LLM4BUS_hw3.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "11iRAQpOONTDKa2nag1kqsd3RmkSdG7B2",
      "authorship_tag": "ABX9TyO+QpavHPkIWt/cOeHUFeIy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iqbalmohomed/LLM4BUS/blob/main/assn3_RAG_LLM4BUS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDqf4viW2IbK",
        "outputId": "bfe283ca-9788-4d67-f532-6ae1996673b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai\n",
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            "Installing collected packages: h11, httpcore, httpx, openai\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
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            "\u001b[0mSuccessfully installed h11-0.14.0 httpcore-1.0.3 httpx-0.26.0 openai-1.12.0\n"
          ]
        }
      ],
      "source": [
        "!pip install openai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from openai import OpenAI\n",
        "from google.colab import userdata\n",
        "\n",
        "open_ai_key = userdata.get('open_ai_key')\n",
        "client = OpenAI(api_key=open_ai_key)"
      ],
      "metadata": {
        "id": "qx2OU_rA2Qyc"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "g7tKm4S6Lj05"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 1 of Assigment**: Comparing methods of summarization (Using LangChain)"
      ],
      "metadata": {
        "id": "L-qQTvLBLl1X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain\n",
        "\n",
        "!pip install langchain-community\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "996115eeLyCj",
        "outputId": "2e410fd5-4bab-46db-d5db-8ef7f7f6aad6"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading dataclasses_json-0.6.4-py3-none-any.whl (28 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
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            "Installing collected packages: mypy-extensions, marshmallow, jsonpointer, typing-inspect, jsonpatch, langsmith, dataclasses-json, langchain-core, langchain-community, langchain\n",
            "Successfully installed dataclasses-json-0.6.4 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.7 langchain-community-0.0.20 langchain-core-0.1.23 langsmith-0.0.87 marshmallow-3.20.2 mypy-extensions-1.0.0 typing-inspect-0.9.0\n",
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            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain-core<0.2,>=0.1.21->langchain-community) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.2 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain-core<0.2,>=0.1.21->langchain-community) (2.16.2)\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<0.7,>=0.5.7->langchain-community) (1.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --quiet  docx2txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjxiJFATNLNc",
        "outputId": "7e0e1908-6add-4027-ddf8-2f610c12a0ff"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import Docx2txtLoader\n",
        "loader = Docx2txtLoader(\"MSFT_FY22Q4_10K.docx\")\n",
        "\n",
        "pages = loader.load()"
      ],
      "metadata": {
        "id": "CDZh4fJ3NUOa"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrVKPkMVN4du",
        "outputId": "e37154d5-6b5e-4507-b6e9-96ab09e1c6ad"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-openai\n",
            "  Downloading langchain_openai-0.0.6-py3-none-any.whl (29 kB)\n",
            "Requirement already satisfied: langchain-core<0.2,>=0.1.16 in /usr/local/lib/python3.10/dist-packages (from langchain-openai) (0.1.23)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain-openai) (1.25.2)\n",
            "Requirement already satisfied: openai<2.0.0,>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from langchain-openai) (1.12.0)\n",
            "Collecting tiktoken<1,>=0.5.2 (from langchain-openai)\n",
            "  Downloading tiktoken-0.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (6.0.1)\n",
            "Requirement already satisfied: anyio<5,>=3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (3.7.1)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (1.33)\n",
            "Requirement already satisfied: langsmith<0.0.88,>=0.0.87 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (0.0.87)\n",
            "Requirement already satisfied: packaging<24.0,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (23.2)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (2.6.1)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (2.31.0)\n",
            "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1.16->langchain-openai) (8.2.3)\n",
            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (1.7.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (0.26.0)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (1.3.0)\n",
            "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (4.66.2)\n",
            "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (4.9.0)\n",
            "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken<1,>=0.5.2->langchain-openai) (2023.12.25)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1.16->langchain-openai) (3.6)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1.16->langchain-openai) (1.2.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai<2.0.0,>=1.10.0->langchain-openai) (2024.2.2)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai<2.0.0,>=1.10.0->langchain-openai) (1.0.3)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai<2.0.0,>=1.10.0->langchain-openai) (0.14.0)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.2,>=0.1.16->langchain-openai) (2.4)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain-core<0.2,>=0.1.16->langchain-openai) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.2 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain-core<0.2,>=0.1.16->langchain-openai) (2.16.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain-core<0.2,>=0.1.16->langchain-openai) (3.3.2)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain-core<0.2,>=0.1.16->langchain-openai) (2.0.7)\n",
            "Installing collected packages: tiktoken, langchain-openai\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed langchain-openai-0.0.6 tiktoken-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *The next two blocks fail as the document loader is not chunking text. It is interesting to see the failure also occurs with map_reduce (with stuff it was expected).*"
      ],
      "metadata": {
        "id": "wWnYF0riP5Bv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains.summarize import load_summarize_chain\n",
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "llm = ChatOpenAI(temperature=0.1,model_name='gpt-3.5-turbo',api_key=open_ai_key)\n",
        "\n",
        "chain = load_summarize_chain(llm,chain_type='stuff')\n",
        "chain.run(pages[0:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "Q-YI52ItNuOI",
        "outputId": "892f0a18-8fec-4101-d066-ef4c5c4fe21f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RateLimitError",
          "evalue": "Error code: 429 - {'error': {'message': 'Request too large for gpt-3.5-turbo in organization org-P5WHVJN9eFvqL8ejIFpXtvZd on tokens per min (TPM): Limit 40000, Requested 98321. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more. You can increase your rate limit by adding a payment method to your account at https://platform.openai.com/account/billing.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRateLimitError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-bfc467904c00>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mchain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_summarize_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mllm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchain_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'stuff'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    536\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`run` supports only one positional argument.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m             return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[\n\u001b[0m\u001b[1;32m    539\u001b[0m                 \u001b[0m_output_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m             ]\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    361\u001b[0m         }\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m         return self.invoke(\n\u001b[0m\u001b[1;32m    364\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m             \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRunnableConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         final_outputs: Dict[str, Any] = self.prep_outputs(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             outputs = (\n\u001b[0;32m--> 156\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/combine_documents/base.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Other keys are assumed to be needed for LLM prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0mother_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_key\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m         output, extra_return_dict = self.combine_docs(\n\u001b[0m\u001b[1;32m    137\u001b[0m             \u001b[0mdocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_run_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mother_keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/combine_documents/stuff.py\u001b[0m in \u001b[0;36mcombine_docs\u001b[0;34m(self, docs, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0;31m# Call predict on the LLM.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     async def acombine_docs(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    291\u001b[0m                 \u001b[0mcompletion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"funny\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \"\"\"\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    361\u001b[0m         }\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m         return self.invoke(\n\u001b[0m\u001b[1;32m    364\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m             \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRunnableConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         final_outputs: Dict[str, Any] = self.prep_outputs(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             outputs = (\n\u001b[0;32m--> 156\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallbackManagerForChainRun\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     ) -> Dict[str, str]:\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_list, run_manager)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_manager\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseLanguageModel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             return self.llm.generate_prompt(\n\u001b[0m\u001b[1;32m    116\u001b[0m                 \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                 \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    542\u001b[0m     ) -> LLMResult:\n\u001b[1;32m    543\u001b[0m         \u001b[0mprompt_messages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_messages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 544\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_messages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m     async def agenerate_prompt(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    406\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mrun_managers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m                     \u001b[0mrun_managers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_llm_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLLMResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m         flattened_outputs = [\n\u001b[1;32m    410\u001b[0m             \u001b[0mLLMResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllm_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    396\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m                 results.append(\n\u001b[0;32m--> 398\u001b[0;31m                     self._generate_with_cache(\n\u001b[0m\u001b[1;32m    399\u001b[0m                         \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m                         \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36m_generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    575\u001b[0m                 )\n\u001b[1;32m    576\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m                 return self._generate(\n\u001b[0m\u001b[1;32m    578\u001b[0m                     \u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_openai/chat_models/base.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, messages, stop, run_manager, stream, **kwargs)\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         }\n\u001b[0;32m--> 438\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessage_dicts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_chat_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_utils/_utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m                         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Missing required argument: {quote(missing[0])}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/resources/chat/completions.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mhttpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeout\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mNotGiven\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNOT_GIVEN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m     ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[0;32m--> 663\u001b[0;31m         return self._post(\n\u001b[0m\u001b[1;32m    664\u001b[0m             \u001b[0;34m\"/chat/completions\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m             body=maybe_transform(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1198\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_httpx_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1199\u001b[0m         )\n\u001b[0;32m-> 1200\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mResponseT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_cls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream_cls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m     def patch(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0mstream_cls\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_StreamT\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m     ) -> ResponseT | _StreamT:\n\u001b[0;32m--> 889\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m    890\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mretries\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m                 \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m                 return self._retry_request(\n\u001b[0m\u001b[1;32m    966\u001b[0m                     \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m                     \u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1011\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m   1014\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mretries\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m                 \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m                 return self._retry_request(\n\u001b[0m\u001b[1;32m    966\u001b[0m                     \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m                     \u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1011\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m   1014\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m             \u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Re-raising status error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 980\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_status_error_from_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m         return self._process_response(\n",
            "\u001b[0;31mRateLimitError\u001b[0m: Error code: 429 - {'error': {'message': 'Request too large for gpt-3.5-turbo in organization org-P5WHVJN9eFvqL8ejIFpXtvZd on tokens per min (TPM): Limit 40000, Requested 98321. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more. You can increase your rate limit by adding a payment method to your account at https://platform.openai.com/account/billing.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = load_summarize_chain(llm,chain_type='map_reduce')\n",
        "chain.run(pages[0:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "33CsaFy5OqLM",
        "outputId": "deb49596-34c1-490e-cd5b-751bc31b27b7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RateLimitError",
          "evalue": "Error code: 429 - {'error': {'message': 'Request too large for gpt-3.5-turbo in organization org-P5WHVJN9eFvqL8ejIFpXtvZd on tokens per min (TPM): Limit 40000, Requested 98321. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more. You can increase your rate limit by adding a payment method to your account at https://platform.openai.com/account/billing.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRateLimitError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-78a6946a4162>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mchain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_summarize_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mllm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchain_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'map_reduce'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    536\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`run` supports only one positional argument.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m             return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[\n\u001b[0m\u001b[1;32m    539\u001b[0m                 \u001b[0m_output_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m             ]\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    361\u001b[0m         }\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m         return self.invoke(\n\u001b[0m\u001b[1;32m    364\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m             \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRunnableConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         final_outputs: Dict[str, Any] = self.prep_outputs(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             outputs = (\n\u001b[0;32m--> 156\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/combine_documents/base.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Other keys are assumed to be needed for LLM prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0mother_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_key\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m         output, extra_return_dict = self.combine_docs(\n\u001b[0m\u001b[1;32m    137\u001b[0m             \u001b[0mdocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_run_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mother_keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/combine_documents/map_reduce.py\u001b[0m in \u001b[0;36mcombine_docs\u001b[0;34m(self, docs, token_max, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0mThis\u001b[0m \u001b[0mreducing\u001b[0m \u001b[0mcan\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mdone\u001b[0m \u001b[0mrecursively\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mneeded\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mif\u001b[0m \u001b[0mthere\u001b[0m \u001b[0mare\u001b[0m \u001b[0mmany\u001b[0m \u001b[0mdocuments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \"\"\"\n\u001b[0;32m--> 225\u001b[0;31m         map_results = self.llm_chain.apply(\n\u001b[0m\u001b[1;32m    226\u001b[0m             \u001b[0;31m# FYI - this is parallelized and so it is fast.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocument_variable_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpage_content\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, input_list, callbacks)\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"outputs\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, input_list, callbacks)\u001b[0m\n\u001b[1;32m    222\u001b[0m         )\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_list, run_manager)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_manager\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseLanguageModel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             return self.llm.generate_prompt(\n\u001b[0m\u001b[1;32m    116\u001b[0m                 \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                 \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    542\u001b[0m     ) -> LLMResult:\n\u001b[1;32m    543\u001b[0m         \u001b[0mprompt_messages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_messages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 544\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_messages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m     async def agenerate_prompt(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    406\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mrun_managers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m                     \u001b[0mrun_managers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_llm_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLLMResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m         flattened_outputs = [\n\u001b[1;32m    410\u001b[0m             \u001b[0mLLMResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllm_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    396\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m                 results.append(\n\u001b[0;32m--> 398\u001b[0;31m                     self._generate_with_cache(\n\u001b[0m\u001b[1;32m    399\u001b[0m                         \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m                         \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36m_generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    575\u001b[0m                 )\n\u001b[1;32m    576\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m                 return self._generate(\n\u001b[0m\u001b[1;32m    578\u001b[0m                     \u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_openai/chat_models/base.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, messages, stop, run_manager, stream, **kwargs)\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         }\n\u001b[0;32m--> 438\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessage_dicts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_chat_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_utils/_utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m                         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Missing required argument: {quote(missing[0])}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/resources/chat/completions.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mhttpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeout\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mNotGiven\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNOT_GIVEN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m     ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[0;32m--> 663\u001b[0;31m         return self._post(\n\u001b[0m\u001b[1;32m    664\u001b[0m             \u001b[0;34m\"/chat/completions\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m             body=maybe_transform(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1198\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_httpx_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1199\u001b[0m         )\n\u001b[0;32m-> 1200\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mResponseT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_cls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream_cls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m     def patch(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0mstream_cls\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_StreamT\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m     ) -> ResponseT | _StreamT:\n\u001b[0;32m--> 889\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m    890\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mretries\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m                 \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m                 return self._retry_request(\n\u001b[0m\u001b[1;32m    966\u001b[0m                     \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m                     \u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1011\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m   1014\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mretries\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m                 \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m                 return self._retry_request(\n\u001b[0m\u001b[1;32m    966\u001b[0m                     \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m                     \u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1011\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m   1014\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m             \u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Re-raising status error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 980\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_status_error_from_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m         return self._process_response(\n",
            "\u001b[0;31mRateLimitError\u001b[0m: Error code: 429 - {'error': {'message': 'Request too large for gpt-3.5-turbo in organization org-P5WHVJN9eFvqL8ejIFpXtvZd on tokens per min (TPM): Limit 40000, Requested 98321. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more. You can increase your rate limit by adding a payment method to your account at https://platform.openai.com/account/billing.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Default uses a recursive text splitter\n",
        "pages = loader.load_and_split()\n",
        "print(len(pages))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "heGat9eJO4KW",
        "outputId": "0b67a659-d234-4b87-a359-0796a657b80d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = load_summarize_chain(llm,chain_type='stuff')\n",
        "chain.run(pages[0:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "dI373guXO7X4",
        "outputId": "8acb9dc0-447b-4835-89d8-5b4c6e742e68"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Microsoft Corporation's Form 10-K for the fiscal year ended June 30, 2022, outlines the company's business operations, financial performance, and corporate social responsibility initiatives. The report highlights Microsoft's focus on empowering individuals and organizations through technology, its commitment to sustainability and addressing racial injustice, and its investments in digital skills and diversity and inclusion. The company's culture is centered around a growth mindset, and it places a strong emphasis on employee feedback, diversity, and inclusion. Microsoft's total rewards programs aim to attract and retain top talent while reinforcing its values of collaboration and growth.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain = load_summarize_chain(llm,chain_type='map_reduce')\n",
        "chain.run(pages[0:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "k6ROLfB0RREe",
        "outputId": "4971abc8-a7ab-471c-fa89-9e8a5f4cf42c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Microsoft Corporation filed its Form 10-K annual report with the SEC, detailing its financial status, market value, and business operations. The report highlights Microsoft's focus on innovation, digital transformation, and sustainability, with a commitment to empowering individuals and organizations worldwide. The company is expanding its portfolio of services and products, investing in AI capabilities, cloud computing, and gaming experiences. Microsoft is dedicated to diversity and inclusion, sustainability, and making a positive impact on the world through its technology and initiatives.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion**: In terms of quality, map_reduce results in a better summary. However, in terms of time-taken, it seems stuff executed much faster."
      ],
      "metadata": {
        "id": "gtMGoxTUSj1N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 2 of Assigment**: RAG on TV Show summaries (Using LlamaIndex)"
      ],
      "metadata": {
        "id": "PI_OZFtVLGyE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install llama-index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZ1pijHt3339",
        "outputId": "fb61f287-e0e3-41f6-efe8-4d5dae96dead"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: dirtyjson, pypdf, PyMuPDFb, mypy-extensions, marshmallow, deprecated, typing-inspect, tiktoken, pymupdf, bs4, llamaindex-py-client, dataclasses-json, llama-index-legacy, llama-index-core, llama-index-readers-file, llama-index-llms-openai, llama-index-embeddings-openai, llama-index-multi-modal-llms-openai, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama-index\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed PyMuPDFb-1.23.22 bs4-0.0.2 dataclasses-json-0.6.4 deprecated-1.2.14 dirtyjson-1.0.8 llama-index-0.10.6 llama-index-agent-openai-0.1.1 llama-index-core-0.10.6.post1 llama-index-embeddings-openai-0.1.1 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.2 llama-index-multi-modal-llms-openai-0.1.1 llama-index-program-openai-0.1.2 llama-index-question-gen-openai-0.1.1 llama-index-readers-file-0.1.3 llamaindex-py-client-0.1.13 marshmallow-3.20.2 mypy-extensions-1.0.0 pymupdf-1.23.22 pypdf-4.0.2 tiktoken-0.6.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# In the RAG portion of the assignment, I used LlamaIndex to create a KB of episodes of a kids tv show called \"Octonauts\". I wrote a program outside of this notebook to download a list of episodes and summaries for each episodes from a Fan Wiki: https://octonauts.fandom.com/wiki/Octonauts_Wiki"
      ],
      "metadata": {
        "id": "T1JhhzAaT-BM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import VectorStoreIndex,SimpleDirectoryReader\n",
        "\n",
        "# only load markdown files\n",
        "required_exts = [\".txt\"]\n",
        "\n",
        "reader = SimpleDirectoryReader(\n",
        "    input_dir=\".\",\n",
        "    required_exts=required_exts,\n",
        "    recursive=True,\n",
        ")\n",
        "\n",
        "docs = reader.load_data()\n",
        "print(f\"Loaded {len(docs)} docs\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-LbcJ_42UsC",
        "outputId": "193c7499-40fd-4044-bf4d-3c8dff13b9fc"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 125 docs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = open_ai_key"
      ],
      "metadata": {
        "id": "yuOuMYDh41fJ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = VectorStoreIndex.from_documents(docs)\n"
      ],
      "metadata": {
        "id": "Y2_pBmDc32v0"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = index.as_query_engine()"
      ],
      "metadata": {
        "id": "WpjtRmIO5U2S"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# First two queries are pretty simple and turn out to be correct."
      ],
      "metadata": {
        "id": "dbVuel0aUDfC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"What is the title of the episode in which the Octonauts celebrate Christmas?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSFrCgVv40Vw",
        "outputId": "3310b37e-3591-4adf-dd1c-68b8afdaf985"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The title of the episode in which the Octonauts celebrate Christmas is \"The Great Christmas Rescue.\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"What is the title of the episode in which Peso is in a race?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUQVYYQI3aP1",
        "outputId": "0aa39261-224d-4247-f38e-9bef5c85272c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"The Great Penguin Race\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## This is a more complex question that the system gets right!\n"
      ],
      "metadata": {
        "id": "ToQfgtkbUK0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"Who is Peso competing in the race?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yrXmFXtD5sb_",
        "outputId": "b718d73a-a317-4e56-800b-dee2beffdd7c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hugo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"In what episodes do the Octonauts recue Penguins?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tqhlZUFi52CU",
        "outputId": "11c3f9dd-3dd0-41e9-b1c5-885b299bbe09"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Octonauts rescue penguins in \"The Emperor Penguins,\" which is Season 4, Episode 10 (102).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat \"The_Emperor_Penguins.txt\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHpnp4DI6F4I",
        "outputId": "f22a0675-c2fa-4b97-ba33-c00be9502049"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title:\"The Emperor Penguins\"\r\n",
            "Order:Season 4, Episode 10 (102)\r\n",
            "Summary:\r\n",
            "The Octonauts follow a group of emperor penguin mothers returning to their families. One of the penguins gets injured, and the Octonauts carry her in theGup-S. However, the Gup falls into an ice chasm, the Octo-Compass is frozen over, and the Octonauts are left without radio connection or a map. So they must carry the injured penguin through ice and snow to return her to her family as she uses her own sense of direction to guide them back to the colony.\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"Which episodes involve Penguins in some fashion?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qyzt25al6dL_",
        "outputId": "8dfb4f6e-2188-42a9-c7e5-833751a3d332"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"The Adelie Penguins\" and \"The Emperor Penguins\" episodes involve penguins in some fashion.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"Which episodes involve the Professor in some manner?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hwjsGAS63YE",
        "outputId": "5d6efcc0-d90e-4234-d75f-2d2d2628f642"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Episodes \"The Flying Fish\" (Season 1, Episode 5) and \"The Giant Squid\" (Season 1, Episode 6) involve the Professor in some manner.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# A complex question that the system got right!\n"
      ],
      "metadata": {
        "id": "6J1bqjmaUYMq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"What sort of animal is the professor?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWeMOTXa6_Ud",
        "outputId": "c87692e9-24e6-447f-8654-a79801f472e4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Professor Inkling is an octopus.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"What sort of animal is Tweak?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWMYnt_G7FSM",
        "outputId": "f3411e53-b41b-43e5-826d-42eac9c7768a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tweak is a character who is associated with the Octonauts team.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Not quite right. It misses some key characters and adds a side actor.\n"
      ],
      "metadata": {
        "id": "C_3UQ2ibUjEP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"Who are the main characters in Octonauts?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tm9S9cQR7IyL",
        "outputId": "822c203a-efb2-4654-e851-97958c0c51db"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The main characters in Octonauts are Shellington, Dashi, Tweak, Kwazii, and the Octobot robot.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Seems like there was an error in the parser or source text. I was asking about a particular vehicle called the GUP-S. It answered about gups."
      ],
      "metadata": {
        "id": "ERlHsmOYUqPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"What episode features the Gup S?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaUCyhnY7Pd8",
        "outputId": "003a3e80-15ae-4523-badf-40913373870d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"The Speedy Sailfish\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat \"The_Speedy_Sailfish.txt\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnVDThDm7c4-",
        "outputId": "77bc7dd3-1c50-4ac3-c7b1-575a0cda18be"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title:\"The Speedy Sailfish\"\r\n",
            "Order:Season 1, Episode 10\r\n",
            "Summary:\r\n",
            "Tweakbrings out her newest invention, the smartgup boxes, that can control all the Gups at once and make them drive themselves. AsShellingtontries out the remote control to the Gups, he accidentally breaks it, causing all the Gups to travel alone without any pilots. It is up toCaptain BarnaclesandKwaziiin theGup-Fto catch them, but when they find a school of sailfish, the fastest fish in the ocean, they enlist their help to round them up.\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Realized I am going to make lots of calls to Index my data if I lose this colab session. Hence, I will attempt to save the index.\n"
      ],
      "metadata": {
        "id": "NiN5YTKzUu7e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "index.storage_context.persist(persist_dir=\"persist_dir\")\n"
      ],
      "metadata": {
        "id": "0-FO4TU67kiU"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r persist_dir.zip persist_dir/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fdvJ9x677RW",
        "outputId": "7a07c1e1-4d28-4e34-99c1-f27619fe28e8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: persist_dir/ (stored 0%)\n",
            "  adding: persist_dir/image__vector_store.json (deflated 19%)\n",
            "  adding: persist_dir/graph_store.json (stored 0%)\n",
            "  adding: persist_dir/index_store.json (deflated 67%)\n",
            "  adding: persist_dir/docstore.json (deflated 79%)\n",
            "  adding: persist_dir/default__vector_store.json (deflated 62%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Display the files that constitute the Knowledge Base. I wrote a web crawler/parser using BeautifulSoup, and ran this on my PC. I just uploaded the resulting files to Collab."
      ],
      "metadata": {
        "id": "CC00cbnrU7Fm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls *.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kMF57YU8gEZ",
        "outputId": "95f7ec19-bfae-49f0-c69f-552c649c6e73"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Platypus.txt\t\t\tThe_Giant_Whirlpool.txt\t\t  The_Octopod_Mystery.txt\n",
            "The_Adelie_Penguins.txt\t\tThe_Great_Algae_Escape.txt\t  The_Operation_Cooperation.txt\n",
            "The_Albino_Humpback_Whale.txt\tThe_Great_Arctic_Adventure.txt\t  The_Operation_Deep_Freeze.txt\n",
            "The_Amazon_Adventure.txt\tThe_Great_Christmas_Rescue.txt\t  The_Orcas.txt\n",
            "The_Arctic_Orcas.txt\t\tThe_Great_Penguin_Race.txt\t  The_Over_and_Under_Adventure.txt\n",
            "The_Artificial_Reef.txt\t\tThe_Great_Swamp_Search.txt\t  The_Pelicans.txt\n",
            "The_Baby_Dolphin.txt\t\tThe_Great_White_Shark.txt\t  The_Pirate_Parrotfish.txt\n",
            "The_Baby_Gator.txt\t\tThe_Gulper_Eels.txt\t\t  The_Poison_Dart_Frogs.txt\n",
            "The_Baby_Sea_Turtles.txt\tThe_Hammerhead_Sharks.txt\t  The_Porcupine_Puffer.txt\n",
            "The_Barracudas.txt\t\tThe_Harbour_Seal.txt\t\t  The_Red_Rock_Crabs.txt\n",
            "The_Beluga_Whales.txt\t\tThe_Hermit_Crab.txt\t\t  The_Remipedes.txt\n",
            "The_Blobfish_Brothers.txt\tThe_Hidden_Lake.txt\t\t  The_Saltwater_Crocodile.txt\n",
            "The_Bomber_Worms.txt\t\tThe_Hippos.txt\t\t\t  The_Sardine_School.txt\n",
            "The_Bowhead_Whales.txt\t\tThe_Humpback_Whales.txt\t\t  The_Scared_Sperm_Whale.txt\n",
            "The_Coconut_Crabs.txt\t\tThe_Humphead_Parrotfish.txt\t  The_Scary_Spookfish.txt\n",
            "The_Coconut_Crisis.txt\t\tThe_Humuhumus.txt\t\t  The_Seahorse_Tale.txt\n",
            "The_Coelacanth.txt\t\tThe_Hungry_Pilot_Fish.txt\t  The_Sea_Pigs.txt\n",
            "The_Colossal_Squid.txt\t\tThe_Immortal_Jellyfish.txt\t  The_Sea_Skaters.txt\n",
            "The_Combtooth_Blenny.txt\tThe_Jawfish.txt\t\t\t  The_Sea_Snakes.txt\n",
            "The_Cone_Snail.txt\t\tThe_Jellyfish_Bloom.txt\t\t  The_Sea_Sponge.txt\n",
            "The_Convict_Fish.txt\t\tThe_Kelp_Forest_Rescue.txt\t  The_Siphonophore.txt\n",
            "The_Cookiecutter_Sharks.txt\tThe_Kelp_Monster_Mystery.txt\t  The_Slime_Eels.txt\n",
            "The_Crab_and_Urchin.txt\t\tThe_Leafy_Sea_Dragons.txt\t  The_Snapping_Shrimp.txt\n",
            "The_Crafty_Cuttlefish.txt\tThe_Lion%27s_Mane_Jellyfish.txt   The_Snot_Sea_Cucumber.txt\n",
            "The_Crawfish.txt\t\tThe_Lionfish.txt\t\t  The_Speedy_Sailfish.txt\n",
            "The_Damselfish.txt\t\tThe_Loggerhead_Sea_Turtle.txt\t  The_Spinner_Dolphins.txt\n",
            "The_Decorator_Crab.txt\t\tThe_Loneliest_Whale.txt\t\t  The_Sunfish.txt\n",
            "The_Dolphin_Reef_Rescue.txt\tThe_Long-Arm_Squid.txt\t\t  The_Surfing_Snails.txt\n",
            "The_Dwarf_Lantern_Shark.txt\tThe_Lost_Lemon_Shark.txt\t  The_Tiger_Shark.txt\n",
            "The_Eel_Ordeal.txt\t\tThe_Lost_Sea_Star.txt\t\t  The_Tree_Lobsters.txt\n",
            "The_Electric_Torpedo_Rays.txt\tThe_Manatees.txt\t\t  The_Triggerfish.txt\n",
            "The_Emperor_Penguins.txt\tThe_Manta_Rays.txt\t\t  The_Undersea_Storm.txt\n",
            "The_Enemy_Anemones.txt\t\tThe_Mantis_Shrimp.txt\t\t  The_Urchin_Invasion.txt\n",
            "The_Enormous_Elephant_Seal.txt\tThe_Mariana_Trench_Adventure.txt  The_Vampire_Squid.txt\n",
            "The_Fiddler_Crabs.txt\t\tThe_Marine_Iguanas.txt\t\t  The_Very_Vegimal_Christmas.txt\n",
            "The_Flamingos.txt\t\tThe_Midnight_Zone.txt\t\t  The_Walrus_Chief.txt\n",
            "The_Flying_Fish.txt\t\tThe_Mimic_Octopus.txt\t\t  The_Walrus_Pups.txt\n",
            "The_Flying_Swords.txt\t\tThe_Mixed_Up_Whales.txt\t\t  The_Water_Bears.txt\n",
            "The_Giant_Jelly.txt\t\tThe_Monster_Map.txt\t\t  The_Whale_Shark.txt\n",
            "The_Giant_Kelp_Forest.txt\tThe_Mudskippers.txt\t\t  The_Yellow_Bellied_Sea_Snakes.txt\n",
            "The_Giant_Spider_Crab.txt\tThe_Narwhal.txt\t\t\t  The_Yeti_Crab.txt\n",
            "The_Giant_Squid.txt\t\tThe_Oarfish.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Shows the format of each data file in the Knowledge Base"
      ],
      "metadata": {
        "id": "Tz3SDYRMUzkP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cat \"The_Flying_Fish.txt\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGFu3cmjAvY1",
        "outputId": "6cc70dbd-ad1a-4a52-cf41-4f0f9a110d76"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title:\"The Flying Fish\"\r\n",
            "Order:Season 1, Episode 5\r\n",
            "Summary:\r\n",
            "Professor InklingshowsShellington,Captain BarnaclesandKwaziian old journal that belonged to his grandfather that shows a missing page. As theOctonautstravel above the ocean a school of flying fish appear and accidentally steal Shellington's waterproof satchel, which had the professor's book inside, soKwaziiin his upgradedGup-Bwith flying fish mode tries to retrieve the satchel.\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "giF4J0nsA0c1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}