Adaptive_Multimodal_RAG.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOgs4SEpDYH1bDAJA6Gnt6O",
      "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/HarshSonaiya/RAGs/blob/main/Adaptive_Multimodal_RAG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pillow\n",
        "from PIL import Image\n",
        "import os"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVzZxmB0FQm0",
        "outputId": "4dd39dd6-33ec-4e7b-ddf0-ff29f1bce2b7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (10.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain langchain_chroma langchain_core langchain_google_genai langchain_huggingface unstructured"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eFVzU3Bg9BCA",
        "outputId": "8d5b6128-ce06-44c6-ac04-4fb158495122"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: deprecated>=1.2.6 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-api>=1.2.0->chromadb!=0.5.4,!=0.5.5,<0.6.0,>=0.4.0->langchain_chroma) (1.2.14)\n",
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            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer>=0.9.0->chromadb!=0.5.4,!=0.5.5,<0.6.0,>=0.4.0->langchain_chroma) (0.1.2)\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install poppler-utils\n",
        "!sudo apt install tesseract-ocr\n",
        "!sudo apt install libtesseract-dev\n",
        "!pip install -U pdfminer.six unstructured pillow-heif pi_heif unstructured_inference pytesseract unstructured.pytesseract \"unstructured[all-docs]\"\n",
        "!pip install -U chromadb langchain langchain_huggingface langchain-google-genai langchain-chroma nltk\n",
        "!python -m nltk.downloader punkt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EfDLrXex9Men",
        "outputId": "fd2c258a-a5c0-4915-d190-0217a78cf5c0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "poppler-utils is already the newest version (22.02.0-2ubuntu0.5).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "tesseract-ocr is already the newest version (4.1.1-2.1build1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "libtesseract-dev is already the newest version (4.1.1-2.1build1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
            "Requirement already satisfied: pdfminer.six in /usr/local/lib/python3.10/dist-packages (20231228)\n",
            "Collecting pdfminer.six\n",
            "  Using cached pdfminer.six-20240706-py3-none-any.whl.metadata (4.1 kB)\n",
            "Requirement already satisfied: unstructured in /usr/local/lib/python3.10/dist-packages (0.15.12)\n",
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            "Requirement already satisfied: setuptools>=16.0 in /usr/local/lib/python3.10/dist-packages (from opentelemetry-instrumentation==0.48b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb) (71.0.4)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface) (3.3)\n",
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            "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime>=1.14.1->chromadb) (10.0)\n",
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            "Requirement already satisfied: google-auth-httplib2<1.0.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (0.2.0)\n",
            "Requirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (4.1.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers>=2.6.0->langchain_huggingface) (3.5.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime>=1.14.1->chromadb) (1.3.0)\n",
            "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.1->google-ai-generativelanguage==0.6.6->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (1.48.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /usr/local/lib/python3.10/dist-packages (from httplib2<1.dev0,>=0.19.0->google-api-python-client->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (3.1.4)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer>=0.9.0->chromadb) (0.1.2)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=2.15.0->google-generativeai<0.8.0,>=0.7.0->langchain-google-genai) (0.6.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface) (2.1.5)\n",
            "/usr/lib/python3.10/runpy.py:126: RuntimeWarning: 'nltk.downloader' found in sys.modules after import of package 'nltk', but prior to execution of 'nltk.downloader'; this may result in unpredictable behaviour\n",
            "  warn(RuntimeWarning(msg))\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "ghuZzwGy8oBV"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "import base64\n",
        "import io\n",
        "import os\n",
        "import re\n",
        "import uuid\n",
        "from typing import Dict, List, Tuple, Union\n",
        "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
        "from langchain.storage import InMemoryStore\n",
        "from langchain_chroma import Chroma\n",
        "from langchain_core.documents import Document\n",
        "from langchain_core.messages import HumanMessage\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "from PIL import Image\n",
        "from unstructured.partition.pdf import partition_pdf\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "from langchain_core.pydantic_v1 import BaseModel, Field\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EMBEDDING_MODEL_NAME = \"jina-embeddings-v2-base-en\"\n",
        "JINA_API_KEY = \"jina_ae4f24b918ea4ba388e184de0b73bf99u49E8MSD2uNdWlw0ryRc96QBfJhj\"\n",
        "LLM_NAME = \"gemini-1.5-flash\"\n",
        "GOOGLE_API_KEY = \"AIzaSyA2uS3oKFZNwHEA_FOK66IjuTyspCK1EMo\"\n",
        "FILE_PATH = \"/content/annual-report-2019-2020.pdf\""
      ],
      "metadata": {
        "id": "M_V9X8hg85FB"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Query classification and document grading classes\n",
        "class QueryClassification(BaseModel):\n",
        "    \"\"\"Classification of the query.\"\"\"  # Add a docstring here\n",
        "    is_finance_related: bool = Field(description=\"True if the query is related to finance domain, else False\")\n",
        "\n",
        "\n",
        "class DocumentGrader(BaseModel):\n",
        "    is_relevant: bool = Field(description=\"Whether the document is useful in answering the query\")\n"
      ],
      "metadata": {
        "id": "KQ5FomNy9VrA"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classify_query(query: str) -> bool:\n",
        "    \"\"\"Classify the query using the LLM.\"\"\"\n",
        "    classifier = llm.with_structured_output(QueryClassification)\n",
        "    out_class = classifier.invoke(query)\n",
        "    return out_class.is_finance_related if out_class else False\n"
      ],
      "metadata": {
        "id": "sADNmY4Z9apF"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def doc_grader(query: str, docs: list[Document]) -> tuple[list[bool], list]:\n",
        "    grades = []\n",
        "    relevant_docs = []\n",
        "    grader = llm.with_structured_output(DocumentGrader)\n",
        "\n",
        "    for doc in docs:\n",
        "        prompt = f\"Document:\\n{doc.page_content}\"\n",
        "        result = grader.invoke(prompt)\n",
        "        grades.append(result.is_relevant)\n",
        "        if result.is_relevant:\n",
        "            relevant_docs.append(doc)\n",
        "\n",
        "    return grades, relevant_docs\n"
      ],
      "metadata": {
        "id": "SMy2KPym9hur"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_pdf_content_with_metadata(file_path: str):\n",
        "    return partition_pdf(\n",
        "        filename=file_path,\n",
        "        extract_images_in_pdf=True,\n",
        "        chunking_strategy=\"by_title\",\n",
        "        max_characters=4000,\n",
        "        combine_text_under_n_chars=2000,\n",
        "        extract_image_block_types=[\"Image\", \"Table\"]\n",
        "    )"
      ],
      "metadata": {
        "id": "qTYKshqR9dqc"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def categorize_pdf_elements_by_type(raw_pdf_elements: list) -> List[str]:\n",
        "    \"\"\"Categorize extracted PDF elements into text content.\"\"\"\n",
        "    texts = []\n",
        "    for element in raw_pdf_elements:\n",
        "        if \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n",
        "            texts.append(str(element))\n",
        "    return texts"
      ],
      "metadata": {
        "id": "MXxO5tCE96oL"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def summarize_text_elements(text_elements: list) -> List[str]:\n",
        "    \"\"\"Generate concise summaries of text elements for efficient retrieval.\"\"\"\n",
        "    summary_prompt = \"\"\"You are an assistant specializing in generating concise and accurate\n",
        "    summaries for retrieval purposes. The summary should capture the essential details of the\n",
        "    provided text, making it easily searchable and optimized for information retrieval.\n",
        "    Please provide a concise summary for the following element: {element}\"\"\"\n",
        "\n",
        "    prompt_template = ChatPromptTemplate.from_template(summary_prompt)\n",
        "    summary_chain = {\"element\": lambda x: x} | prompt_template | llm | StrOutputParser()\n",
        "    summaries = summary_chain.batch(text_elements, {\"max_concurrency\": 5})\n",
        "    return summaries"
      ],
      "metadata": {
        "id": "F-T9fSa-96-r"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_media_to_base64(media_path: str) -> str:\n",
        "    \"\"\"Encode media (image/table) to a base64 string.\"\"\"\n",
        "    with open(media_path, \"rb\") as media_file:\n",
        "        return base64.b64encode(media_file.read()).decode(\"utf-8\")"
      ],
      "metadata": {
        "id": "7m-sVu2p9-Rr"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def summarize_media(base64_media: str, prompt: str) -> str:\n",
        "    \"\"\"Generate a summary for media (image/table) using the Google Generative AI model.\"\"\"\n",
        "    msg = llm.invoke(\n",
        "        [\n",
        "            HumanMessage(\n",
        "                content=[\n",
        "                    {\"type\": \"text\", \"text\": prompt},\n",
        "                    {\n",
        "                        \"type\": \"image_url\",\n",
        "                        \"image_url\": {\"url\": f\"data:image/jpeg;base64,{base64_media}\"},\n",
        "                    },\n",
        "                ]\n",
        "            )\n",
        "        ]\n",
        "    )\n",
        "    return msg.content"
      ],
      "metadata": {
        "id": "sexgxpSF-A3_"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_media_summaries(media_dir: str) -> Tuple[List[str], List[str]]:\n",
        "    \"\"\"Generate summaries and base64 encoded strings for media (images/tables) in the given directory.\"\"\"\n",
        "    base64_media_list = []\n",
        "    media_summaries = []\n",
        "    summary_prompt = \"\"\"You are an assistant tasked with summarizing images and tables for retrieval.\n",
        "    These summaries will be embedded and used to retrieve the raw media.\n",
        "    Provide concise summaries optimized for retrieval.\"\"\"\n",
        "\n",
        "    for media_file in sorted(os.listdir(media_dir)):\n",
        "        if media_file.endswith(\".jpg\"):\n",
        "            media_path = os.path.join(media_dir, media_file)\n",
        "            base64_media = encode_media_to_base64(media_path)\n",
        "            base64_media_list.append(base64_media)\n",
        "            media_summaries.append(summarize_media(base64_media, summary_prompt))\n",
        "\n",
        "    return base64_media_list, media_summaries\n"
      ],
      "metadata": {
        "id": "zmx4mz-u-DhX"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_multi_vector_retriever(\n",
        "    vectorstore: Chroma,\n",
        "    text_summaries: List[str],\n",
        "    raw_texts: List[str],\n",
        "    image_summaries: List[str],\n",
        "    raw_images: List[str]\n",
        ") -> MultiVectorRetriever:\n",
        "    \"\"\"Create a retriever that indexes text and image summaries and returns raw text or image content.\"\"\"\n",
        "    store = InMemoryStore()\n",
        "    id_key = \"doc_id\"\n",
        "    retriever = MultiVectorRetriever(\n",
        "        vectorstore=vectorstore,\n",
        "        docstore=store,\n",
        "        id_key=id_key,\n",
        "    )"
      ],
      "metadata": {
        "id": "Sl9_rq64-Frr"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_documents(\n",
        "        retriever: MultiVectorRetriever,\n",
        "        summaries: List[str],\n",
        "        contents: List[Union[str, bytes]]\n",
        "    ):\n",
        "\n",
        "  \"\"\"Add documents to the vectorstore and docstore.\"\"\"\n",
        "  doc_ids = [str(uuid.uuid4()) for _ in contents]\n",
        "  summary_docs = [Document(page_content=summary, metadata={id_key: doc_ids[i]})for i, summary in enumerate(summaries)]\n",
        "\n",
        "  retriever.vectorstore.add_documents(summary_docs)\n",
        "  retriever.docstore.mset(list(zip(doc_ids, contents)))\n",
        "\n",
        "  if text_summaries and raw_texts:\n",
        "    add_documents(retriever, text_summaries, raw_texts)\n",
        "\n",
        "  if image_summaries and raw_images:\n",
        "    add_documents(retriever, image_summaries, raw_images)\n",
        "\n",
        "  return retriever"
      ],
      "metadata": {
        "id": "LfEsq4pa-Hyc"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def is_base64_encoded(data: str) -> bool:\n",
        "    \"\"\"Check if the string is a valid base64 encoded data.\"\"\"\n",
        "    return re.match(r\"^[A-Za-z0-9+/]+[=]{0,2}$\", data) is not None\n",
        "\n",
        "def is_media_base64(data: str) -> bool:\n",
        "    \"\"\"Check if the base64 data represents a media item.\"\"\"\n",
        "    media_signatures = {\n",
        "        b\"\\xff\\xd8\\xff\": \"jpg\",\n",
        "        b\"\\x89\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\": \"png\",\n",
        "        b\"\\x47\\x49\\x46\\x38\": \"gif\",\n",
        "        b\"\\x52\\x49\\x46\\x46\": \"webp\",\n",
        "    }\n",
        "    try:\n",
        "        header = base64.b64decode(data)[:8]\n",
        "        return any(header.startswith(sig) for sig in media_signatures)\n",
        "    except Exception:\n",
        "        return False"
      ],
      "metadata": {
        "id": "kypo3MpH-J6p"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def resize_base64_media(base64_string: str, size: tuple = (128, 128)) -> str:\n",
        "    \"\"\"Resize a base64-encoded media item.\"\"\"\n",
        "    media_data = base64.b64decode(base64_string)\n",
        "    media = Image.open(io.BytesIO(media_data))\n",
        "    resized_media = media.resize(size, Image.LANCZOS)\n",
        "\n",
        "    buffered = io.BytesIO()\n",
        "    resized_media.save(buffered, format=media.format)\n",
        "\n",
        "    return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")"
      ],
      "metadata": {
        "id": "fADXt1XI--MH"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def split_media_and_texts(docs: List[str]) -> Dict[str, List[str]]:\n",
        "    \"\"\"Split base64-encoded media and texts from a list of documents.\"\"\"\n",
        "    media_list = []\n",
        "    texts = []\n",
        "\n",
        "    for doc in docs:\n",
        "        if is_base64_encoded(doc) and is_media_base64(doc):\n",
        "            media_list.append(resize_base64_media(doc, size=(1300, 600)))\n",
        "        else:\n",
        "            texts.append(doc)\n",
        "\n",
        "    return {\"media\": media_list, \"texts\": texts}"
      ],
      "metadata": {
        "id": "5V6IhKHv_AM7"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_prompt(data: Dict[str, Union[List[str], str]]) -> List[HumanMessage]:\n",
        "    \"\"\"Format data into a prompt for multi-modal analysis.\"\"\"\n",
        "    formatted_texts = \"\\n\".join(data[\"context\"][\"texts\"])\n",
        "    messages = []\n",
        "\n",
        "    if data[\"context\"][\"media\"]:\n",
        "        for media in data[\"context\"][\"media\"]:\n",
        "            media_message = {\n",
        "                \"type\": \"image_url\",\n",
        "                \"image_url\": {\"url\": f\"data:image/jpeg;base64,{media}\"},\n",
        "            }\n",
        "            messages.append(media_message)\n",
        "\n",
        "    text_message = {\n",
        "        \"type\": \"text\",\n",
        "        \"text\": (\n",
        "            \"You are an expert tasked with providing analysis and insights based on mixed media inputs.\\n\"\n",
        "            \"You will receive a combination of text, tables, and media items, including charts and graphs.\\n\"\n",
        "            \"Your goal is to provide a detailed analysis or answer based on the provided data.\\n\"\n",
        "            f\"User question: {data['question']}\\n\\n\"\n",
        "            \"Text and/or tables:\\n\"\n",
        "            f\"{formatted_texts}\"\n",
        "        ),\n",
        "    }\n",
        "    messages.append(text_message)\n",
        "\n",
        "    return [HumanMessage(content=messages)]"
      ],
      "metadata": {
        "id": "cIBCt4JZ_Dmz"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_multi_modal_rag_chain(retriever) -> RunnableLambda:\n",
        "    \"\"\"Create a multi-modal RAG chain for processing.\"\"\"\n",
        "    chain = (\n",
        "        {\n",
        "            \"context\": retriever | RunnableLambda(split_media_and_texts),\n",
        "            \"question\": RunnablePassthrough(),\n",
        "        }\n",
        "        | RunnableLambda(format_prompt)\n",
        "        | llm\n",
        "        | StrOutputParser()\n",
        "    )\n",
        "    return chain"
      ],
      "metadata": {
        "id": "2-5Hy-BX_Ghh"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def query_system(\n",
        "    question: str,\n",
        "    docs: List[str],\n",
        "    retriever: MultiVectorRetriever\n",
        ") -> str:\n",
        "    \"\"\"Query the system with a question and documents.\"\"\"\n",
        "    context_data = split_media_and_texts(docs)\n",
        "    chain = create_multi_modal_rag_chain(retriever)\n",
        "    result = chain({\"question\": question, \"context\": context_data})\n",
        "    return result"
      ],
      "metadata": {
        "id": "Mthuo96N_NHF"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = ChatGoogleGenerativeAI(model=LLM_NAME, google_api_key=GOOGLE_API_KEY)\n"
      ],
      "metadata": {
        "id": "y1T00RUE_P4I"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract and process documents\n",
        "raw_pdf_elements = extract_pdf_content_with_metadata(FILE_PATH)\n",
        "text_elements = categorize_pdf_elements_by_type(raw_pdf_elements)\n",
        "text_summaries = summarize_text_elements(text_elements)"
      ],
      "metadata": {
        "id": "xAjOYb51EM6u"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_pipeline(query: str, vectorstore: Chroma) -> str:\n",
        "    docs = retriever(vectorstore, query)\n",
        "    grades, relevant_docs = doc_grader(query, docs)\n",
        "\n",
        "    if sum(grades) / len(grades) < 0.75:\n",
        "        new_docs = retriever(vectorstore, query, relevant_docs)\n",
        "        relevant_docs.extend(new_docs)\n",
        "\n",
        "    return query_system(query, relevant_docs, retriever)"
      ],
      "metadata": {
        "id": "0JmxQDVYBoDY"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "media_dir = \"figures\"  # Example directory for media files\n",
        "base64_media_list, media_summaries = generate_media_summaries(media_dir)\n",
        "\n",
        "# Create vector store and retriever\n",
        "vectorstore = Chroma(persist_directory=\"./chroma_db\")\n",
        "retriever = create_multi_vector_retriever(\n",
        "        vectorstore,\n",
        "        text_summaries,\n",
        "        text_elements,\n",
        "        media_summaries,\n",
        "        base64_media_list\n",
        "    )\n",
        "\n",
        "    # Example query\n",
        "query = \"What are the insights from the provided data?\"\n",
        "is_complex = classify_query(query)\n",
        "\n",
        "if is_complex:\n",
        "        final_answer = run_pipeline(query=query, vectorstore=vectorstore)\n",
        "        print(final_answer)\n",
        "else:\n",
        "        messages = [\n",
        "            (\"system\",\n",
        "             \"You are a helpful assistant that answers user queries. Be respectful and concise in your responses.\"),\n",
        "            (\"human\", query)\n",
        "        ]\n",
        "        final_answer = llm.invoke(messages)\n",
        "        print(final_answer.content)\n",
        "\n",
        "docs = text_elements + base64_media_list\n",
        "answer = query_system(query, docs, retriever)\n",
        "print(answer)"
      ],
      "metadata": {
        "id": "K1t498xzEqZ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "fe72ed28-396a-405d-d6bf-74f075e54fd6"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:langchain_google_genai.chat_models:Retrying langchain_google_genai.chat_models._chat_with_retry.<locals>._chat_with_retry in 2.0 seconds as it raised ResourceExhausted: 429 Resource has been exhausted (e.g. check quota)..\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ResourceExhausted",
          "evalue": "429 Resource has been exhausted (e.g. check quota).",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mResourceExhausted\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-54-a62b7df5b345>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmedia_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"figures\"\u001b[0m  \u001b[0;31m# Example directory for media files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbase64_media_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmedia_summaries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_media_summaries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmedia_dir\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Create vector store and retriever\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvectorstore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mChroma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpersist_directory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"./chroma_db\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-36-dec9a49e3ee3>\u001b[0m in \u001b[0;36mgenerate_media_summaries\u001b[0;34m(media_dir)\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0mbase64_media\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencode_media_to_base64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmedia_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mbase64_media_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase64_media\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0mmedia_summaries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msummarize_media\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase64_media\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msummary_prompt\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     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mbase64_media_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmedia_summaries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-35-d27ddd1ff910>\u001b[0m in \u001b[0;36msummarize_media\u001b[0;34m(base64_media, prompt)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msummarize_media\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase64_media\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprompt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\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[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Generate a summary for media (image/table) using the Google Generative AI model.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     msg = llm.invoke(\n\u001b[0m\u001b[1;32m      4\u001b[0m         [\n\u001b[1;32m      5\u001b[0m             HumanMessage(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    284\u001b[0m         return cast(\n\u001b[1;32m    285\u001b[0m             \u001b[0mChatGeneration\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m             self.generate_prompt(\n\u001b[0m\u001b[1;32m    287\u001b[0m                 \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\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[1;32m    288\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;36mgenerate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    784\u001b[0m     ) -> LLMResult:\n\u001b[1;32m    785\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--> 786\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    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\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, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    641\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    642\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--> 643\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    644\u001b[0m         flattened_outputs = [\n\u001b[1;32m    645\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;31m# type: ignore[list-item]\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, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    631\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    632\u001b[0m                 results.append(\n\u001b[0;32m--> 633\u001b[0;31m                     self._generate_with_cache(\n\u001b[0m\u001b[1;32m    634\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    635\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    853\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_generate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"run_manager\"\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--> 855\u001b[0;31m                 result = self._generate(\n\u001b[0m\u001b[1;32m    856\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    857\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, messages, stop, run_manager, tools, functions, safety_settings, tool_config, generation_config, **kwargs)\u001b[0m\n\u001b[1;32m    978\u001b[0m             \u001b[0mgeneration_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgeneration_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m         )\n\u001b[0;32m--> 980\u001b[0;31m         response: GenerateContentResponse = _chat_with_retry(\n\u001b[0m\u001b[1;32m    981\u001b[0m             \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\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[0;32m/usr/local/lib/python3.10/dist-packages/langchain_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_chat_with_retry\u001b[0;34m(generation_method, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_chat_with_retry\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    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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    335\u001b[0m             \u001b[0mwrapped_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatistics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatistics\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\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[0mkw\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    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mretry_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\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[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mWrappedFn\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/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0mretry_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRetryCallState\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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    474\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m             \u001b[0mdo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry_state\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    476\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\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    477\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/usr/local/lib/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36miter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maction\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\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    377\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36mexc_check\u001b[0;34m(rs)\u001b[0m\n\u001b[1;32m    416\u001b[0m                 \u001b[0mretry_exc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretry_error_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfut\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mretry_exc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\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    419\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mretry_exc\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\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    420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_attempt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfailed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_attempt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\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    186\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    449\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\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    450\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\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    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\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[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\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--> 403\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0;31m# Break a reference cycle with the exception in self._exception\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/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\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    477\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--> 478\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\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    479\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# noqa: B902\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    480\u001b[0m                     \u001b[0mretry_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\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_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_chat_with_retry\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    201\u001b[0m             ) from e\n\u001b[1;32m    202\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\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[0;32m--> 203\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    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_chat_with_retry\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[0;32m/usr/local/lib/python3.10/dist-packages/langchain_google_genai/chat_models.py\u001b[0m in \u001b[0;36m_chat_with_retry\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_chat_with_retry\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[1;32m    186\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--> 187\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mgeneration_method\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    188\u001b[0m         \u001b[0;31m# Do not retry for these errors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFailedPrecondition\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\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/google/ai/generativelanguage_v1beta/services/generative_service/client.py\u001b[0m in \u001b[0;36mgenerate_content\u001b[0;34m(self, request, model, contents, retry, timeout, metadata)\u001b[0m\n\u001b[1;32m    825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m         \u001b[0;31m# Send the request.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 827\u001b[0;31m         response = rpc(\n\u001b[0m\u001b[1;32m    828\u001b[0m             \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    829\u001b[0m             \u001b[0mretry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry\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/google/api_core/gapic_v1/method.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, timeout, retry, compression, *args, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"compression\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_func\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    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/api_core/retry/retry_unary.py\u001b[0m in \u001b[0;36mretry_wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    291\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maximum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiplier\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_multiplier\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             return retry_target(\n\u001b[0m\u001b[1;32m    294\u001b[0m                 \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_predicate\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/google/api_core/retry/retry_unary.py\u001b[0m in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;31m# defer to shared logic for handling errors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m             _retry_error_helper(\n\u001b[0m\u001b[1;32m    154\u001b[0m                 \u001b[0mexc\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                 \u001b[0mdeadline\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/google/api_core/retry/retry_base.py\u001b[0m in \u001b[0;36m_retry_error_helper\u001b[0;34m(exc, deadline, next_sleep, error_list, predicate_fn, on_error_fn, exc_factory_fn, original_timeout)\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0moriginal_timeout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         )\n\u001b[0;32m--> 212\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mfinal_exc\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msource_exc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mon_error_fn\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[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0mon_error_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\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/google/api_core/retry/retry_unary.py\u001b[0m in \u001b[0;36mretry_target\u001b[0;34m(target, predicate, sleep_generator, timeout, on_error, exception_factory, **kwargs)\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0msleep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msleep_generator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\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--> 144\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\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    145\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misawaitable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\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    146\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ASYNC_RETRY_WARNING\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/google/api_core/timeout.py\u001b[0m in \u001b[0;36mfunc_with_timeout\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"timeout\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime_since_first_attempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\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    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc_with_timeout\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/google/api_core/grpc_helpers.py\u001b[0m in \u001b[0;36merror_remapped_callable\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcallable_\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[1;32m     77\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mgrpc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRpcError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_grpc_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0merror_remapped_callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mResourceExhausted\u001b[0m: 429 Resource has been exhausted (e.g. check quota)."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4j1SPfs3D9rJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}