Langchain_Tutorial_RAG.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Installs"
      ],
      "metadata": {
        "id": "XORbazN4blRF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5FTyKnqlN4Hk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "927379ea-943c-40aa-93fe-8a3313d33249"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain\n",
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            "Installing collected packages: mypy-extensions, marshmallow, jsonpointer, typing-inspect, langsmith, jsonpatch, dataclasses-json, langchain\n",
            "Successfully installed dataclasses-json-0.6.1 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.321 langsmith-0.0.49 marshmallow-3.20.1 mypy-extensions-1.0.0 typing-inspect-0.9.0\n",
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            "Installing collected packages: google-ai-generativelanguage, google-generativeai\n",
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53723 sha256=2326ac91010e62cb7ede6eec5e0e69d35394be9b41c7553950006cd7d8cd088c\n",
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            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, websockets, uvloop, typing-extensions, python-dotenv, pulsar-client, overrides, humanfriendly, httptools, h11, chroma-hnswlib, bcrypt, backoff, watchfiles, uvicorn, starlette, posthog, huggingface_hub, coloredlogs, tokenizers, onnxruntime, fastapi, chromadb\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed backoff-2.2.1 bcrypt-4.0.1 chroma-hnswlib-0.7.3 chromadb-0.4.14 coloredlogs-15.0.1 fastapi-0.104.0 h11-0.14.0 httptools-0.6.1 huggingface_hub-0.17.3 humanfriendly-10.0 monotonic-1.6 onnxruntime-1.16.1 overrides-7.4.0 posthog-3.0.2 pulsar-client-3.3.0 pypika-0.48.9 python-dotenv-1.0.0 starlette-0.27.0 tokenizers-0.14.1 typing-extensions-4.8.0 uvicorn-0.23.2 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n",
            "Collecting colorama\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Installing collected packages: colorama\n",
            "Successfully installed colorama-0.4.6\n"
          ]
        }
      ],
      "source": [
        "! pip install langchain\n",
        "\n",
        "# Load PDFs.\n",
        "! pip install pypdf\n",
        "\n",
        "# Access to the Google Palm API\n",
        "! pip install google-generativeai\n",
        "\n",
        "# Database for embeddings\n",
        "! pip install chromadb\n",
        "\n",
        "# Colored output\n",
        "! pip install colorama"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports and setup"
      ],
      "metadata": {
        "id": "fm89a4zlbsNY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from colorama import Fore"
      ],
      "metadata": {
        "id": "Z5UWcUGQOHwb"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Connect to drive.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Rz3iBmRcCM0",
        "outputId": "601e4c7e-abdc-429a-b2d0-05175824ad9b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Palm Setup"
      ],
      "metadata": {
        "id": "W7yYP6c9c3_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate yours @ https://developers.generativeai.google/products/palm\n",
        "with open('/content/drive/My Drive/key.txt', 'r') as f:\n",
        "  palm_api_key = f.readlines()[0].strip('\\n')\n",
        "\n",
        "os.environ[\"GOOGLE_API_KEY\"] = palm_api_key"
      ],
      "metadata": {
        "id": "kiw-kyJ4OKRU"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import langchain\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.llms.google_palm import GooglePalm\n",
        "from langchain.embeddings.google_palm import GooglePalmEmbeddings\n",
        "\n",
        "llm = GooglePalm(temperature=0.0, max_output_tokens=256)\n",
        "embeddings = GooglePalmEmbeddings()"
      ],
      "metadata": {
        "id": "b8qGy9soc8KL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ingestion Phase"
      ],
      "metadata": {
        "id": "N_XHkgiLdHJB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "from langchain.vectorstores import Chroma"
      ],
      "metadata": {
        "id": "BOBWNXf2dLBf"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_path = \"https://www.abc.xyz/assets/86/99/68122c444c4a93d2228e21ecc16b/20230426-alphabet-10q.pdf\"\n",
        "loader = PyPDFLoader(doc_path)\n",
        "text_splitter = CharacterTextSplitter(\n",
        "    chunk_size=512, chunk_overlap=0, separator=\"\\n\"\n",
        ")\n",
        "\n",
        "# load\n",
        "documents = loader.load()\n",
        "# split\n",
        "texts = text_splitter.split_documents(documents)\n",
        "# embed and store\n",
        "vector_store = Chroma.from_documents(\n",
        "    texts, embeddings, collection_name=\"google_2023\", persist_directory=\"./\"\n",
        ")"
      ],
      "metadata": {
        "id": "30Hl5HNUSFpX"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Exploring Stored Data\n",
        "data = vector_store._collection.peek(5)\n",
        "for idx, (id, embedding, metadata, document) in enumerate(zip(\n",
        "    data[\"ids\"], data[\"embeddings\"], data[\"metadatas\"], data[\"documents\"]\n",
        ")):\n",
        "  print(Fore.YELLOW + \"# Document \" + str(idx + 1) + \"\\n\\n\" + Fore.RESET)\n",
        "  print(Fore.BLUE + \"Id: \" + Fore.RESET + id + \"\\n\")\n",
        "  print(Fore.BLUE + \"Embedding: \" + Fore.RESET + str(embedding)[:40] + \" ...\\n\")\n",
        "  print(Fore.BLUE + \"Metadata: \" + Fore.RESET + str(metadata) + \"\\n\")\n",
        "  print(\n",
        "      Fore.BLUE + \"Text: \\n\\n\" + Fore.RESET\n",
        "      + document\n",
        "      + \"\\n\\n------------------------------------------------------------ \\n\"\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "nUD1uMGGNnH6",
        "outputId": "673e5f1c-93b5-4c6b-c930-df38e9544dc1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m# Document 1\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mId: \u001b[39m6adfd2ca-71d9-11ee-82fb-0242ac1c000c\n",
            "\n",
            "\u001b[34mEmbedding: \u001b[39m[-0.007248384412378073, 0.01239798404276 ...\n",
            "\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 0, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39mUNITED STATES\n",
            "SECURITIES AND EXCHANGE COMMISSION\n",
            "Washington, D.C. 20549\n",
            "________________________________________________________________________________________\n",
            "FORM 10-Q  \n",
            "________________________________________________________________________________________\n",
            "(Mark One)\n",
            "☒ QUARTERLY REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\n",
            "For the quarterly period ended March 31, 2023\n",
            "OR\n",
            "☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 2\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mId: \u001b[39m6adfd4fa-71d9-11ee-82fb-0242ac1c000c\n",
            "\n",
            "\u001b[34mEmbedding: \u001b[39m[-0.004968447610735893, -0.0063596065156 ...\n",
            "\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 0, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39mFor the transition period from _______ to _______\n",
            "Commission file number: 001-37580  \n",
            "________________________________________________________________________________________\n",
            "Alphabet Inc.  \n",
            "(Exact name of registrant as specified in its charter)\n",
            "________________________________________________________________________________________\n",
            "Delaware 61-1767919\n",
            "(State or other jurisdiction of incorporation or organization) (I.R.S. Employer Identification Number)\n",
            "1600 Amphitheatre Parkway\n",
            "Mountain View , CA 94043\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 3\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mId: \u001b[39m6adfd6b2-71d9-11ee-82fb-0242ac1c000c\n",
            "\n",
            "\u001b[34mEmbedding: \u001b[39m[-0.000964079808909446, -0.0158256012946 ...\n",
            "\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 0, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39m(Address of principal executive offices, including zip code)\n",
            "(650) 253-0000\n",
            "(Registrant's telephone number, including area code)\n",
            "Securities registered pursuant to Section 12(b) of the Act:\n",
            "Title of each class Trading Symbol(s) Name of each exchange on which registered\n",
            "Class A Common Stock, $0.001 par value GOOGL Nasdaq Stock Market LLC\n",
            "(Nasdaq Global Select Market)\n",
            "Class C Capital Stock, $0.001 par value GOOG Nasdaq Stock Market LLC\n",
            "(Nasdaq Global Select Market)\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 4\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mId: \u001b[39m6adfd77a-71d9-11ee-82fb-0242ac1c000c\n",
            "\n",
            "\u001b[34mEmbedding: \u001b[39m[0.026943108066916466, 0.008100776001811 ...\n",
            "\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 0, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39m________________________________________________________________________________________\n",
            "Indicate by check mark whether the registrant: (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities \n",
            "Exchange Act of 1934 during the preceding 12 months (or for such shorter period that the registrant was required to file such \n",
            "reports), and (2) has been subject to such filing requirements for the past 90 days.    Yes  ☒    No  ☐\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 5\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mId: \u001b[39m6adfd7fc-71d9-11ee-82fb-0242ac1c000c\n",
            "\n",
            "\u001b[34mEmbedding: \u001b[39m[0.010438675992190838, -0.02086065150797 ...\n",
            "\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 0, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39mIndicate by check mark whether the registrant has submitted electronically every Interactive Data File required to be submitted \n",
            "pursuant to Rule 405 of Regulation S-T (§232.405 of this chapter) during the preceding 12 months (or for such shorter period \n",
            "that the registrant was required to submit such files).    Yes  ☒    No  ☐\n",
            "Indicate by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generation Phase"
      ],
      "metadata": {
        "id": "EEZgIdQld63o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title prompt\n",
        "prompt_template = PromptTemplate.from_template(\n",
        "    \"You are finance expert who is tasked with finidng insights about a  \\n\"\n",
        "    \"company from text. Please answer the question at the end using the \\n\"\n",
        "    \"information given below. \\n\"\n",
        "    \"\\n------------------------------------\\n\"\n",
        "    \"Information: \\n\"\n",
        "    \"{context}\"\n",
        "    \"\\n------------------------------------\\n\"\n",
        "    \"Question: \\n\"\n",
        "    \"{question}\"\n",
        ")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "YZyUeN5p_bsa"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. user asks query\n",
        "query = \"What was Google's total income from operations in 2022?\"\n",
        "# 2. langchain gets context\n",
        "retriever = vector_store.as_retriever(search_kwargs={\"k\": 3})\n",
        "relevant_docs = retriever.get_relevant_documents(query=query)\n",
        "context = \"\\n ------------------------------------------------\\n\".join(\n",
        "    [doc.page_content for doc in relevant_docs]\n",
        ")\n",
        "# 3. generate response\n",
        "prompt = prompt_template.format(context=context, question=query)\n",
        "answer = llm(prompt)\n",
        "# 4. return response\n",
        "print(answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vu0PzcAWSljL",
        "outputId": "78908068-d6f4-4b16-c496-6eec98654526"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Google's total income from operations in 2022 was $20,094.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Exploring Context\n",
        "\n",
        "for idx, document in enumerate(relevant_docs):\n",
        "  print(Fore.YELLOW + \"# Document \" + str(idx + 1) + \"\\n\\n\" + Fore.RESET)\n",
        "  print(\n",
        "      document.page_content\n",
        "      + \"\\n\\n------------------------------------------------------------ \\n\"\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "OzoZbTKJk4cm",
        "outputId": "0fd8d9b0-3ac1-4c77-9635-bdba4e4fc5ae"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m# Document 1\n",
            "\n",
            "\u001b[39m\n",
            "Total revenues $ 68,011 $ 69,787 \n",
            " Three Months Ended\n",
            "March 31,\n",
            " 2022 2023\n",
            "Operating income (loss):\n",
            "Google Services $ 21,973 $ 21,737 \n",
            "Google Cloud  (706)  191 \n",
            "Other Bets  (835)  (1,225) \n",
            "Corporate costs, unallocated  (338)  (3,288) \n",
            "Total income from operations $ 20,094 $ 17,415 \n",
            "For revenues by geography, see Note 2 .\n",
            "The following table presents long-lived assets by geographic area, which includes property and equipment, net \n",
            "and operating lease assets (in millions) :\n",
            "As of \n",
            "December 31, 2022As of\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 2\n",
            "\n",
            "\u001b[39m\n",
            "2022 2023\n",
            "Net cash provided by operating activities $ 25,106 $ 23,509 \n",
            "Net cash used in investing activities $ (9,051) $ (2,946) \n",
            "Net cash used in financing activities $ (16,214) $ (16,568) \n",
            "Cash Provided by Operating Activities\n",
            "Our largest source of cash provided by operations are advertising revenues generated by Google Search & \n",
            "other properties, Google Network properties, and YouTube properties. Additionally, we generate cash through sales\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 3\n",
            "\n",
            "\u001b[39m\n",
            "Financial Results\n",
            "Revenues\n",
            "The following table presents revenues by type (in millions): \n",
            " Three Months Ended\n",
            "March 31,\n",
            " 2022 2023\n",
            "Google Search & other $ 39,618 $ 40,359 \n",
            "YouTube ads  6,869  6,693 \n",
            "Google Network  8,174  7,496 \n",
            "Google advertising  54,661  54,548 \n",
            "Google other  6,811  7,413 \n",
            "Google Services total  61,472  61,961 \n",
            "Google Cloud  5,821  7,454 \n",
            "Other Bets  440  288 \n",
            "Hedging gains (losses)  278  84 \n",
            "Total revenues $ 68,011 $ 69,787 \n",
            "Google Services\n",
            "Google advertising revenues\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Improving RAG"
      ],
      "metadata": {
        "id": "aYWmkhOAfE5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.retrievers import ContextualCompressionRetriever\n",
        "from langchain.retrievers.document_compressors import LLMChainExtractor\n",
        "\n",
        "compressor = LLMChainExtractor.from_llm(llm)\n",
        "compression_retriever = ContextualCompressionRetriever(\n",
        "    base_compressor=compressor,\n",
        "    base_retriever=vector_store.as_retriever(search_kwargs={\"k\": 3})\n",
        ")\n",
        "\n",
        "compressed_docs = compression_retriever.get_relevant_documents(query)"
      ],
      "metadata": {
        "id": "J0WU8S6ZJnOU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9a3f055-2e29-4078-ec6f-f831e2c679a0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Compressed contexts\n",
        "data = vector_store._collection.peek(5)\n",
        "for idx, document in enumerate(compressed_docs):\n",
        "  print(Fore.YELLOW + \"# Document \" + str(idx + 1) + \"\\n\\n\" + Fore.RESET)\n",
        "  print(Fore.BLUE + \"Metadata: \" + Fore.RESET + str(document.metadata) + \"\\n\")\n",
        "  print(\n",
        "      Fore.BLUE + \"Text: \\n\\n\" + Fore.RESET\n",
        "      + document.page_content\n",
        "      + \"\\n\\n------------------------------------------------------------ \\n\"\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "WAe8olSyBxb8",
        "outputId": "870984a9-9eab-49fd-c9e5-3067e46b5118"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m# Document 1\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 28, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39mTotal income from operations $ 20,094\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 2\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 39, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39m2022 2023\n",
            "Net cash provided by operating activities $ 25,106 $ 23,509\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n",
            "\u001b[33m# Document 3\n",
            "\n",
            "\u001b[39m\n",
            "\u001b[34mMetadata: \u001b[39m{'page': 34, 'source': '/tmp/tmp2cvv8we5/tmp.pdf'}\n",
            "\n",
            "\u001b[34mText: \n",
            "\n",
            "\u001b[39mTotal revenues $ 68,011 $ 69,787\n",
            "\n",
            "------------------------------------------------------------ \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Generating Answer\n",
        "context = \"\\n ------------------------------------------------\\n\".join(\n",
        "    [doc.page_content for doc in compressed_docs]\n",
        ")\n",
        "# 3. generate response\n",
        "prompt = prompt_template.format(context=context, question=query)\n",
        "answer = llm(prompt)\n",
        "# 4. return response\n",
        "print(answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "w_RIOb2EDyZZ",
        "outputId": "6d0dc6e3-c176-4bd8-a2f3-b03763e7bf6f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20,094\n"
          ]
        }
      ]
    }
  ]
}