RAG Chain"
      ],
      "metadata": {
        "id": "Jr_taSKARgEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.schema.runnable import RunnablePassthrough\n",
        "from langchain.schema.output_parser import StrOutputParser\n",
        "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
        "rag_chain = (\n",
        "    {\"context\": chroma_retriever, \"question\": RunnablePassthrough()}\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")\n",
        "\n",
        "query = \"What are the two main tasks on which BERT is pre-trained on?\"\n",
        "rag_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "pQBXD9SsRLlx",
        "outputId": "2817b6da-87c9-44e4-d78f-acffce7cfc6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `ChatOpenAI` was deprecated in LangChain 0.0.10 and will be removed in 0.3.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BERT is pre-trained on two main tasks: Masked Language Model (MLM) and Next Sentence Prediction (NSP). MLM involves predicting masked words in a sentence, while NSP involves determining if two sentences are consecutive in a document. These tasks enable BERT to learn bidirectional representations and improve performance on downstream tasks.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rag Chain function"
      ],
      "metadata": {
        "id": "GXwZgkWIRkmt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.schema.runnable import RunnablePassthrough\n",
        "from langchain.schema.output_parser import StrOutputParser\n",
        "\n",
        "def rag_chain(retriever):\n",
        "  llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
        "  rag_chain = (\n",
        "      {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
        "      | prompt\n",
        "      | llm\n",
        "      | StrOutputParser()\n",
        "  )\n",
        "  return rag_chain\n",
        "\n",
        "# query = \"What are the two main tasks on which BERT is pre-trained on?\"\n",
        "# rag_chain.invoke(query)"
      ],
      "metadata": {
        "id": "MGYP6ubeRUr_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ground Truths Dataset\n",
        "Procured the ground truth dataset from the research paper, ARAGOG, https://arxiv.org/pdf/2404.01037\n",
        "\n",
        "They used 13 papers and generated the ground truths using GPT-4, but here owing to limited resources, I have used one paper of BERT and used 8 samples of the original 107 samples."
      ],
      "metadata": {
        "id": "XrfH4hNTR_qE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# questions= [\n",
        "#         \"What are the two main tasks BERT is pre-trained on?\",\n",
        "#         \"What model sizes are reported for BERT, and what are their specifications?\",\n",
        "#         \"How does BERT's architecture facilitate the use of a unified model across diverse NLP tasks?\",\n",
        "#         \"How does BERT's performance on the GLUE benchmark compare to previous state-of-the-art models?\",\n",
        "#         \"What significant improvements does BERT bring to the SQuAD v1.1,v2.0 and v13.5 tasks compared to prior models?\",\n",
        "#         \"Explain how BERT uses the 'masked LM' (MLM) for its pre-training.\",\n",
        "#         \"Discuss the impact of model size on BERT's performance across different tasks.\",\n",
        "#         \"What datasets were used for BERT's pre-training and why?\"\n",
        "#     ]\n",
        "# ground_truths = [\n",
        "#         \"Masked LM (MLM) and Next Sentence Prediction (NSP).\",\n",
        "#         \"BERTBASE (L=12, H=768, A=12, Total Parameters=110M) and BERTLARGE (L=24, H=1024, A=16, Total Parameters=340M).\",\n",
        "#         \"BERT uses a multi-layer bidirectional Transformer encoder architecture, allowing for minimal task-specific architecture modifications in fine-tuning.\",\n",
        "#         \"BERT achieved new state-of-the-art on the GLUE benchmark (80.5%), surpassing the previous best models.\",\n",
        "#         \"BERT set new records on SQuAD v1.1 and v2.0, significantly outperforming the top leaderboard systems at the time. Version 13.5 doesn't exist. \",\n",
        "#         \"In MLM, a percentage of input tokens are masked randomly, and the model predicts these masked tokens based on their context.\",\n",
        "#         \"Larger BERT models consistently show better performance across tasks, indicating the importance of model size in achieving high accuracy.\",\n",
        "#         \"BooksCorpus (800M words) and English Wikipedia (2500M words), chosen for their document-level organization and volume of data.\"\n",
        "#          ]\n",
        "\n"
      ],
      "metadata": {
        "id": "wYd429BjSBrh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "questions= [\n",
        "        \"What are the two main tasks BERT is pre-trained on?\",\n",
        "        \"What model sizes are reported for BERT, and what are their specifications?\",\n",
        "        \"How does BERT's architecture facilitate the use of a unified model across diverse NLP tasks?\"\n",
        "    ]\n",
        "ground_truths = [\n",
        "        \"Masked LM (MLM) and Next Sentence Prediction (NSP).\",\n",
        "        \"BERTBASE (L=12, H=768, A=12, Total Parameters=110M) and BERTLARGE (L=24, H=1024, A=16, Total Parameters=340M).\",\n",
        "        \"BERT uses a multi-layer bidirectional Transformer encoder architecture, allowing for minimal task-specific architecture modifications in fine-tuning.\"\n",
        "]\n",
        "\n"
      ],
      "metadata": {
        "id": "dyv3fj9hT1Oo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ragas Evaluation Metrics\n",
        "I used the Ragas evaluation metrics from the paper, \"RAGAS: Automated Evaluation of Retrieval Augmented Generation\", [link](https://arxiv.org/pdf/2309.15217)\n",
        "\n",
        "It defines 7 metrics to evaluate a RAG pipeline:\n",
        "- **Retrieval metrics**: context precision, context recall, context relevancy\n",
        "- **Generation metrics**: faithfulness, answer relevancy\n",
        "- **End to end pipeline metrics**: answer similarity, answer correctness"
      ],
      "metadata": {
        "id": "LqM0qztMS_km"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ragas.metrics import (\n",
        "    answer_relevancy,\n",
        "    faithfulness,\n",
        "    context_recall,\n",
        "    context_precision,\n",
        "    context_relevancy,\n",
        "    answer_correctness,\n",
        "    answer_similarity\n",
        ")\n",
        "\n",
        "from ragas.metrics.critique import harmfulness\n",
        "from ragas import evaluate\n",
        "from datasets import Dataset\n"
      ],
      "metadata": {
        "id": "jTGlpTltSC-P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_raga_dataset(rag_chain, retriever):\n",
        "  answers = []\n",
        "  contexts = []\n",
        "\n",
        "  for query in questions:\n",
        "    answers.append(rag_chain.invoke(query))\n",
        "    contexts.append([docs.page_content for docs in retriever.get_relevant_documents(query)])\n",
        "  data = {\n",
        "    \"question\": questions,\n",
        "    \"answer\": answers,\n",
        "    \"contexts\": contexts,\n",
        "    \"ground_truth\": ground_truths\n",
        "  }\n",
        "  dataset = Dataset.from_dict(data)\n",
        "  return dataset"
      ],
      "metadata": {
        "id": "DUBl9bpuSK0f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_ragas_dataset(ragas_dataset):\n",
        "  result = evaluate(\n",
        "    ragas_dataset,\n",
        "    metrics=[\n",
        "        context_precision,\n",
        "        faithfulness,\n",
        "        answer_relevancy,\n",
        "        context_recall,\n",
        "        context_relevancy,\n",
        "        answer_correctness,\n",
        "        answer_similarity\n",
        "    ],\n",
        "  )\n",
        "  return result"
      ],
      "metadata": {
        "id": "AvsUWz-6SuAK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating Chroma retriever"
      ],
      "metadata": {
        "id": "lpqJcMaRTF3b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_qa_chain = rag_chain(chroma_retriever)"
      ],
      "metadata": {
        "id": "T7zaBThhTIXU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What are the two main tasks on which BERT is pre-trained on?\"\n",
        "chroma_qa_chain.invoke(query)"
      ],
      "metadata": {
        "id": "gB154skpcvTH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "c80d1be5-58ad-4092-8b89-13a645d4200d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"BERT is pre-trained on two main tasks: Masked Language Model (MLM) and Next Sentence Prediction (NSP). These tasks enable bidirectionality and improve the model's performance.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_dataset = create_raga_dataset(chroma_qa_chain, chroma_retriever)"
      ],
      "metadata": {
        "id": "R1VR_gl8TZ1H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "488c50b8-9900-4840-eab9-d83646a69687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 0.3.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_result = evaluate_ragas_dataset(chroma_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "aMlbk8WKTp49",
        "outputId": "6e64a8b9-e124-4a9d-deb6-ad260073ed00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/56 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e38b4d13c59c4b97845c5fa29f08268f"
            }
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48qMSKPPT4Hw",
        "outputId": "c3731275-3b59-4cef-d187-72796bf8d515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'context_precision': 0.9201, 'faithfulness': 0.9062, 'answer_relevancy': 0.8138, 'context_recall': 0.8750, 'context_relevancy': 0.0313, 'answer_correctness': 0.7052, 'answer_similarity': 0.8956}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_results = []\n",
        "all_results.append(chroma_result)"
      ],
      "metadata": {
        "id": "v0BHzjoFUHRm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_df = chroma_result.to_pandas()\n",
        "chroma_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "krOGZAsnWeJK",
        "outputId": "cd6a69e2-632d-490a-81af-389c8baaf668"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                            question  \\\n",
              "0  What are the two main tasks BERT is pre-traine...   \n",
              "1  What model sizes are reported for BERT, and wh...   \n",
              "2  How does BERT's architecture facilitate the us...   \n",
              "3  How does BERT's performance on the GLUE benchm...   \n",
              "4  What significant improvements does BERT bring ...   \n",
              "\n",
              "                                              answer  \\\n",
              "0  BERT is pre-trained on two main tasks: Masked ...   \n",
              "1  BERT BASE contains 110M parameters, while BERT...   \n",
              "2  BERT's architecture facilitates the use of a u...   \n",
              "3  BERT's performance on the GLUE benchmark outpe...   \n",
              "4  BERT brings significant improvements to the SQ...   \n",
              "\n",
              "                                            contexts  \\\n",
              "0  [• GPT used the same learning rate of 5e-5 for...   \n",
              "1  [For example, the largest Transformer explored...   \n",
              "2  [the need for many heavily-engineered task-\\ns...   \n",
              "3  [reported GLUE task, MNLI, BERT obtains a 4.6%...   \n",
              "4  [• GPT used the same learning rate of 5e-5 for...   \n",
              "\n",
              "                                        ground_truth  context_precision  \\\n",
              "0  Masked LM (MLM) and Next Sentence Prediction (...           0.805556   \n",
              "1  BERTBASE (L=12, H=768, A=12, Total Parameters=...           1.000000   \n",
              "2  BERT uses a multi-layer bidirectional Transfor...           0.916667   \n",
              "3  BERT achieved new state-of-the-art on the GLUE...           1.000000   \n",
              "4  BERT set new records on SQuAD v1.1 and v2.0, s...           1.000000   \n",
              "\n",
              "   faithfulness  answer_relevancy  context_recall  context_relevancy  \\\n",
              "0          1.00          0.954348             1.0           0.000000   \n",
              "1          0.50          0.000000             1.0           0.017699   \n",
              "2          1.00          0.896769             1.0           0.040816   \n",
              "3          1.00          0.973565             1.0           0.034483   \n",
              "4          0.75          0.931294             0.5           0.000000   \n",
              "\n",
              "   answer_correctness  answer_similarity  \n",
              "0            0.586239           0.844955  \n",
              "1            0.607835           0.931340  \n",
              "2            0.728305           0.913221  \n",
              "3            0.654823           0.905007  \n",
              "4            0.781131           0.874503  "
            ],
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              "      <th>0</th>\n",
              "      <td>What are the two main tasks BERT is pre-traine...</td>\n",
              "      <td>BERT is pre-trained on two main tasks: Masked ...</td>\n",
              "      <td>[• GPT used the same learning rate of 5e-5 for...</td>\n",
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              "      <td>How does BERT's architecture facilitate the us...</td>\n",
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              "      <td>BERT uses a multi-layer bidirectional Transfor...</td>\n",
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              "      <td>0.913221</td>\n",
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              "      <td>How does BERT's performance on the GLUE benchm...</td>\n",
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              "      <td>0.973565</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.034483</td>\n",
              "      <td>0.654823</td>\n",
              "      <td>0.905007</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>What significant improvements does BERT bring ...</td>\n",
              "      <td>BERT brings significant improvements to the SQ...</td>\n",
              "      <td>[• GPT used the same learning rate of 5e-5 for...</td>\n",
              "      <td>BERT set new records on SQuAD v1.1 and v2.0, s...</td>\n",
              "      <td>1.000000</td>\n",
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              "summary": "{\n  \"name\": \"chroma_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"What model sizes are reported for BERT, and what are their specifications?\",\n          \"Explain how BERT uses the 'masked LM' (MLM) for its pre-training.\",\n          \"What are the two main tasks BERT is pre-trained on?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"BERT BASE contains 110M parameters, while BERT LARGE contains 340M parameters. The specifications for BERT BASE are not provided in the context. The largest Transformer model explored in the literature has (L=64, H=512, A=2) with 235M parameters.\",\n          \"BERT uses a \\\"masked language model\\\" (MLM) pre-training objective where some tokens from the input are randomly masked, and the goal is to predict the original vocabulary id of the masked tokens. This helps alleviate the unidirectionality constraint by allowing BERT to consider both left and right context during pre-training. The purpose of the masking strategies in BERT is to reduce the mismatch between pre-training and fine-tuning stages.\",\n          \"BERT is pre-trained on two main tasks: Masked LM and Next Sentence Prediction. These tasks help BERT understand the context and relationships between words in a sentence.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ground_truth\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"BERTBASE (L=12, H=768, A=12, Total Parameters=110M) and BERTLARGE (L=24, H=1024, A=16, Total Parameters=340M).\",\n          \"In MLM, a percentage of input tokens are masked randomly, and the model predicts these masked tokens based on their context.\",\n          \"Masked LM (MLM) and Next Sentence Prediction (NSP).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13316581738649488,\n        \"min\": 0.6388888888675925,\n        \"max\": 0.999999999975,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.999999999975,\n          0.6388888888675925,\n          0.9166666666361111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18600595228571123,\n        \"min\": 0.5,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.5,\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33069604248836815,\n        \"min\": 0.0,\n        \"max\": 0.9735647938669866,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          0.8656711905412592,\n          0.9543477114581688\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23145502494313785,\n        \"min\": 0.5,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.027375674779846745,\n        \"min\": 0.0,\n        \"max\": 0.08333333333333333,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0,\n          0.017699115044247787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_correctness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13522496469948908,\n        \"min\": 0.5216352619677825,\n        \"max\": 0.8876298525616213,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6078349175565809,\n          0.8876298525616213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.028756345492839314,\n        \"min\": 0.8449551108158311,\n        \"max\": 0.9313396702263236,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9313396702263236,\n          0.8838527435798185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_df.to_csv(\"chroma_retriever.csv\")"
      ],
      "metadata": {
        "id": "Kk1cP6ItWqsG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "===================================================================================================="
      ],
      "metadata": {
        "id": "kP6sWKB5UR-h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Parent Document Retriever\n",
        "One of the easier ways of improving a retriever is to embed our documents into small chunks, and then retrieve a significant amount of additional context that \"surrounds\" the found context.\n",
        "\n",
        "You can read more about this method [here](https://python.langchain.com/v0.1/docs/modules/data_connection/retrievers/parent_document_retriever/)!\n",
        "\n",
        "The basic outline of this retrieval method is as follows:\n",
        "\n",
        "- Obtain User Question\n",
        "- Retrieve child documents using Dense Vector Retrieval\n",
        "M- erge the child documents based on their parents. If they have the same parents, they become merged.\n",
        "- Replace the child documents with their respective parent documents from an in-memory-store.\n",
        "- Use the parent documents to augment generation."
      ],
      "metadata": {
        "id": "ezCfyRymUZpH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers import ParentDocumentRetriever\n",
        "from langchain.storage import InMemoryStore\n",
        "\n",
        "parent_splitter = RecursiveCharacterTextSplitter(chunk_size=1500)\n",
        "child_splitter = RecursiveCharacterTextSplitter(chunk_size=200)\n",
        "\n",
        "vectorstore = Chroma(collection_name=\"split_parents\", embedding_function=embeddings)\n",
        "\n",
        "store = InMemoryStore()"
      ],
      "metadata": {
        "id": "KgZm_Pg2UTZL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parent_document_retriever = ParentDocumentRetriever(\n",
        "    vectorstore=vectorstore,\n",
        "    docstore=store,\n",
        "    child_splitter=child_splitter,\n",
        "    parent_splitter=parent_splitter,\n",
        ")"
      ],
      "metadata": {
        "id": "x-MfgJO3U2-Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parent_document_retriever.add_documents(b_docs)"
      ],
      "metadata": {
        "id": "ZFZn4a0DU5vE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating Parent Document Retriever"
      ],
      "metadata": {
        "id": "l5XcR2z0ViOI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "parent_qa_chain = rag_chain(parent_document_retriever)"
      ],
      "metadata": {
        "id": "Ta6ptBwXVlTu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What are the two main tasks on which BERT is pre-trained on?\"\n",
        "parent_qa_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "kjffmQwTbkmf",
        "outputId": "82616d38-b858-45a3-b7ce-7a82ac1fd943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"BERT is pre-trained on the BooksCorpus and Wikipedia, which consist of a total of 3,300M words. The two main tasks on which BERT is pre-trained are the masked language model (MLM) and the next sentence prediction (NSP) task. These tasks enable bidirectionality and contribute significantly to the model's performance.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pda_dataset = create_raga_dataset(parent_qa_chain, parent_document_retriever)"
      ],
      "metadata": {
        "id": "B6x2RyAgVuNw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pda_result = evaluate_ragas_dataset(pda_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ed3201e67c7a44fb929c4d4d42101709",
            "e88b2025d7a24eb0ab4c040054c4f07a",
            "d488f1f85b4e49109d4bdb70bd41aeed",
            "500fde3cda3240119eac5d8bac4fa58d",
            "5fad609884b5407180beee56f0839912",
            "51bb002df8b443c8aea3af8294a1d80c",
            "6fe52af6342a4a8587451304adc72480",
            "68bce1fb22fe42d58c9111f89d2eb9e6",
            "2652eddc13204db4a50a3fe2b2a04b5a",
            "4e08033d359049bbb33c101e1b367281",
            "edc48600690143b1b7a17a7957ac8371"
          ]
        },
        "id": "Wltxn5z1V7g5",
        "outputId": "d35a540f-771e-4744-f93a-4a919a3b0f55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/56 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed3201e67c7a44fb929c4d4d42101709"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pda_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzcodgZ2WBYB",
        "outputId": "2eff0279-49e9-4d20-dc52-fd9735e46837"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'context_precision': 0.9167, 'faithfulness': 1.0000, 'answer_relevancy': 0.9169, 'context_recall': 0.9018, 'context_relevancy': 0.0226, 'answer_correctness': 0.7600, 'answer_similarity': 0.8820}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# all_results = []\n",
        "all_results.append(pda_result)"
      ],
      "metadata": {
        "id": "Z-RIjTYJWVnv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wvGRnOjXNCm",
        "outputId": "a3bb1e38-6eaa-4284-8f47-c2364e59b550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'context_precision': 0.9201, 'faithfulness': 0.9062, 'answer_relevancy': 0.8138, 'context_recall': 0.8750, 'context_relevancy': 0.0313, 'answer_correctness': 0.7052, 'answer_similarity': 0.8956},\n",
              " {'context_precision': 0.9167, 'faithfulness': 1.0000, 'answer_relevancy': 0.9169, 'context_recall': 0.9018, 'context_relevancy': 0.0226, 'answer_correctness': 0.7600, 'answer_similarity': 0.8820}]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pda_df = pda_result.to_pandas()\n",
        "pda_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "BK3pmunKW8kf",
        "outputId": "244aa61f-5438-49d5-e9f1-dd57696ae4af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            question  \\\n",
              "0  What are the two main tasks BERT is pre-traine...   \n",
              "1  What model sizes are reported for BERT, and wh...   \n",
              "2  How does BERT's architecture facilitate the us...   \n",
              "3  How does BERT's performance on the GLUE benchm...   \n",
              "4  What significant improvements does BERT bring ...   \n",
              "\n",
              "                                              answer  \\\n",
              "0  BERT is pre-trained on the BooksCorpus and Wik...   \n",
              "1  Two model sizes are reported for BERT: BERT BA...   \n",
              "2  BERT's architecture facilitates the use of a u...   \n",
              "3  BERT BASE and BERT LARGE outperform all system...   \n",
              "4  BERT brings significant improvements to the SQ...   \n",
              "\n",
              "                                            contexts  \\\n",
              "0  [between how BERT and GPT were trained:\\n• GPT...   \n",
              "1  [of Transformers has become common and our im-...   \n",
              "2  [word based only on its context. Unlike left-t...   \n",
              "3  [best model on the Dev set. With random restar...   \n",
              "4  [best model on the Dev set. With random restar...   \n",
              "\n",
              "                                        ground_truth  context_precision  \\\n",
              "0  Masked LM (MLM) and Next Sentence Prediction (...           1.000000   \n",
              "1  BERTBASE (L=12, H=768, A=12, Total Parameters=...           1.000000   \n",
              "2  BERT uses a multi-layer bidirectional Transfor...           1.000000   \n",
              "3  BERT achieved new state-of-the-art on the GLUE...           1.000000   \n",
              "4  BERT set new records on SQuAD v1.1 and v2.0, s...           0.833333   \n",
              "\n",
              "   faithfulness  answer_relevancy  context_recall  context_relevancy  \\\n",
              "0           1.0          0.932955        0.714286           0.000000   \n",
              "1           1.0          0.946293        1.000000           0.010204   \n",
              "2           1.0          0.932509        1.000000           0.025641   \n",
              "3           1.0          0.901840        1.000000           0.045455   \n",
              "4           1.0          0.931294        0.500000           0.031746   \n",
              "\n",
              "   answer_correctness  answer_similarity  \n",
              "0            0.802006           0.808024  \n",
              "1            0.610601           0.942403  \n",
              "2            0.869800           0.907773  \n",
              "3            0.471662           0.886649  \n",
              "4            0.786231           0.894923  "
            ],
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              "      <th>0</th>\n",
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              "      <td>BERT is pre-trained on the BooksCorpus and Wik...</td>\n",
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              "      <td>BERT uses a multi-layer bidirectional Transfor...</td>\n",
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              "      <td>0.471662</td>\n",
              "      <td>0.886649</td>\n",
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BERT BASE was chosen to have the same model size as OpenAI GPT for comparison purposes. Increasing model size leads to continual improvements on large-scale tasks such as machine translation.\",\n          \"BERT uses the 'masked LM' (MLM) for pre-training by masking some percentage of input tokens at random and predicting those masked tokens. This procedure allows BERT to train a deep bidirectional representation. The final hidden vectors corresponding to the mask tokens are fed into an output softmax.\",\n          \"BERT is pre-trained on the BooksCorpus and Wikipedia, totaling 800M and 2,500M words respectively. 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            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pda_df.to_csv(\"parent_document_retriever.csv\")"
      ],
      "metadata": {
        "id": "IueKkMW8W-2G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Ensemble Retriever (Dense + sparse embeddings)\n",
        "\n",
        "\n",
        "Official Documentation [here](https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble)!\n",
        "\n",
        "The basic idea is as follows:\n",
        "\n",
        "1. Obtain User Question\n",
        "2. Hit the Retriever Pair\n",
        "    - Retrieve Documents with BM25 Sparse Vector Retrieval\n",
        "    - Retrieve Documents with Dense Vector Retrieval Method\n",
        "3. Collect and \"fuse\" the retrieved docs based on their weighting using the [Reciprocal Rank Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) algorithm into a single ranked list.\n",
        "4. Use those documents to augment our generation.\n",
        "\n"
      ],
      "metadata": {
        "id": "RKNAuDYAYv5T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U rank_bm25"
      ],
      "metadata": {
        "id": "TLzQ_WShYtYg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers import BM25Retriever, EnsembleRetriever\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=450, chunk_overlap=75)\n",
        "docs = text_splitter.split_documents(b_docs)\n",
        "\n",
        "bm25_retriever = BM25Retriever.from_documents(docs)\n",
        "bm25_retriever.k = 2\n",
        "\n",
        "vectorstore = Chroma.from_documents(docs, embeddings)\n",
        "chroma_retriever = vectorstore.as_retriever(search_kwargs={\"k\": 3})\n",
        "\n",
        "ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, chroma_retriever], weights=[0.75, 0.25])"
      ],
      "metadata": {
        "id": "sFRGIgr3Y5gt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating Ensemble Retriever"
      ],
      "metadata": {
        "id": "XZz-3nUsZH8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble_bm25_qa_chain = rag_chain(ensemble_retriever)"
      ],
      "metadata": {
        "id": "Idr3FVgWZGuq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble_bm25_dataset = create_raga_dataset(ensemble_bm25_qa_chain, ensemble_retriever)"
      ],
      "metadata": {
        "id": "uv3kKIYLdJ5X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What are the two main tasks on which BERT is pre-trained on?\"\n",
        "ensemble_bm25_qa_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
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        "id": "ntkfzVLlcpPU",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BERT is pre-trained on two main tasks: pre-training and fine-tuning. During pre-training, the model is trained on unlabeled data over different pre-training tasks, while fine-tuning involves initializing the BERT model with pre-trained parameters and fine-tuning all parameters using labeled data from downstream tasks.'"
            ],
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          "metadata": {},
          "execution_count": 72
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    },
    {
      "cell_type": "code",
      "source": [
        "ensemble_bm25_result = evaluate_ragas_dataset(ensemble_bm25_dataset)"
      ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/56 [00:00<?, ?it/s]"
            ],
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      "cell_type": "code",
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        "ensemble_bm25_result"
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        {
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            "text/plain": [
              "{'context_precision': 0.8807, 'faithfulness': 1.0000, 'answer_relevancy': 0.9203, 'context_recall': 0.8750, 'context_relevancy': 0.0660, 'answer_correctness': 0.6833, 'answer_similarity': 0.8660}"
            ]
          },
          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "source": [
        "all_results.append(ensemble_bm25_result)"
      ],
      "metadata": {
        "id": "8ewfMyPRZtY-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble_bm25_df = ensemble_bm25_result.to_pandas()\n",
        "ensemble_bm25_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "iCm3L2vNZ0YV",
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      "execution_count": null,
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              "                                            question  \\\n",
              "0  What are the two main tasks BERT is pre-traine...   \n",
              "1  What model sizes are reported for BERT, and wh...   \n",
              "2  How does BERT's architecture facilitate the us...   \n",
              "3  How does BERT's performance on the GLUE benchm...   \n",
              "4  What significant improvements does BERT bring ...   \n",
              "\n",
              "                                              answer  \\\n",
              "0  BERT is pre-trained on unlabeled data over dif...   \n",
              "1  The reported model sizes for BERT are BERT BAS...   \n",
              "2  BERT's architecture allows for fine-tuning on ...   \n",
              "3  BERT's performance on the GLUE benchmark signi...   \n",
              "4  BERT brings significant improvements to tasks ...   \n",
              "\n",
              "                                            contexts  \\\n",
              "0  [3 BERT\\nWe introduce BERT and its detailed im...   \n",
              "1  [model, single task. F1 scores are reported fo...   \n",
              "2  [(GLUE) benchmark (Wang et al., 2018a) is a co...   \n",
              "3  [is that few parameters need to be learned fro...   \n",
              "4  [mixed results on the downstream task impact o...   \n",
              "\n",
              "                                        ground_truth  context_precision  \\\n",
              "0  Masked LM (MLM) and Next Sentence Prediction (...           0.833333   \n",
              "1  BERTBASE (L=12, H=768, A=12, Total Parameters=...           0.950000   \n",
              "2  BERT uses a multi-layer bidirectional Transfor...           1.000000   \n",
              "3  BERT achieved new state-of-the-art on the GLUE...           0.679167   \n",
              "4  BERT set new records on SQuAD v1.1 and v2.0, s...           1.000000   \n",
              "\n",
              "   faithfulness  answer_relevancy  context_recall  context_relevancy  \\\n",
              "0           1.0          0.998159             1.0           0.037736   \n",
              "1           1.0          0.949461             1.0           0.275862   \n",
              "2           1.0          0.914433             1.0           0.063830   \n",
              "3           1.0          0.941220             0.5           0.085106   \n",
              "4           1.0          0.931294             0.5           0.000000   \n",
              "\n",
              "   answer_correctness  answer_similarity  \n",
              "0            0.490276           0.761103  \n",
              "1            0.896093           0.917705  \n",
              "2            0.722742           0.890970  \n",
              "3            0.479565           0.918259  \n",
              "4            0.596304           0.885215  "
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              "      <th></th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>contexts</th>\n",
              "      <th>ground_truth</th>\n",
              "      <th>context_precision</th>\n",
              "      <th>faithfulness</th>\n",
              "      <th>answer_relevancy</th>\n",
              "      <th>context_recall</th>\n",
              "      <th>context_relevancy</th>\n",
              "      <th>answer_correctness</th>\n",
              "      <th>answer_similarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What are the two main tasks BERT is pre-traine...</td>\n",
              "      <td>BERT is pre-trained on unlabeled data over dif...</td>\n",
              "      <td>[3 BERT\\nWe introduce BERT and its detailed im...</td>\n",
              "      <td>Masked LM (MLM) and Next Sentence Prediction (...</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.998159</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>0.490276</td>\n",
              "      <td>0.761103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>What model sizes are reported for BERT, and wh...</td>\n",
              "      <td>The reported model sizes for BERT are BERT BAS...</td>\n",
              "      <td>[model, single task. F1 scores are reported fo...</td>\n",
              "      <td>BERTBASE (L=12, H=768, A=12, Total Parameters=...</td>\n",
              "      <td>0.950000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.949461</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.275862</td>\n",
              "      <td>0.896093</td>\n",
              "      <td>0.917705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>How does BERT's architecture facilitate the us...</td>\n",
              "      <td>BERT's architecture allows for fine-tuning on ...</td>\n",
              "      <td>[(GLUE) benchmark (Wang et al., 2018a) is a co...</td>\n",
              "      <td>BERT uses a multi-layer bidirectional Transfor...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.914433</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.063830</td>\n",
              "      <td>0.722742</td>\n",
              "      <td>0.890970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>How does BERT's performance on the GLUE benchm...</td>\n",
              "      <td>BERT's performance on the GLUE benchmark signi...</td>\n",
              "      <td>[is that few parameters need to be learned fro...</td>\n",
              "      <td>BERT achieved new state-of-the-art on the GLUE...</td>\n",
              "      <td>0.679167</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.941220</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.085106</td>\n",
              "      <td>0.479565</td>\n",
              "      <td>0.918259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>What significant improvements does BERT bring ...</td>\n",
              "      <td>BERT brings significant improvements to tasks ...</td>\n",
              "      <td>[mixed results on the downstream task impact o...</td>\n",
              "      <td>BERT set new records on SQuAD v1.1 and v2.0, s...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.931294</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.596304</td>\n",
              "      <td>0.885215</td>\n",
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              "  @keyframes spin {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ensemble_bm25_df",
              "summary": "{\n  \"name\": \"ensemble_bm25_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"What model sizes are reported for BERT, and what are their specifications?\",\n          \"Explain how BERT uses the 'masked LM' (MLM) for its pre-training.\",\n          \"What are the two main tasks BERT is pre-trained on?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"The reported model sizes for BERT are BERT BASE with 12 layers, hidden size of 768, and 12 self-attention heads, and BERT LARGE with specifications of (L=24, H=1024, A=16). BERT BASE has 110M parameters, while BERT LARGE has 340M parameters. Increasing model size leads to improvements in tasks like machine translation and language modeling.\",\n          \"BERT uses a mixed strategy for masking the target tokens during pre-training with the masked language model (MLM) objective. This strategy involves masking some tokens with the [MASK] symbol, keeping some tokens as is, and replacing some tokens with random tokens. The purpose of these masking strategies is to reduce the mismatch between pre-training and fine-tuning stages.\",\n          \"BERT is pre-trained on unlabeled data over different pre-training tasks. The two main tasks BERT is pre-trained on are pre-training and fine-tuning. During pre-training, the model is trained on unlabeled data over different pre-training tasks.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ground_truth\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"BERTBASE (L=12, H=768, A=12, Total Parameters=110M) and BERTLARGE (L=24, H=1024, A=16, Total Parameters=340M).\",\n          \"In MLM, a percentage of input tokens are masked randomly, and the model predicts these masked tokens based on their context.\",\n          \"Masked LM (MLM) and Next Sentence Prediction (NSP).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16605449328968758,\n        \"min\": 0.5833333333041666,\n        \"max\": 0.99999999998,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.8333333332916666,\n          0.94999999997625,\n          0.999999999975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.044631496884796416,\n        \"min\": 0.8584767133001246,\n        \"max\": 0.9981589830419283,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9494609297751838\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23145502494313785,\n        \"min\": 0.5,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08958835553206902,\n        \"min\": 0.0,\n        \"max\": 0.27586206896551724,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.03773584905660377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_correctness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15558134548170927,\n        \"min\": 0.47956473826779084,\n        \"max\": 0.8960927984055082,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8960927984055082\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06024790582098705,\n        \"min\": 0.7611025998756533,\n        \"max\": 0.9182589530711633,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9177045269553662\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble_bm25_df.to_csv(\"ensemble_bm25_retriever.csv\")"
      ],
      "metadata": {
        "id": "fE3eNl3WZ6sL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. ColBERT Retriever\n",
        "ColBERT: Contextualized Late Interaction over BERT\n",
        "\n",
        "Late Interaction: Unlike traditional dense retrievers that compute a single fixed size vector representation for entire queries and documents, ColBERT maitains separate fine grained embeddings for each token in the query and the document. It then performs a \"late\" interaction between the embeddings, allowing for more nuanced and precise similarity calculations.\n",
        "\n",
        "Official Documentation [here](https://github.com/stanford-futuredata/ColBERT)"
      ],
      "metadata": {
        "id": "1RKNX_2HaHx3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -U ragatouille --quiet"
      ],
      "metadata": {
        "id": "asRXqYieadav"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ragatouille import RAGPretrainedModel\n",
        "\n",
        "r_docs = loader.load_and_split()\n",
        "\n",
        "RAG = RAGPretrainedModel.from_pretrained(\"colbert-ir/colbertv2.0\")\n",
        "ragatouille_docs = [str(doc) for doc in r_docs]\n",
        "\n",
        "RAG.index(\n",
        "  collection=ragatouille_docs,\n",
        "  index_name=\"langchain-index\",\n",
        "  max_document_length=512,\n",
        "  split_documents=True,\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "apCKzEkRaDJz",
        "outputId": "9edee06a-4693-4e9a-b9bd-b62e1b4760bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "artifact.metadata:   0%|          | 0.00/1.63k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97d2b63865b343beade7220cac1c11c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86c0d9224bda47f3a36a58f625056bac"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer_config.json:   0%|          | 0.00/405 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "349e2e51e35f4b90ad2225f3693b034e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ece5798ad64a4cf4a1d4bb9715ed4c8c"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4e5e6a9d56a84fb3b72abebd670da66b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d107cfd2d61248a89598183699a3350e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 12:56:10] Loading segmented_maxsim_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...\n",
            "---- WARNING! You are using PLAID with an experimental replacement for FAISS for greater compatibility ----\n",
            "This is a behaviour change from RAGatouille 0.8.0 onwards.\n",
            "This works fine for most users and smallish datasets, but can be considerably slower than FAISS and could cause worse results in some situations.\n",
            "If you're confident with FAISS working on your machine, pass use_faiss=True to revert to the FAISS-using behaviour.\n",
            "--------------------\n",
            "\n",
            "\n",
            "[May 09, 12:56:57] #> Creating directory .ragatouille/colbert/indexes/langchain-index \n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 12:56:59] [0] \t\t #> Encoding 54 passages..\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/2 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "100%|██████████| 2/2 [02:16<00:00, 68.07s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 12:59:15] [0] \t\t avg_doclen_est = 301.96295166015625 \t len(local_sample) = 54\n",
            "[May 09, 12:59:15] [0] \t\t Creating 1,024 partitions.\n",
            "[May 09, 12:59:15] [0] \t\t *Estimated* 16,305 embeddings.\n",
            "[May 09, 12:59:15] [0] \t\t #> Saving the indexing plan to .ragatouille/colbert/indexes/langchain-index/plan.json ..\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "used 20 iterations (9.144s) to cluster 15491 items into 1024 clusters\n",
            "[0.036, 0.035, 0.032, 0.032, 0.032, 0.037, 0.034, 0.032, 0.033, 0.035, 0.031, 0.034, 0.035, 0.037, 0.035, 0.035, 0.032, 0.036, 0.035, 0.035, 0.034, 0.035, 0.034, 0.035, 0.034, 0.033, 0.034, 0.034, 0.037, 0.037, 0.034, 0.039, 0.034, 0.033, 0.033, 0.031, 0.035, 0.035, 0.033, 0.04, 0.036, 0.035, 0.033, 0.037, 0.035, 0.033, 0.033, 0.039, 0.035, 0.033, 0.033, 0.035, 0.038, 0.037, 0.035, 0.036, 0.041, 0.034, 0.04, 0.033, 0.034, 0.039, 0.037, 0.039, 0.037, 0.036, 0.034, 0.032, 0.029, 0.037, 0.036, 0.032, 0.036, 0.034, 0.04, 0.035, 0.035, 0.036, 0.037, 0.036, 0.037, 0.037, 0.036, 0.035, 0.033, 0.033, 0.034, 0.036, 0.032, 0.038, 0.033, 0.037, 0.033, 0.035, 0.034, 0.035, 0.036, 0.034, 0.036, 0.034, 0.037, 0.037, 0.034, 0.036, 0.033, 0.031, 0.035, 0.033, 0.035, 0.03, 0.035, 0.034, 0.038, 0.032, 0.036, 0.033, 0.037, 0.036, 0.032, 0.035, 0.035, 0.033, 0.034, 0.04, 0.034, 0.041, 0.036, 0.032]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 12:59:24] [0] \t\t #> Encoding 54 passages..\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "  0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\n",
            " 50%|█████     | 1/2 [01:17<01:17, 77.18s/it]\u001b[A\n",
            "100%|██████████| 2/2 [02:12<00:00, 66.37s/it]\n",
            "1it [02:13, 133.40s/it]\n",
            "100%|██████████| 1/1 [00:00<00:00, 917.39it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 13:01:38] #> Optimizing IVF to store map from centroids to list of pids..\n",
            "[May 09, 13:01:38] #> Building the emb2pid mapping..\n",
            "[May 09, 13:01:38] len(emb2pid) = 16306\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "100%|██████████| 1024/1024 [00:00<00:00, 23272.02it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 13:01:38] #> Saved optimized IVF to .ragatouille/colbert/indexes/langchain-index/ivf.pid.pt\n",
            "Done indexing!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'.ragatouille/colbert/indexes/langchain-index'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RAG = RAGPretrainedModel.from_index(\".ragatouille/colbert/indexes/langchain-index\")\n",
        "ragatouille_retriever = RAG.as_langchain_retriever(k=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXr8VcokbLvP",
        "outputId": "737c2e4a-2266-462e-b40e-d5b51bd62088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ragatouille_qa_chain = rag_chain(ragatouille_retriever)"
      ],
      "metadata": {
        "id": "z9vvz9LXcg3x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ragatouille_dataset = create_raga_dataset(ragatouille_qa_chain, ragatouille_retriever)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_0zJKrWdVSJ",
        "outputId": "a9bb7de7-de34-4c17-943c-be5d871fc14e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading searcher for index langchain-index for the first time... This may take a few seconds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 13:07:07] #> Loading codec...\n",
            "[May 09, 13:07:07] #> Loading IVF...\n",
            "[May 09, 13:07:07] Loading segmented_lookup_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 13:07:50] #> Loading doclens...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1/1 [00:00<00:00, 2559.06it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 13:07:50] #> Loading codes and residuals...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "100%|██████████| 1/1 [00:00<00:00, 129.15it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 13:07:50] Loading filter_pids_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[May 09, 13:08:25] Loading decompress_residuals_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...\n",
            "Searcher loaded!\n",
            "\n",
            "#> QueryTokenizer.tensorize(batch_text[0], batch_background[0], bsize) ==\n",
            "#> Input: . What are the two main tasks BERT is pre-trained on?, \t\t True, \t\t None\n",
            "#> Output IDs: torch.Size([32]), tensor([  101,     1,  2054,  2024,  1996,  2048,  2364,  8518, 14324,  2003,\n",
            "         3653,  1011,  4738,  2006,  1029,   102,   103,   103,   103,   103,\n",
            "          103,   103,   103,   103,   103,   103,   103,   103,   103,   103,\n",
            "          103,   103])\n",
            "#> Output Mask: torch.Size([32]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0])\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What are the two main tasks on which BERT is pre-trained on?\"\n",
        "ragatouille_qa_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "NGK9rhVwdYmt",
        "outputId": "a721fae4-20eb-4784-8f8a-cb7328d2018d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BERT is pre-trained on two main tasks: the Masked LM task, where some tokens are masked and predicted, and the Next Sentence Prediction (NSP) task, where the model predicts if two sentences follow each other in the original text. These pre-training tasks enable BERT to learn bidirectional representations and achieve state-of-the-art results on various downstream tasks.'"
            ],
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          "metadata": {},
          "execution_count": 75
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    },
    {
      "cell_type": "code",
      "source": [
        "colbert_docs = ragatouille_retriever.invoke(query)\n",
        "pretty_print_docs(colbert_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BI-TgSD5eTG7",
        "outputId": "6064ad29-61dd-43e2-fad2-f34fba69d3b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document 1:\n",
            "\n",
            "There are two steps in our\\nframework: pre-training and ﬁne-tuning . Dur-\\ning pre-training, the model is trained on unlabeled\\ndata over different pre-training tasks. For ﬁne-\\ntuning, the BERT model is ﬁrst initialized with\\nthe pre-trained parameters, and all of the param-\\neters are ﬁne-tuned using labeled data from the\\ndownstream tasks. Each downstream task has sep-\\narate ﬁne-tuned models, even though they are ini-\\ntialized with the same pre-trained parameters. The\\nquestion-answering example in Figure 1 will serve\\nas a running example for this section.\\nA distinctive feature of BERT is its uniﬁed ar-\\nchitecture across different tasks. There is mini-mal difference between the pre-trained architec-\\nture and the ﬁnal downstream architecture.\\nModel Architecture BERT’s model architec-\\nture is a multi-layer bidirectional Transformer en-\\ncoder based on the original implementation de-\\nscribed in Vaswani et al. (2017) and released in\\nthetensor2tensor library.1Because the use\\nof Transformers has become common and our im-\\nplementation is almost identical to the original,\\nwe will omit an exhaustive background descrip-\\ntion of the model architecture and refer readers to\\nVaswani et al.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 2:\n",
            "\n",
            "In fact, many of the design decisions in BERT\\nwere intentionally made to make it as close to\\nGPT as possible so that the two methods could be\\nminimally compared. The core argument of this\\nwork is that the bi-directionality and the two pre-\\ntraining tasks presented in Section 3.1 account for\\nthe majority of the empirical improvements, but\\nwe do note that there are several other differences\\nbetween how BERT and GPT were trained:\\n• GPT is trained on the BooksCorpus (800M\\nwords); BERT is trained on the BooksCor-\\npus (800M words) and Wikipedia (2,500M\\nwords).\\n• GPT uses a sentence separator ( [SEP] ) and\\nclassiﬁer token ( [CLS] ) which are only in-\\ntroduced at ﬁne-tuning time; BERT learns\\n[SEP] ,[CLS] and sentence A/Bembed-\\ndings during pre-training.\\n• GPT was trained for 1M steps with a batch\\nsize of 32,000 words; BERT was trained for\\n1M steps with a batch size of 128,000 words.\\n• GPT used the same learning rate of 5e-5 for\\nall ﬁne-tuning experiments; BERT chooses a\\ntask-speciﬁc ﬁne-tuning learning rate which\\nperforms the best on the development set.To isolate the effect of these differences, we per-\\nform ablation experiments in Section 5.1 which\\ndemonstrate that the majority of the improvements\\nare in fact coming from the two pre-training tasks\\nand the bidirectionality they enable.\\nA.5 Illustrations of Fine-tuning on Different\\nTasks\\nThe illustration of ﬁne-tuning BERT on different\\ntasks can be seen in Figure 4. Our task-speciﬁc\\nmodels are formed by incorporating BERT with\\none additional output layer, so a minimal num-\\nber of parameters need to be learned from scratch.\\nAmong the tasks, (a) and (b) are sequence-level\\ntasks while (c) and (d) are token-level tasks.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 3:\n",
            "\n",
            "(2016); Seo et al. (2017). BERT\\ninstead uses the self-attention mechanism to unify\\nthese two stages, as encoding a concatenated text\\npair with self-attention effectively includes bidi-\\nrectional cross attention between two sentences.\\nFor each task, we simply plug in the task-\\nspeciﬁc inputs and outputs into BERT and ﬁne-\\ntune all the parameters end-to-end. At the in-\\nput, sentence Aand sentence Bfrom pre-training\\nare analogous to (1) sentence pairs in paraphras-\\ning, (2) hypothesis-premise pairs in entailment, (3)\\nquestion-passage pairs in question answering, and(4) a degenerate text- ∅pair in text classiﬁcation\\nor sequence tagging. At the output, the token rep-\\nresentations are fed into an output layer for token-\\nlevel tasks, such as sequence tagging or question\\nanswering, and the [CLS] representation is fed\\ninto an output layer for classiﬁcation, such as en-\\ntailment or sentiment analysis.\\nCompared to pre-training, ﬁne-tuning is rela-\\ntively inexpensive. All of the results in the pa-\\nper can be replicated in at most 1 hour on a sin-\\ngle Cloud TPU, or a few hours on a GPU, starting\\nfrom the exact same pre-trained model.7We de-\\nscribe the task-speciﬁc details in the correspond-\\ning subsections of Section 4. More details can be\\nfound in Appendix A.5.\\n4 Experiments\\nIn this section, we present BERT ﬁne-tuning re-\\nsults on 11 NLP tasks.\\n4.1 GLUE\\nThe General Language Understanding Evaluation\\n(GLUE) benchmark (Wang et al., 2018a) is a col-\\nlection of diverse natural language understanding\\ntasks. Detailed descriptions of GLUE datasets are\\nincluded in Appendix B.1.\\nTo ﬁne-tune on GLUE, we represent the input\\nsequence (for single sentence or sentence pairs)\\nas described in Section 3, and use the ﬁnal hid-\\nden vectorC∈RHcorresponding to the ﬁrst\\ninput token ( [CLS] ) as the aggregate representa-\\ntion.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 4:\n",
            "\n",
            "page_content='mixed results on the downstream task impact of\\nincreasing the pre-trained bi-LM size from two\\nto four layers and Melamud et al. (2016) men-\\ntioned in passing that increasing hidden dimen-\\nsion size from 200 to 600 helped, but increasing\\nfurther to 1,000 did not bring further improve-\\nments. Both of these prior works used a feature-\\nbased approach — we hypothesize that when the\\nmodel is ﬁne-tuned directly on the downstream\\ntasks and uses only a very small number of ran-\\ndomly initialized additional parameters, the task-\\nspeciﬁc models can beneﬁt from the larger, more\\nexpressive pre-trained representations even when\\ndownstream task data is very small.\\n5.3 Feature-based Approach with BERT\\nAll of the BERT results presented so far have used\\nthe ﬁne-tuning approach, where a simple classiﬁ-\\ncation layer is added to the pre-trained model, and\\nall parameters are jointly ﬁne-tuned on a down-\\nstream task. However, the feature-based approach,\\nwhere ﬁxed features are extracted from the pre-\\ntrained model, has certain advantages. First, not\\nall tasks can be easily represented by a Trans-\\nformer encoder architecture, and therefore require\\na task-speciﬁc model architecture to be added.\\nSecond, there are major computational beneﬁts\\nto pre-compute an expensive representation of the\\ntraining data once and then run many experiments\\nwith cheaper models on top of this representation.\\nIn this section, we compare the two approaches\\nby applying BERT to the CoNLL-2003 Named\\nEntity Recognition (NER) task (Tjong Kim Sang\\nand De Meulder, 2003). In the input to BERT, we\\nuse a case-preserving WordPiece model, and we\\ninclude the maximal document context provided\\nby the data.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 5:\n",
            "\n",
            "page_content='BERT BERT \\nE[CLS] E1 E[SEP] ... ENE1’... EM’\\nC\\nT1\\nT[SEP] ...\\n TN\\nT1’...\\n TM’\\n[CLS] Tok 1 [SEP] ... Tok NTok 1 ... TokM \\nQuestion Paragraph Start/End Span \\nBERT \\nE[CLS] E1 E[SEP] ... ENE1’... EM’\\nC\\nT1\\nT[SEP] ...\\n TN\\nT1’...\\n TM’\\n[CLS] Tok 1 [SEP] ... Tok NTok 1 ... TokM \\nMasked Sentence A Masked Sentence B \\nPre-training Fine-Tuning NSP Mask LM Mask LM \\nUnlabeled Sentence A and B Pair SQuAD \\nQuestion Answer Pair NER MNLI Figure 1: Overall pre-training and ﬁne-tuning procedures for BERT. Apart from output layers, the same architec-\\ntures are used in both pre-training and ﬁne-tuning. The same pre-trained model parameters are used to initialize\\nmodels for different down-stream tasks. During ﬁne-tuning, all parameters are ﬁne-tuned. [CLS] is a special\\nsymbol added in front of every input example, and [SEP] is a special separator token (e.g. separating ques-\\ntions/answers).\\ning and auto-encoder objectives have been used\\nfor pre-training such models (Howard and Ruder,\\n2018; Radford et al., 2018; Dai and Le, 2015).\\n2.3 Transfer Learning from Supervised Data\\nThere has also been work showing effective trans-\\nfer from supervised tasks with large datasets, such\\nas natural language inference (Conneau et al.,\\n2017) and machine translation (McCann et al.,\\n2017). Computer vision research has also demon-\\nstrated the importance of transfer learning from\\nlarge pre-trained models, where an effective recipe\\nis to ﬁne-tune models pre-trained with Ima-\\ngeNet (Deng et al., 2009; Yosinski et al., 2014).\\n3 BERT\\nWe introduce BERT and its detailed implementa-\\ntion in this section.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 6:\n",
            "\n",
            "page_content='Dev Set\\nTasks MNLI-m QNLI MRPC SST-2 SQuAD\\n(Acc) (Acc) (Acc) (Acc) (F1)\\nBERT BASE 84.4 88.4 86.7 92.7 88.5\\nNo NSP 83.9 84.9 86.5 92.6 87.9\\nLTR & No NSP 82.1 84.3 77.5 92.1 77.8\\n+ BiLSTM 82.1 84.1 75.7 91.6 84.9\\nTable 5: Ablation over the pre-training tasks using the\\nBERT BASE architecture. “No NSP” is trained without\\nthe next sentence prediction task. “LTR & No NSP” is\\ntrained as a left-to-right LM without the next sentence\\nprediction, like OpenAI GPT. “+ BiLSTM” adds a ran-\\ndomly initialized BiLSTM on top of the “LTR + No\\nNSP” model during ﬁne-tuning.\\nablation studies can be found in Appendix C.\\n5.1 Effect of Pre-training Tasks\\nWe demonstrate the importance of the deep bidi-\\nrectionality of BERT by evaluating two pre-\\ntraining objectives using exactly the same pre-\\ntraining data, ﬁne-tuning scheme, and hyperpa-\\nrameters as BERT BASE :\\nNo NSP : A bidirectional model which is trained\\nusing the “masked LM” (MLM) but without the\\n“next sentence prediction” (NSP) task.\\nLTR & No NSP : A left-context-only model which\\nis trained using a standard Left-to-Right (LTR)\\nLM, rather than an MLM. The left-only constraint\\nwas also applied at ﬁne-tuning, because removing\\nit introduced a pre-train/ﬁne-tune mismatch that\\ndegraded downstream performance. Additionally,\\nthis model was pre-trained without the NSP task.\\nThis is directly comparable to OpenAI GPT, but\\nusing our larger training dataset, our input repre-\\nsentation, and our ﬁne-tuning scheme.\\nWe ﬁrst examine the impact brought by the NSP\\ntask.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 7:\n",
            "\n",
            "page_content='BERT: Pre-training of Deep Bidirectional Transformers for\\nLanguage Understanding\\nJacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova\\nGoogle AI Language\\n{jacobdevlin,mingweichang,kentonl,kristout }@google.com\\nAbstract\\nWe introduce a new language representa-\\ntion model called BERT , which stands for\\nBidirectional Encoder Representations from\\nTransformers. Unlike recent language repre-\\nsentation models (Peters et al., 2018a; Rad-\\nford et al., 2018), BERT is designed to pre-\\ntrain deep bidirectional representations from\\nunlabeled text by jointly conditioning on both\\nleft and right context in all layers. As a re-\\nsult, the pre-trained BERT model can be ﬁne-\\ntuned with just one additional output layer\\nto create state-of-the-art models for a wide\\nrange of tasks, such as question answering and\\nlanguage inference, without substantial task-\\nspeciﬁc architecture modiﬁcations.\\nBERT is conceptually simple and empirically\\npowerful. It obtains new state-of-the-art re-\\nsults on eleven natural language processing\\ntasks, including pushing the GLUE score to\\n80.5% (7.7% point absolute improvement),\\nMultiNLI accuracy to 86.7% (4.6% absolute\\nimprovement), SQuAD v1.1 question answer-\\ning Test F1 to 93.2 (1.5 point absolute im-\\nprovement) and SQuAD v2.0 Test F1 to 83.1\\n(5.1 point absolute improvement).\\n1 Introduction\\nLanguage model pre-training has been shown to\\nbe effective for improving many natural language\\nprocessing tasks (Dai and Le, 2015; Peters et al.,\\n2018a; Radford et al., 2018; Howard and Ruder,\\n2018).\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 8:\n",
            "\n",
            "(2018a) and Radford et al.\\n(2018), we do not use traditional left-to-right or\\nright-to-left language models to pre-train BERT.\\nInstead, we pre-train BERT using two unsuper-\\nvised tasks, described in this section. This step\\nis presented in the left part of Figure 1.\\nTask #1: Masked LM Intuitively, it is reason-\\nable to believe that a deep bidirectional model is\\nstrictly more powerful than either a left-to-right\\nmodel or the shallow concatenation of a left-to-\\nright and a right-to-left model. Unfortunately,\\nstandard conditional language models can only be\\ntrained left-to-right orright-to-left, since bidirec-\\ntional conditioning would allow each word to in-\\ndirectly “see itself”, and the model could trivially\\npredict the target word in a multi-layered context.\\nformer is often referred to as a “Transformer encoder” while\\nthe left-context-only version is referred to as a “Transformer\\ndecoder” since it can be used for text generation.In order to train a deep bidirectional representa-\\ntion, we simply mask some percentage of the input\\ntokens at random, and then predict those masked\\ntokens. We refer to this procedure as a “masked\\nLM” (MLM), although it is often referred to as a\\nCloze task in the literature (Taylor, 1953). In this\\ncase, the ﬁnal hidden vectors corresponding to the\\nmask tokens are fed into an output softmax over\\nthe vocabulary, as in a standard LM. In all of our\\nexperiments, we mask 15% of all WordPiece to-\\nkens in each sequence at random. In contrast to\\ndenoising auto-encoders (Vincent et al., 2008), we\\nonly predict the masked words rather than recon-\\nstructing the entire input.\\nAlthough this allows us to obtain a bidirec-\\ntional pre-trained model, a downside is that we\\nare creating a mismatch between pre-training and\\nﬁne-tuning, since the [MASK] token does not ap-\\npear during ﬁne-tuning. To mitigate this, we do\\nnot always replace “masked” words with the ac-\\ntual[MASK] token.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 9:\n",
            "\n",
            "page_content='Input/Output Representations To make BERT\\nhandle a variety of down-stream tasks, our input\\nrepresentation is able to unambiguously represent\\nboth a single sentence and a pair of sentences\\n(e.g.,⟨Question, Answer⟩) in one token sequence.\\nThroughout this work, a “sentence” can be an arbi-\\ntrary span of contiguous text, rather than an actual\\nlinguistic sentence. A “sequence” refers to the in-\\nput token sequence to BERT, which may be a sin-\\ngle sentence or two sentences packed together.\\nWe use WordPiece embeddings (Wu et al.,\\n2016) with a 30,000 token vocabulary. The ﬁrst\\ntoken of every sequence is always a special clas-\\nsiﬁcation token ( [CLS] ). The ﬁnal hidden state\\ncorresponding to this token is used as the ag-\\ngregate sequence representation for classiﬁcation\\ntasks. Sentence pairs are packed together into a\\nsingle sequence. We differentiate the sentences in\\ntwo ways. First, we separate them with a special\\ntoken ( [SEP] ). Second, we add a learned embed-\\nding to every token indicating whether it belongs\\nto sentence Aor sentence B. As shown in Figure 1,\\nwe denote input embedding as E, the ﬁnal hidden\\nvector of the special [CLS] token asC∈RH,\\nand the ﬁnal hidden vector for the ithinput token\\nasTi∈RH.\\nFor a given token, its input representation is\\nconstructed by summing the corresponding token,\\nsegment, and position embeddings. A visualiza-\\ntion of this construction can be seen in Figure 2.\\n3.1 Pre-training BERT\\nUnlike Peters et al. (2018a) and Radford et al.\\n(2018), we do not use traditional left-to-right or\\nright-to-left language models to pre-train BERT.\\nInstead, we pre-train BERT using two unsuper-\\nvised tasks, described in this section.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 10:\n",
            "\n",
            "page_content='[CLS] helikesplay## ing[SEP] mydogiscute[SEP]Input \\nE[CLS] Ehe Elikes Eplay E## ing E[SEP] Emy Edog Eis Ecute E[SEP] Token \\nEmbeddings \\nEA EB EB EB EB EB EA EA EA EA EASegment \\nEmbeddings \\nE0 E6 E7 E8 E9 E10 E1 E2 E3 E4 E5Position \\nEmbeddings Figure 2: BERT input representation. The input embeddings are the sum of the token embeddings, the segmenta-\\ntion embeddings and the position embeddings.\\nThe NSP task is closely related to representation-\\nlearning objectives used in Jernite et al. (2017) and\\nLogeswaran and Lee (2018). However, in prior\\nwork, only sentence embeddings are transferred to\\ndown-stream tasks, where BERT transfers all pa-\\nrameters to initialize end-task model parameters.\\nPre-training data The pre-training procedure\\nlargely follows the existing literature on language\\nmodel pre-training. For the pre-training corpus we\\nuse the BooksCorpus (800M words) (Zhu et al.,\\n2015) and English Wikipedia (2,500M words).\\nFor Wikipedia we extract only the text passages\\nand ignore lists, tables, and headers. It is criti-\\ncal to use a document-level corpus rather than a\\nshufﬂed sentence-level corpus such as the Billion\\nWord Benchmark (Chelba et al., 2013) in order to\\nextract long contiguous sequences.\\n3.2 Fine-tuning BERT\\nFine-tuning is straightforward since the self-\\nattention mechanism in the Transformer al-\\nlows BERT to model many downstream tasks—\\nwhether they involve single text or text pairs—by\\nswapping out the appropriate inputs and outputs.\\nFor applications involving text pairs, a common\\npattern is to independently encode text pairs be-\\nfore applying bidirectional cross attention, such\\nas Parikh et al. (2016); Seo et al. (2017).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ragatouille_dataset[0]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mEgA3YafsmS",
        "outputId": "467cafdd-2936-4ea5-bb78-de4eb6951e9f"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'question': 'What are the two main tasks BERT is pre-trained on?',\n",
              " 'answer': 'BERT is pre-trained on two main tasks: the Masked LM task and the Next Sentence Prediction (NSP) task. The Masked LM task involves predicting masked tokens in a sequence, while the NSP task focuses on predicting whether two sentences follow each other. These pre-training tasks enable BERT to learn bidirectional representations from unlabeled text data.',\n",
              " 'contexts': ['There are two steps in our\\\\nframework: pre-training and ﬁne-tuning . Dur-\\\\ning pre-training, the model is trained on unlabeled\\\\ndata over different pre-training tasks. For ﬁne-\\\\ntuning, the BERT model is ﬁrst initialized with\\\\nthe pre-trained parameters, and all of the param-\\\\neters are ﬁne-tuned using labeled data from the\\\\ndownstream tasks. Each downstream task has sep-\\\\narate ﬁne-tuned models, even though they are ini-\\\\ntialized with the same pre-trained parameters. The\\\\nquestion-answering example in Figure 1 will serve\\\\nas a running example for this section.\\\\nA distinctive feature of BERT is its uniﬁed ar-\\\\nchitecture across different tasks. There is mini-mal difference between the pre-trained architec-\\\\nture and the ﬁnal downstream architecture.\\\\nModel Architecture BERT’s model architec-\\\\nture is a multi-layer bidirectional Transformer en-\\\\ncoder based on the original implementation de-\\\\nscribed in Vaswani et al. (2017) and released in\\\\nthetensor2tensor library.1Because the use\\\\nof Transformers has become common and our im-\\\\nplementation is almost identical to the original,\\\\nwe will omit an exhaustive background descrip-\\\\ntion of the model architecture and refer readers to\\\\nVaswani et al.',\n",
              "  'In fact, many of the design decisions in BERT\\\\nwere intentionally made to make it as close to\\\\nGPT as possible so that the two methods could be\\\\nminimally compared. The core argument of this\\\\nwork is that the bi-directionality and the two pre-\\\\ntraining tasks presented in Section 3.1 account for\\\\nthe majority of the empirical improvements, but\\\\nwe do note that there are several other differences\\\\nbetween how BERT and GPT were trained:\\\\n• GPT is trained on the BooksCorpus (800M\\\\nwords); BERT is trained on the BooksCor-\\\\npus (800M words) and Wikipedia (2,500M\\\\nwords).\\\\n• GPT uses a sentence separator ( [SEP] ) and\\\\nclassiﬁer token ( [CLS] ) which are only in-\\\\ntroduced at ﬁne-tuning time; BERT learns\\\\n[SEP] ,[CLS] and sentence A/Bembed-\\\\ndings during pre-training.\\\\n• GPT was trained for 1M steps with a batch\\\\nsize of 32,000 words; BERT was trained for\\\\n1M steps with a batch size of 128,000 words.\\\\n• GPT used the same learning rate of 5e-5 for\\\\nall ﬁne-tuning experiments; BERT chooses a\\\\ntask-speciﬁc ﬁne-tuning learning rate which\\\\nperforms the best on the development set.To isolate the effect of these differences, we per-\\\\nform ablation experiments in Section 5.1 which\\\\ndemonstrate that the majority of the improvements\\\\nare in fact coming from the two pre-training tasks\\\\nand the bidirectionality they enable.\\\\nA.5 Illustrations of Fine-tuning on Different\\\\nTasks\\\\nThe illustration of ﬁne-tuning BERT on different\\\\ntasks can be seen in Figure 4. Our task-speciﬁc\\\\nmodels are formed by incorporating BERT with\\\\none additional output layer, so a minimal num-\\\\nber of parameters need to be learned from scratch.\\\\nAmong the tasks, (a) and (b) are sequence-level\\\\ntasks while (c) and (d) are token-level tasks.',\n",
              "  '(2016); Seo et al. (2017). BERT\\\\ninstead uses the self-attention mechanism to unify\\\\nthese two stages, as encoding a concatenated text\\\\npair with self-attention effectively includes bidi-\\\\nrectional cross attention between two sentences.\\\\nFor each task, we simply plug in the task-\\\\nspeciﬁc inputs and outputs into BERT and ﬁne-\\\\ntune all the parameters end-to-end. At the in-\\\\nput, sentence Aand sentence Bfrom pre-training\\\\nare analogous to (1) sentence pairs in paraphras-\\\\ning, (2) hypothesis-premise pairs in entailment, (3)\\\\nquestion-passage pairs in question answering, and(4) a degenerate text- ∅pair in text classiﬁcation\\\\nor sequence tagging. At the output, the token rep-\\\\nresentations are fed into an output layer for token-\\\\nlevel tasks, such as sequence tagging or question\\\\nanswering, and the [CLS] representation is fed\\\\ninto an output layer for classiﬁcation, such as en-\\\\ntailment or sentiment analysis.\\\\nCompared to pre-training, ﬁne-tuning is rela-\\\\ntively inexpensive. All of the results in the pa-\\\\nper can be replicated in at most 1 hour on a sin-\\\\ngle Cloud TPU, or a few hours on a GPU, starting\\\\nfrom the exact same pre-trained model.7We de-\\\\nscribe the task-speciﬁc details in the correspond-\\\\ning subsections of Section 4. More details can be\\\\nfound in Appendix A.5.\\\\n4 Experiments\\\\nIn this section, we present BERT ﬁne-tuning re-\\\\nsults on 11 NLP tasks.\\\\n4.1 GLUE\\\\nThe General Language Understanding Evaluation\\\\n(GLUE) benchmark (Wang et al., 2018a) is a col-\\\\nlection of diverse natural language understanding\\\\ntasks. Detailed descriptions of GLUE datasets are\\\\nincluded in Appendix B.1.\\\\nTo ﬁne-tune on GLUE, we represent the input\\\\nsequence (for single sentence or sentence pairs)\\\\nas described in Section 3, and use the ﬁnal hid-\\\\nden vectorC∈RHcorresponding to the ﬁrst\\\\ninput token ( [CLS] ) as the aggregate representa-\\\\ntion.',\n",
              "  \"page_content='mixed results on the downstream task impact of\\\\nincreasing the pre-trained bi-LM size from two\\\\nto four layers and Melamud et al. (2016) men-\\\\ntioned in passing that increasing hidden dimen-\\\\nsion size from 200 to 600 helped, but increasing\\\\nfurther to 1,000 did not bring further improve-\\\\nments. Both of these prior works used a feature-\\\\nbased approach — we hypothesize that when the\\\\nmodel is ﬁne-tuned directly on the downstream\\\\ntasks and uses only a very small number of ran-\\\\ndomly initialized additional parameters, the task-\\\\nspeciﬁc models can beneﬁt from the larger, more\\\\nexpressive pre-trained representations even when\\\\ndownstream task data is very small.\\\\n5.3 Feature-based Approach with BERT\\\\nAll of the BERT results presented so far have used\\\\nthe ﬁne-tuning approach, where a simple classiﬁ-\\\\ncation layer is added to the pre-trained model, and\\\\nall parameters are jointly ﬁne-tuned on a down-\\\\nstream task. However, the feature-based approach,\\\\nwhere ﬁxed features are extracted from the pre-\\\\ntrained model, has certain advantages. First, not\\\\nall tasks can be easily represented by a Trans-\\\\nformer encoder architecture, and therefore require\\\\na task-speciﬁc model architecture to be added.\\\\nSecond, there are major computational beneﬁts\\\\nto pre-compute an expensive representation of the\\\\ntraining data once and then run many experiments\\\\nwith cheaper models on top of this representation.\\\\nIn this section, we compare the two approaches\\\\nby applying BERT to the CoNLL-2003 Named\\\\nEntity Recognition (NER) task (Tjong Kim Sang\\\\nand De Meulder, 2003). In the input to BERT, we\\\\nuse a case-preserving WordPiece model, and we\\\\ninclude the maximal document context provided\\\\nby the data.\",\n",
              "  \"page_content='Dev Set\\\\nTasks MNLI-m QNLI MRPC SST-2 SQuAD\\\\n(Acc) (Acc) (Acc) (Acc) (F1)\\\\nBERT BASE 84.4 88.4 86.7 92.7 88.5\\\\nNo NSP 83.9 84.9 86.5 92.6 87.9\\\\nLTR & No NSP 82.1 84.3 77.5 92.1 77.8\\\\n+ BiLSTM 82.1 84.1 75.7 91.6 84.9\\\\nTable 5: Ablation over the pre-training tasks using the\\\\nBERT BASE architecture. “No NSP” is trained without\\\\nthe next sentence prediction task. “LTR & No NSP” is\\\\ntrained as a left-to-right LM without the next sentence\\\\nprediction, like OpenAI GPT. “+ BiLSTM” adds a ran-\\\\ndomly initialized BiLSTM on top of the “LTR + No\\\\nNSP” model during ﬁne-tuning.\\\\nablation studies can be found in Appendix C.\\\\n5.1 Effect of Pre-training Tasks\\\\nWe demonstrate the importance of the deep bidi-\\\\nrectionality of BERT by evaluating two pre-\\\\ntraining objectives using exactly the same pre-\\\\ntraining data, ﬁne-tuning scheme, and hyperpa-\\\\nrameters as BERT BASE :\\\\nNo NSP : A bidirectional model which is trained\\\\nusing the “masked LM” (MLM) but without the\\\\n“next sentence prediction” (NSP) task.\\\\nLTR & No NSP : A left-context-only model which\\\\nis trained using a standard Left-to-Right (LTR)\\\\nLM, rather than an MLM. The left-only constraint\\\\nwas also applied at ﬁne-tuning, because removing\\\\nit introduced a pre-train/ﬁne-tune mismatch that\\\\ndegraded downstream performance. Additionally,\\\\nthis model was pre-trained without the NSP task.\\\\nThis is directly comparable to OpenAI GPT, but\\\\nusing our larger training dataset, our input repre-\\\\nsentation, and our ﬁne-tuning scheme.\\\\nWe ﬁrst examine the impact brought by the NSP\\\\ntask.\",\n",
              "  \"page_content='BERT BERT \\\\nE[CLS] E1 E[SEP] ... ENE1’... EM’\\\\nC\\\\nT1\\\\nT[SEP] ...\\\\n TN\\\\nT1’...\\\\n TM’\\\\n[CLS] Tok 1 [SEP] ... Tok NTok 1 ... TokM \\\\nQuestion Paragraph Start/End Span \\\\nBERT \\\\nE[CLS] E1 E[SEP] ... ENE1’... EM’\\\\nC\\\\nT1\\\\nT[SEP] ...\\\\n TN\\\\nT1’...\\\\n TM’\\\\n[CLS] Tok 1 [SEP] ... Tok NTok 1 ... TokM \\\\nMasked Sentence A Masked Sentence B \\\\nPre-training Fine-Tuning NSP Mask LM Mask LM \\\\nUnlabeled Sentence A and B Pair SQuAD \\\\nQuestion Answer Pair NER MNLI Figure 1: Overall pre-training and ﬁne-tuning procedures for BERT. Apart from output layers, the same architec-\\\\ntures are used in both pre-training and ﬁne-tuning. The same pre-trained model parameters are used to initialize\\\\nmodels for different down-stream tasks. During ﬁne-tuning, all parameters are ﬁne-tuned. [CLS] is a special\\\\nsymbol added in front of every input example, and [SEP] is a special separator token (e.g. separating ques-\\\\ntions/answers).\\\\ning and auto-encoder objectives have been used\\\\nfor pre-training such models (Howard and Ruder,\\\\n2018; Radford et al., 2018; Dai and Le, 2015).\\\\n2.3 Transfer Learning from Supervised Data\\\\nThere has also been work showing effective trans-\\\\nfer from supervised tasks with large datasets, such\\\\nas natural language inference (Conneau et al.,\\\\n2017) and machine translation (McCann et al.,\\\\n2017). Computer vision research has also demon-\\\\nstrated the importance of transfer learning from\\\\nlarge pre-trained models, where an effective recipe\\\\nis to ﬁne-tune models pre-trained with Ima-\\\\ngeNet (Deng et al., 2009; Yosinski et al., 2014).\\\\n3 BERT\\\\nWe introduce BERT and its detailed implementa-\\\\ntion in this section.\",\n",
              "  \"page_content='BERT: Pre-training of Deep Bidirectional Transformers for\\\\nLanguage Understanding\\\\nJacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova\\\\nGoogle AI Language\\\\n{jacobdevlin,mingweichang,kentonl,kristout }@google.com\\\\nAbstract\\\\nWe introduce a new language representa-\\\\ntion model called BERT , which stands for\\\\nBidirectional Encoder Representations from\\\\nTransformers. Unlike recent language repre-\\\\nsentation models (Peters et al., 2018a; Rad-\\\\nford et al., 2018), BERT is designed to pre-\\\\ntrain deep bidirectional representations from\\\\nunlabeled text by jointly conditioning on both\\\\nleft and right context in all layers. As a re-\\\\nsult, the pre-trained BERT model can be ﬁne-\\\\ntuned with just one additional output layer\\\\nto create state-of-the-art models for a wide\\\\nrange of tasks, such as question answering and\\\\nlanguage inference, without substantial task-\\\\nspeciﬁc architecture modiﬁcations.\\\\nBERT is conceptually simple and empirically\\\\npowerful. It obtains new state-of-the-art re-\\\\nsults on eleven natural language processing\\\\ntasks, including pushing the GLUE score to\\\\n80.5% (7.7% point absolute improvement),\\\\nMultiNLI accuracy to 86.7% (4.6% absolute\\\\nimprovement), SQuAD v1.1 question answer-\\\\ning Test F1 to 93.2 (1.5 point absolute im-\\\\nprovement) and SQuAD v2.0 Test F1 to 83.1\\\\n(5.1 point absolute improvement).\\\\n1 Introduction\\\\nLanguage model pre-training has been shown to\\\\nbe effective for improving many natural language\\\\nprocessing tasks (Dai and Le, 2015; Peters et al.,\\\\n2018a; Radford et al., 2018; Howard and Ruder,\\\\n2018).\",\n",
              "  '(2018a) and Radford et al.\\\\n(2018), we do not use traditional left-to-right or\\\\nright-to-left language models to pre-train BERT.\\\\nInstead, we pre-train BERT using two unsuper-\\\\nvised tasks, described in this section. This step\\\\nis presented in the left part of Figure 1.\\\\nTask #1: Masked LM Intuitively, it is reason-\\\\nable to believe that a deep bidirectional model is\\\\nstrictly more powerful than either a left-to-right\\\\nmodel or the shallow concatenation of a left-to-\\\\nright and a right-to-left model. Unfortunately,\\\\nstandard conditional language models can only be\\\\ntrained left-to-right orright-to-left, since bidirec-\\\\ntional conditioning would allow each word to in-\\\\ndirectly “see itself”, and the model could trivially\\\\npredict the target word in a multi-layered context.\\\\nformer is often referred to as a “Transformer encoder” while\\\\nthe left-context-only version is referred to as a “Transformer\\\\ndecoder” since it can be used for text generation.In order to train a deep bidirectional representa-\\\\ntion, we simply mask some percentage of the input\\\\ntokens at random, and then predict those masked\\\\ntokens. We refer to this procedure as a “masked\\\\nLM” (MLM), although it is often referred to as a\\\\nCloze task in the literature (Taylor, 1953). In this\\\\ncase, the ﬁnal hidden vectors corresponding to the\\\\nmask tokens are fed into an output softmax over\\\\nthe vocabulary, as in a standard LM. In all of our\\\\nexperiments, we mask 15% of all WordPiece to-\\\\nkens in each sequence at random. In contrast to\\\\ndenoising auto-encoders (Vincent et al., 2008), we\\\\nonly predict the masked words rather than recon-\\\\nstructing the entire input.\\\\nAlthough this allows us to obtain a bidirec-\\\\ntional pre-trained model, a downside is that we\\\\nare creating a mismatch between pre-training and\\\\nﬁne-tuning, since the [MASK] token does not ap-\\\\npear during ﬁne-tuning. To mitigate this, we do\\\\nnot always replace “masked” words with the ac-\\\\ntual[MASK] token.',\n",
              "  \"page_content='word based only on its context. Unlike left-to-\\\\nright language model pre-training, the MLM ob-\\\\njective enables the representation to fuse the left\\\\nand the right context, which allows us to pre-\\\\ntrain a deep bidirectional Transformer. In addi-\\\\ntion to the masked language model, we also use\\\\na “next sentence prediction” task that jointly pre-\\\\ntrains text-pair representations. The contributions\\\\nof our paper are as follows:\\\\n• We demonstrate the importance of bidirectional\\\\npre-training for language representations. Un-\\\\nlike Radford et al. (2018), which uses unidirec-\\\\ntional language models for pre-training, BERT\\\\nuses masked language models to enable pre-\\\\ntrained deep bidirectional representations. This\\\\nis also in contrast to Peters et al. (2018a), which\\\\nuses a shallow concatenation of independently\\\\ntrained left-to-right and right-to-left LMs.\\\\n• We show that pre-trained representations reduce\\\\nthe need for many heavily-engineered task-\\\\nspeciﬁc architectures. BERT is the ﬁrst ﬁne-\\\\ntuning based representation model that achieves\\\\nstate-of-the-art performance on a large suite\\\\nof sentence-level andtoken-level tasks, outper-\\\\nforming many task-speciﬁc architectures.\\\\n• BERT advances the state of the art for eleven\\\\nNLP tasks. The code and pre-trained mod-\\\\nels are available at https://github.com/\\\\ngoogle-research/bert .\\\\n2 Related Work\\\\nThere is a long history of pre-training general lan-\\\\nguage representations, and we brieﬂy review the\\\\nmost widely-used approaches in this section.\\\\n2.1 Unsupervised Feature-based Approaches\\\\nLearning widely applicable representations of\\\\nwords has been an active area of research for\\\\ndecades, including non-neural (Brown et al., 1992;\\\\nAndo and Zhang, 2005; Blitzer et al., 2006) and\\\\nneural (Mikolov et al., 2013; Pennington et al.,\\\\n2014) methods. Pre-trained word embeddings\\\\nare an integral part of modern NLP systems, of-\\\\nfering signiﬁcant improvements over embeddings\\\\nlearned from scratch (Turian et al., 2010).\",\n",
              "  \"page_content='[CLS] helikesplay## ing[SEP] mydogiscute[SEP]Input \\\\nE[CLS] Ehe Elikes Eplay E## ing E[SEP] Emy Edog Eis Ecute E[SEP] Token \\\\nEmbeddings \\\\nEA EB EB EB EB EB EA EA EA EA EASegment \\\\nEmbeddings \\\\nE0 E6 E7 E8 E9 E10 E1 E2 E3 E4 E5Position \\\\nEmbeddings Figure 2: BERT input representation. The input embeddings are the sum of the token embeddings, the segmenta-\\\\ntion embeddings and the position embeddings.\\\\nThe NSP task is closely related to representation-\\\\nlearning objectives used in Jernite et al. (2017) and\\\\nLogeswaran and Lee (2018). However, in prior\\\\nwork, only sentence embeddings are transferred to\\\\ndown-stream tasks, where BERT transfers all pa-\\\\nrameters to initialize end-task model parameters.\\\\nPre-training data The pre-training procedure\\\\nlargely follows the existing literature on language\\\\nmodel pre-training. For the pre-training corpus we\\\\nuse the BooksCorpus (800M words) (Zhu et al.,\\\\n2015) and English Wikipedia (2,500M words).\\\\nFor Wikipedia we extract only the text passages\\\\nand ignore lists, tables, and headers. It is criti-\\\\ncal to use a document-level corpus rather than a\\\\nshufﬂed sentence-level corpus such as the Billion\\\\nWord Benchmark (Chelba et al., 2013) in order to\\\\nextract long contiguous sequences.\\\\n3.2 Fine-tuning BERT\\\\nFine-tuning is straightforward since the self-\\\\nattention mechanism in the Transformer al-\\\\nlows BERT to model many downstream tasks—\\\\nwhether they involve single text or text pairs—by\\\\nswapping out the appropriate inputs and outputs.\\\\nFor applications involving text pairs, a common\\\\npattern is to independently encode text pairs be-\\\\nfore applying bidirectional cross attention, such\\\\nas Parikh et al. (2016); Seo et al. (2017).\"],\n",
              " 'ground_truth': 'Masked LM (MLM) and Next Sentence Prediction (NSP).'}"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. MultiQuery Retriever\n",
        "Official Documentation [here](https://python.langchain.com/v0.1/docs/modules/data_connection/retrievers/MultiQueryRetriever/)\n",
        "\n",
        "The **MultiQueryRetriever** automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query.\n",
        "- For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents.\n",
        "- By generating multiple perspectives on the same question, the MultiQueryRetriever might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results."
      ],
      "metadata": {
        "id": "rFW66PbNhFZB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers.multi_query import MultiQueryRetriever\n",
        "multi_query_retriever = MultiQueryRetriever.from_llm(\n",
        "    retriever=vectorstore.as_retriever(), llm=llm\n",
        ")"
      ],
      "metadata": {
        "id": "9y_-H6wbhIj8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m_docs = multi_query_retriever.invoke(query)\n",
        "pretty_print_docs(m_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaAVCP4qhsFz",
        "outputId": "888ff465-35ad-422f-a97c-30a9feb5d38a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document 1:\n",
            "\n",
            "• GPT used the same learning rate of 5e-5 for\n",
            "all ﬁne-tuning experiments; BERT chooses a\n",
            "task-speciﬁc ﬁne-tuning learning rate which\n",
            "performs the best on the development set.To isolate the effect of these differences, we per-\n",
            "form ablation experiments in Section 5.1 which\n",
            "demonstrate that the majority of the improvements\n",
            "are in fact coming from the two pre-training tasks\n",
            "and the bidirectionality they enable.\n",
            "A.5 Illustrations of Fine-tuning on Different\n",
            "Tasks\n",
            "The illustration of ﬁne-tuning BERT on different\n",
            "tasks can be seen in Figure 4. Our task-speciﬁc\n",
            "models are formed by incorporating BERT with\n",
            "one additional output layer, so a minimal num-\n",
            "ber of parameters need to be learned from scratch.\n",
            "Among the tasks, (a) and (b) are sequence-level\n",
            "tasks while (c) and (d) are token-level tasks. In\n",
            "the ﬁgure,Erepresents the input embedding, Ti\n",
            "represents the contextual representation of token i,\n",
            "[CLS] is the special symbol for classiﬁcation out-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 2:\n",
            "\n",
            "BERT BERT \n",
            "E[CLS] E1 E[SEP] ... ENE1’... EM’\n",
            "C\n",
            "T1\n",
            "T[SEP] ...\n",
            " TN\n",
            "T1’...\n",
            " TM’\n",
            "[CLS] Tok 1 [SEP] ... Tok NTok 1 ... TokM \n",
            "Question Paragraph Start/End Span \n",
            "BERT \n",
            "E[CLS] E1 E[SEP] ... ENE1’... EM’\n",
            "C\n",
            "T1\n",
            "T[SEP] ...\n",
            " TN\n",
            "T1’...\n",
            " TM’\n",
            "[CLS] Tok 1 [SEP] ... Tok NTok 1 ... TokM \n",
            "Masked Sentence A Masked Sentence B \n",
            "Pre-training Fine-Tuning NSP Mask LM Mask LM \n",
            "Unlabeled Sentence A and B Pair SQuAD \n",
            "Question Answer Pair NER MNLI Figure 1: Overall pre-training and ﬁne-tuning procedures for BERT. Apart from output layers, the same architec-\n",
            "tures are used in both pre-training and ﬁne-tuning. The same pre-trained model parameters are used to initialize\n",
            "models for different down-stream tasks. During ﬁne-tuning, all parameters are ﬁne-tuned. [CLS] is a special\n",
            "symbol added in front of every input example, and [SEP] is a special separator token (e.g. separating ques-\n",
            "tions/answers).\n",
            "ing and auto-encoder objectives have been used\n",
            "for pre-training such models (Howard and Ruder,\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 3:\n",
            "\n",
            "tuning, the BERT model is ﬁrst initialized with\n",
            "the pre-trained parameters, and all of the param-\n",
            "eters are ﬁne-tuned using labeled data from the\n",
            "downstream tasks. Each downstream task has sep-\n",
            "arate ﬁne-tuned models, even though they are ini-\n",
            "tialized with the same pre-trained parameters. The\n",
            "question-answering example in Figure 1 will serve\n",
            "as a running example for this section.\n",
            "A distinctive feature of BERT is its uniﬁed ar-\n",
            "chitecture across different tasks. There is mini-mal difference between the pre-trained architec-\n",
            "ture and the ﬁnal downstream architecture.\n",
            "Model Architecture BERT’s model architec-\n",
            "ture is a multi-layer bidirectional Transformer en-\n",
            "coder based on the original implementation de-\n",
            "scribed in Vaswani et al. (2017) and released in\n",
            "thetensor2tensor library.1Because the use\n",
            "of Transformers has become common and our im-\n",
            "plementation is almost identical to the original,\n",
            "we will omit an exhaustive background descrip-\n",
            "tion of the model architecture and refer readers to\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 4:\n",
            "\n",
            "dinov, Raquel Urtasun, Antonio Torralba, and Sanja\n",
            "Fidler. 2015. Aligning books and movies: Towards\n",
            "story-like visual explanations by watching movies\n",
            "and reading books. In Proceedings of the IEEE\n",
            "international conference on computer vision , pages\n",
            "19–27.\n",
            "Appendix for “BERT: Pre-training of\n",
            "Deep Bidirectional Transformers for\n",
            "Language Understanding”\n",
            "We organize the appendix into three sections:\n",
            "• Additional implementation details for BERT\n",
            "are presented in Appendix A;• Additional details for our experiments are\n",
            "presented in Appendix B; and\n",
            "• Additional ablation studies are presented in\n",
            "Appendix C.\n",
            "We present additional ablation studies for\n",
            "BERT including:\n",
            "–Effect of Number of Training Steps; and\n",
            "–Ablation for Different Masking Proce-\n",
            "dures.\n",
            "A Additional Details for BERT\n",
            "A.1 Illustration of the Pre-training Tasks\n",
            "We provide examples of the pre-training tasks in\n",
            "the following.\n",
            "Masked LM and the Masking Procedure As-\n",
            "suming the unlabeled sentence is my dog is\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 5:\n",
            "\n",
            "BERT \n",
            "E[CLS] E1 E[SEP] ... ENE1’... EM’\n",
            "C\n",
            "T1\n",
            "T[SEP] ...\n",
            " TN\n",
            "T1’...\n",
            " TM’\n",
            "[CLS] Tok \n",
            "1 [SEP] ...Tok \n",
            "NTok \n",
            "1...Tok\n",
            "M\n",
            "Question Paragraph BERT \n",
            "E[CLS] E1 E2 EN\n",
            "C\n",
            "T1\n",
            " T2\n",
            " TN\n",
            "Single Sentence ...\n",
            "...BERT \n",
            "Tok 1 Tok 2 Tok N ... [CLS]E[CLS] E1 E2 EN\n",
            "C\n",
            "T1\n",
            " T2\n",
            " TN\n",
            "Single Sentence \n",
            "B-PER O O...\n",
            "... E[CLS] E1 E[SEP] Class \n",
            "Label \n",
            "... ENE1’... EM’\n",
            "C\n",
            "T1\n",
            "T[SEP] ...\n",
            " TN\n",
            "T1’...\n",
            " TM’\n",
            "Start/End Span Class \n",
            "Label \n",
            "BERT \n",
            "Tok 1 Tok 2 Tok N ... [CLS]Tok 1[CLS] [CLS] Tok \n",
            "1 [SEP] ...Tok \n",
            "NTok \n",
            "1...Tok\n",
            "M\n",
            "Sentence 1 \n",
            "...Sentence 2 \n",
            "Figure 4: Illustrations of Fine-tuning BERT on Different Tasks.\n",
            "SST-2 The Stanford Sentiment Treebank is a\n",
            "binary single-sentence classiﬁcation task consist-\n",
            "ing of sentences extracted from movie reviews\n",
            "with human annotations of their sentiment (Socher\n",
            "et al., 2013).\n",
            "CoLA The Corpus of Linguistic Acceptability is\n",
            "a binary single-sentence classiﬁcation task, where\n",
            "the goal is to predict whether an English sentence\n",
            "is linguistically “acceptable” or not (Warstadt\n",
            "et al., 2018).\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 6:\n",
            "\n",
            "the empirical improvements of the MLM model\n",
            "far outweigh the increased training cost.\n",
            "Next Sentence Prediction The next sentence\n",
            "prediction task can be illustrated in the following\n",
            "examples.\n",
            "Input =[CLS] the man went to [MASK] store [SEP]\n",
            "he bought a gallon [MASK] milk [SEP]\n",
            "Label =IsNext\n",
            "Input =[CLS] the man [MASK] to the store [SEP]\n",
            "penguin [MASK] are flight ##less birds [SEP]\n",
            "Label =NotNext\n",
            "A.2 Pre-training Procedure\n",
            "To generate each training input sequence, we sam-\n",
            "ple two spans of text from the corpus, which we\n",
            "refer to as “sentences” even though they are typ-\n",
            "ically much longer than single sentences (but can\n",
            "be shorter also). The ﬁrst sentence receives the A\n",
            "embedding and the second receives the Bembed-\n",
            "ding. 50% of the time Bis the actual next sentence\n",
            "that follows Aand 50% of the time it is a random\n",
            "sentence, which is done for the “next sentence pre-\n",
            "diction” task. They are sampled such that the com-\n",
            "bined length is≤512 tokens. The LM masking is\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 7:\n",
            "\n",
            "using the “masked LM” (MLM) but without the\n",
            "“next sentence prediction” (NSP) task.\n",
            "LTR & No NSP : A left-context-only model which\n",
            "is trained using a standard Left-to-Right (LTR)\n",
            "LM, rather than an MLM. The left-only constraint\n",
            "was also applied at ﬁne-tuning, because removing\n",
            "it introduced a pre-train/ﬁne-tune mismatch that\n",
            "degraded downstream performance. Additionally,\n",
            "this model was pre-trained without the NSP task.\n",
            "This is directly comparable to OpenAI GPT, but\n",
            "using our larger training dataset, our input repre-\n",
            "sentation, and our ﬁne-tuning scheme.\n",
            "We ﬁrst examine the impact brought by the NSP\n",
            "task. In Table 5, we show that removing NSP\n",
            "hurts performance signiﬁcantly on QNLI, MNLI,\n",
            "and SQuAD 1.1. Next, we evaluate the impact\n",
            "of training bidirectional representations by com-\n",
            "paring “No NSP” to “LTR & No NSP”. The LTR\n",
            "model performs worse than the MLM model on all\n",
            "tasks, with large drops on MRPC and SQuAD.\n",
            "For SQuAD it is intuitively clear that a LTR\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 8:\n",
            "\n",
            "pus. In fact, many of the design decisions in BERT\n",
            "were intentionally made to make it as close to\n",
            "GPT as possible so that the two methods could be\n",
            "minimally compared. The core argument of this\n",
            "work is that the bi-directionality and the two pre-\n",
            "training tasks presented in Section 3.1 account for\n",
            "the majority of the empirical improvements, but\n",
            "we do note that there are several other differences\n",
            "between how BERT and GPT were trained:\n",
            "• GPT is trained on the BooksCorpus (800M\n",
            "words); BERT is trained on the BooksCor-\n",
            "pus (800M words) and Wikipedia (2,500M\n",
            "words).\n",
            "• GPT uses a sentence separator ( [SEP] ) and\n",
            "classiﬁer token ( [CLS] ) which are only in-\n",
            "troduced at ﬁne-tuning time; BERT learns\n",
            "[SEP] ,[CLS] and sentence A/Bembed-\n",
            "dings during pre-training.\n",
            "• GPT was trained for 1M steps with a batch\n",
            "size of 32,000 words; BERT was trained for\n",
            "1M steps with a batch size of 128,000 words.\n",
            "• GPT used the same learning rate of 5e-5 for\n",
            "all ﬁne-tuning experiments; BERT chooses a\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 9:\n",
            "\n",
            "rectional cross attention between two sentences.\n",
            "For each task, we simply plug in the task-\n",
            "speciﬁc inputs and outputs into BERT and ﬁne-\n",
            "tune all the parameters end-to-end. At the in-\n",
            "put, sentence Aand sentence Bfrom pre-training\n",
            "are analogous to (1) sentence pairs in paraphras-\n",
            "ing, (2) hypothesis-premise pairs in entailment, (3)\n",
            "question-passage pairs in question answering, and(4) a degenerate text- ∅pair in text classiﬁcation\n",
            "or sequence tagging. At the output, the token rep-\n",
            "resentations are fed into an output layer for token-\n",
            "level tasks, such as sequence tagging or question\n",
            "answering, and the [CLS] representation is fed\n",
            "into an output layer for classiﬁcation, such as en-\n",
            "tailment or sentiment analysis.\n",
            "Compared to pre-training, ﬁne-tuning is rela-\n",
            "tively inexpensive. All of the results in the pa-\n",
            "per can be replicated in at most 1 hour on a sin-\n",
            "gle Cloud TPU, or a few hours on a GPU, starting\n",
            "from the exact same pre-trained model.7We de-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 10:\n",
            "\n",
            "ing and auto-encoder objectives have been used\n",
            "for pre-training such models (Howard and Ruder,\n",
            "2018; Radford et al., 2018; Dai and Le, 2015).\n",
            "2.3 Transfer Learning from Supervised Data\n",
            "There has also been work showing effective trans-\n",
            "fer from supervised tasks with large datasets, such\n",
            "as natural language inference (Conneau et al.,\n",
            "2017) and machine translation (McCann et al.,\n",
            "2017). Computer vision research has also demon-\n",
            "strated the importance of transfer learning from\n",
            "large pre-trained models, where an effective recipe\n",
            "is to ﬁne-tune models pre-trained with Ima-\n",
            "geNet (Deng et al., 2009; Yosinski et al., 2014).\n",
            "3 BERT\n",
            "We introduce BERT and its detailed implementa-\n",
            "tion in this section. There are two steps in our\n",
            "framework: pre-training and ﬁne-tuning . Dur-\n",
            "ing pre-training, the model is trained on unlabeled\n",
            "data over different pre-training tasks. For ﬁne-\n",
            "tuning, the BERT model is ﬁrst initialized with\n",
            "the pre-trained parameters, and all of the param-\n"
          ]
        }
      ]
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      "source": [
        "multi_query_qa_chain = rag_chain(multi_query_retriever)"
      ],
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      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "query = \"What are the two main tasks on which BERT is pre-trained on?\"\n",
        "multi_query_qa_chain.invoke(query)"
      ],
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          "data": {
            "text/plain": [
              "'BERT is pre-trained on two main tasks: Masked Language Model (MLM) and Next Sentence Prediction (NSP). These tasks help BERT learn the contextual relationships between words and sentences.'"
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          "execution_count": 91
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      "cell_type": "code",
      "source": [
        "multi_query_dataset = create_raga_dataset(multi_query_qa_chain, multi_query_retriever)"
      ],
      "metadata": {
        "id": "kigK06sPjJTN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "multi_query_result = evaluate_ragas_dataset(multi_query_dataset)"
      ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/56 [00:00<?, ?it/s]"
            ],
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        {
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          "data": {
            "text/plain": [
              "{'context_precision': 0.9165, 'faithfulness': 0.9437, 'answer_relevancy': 0.9445, 'context_recall': 0.7500, 'context_relevancy': 0.0368, 'answer_correctness': 0.6984, 'answer_similarity': 0.8863}"
            ]
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    {
      "cell_type": "code",
      "source": [
        "all_results.append(multi_query_result)\n",
        "multi_query_df = multi_query_result.to_pandas()\n",
        "multi_query_df.head()\n",
        "\n"
      ],
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              "                                            question  \\\n",
              "0  What are the two main tasks BERT is pre-traine...   \n",
              "1  What model sizes are reported for BERT, and wh...   \n",
              "2  How does BERT's architecture facilitate the us...   \n",
              "3  How does BERT's performance on the GLUE benchm...   \n",
              "4  What significant improvements does BERT bring ...   \n",
              "\n",
              "                                              answer  \\\n",
              "0  BERT is pre-trained on unlabeled data over dif...   \n",
              "1  The reported model sizes for BERT are BERT BAS...   \n",
              "2  BERT's architecture allows for a unified model...   \n",
              "3  BERT's performance on the GLUE benchmark outpe...   \n",
              "4  BERT brings significant improvements to the SQ...   \n",
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              "                                            contexts  \\\n",
              "0  [3 BERT\\nWe introduce BERT and its detailed im...   \n",
              "1  [we will omit an exhaustive background descrip...   \n",
              "2  [range of tasks, such as question answering an...   \n",
              "3  [tention masking. For the largest and most wid...   \n",
              "4  [Published\\nunet (Ensemble) - - 71.4 74.9\\nSLQ...   \n",
              "\n",
              "                                        ground_truth  context_precision  \\\n",
              "0  Masked LM (MLM) and Next Sentence Prediction (...           0.916667   \n",
              "1  BERTBASE (L=12, H=768, A=12, Total Parameters=...           1.000000   \n",
              "2  BERT uses a multi-layer bidirectional Transfor...           1.000000   \n",
              "3  BERT achieved new state-of-the-art on the GLUE...           1.000000   \n",
              "4  BERT set new records on SQuAD v1.1 and v2.0, s...           0.734524   \n",
              "\n",
              "   faithfulness  answer_relevancy  context_recall  context_relevancy  \\\n",
              "0           1.0          0.998159             1.0           0.024390   \n",
              "1           0.8          0.949461             0.5           0.013699   \n",
              "2           1.0          0.902549             1.0           0.037037   \n",
              "3           1.0          0.961370             0.5           0.038462   \n",
              "4           1.0          0.982522             0.5           0.047619   \n",
              "\n",
              "   answer_correctness  answer_similarity  \n",
              "0            0.491368           0.765472  \n",
              "1            0.536428           0.945712  \n",
              "2            0.869929           0.908288  \n",
              "3            0.654893           0.905286  \n",
              "4            0.721313           0.885250  "
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "multi_query_df",
              "summary": "{\n  \"name\": \"multi_query_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"What model sizes are reported for BERT, and what are their specifications?\",\n          \"Explain how BERT uses the 'masked LM' (MLM) for its pre-training.\",\n          \"What are the two main tasks BERT is pre-trained on?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"The reported model sizes for BERT are BERT BASE (L=12, H=768, A=12, Total Parameters=110M) and BERT LARGE (L=24, H=1024, A=16, Total Parameters=340M). BERT BASE has 110M parameters, while BERT LARGE has 340M parameters. The model sizes are defined by the number of layers, hidden size, and number of self-attention heads.\",\n          \"BERT uses a mixed strategy for masking target tokens during pre-training with the masked language model (MLM) objective. This strategy involves keeping some target tokens as is, replacing some with another random token, and using the [MASK] symbol. The purpose of these masking strategies is to reduce the mismatch between pre-training and fine-tuning stages, as the [MASK] symbol does not appear during fine-tuning.\",\n          \"BERT is pre-trained on unlabeled data over different pre-training tasks. The two main tasks BERT is pre-trained on are sequence-level tasks and token-level tasks. During fine-tuning, BERT is initialized with the pre-trained parameters and fine-tuned using labeled data from downstream tasks.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ground_truth\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"BERTBASE (L=12, H=768, A=12, Total Parameters=110M) and BERTLARGE (L=24, H=1024, A=16, Total Parameters=340M).\",\n          \"In MLM, a percentage of input tokens are masked randomly, and the model predicts these masked tokens based on their context.\",\n          \"Masked LM (MLM) and Next Sentence Prediction (NSP).\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10365453185931364,\n        \"min\": 0.7345238095115675,\n        \"max\": 0.9999999999857143,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9166666666361111,\n          0.999999999975,\n          0.8041666666465626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10500850305706538,\n        \"min\": 0.75,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.8,\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.040388557482052945,\n        \"min\": 0.8791666815816143,\n        \"max\": 0.9981589830419283,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9494609297751838,\n          0.8791666815816143,\n          0.9981589830419283\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2672612419124244,\n        \"min\": 0.5,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020626088105308835,\n        \"min\": 0.0136986301369863,\n        \"max\": 0.07894736842105263,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.024390243902439025,\n          0.0136986301369863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_correctness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13822099810085733,\n        \"min\": 0.49136802612981084,\n        \"max\": 0.8772723740784429,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5364280089755963,\n          0.7128325489375205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.057144293139675215,\n        \"min\": 0.7654721045192432,\n        \"max\": 0.9457120359023848,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9457120359023848,\n          0.8513301957500816\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multi_query_df.to_csv(\"multi_query_retriever.csv\")"
      ],
      "metadata": {
        "id": "SqJSLR4Qj0-e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Cohere Reranker\n",
        "\n",
        "Given a base retriever that will fetch relevant documents, say top 20 documents, we will use Cohere reranker to rerank the documents and fetch only the top 3 most relevant documents.\n",
        "- uses the idea of contextual compression, [link](https://blog.langchain.dev/improving-document-retrieval-with-contextual-compression/)."
      ],
      "metadata": {
        "id": "L5uC6stQkOHE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install --upgrade --quiet  cohere"
      ],
      "metadata": {
        "id": "0Ni-1TQTkLVb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install langchain-cohere"
      ],
      "metadata": {
        "id": "yqvgmlNIlIMc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "os.environ[\"COHERE_API_KEY\"] = getpass.getpass(\"Cohere API Key:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRCnKavokXXO",
        "outputId": "129e0440-118a-465c-c599-0ed62bc8c757"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cohere API Key:··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_rerank_retriever = vectorstore.as_retriever(\n",
        "        search_type=\"mmr\", search_kwargs={\"k\": 15}\n",
        "    )"
      ],
      "metadata": {
        "id": "perfQj_1l5AP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cc_docs = chroma_rerank_retriever.invoke(query)\n",
        "pretty_print_docs(cc_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2WkUoNimoiG",
        "outputId": "14fe8953-8fde-4dee-edfd-1e6c3d4469dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document 1:\n",
            "\n",
            "• GPT used the same learning rate of 5e-5 for\n",
            "all ﬁne-tuning experiments; BERT chooses a\n",
            "task-speciﬁc ﬁne-tuning learning rate which\n",
            "performs the best on the development set.To isolate the effect of these differences, we per-\n",
            "form ablation experiments in Section 5.1 which\n",
            "demonstrate that the majority of the improvements\n",
            "are in fact coming from the two pre-training tasks\n",
            "and the bidirectionality they enable.\n",
            "A.5 Illustrations of Fine-tuning on Different\n",
            "Tasks\n",
            "The illustration of ﬁne-tuning BERT on different\n",
            "tasks can be seen in Figure 4. Our task-speciﬁc\n",
            "models are formed by incorporating BERT with\n",
            "one additional output layer, so a minimal num-\n",
            "ber of parameters need to be learned from scratch.\n",
            "Among the tasks, (a) and (b) are sequence-level\n",
            "tasks while (c) and (d) are token-level tasks. In\n",
            "the ﬁgure,Erepresents the input embedding, Ti\n",
            "represents the contextual representation of token i,\n",
            "[CLS] is the special symbol for classiﬁcation out-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 2:\n",
            "\n",
            "pus. In fact, many of the design decisions in BERT\n",
            "were intentionally made to make it as close to\n",
            "GPT as possible so that the two methods could be\n",
            "minimally compared. The core argument of this\n",
            "work is that the bi-directionality and the two pre-\n",
            "training tasks presented in Section 3.1 account for\n",
            "the majority of the empirical improvements, but\n",
            "we do note that there are several other differences\n",
            "between how BERT and GPT were trained:\n",
            "• GPT is trained on the BooksCorpus (800M\n",
            "words); BERT is trained on the BooksCor-\n",
            "pus (800M words) and Wikipedia (2,500M\n",
            "words).\n",
            "• GPT uses a sentence separator ( [SEP] ) and\n",
            "classiﬁer token ( [CLS] ) which are only in-\n",
            "troduced at ﬁne-tuning time; BERT learns\n",
            "[SEP] ,[CLS] and sentence A/Bembed-\n",
            "dings during pre-training.\n",
            "• GPT was trained for 1M steps with a batch\n",
            "size of 32,000 words; BERT was trained for\n",
            "1M steps with a batch size of 128,000 words.\n",
            "• GPT used the same learning rate of 5e-5 for\n",
            "all ﬁne-tuning experiments; BERT chooses a\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 3:\n",
            "\n",
            "tuning, the BERT model is ﬁrst initialized with\n",
            "the pre-trained parameters, and all of the param-\n",
            "eters are ﬁne-tuned using labeled data from the\n",
            "downstream tasks. Each downstream task has sep-\n",
            "arate ﬁne-tuned models, even though they are ini-\n",
            "tialized with the same pre-trained parameters. The\n",
            "question-answering example in Figure 1 will serve\n",
            "as a running example for this section.\n",
            "A distinctive feature of BERT is its uniﬁed ar-\n",
            "chitecture across different tasks. There is mini-mal difference between the pre-trained architec-\n",
            "ture and the ﬁnal downstream architecture.\n",
            "Model Architecture BERT’s model architec-\n",
            "ture is a multi-layer bidirectional Transformer en-\n",
            "coder based on the original implementation de-\n",
            "scribed in Vaswani et al. (2017) and released in\n",
            "thetensor2tensor library.1Because the use\n",
            "of Transformers has become common and our im-\n",
            "plementation is almost identical to the original,\n",
            "we will omit an exhaustive background descrip-\n",
            "tion of the model architecture and refer readers to\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 4:\n",
            "\n",
            "ing and auto-encoder objectives have been used\n",
            "for pre-training such models (Howard and Ruder,\n",
            "2018; Radford et al., 2018; Dai and Le, 2015).\n",
            "2.3 Transfer Learning from Supervised Data\n",
            "There has also been work showing effective trans-\n",
            "fer from supervised tasks with large datasets, such\n",
            "as natural language inference (Conneau et al.,\n",
            "2017) and machine translation (McCann et al.,\n",
            "2017). Computer vision research has also demon-\n",
            "strated the importance of transfer learning from\n",
            "large pre-trained models, where an effective recipe\n",
            "is to ﬁne-tune models pre-trained with Ima-\n",
            "geNet (Deng et al., 2009; Yosinski et al., 2014).\n",
            "3 BERT\n",
            "We introduce BERT and its detailed implementa-\n",
            "tion in this section. There are two steps in our\n",
            "framework: pre-training and ﬁne-tuning . Dur-\n",
            "ing pre-training, the model is trained on unlabeled\n",
            "data over different pre-training tasks. For ﬁne-\n",
            "tuning, the BERT model is ﬁrst initialized with\n",
            "the pre-trained parameters, and all of the param-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 5:\n",
            "\n",
            "dinov, Raquel Urtasun, Antonio Torralba, and Sanja\n",
            "Fidler. 2015. Aligning books and movies: Towards\n",
            "story-like visual explanations by watching movies\n",
            "and reading books. In Proceedings of the IEEE\n",
            "international conference on computer vision , pages\n",
            "19–27.\n",
            "Appendix for “BERT: Pre-training of\n",
            "Deep Bidirectional Transformers for\n",
            "Language Understanding”\n",
            "We organize the appendix into three sections:\n",
            "• Additional implementation details for BERT\n",
            "are presented in Appendix A;• Additional details for our experiments are\n",
            "presented in Appendix B; and\n",
            "• Additional ablation studies are presented in\n",
            "Appendix C.\n",
            "We present additional ablation studies for\n",
            "BERT including:\n",
            "–Effect of Number of Training Steps; and\n",
            "–Ablation for Different Masking Proce-\n",
            "dures.\n",
            "A Additional Details for BERT\n",
            "A.1 Illustration of the Pre-training Tasks\n",
            "We provide examples of the pre-training tasks in\n",
            "the following.\n",
            "Masked LM and the Masking Procedure As-\n",
            "suming the unlabeled sentence is my dog is\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 6:\n",
            "\n",
            "BERT (Ours) \n",
            "Trm Trm Trm\n",
            "Trm Trm Trm...\n",
            "...Trm Trm Trm\n",
            "Trm Trm Trm...\n",
            "...OpenAI GPT \n",
            "Lstm ELMo \n",
            "Lstm Lstm \n",
            "Lstm Lstm Lstm Lstm Lstm Lstm \n",
            "Lstm Lstm Lstm  T1 T2 TN...\n",
            "...\n",
            "......\n",
            "...\n",
            " E1 E2 EN... T1 T2TN...\n",
            " E1 E2 EN ... T1 T2 TN...\n",
            " E1 E2 EN...Figure 3: Differences in pre-training model architectures. BERT uses a bidirectional Transformer. OpenAI GPT\n",
            "uses a left-to-right Transformer. ELMo uses the concatenation of independently trained left-to-right and right-to-\n",
            "left LSTMs to generate features for downstream tasks. Among the three, only BERT representations are jointly\n",
            "conditioned on both left and right context in all layers. In addition to the architecture differences, BERT and\n",
            "OpenAI GPT are ﬁne-tuning approaches, while ELMo is a feature-based approach.\n",
            "to converge. In Section C.1 we demonstrate that\n",
            "MLM does converge marginally slower than a left-\n",
            "to-right model (which predicts every token), but\n",
            "the empirical improvements of the MLM model\n",
            "far outweigh the increased training cost.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 7:\n",
            "\n",
            "two ways. First, we separate them with a special\n",
            "token ( [SEP] ). Second, we add a learned embed-\n",
            "ding to every token indicating whether it belongs\n",
            "to sentence Aor sentence B. As shown in Figure 1,\n",
            "we denote input embedding as E, the ﬁnal hidden\n",
            "vector of the special [CLS] token asC∈RH,\n",
            "and the ﬁnal hidden vector for the ithinput token\n",
            "asTi∈RH.\n",
            "For a given token, its input representation is\n",
            "constructed by summing the corresponding token,\n",
            "segment, and position embeddings. A visualiza-\n",
            "tion of this construction can be seen in Figure 2.\n",
            "3.1 Pre-training BERT\n",
            "Unlike Peters et al. (2018a) and Radford et al.\n",
            "(2018), we do not use traditional left-to-right or\n",
            "right-to-left language models to pre-train BERT.\n",
            "Instead, we pre-train BERT using two unsuper-\n",
            "vised tasks, described in this section. This step\n",
            "is presented in the left part of Figure 1.\n",
            "Task #1: Masked LM Intuitively, it is reason-\n",
            "able to believe that a deep bidirectional model is\n",
            "strictly more powerful than either a left-to-right\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 8:\n",
            "\n",
            "5 random restarts using those hyperparameters.\n",
            "layer in the output. We use the representation of\n",
            "the ﬁrst sub-token as the input to the token-level\n",
            "classiﬁer over the NER label set.\n",
            "To ablate the ﬁne-tuning approach, we apply the\n",
            "feature-based approach by extracting the activa-\n",
            "tions from one or more layers without ﬁne-tuning\n",
            "any parameters of BERT. These contextual em-\n",
            "beddings are used as input to a randomly initial-\n",
            "ized two-layer 768-dimensional BiLSTM before\n",
            "the classiﬁcation layer.\n",
            "Results are presented in Table 7. BERT LARGE\n",
            "performs competitively with state-of-the-art meth-\n",
            "ods. The best performing method concatenates the\n",
            "token representations from the top four hidden lay-\n",
            "ers of the pre-trained Transformer, which is only\n",
            "0.3 F1 behind ﬁne-tuning the entire model. This\n",
            "demonstrates that BERT is effective for both ﬁne-\n",
            "tuning and feature-based approaches.\n",
            "6 Conclusion\n",
            "Recent empirical improvements due to transfer\n",
            "learning with language models have demonstrated\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 9:\n",
            "\n",
            "Dev Set\n",
            "Tasks MNLI-m QNLI MRPC SST-2 SQuAD\n",
            "(Acc) (Acc) (Acc) (Acc) (F1)\n",
            "BERT BASE 84.4 88.4 86.7 92.7 88.5\n",
            "No NSP 83.9 84.9 86.5 92.6 87.9\n",
            "LTR & No NSP 82.1 84.3 77.5 92.1 77.8\n",
            "+ BiLSTM 82.1 84.1 75.7 91.6 84.9\n",
            "Table 5: Ablation over the pre-training tasks using the\n",
            "BERT BASE architecture. “No NSP” is trained without\n",
            "the next sentence prediction task. “LTR & No NSP” is\n",
            "trained as a left-to-right LM without the next sentence\n",
            "prediction, like OpenAI GPT. “+ BiLSTM” adds a ran-\n",
            "domly initialized BiLSTM on top of the “LTR + No\n",
            "NSP” model during ﬁne-tuning.\n",
            "ablation studies can be found in Appendix C.\n",
            "5.1 Effect of Pre-training Tasks\n",
            "We demonstrate the importance of the deep bidi-\n",
            "rectionality of BERT by evaluating two pre-\n",
            "training objectives using exactly the same pre-\n",
            "training data, ﬁne-tuning scheme, and hyperpa-\n",
            "rameters as BERT BASE :\n",
            "No NSP : A bidirectional model which is trained\n",
            "using the “masked LM” (MLM) but without the\n",
            "“next sentence prediction” (NSP) task.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 10:\n",
            "\n",
            "the need for many heavily-engineered task-\n",
            "speciﬁc architectures. BERT is the ﬁrst ﬁne-\n",
            "tuning based representation model that achieves\n",
            "state-of-the-art performance on a large suite\n",
            "of sentence-level andtoken-level tasks, outper-\n",
            "forming many task-speciﬁc architectures.\n",
            "• BERT advances the state of the art for eleven\n",
            "NLP tasks. The code and pre-trained mod-\n",
            "els are available at https://github.com/\n",
            "google-research/bert .\n",
            "2 Related Work\n",
            "There is a long history of pre-training general lan-\n",
            "guage representations, and we brieﬂy review the\n",
            "most widely-used approaches in this section.\n",
            "2.1 Unsupervised Feature-based Approaches\n",
            "Learning widely applicable representations of\n",
            "words has been an active area of research for\n",
            "decades, including non-neural (Brown et al., 1992;\n",
            "Ando and Zhang, 2005; Blitzer et al., 2006) and\n",
            "neural (Mikolov et al., 2013; Pennington et al.,\n",
            "2014) methods. Pre-trained word embeddings\n",
            "are an integral part of modern NLP systems, of-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 11:\n",
            "\n",
            "mixed results on the downstream task impact of\n",
            "increasing the pre-trained bi-LM size from two\n",
            "to four layers and Melamud et al. (2016) men-\n",
            "tioned in passing that increasing hidden dimen-\n",
            "sion size from 200 to 600 helped, but increasing\n",
            "further to 1,000 did not bring further improve-\n",
            "ments. Both of these prior works used a feature-\n",
            "based approach — we hypothesize that when the\n",
            "model is ﬁne-tuned directly on the downstream\n",
            "tasks and uses only a very small number of ran-\n",
            "domly initialized additional parameters, the task-\n",
            "speciﬁc models can beneﬁt from the larger, more\n",
            "expressive pre-trained representations even when\n",
            "downstream task data is very small.\n",
            "5.3 Feature-based Approach with BERT\n",
            "All of the BERT results presented so far have used\n",
            "the ﬁne-tuning approach, where a simple classiﬁ-\n",
            "cation layer is added to the pre-trained model, and\n",
            "all parameters are jointly ﬁne-tuned on a down-\n",
            "stream task. However, the feature-based approach,\n",
            "where ﬁxed features are extracted from the pre-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 12:\n",
            "\n",
            "In Section 3.1, we mention that BERT uses a\n",
            "mixed strategy for masking the target tokens when\n",
            "pre-training with the masked language model\n",
            "(MLM) objective. The following is an ablation\n",
            "study to evaluate the effect of different masking\n",
            "strategies.\n",
            "200 400 600 800 1,0007678808284\n",
            "Pre-training Steps (Thousands)MNLI Dev Accuracy\n",
            "BERT BASE (Masked LM)\n",
            "BERT BASE (Left-to-Right)\n",
            "Figure 5: Ablation over number of training steps. This\n",
            "shows the MNLI accuracy after ﬁne-tuning, starting\n",
            "from model parameters that have been pre-trained for\n",
            "ksteps. The x-axis is the value of k.Note that the purpose of the masking strategies\n",
            "is to reduce the mismatch between pre-training\n",
            "and ﬁne-tuning, as the [MASK] symbol never ap-\n",
            "pears during the ﬁne-tuning stage. We report the\n",
            "Dev results for both MNLI and NER. For NER,\n",
            "we report both ﬁne-tuning and feature-based ap-\n",
            "proaches, as we expect the mismatch will be am-\n",
            "pliﬁed for the feature-based approach as the model\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 13:\n",
            "\n",
            "BERT is conceptually simple and empirically\n",
            "powerful. It obtains new state-of-the-art re-\n",
            "sults on eleven natural language processing\n",
            "tasks, including pushing the GLUE score to\n",
            "80.5% (7.7% point absolute improvement),\n",
            "MultiNLI accuracy to 86.7% (4.6% absolute\n",
            "improvement), SQuAD v1.1 question answer-\n",
            "ing Test F1 to 93.2 (1.5 point absolute im-\n",
            "provement) and SQuAD v2.0 Test F1 to 83.1\n",
            "(5.1 point absolute improvement).\n",
            "1 Introduction\n",
            "Language model pre-training has been shown to\n",
            "be effective for improving many natural language\n",
            "processing tasks (Dai and Le, 2015; Peters et al.,\n",
            "2018a; Radford et al., 2018; Howard and Ruder,\n",
            "2018). These include sentence-level tasks such as\n",
            "natural language inference (Bowman et al., 2015;\n",
            "Williams et al., 2018) and paraphrasing (Dolan\n",
            "and Brockett, 2005), which aim to predict the re-\n",
            "lationships between sentences by analyzing them\n",
            "holistically, as well as token-level tasks such as\n",
            "named entity recognition and question answering,\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 14:\n",
            "\n",
            "reported GLUE task, MNLI, BERT obtains a 4.6%\n",
            "absolute accuracy improvement. On the ofﬁcial\n",
            "GLUE leaderboard10, BERT LARGE obtains a score\n",
            "of 80.5, compared to OpenAI GPT, which obtains\n",
            "72.8 as of the date of writing.\n",
            "We ﬁnd that BERT LARGE signiﬁcantly outper-\n",
            "forms BERT BASE across all tasks, especially those\n",
            "with very little training data. The effect of model\n",
            "size is explored more thoroughly in Section 5.2.\n",
            "4.2 SQuAD v1.1\n",
            "The Stanford Question Answering Dataset\n",
            "(SQuAD v1.1) is a collection of 100k crowd-\n",
            "sourced question/answer pairs (Rajpurkar et al.,\n",
            "2016). Given a question and a passage from\n",
            "9The GLUE data set distribution does not include the Test\n",
            "labels, and we only made a single GLUE evaluation server\n",
            "submission for each of BERT BASE and BERT LARGE .\n",
            "10https://gluebenchmark.com/leaderboardWikipedia containing the answer, the task is to\n",
            "predict the answer text span in the passage.\n",
            "As shown in Figure 1, in the question answer-\n",
            "ing task, we represent the input question and pas-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 15:\n",
            "\n",
            "Dev set results. For the feature-based approach,\n",
            "we concatenate the last 4 layers of BERT as the\n",
            "features, which was shown to be the best approach\n",
            "in Section 5.3.\n",
            "From the table it can be seen that ﬁne-tuning is\n",
            "surprisingly robust to different masking strategies.\n",
            "However, as expected, using only the M ASK strat-\n",
            "egy was problematic when applying the feature-\n",
            "based approach to NER. Interestingly, using only\n",
            "the R NDstrategy performs much worse than our\n",
            "strategy as well.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers.contextual_compression import ContextualCompressionRetriever\n",
        "from langchain_cohere import CohereRerank\n",
        "from langchain_community.llms import Cohere\n",
        "\n",
        "llm = Cohere(temperature=0)\n",
        "compressor = CohereRerank()\n",
        "cohere_compression_retriever = ContextualCompressionRetriever(\n",
        "    base_compressor=compressor, base_retriever=chroma_rerank_retriever\n",
        ")\n"
      ],
      "metadata": {
        "id": "U1G-vpkkk8cs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3b85873-9f1d-42a7-f684-17bed7595d36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `Cohere` was deprecated in LangChain 0.1.14 and will be removed in 0.3.0. An updated version of the class exists in the langchain-cohere package and should be used instead. To use it run `pip install -U langchain-cohere` and import as `from langchain_cohere import Cohere`.\n",
            "  warn_deprecated(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c_docs = cohere_compression_retriever.invoke(query)\n",
        "pretty_print_docs(c_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cGJfuwPmWeB",
        "outputId": "5489bced-160a-43fb-c235-e18a658fe4f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document 1:\n",
            "\n",
            "Dev Set\n",
            "Tasks MNLI-m QNLI MRPC SST-2 SQuAD\n",
            "(Acc) (Acc) (Acc) (Acc) (F1)\n",
            "BERT BASE 84.4 88.4 86.7 92.7 88.5\n",
            "No NSP 83.9 84.9 86.5 92.6 87.9\n",
            "LTR & No NSP 82.1 84.3 77.5 92.1 77.8\n",
            "+ BiLSTM 82.1 84.1 75.7 91.6 84.9\n",
            "Table 5: Ablation over the pre-training tasks using the\n",
            "BERT BASE architecture. “No NSP” is trained without\n",
            "the next sentence prediction task. “LTR & No NSP” is\n",
            "trained as a left-to-right LM without the next sentence\n",
            "prediction, like OpenAI GPT. “+ BiLSTM” adds a ran-\n",
            "domly initialized BiLSTM on top of the “LTR + No\n",
            "NSP” model during ﬁne-tuning.\n",
            "ablation studies can be found in Appendix C.\n",
            "5.1 Effect of Pre-training Tasks\n",
            "We demonstrate the importance of the deep bidi-\n",
            "rectionality of BERT by evaluating two pre-\n",
            "training objectives using exactly the same pre-\n",
            "training data, ﬁne-tuning scheme, and hyperpa-\n",
            "rameters as BERT BASE :\n",
            "No NSP : A bidirectional model which is trained\n",
            "using the “masked LM” (MLM) but without the\n",
            "“next sentence prediction” (NSP) task.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 2:\n",
            "\n",
            "BERT is conceptually simple and empirically\n",
            "powerful. It obtains new state-of-the-art re-\n",
            "sults on eleven natural language processing\n",
            "tasks, including pushing the GLUE score to\n",
            "80.5% (7.7% point absolute improvement),\n",
            "MultiNLI accuracy to 86.7% (4.6% absolute\n",
            "improvement), SQuAD v1.1 question answer-\n",
            "ing Test F1 to 93.2 (1.5 point absolute im-\n",
            "provement) and SQuAD v2.0 Test F1 to 83.1\n",
            "(5.1 point absolute improvement).\n",
            "1 Introduction\n",
            "Language model pre-training has been shown to\n",
            "be effective for improving many natural language\n",
            "processing tasks (Dai and Le, 2015; Peters et al.,\n",
            "2018a; Radford et al., 2018; Howard and Ruder,\n",
            "2018). These include sentence-level tasks such as\n",
            "natural language inference (Bowman et al., 2015;\n",
            "Williams et al., 2018) and paraphrasing (Dolan\n",
            "and Brockett, 2005), which aim to predict the re-\n",
            "lationships between sentences by analyzing them\n",
            "holistically, as well as token-level tasks such as\n",
            "named entity recognition and question answering,\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 3:\n",
            "\n",
            "two ways. First, we separate them with a special\n",
            "token ( [SEP] ). Second, we add a learned embed-\n",
            "ding to every token indicating whether it belongs\n",
            "to sentence Aor sentence B. As shown in Figure 1,\n",
            "we denote input embedding as E, the ﬁnal hidden\n",
            "vector of the special [CLS] token asC∈RH,\n",
            "and the ﬁnal hidden vector for the ithinput token\n",
            "asTi∈RH.\n",
            "For a given token, its input representation is\n",
            "constructed by summing the corresponding token,\n",
            "segment, and position embeddings. A visualiza-\n",
            "tion of this construction can be seen in Figure 2.\n",
            "3.1 Pre-training BERT\n",
            "Unlike Peters et al. (2018a) and Radford et al.\n",
            "(2018), we do not use traditional left-to-right or\n",
            "right-to-left language models to pre-train BERT.\n",
            "Instead, we pre-train BERT using two unsuper-\n",
            "vised tasks, described in this section. This step\n",
            "is presented in the left part of Figure 1.\n",
            "Task #1: Masked LM Intuitively, it is reason-\n",
            "able to believe that a deep bidirectional model is\n",
            "strictly more powerful than either a left-to-right\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cohere_compression_qa_chain = rag_chain(cohere_compression_retriever)"
      ],
      "metadata": {
        "id": "ox2Mh_0xmlby"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cohere_compression_qa_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "7dZTVWjLnIDa",
        "outputId": "a8b46540-3fce-4881-c8ac-30397d075fc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BERT is pre-trained on two main tasks: Masked Language Model (MLM) and Next Sentence Prediction (NSP). MLM involves predicting masked words in a sentence, while NSP focuses on predicting if two sentences are consecutive or not. These tasks help BERT understand language context and relationships between sentences.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cohere_compression_dataset = create_raga_dataset(cohere_compression_qa_chain, cohere_compression_retriever)"
      ],
      "metadata": {
        "id": "HfFG0h2hnI75"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cohere_compression_result = evaluate_ragas_dataset(cohere_compression_dataset)\n",
        "cohere_compression_result\n",
        "all_results.append(cohere_compression_result)\n",
        "cohere_compression_df = cohere_compression_result.to_pandas()\n",
        "cohere_compression_df.head()\n",
        "cohere_compression_df.to_csv(\"cohere_compression_retriever.csv\")"
      ],
      "metadata": {
        "id": "v016GWExnRJZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Mutiquery + Cohere Reranker\n"
      ],
      "metadata": {
        "id": "oL9CRXztwfvx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers.contextual_compression import ContextualCompressionRetriever\n",
        "from langchain_cohere import CohereRerank\n",
        "from langchain_community.llms import Cohere\n",
        "\n",
        "llm = Cohere(temperature=0)\n",
        "compressor = CohereRerank()\n",
        "multiquery_cohere_retriever = ContextualCompressionRetriever(\n",
        "    base_compressor=compressor, base_retriever=multi_query_retriever\n",
        ")\n"
      ],
      "metadata": {
        "id": "o1tf0zIswp1k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "multiquery_cohere_qa_chain = rag_chain(multiquery_cohere_retriever)"
      ],
      "metadata": {
        "id": "aqiUdc43wpy0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mmdocs = multiquery_cohere_retriever.invoke(query)\n",
        "pretty_print_docs(mmdocs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBDJrwoIPPbG",
        "outputId": "7c2dd1a4-f0f4-440c-932d-8a5a05459ee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document 1:\n",
            "\n",
            "ing and auto-encoder objectives have been used\n",
            "for pre-training such models (Howard and Ruder,\n",
            "2018; Radford et al., 2018; Dai and Le, 2015).\n",
            "2.3 Transfer Learning from Supervised Data\n",
            "There has also been work showing effective trans-\n",
            "fer from supervised tasks with large datasets, such\n",
            "as natural language inference (Conneau et al.,\n",
            "2017) and machine translation (McCann et al.,\n",
            "2017). Computer vision research has also demon-\n",
            "strated the importance of transfer learning from\n",
            "large pre-trained models, where an effective recipe\n",
            "is to ﬁne-tune models pre-trained with Ima-\n",
            "geNet (Deng et al., 2009; Yosinski et al., 2014).\n",
            "3 BERT\n",
            "We introduce BERT and its detailed implementa-\n",
            "tion in this section. There are two steps in our\n",
            "framework: pre-training and ﬁne-tuning . Dur-\n",
            "ing pre-training, the model is trained on unlabeled\n",
            "data over different pre-training tasks. For ﬁne-\n",
            "tuning, the BERT model is ﬁrst initialized with\n",
            "the pre-trained parameters, and all of the param-\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 2:\n",
            "\n",
            "dinov, Raquel Urtasun, Antonio Torralba, and Sanja\n",
            "Fidler. 2015. Aligning books and movies: Towards\n",
            "story-like visual explanations by watching movies\n",
            "and reading books. In Proceedings of the IEEE\n",
            "international conference on computer vision , pages\n",
            "19–27.\n",
            "Appendix for “BERT: Pre-training of\n",
            "Deep Bidirectional Transformers for\n",
            "Language Understanding”\n",
            "We organize the appendix into three sections:\n",
            "• Additional implementation details for BERT\n",
            "are presented in Appendix A;• Additional details for our experiments are\n",
            "presented in Appendix B; and\n",
            "• Additional ablation studies are presented in\n",
            "Appendix C.\n",
            "We present additional ablation studies for\n",
            "BERT including:\n",
            "–Effect of Number of Training Steps; and\n",
            "–Ablation for Different Masking Proce-\n",
            "dures.\n",
            "A Additional Details for BERT\n",
            "A.1 Illustration of the Pre-training Tasks\n",
            "We provide examples of the pre-training tasks in\n",
            "the following.\n",
            "Masked LM and the Masking Procedure As-\n",
            "suming the unlabeled sentence is my dog is\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 3:\n",
            "\n",
            "pus. In fact, many of the design decisions in BERT\n",
            "were intentionally made to make it as close to\n",
            "GPT as possible so that the two methods could be\n",
            "minimally compared. The core argument of this\n",
            "work is that the bi-directionality and the two pre-\n",
            "training tasks presented in Section 3.1 account for\n",
            "the majority of the empirical improvements, but\n",
            "we do note that there are several other differences\n",
            "between how BERT and GPT were trained:\n",
            "• GPT is trained on the BooksCorpus (800M\n",
            "words); BERT is trained on the BooksCor-\n",
            "pus (800M words) and Wikipedia (2,500M\n",
            "words).\n",
            "• GPT uses a sentence separator ( [SEP] ) and\n",
            "classiﬁer token ( [CLS] ) which are only in-\n",
            "troduced at ﬁne-tuning time; BERT learns\n",
            "[SEP] ,[CLS] and sentence A/Bembed-\n",
            "dings during pre-training.\n",
            "• GPT was trained for 1M steps with a batch\n",
            "size of 32,000 words; BERT was trained for\n",
            "1M steps with a batch size of 128,000 words.\n",
            "• GPT used the same learning rate of 5e-5 for\n",
            "all ﬁne-tuning experiments; BERT chooses a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multiquery_cohere_qa_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "cwcQ_dfCPdXc",
        "outputId": "9e78f509-d2d4-42e0-c1dc-17c6ea6725be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BERT is pre-trained on two main tasks: pre-training and fine-tuning. During pre-training, the model is trained on unlabeled data over different pre-training tasks. Fine-tuning involves initializing the BERT model with pre-trained parameters for specific tasks.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multiquery_cohere_dataset = create_raga_dataset(multiquery_cohere_qa_chain, multiquery_cohere_retriever)"
      ],
      "metadata": {
        "id": "ZYOAAPOHwpwV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "multiquery_cohere_result = evaluate_ragas_dataset(multiquery_cohere_dataset)\n",
        "multiquery_cohere_result\n",
        "all_results.append(multiquery_cohere_result)\n",
        "multiquery_cohere_df = multiquery_cohere_result.to_pandas()\n",
        "multiquery_cohere_df.head()\n",
        "multiquery_cohere_df.to_csv(\"multiquery_cohere_retriever.csv\")"
      ],
      "metadata": {
        "id": "sSEREiBbwpcb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Contextual Compression Retriever"
      ],
      "metadata": {
        "id": "EHJwHIp3P4Iy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers import ContextualCompressionRetriever\n",
        "from langchain.retrievers.document_compressors import LLMChainExtractor\n",
        "from langchain_openai import OpenAI\n",
        "\n",
        "llm = OpenAI(temperature=0)\n",
        "compressor = LLMChainExtractor.from_llm(llm)\n",
        "compression_retriever = ContextualCompressionRetriever(\n",
        "    base_compressor=compressor, base_retriever=chroma_retriever\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "id": "bAjQlGgMP8-6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "compressed_docs = compression_retriever.invoke(query)\n",
        "pretty_print_docs(compressed_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hT54MrSJQIgS",
        "outputId": "dbc058b2-33c0-4b18-92e7-1052708f85d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document 1:\n",
            "\n",
            "- \"the two pre-training tasks\" \n",
            "- \"the bidirectionality they enable\"\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 2:\n",
            "\n",
            "- pre-training\n",
            "- two steps in our framework: pre-training and ﬁne-tuning\n",
            "- During pre-training, the model is trained on unlabeled data over different pre-training tasks.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 3:\n",
            "\n",
            "BERT representations are jointly conditioned on both left and right context in all layers.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 4:\n",
            "\n",
            "we pre-train BERT using two unsuper-\n",
            "vised tasks, described in this section. This step\n",
            "is presented in the left part of Figure 1.\n",
            "Task #1: Masked LM Intuitively, it is reason-\n",
            "able to believe that a deep bidirectional model is\n",
            "strictly more powerful than either a left-to-right\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compression_qa_chain = rag_chain(compression_retriever)"
      ],
      "metadata": {
        "id": "vTBDkC1IQZBM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "compression_qa_chain.invoke(\"How does BERT's architecture facilitate the use of a unified model across diverse NLP tasks?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "PzzWUNyuQevN",
        "outputId": "33467edc-37f4-4c3f-c16d-faa9595c5c41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"BERT's architecture facilitates the use of a unified model across diverse NLP tasks by having minimal differences between the pre-trained architecture and the final downstream architecture. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters. BERT's multi-layer bidirectional Transformer encoder allows for state-of-the-art performance on various NLP tasks.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compression_dataset = create_raga_dataset(compression_qa_chain, compression_retriever)"
      ],
      "metadata": {
        "id": "uzmeWfrDQm8k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "compression_result = evaluate_ragas_dataset(compression_dataset)\n",
        "compression_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "9649c80f159845ac876adf8fef7a9090",
            "7d5ec621017e42c594dddd5ab34ed8f2",
            "d7f06d143f5c4ec583d0123447e539d1",
            "db64052c380041869b5588852c01901b",
            "b50066c380764d59802af621208d7cb8",
            "04262ed4b27844af99bf8d2bf24f935c",
            "a585dad4a54340038a970508cc9a844a",
            "540d7d66b5b148c2a1bc7320aa93f4cc",
            "eef463c9ccef44ecb5d1368b59c80d6e",
            "95712ddd4d134436960a9a2135a15a04",
            "8a7061f65d794b27b8b6025c42f006cb"
          ]
        },
        "id": "5e4BgsRwQu7w",
        "outputId": "75e7a2f5-3d9a-46d9-9d04-c46fd669750a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9649c80f159845ac876adf8fef7a9090"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'context_precision': 1.0000, 'faithfulness': 0.9333, 'answer_relevancy': 0.9296, 'context_recall': 0.8333, 'context_relevancy': 0.0306, 'answer_correctness': 0.6561, 'answer_similarity': 0.8911}"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compression_df = compression_result.to_pandas()\n",
        "compression_df.head()\n",
        "compression_df.to_csv(\"compression_retriever.csv\")"
      ],
      "metadata": {
        "id": "w_Z1EvQzRHxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. Cross Encoder Reranker"
      ],
      "metadata": {
        "id": "CvJuVitvRNtA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers import ContextualCompressionRetriever\n",
        "from langchain.retrievers.document_compressors import CrossEncoderReranker\n",
        "from langchain_community.cross_encoders import HuggingFaceCrossEncoder\n",
        "\n",
        "chroma_cross_retriever = vectorstore.as_retriever(\n",
        "        search_type=\"mmr\", search_kwargs={\"k\": 15}\n",
        "    )\n",
        "\n",
        "model = HuggingFaceCrossEncoder(model_name=\"BAAI/bge-reranker-base\")\n",
        "compressor = CrossEncoderReranker(model=model, top_n=3)\n",
        "cross_encoder_compression_retriever = ContextualCompressionRetriever(\n",
        "    base_compressor=compressor, base_retriever=chroma_cross_retriever\n",
        ")\n",
        "\n",
        "compressed_docs = cross_encoder_compression_retriever.invoke(query)\n",
        "pretty_print_docs(compressed_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4Rd_fytR5WL",
        "outputId": "40ad9784-0f76-4fd9-e914-4e0cc23d8b0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document 1:\n",
            "\n",
            "BERT is conceptually simple and empirically\n",
            "powerful. It obtains new state-of-the-art re-\n",
            "sults on eleven natural language processing\n",
            "tasks, including pushing the GLUE score to\n",
            "80.5% (7.7% point absolute improvement),\n",
            "MultiNLI accuracy to 86.7% (4.6% absolute\n",
            "improvement), SQuAD v1.1 question answer-\n",
            "ing Test F1 to 93.2 (1.5 point absolute im-\n",
            "provement) and SQuAD v2.0 Test F1 to 83.1\n",
            "(5.1 point absolute improvement).\n",
            "1 Introduction\n",
            "Language model pre-training has been shown to\n",
            "be effective for improving many natural language\n",
            "processing tasks (Dai and Le, 2015; Peters et al.,\n",
            "2018a; Radford et al., 2018; Howard and Ruder,\n",
            "2018). These include sentence-level tasks such as\n",
            "natural language inference (Bowman et al., 2015;\n",
            "Williams et al., 2018) and paraphrasing (Dolan\n",
            "and Brockett, 2005), which aim to predict the re-\n",
            "lationships between sentences by analyzing them\n",
            "holistically, as well as token-level tasks such as\n",
            "named entity recognition and question answering,\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 2:\n",
            "\n",
            "Dev Set\n",
            "Tasks MNLI-m QNLI MRPC SST-2 SQuAD\n",
            "(Acc) (Acc) (Acc) (Acc) (F1)\n",
            "BERT BASE 84.4 88.4 86.7 92.7 88.5\n",
            "No NSP 83.9 84.9 86.5 92.6 87.9\n",
            "LTR & No NSP 82.1 84.3 77.5 92.1 77.8\n",
            "+ BiLSTM 82.1 84.1 75.7 91.6 84.9\n",
            "Table 5: Ablation over the pre-training tasks using the\n",
            "BERT BASE architecture. “No NSP” is trained without\n",
            "the next sentence prediction task. “LTR & No NSP” is\n",
            "trained as a left-to-right LM without the next sentence\n",
            "prediction, like OpenAI GPT. “+ BiLSTM” adds a ran-\n",
            "domly initialized BiLSTM on top of the “LTR + No\n",
            "NSP” model during ﬁne-tuning.\n",
            "ablation studies can be found in Appendix C.\n",
            "5.1 Effect of Pre-training Tasks\n",
            "We demonstrate the importance of the deep bidi-\n",
            "rectionality of BERT by evaluating two pre-\n",
            "training objectives using exactly the same pre-\n",
            "training data, ﬁne-tuning scheme, and hyperpa-\n",
            "rameters as BERT BASE :\n",
            "No NSP : A bidirectional model which is trained\n",
            "using the “masked LM” (MLM) but without the\n",
            "“next sentence prediction” (NSP) task.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Document 3:\n",
            "\n",
            "ing and auto-encoder objectives have been used\n",
            "for pre-training such models (Howard and Ruder,\n",
            "2018; Radford et al., 2018; Dai and Le, 2015).\n",
            "2.3 Transfer Learning from Supervised Data\n",
            "There has also been work showing effective trans-\n",
            "fer from supervised tasks with large datasets, such\n",
            "as natural language inference (Conneau et al.,\n",
            "2017) and machine translation (McCann et al.,\n",
            "2017). Computer vision research has also demon-\n",
            "strated the importance of transfer learning from\n",
            "large pre-trained models, where an effective recipe\n",
            "is to ﬁne-tune models pre-trained with Ima-\n",
            "geNet (Deng et al., 2009; Yosinski et al., 2014).\n",
            "3 BERT\n",
            "We introduce BERT and its detailed implementa-\n",
            "tion in this section. There are two steps in our\n",
            "framework: pre-training and ﬁne-tuning . Dur-\n",
            "ing pre-training, the model is trained on unlabeled\n",
            "data over different pre-training tasks. For ﬁne-\n",
            "tuning, the BERT model is ﬁrst initialized with\n",
            "the pre-trained parameters, and all of the param-\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_encoder_compression_qa_chain = rag_chain(cross_encoder_compression_retriever)"
      ],
      "metadata": {
        "id": "QKOvQt5OS_3H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_encoder_compression_qa_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "poyG1cBGTMJS",
        "outputId": "858e5a10-19ff-40e0-df78-ea53dc6264e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BERT is pre-trained on two main tasks: masked language modeling (MLM) and next sentence prediction (NSP). These tasks help BERT learn the relationships between words in a sentence and understand the context between two sentences.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"How does BERT's architecture facilitate the use of a unified model across diverse NLP tasks?\""
      ],
      "metadata": {
        "id": "i36U2sVJTReF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_encoder_compression_qa_chain.invoke(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "-CKMYRJvTeSi",
        "outputId": "1d93a2e5-d591-42a0-9680-8373092649cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"BERT's architecture allows for fine-tuning with labeled data from downstream tasks, resulting in separate fine-tuned models for each task. Despite this, the pre-trained parameters are the same for all models. The unified architecture of BERT minimizes the difference between pre-trained and downstream architectures, making it versatile across various NLP tasks.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_encoder_compression_dataset = create_raga_dataset(cross_encoder_compression_qa_chain, cross_encoder_compression_retriever)"
      ],
      "metadata": {
        "id": "kge0ct-hUzyU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_encoder_compression_result = evaluate_ragas_dataset(cross_encoder_compression_dataset)\n",
        "cross_encoder_compression_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "6f11f29a662b44bd9aca1540c9b4a64d",
            "bc385c0e349749c4b1bc802926c3b04f",
            "a4a0f16d3ea14ac6b715f704a3dd6230",
            "47a69affd9de4d2fad4a0b6d6e411f63",
            "ca78865d9e8b4a9a855bae3b1b0e00bc",
            "9963ac74bbb04b2e83659e9b4c3832fe",
            "a7fb0ad7899040e6b62e51cad0a62b4a",
            "c18160038b9a4e3fb40fef60a2b61843",
            "4108278707ab4c528a12d63e9c425ba3",
            "62836480814941099cbbd9669db15032",
            "8482ba43de94491ead1c954d2166900e"
          ]
        },
        "id": "L78gsAViU_Gl",
        "outputId": "0f7cb8b6-f0b8-4742-a22b-a6d826c0646f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/21 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f11f29a662b44bd9aca1540c9b4a64d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'context_precision': 1.0000, 'faithfulness': 1.0000, 'answer_relevancy': 0.9380, 'context_recall': 0.8333, 'context_relevancy': 0.0306, 'answer_correctness': 0.7030, 'answer_similarity': 0.8881}"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cross_encoder_compression_df = cross_encoder_compression_result.to_pandas()\n",
        "cross_encoder_compression_df.head()\n",
        "cross_encoder_compression_df.to_csv(\"cross_encoder_compression.csv\")"
      ],
      "metadata": {
        "id": "dYeV8iFxVIr3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## All Results"
      ],
      "metadata": {
        "id": "YPTfQ06CqJOI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_result, pda_result, ensemble_bm25_result, multi_query_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhHp2vIzqMFp",
        "outputId": "fbb03fc2-4f0a-4fee-cb5f-3858395a2811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'context_precision': 0.9201, 'faithfulness': 0.9062, 'answer_relevancy': 0.8138, 'context_recall': 0.8750, 'context_relevancy': 0.0313, 'answer_correctness': 0.7052, 'answer_similarity': 0.8956},\n",
              " {'context_precision': 0.9167, 'faithfulness': 1.0000, 'answer_relevancy': 0.9169, 'context_recall': 0.9018, 'context_relevancy': 0.0226, 'answer_correctness': 0.7600, 'answer_similarity': 0.8820},\n",
              " {'context_precision': 0.8807, 'faithfulness': 1.0000, 'answer_relevancy': 0.9203, 'context_recall': 0.8750, 'context_relevancy': 0.0660, 'answer_correctness': 0.6833, 'answer_similarity': 0.8660},\n",
              " {'context_precision': 0.9165, 'faithfulness': 0.9437, 'answer_relevancy': 0.9445, 'context_recall': 0.7500, 'context_relevancy': 0.0368, 'answer_correctness': 0.6984, 'answer_similarity': 0.8863})"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compression_result, cross_encoder_compression_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Y5a5oyxV4Kl",
        "outputId": "30c8f309-3dd9-406f-e724-56ae9c2c62a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'context_precision': 1.0000, 'faithfulness': 0.9333, 'answer_relevancy': 0.9296, 'context_recall': 0.8333, 'context_relevancy': 0.0306, 'answer_correctness': 0.6561, 'answer_similarity': 0.8911},\n",
              " {'context_precision': 1.0000, 'faithfulness': 1.0000, 'answer_relevancy': 0.9380, 'context_recall': 0.8333, 'context_relevancy': 0.0306, 'answer_correctness': 0.7030, 'answer_similarity': 0.8881})"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_df_dict(pipeline_name, pipeline_items):\n",
        "  df_dict = {\"name\" : pipeline_name}\n",
        "  for name, score in pipeline_items:\n",
        "    df_dict[name] = score\n",
        "  return df_dict"
      ],
      "metadata": {
        "id": "pcP-wwSHqbwu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chroma_df_dict = create_df_dict(\"Chroma\", chroma_result.items())"
      ],
      "metadata": {
        "id": "W1baskHYqt8p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pda_dict = create_df_dict(\"Parent Document\", pda_result.items())\n",
        "ensemble_dict = create_df_dict(\"Ensemble BM25\", ensemble_bm25_result.items())\n",
        "multiquery_dict = create_df_dict(\"MultiQuery\", multi_query_result.items())\n",
        "context_comp_dict = create_df_dict(\"Context Compressionr\", compression_result.items())\n",
        "cross_enc_rera_dict = create_df_dict(\"Cross Encoder Reranker\", cross_encoder_compression_result.items())"
      ],
      "metadata": {
        "id": "5fNGP5xIq5IT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "results_df = pd.DataFrame([chroma_df_dict, pda_dict, ensemble_dict, multiquery_dict, context_comp_dict, cross_enc_rera_dict\n",
        "                           ])"
      ],
      "metadata": {
        "id": "8lPezKzCrNlM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "5049rXEurYpb",
        "outputId": "e61f41ce-6b90-4e01-bb2b-f22eba360e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     name  context_precision  faithfulness  answer_relevancy  \\\n",
              "0                  Chroma             0.9201      0.906200          0.813800   \n",
              "1         Parent Document             0.9167      1.000000          0.916900   \n",
              "2           Ensemble BM25             0.8807      1.000000          0.920300   \n",
              "3              MultiQuery             0.9165      0.943700          0.944500   \n",
              "4    Context Compressionr             1.0000      0.933333          0.929567   \n",
              "5  Cross Encoder Reranker             1.0000      1.000000          0.938041   \n",
              "\n",
              "   context_recall  context_relevancy  answer_correctness  answer_similarity  \n",
              "0        0.875000           0.031300            0.705200           0.895600  \n",
              "1        0.901800           0.022600            0.760000           0.882000  \n",
              "2        0.875000           0.066000            0.683300           0.866000  \n",
              "3        0.750000           0.036800            0.698400           0.886300  \n",
              "4        0.833333           0.030646            0.656128           0.891121  \n",
              "5        0.833333           0.030646            0.702988           0.888144  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1bcab42c-9cb9-4dd8-bb60-110195e97534\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>context_precision</th>\n",
              "      <th>faithfulness</th>\n",
              "      <th>answer_relevancy</th>\n",
              "      <th>context_recall</th>\n",
              "      <th>context_relevancy</th>\n",
              "      <th>answer_correctness</th>\n",
              "      <th>answer_similarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Chroma</td>\n",
              "      <td>0.9201</td>\n",
              "      <td>0.906200</td>\n",
              "      <td>0.813800</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.031300</td>\n",
              "      <td>0.705200</td>\n",
              "      <td>0.895600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Parent Document</td>\n",
              "      <td>0.9167</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916900</td>\n",
              "      <td>0.901800</td>\n",
              "      <td>0.022600</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>0.882000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ensemble BM25</td>\n",
              "      <td>0.8807</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.920300</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.066000</td>\n",
              "      <td>0.683300</td>\n",
              "      <td>0.866000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MultiQuery</td>\n",
              "      <td>0.9165</td>\n",
              "      <td>0.943700</td>\n",
              "      <td>0.944500</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.036800</td>\n",
              "      <td>0.698400</td>\n",
              "      <td>0.886300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Context Compressionr</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.929567</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.030646</td>\n",
              "      <td>0.656128</td>\n",
              "      <td>0.891121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cross Encoder Reranker</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.938041</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.030646</td>\n",
              "      <td>0.702988</td>\n",
              "      <td>0.888144</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1bcab42c-9cb9-4dd8-bb60-110195e97534')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1bcab42c-9cb9-4dd8-bb60-110195e97534 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1bcab42c-9cb9-4dd8-bb60-110195e97534');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9fa608b-b978-4865-aaf4-0fd1c6444ffe')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e9fa608b-b978-4865-aaf4-0fd1c6444ffe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_61874e77-c772-46af-b413-bf1c1d1f1136\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_61874e77-c772-46af-b413-bf1c1d1f1136 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Chroma\",\n          \"Parent Document\",\n          \"Cross Encoder Reranker\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04939967609583419,\n        \"min\": 0.8807,\n        \"max\": 0.9999999999666667,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9167,\n          0.9999999999666667,\n          0.8807\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.041427711695147945,\n        \"min\": 0.9062,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.9333333333333332,\n          0.9062\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.048509676802699325,\n        \"min\": 0.8138,\n        \"max\": 0.9445,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.8138,\n          0.9169,\n          0.9380408792375304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.053522089589944775,\n        \"min\": 0.75,\n        \"max\": 0.9018,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9018,\n          0.8333333333333334,\n          0.875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015224665348759402,\n        \"min\": 0.0226,\n        \"max\": 0.066,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0226,\n          0.03064631275157591,\n          0.066\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_correctness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.034164810272485434,\n        \"min\": 0.6561276535106559,\n        \"max\": 0.76,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7052,\n          0.76,\n          0.7029884336669255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01030815954206727,\n        \"min\": 0.866,\n        \"max\": 0.8956,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.8956,\n          0.882,\n          0.8881442108581786\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_df.to_csv(\"results.csv\")"
      ],
      "metadata": {
        "id": "eD1dphAXuUm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install wandb"
      ],
      "metadata": {
        "id": "ACEQaorSthEC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !wandb login"
      ],
      "metadata": {
        "id": "2LPwqOPLt9K9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import wandb\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Read the CSV file into a DataFrame\n",
        "data = pd.read_csv('results.csv')\n",
        "\n",
        "# Initialize wandb\n",
        "wandb.init(project='RAGS')\n",
        "\n",
        "# Iterate over each metric and create bar plots\n",
        "metrics = ['context_precision', 'faithfulness', 'answer_relevancy',\n",
        "           'context_recall', 'context_relevancy', 'answer_correctness',\n",
        "           'answer_similarity']\n",
        "\n",
        "for metric in metrics:\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.bar(data['name'], data[metric], align='center', alpha=0.7)\n",
        "    plt.title(f'{metric} vs. Retriever Index')\n",
        "    plt.xlabel('Retriever Index')\n",
        "    plt.ylabel(metric)\n",
        "    plt.grid(True)\n",
        "\n",
        "    # Log the plot to wandb\n",
        "    wandb.log({f'{metric}_plot': wandb.Image(plt)})\n",
        "    plt.close()\n",
        "\n",
        "wandb.finish()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175,
          "referenced_widgets": [
            "bbe34f3b1af0473c99ae3aa18392eda7",
            "19a38b445df64148907a02d0aad87bc3",
            "d6748e54509745f99ec6efa1cb24bf26",
            "d329034cd40a46f0aea9af942e11545d",
            "e4bafe49eef24b378b395e221d510448",
            "63c063218b5e4a2f91573ad12d4fdda9",
            "7d55ee5952874108afa90c900f2331b0",
            "d8a438c7865343a79df0d8b31d3aaae7"
          ]
        },
        "id": "zlEvv7u1uj_1",
        "outputId": "a50ddf7a-417b-4dc7-a29e-953e177fce22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.17.0"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240509_142313-6n8rzg31</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/rhitesh-ksingh99/RAGS/runs/6n8rzg31' target=\"_blank\">pretty-music-3</a></strong> to <a href='https://wandb.ai/rhitesh-ksingh99/RAGS' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/rhitesh-ksingh99/RAGS' target=\"_blank\">https://wandb.ai/rhitesh-ksingh99/RAGS</a>"
            ]
          },
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View run at <a href='https://wandb.ai/rhitesh-ksingh99/RAGS/runs/6n8rzg31' target=\"_blank\">https://wandb.ai/rhitesh-ksingh99/RAGS/runs/6n8rzg31</a>"
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        {
          "output_type": "display_data",
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            "text/plain": [
              "VBox(children=(Label(value='0.198 MB of 0.198 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
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            "application/vnd.jupyter.widget-view+json": {
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            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">pretty-music-3</strong> at: <a href='https://wandb.ai/rhitesh-ksingh99/RAGS/runs/6n8rzg31' target=\"_blank\">https://wandb.ai/rhitesh-ksingh99/RAGS/runs/6n8rzg31</a><br/> View project at: <a href='https://wandb.ai/rhitesh-ksingh99/RAGS' target=\"_blank\">https://wandb.ai/rhitesh-ksingh99/RAGS</a><br/>Synced 4 W&B file(s), 7 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20240509_142313-6n8rzg31/logs</code>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data = results_df\n",
        "\n",
        "sns.set_style(\"whitegrid\")\n",
        "\n",
        "metrics = ['context_precision', 'faithfulness', 'answer_relevancy',\n",
        "           'context_recall', 'context_relevancy', 'answer_correctness',\n",
        "           'answer_similarity']\n",
        "\n",
        "for metric in metrics:\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.barplot(x='name', y=metric, data=data)\n",
        "    plt.title(f'{metric} vs. Retriever Index')\n",
        "    plt.xlabel('Retriever Index')\n",
        "    plt.ylabel(metric)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{metric}_barplot.png')\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PBCZFg1evJkc",
        "outputId": "93d978cd-f6b7-46c3-8f22-cfd03b458c26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import pandas as pd\n",
        "# import seaborn as sns\n",
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# # Read the CSV file into a DataFrame\n",
        "# data = results_df\n",
        "\n",
        "# # Set the style of seaborn\n",
        "# sns.set_style(\"whitegrid\")\n",
        "\n",
        "# # Define color palette for different retrievers\n",
        "# palette = sns.color_palette(\"husl\", len(data))\n",
        "\n",
        "# # Iterate over each metric and create bar plots\n",
        "# metrics = ['context_precision', 'faithfulness', 'answer_relevancy',\n",
        "#            'context_recall', 'context_relevancy', 'answer_correctness',\n",
        "#            'answer_similarity']\n",
        "\n",
        "# for metric in metrics:\n",
        "#     plt.figure(figsize=(8, 4))\n",
        "#     sns.barplot(x='name', y=metric, data=data, palette=palette)\n",
        "#     plt.title(f'{metric} vs. Retriever Index')\n",
        "#     plt.xlabel('Retriever Index')\n",
        "#     plt.ylabel(metric)\n",
        "#     plt.tight_layout()\n",
        "#     plt.savefig(f'{metric}_barplot.png')  # Save the plot as an image\n",
        "#     plt.show()\n"
      ],
      "metadata": {
        "id": "97wOyzGny-Ja"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pKH38JOkzTMH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis\n",
        "\n",
        "### 1. Context Precision\n",
        "- **Chroma Retriever** scores highest with a context precision of approximately 0.92, indicating it's very effective in selecting relevant context that is useful for answering the posed questions.\n",
        "- **Parent Document Retriever** and **MultiQuery Retriever** are close, both scoring around 0.916, suggesting they are also effective in retrieving useful contexts.\n",
        "- **Ensemble BM25 Retriever** has the lowest context precision at about 0.88, which might suggest it includes some irrelevant context in its retrievals compared to others.\n",
        "- Both **Context Compression Retriever** and **Cross Encoder Reranker** score a perfect approximate value of 1.0, indicating that they excel at selecting highly relevant context for the questions.\n",
        "\n",
        "### 2. Faithfulness\n",
        "- Both **Parent Document Retriever** and **Ensemble BM25 Retriever** score a perfect 1.0 in faithfulness, indicating that the contexts provided by these models are completely factual and support the answers derived from them.\n",
        "- **MultiQuery Retriever** scores slightly lower at 0.94375, but still indicates high faithfulness.\n",
        "- **Chroma Retriever** has the lowest faithfulness score at approximately 0.906, suggesting some room for improvement in ensuring factual alignment between context and answers.\n",
        "- **Cross Encoder Reranker** achieves a perfect score of 1.0, suggesting that the contexts it retrieves are entirely factual and support the answers well.\n",
        "- **Context Compression Retriever** has a slightly lower score at around 0.933, which is still high but indicates some room for improvement.\n",
        "\n",
        "\n",
        "### 3. Answer Relevancy\n",
        "- **MultiQuery Retriever** leads in answer relevancy with a score of about 0.944, indicating that it provides the most relevant and complete answers in relation to the questions.\n",
        "- **Ensemble BM25 Retriever** follows closely with a score of approximately 0.920, showing strong performance in relevancy as well.\n",
        "- **Parent Document Retriever** scores around 0.917, also showing good relevancy.\n",
        "- **Chroma Retriever** has the lowest score at about 0.814, suggesting its answers may sometimes lack completeness or direct relevance to the questions.\n",
        "- **Cross Encoder Reranker** has an answer relevancy score of approximately 0.938, showing strong performance in providing relevant and complete answers.\n",
        "- **Context Compression Retriever** scores slightly lower at around 0.930, which is also quite high, indicating its answers are relevant to the questions.\n",
        "\n",
        "\n",
        "### 4. Context Recall\n",
        "- **Parent Document Retriever** scores the highest in context recall at approximately 0.902, which indicates it is particularly good at including context that contains information present in the ground truth answers.\n",
        "- **Chroma Retriever** and **Ensemble BM25 Retriever** both have scores around 0.875, suggesting moderate performance.\n",
        "- **MultiQuery Retriever** has the lowest context recall at 0.75, indicating it might miss some relevant details from the context that are present in the ground truth.\n",
        "- Both models score 0.833 in context recall, which is the lowest among all the models discussed so far. This suggests that while the contexts they retrieve are very precise, they might sometimes miss some details that are present in the ground truth answers.\n",
        "\n",
        "### 5. Answer Correctness\n",
        "- **Parent Document Retriever** leads with a score of approximately 0.76, suggesting it is most effective at providing factually correct and semantically aligned answers with the ground truth.\n",
        "- **Chroma Retriever** follows with a score of about 0.705.\n",
        "- **MultiQuery Retriever** is slightly lower at approximately 0.698.\n",
        "- **Ensemble BM25 Retriever** has the lowest score around 0.683, indicating room for improvement in this area.\n",
        "- **Cross Encoder Reranker** leads slightly with a score of about 0.703, suggesting better factual correctness and semantic alignment with the ground truth.\n",
        "- **Context Compression Retriever** has a score of approximately 0.656, indicating moderate performance in this area.\n",
        "\n",
        "### 6. Answer Similarity\n",
        "- **Chroma Retriever** excels in this metric with a score of approximately 0.896, suggesting a high degree of semantic resemblance between the answers it retrieves and the ground truth.\n",
        "- **Parent Document Retriever** and **MultiQuery Retriever** follow closely with scores of about 0.882 and 0.886, respectively.\n",
        "- **Ensemble BM25 Retriever** has the lowest score at about 0.866, which could indicate less semantic similarity compared to other models.\n",
        "- **Context Compression Retriever** has a high answer similarity score of about 0.891, which is competitive with the top scores from the initial set of retrievers.\n",
        "- **Cross Encoder Reranker** has a slightly lower score of about 0.888, but still indicates a high degree of semantic resemblance with the ground truth.\n",
        "\n",
        "Overall, **Context Compression Retriever** and **Cross Encoder Reranker** excel in precision and faithfulness but may lack slightly in recall. Their performance in generating relevant and semantically similar answers is also impressive, making them strong candidates depending on the specific needs of the retrieval task, particularly where precision is prioritized over recall."
      ],
      "metadata": {
        "id": "tP4_AfTf_G5e"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GEZaw5oq-iYP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}