FinRAG_v0.0.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piBrI5ukuZSg"
      },
      "source": [
        "# Chat with Data Notebook (Embedded Tables)\n",
        "\n",
        "In this notebook we walk you through an advanced RAG use case - parsing a complex document with embedded tables - and how to handle that in LlamaIndex.\n",
        "\n",
        "We compare our approach against a \"naive\" RAG stack (using top-k retrieval with a fixed chunk size).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4aWI5yfuC22",
        "outputId": "4fb1894c-b30b-46ab-c7ca-4a9463061216"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "\u001b[?25hInstalling collected packages: urllib3, retrying, pypdf, mypy-extensions, marshmallow, jsonpointer, html2text, h11, deprecated, aiostream, typing-inspect, jsonpatch, httpcore, tiktoken, langsmith, httpx, dataclasses-json, openai, langchain, atlassian-python-api, llama-index, llama-hub\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.7\n",
            "    Uninstalling urllib3-2.0.7:\n",
            "      Successfully uninstalled urllib3-2.0.7\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",
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          ]
        }
      ],
      "source": [
        "!pip install llama-index llama-hub pypdf --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ht4oSN2PvzUJ"
      },
      "outputs": [],
      "source": [
        "# setup OpenAI\n",
        "import openai\n",
        "\n",
        "openai.api_key = \"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k3CGRBjsuLCB"
      },
      "outputs": [],
      "source": [
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VhuXtvSX2mWo"
      },
      "outputs": [],
      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css():\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "get_ipython().events.register('pre_run_cell', set_css)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Y0CWFcEv61y"
      },
      "source": [
        "## Complex Document (with Embedded Tables)\n",
        "\n",
        "In this setting we walk through a document that has an embedded table inside of it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XVTmmtjp8qvu",
        "outputId": "bd346df3-90c4-4aa8-b793-abccd87d4494"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
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            "python3-tk is already the newest version (3.10.8-1~22.04).\n",
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            "  libjbig2dec0 poppler-data\n",
            "Suggested packages:\n",
            "  fonts-noto fonts-freefont-otf | fonts-freefont-ttf fonts-texgyre ghostscript-x poppler-utils\n",
            "  fonts-japanese-mincho | fonts-ipafont-mincho fonts-japanese-gothic | fonts-ipafont-gothic\n",
            "  fonts-arphic-ukai fonts-arphic-uming fonts-nanum\n",
            "The following NEW packages will be installed:\n",
            "  fonts-droid-fallback fonts-noto-mono fonts-urw-base35 ghostscript libgs9 libgs9-common libidn12\n",
            "  libijs-0.35 libjbig2dec0 poppler-data\n",
            "0 upgraded, 10 newly installed, 0 to remove and 19 not upgraded.\n",
            "Need to get 16.7 MB of archives.\n",
            "After this operation, 63.0 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1build1 [1,805 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 poppler-data all 0.4.11-1 [2,171 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-noto-mono all 20201225-1build1 [397 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-urw-base35 all 20200910-1 [6,367 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9-common all 9.55.0~dfsg1-0ubuntu5.5 [752 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libidn12 amd64 1.38-4ubuntu1 [60.0 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy/main amd64 libijs-0.35 amd64 0.35-15build2 [16.5 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjbig2dec0 amd64 0.19-3build2 [64.7 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9 amd64 9.55.0~dfsg1-0ubuntu5.5 [5,030 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 ghostscript amd64 9.55.0~dfsg1-0ubuntu5.5 [49.5 kB]\n",
            "Fetched 16.7 MB in 1s (13.0 MB/s)\n",
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            "(Reading database ... 120874 files and directories currently installed.)\n",
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            "Selecting previously unselected package ghostscript.\n",
            "Preparing to unpack .../9-ghostscript_9.55.0~dfsg1-0ubuntu5.5_amd64.deb ...\n",
            "Unpacking ghostscript (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Setting up fonts-noto-mono (20201225-1build1) ...\n",
            "Setting up libijs-0.35:amd64 (0.35-15build2) ...\n",
            "Setting up fonts-urw-base35 (20200910-1) ...\n",
            "Setting up poppler-data (0.4.11-1) ...\n",
            "Setting up libjbig2dec0:amd64 (0.19-3build2) ...\n",
            "Setting up libidn12:amd64 (1.38-4ubuntu1) ...\n",
            "Setting up fonts-droid-fallback (1:6.0.1r16-1.1build1) ...\n",
            "Setting up libgs9-common (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Setting up libgs9:amd64 (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Setting up ghostscript (9.55.0~dfsg1-0ubuntu5.5) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.10.2-1) ...\n"
          ]
        }
      ],
      "source": [
        "!apt install ghostscript python3-tk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kv6qso2Bv6ZA",
        "outputId": "dd2a3a42-782d-45db-f359-9a7fb3c0d6e9"
      },
      "outputs": [
        {
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          "metadata": {},
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        },
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          "text": [
            "Collecting camelot-py\n",
            "  Downloading camelot_py-0.11.0-py3-none-any.whl (40 kB)\n",
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            "  Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.23.4->camelot-py) (1.16.0)\n",
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            "Installing collected packages: uvicorn, PyMuPDFb, ghostscript, aiofiles, starlette, pymupdf, pdfminer.six, frontend, camelot-py\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 fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed PyMuPDFb-1.23.6 aiofiles-23.2.1 camelot-py-0.11.0 frontend-0.0.3 ghostscript-0.7 pdfminer.six-20221105 pymupdf-1.23.6 starlette-0.32.0.post1 uvicorn-0.24.0.post1\n"
          ]
        }
      ],
      "source": [
        "!pip install camelot-py pymupdf frontend ghostscript"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "BqJQDrZqzAEp",
        "outputId": "9e9c0ab8-b02c-453f-b772-3fb98d1dd6f4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
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              "<IPython.core.display.HTML object>"
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          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import camelot\n",
        "from llama_index import Document, SummaryIndex\n",
        "\n",
        "# https://en.wikipedia.org/wiki/The_World%27s_Billionaires\n",
        "from llama_index import VectorStoreIndex, ServiceContext, LLMPredictor\n",
        "from llama_index.query_engine import PandasQueryEngine, RetrieverQueryEngine\n",
        "from llama_index.retrievers import RecursiveRetriever\n",
        "from llama_index.schema import IndexNode\n",
        "from llama_index.llms import OpenAI\n",
        "\n",
        "from llama_hub.file.pymu_pdf.base import PyMuPDFReader\n",
        "from pathlib import Path\n",
        "from typing import List"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ok7qgGuyzRMx"
      },
      "source": [
        "### Parse out Table, build Pandas Query Engine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "-FyhtIy43ZWQ",
        "outputId": "5de06145-f95d-4f0a-9459-ce30f9938b30"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-11-08 08:39:04--  https://www.dropbox.com/scl/fi/waoz9bo9yiemnhnqvu0cc/billionaires_page.pdf?rlkey=4i08msa7zr1lpnuq2y1vs2xgw&dl=1\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.67.18, 2620:100:6025:18::a27d:4512\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.67.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com/cd/0/inline/CHILPsnmWsPf2A36gkigVOpcEGOEPC5ozf159kcAuew7RElvDPzsnCSDtWgUAhofhJWPHM7BUp-qrES7oGQnSWKUQBGFH126pEgeN72xQsYTBjw4h5Yq56h2KBJ5w3Rrx20/file?dl=1# [following]\n",
            "--2023-11-08 08:39:04--  https://uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com/cd/0/inline/CHILPsnmWsPf2A36gkigVOpcEGOEPC5ozf159kcAuew7RElvDPzsnCSDtWgUAhofhJWPHM7BUp-qrES7oGQnSWKUQBGFH126pEgeN72xQsYTBjw4h5Yq56h2KBJ5w3Rrx20/file?dl=1\n",
            "Resolving uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com (uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com)... 162.125.71.15, 2620:100:6021:15::a27d:410f\n",
            "Connecting to uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com (uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com)|162.125.71.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/CHKjbWpWIGqoqrztPc1KS0mhb7GEzH3F_Tl7PnbSNZaXwZRHts8t_rGMnHT5EDLnPRocjJu3VvNdEVfw1GZcL7OKJbIpaAHpTEpHoIKQmZIG5jXnrVQe75OG3_z0veVD81LBYoDrKD8vYpHqk6257ZOZuq7W05m_VOkARB219b31V_CI0HFBmNRicWw83b4PDrBYj0n9ecB4TMXts8HEjjG7eQAK03z2qi8dOe9RVud5XAENDle-l3LXHRhiqClbP-buWdRKtSIS4PXHOpEDazrt1n7er4JXaR-GNQvnEzaqpCHRGh83P9h9Q0anQZ7xt8AajYFUM0P-iqKCYYyMZBA9korX3RKYNHYTdJyEmninNw/file?dl=1 [following]\n",
            "--2023-11-08 08:39:05--  https://uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com/cd/0/inline2/CHKjbWpWIGqoqrztPc1KS0mhb7GEzH3F_Tl7PnbSNZaXwZRHts8t_rGMnHT5EDLnPRocjJu3VvNdEVfw1GZcL7OKJbIpaAHpTEpHoIKQmZIG5jXnrVQe75OG3_z0veVD81LBYoDrKD8vYpHqk6257ZOZuq7W05m_VOkARB219b31V_CI0HFBmNRicWw83b4PDrBYj0n9ecB4TMXts8HEjjG7eQAK03z2qi8dOe9RVud5XAENDle-l3LXHRhiqClbP-buWdRKtSIS4PXHOpEDazrt1n7er4JXaR-GNQvnEzaqpCHRGh83P9h9Q0anQZ7xt8AajYFUM0P-iqKCYYyMZBA9korX3RKYNHYTdJyEmninNw/file?dl=1\n",
            "Reusing existing connection to uc19051c67f1e7f1353ff651529f.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3063791 (2.9M) [application/binary]\n",
            "Saving to: ‘billionaires_page.pdf’\n",
            "\n",
            "billionaires_page.p 100%[===================>]   2.92M  2.16MB/s    in 1.4s    \n",
            "\n",
            "2023-11-08 08:39:07 (2.16 MB/s) - ‘billionaires_page.pdf’ saved [3063791/3063791]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget \"https://www.dropbox.com/scl/fi/waoz9bo9yiemnhnqvu0cc/billionaires_page.pdf?rlkey=4i08msa7zr1lpnuq2y1vs2xgw&dl=1\" -O billionaires_page.pdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "bk7Aj3x1zC0P",
        "outputId": "547b70d0-19e5-4226-c6a6-0377f232e56e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# initialize PDF reader\n",
        "reader = PyMuPDFReader()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "A48MFvCHzELc",
        "outputId": "0da1b6d9-dc24-4367-b53a-ba878978db39"
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      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "file_path = \"billionaires_page.pdf\"\n",
        "docs = reader.load(file_path=file_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "qUFUJFMjzFaR",
        "outputId": "be94b1e3-508f-4bc2-a170-ba24a3779cc4"
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      "outputs": [
        {
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              "\n",
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              "    }\n",
              "  </style>\n",
              "  "
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
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      ],
      "source": [
        "# use camelot to parse tables\n",
        "def get_tables(path: str, pages: List[int]):\n",
        "    table_dfs = []\n",
        "    for page in pages:\n",
        "        table_list = camelot.read_pdf(path, pages=str(page))\n",
        "        table_df = table_list[0].df\n",
        "        table_df = (\n",
        "            table_df.rename(columns=table_df.iloc[0])\n",
        "            .drop(table_df.index[0])\n",
        "            .reset_index(drop=True)\n",
        "        )\n",
        "        table_dfs.append(table_df)\n",
        "    return table_dfs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "prSCnWjWzGvN",
        "outputId": "02283c68-8211-4b55-952d-d7c75e7688f7"
      },
      "outputs": [
        {
          "data": {
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      ],
      "source": [
        "table_dfs = get_tables(file_path, pages=[3, 25])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "R7_MxDU_zIEx",
        "outputId": "7185950a-8fc1-4a61-e068-5fa7486c2405"
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      "outputs": [
        {
          "data": {
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>No.</th>\n",
              "      <th>Name</th>\n",
              "      <th>Net worth\\n(USD)</th>\n",
              "      <th>Age</th>\n",
              "      <th>Nationality</th>\n",
              "      <th>Primary source(s) of wealth</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <td>1</td>\n",
              "      <td>Bernard Arnault &amp;\\nfamily</td>\n",
              "      <td>$211 billion</td>\n",
              "      <td>74</td>\n",
              "      <td>France</td>\n",
              "      <td>LVMH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>$180 billion</td>\n",
              "      <td>51</td>\n",
              "      <td>United\\nStates</td>\n",
              "      <td>Tesla, SpaceX, X Corp.</td>\n",
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              "      <th>2</th>\n",
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              "      <td>59</td>\n",
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              "      <td>$106 billion</td>\n",
              "      <td>92</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Bill Gates</td>\n",
              "      <td>$104 billion</td>\n",
              "      <td>67</td>\n",
              "      <td>United\\nStates</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Michael Bloomberg</td>\n",
              "      <td>$94.5 billion</td>\n",
              "      <td>81</td>\n",
              "      <td>United\\nStates</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Carlos Slim &amp; family</td>\n",
              "      <td>$93 billion</td>\n",
              "      <td>83</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>Telmex, América Móvil, Grupo\\nCarso</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Mukesh Ambani</td>\n",
              "      <td>$83.4 billion</td>\n",
              "      <td>65</td>\n",
              "      <td>India</td>\n",
              "      <td>Reliance Industries</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Steve Ballmer</td>\n",
              "      <td>$80.7 billion</td>\n",
              "      <td>67</td>\n",
              "      <td>United\\nStates</td>\n",
              "      <td>Microsoft</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-c3f72bd8-bb2b-4088-8b66-07124556f231 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-c3f72bd8-bb2b-4088-8b66-07124556f231');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1d774f2b-92df-444c-9d7c-6b2b6a5d7c2b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d774f2b-92df-444c-9d7c-6b2b6a5d7c2b')\"\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",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-1d774f2b-92df-444c-9d7c-6b2b6a5d7c2b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  No.                       Name Net worth\\n(USD) Age     Nationality  \\\n",
              "0   1  Bernard Arnault &\\nfamily     $211 billion  74          France   \n",
              "1   2                  Elon Musk     $180 billion  51  United\\nStates   \n",
              "2   3                 Jeff Bezos     $114 billion  59  United\\nStates   \n",
              "3   4              Larry Ellison     $107 billion  78  United\\nStates   \n",
              "4   5             Warren Buffett     $106 billion  92  United\\nStates   \n",
              "5   6                 Bill Gates     $104 billion  67  United\\nStates   \n",
              "6   7          Michael Bloomberg    $94.5 billion  81  United\\nStates   \n",
              "7   8       Carlos Slim & family      $93 billion  83          Mexico   \n",
              "8   9              Mukesh Ambani    $83.4 billion  65           India   \n",
              "9  10              Steve Ballmer    $80.7 billion  67  United\\nStates   \n",
              "\n",
              "           Primary source(s) of wealth  \n",
              "0                                 LVMH  \n",
              "1               Tesla, SpaceX, X Corp.  \n",
              "2                               Amazon  \n",
              "3                   Oracle Corporation  \n",
              "4                   Berkshire Hathaway  \n",
              "5                            Microsoft  \n",
              "6                       Bloomberg L.P.  \n",
              "7  Telmex, América Móvil, Grupo\\nCarso  \n",
              "8                  Reliance Industries  \n",
              "9                            Microsoft  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# shows list of top billionaires in 2023\n",
        "table_dfs[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "wnvCdO78zJ3v",
        "outputId": "1778ef06-3e3b-4275-fc84-ba672adb0aba"
      },
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        {
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              "    pre {\n",
              "        white-space: pre-wrap;\n",
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              "\n",
              "  <div id=\"df-0f8f935e-0e12-45ea-adf9-168ac16e281a\" 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",
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              "    }\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>Year</th>\n",
              "      <th>Number of billionaires</th>\n",
              "      <th>Group's combined net worth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023[2]</td>\n",
              "      <td>2,640</td>\n",
              "      <td>$12.2 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022[6]</td>\n",
              "      <td>2,668</td>\n",
              "      <td>$12.7 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021[11]</td>\n",
              "      <td>2,755</td>\n",
              "      <td>$13.1 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020</td>\n",
              "      <td>2,095</td>\n",
              "      <td>$8.0 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019</td>\n",
              "      <td>2,153</td>\n",
              "      <td>$8.7 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2018</td>\n",
              "      <td>2,208</td>\n",
              "      <td>$9.1 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2017</td>\n",
              "      <td>2,043</td>\n",
              "      <td>$7.7 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016</td>\n",
              "      <td>1,810</td>\n",
              "      <td>$6.5 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2015[18]</td>\n",
              "      <td>1,826</td>\n",
              "      <td>$7.1 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2014[67]</td>\n",
              "      <td>1,645</td>\n",
              "      <td>$6.4 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2013[68]</td>\n",
              "      <td>1,426</td>\n",
              "      <td>$5.4 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2012</td>\n",
              "      <td>1,226</td>\n",
              "      <td>$4.6 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2011</td>\n",
              "      <td>1,210</td>\n",
              "      <td>$4.5 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2010</td>\n",
              "      <td>1,011</td>\n",
              "      <td>$3.6 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2009</td>\n",
              "      <td>793</td>\n",
              "      <td>$2.4 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2008</td>\n",
              "      <td>1,125</td>\n",
              "      <td>$4.4 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2007</td>\n",
              "      <td>946</td>\n",
              "      <td>$3.5 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2006</td>\n",
              "      <td>793</td>\n",
              "      <td>$2.6 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2005</td>\n",
              "      <td>691</td>\n",
              "      <td>$2.2 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2004</td>\n",
              "      <td>587</td>\n",
              "      <td>$1.9 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2003</td>\n",
              "      <td>476</td>\n",
              "      <td>$1.4 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2002</td>\n",
              "      <td>497</td>\n",
              "      <td>$1.5 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2001</td>\n",
              "      <td>538</td>\n",
              "      <td>$1.8 trillion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2000</td>\n",
              "      <td>470</td>\n",
              "      <td>$898 billion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Sources: Forbes.[18][67][66][68]</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
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            "text/plain": [
              "                                Year Number of billionaires  \\\n",
              "0                            2023[2]                  2,640   \n",
              "1                            2022[6]                  2,668   \n",
              "2                           2021[11]                  2,755   \n",
              "3                               2020                  2,095   \n",
              "4                               2019                  2,153   \n",
              "5                               2018                  2,208   \n",
              "6                               2017                  2,043   \n",
              "7                               2016                  1,810   \n",
              "8                           2015[18]                  1,826   \n",
              "9                           2014[67]                  1,645   \n",
              "10                          2013[68]                  1,426   \n",
              "11                              2012                  1,226   \n",
              "12                              2011                  1,210   \n",
              "13                              2010                  1,011   \n",
              "14                              2009                    793   \n",
              "15                              2008                  1,125   \n",
              "16                              2007                    946   \n",
              "17                              2006                    793   \n",
              "18                              2005                    691   \n",
              "19                              2004                    587   \n",
              "20                              2003                    476   \n",
              "21                              2002                    497   \n",
              "22                              2001                    538   \n",
              "23                              2000                    470   \n",
              "24  Sources: Forbes.[18][67][66][68]                          \n",
              "\n",
              "   Group's combined net worth  \n",
              "0              $12.2 trillion  \n",
              "1              $12.7 trillion  \n",
              "2              $13.1 trillion  \n",
              "3               $8.0 trillion  \n",
              "4               $8.7 trillion  \n",
              "5               $9.1 trillion  \n",
              "6               $7.7 trillion  \n",
              "7               $6.5 trillion  \n",
              "8               $7.1 trillion  \n",
              "9               $6.4 trillion  \n",
              "10              $5.4 trillion  \n",
              "11              $4.6 trillion  \n",
              "12              $4.5 trillion  \n",
              "13              $3.6 trillion  \n",
              "14              $2.4 trillion  \n",
              "15              $4.4 trillion  \n",
              "16              $3.5 trillion  \n",
              "17              $2.6 trillion  \n",
              "18              $2.2 trillion  \n",
              "19              $1.9 trillion  \n",
              "20              $1.4 trillion  \n",
              "21              $1.5 trillion  \n",
              "22              $1.8 trillion  \n",
              "23               $898 billion  \n",
              "24                             "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# shows list of top billionaires\n",
        "table_dfs[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 52
        },
        "id": "Q-NdLsc9zLZg",
        "outputId": "c1f39034-4673-4b14-974c-ea48a209a0a5"
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      "outputs": [
        {
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              "\n",
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          "text": [
            "[nltk_data] Downloading package punkt to /tmp/llama_index...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "# define query engines over these tables\n",
        "df_query_engines = [PandasQueryEngine(table_df) for table_df in table_dfs]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "tmksOoRuzMxe",
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          "text": [
            "$180 billion\n"
          ]
        }
      ],
      "source": [
        "response = df_query_engines[0].query(\n",
        "    \"What's the net worth of the second richest billionaire in 2023?\"\n",
        ")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "iYeIkl0wzN4H",
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          "text": [
            "793\n"
          ]
        }
      ],
      "source": [
        "response = df_query_engines[1].query(\"How many billionaires were there in 2009?\")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPhTPOALzTf8"
      },
      "source": [
        "### Build Recursive Retriever\n",
        "\n",
        "We define a top-level vector index that does top-k lookup over a set of Nodes. We define two special nodes (`IndexNode` objects) linking to each of these tables.\n",
        "\n",
        "We define a `RecursiveRetriever` object to recursively retrieve/query nodes. We then put this in our `RetrieverQueryEngine` along with a `ResponseSynthesizer` to synthesize a response.\n",
        "\n",
        "We pass in mappings from id to retriever and id to query engine. We then pass in a root id representing the retriever we query first."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 17
        },
        "id": "Q16ooq0vzenz",
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      "outputs": [
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      ],
      "source": [
        "llm = OpenAI(temperature=0, model=\"gpt-4\")\n",
        "\n",
        "service_context = ServiceContext.from_defaults(\n",
        "    llm=llm,\n",
        ")"
      ]
    },
    {
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      "execution_count": null,
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "doc_nodes = service_context.node_parser.get_nodes_from_documents(docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Ix5wBtHTzg8o",
        "outputId": "4b240741-885a-4f60-b029-dd696ed119a2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# define index nodes\n",
        "summaries = [\n",
        "    \"This node provides information about the world's richest billionaires in 2023\",\n",
        "    \"This node provides information on the number of billionaires and their combined net worth from 2000 to 2023.\",\n",
        "]\n",
        "\n",
        "df_nodes = [\n",
        "    IndexNode(text=summary, index_id=f\"pandas{idx}\")\n",
        "    for idx, summary in enumerate(summaries)\n",
        "]\n",
        "\n",
        "df_id_query_engine_mapping = {\n",
        "    f\"pandas{idx}\": df_query_engine\n",
        "    for idx, df_query_engine in enumerate(df_query_engines)\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "I_8SoJwEzh6C",
        "outputId": "3eec8820-88af-4745-92ff-c0e3095f59e0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# construct top-level vector index + query engine\n",
        "vector_index = VectorStoreIndex(doc_nodes + df_nodes)\n",
        "vector_retriever = vector_index.as_retriever(similarity_top_k=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "_IlcUcCQzpdG",
        "outputId": "f476e6a3-b8c7-4201-c3f4-fbdfc48e78fb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from llama_index.retrievers import RecursiveRetriever\n",
        "from llama_index.query_engine import RetrieverQueryEngine\n",
        "from llama_index.response_synthesizers import get_response_synthesizer\n",
        "\n",
        "recursive_retriever = RecursiveRetriever(\n",
        "    \"vector\",\n",
        "    retriever_dict={\"vector\": vector_retriever},\n",
        "    query_engine_dict=df_id_query_engine_mapping,\n",
        "    verbose=True,\n",
        ")\n",
        "\n",
        "response_synthesizer = get_response_synthesizer(\n",
        "    # service_context=service_context,\n",
        "    response_mode=\"compact\"\n",
        ")\n",
        "\n",
        "query_engine = RetrieverQueryEngine.from_args(\n",
        "    recursive_retriever, response_synthesizer=response_synthesizer\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ol-2hQ9hzqp_"
      },
      "source": [
        "### Define Baseline Retriever\n",
        "\n",
        "We also define a baseline retriever that does top-k lookup over the raw document."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "KxMk4zehipBf",
        "outputId": "4cdc5cb3-30b6-4144-abb4-f740cface9e8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# baseline vector index (that doesn't include the extra df nodes).\n",
        "# used to benchmark\n",
        "vector_index0 = VectorStoreIndex(doc_nodes)\n",
        "vector_query_engine0 = vector_index0.as_query_engine()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRsB_28_isCA"
      },
      "source": [
        "### Compare Results\n",
        "\n",
        "We compare results between the recursive retriever vs. baseline retriever."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "Ymguy0KGztxd",
        "outputId": "9fcb5ae8-ea65-45c3-df73-faecdca71f8e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34mRetrieving with query id None: How many billionaires were there in 2009?\n",
            "\u001b[0m\u001b[1;3;38;5;200mRetrieved node with id, entering: pandas1\n",
            "\u001b[0m\u001b[1;3;34mRetrieving with query id pandas1: How many billionaires were there in 2009?\n",
            "\u001b[0m\u001b[1;3;32mGot response: 793\n",
            "\u001b[0m793\n"
          ]
        }
      ],
      "source": [
        "response = query_engine.query(\n",
        "    \"How many billionaires were there in 2009?\"\n",
        ")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "TIM0KPcYz2t2",
        "outputId": "cf41d562-7ae3-43cc-a7e3-48b4b4ec43b6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Query: How many billionaires were there in 2009?\\nResponse: 793'"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "response.source_nodes[0].node.get_content()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "Ylgg7AYYzxLd",
        "outputId": "7c682903-18ef-4bc0-f1ac-3f0e2632f108"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Based on the context information, it is not possible to determine the exact number of billionaires in 2009.\n"
          ]
        }
      ],
      "source": [
        "response = vector_query_engine0.query(\n",
        "    \"How many billionaires were there in 2009?\"\n",
        ")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MCLtfVdNz5Op",
        "outputId": "6be1aa74-a7b0-42de-8fc5-f7ce82d36600"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "7/1/23, 11:31 PM\n",
            "The World's Billionaires - Wikipedia\n",
            "https://en.wikipedia.org/wiki/The_World%27s_Billionaires\n",
            "6/33\n",
            "In the 32nd annual Forbes list of the world's billionaires, the aggregate wealth of the top 20 richest\n",
            "people on Earth amounted to about 13 percent of all billionaires' fortunes combined.[15] A record of\n",
            "2,208 billionaires were in the ranking and the total wealth was $9.1 trillion, up 18% since 2017. For\n",
            "the first time, Jeff Bezos was listed as the top billionaire due to Amazon's rising stock price that\n",
            "resulted in one person's biggest one-year gain in wealth ($35 billion) since Forbes started tracking in\n",
            "1987.[15] The U.S. had the most billionaires in the world, with 585, while China was catching up with\n",
            "476 when including Hong Kong, Macau and Taiwan; it had 372 when excluding those three places.[15]\n",
            "Forbes excluded Al-Walid bin Talal and all other Saudi billionaires due to the absence of accurate\n",
            "wealth estimations as a result of the 2017–19 Saudi Arabian purge.[16]\n",
            "No.\n",
            "Name\n",
            "Net worth (USD)\n",
            "Age\n",
            "Nationality\n",
            "Source(s) of wealth\n",
            "1 \n",
            "Jeff Bezos\n",
            "$112 billion \n",
            "54\n",
            " United States\n",
            "Amazon\n",
            "2 \n",
            "Bill Gates\n",
            "$90 billion \n",
            "62\n",
            " United States\n",
            "Microsoft\n",
            "3 \n",
            "Warren Buffett\n",
            "$84 billion \n",
            "87\n",
            " United States\n",
            "Berkshire Hathaway\n",
            "4 \n",
            "Bernard Arnault\n",
            "$72 billion \n",
            "69\n",
            " France\n",
            "LVMH\n",
            "5 \n",
            "Mark Zuckerberg\n",
            "$71 billion \n",
            "33\n",
            " United States\n",
            "Facebook, Inc.\n",
            "6 \n",
            "Amancio Ortega\n",
            "$70 billion \n",
            "81\n",
            " Spain\n",
            "Inditex, Zara\n",
            "7 \n",
            "Carlos Slim\n",
            "$67.1 billion \n",
            "78\n",
            " Mexico\n",
            "América Móvil, Grupo Carso\n",
            "8 \n",
            "Charles Koch\n",
            "$60 billion \n",
            "82\n",
            " United States\n",
            "Koch Industries\n",
            "8 \n",
            "David Koch\n",
            "$60 billion \n",
            "77\n",
            " United States\n",
            "Koch Industries\n",
            "10 \n",
            "Larry Ellison\n",
            "$58.5 billion \n",
            "73\n",
            " United States\n",
            "Oracle Corporation\n",
            "On the 30th anniversary of the Forbes list of the world's billionaires, for the fourth year in a row, Bill\n",
            "Gates was named the richest man in the world.[5] In 2017, there was a record of 2,043 people on the\n",
            "list, which is the first time over 2,000 people were listed. This included 195 newcomers of whom 76\n",
            "were from China and 25 from the U.S.; there were 56 people under 40 and it had a record of 227\n",
            "women.[17] The number of billionaires increased 13% to 2,043 from 1,810 in 2016; this was the biggest\n",
            "change in over 30 years of tracking billionaires globally.[5] Added together, the total net worth for\n",
            "2017's billionaires was US$7.67 trillion, up from US$7.1 trillion in 2015.[5][18] This was the first time\n",
            "after 12 years that Carlos Slim was not within the top five. The U.S. had the most billionaires in the\n",
            "world, with a record of 565. China had 319 (not including Hong Kong, Taiwan or Macau), Germany\n",
            "had 114, and India had the fourth most with 101; India reached over 100 billionaires for its first time.\n",
            "2017\n"
          ]
        }
      ],
      "source": [
        "print(response.source_nodes[1].node.get_content())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "kQZhiiIJjB_s",
        "outputId": "9f0197c3-8331-434e-ea00-18a402f061cb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34mRetrieving with query id None: What is the average age of top 5 billionaires in 2023? Make sure age is a float.\n",
            "\u001b[0m\u001b[1;3;38;5;200mRetrieved node with id, entering: pandas0\n",
            "\u001b[0m\u001b[1;3;34mRetrieving with query id pandas0: What is the average age of top 5 billionaires in 2023? Make sure age is a float.\n",
            "\u001b[0m\u001b[1;3;32mGot response: 70.8\n",
            "\u001b[0mThe average age of the top 5 billionaires in 2023 is 70.8.\n"
          ]
        }
      ],
      "source": [
        "response = query_engine.query(\n",
        "    \"What is the average age of top 5 billionaires in 2023? Make sure age is a float.\"\n",
        ")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "gbWsicqqjXYi",
        "outputId": "c017b4fc-baa4-42b9-93ef-f4facbc46822"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The average age of the top 5 billionaires in 2023 is 72.2.\n"
          ]
        }
      ],
      "source": [
        "response = vector_query_engine0.query(\n",
        "    \"What is the average age of top 5 billionaires in 2023? Make sure age is a float.\"\n",
        ")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVEtBgX4kiaj"
      },
      "source": [
        "Of course, just like the baseline vector query engine, the recursive retriever can answer semantic queries over the article as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cpe2JqBfkf7K",
        "outputId": "64556143-ae02-4699-997b-17807ddb6151"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34mRetrieving with query id None: How is wealth accounted for in recipients if the billionaire is deceased?\n",
            "\u001b[0m\u001b[1;3;38;5;200mRetrieving text node: 7/1/23, 11:31 PM\n",
            "The World's Billionaires - Wikipedia\n",
            "https://en.wikipedia.org/wiki/The_World%27s_Billionaires\n",
            "2/33\n",
            "stock are priced to market on a date roughly a month before publication. Privately held companies are\n",
            "priced by the prevailing price-to-sales or price-to-earnings ratios. Known debt is subtracted from\n",
            "assets to get a final estimate of an individual's estimated worth in United States dollars. Since stock\n",
            "prices fluctuate rapidly, an individual's true wealth and ranking at the time of publication may vary\n",
            "from their situation when the list was compiled.[7]\n",
            "When a living individual has dispersed his or her wealth to immediate family members it is included\n",
            "under a single listing (as a single \"family fortune\") provided that individual (the grantor) is still living.\n",
            "However, if a deceased billionaire's fortune has been dispersed, it will not appear as a single listing,\n",
            "and each recipient will only appear if his or her own total net worth is over a $Billion (his or her net\n",
            "worth will not be combined with family members')[7] Royal families and dictators that have their\n",
            "wealth contingent on a position are always excluded from these lists.[9]\n",
            "The rankings are published annually in March, so the net worths listed are snapshots taken at that\n",
            "time. These lists only show the top 10 wealthiest billionaires for each year.\n",
            "Icon\n",
            "Description\n",
            "Has not changed from the previous ranking.\n",
            "Has increased from the previous ranking.\n",
            "Has decreased from the previous ranking.\n",
            "In the 37th annual Forbes list of the world's billionaires, the list included 2,640 billionaires with a\n",
            "total net wealth of $12.2 trillion, down 28 members and $500 billion from 2022. Nearly half the list is\n",
            "poorer than the previous year, including Elon Musk, who fell from No. 1 to No. 2.[2] The list also\n",
            "marks for the first time a French citizen was in the top position as well as a non-American for the first\n",
            "time since 2013 when the Mexican Carlos Slim Helu was the world's richest person. The list, like in\n",
            "2022, counted 15 under 30 billionaires with the richest of them being Red Bull heir Mark Mateschitz\n",
            "with a net worth of $34.7 billion. The youngest of the lot were Clemente Del Vecchio, heir to the\n",
            "Luxottica fortune shared with his six siblings and stepmother and Kim Jung-yang, whose fortune lies\n",
            "in Japanese-South Korean gaming giant Nexon, both under-20s.[10]\n",
            "Annual rankings\n",
            "Legend\n",
            "2023\n",
            "\u001b[0mIf a deceased billionaire's fortune has been dispersed, it will not appear as a single listing. Instead, each recipient will only appear if their own total net worth is over a billion dollars. In other words, the net worth of each individual recipient will not be combined with that of other family members.\n"
          ]
        }
      ],
      "source": [
        "response = query_engine.query(\n",
        "    \"How is wealth accounted for in recipients if the billionaire is deceased?\"\n",
        ")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "IPZJRbJnk_s4",
        "outputId": "e1a917b1-abe1-40f8-b55e-523c5f153b7b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "If a deceased billionaire's fortune has been dispersed, it will not appear as a single listing. Instead, each recipient will only appear if their own total net worth is over a billion dollars. In other words, the net worth of each individual recipient will not be combined with that of other family members.\n"
          ]
        }
      ],
      "source": [
        "response = vector_query_engine0.query(\n",
        "    \"How is wealth accounted for in recipients if the billionaire is deceased?\"\n",
        ")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3ZaPyxG_4j2"
      },
      "source": [
        "## Extended Use Case: Parsing Tesla 10Qs\n",
        "\n",
        "Here we deal with an even messier document format - parsing tables within a 10K.\n",
        "\n",
        "We use the Unstructured library to help us extract tables.\n",
        "\n",
        "The tables aren't perfectly formatted, but work well enough."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oJT0MpiCCqiK",
        "outputId": "1e3985da-df4f-489a-b628-8400be5ed1d4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting unstructured\n",
            "  Downloading unstructured-0.10.29-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (from unstructured) (5.2.0)\n",
            "Collecting filetype (from unstructured)\n",
            "  Downloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\n",
            "Collecting python-magic (from unstructured)\n",
            "  Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from unstructured) (4.9.3)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from unstructured) (3.8.1)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from unstructured) (0.9.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from unstructured) (2.31.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from unstructured) (4.11.2)\n",
            "Collecting emoji (from unstructured)\n",
            "  Downloading emoji-2.8.0-py2.py3-none-any.whl (358 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m358.9/358.9 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: dataclasses-json in /usr/local/lib/python3.10/dist-packages (from unstructured) (0.5.14)\n",
            "Collecting python-iso639 (from unstructured)\n",
            "  Downloading python_iso639-2023.6.15-py3-none-any.whl (275 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m275.1/275.1 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langdetect (from unstructured)\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from unstructured) (1.23.5)\n",
            "Collecting rapidfuzz (from unstructured)\n",
            "  Downloading rapidfuzz-3.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m44.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting backoff (from unstructured)\n",
            "  Downloading backoff-2.2.1-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from unstructured) (4.5.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->unstructured) (2.5)\n",
            "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json->unstructured) (3.20.1)\n",
            "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json->unstructured) (0.9.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from langdetect->unstructured) (1.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (2023.6.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured) (4.66.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->unstructured) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->unstructured) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->unstructured) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->unstructured) (2023.7.22)\n",
            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json->unstructured) (23.2)\n",
            "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json->unstructured) (1.0.0)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993224 sha256=0bb73d4428e2d33042026c0aa9289324682e0cd7a4cf8d9edecc7f6a7bc1a81f\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
            "Successfully built langdetect\n",
            "Installing collected packages: filetype, rapidfuzz, python-magic, python-iso639, langdetect, emoji, backoff, unstructured\n",
            "Successfully installed backoff-2.2.1 emoji-2.8.0 filetype-1.2.0 langdetect-1.0.9 python-iso639-2023.6.15 python-magic-0.4.27 rapidfuzz-3.5.2 unstructured-0.10.29\n"
          ]
        }
      ],
      "source": [
        "!pip install unstructured"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rgNa4raBP91"
      },
      "source": [
        "### Extract Elements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "_lIcAz6sAEnI",
        "outputId": "c7827bed-2b4c-488d-8057-22427f3a99d4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from pydantic import BaseModel\n",
        "from unstructured.partition.html import partition_html\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "wf5G_ne_AG-Z",
        "outputId": "e2c32758-06f0-4722-ac51-3352509de6a1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-11-08 15:51:33--  https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.65.18, 2620:100:601b:18::a27d:812\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.65.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc8c2f9df597ecdae58e0a717b26.dl.dropboxusercontent.com/cd/0/inline/CHLjbU75m2bMyWOrogDeJxZxDTWRHX3aJdMvB30xWHi54AHyO71ak9byoVYTwIWB7wOons_Wnr_w5fqpNv-HwOW2IkWj5tg2_87-PbYhMwV2iF7xl7uD17ut9ulTnPH01ZY/file?dl=1# [following]\n",
            "--2023-11-08 15:51:34--  https://uc8c2f9df597ecdae58e0a717b26.dl.dropboxusercontent.com/cd/0/inline/CHLjbU75m2bMyWOrogDeJxZxDTWRHX3aJdMvB30xWHi54AHyO71ak9byoVYTwIWB7wOons_Wnr_w5fqpNv-HwOW2IkWj5tg2_87-PbYhMwV2iF7xl7uD17ut9ulTnPH01ZY/file?dl=1\n",
            "Resolving uc8c2f9df597ecdae58e0a717b26.dl.dropboxusercontent.com (uc8c2f9df597ecdae58e0a717b26.dl.dropboxusercontent.com)... 162.125.65.15, 2620:100:6022:15::a27d:420f\n",
            "Connecting to uc8c2f9df597ecdae58e0a717b26.dl.dropboxusercontent.com (uc8c2f9df597ecdae58e0a717b26.dl.dropboxusercontent.com)|162.125.65.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7898923 (7.5M) [application/binary]\n",
            "Saving to: ‘tesla_2021_10k.htm’\n",
            "\n",
            "tesla_2021_10k.htm  100%[===================>]   7.53M  34.9MB/s    in 0.2s    \n",
            "\n",
            "2023-11-08 15:51:34 (34.9 MB/s) - ‘tesla_2021_10k.htm’ saved [7898923/7898923]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget \"https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1\" -O tesla_2021_10k.htm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "QhkoN2ayAH7F",
        "outputId": "1ff1f8d8-4a43-46d5-bc1c-6ea5c060f6b3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from llama_index.readers.file.flat_reader import FlatReader\n",
        "from pathlib import Path\n",
        "\n",
        "reader = FlatReader()\n",
        "docs_2021 = reader.load_data(Path(\"tesla_2021_10k.htm\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "omfHCg8IAJ6c",
        "outputId": "ffbc66b5-6396-4634-af7c-d4af3b8d59dd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from llama_index.node_parser import (\n",
        "    UnstructuredElementNodeParser,\n",
        ")\n",
        "\n",
        "node_parser = UnstructuredElementNodeParser()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "id": "B43MNRgSAK4F",
        "outputId": "5c4e4c1e-0580-410c-afa7-2120bc4bb1c0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "100%|██████████| 105/105 [21:36<00:00, 12.35s/it]\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import pickle\n",
        "\n",
        "if not os.path.exists(\"2021_nodes.pkl\"):\n",
        "    raw_nodes_2021 = node_parser.get_nodes_from_documents(docs_2021)\n",
        "    pickle.dump(raw_nodes_2021, open(\"2021_nodes.pkl\", \"wb\"))\n",
        "else:\n",
        "    raw_nodes_2021 = pickle.load(open(\"2021_nodes.pkl\", \"rb\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LQclEz8TAnsc"
      },
      "outputs": [],
      "source": [
        "base_nodes_2021, node_mappings_2021 = node_parser.get_base_nodes_and_mappings(\n",
        "    raw_nodes_2021\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cAh5Q9MrAo7x"
      },
      "outputs": [],
      "source": [
        "example_index_node = [b for b in base_nodes_2021 if isinstance(b, IndexNode)][\n",
        "    20\n",
        "]\n",
        "\n",
        "# Index Node\n",
        "print(\n",
        "    f\"\\n--------\\n{example_index_node.get_content(metadata_mode='all')}\\n--------\\n\"\n",
        ")\n",
        "# Index Node ID\n",
        "print(f\"\\n--------\\nIndex ID: {example_index_node.index_id}\\n--------\\n\")\n",
        "# Referenceed Table\n",
        "print(\n",
        "    f\"\\n--------\\n{node_mappings_2021[example_index_node.index_id].get_content()}\\n--------\\n\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jiEXTOJJArVZ"
      },
      "source": [
        "### Build Recursive Retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HoqKWlF2Asrk"
      },
      "outputs": [],
      "source": [
        "from llama_index.retrievers import RecursiveRetriever\n",
        "from llama_index.query_engine import RetrieverQueryEngine\n",
        "from llama_index import VectorStoreIndex, ServiceContext\n",
        "from llama_index.llms import OpenAI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5WwTWCedAuib"
      },
      "outputs": [],
      "source": [
        "llm = OpenAI(model=\"gpt-4-1106-preview\")\n",
        "service_context = ServiceContext.from_defaults(llm=llm)\n",
        "\n",
        "# construct top-level vector index + query engine\n",
        "vector_index = VectorStoreIndex(base_nodes_2021, service_context=service_context)\n",
        "vector_retriever = vector_index.as_retriever(similarity_top_k=1)\n",
        "vector_query_engine = vector_index.as_query_engine(similarity_top_k=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_vUeCatwAvZ1"
      },
      "outputs": [],
      "source": [
        "from llama_index.retrievers import RecursiveRetriever\n",
        "\n",
        "recursive_retriever = RecursiveRetriever(\n",
        "    \"vector\",\n",
        "    retriever_dict={\"vector\": vector_retriever},\n",
        "    node_dict=node_mappings_2021,\n",
        "    verbose=True,\n",
        ")\n",
        "query_engine = RetrieverQueryEngine.from_args(recursive_retriever)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qurWIG1JAwjK"
      },
      "source": [
        "### Run Some Queries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eFQF6aXdAxTD"
      },
      "outputs": [],
      "source": [
        "response = query_engine.query(\"What was the revenue in 2020?\")\n",
        "print(str(response))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9L0XyEDmAyY9"
      },
      "outputs": [],
      "source": [
        "# compare against the baseline retriever\n",
        "response = vector_query_engine.query(\"What was the revenue in 2020?\")\n",
        "print(str(response))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "V100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}