language/use-cases/document-qa/question_answering_documentai_vector_store_palm.ipynb

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   "source": [
    "# Copyright 2023 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "JAPoU8Sm5E6e",
    "tags": []
   },
   "source": [
    "# Question answering with Documents using Document AI, Pandas, and PaLM\n",
    "\n",
    "<table align=\"left\">\n",
    "  <td style=\"text-align: center\">\n",
    "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-qa/question_answering_documentai_vector_store_palm.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Run in Colab\n",
    "    </a>\n",
    "  </td>\n",
    "  <td style=\"text-align: center\">\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-qa/question_answering_documentai_vector_store_palm.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "  <td style=\"text-align: center\">\n",
    "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/main/language/use-cases/document-qa/question_answering_documentai_vector_store_palm.ipynb\">\n",
    "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
    "    </a>\n",
    "  </td>\n",
    "</table>\n"
   ]
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   "source": [
    "| | |\n",
    "|-|-|\n",
    "|Author(s) | [Holt Skinner](https://github.com/holtskinner), [Mona Mona](https://github.com/Mona19) |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tvgnzT1CKxrO"
   },
   "source": [
    "## Overview\n",
    "\n",
    "Question answering for large documents is a challenging task because it requires the ability to understand and process a large amount of text.\n",
    "Question answering for large documents using Document AI and PaLM is a powerful approach that can be used to extract information from large amounts of text. Document AI can be used to extract structured data from documents, such as tables, lists, and images. PaLM can then be used to answer questions about the extracted data.\n",
    "\n",
    "[Document AI](https://cloud.google.com/document-ai) provides a scalable and managed way to extract data from documents using AI. In this notebook, you will use the [Document OCR processor](https://cloud.google.com/document-ai/docs/document-ocr), which is a pre-trained model that will extract text and layout information from document files.\n",
    "\n",
    "Here are some of the benefits of using Document AI and PaLM for question answering for large documents:\n",
    "\n",
    "It can be used to extract information from a variety of document formats.\n",
    "It can be used to answer questions about a variety of topics.\n",
    "It can be used to answer questions in natural language.\n",
    "It is a scalable solution that can be used to answer questions about large amounts of data.\n"
   ]
  },
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    "![Palm_pandas.png](data:image/png;base64,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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d975e698c9a4"
   },
   "source": [
    "### Objective\n",
    "\n",
    "In this notebook we will show how you can do the following:\n",
    "\n",
    "1. Extract text from pdf documents using the Document AI OCR processor.\n",
    "2. Use embedding model [Gecko](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) to generate embeddings for the extracted text\n",
    "3. Use PaLM `text-bison` model to answer questions on the embeddings datastore.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4j1gvi3jqG6U"
   },
   "source": [
    "### Costs\n",
    "\n",
    "This tutorial uses billable components of Google Cloud:\n",
    "\n",
    "- Vertex AI PaLM APIs offered by Generative AI studio\n",
    "- Document AI\n",
    "\n",
    "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
    "Learn about [Document AI pricing](https://cloud.google.com/document-ai/pricing),\n",
    "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
    "to generate a cost estimate based on your projected usage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QDU0XJ1xRDlL"
   },
   "source": [
    "## Getting Started\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2a5AEr0lkLKD"
   },
   "source": [
    "### Install Vertex AI SDK & Other dependencies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "GYACuZHAF3DQ"
   },
   "outputs": [],
   "source": [
    "%pip install --upgrade google-cloud-aiplatform==1.35.0 google-cloud-documentai==2.20.1 backoff==2.2.1 --user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UG3WsMySF3DQ"
   },
   "source": [
    "**Colab only**: Run the following cell to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_Hsqwn4hkLKE"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "if \"google.colab\" in sys.modules:\n",
    "    # Automatically restart kernel after installs so that your environment can access the new packages\n",
    "    import IPython\n",
    "\n",
    "    app = IPython.Application.instance()\n",
    "    app.kernel.do_shutdown(True)\n",
    "else:\n",
    "    # Otherwise, attempt to discover local credentials as described on https://cloud.google.com/docs/authentication/application-default-credentials\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5XtUQRDqlwTY"
   },
   "source": [
    "## Colab Only\n",
    "\n",
    "You will need to run the following cell to authenticates your Colab environment with your Google Cloud account.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "P_100VNRj-xn"
   },
   "outputs": [],
   "source": [
    "if \"google.colab\" in sys.modules:\n",
    "    from google.colab import auth\n",
    "\n",
    "    auth.authenticate_user()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R-yyDsflbCc0"
   },
   "source": [
    "### Enable the APIs in your project\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ElgyIjJ7eSIy"
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "import backoff\n",
    "from tenacity import retry, stop_after_attempt, wait_random_exponential\n",
    "from google.api_core.exceptions import ResourceExhausted\n",
    "from google.api_core.client_options import ClientOptions\n",
    "from google.api_core.exceptions import AlreadyExists\n",
    "from google.cloud import documentai\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "from typing import Dict, List\n",
    "import pandas as pd\n",
    "from logging import error\n",
    "import re\n",
    "import textwrap\n",
    "from typing import Tuple, List\n",
    "import vertexai\n",
    "from vertexai.language_models import TextEmbeddingModel, TextGenerationModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ai62J2sLPDFX"
   },
   "outputs": [],
   "source": [
    "!gcloud config set project \"YOUR_PROJECT_ID\"\n",
    "\n",
    "!gcloud services enable documentai.googleapis.com storage.googleapis.com aiplatform.googleapis.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UP76a2la7O-a"
   },
   "source": [
    "## Document AI\n",
    "\n",
    "The following [limits](https://cloud.google.com/document-ai/quotas) apply for online processing with the Document OCR processor.\n",
    "\n",
    "| Limit             | Value |\n",
    "| ----------------- | ----: |\n",
    "| Maximum file size | 20 MB |\n",
    "| Maximum pages     |    15 |\n",
    "\n",
    "For documents that don't meet these limits, you can use [batch processing](https://cloud.google.com/document-ai/docs/send-request#batch-process) to extract the document text. (Not covered in this notebook.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HZkLDRTjTcfm"
   },
   "source": [
    "### Preparing data files\n",
    "\n",
    "To begin, you will need to download PDFs for the summarizing tasks below.\n",
    "For this notebook, you will be using Alphabet earnings report PDFs hosted in a public Google Cloud Storage bucket.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "f-IWo-lb-gbn"
   },
   "outputs": [],
   "source": [
    "# Copying the files from the GCS bucket to local storage\n",
    "!gsutil -m cp -r gs://github-repo/documents/docai ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FxmWYA05o4jj"
   },
   "source": [
    "### Create Document AI OCR Processor\n",
    "\n",
    "A [Document AI processor](https://cloud.google.com/document-ai/docs/overview#dai-processors) is an interface between a document file and a machine learning model that performs document processing actions. They can be used to classify, split, parse, or analyze a document. Each Google Cloud project needs to create its own processor instances.\n",
    "\n",
    "There are two types of Document AI processors:\n",
    "\n",
    "- Pre-trained processors: These processors are pre-trained on a large dataset of documents and can be used to perform common document processing tasks, such as Optical Character Recognition (OCR), form parsing, and entity extraction.\n",
    "- Custom processors: These processors can be trained on your own dataset of documents to perform specific tasks that are not covered by the pre-trained processors.\n",
    "\n",
    "Refer to [Full processor and detail list](https://cloud.google.com/document-ai/docs/processors-list) for all supported processors.\n",
    "\n",
    "Processors take a PDF or image file as input and output the data in the [`Document`](https://cloud.google.com/document-ai/docs/reference/rest/v1/Document) format.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9URyhLDo4jk"
   },
   "source": [
    "### Create a processor\n",
    "\n",
    "Set these variables below:\n",
    "\n",
    "1. Enter `YOUR_PROJECT_ID` in project_id\n",
    "2. Enter `YOUR_PROCESSOR_DISPLAY_NAME` for `processor_display_name`. For example `processor_display_name = \"my processor\"`, before running the code below.\n",
    "\n",
    "Note: Run this code only once to create the processor.\n",
    "You cannot create multiple processors with the same display name. If you receive an error, change the name of the processor and rerun.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "7isig7e07O-a"
   },
   "outputs": [],
   "source": [
    "# TODO(developer): Edit these variables before running the code.\n",
    "project_id = \"YOUR_PROJECT_ID\"\n",
    "\n",
    "# See https://cloud.google.com/document-ai/docs/regions for all options.\n",
    "location = \"us\"\n",
    "\n",
    "# Must be unique per project, e.g.: \"My Processor\"\n",
    "processor_display_name = \"YOUR_PROCESSOR_DISPLAY_NAME\"\n",
    "\n",
    "# You must set the `api_endpoint` if you use a location other than \"us\".\n",
    "client_options = ClientOptions(api_endpoint=f\"{location}-documentai.googleapis.com\")\n",
    "\n",
    "\n",
    "def create_processor(\n",
    "    project_id: str, location: str, processor_display_name: str\n",
    ") -> documentai.Processor:\n",
    "    client = documentai.DocumentProcessorServiceClient(client_options=client_options)\n",
    "\n",
    "    # The full resource name of the location\n",
    "    # e.g.: projects/project_id/locations/location\n",
    "    parent = client.common_location_path(project_id, location)\n",
    "\n",
    "    # Create a processor\n",
    "    return client.create_processor(\n",
    "        parent=parent,\n",
    "        processor=documentai.Processor(\n",
    "            display_name=processor_display_name, type_=\"OCR_PROCESSOR\"\n",
    "        ),\n",
    "    )\n",
    "\n",
    "\n",
    "try:\n",
    "    processor = create_processor(project_id, location, processor_display_name)\n",
    "    print(f\"Created Processor {processor.name}\")\n",
    "except AlreadyExists as e:\n",
    "    print(\n",
    "        f\"Processor already exits, change the processor name and rerun this code. {e.message}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JkK74RRio4jk",
    "tags": []
   },
   "source": [
    "### Process the documents\n",
    "\n",
    "Process document takes the processor name and file path of the document and extracts the text from the document.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "L7bE7ZGtlcxv"
   },
   "outputs": [],
   "source": [
    "def process_document(\n",
    "    processor_name: str,\n",
    "    file_path: str,\n",
    ") -> documentai.Document:\n",
    "    client = documentai.DocumentProcessorServiceClient(client_options=client_options)\n",
    "\n",
    "    # Read the file into memory\n",
    "    with open(file_path, \"rb\") as image:\n",
    "        image_content = image.read()\n",
    "\n",
    "    # Load Binary Data into Document AI RawDocument Object\n",
    "    raw_document = documentai.RawDocument(\n",
    "        content=image_content, mime_type=\"application/pdf\"\n",
    "    )\n",
    "\n",
    "    # Configure the process request\n",
    "    request = documentai.ProcessRequest(name=processor_name, raw_document=raw_document)\n",
    "\n",
    "    result = client.process_document(request=request)\n",
    "\n",
    "    return result.document"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "usKPLAna5mLd"
   },
   "source": [
    "#### Create data chunks\n",
    "\n",
    "LLMs produce the best results when documents text is broken up into small \"chunks\" before being added to the prompt.\n",
    "\n",
    "Chunking is a technique used to break a document into smaller chunks that are easier to process. This can be done by dividing the document into sentences, paragraphs, or even sections.\n",
    "\n",
    "The current token size limit for PaLM is 8196 tokens. This means that a single request to the PaLM API can only process a document that is up to 8196 tokens long. If the document is longer than this, it will need to be broken up into smaller chunks.\n",
    "\n",
    "For the provided documents, we are using the paragraphs detected by Document AI to separate the chunks.\n",
    "You should experiment with other values as well and see how it affects your summarization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "nDf7Zw3SGuyW"
   },
   "outputs": [],
   "source": [
    "def layout_to_text(layout: documentai.Document.Page.Layout, text: str) -> str:\n",
    "    \"\"\"\n",
    "    Document AI identifies text in different parts of the document by their\n",
    "    offsets in the entirety of the document\"s text. This function converts\n",
    "    offsets to a string.\n",
    "    \"\"\"\n",
    "    # If a text segment spans several lines, it will\n",
    "    # be stored in different text segments.\n",
    "    return \"\".join(\n",
    "        text[int(segment.start_index) : int(segment.end_index)]\n",
    "        for segment in layout.text_anchor.text_segments\n",
    "    )\n",
    "\n",
    "\n",
    "# If you already have a Document AI Processor in your project, assign the full processor resource name here.\n",
    "processor_name = processor.name\n",
    "chunk_size = 5000\n",
    "extracted_data: List[Dict] = []\n",
    "\n",
    "# Loop through each PDF file in the \"docai\" directory.\n",
    "for path in glob.glob(\"docai/*.pdf\"):\n",
    "    # Extract the file name and type from the path.\n",
    "    file_name, file_type = os.path.splitext(path)\n",
    "\n",
    "    print(f\"Processing {file_name}\")\n",
    "\n",
    "    # Process the document.\n",
    "    document = process_document(processor_name, file_path=path)\n",
    "\n",
    "    if not document:\n",
    "        print(\"Processing did not complete successfully.\")\n",
    "        continue\n",
    "\n",
    "    # Split the text into chunks based on paragraphs.\n",
    "    document_chunks = [\n",
    "        layout_to_text(paragraph.layout, document.text)\n",
    "        for page in document.pages\n",
    "        for paragraph in page.paragraphs\n",
    "    ]\n",
    "\n",
    "    # Can also split into chunks by page or blocks.\n",
    "    # document_chunks = [page.text for page in wrapped_document.pages]\n",
    "    # document_chunks = [block.text for page in wrapped_document.pages for block in page.blocks]\n",
    "\n",
    "    # Loop through each chunk and create a dictionary with metadata and content.\n",
    "    for chunk_number, chunk_content in enumerate(document_chunks, start=1):\n",
    "        # Append the chunk information to the extracted_data list.\n",
    "        extracted_data.append(\n",
    "            {\n",
    "                \"file_name\": file_name,\n",
    "                \"file_type\": file_type,\n",
    "                \"chunk_number\": chunk_number,\n",
    "                \"content\": chunk_content,\n",
    "            }\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "zbPhNWEGw1hC"
   },
   "outputs": [],
   "source": [
    "# Convert extracted_data to a sorted Pandas DataFrame\n",
    "pdf_data = (\n",
    "    pd.DataFrame.from_dict(extracted_data)\n",
    "    .sort_values(by=[\"file_name\"])\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "pdf_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y3r_0YLUw1hD"
   },
   "outputs": [],
   "source": [
    "# Define the maximum number of characters in each chunk.\n",
    "chunk_size = 5000\n",
    "\n",
    "pdf_data_sample = pdf_data.copy()\n",
    "\n",
    "# Remove all non-alphabets and numbers from the data to clean it up.\n",
    "# This is harsh cleaning. You can define your custom logic for cleansing here.\n",
    "pdf_data_sample[\"content\"] = pdf_data_sample[\"content\"].apply(\n",
    "    lambda x: re.sub(\"[^A-Za-z0-9]+\", \" \", x)\n",
    ")\n",
    "\n",
    "# Apply chunk splitting logic to each row of content in the DataFrame.\n",
    "pdf_data_sample[\"chunks\"] = pdf_data_sample[\"content\"].apply(\n",
    "    lambda row: textwrap.wrap(row, width=chunk_size)\n",
    ")\n",
    "\n",
    "# Now, each row in 'chunks' contains list of all chunks and hence we need to explode them into individual rows.\n",
    "pdf_data_sample = pdf_data_sample.explode(\"chunks\")\n",
    "\n",
    "# Sort and reset index\n",
    "pdf_data_sample = pdf_data_sample.sort_values(by=[\"file_name\"]).reset_index(drop=True)\n",
    "pdf_data_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-Vp9LwVww1hE"
   },
   "outputs": [],
   "source": [
    "print(\"The original dataframe has :\", pdf_data.shape[0], \" rows without chunking\")\n",
    "print(\"The chunked dataframe has :\", pdf_data_sample.shape[0], \" rows with chunking\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jj7KMupOPUX-",
    "tags": []
   },
   "source": [
    "## Question Answering using the [PaLM](https://ai.google/discover/palm2/) and Gecko Embedding model\n",
    "\n",
    "You have just used Document AI to extract text from PDF files.\n",
    "\n",
    "In the next section, you will perform question answering on the extracted text using the PaLM model and Gecko Embedding model with Vertex AI.\n",
    "\n",
    "Question answering using PaLM model and Gecko embedding model is a technique that can be used to improve the accuracy and efficiency of question answering.\n",
    "\n",
    "The Gecko embedding model is a pre-trained embedding model that has been trained on a large dataset of text. It can be used to represent text as vectors that can be used to measure the similarity between different pieces of text.\n",
    "\n",
    "The PaLM model is a large language model that has been trained on a massive dataset of text and code. It can be used to answer questions in a variety of ways, including factual question answering, open-ended question answering, and answering questions in context.\n",
    "\n",
    "To use the PaLM model and Gecko embedding model for question answering, you can use the following steps:\n",
    "\n",
    "1. Represent the documents that contain the answer as vectors using the Gecko embedding model.\n",
    "2. Compare the question to the document vector store.\n",
    "3. The document with the most similar vector to the question is the document that contains the answer.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xe7OuYuGkLKF"
   },
   "source": [
    "### Authenticating your notebook environment\n",
    "\n",
    "- If you are using **Colab** to run this notebook, run the cell below and continue.\n",
    "- If you are using **Vertex AI Workbench**, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8YRKSFYOqSH4"
   },
   "outputs": [],
   "source": [
    "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
    "vertexai.init(project=PROJECT_ID, location=\"us-central1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H3edk4BiDkQ4"
   },
   "source": [
    "### Import models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "CKzpNLuzDeC4"
   },
   "outputs": [],
   "source": [
    "generation_model = TextGenerationModel.from_pretrained(\"text-bison\")\n",
    "embedding_model = TextEmbeddingModel.from_pretrained(\"textembedding-gecko@001\")\n",
    "\n",
    "\n",
    "# This decorator is used to handle exceptions and apply exponential backoff in case of ResourceExhausted errors.\n",
    "# It means the function will be retried with increasing time intervals in case of this specific exception.\n",
    "@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(5))\n",
    "def text_generation_model_with_backoff(**kwargs):\n",
    "    return generation_model.predict(**kwargs).text\n",
    "\n",
    "\n",
    "@retry(wait=wait_random_exponential(min=10, max=120), stop=stop_after_attempt(5))\n",
    "def embedding_model_with_backoff(text=[]):\n",
    "    embeddings = embedding_model.get_embeddings(text)\n",
    "    return [each.values for each in embeddings][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kU-s4MWnF3DV"
   },
   "source": [
    "You can start the implementation first by simply getting the embeddings for each chunk.\n",
    "\n",
    "This will add the embeddings (vector/number representation) of each chunk as a separate column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "PeJCuEx5w1hF"
   },
   "outputs": [],
   "source": [
    "pdf_data_sample[\"embedding\"] = pdf_data_sample[\"chunks\"].apply(\n",
    "    lambda x: embedding_model_with_backoff([x])\n",
    ")\n",
    "pdf_data_sample[\"embedding\"] = pdf_data_sample.embedding.apply(np.array)\n",
    "pdf_data_sample.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8aGR1MrCw1hF"
   },
   "outputs": [],
   "source": [
    "def get_context_from_question(\n",
    "    question: str, vector_store: pd.DataFrame, sort_index_value: int = 2\n",
    ") -> Tuple[str, pd.DataFrame]:\n",
    "    query_vector = np.array(embedding_model_with_backoff([question]))\n",
    "    vector_store[\"dot_product\"] = vector_store[\"embedding\"].apply(\n",
    "        lambda row: np.dot(row, query_vector)\n",
    "    )\n",
    "    top_matched = vector_store.sort_values(by=\"dot_product\", ascending=False)[\n",
    "        :sort_index_value\n",
    "    ].index\n",
    "    top_matched_df = vector_store.loc[top_matched, [\"file_name\", \"chunks\"]]\n",
    "    context = \"\\n\".join(top_matched_df[\"chunks\"].values)\n",
    "    return context, top_matched_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t_f5ZBqLF3DV"
   },
   "source": [
    "Now that you have a general function that always gets you custom relevant context for the question, you can call it with every new question.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nF7J1tanw1hG"
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# your question for the documents\n",
    "question = \"When did google become carbon neutral?\"\n",
    "\n",
    "# get the custom relevant chunks from all the chunks in vector store.\n",
    "context, top_matched_df = get_context_from_question(\n",
    "    question,\n",
    "    vector_store=pdf_data_sample,\n",
    "    sort_index_value=2,  # Top N results to pick from embedding vector search\n",
    ")\n",
    "# top 5 data that has been picked by model based on user question. This becomes the context.\n",
    "top_matched_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KtYluejcw1hG"
   },
   "outputs": [],
   "source": [
    "# Prompt for Q&A which takes the custom context found in last step.\n",
    "prompt = f\"\"\" Answer the question as precise as possible using the provided context. \\n\\n\n",
    "            Context: \\n {context}?\\n\n",
    "            Question: \\n {question} \\n\n",
    "            Answer:\n",
    "          \"\"\"\n",
    "\n",
    "# Call the PaLM API on the prompt.\n",
    "print(\"PaLM Predicted:\", text_generation_model_with_backoff(prompt=prompt), \"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WLFuRGrtN-9l"
   },
   "source": [
    "# Conclusion\n",
    "\n",
    "In this notebook, you learned:\n",
    "\n",
    "1. Extract text from pdf documents using the Document AI OCR processor.\n",
    "2. Use Embedding model Gecko to generate embeddings for the extracted text\n",
    "3. Use PaLM chat-bison@latest model to answer questions on the embeddings datastore.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rhR1vTtpXj1q"
   },
   "source": [
    "## Clean Up\n",
    "\n",
    "If you no longer need the Document AI processor, you can delete it using the following code.\n",
    "\n",
    "Alternatively, you can use the Cloud Console to delete the processor as outlined in [Creating and managing processors > Delete a processor](https://cloud.google.com/document-ai/docs/create-processor#documentai_delete_processor-web).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EuTdzGhbe9C3"
   },
   "outputs": [],
   "source": [
    "def delete_processor(processor_name: str) -> None:\n",
    "    client = documentai.DocumentProcessorServiceClient(client_options=client_options)\n",
    "\n",
    "    # Delete a processor\n",
    "    operation = client.delete_processor(name=processor_name)\n",
    "    # Print operation details\n",
    "    print(operation.operation.name)\n",
    "    # Wait for operation to complete\n",
    "    operation.result()\n",
    "\n",
    "\n",
    "delete_processor(processor_name)"
   ]
  }
 ],
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