Advanced_Document_Parsing_For_Enterprises.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jslinuxta/Colab/blob/main/Advanced_Document_Parsing_For_Enterprises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssw34RVKHmsJ"
      },
      "source": [
        "# Advanced Document Parsing For Enterprises"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2oYelmWW35Q"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "The bread and butter of natural language processing technology is text. Once we can reduce a set of data into text, we can do all kinds of things with it: question answering, summarization, classification, sentiment analysis, searching and indexing, and more.\n",
        "<br>\n",
        "<br>\n",
        "In the context of enterprise Retrieval Augmented Generation (RAG), the information is often locked in complex file types such as PDFs. These formats are made for sharing information between humans, but not so much with language models.\n",
        "<br>\n",
        "<br>\n",
        "In this notebook, we will use a real-world pharmaceutical drug label to test out various performant approaches to parsing PDFs. This will allow us to use [Cohere's Command-R model](https://txt.cohere.com/command-r/) in a RAG setting to answer questions and asks about this label, such as \"I need a succinct summary of the compound name, indication, route of administration, and mechanism of action of\" a given pharmaceutical.\n",
        "<br>\n",
        "<br>\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-mq1FCojI2p"
      },
      "source": [
        "<a name=\"top\"></a>\n",
        "## PDF Parsing\n",
        "\n",
        "We will go over five proprietary as well as open source options for processing PDFs. The parsing mechanisms demonstrated in the following sections are\n",
        "- [Google Document AI](#gcp)\n",
        "- [AWS Textract](#aws)\n",
        "- [Unstructured.io](#unstructured)\n",
        "- [LlamaParse](#llama)\n",
        "- [pdf2image + pytesseract](#pdf2image)\n",
        "\n",
        "By way of example, we will be parsing a [21-page PDF](https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/215500s000lbl.pdf) containing the label for a recent FDA drug approval, the beginning of which is shown below. Then, we will perform a series of basic RAG tasks with our different parsings and evaluate their performance.\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lB0o4L4jh5Sv",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "## Getting Set Up\n",
        "\n",
        "Before we dive into the technical weeds, we need to set up the notebook's runtime and filesystem environments. The code cells below do the following:\n",
        "- Install required libraries\n",
        "- Confirm that data dependencies from the GitHub repo have been downloaded. These will be under `data/document-parsing` and contain the following:\n",
        "    - the PDF document that we will be working with, `fda-approved-drug.pdf` (this can also be found here: https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/215500s000lbl.pdf)\n",
        "    - precomputed parsed documents for each parsing solution. While the point of this notebook is to illustrate how this is done, we provide the parsed final results to allow readers to skip ahead to the RAG section without having to set up the required infrastructure for each solution.)\n",
        "- Add utility functions needed for later sections"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZP1v0aF6Ij3U"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "! sudo apt install tesseract-ocr poppler-utils\n",
        "! pip install \"cohere<5\" fsspec hnswlib google-cloud-documentai google-cloud-storage boto3 langchain-text-splitters llama_parse pytesseract pdf2image pandas\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A5yWsKDSL_kY"
      },
      "outputs": [],
      "source": [
        "data_dir = \"data/document-parsing\"\n",
        "source_filename = \"example-drug-label\"\n",
        "extension = \"pdf\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9maXEJ8WpIaE"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "sources = [\"gcp\", \"aws\", \"unstructured-io\", \"llamaparse-text\", \"llamaparse-markdown\", \"pytesseract\"]\n",
        "\n",
        "filenames = [\"{}-parsed-fda-approved-drug.txt\".format(source) for source in sources]\n",
        "filenames.append(\"fda-approved-drug.pdf\")\n",
        "\n",
        "for filename in filenames:\n",
        "    file_path = Path(f\"{data_dir}/{filename}\")\n",
        "    if file_path.is_file() == False:\n",
        "        print(f\"File {filename} not found at {data_dir}!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BoM6-Tq-Sm-1"
      },
      "source": [
        "### Utility Functions\n",
        "Make sure to include the notebook's utility functions in the runtime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jl7BJsvdSrr5"
      },
      "outputs": [],
      "source": [
        "def store_document(path: str, doc_content: str):\n",
        "    with open(path, 'w') as f:\n",
        "      f.write(doc_content)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R2u-drbt7SOQ"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "def insert_citations_in_order(text, citations, documents):\n",
        "    \"\"\"\n",
        "    A helper function to pretty print citations.\n",
        "    \"\"\"\n",
        "\n",
        "    citations_reference = {}\n",
        "    for index, doc in enumerate(documents):\n",
        "        citations_reference[index] = doc\n",
        "\n",
        "    offset = 0\n",
        "    # Process citations in the order they were provided\n",
        "    for citation in citations:\n",
        "        # Adjust start/end with offset\n",
        "        start, end = citation['start'] + offset, citation['end'] + offset\n",
        "        citation_numbers = []\n",
        "        for doc_id in citation[\"document_ids\"]:\n",
        "            for citation_index, doc in citations_reference.items():\n",
        "                if doc[\"id\"] == doc_id:\n",
        "                    citation_numbers.append(citation_index)\n",
        "        references = \"(\" + \", \".join(\"[{}]\".format(num) for num in citation_numbers) + \")\"\n",
        "        modification = f'{text[start:end]} {references}'\n",
        "        # Replace the cited text with its bolded version + placeholder\n",
        "        text = text[:start] + modification + text[end:]\n",
        "        # Update the offset for subsequent replacements\n",
        "        offset += len(modification) - (end - start)\n",
        "\n",
        "    # Add the citations at the bottom of the text\n",
        "    text_with_citations = f'{text}'\n",
        "    citations_reference = [\"[{}]: {}\".format(x[\"id\"], x[\"text\"]) for x in citations_reference.values()]\n",
        "\n",
        "    return text_with_citations, \"\\n\".join(citations_reference)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SULVG_HwKdm5"
      },
      "outputs": [],
      "source": [
        "def format_docs_for_chat(documents):\n",
        "  return [{\"id\": str(index), \"text\": x} for index, x in enumerate(documents)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-VohvR3X6S6",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "## Document Parsing Solutions\n",
        "\n",
        "For demonstration purposes, we have collected and saved the parsed documents from each solution in this notebook. Skip to the [next section](#document-questions) to run RAG with Command-R on the pre-fetched versions. You can find all parsed resources in detail at the link [here](https://github.com/gchatz22/temp-cohere-resources/tree/main/data)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLtrGjXiJE9M"
      },
      "source": [
        "<a name=\"gcp\"></a>\n",
        "### Solution 1: Google Cloud Document AI [[Back to Solutions]](#top)\n",
        "\n",
        "Document AI helps developers create high-accuracy processors to extract, classify, and split documents.\n",
        "<br>\n",
        "External documentation: https://cloud.google.com/document-ai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxwJ_jZpgNDo"
      },
      "source": [
        "#### Parsing the document\n",
        "\n",
        "The following block can be executed in one of two ways\n",
        "1. Inside a Google Vertex AI environment\n",
        "  - No authentication is needed\n",
        "2. Inside the notebook\n",
        "  - Authentication needed\n",
        "  - There are pointers inside the code on which lines to uncomment in order to achieve that\n",
        "<br>\n",
        "**Note: You can skip to the next block if you want to use the pre-existing parsed version.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uZdjNlfxJEXv"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Extracted from https://cloud.google.com/document-ai/docs/samples/documentai-batch-process-document\n",
        "\"\"\"\n",
        "\n",
        "import re\n",
        "from typing import Optional\n",
        "\n",
        "from google.api_core.client_options import ClientOptions\n",
        "from google.api_core.exceptions import InternalServerError\n",
        "from google.api_core.exceptions import RetryError\n",
        "from google.cloud import documentai  # type: ignore\n",
        "from google.cloud import storage\n",
        "\n",
        "project_id = \"\"\n",
        "location = \"\"\n",
        "processor_id = \"\"\n",
        "gcs_output_uri = \"\"\n",
        "# credentials_file = \"populate if you are running in a non Vertex AI environment.\"\n",
        "gcs_input_prefix = \"\"\n",
        "\n",
        "\n",
        "def batch_process_documents(\n",
        "    project_id: str,\n",
        "    location: str,\n",
        "    processor_id: str,\n",
        "    gcs_output_uri: str,\n",
        "    gcs_input_prefix: str,\n",
        "    timeout: int = 400\n",
        ") -> None:\n",
        "    parsed_documents = []\n",
        "\n",
        "    # Client configs\n",
        "    opts = ClientOptions(api_endpoint=f\"{location}-documentai.googleapis.com\")\n",
        "    # With credentials\n",
        "    # opts = ClientOptions(api_endpoint=f\"{location}-documentai.googleapis.com\", credentials_file=credentials_file)\n",
        "\n",
        "    client = documentai.DocumentProcessorServiceClient(client_options=opts)\n",
        "    processor_name = client.processor_path(project_id, location, processor_id)\n",
        "\n",
        "    # Input storage configs\n",
        "    gcs_prefix = documentai.GcsPrefix(gcs_uri_prefix=gcs_input_prefix)\n",
        "    input_config = documentai.BatchDocumentsInputConfig(gcs_prefix=gcs_prefix)\n",
        "\n",
        "    # Output storage configs\n",
        "    gcs_output_config = documentai.DocumentOutputConfig.GcsOutputConfig(gcs_uri=gcs_output_uri, field_mask=None)\n",
        "    output_config = documentai.DocumentOutputConfig(gcs_output_config=gcs_output_config)\n",
        "    storage_client = storage.Client()\n",
        "    # With credentials\n",
        "    # storage_client = storage.Client.from_service_account_json(json_credentials_path=credentials_file)\n",
        "\n",
        "    # Batch process docs request\n",
        "    request = documentai.BatchProcessRequest(\n",
        "        name=processor_name,\n",
        "        input_documents=input_config,\n",
        "        document_output_config=output_config,\n",
        "    )\n",
        "\n",
        "    # batch_process_documents returns a long running operation\n",
        "    operation = client.batch_process_documents(request)\n",
        "\n",
        "    # Continually polls the operation until it is complete.\n",
        "    # This could take some time for larger files\n",
        "    try:\n",
        "        print(f\"Waiting for operation {operation.operation.name} to complete...\")\n",
        "        operation.result(timeout=timeout)\n",
        "    except (RetryError, InternalServerError) as e:\n",
        "        print(e.message)\n",
        "\n",
        "    # Get output document information from completed operation metadata\n",
        "    metadata = documentai.BatchProcessMetadata(operation.metadata)\n",
        "    if metadata.state != documentai.BatchProcessMetadata.State.SUCCEEDED:\n",
        "        raise ValueError(f\"Batch Process Failed: {metadata.state_message}\")\n",
        "\n",
        "    print(\"Output files:\")\n",
        "    # One process per Input Document\n",
        "    for process in list(metadata.individual_process_statuses):\n",
        "        matches = re.match(r\"gs://(.*?)/(.*)\", process.output_gcs_destination)\n",
        "        if not matches:\n",
        "            print(\"Could not parse output GCS destination:\", process.output_gcs_destination)\n",
        "            continue\n",
        "\n",
        "        output_bucket, output_prefix = matches.groups()\n",
        "        output_blobs = storage_client.list_blobs(output_bucket, prefix=output_prefix)\n",
        "\n",
        "        # Document AI may output multiple JSON files per source file\n",
        "        # (Large documents get split in multiple file \"versions\" doc --> parsed_doc_0 + parsed_doc_1 ...)\n",
        "        for blob in output_blobs:\n",
        "            # Document AI should only output JSON files to GCS\n",
        "            if blob.content_type != \"application/json\":\n",
        "                print(f\"Skipping non-supported file: {blob.name} - Mimetype: {blob.content_type}\")\n",
        "                continue\n",
        "\n",
        "            # Download JSON file as bytes object and convert to Document Object\n",
        "            print(f\"Fetching {blob.name}\")\n",
        "            document = documentai.Document.from_json(blob.download_as_bytes(), ignore_unknown_fields=True)\n",
        "            # Store the filename and the parsed versioned document content as a tuple\n",
        "            parsed_documents.append((blob.name.split(\"/\")[-1].split(\".\")[0], document.text))\n",
        "\n",
        "    print(\"Finished document parsing process.\")\n",
        "    return parsed_documents\n",
        "\n",
        "# Call service\n",
        "# versioned_parsed_documents = batch_process_documents(\n",
        "#     project_id=project_id,\n",
        "#     location=location,\n",
        "#     processor_id=processor_id,\n",
        "#     gcs_output_uri=gcs_output_uri,\n",
        "#     gcs_input_prefix=gcs_input_prefix\n",
        "# )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CT4QwMTwLPad",
        "outputId": "1c85c3aa-a3b5-48c4-bb6e-5033c2c604a0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nPost process parsed document and store it locally.\\nMake sure to run in Google Vertex AI environment or include a credentials file.\\n'"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\"\n",
        "Post process parsed document and store it locally.\n",
        "Make sure to run in Google Vertex AI environment or include a credentials file.\n",
        "\"\"\"\n",
        "\n",
        "# from pathlib import Path\n",
        "# from collections import defaultdict\n",
        "\n",
        "# parsed_documents = []\n",
        "# combined_versioned_parsed_documents = defaultdict(list)\n",
        "\n",
        "# # Assemble versioned documents together ({\"doc_name\": [(0, doc_content_0), (1, doc_content_1), ...]}).\n",
        "# for filename, doc_content in versioned_parsed_documents:\n",
        "#   filename, version = \"-\".join(filename.split(\"-\")[:-1]), filename.split(\"-\")[-1]\n",
        "#   combined_versioned_parsed_documents[filename].append((version, doc_content))\n",
        "\n",
        "# # Sort documents by version and join the content together.\n",
        "# for filename, docs in combined_versioned_parsed_documents.items():\n",
        "#   doc_content = \" \".join([x[1] for x in sorted(docs, key=lambda x: x[0])])\n",
        "#   parsed_documents.append((filename, doc_content))\n",
        "\n",
        "# # Store parsed documents in local storage.\n",
        "# for filename, doc_content in parsed_documents:\n",
        "#  file_path = \"{}/{}-parsed-{}.txt\".format(data_dir, \"gcp\", source_filename)\n",
        "#  store_document(file_path, doc_content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmkya9saFN8R"
      },
      "source": [
        "#### Visualize the parsed document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S6SMCDb1FhqZ"
      },
      "outputs": [],
      "source": [
        "filename = \"gcp-parsed-{}.txt\".format(source_filename)\n",
        "with open(\"{}/{}\".format(data_dir, filename), \"r\") as doc:\n",
        "    parsed_document = doc.read()\n",
        "\n",
        "print(parsed_document[:1000])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlRxuddi5w0E"
      },
      "source": [
        "<a name=\"aws\"></a>\n",
        "### Solution 2: AWS Textract [[Back to Solutions]](#top)\n",
        "\n",
        "[Amazon Textract](https://aws.amazon.com/textract/) is an OCR service offered by AWS. It can detect text, forms, tables, and more in PDFs and images. In this section, we go over how to use Textract's asynchronous API.\n",
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YCszLJnge4j"
      },
      "source": [
        "#### Parsing the document"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFBasw782Gho"
      },
      "source": [
        "We assume you are working within the AWS ecosystem (from a SageMaker notebook, EC2 instance, a Lambda function, ...) with valid credentials. Much of the code here is from supplemental materials created by AWS and offered here:\n",
        "\n",
        "- https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract\n",
        "- https://github.com/aws-samples/textract-paragraph-identification/tree/main\n",
        "\n",
        "At minimum, you will need access to the following AWS resources to get started:\n",
        "\n",
        "- Textract\n",
        "- an S3 bucket containing the document(s) to process - in this case, our `example-drug-label.pdf` file\n",
        "- an SNS topic that Textract can publish to. This is used to send a notification that parsing is complete.\n",
        "- an IAM role that Textract will assume, granting access to the S3 bucket and SNS topic\n",
        "\n",
        "First, we bring in the `TextractWrapper` class provided in the [AWS Code Examples repository](https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/python/example_code/textract/textract_wrapper.py). This class makes it simpler to interface with the Textract service."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "casiElly3G2C"
      },
      "outputs": [],
      "source": [
        "# source: https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract\n",
        "\n",
        "# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n",
        "# SPDX-License-Identifier: Apache-2.0\n",
        "\n",
        "\"\"\"\n",
        "Purpose\n",
        "\n",
        "Shows how to use the AWS SDK for Python (Boto3) with Amazon Textract to\n",
        "detect text, form, and table elements in document images.\n",
        "\"\"\"\n",
        "\n",
        "import json\n",
        "import logging\n",
        "from botocore.exceptions import ClientError\n",
        "\n",
        "logger = logging.getLogger(__name__)\n",
        "\n",
        "\n",
        "# snippet-start:[python.example_code.textract.TextractWrapper]\n",
        "class TextractWrapper:\n",
        "    \"\"\"Encapsulates Textract functions.\"\"\"\n",
        "\n",
        "    def __init__(self, textract_client, s3_resource, sqs_resource):\n",
        "        \"\"\"\n",
        "        :param textract_client: A Boto3 Textract client.\n",
        "        :param s3_resource: A Boto3 Amazon S3 resource.\n",
        "        :param sqs_resource: A Boto3 Amazon SQS resource.\n",
        "        \"\"\"\n",
        "        self.textract_client = textract_client\n",
        "        self.s3_resource = s3_resource\n",
        "        self.sqs_resource = sqs_resource\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.TextractWrapper]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.DetectDocumentText]\n",
        "    def detect_file_text(self, *, document_file_name=None, document_bytes=None):\n",
        "        \"\"\"\n",
        "        Detects text elements in a local image file or from in-memory byte data.\n",
        "        The image must be in PNG or JPG format.\n",
        "\n",
        "        :param document_file_name: The name of a document image file.\n",
        "        :param document_bytes: In-memory byte data of a document image.\n",
        "        :return: The response from Amazon Textract, including a list of blocks\n",
        "                 that describe elements detected in the image.\n",
        "        \"\"\"\n",
        "        if document_file_name is not None:\n",
        "            with open(document_file_name, \"rb\") as document_file:\n",
        "                document_bytes = document_file.read()\n",
        "        try:\n",
        "            response = self.textract_client.detect_document_text(\n",
        "                Document={\"Bytes\": document_bytes}\n",
        "            )\n",
        "            logger.info(\"Detected %s blocks.\", len(response[\"Blocks\"]))\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't detect text.\")\n",
        "            raise\n",
        "        else:\n",
        "            return response\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.DetectDocumentText]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.AnalyzeDocument]\n",
        "    def analyze_file(\n",
        "        self, feature_types, *, document_file_name=None, document_bytes=None\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Detects text and additional elements, such as forms or tables, in a local image\n",
        "        file or from in-memory byte data.\n",
        "        The image must be in PNG or JPG format.\n",
        "\n",
        "        :param feature_types: The types of additional document features to detect.\n",
        "        :param document_file_name: The name of a document image file.\n",
        "        :param document_bytes: In-memory byte data of a document image.\n",
        "        :return: The response from Amazon Textract, including a list of blocks\n",
        "                 that describe elements detected in the image.\n",
        "        \"\"\"\n",
        "        if document_file_name is not None:\n",
        "            with open(document_file_name, \"rb\") as document_file:\n",
        "                document_bytes = document_file.read()\n",
        "        try:\n",
        "            response = self.textract_client.analyze_document(\n",
        "                Document={\"Bytes\": document_bytes}, FeatureTypes=feature_types\n",
        "            )\n",
        "            logger.info(\"Detected %s blocks.\", len(response[\"Blocks\"]))\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't detect text.\")\n",
        "            raise\n",
        "        else:\n",
        "            return response\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.AnalyzeDocument]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.helper.prepare_job]\n",
        "    def prepare_job(self, bucket_name, document_name, document_bytes):\n",
        "        \"\"\"\n",
        "        Prepares a document image for an asynchronous detection job by uploading\n",
        "        the image bytes to an Amazon S3 bucket. Amazon Textract must have permission\n",
        "        to read from the bucket to process the image.\n",
        "\n",
        "        :param bucket_name: The name of the Amazon S3 bucket.\n",
        "        :param document_name: The name of the image stored in Amazon S3.\n",
        "        :param document_bytes: The image as byte data.\n",
        "        \"\"\"\n",
        "        try:\n",
        "            bucket = self.s3_resource.Bucket(bucket_name)\n",
        "            bucket.upload_fileobj(document_bytes, document_name)\n",
        "            logger.info(\"Uploaded %s to %s.\", document_name, bucket_name)\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't upload %s to %s.\", document_name, bucket_name)\n",
        "            raise\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.helper.prepare_job]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.helper.check_job_queue]\n",
        "    def check_job_queue(self, queue_url, job_id):\n",
        "        \"\"\"\n",
        "        Polls an Amazon SQS queue for messages that indicate a specified Textract\n",
        "        job has completed.\n",
        "\n",
        "        :param queue_url: The URL of the Amazon SQS queue to poll.\n",
        "        :param job_id: The ID of the Textract job.\n",
        "        :return: The status of the job.\n",
        "        \"\"\"\n",
        "        status = None\n",
        "        try:\n",
        "            queue = self.sqs_resource.Queue(queue_url)\n",
        "            messages = queue.receive_messages()\n",
        "            if messages:\n",
        "                msg_body = json.loads(messages[0].body)\n",
        "                msg = json.loads(msg_body[\"Message\"])\n",
        "                if msg.get(\"JobId\") == job_id:\n",
        "                    messages[0].delete()\n",
        "                    status = msg.get(\"Status\")\n",
        "                    logger.info(\n",
        "                        \"Got message %s with status %s.\", messages[0].message_id, status\n",
        "                    )\n",
        "            else:\n",
        "                logger.info(\"No messages in queue %s.\", queue_url)\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't get messages from queue %s.\", queue_url)\n",
        "        else:\n",
        "            return status\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.helper.check_job_queue]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.StartDocumentTextDetection]\n",
        "    def start_detection_job(\n",
        "        self, bucket_name, document_file_name, sns_topic_arn, sns_role_arn\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Starts an asynchronous job to detect text elements in an image stored in an\n",
        "        Amazon S3 bucket. Textract publishes a notification to the specified Amazon SNS\n",
        "        topic when the job completes.\n",
        "        The image must be in PNG, JPG, or PDF format.\n",
        "\n",
        "        :param bucket_name: The name of the Amazon S3 bucket that contains the image.\n",
        "        :param document_file_name: The name of the document image stored in Amazon S3.\n",
        "        :param sns_topic_arn: The Amazon Resource Name (ARN) of an Amazon SNS topic\n",
        "                              where the job completion notification is published.\n",
        "        :param sns_role_arn: The ARN of an AWS Identity and Access Management (IAM)\n",
        "                             role that can be assumed by Textract and grants permission\n",
        "                             to publish to the Amazon SNS topic.\n",
        "        :return: The ID of the job.\n",
        "        \"\"\"\n",
        "        try:\n",
        "            response = self.textract_client.start_document_text_detection(\n",
        "                DocumentLocation={\n",
        "                    \"S3Object\": {\"Bucket\": bucket_name, \"Name\": document_file_name}\n",
        "                },\n",
        "                NotificationChannel={\n",
        "                    \"SNSTopicArn\": sns_topic_arn,\n",
        "                    \"RoleArn\": sns_role_arn,\n",
        "                },\n",
        "            )\n",
        "            job_id = response[\"JobId\"]\n",
        "            logger.info(\n",
        "                \"Started text detection job %s on %s.\", job_id, document_file_name\n",
        "            )\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't detect text in %s.\", document_file_name)\n",
        "            raise\n",
        "        else:\n",
        "            return job_id\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.StartDocumentTextDetection]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.GetDocumentTextDetection]\n",
        "    def get_detection_job(self, job_id):\n",
        "        \"\"\"\n",
        "        Gets data for a previously started text detection job.\n",
        "\n",
        "        :param job_id: The ID of the job to retrieve.\n",
        "        :return: The job data, including a list of blocks that describe elements\n",
        "                 detected in the image.\n",
        "        \"\"\"\n",
        "        try:\n",
        "            response = self.textract_client.get_document_text_detection(JobId=job_id)\n",
        "            job_status = response[\"JobStatus\"]\n",
        "            logger.info(\"Job %s status is %s.\", job_id, job_status)\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't get data for job %s.\", job_id)\n",
        "            raise\n",
        "        else:\n",
        "            return response\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.GetDocumentTextDetection]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.StartDocumentAnalysis]\n",
        "    def start_analysis_job(\n",
        "        self,\n",
        "        bucket_name,\n",
        "        document_file_name,\n",
        "        feature_types,\n",
        "        sns_topic_arn,\n",
        "        sns_role_arn,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Starts an asynchronous job to detect text and additional elements, such as\n",
        "        forms or tables, in an image stored in an Amazon S3 bucket. Textract publishes\n",
        "        a notification to the specified Amazon SNS topic when the job completes.\n",
        "        The image must be in PNG, JPG, or PDF format.\n",
        "\n",
        "        :param bucket_name: The name of the Amazon S3 bucket that contains the image.\n",
        "        :param document_file_name: The name of the document image stored in Amazon S3.\n",
        "        :param feature_types: The types of additional document features to detect.\n",
        "        :param sns_topic_arn: The Amazon Resource Name (ARN) of an Amazon SNS topic\n",
        "                              where job completion notification is published.\n",
        "        :param sns_role_arn: The ARN of an AWS Identity and Access Management (IAM)\n",
        "                             role that can be assumed by Textract and grants permission\n",
        "                             to publish to the Amazon SNS topic.\n",
        "        :return: The ID of the job.\n",
        "        \"\"\"\n",
        "        try:\n",
        "            response = self.textract_client.start_document_analysis(\n",
        "                DocumentLocation={\n",
        "                    \"S3Object\": {\"Bucket\": bucket_name, \"Name\": document_file_name}\n",
        "                },\n",
        "                NotificationChannel={\n",
        "                    \"SNSTopicArn\": sns_topic_arn,\n",
        "                    \"RoleArn\": sns_role_arn,\n",
        "                },\n",
        "                FeatureTypes=feature_types,\n",
        "            )\n",
        "            job_id = response[\"JobId\"]\n",
        "            logger.info(\n",
        "                \"Started text analysis job %s on %s.\", job_id, document_file_name\n",
        "            )\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't analyze text in %s.\", document_file_name)\n",
        "            raise\n",
        "        else:\n",
        "            return job_id\n",
        "\n",
        "    # snippet-end:[python.example_code.textract.StartDocumentAnalysis]\n",
        "\n",
        "    # snippet-start:[python.example_code.textract.GetDocumentAnalysis]\n",
        "    def get_analysis_job(self, job_id):\n",
        "        \"\"\"\n",
        "        Gets data for a previously started detection job that includes additional\n",
        "        elements.\n",
        "\n",
        "        :param job_id: The ID of the job to retrieve.\n",
        "        :return: The job data, including a list of blocks that describe elements\n",
        "                 detected in the image.\n",
        "        \"\"\"\n",
        "        try:\n",
        "            response = self.textract_client.get_document_analysis(JobId=job_id)\n",
        "            job_status = response[\"JobStatus\"]\n",
        "            logger.info(\"Job %s status is %s.\", job_id, job_status)\n",
        "        except ClientError:\n",
        "            logger.exception(\"Couldn't get data for job %s.\", job_id)\n",
        "            raise\n",
        "        else:\n",
        "            return response\n",
        "\n",
        "\n",
        "# snippet-end:[python.example_code.textract.GetDocumentAnalysis]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpF9UagY01-l"
      },
      "source": [
        "Next, we set up Textract and S3, and provide this to an instance of `TextractWrapper`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aetKe-lg0-58"
      },
      "outputs": [],
      "source": [
        "import boto3\n",
        "\n",
        "textract_client = boto3.client('textract')\n",
        "s3_client = boto3.client('s3')\n",
        "\n",
        "textractWrapper = TextractWrapper(textract_client, s3_client, None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3Qr-MTL4dkT"
      },
      "source": [
        "We are now ready to make calls to Textract. At a high level, Textract has two modes: synchronous and asynchronous. Synchronous calls return the parsed output once it is completed. As of the time of writing (March 2024), however, multipage PDF processing is only supported [asynchronously](https://docs.aws.amazon.com/textract/latest/dg/sync.html). So for our purposes here, we will only explore the asynchronous route.\n",
        "\n",
        "Asynchronous calls follow the below process:\n",
        "\n",
        "1. Send a request to Textract with an SNS topic, S3 bucket, and the name (key) of the document inside that bucket to process. Textract returns a Job ID that can be used to track the status of the request\n",
        "2. Textract fetches the document from S3 and processes it\n",
        "3. Once the request is complete, Textract sends out a message to the SNS topic. This can be used in conjunction with other services such as Lambda or SQS for downstream processes.\n",
        "4. The parsed results can be fetched from Textract in chunks via the job ID."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pb9AIE4v4Yww"
      },
      "outputs": [],
      "source": [
        "bucket_name = \"your-bucket-name\"\n",
        "sns_topic_arn = \"your-sns-arn\" # this can be found under the topic you created in the Amazon SNS dashboard\n",
        "sns_role_arn = \"sns-role-arn\" # this is an IAM role that allows Textract to interact with SNS\n",
        "\n",
        "file_name = \"example-drug-label.pdf\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tPG-P7c79xln"
      },
      "outputs": [],
      "source": [
        "# kick off a text detection job. This returns a job ID.\n",
        "job_id = textractWrapper.start_detection_job(bucket_name=bucket_name, document_file_name=file_name,\n",
        "                                    sns_topic_arn=sns_topic_arn, sns_role_arn=sns_role_arn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0e0hZZJ-yCf"
      },
      "source": [
        "Once the job completes, this will return a dictionary with the following keys:\n",
        "\n",
        "```dict_keys(['DocumentMetadata', 'JobStatus', 'NextToken', 'Blocks', 'AnalyzeDocumentModelVersion', 'ResponseMetadata'])```\n",
        "\n",
        "This response corresponds to one chunk of information parsed by Textract. The number of chunks a document is parsed into depends on the length of the document. The two keys we are most interested in are `Blocks` and `NextToken`. `Blocks` contains all of the information that was extracted from this chunk, while `NextToken` tells us what chunk comes next, if any.\n",
        "\n",
        "Textract returns an information-rich representation of the extracted text, such as their position on the page and hierarchical relationships with other entities, all the way down to the individual word level. Since we are only interested in the raw text, we need a way to parse through all of the chunks and their `Blocks`. Lucky for us, Amazon provides some [helper functions](https://github.com/aws-samples/textract-paragraph-identification/tree/main) for this purpose, which we utilize below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9h-6yBBJCYC-"
      },
      "outputs": [],
      "source": [
        "def get_text_results_from_textract(job_id):\n",
        "    response = textract_client.get_document_text_detection(JobId=job_id)\n",
        "    collection_of_textract_responses = []\n",
        "    pages = [response]\n",
        "\n",
        "    collection_of_textract_responses.append(response)\n",
        "\n",
        "    while 'NextToken' in response:\n",
        "        next_token = response['NextToken']\n",
        "        response = textract_client.get_document_text_detection(JobId=job_id, NextToken=next_token)\n",
        "        pages.append(response)\n",
        "        collection_of_textract_responses.append(response)\n",
        "    return collection_of_textract_responses\n",
        "\n",
        "def get_the_text_with_required_info(collection_of_textract_responses):\n",
        "    total_text = []\n",
        "    total_text_with_info = []\n",
        "    running_sequence_number = 0\n",
        "\n",
        "    font_sizes_and_line_numbers = {}\n",
        "    for page in collection_of_textract_responses:\n",
        "        per_page_text = []\n",
        "        blocks = page['Blocks']\n",
        "        for block in blocks:\n",
        "            if block['BlockType'] == 'LINE':\n",
        "                block_text_dict = {}\n",
        "                running_sequence_number += 1\n",
        "                block_text_dict.update(text=block['Text'])\n",
        "                block_text_dict.update(page=block['Page'])\n",
        "                block_text_dict.update(left_indent=round(block['Geometry']['BoundingBox']['Left'], 2))\n",
        "                font_height = round(block['Geometry']['BoundingBox']['Height'], 3)\n",
        "                line_number = running_sequence_number\n",
        "                block_text_dict.update(font_height=round(block['Geometry']['BoundingBox']['Height'], 3))\n",
        "                block_text_dict.update(indent_from_top=round(block['Geometry']['BoundingBox']['Top'], 2))\n",
        "                block_text_dict.update(text_width=round(block['Geometry']['BoundingBox']['Width'], 2))\n",
        "                block_text_dict.update(line_number=running_sequence_number)\n",
        "\n",
        "                if font_height in font_sizes_and_line_numbers:\n",
        "                    line_numbers = font_sizes_and_line_numbers[font_height]\n",
        "                    line_numbers.append(line_number)\n",
        "                    font_sizes_and_line_numbers[font_height] = line_numbers\n",
        "                else:\n",
        "                    line_numbers = []\n",
        "                    line_numbers.append(line_number)\n",
        "                    font_sizes_and_line_numbers[font_height] = line_numbers\n",
        "\n",
        "                total_text.append(block['Text'])\n",
        "                per_page_text.append(block['Text'])\n",
        "                total_text_with_info.append(block_text_dict)\n",
        "\n",
        "    return total_text, total_text_with_info, font_sizes_and_line_numbers\n",
        "\n",
        "def get_text_with_line_spacing_info(total_text_with_info):\n",
        "    i = 1\n",
        "    text_info_with_line_spacing_info = []\n",
        "    while (i < len(total_text_with_info) - 1):\n",
        "        previous_line_info = total_text_with_info[i - 1]\n",
        "        current_line_info = total_text_with_info[i]\n",
        "        next_line_info = total_text_with_info[i + 1]\n",
        "        if current_line_info['page'] == next_line_info['page'] and previous_line_info['page'] == current_line_info[\n",
        "            'page']:\n",
        "            line_spacing_after = round((next_line_info['indent_from_top'] - current_line_info['indent_from_top']), 2)\n",
        "            spacing_with_prev = round((current_line_info['indent_from_top'] - previous_line_info['indent_from_top']), 2)\n",
        "            current_line_info.update(line_space_before=spacing_with_prev)\n",
        "            current_line_info.update(line_space_after=line_spacing_after)\n",
        "            text_info_with_line_spacing_info.append(current_line_info)\n",
        "        else:\n",
        "            text_info_with_line_spacing_info.append(None)\n",
        "        i += 1\n",
        "    return text_info_with_line_spacing_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McBARKH_ClEU"
      },
      "source": [
        "We feed in the Job ID from before into the function `get_text_results_from_textract` to fetch all of the chunks associated with this job. Then, we pass the resulting list into `get_the_text_with_required_info` and `get_text_with_line_spacing_info` to organize the text into lines.\n",
        "\n",
        "Finally, we can concatenate the lines into one string to pass into our downstream RAG pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0CieHFURIhBm"
      },
      "outputs": [],
      "source": [
        "all_text = \"\\n\".join([line[\"text\"] if line else \"\" for line in text_info_with_line_spacing])\n",
        "\n",
        "with open(f\"aws-parsed-{source_filename}.txt\", \"w\") as f:\n",
        "  f.write(all_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fy8iAFpbggcD"
      },
      "source": [
        "#### Visualize the parsed document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3AwZQnFRghv8"
      },
      "outputs": [],
      "source": [
        "filename = \"aws-parsed-{}.txt\".format(source_filename)\n",
        "with open(\"{}/{}\".format(data_dir, filename), \"r\") as doc:\n",
        "    parsed_document = doc.read()\n",
        "\n",
        "print(parsed_document[:1000])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LEO2tP6WJmtn"
      },
      "source": [
        "<a name=\"unstructured\"></a>\n",
        "### Solution 3: Unstructured.io [[Back to Solutions]](#top)\n",
        "\n",
        "Unstructured.io provides libraries with open-source components for pre-processing text documents such as PDFs, HTML and Word Documents.\n",
        "<br>\n",
        "External documentation: https://github.com/Unstructured-IO/unstructured-api"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "julG3fBrgmqQ"
      },
      "source": [
        "#### Parsing the document\n",
        "\n",
        "The guide assumes an endpoint exists that hosts this service. The API is offered in two forms\n",
        "1. [a hosted version](https://unstructured.io/)\n",
        "2. [an OSS docker image](https://github.com/Unstructured-IO/unstructured-api?tab=readme-ov-file#dizzy-instructions-for-using-the-docker-image)\n",
        "<br>\n",
        "**Note: You can skip to the next block if you want to use the pre-existing parsed version.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5-jaa4-aJiJG"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import requests\n",
        "\n",
        "UNSTRUCTURED_URL = \"\" # enter service endpoint\n",
        "\n",
        "parsed_documents = []\n",
        "\n",
        "input_path = \"{}/{}.{}\".format(data_dir, source_filename, extension)\n",
        "with open(input_path, 'rb') as file_data:\n",
        "    response = requests.post(\n",
        "        url=UNSTRUCTURED_URL,\n",
        "        files={\"files\": (\"{}.{}\".format(source_filename, extension), file_data)},\n",
        "        data={\n",
        "            \"output_format\": (None, \"application/json\"),\n",
        "            \"stratergy\": \"hi_res\",\n",
        "            \"pdf_infer_table_structure\": \"true\",\n",
        "            \"include_page_breaks\": \"true\"\n",
        "        },\n",
        "        headers={\"Accept\": \"application/json\"}\n",
        "    )\n",
        "\n",
        "parsed_response = response.json()\n",
        "\n",
        "parsed_document = \" \".join([parsed_entry[\"text\"] for parsed_entry in parsed_response])\n",
        "print(\"Parsed {}\".format(source_filename))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5qI5Q85vTfxs"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Post process parsed document and store it locally.\n",
        "\"\"\"\n",
        "\n",
        "file_path = \"{}/{}-parsed-fda-approved-drug.txt\".format(data_dir, \"unstructured-io\")\n",
        "store_document(file_path, parsed_document)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOAYMvt1HlAj"
      },
      "source": [
        "#### Visualize the parsed document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cBr_roPTHonz"
      },
      "outputs": [],
      "source": [
        "filename = \"unstructured-io-parsed-{}.txt\".format(source_filename)\n",
        "with open(\"{}/{}\".format(data_dir, filename), \"r\") as doc:\n",
        "    parsed_document = doc.read()\n",
        "\n",
        "print(parsed_document[:1000])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KF528hYyEQZx"
      },
      "source": [
        "<a name=\"llama\"></a>\n",
        "\n",
        "### Solution 4: LlamaParse [[Back to Solutions]](#top)\n",
        "\n",
        "LlamaParse is an API created by LlamaIndex to efficiently parse and represent files for efficient retrieval and context augmentation using LlamaIndex frameworks.\n",
        "<br>\n",
        "External documentation: https://github.com/run-llama/llama_parse"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBLNlEVXgshj"
      },
      "source": [
        "#### Parsing the document\n",
        "\n",
        "The following block uses the LlamaParse cloud offering. You can learn more and fetch a respective API key for the service [here](https://cloud.llamaindex.ai/parse).\n",
        "<br>\n",
        "Parsing documents with LlamaParse offers an option for two output modes both of which we will explore and compare below\n",
        "- Text\n",
        "- Markdown\n",
        "<br>\n",
        "\n",
        "**Note: You can skip to the next block if you want to use the pre-existing parsed version.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hR_85riD9ApM"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from llama_parse import LlamaParse\n",
        "\n",
        "import nest_asyncio # needed to notebook env\n",
        "nest_asyncio.apply() # needed to notebook env\n",
        "\n",
        "llama_index_api_key = \"{API_KEY}\"\n",
        "input_path = \"{}/{}.{}\".format(data_dir, source_filename, extension)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e4TskJQ4EdpA"
      },
      "outputs": [],
      "source": [
        "# Text mode\n",
        "text_parser = LlamaParse(\n",
        "    api_key=llama_index_api_key,\n",
        "    result_type=\"text\"\n",
        ")\n",
        "\n",
        "text_response = text_parser.load_data(input_path)\n",
        "text_parsed_document = \" \".join([parsed_entry.text for parsed_entry in text_response])\n",
        "\n",
        "print(\"Parsed {} to text\".format(source_filename))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GKca2BfI9X5O"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Post process parsed document and store it locally.\n",
        "\"\"\"\n",
        "\n",
        "file_path = \"{}/{}-text-parsed-fda-approved-drug.txt\".format(data_dir, \"llamaparse\")\n",
        "store_document(file_path, text_parsed_document)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mjig7g-f89PQ"
      },
      "outputs": [],
      "source": [
        "# Markdown mode\n",
        "markdown_parser = LlamaParse(\n",
        "    api_key=llama_index_api_key,\n",
        "    result_type=\"markdown\"\n",
        ")\n",
        "\n",
        "markdown_response = markdown_parser.load_data(input_path)\n",
        "markdown_parsed_document = \" \".join([parsed_entry.text for parsed_entry in markdown_response])\n",
        "\n",
        "print(\"Parsed {} to markdown\".format(source_filename))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upmHMI8SLcXZ"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Post process parsed document and store it locally.\n",
        "\"\"\"\n",
        "\n",
        "file_path = \"{}/{}-markdown-parsed-fda-approved-drug.txt\".format(data_dir, \"llamaparse\")\n",
        "store_document(file_path, markdown_parsed_document)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P88GmKarHsTQ"
      },
      "source": [
        "#### Visualize the parsed document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y0Cs-a03Huob"
      },
      "outputs": [],
      "source": [
        "# Text parsing\n",
        "\n",
        "filename = \"llamaparse-text-parsed-{}.txt\".format(source_filename)\n",
        "\n",
        "with open(\"{}/{}\".format(data_dir, filename), \"r\") as doc:\n",
        "    parsed_document = doc.read()\n",
        "\n",
        "print(parsed_document[:1000])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XW6SIVEg_fRN"
      },
      "outputs": [],
      "source": [
        "# Markdown parsing\n",
        "\n",
        "filename = \"llamaparse-markdown-parsed-fda-approved-drug.txt\"\n",
        "with open(\"{}/{}\".format(data_dir, filename), \"r\") as doc:\n",
        "    parsed_document = doc.read()\n",
        "\n",
        "print(parsed_document[:1000])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8QQ_RopYZ_mf"
      },
      "source": [
        "<a name=\"pdf2image\"></a>\n",
        "\n",
        "### Solution 5: pdf2image + pytesseract [[Back to Solutions]](#top)\n",
        "\n",
        "The final parsing method we examine does not rely on cloud services, but rather relies on two libraries: `pdf2image`, and `pytesseract`. `pytesseract` lets you perform OCR locally on images, but not PDF files. So, we first convert our PDF into a set of images via `pdf2image`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unM8RfqYgzWC"
      },
      "source": [
        "#### Parsing the document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4kt-bmbGeWsf"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from pdf2image import convert_from_path\n",
        "import pytesseract"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5bE2Sebtf_eU"
      },
      "outputs": [],
      "source": [
        "# pdf2image extracts as a list of PIL.Image objects\n",
        "pages = convert_from_path(filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ViLTaLPYeT-o"
      },
      "outputs": [],
      "source": [
        "# we look at the first page as a sanity check:\n",
        "\n",
        "plt.imshow(pages[0])\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZhXeNTbBKU_"
      },
      "source": [
        "Now, we can process the image of each page with `pytesseract` and concatenate the results to get our parsed document."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W1SNCEd91k7N"
      },
      "outputs": [],
      "source": [
        "label_ocr_pytesseract = \"\".join([pytesseract.image_to_string(page) for page in pages])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tv9hLiJgBANJ",
        "outputId": "1c3bd5dc-cfe9-43e6-9cc7-bc695a210d31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HIGHLIGHTS OF PRESCRIBING INFORMATION\n",
            "\n",
            "These highlights do not include all the information needed to use\n",
            "IWILFIN™ safely and effectively. See full prescribing information for\n",
            "IWILFIN.\n",
            "\n",
            "IWILFIN™ (eflor\n"
          ]
        }
      ],
      "source": [
        "print(label_ocr_pytesseract[:200])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TIwcDmjXejKV"
      },
      "outputs": [],
      "source": [
        "label_ocr_pytesseract = \"\".join([pytesseract.image_to_string(page) for page in pages])\n",
        "\n",
        "with open(f\"pytesseract-parsed-{source_filename}.txt\", \"w\") as f:\n",
        "  f.write(label_ocr_pytesseract)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0u1UfNdaFt4"
      },
      "source": [
        "#### Visualize the parsed document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HaJutaBkf9Yj"
      },
      "outputs": [],
      "source": [
        "filename = \"pytesseract-parsed-{}.txt\".format(source_filename)\n",
        "with open(\"{}/{}\".format(data_dir, filename), \"r\") as doc:\n",
        "    parsed_document = doc.read()\n",
        "\n",
        "print(parsed_document[:1000])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCbkT4oZSfs9",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "<a name=\"document-questions\"></a>\n",
        "## Document Questions\n",
        "\n",
        "We can now ask a set of simple + complex questions and see how each parsing solution performs with Command-R. The questions are\n",
        "- **What are the most common adverse reactions of Iwilfin?**\n",
        "  - Task: Simple information extraction\n",
        "- **What is the recommended dosage of IWILFIN on body surface area between 0.5 and 0.75?**\n",
        "  - Task: Tabular data extraction\n",
        "- **I need a succinct summary of the compound name, indication, route of administration, and mechanism of action of Iwilfin.**\n",
        "  - Task: Overall document summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WSo0xA4vZAHV"
      },
      "outputs": [],
      "source": [
        "import cohere\n",
        "co = cohere.Client(api_key=\"{API_KEY}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gyPYqKErY7ni"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Document Questions\n",
        "\"\"\"\n",
        "prompt = \"What are the most common adverse reactions of Iwilfin?\"\n",
        "# prompt = \"What is the recommended dosage of Iwilfin on body surface area between 0.5 m2 and 0.75 m2?\"\n",
        "# prompt = \"I need a succinct summary of the compound name, indication, route of administration, and mechanism of action of Iwilfin.\"\n",
        "\n",
        "\"\"\"\n",
        "Choose one of the above solutions\n",
        "\"\"\"\n",
        "source = \"gcp\"\n",
        "# source = \"aws\"\n",
        "# source = \"unstructured-io\"\n",
        "# source = \"llamaparse-text\"\n",
        "# source = \"llamaparse-markdown\"\n",
        "# source = \"pytesseract\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwdK7trMKykt",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "## Data Ingestion\n",
        "<a name=\"ingestion\"></a>\n",
        "\n",
        "In order to set up our RAG implementation, we need to separate the parsed text into chunks and load the chunks to an index. The index will allow us to retrieve relevant passages from the document for different queries. Here, we use a simple implementation of indexing using the `hnswlib` library. Note that there are many different indexing solutions that are appropriate for specific production use cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46rPn1uoLQDa"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Read parsed document content and chunk data\n",
        "\"\"\"\n",
        "\n",
        "import os\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "\n",
        "documents = []\n",
        "\n",
        "with open(\"{}/{}-parsed-fda-approved-drug.txt\".format(data_dir, source), \"r\") as doc:\n",
        "doc_content = doc.read()\n",
        "\n",
        "\"\"\"\n",
        "Personal notes on chunking\n",
        "https://medium.com/@ayhamboucher/llm-based-context-splitter-for-large-documents-445d3f02b01b\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Chunk doc content\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=512,\n",
        "    chunk_overlap=200,\n",
        "    length_function=len,\n",
        "    is_separator_regex=False\n",
        ")\n",
        "\n",
        "# Split the text into chunks with some overlap\n",
        "chunks_ = text_splitter.create_documents([doc_content])\n",
        "documents = [c.page_content for c in chunks_]\n",
        "\n",
        "print(\"Source document has been broken down to {} chunks\".format(len(documents)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lk4YgREV7LgC"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Embed document chunks\n",
        "\"\"\"\n",
        "document_embeddings = co.embed(texts=documents, model=\"embed-english-v3.0\", input_type=\"search_document\").embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtG3eblo7Mkd",
        "outputId": "6dfb3a1f-d4a5-480b-b8e3-233950fce701"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Count: 115\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "Create document index and add embedded chunks\n",
        "\"\"\"\n",
        "\n",
        "import hnswlib\n",
        "\n",
        "index = hnswlib.Index(space='ip', dim=1024) # space: inner product\n",
        "index.init_index(max_elements=len(document_embeddings), ef_construction=512, M=64)\n",
        "index.add_items(document_embeddings, list(range(len(document_embeddings))))\n",
        "print(\"Count:\", index.element_count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIncJz3qhWkg",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "## Retrieval\n",
        "\n",
        "In this step, we use k-nearest neighbors to fetch the most relevant documents for our query. Once the nearest neighbors are retrieved, we use Cohere's reranker to reorder the documents in the most relevant order with regards to our input search query."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5rTZcKQ48tAJ"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Embed search query\n",
        "Fetch k nearest neighbors\n",
        "\"\"\"\n",
        "\n",
        "query_emb = co.embed(texts=[prompt], model='embed-english-v3.0', input_type=\"search_query\").embeddings\n",
        "default_knn = 10\n",
        "knn = default_knn if default_knn <= index.element_count else index.element_count\n",
        "result = index.knn_query(query_emb, k=knn)\n",
        "neighbors = [(result[0][0][i], result[1][0][i]) for i in range(len(result[0][0]))]\n",
        "relevant_docs = [documents[x[0]] for x in sorted(neighbors, key=lambda x: x[1])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vz8jbX8A9RO_"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Rerank retrieved documents\n",
        "\"\"\"\n",
        "\n",
        "rerank_results = co.rerank(query=prompt, documents=relevant_docs, top_n=3, model='rerank-english-v2.0').results\n",
        "reranked_relevant_docs = format_docs_for_chat([x.document[\"text\"] for x in rerank_results])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KX0RYtx1HW_h",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "## Final Step: Call Command-R + RAG!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "beUFAEKnHYCQ"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Call the /chat endpoint with command-r\n",
        "\"\"\"\n",
        "\n",
        "response = co.chat(\n",
        "    message=prompt,\n",
        "    model=\"command-r\",\n",
        "    documents=reranked_relevant_docs\n",
        ")\n",
        "\n",
        "cited_response, citations_reference = insert_citations_in_order(response.text, response.citations, reranked_relevant_docs)\n",
        "print(cited_response)\n",
        "print(\"\\n\")\n",
        "print(\"References:\")\n",
        "print(citations_reference)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzJclPa6hnpi",
        "jp-MarkdownHeadingCollapsed": true
      },
      "source": [
        "## Head-to-head Comparisons\n",
        "\n",
        "Run the code cells below to make head to head comparisons of the different parsing techniques across different questions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52wdFoILLy85"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "results = pd.read_csv(\"{}/results-table.csv\".format(data_dir))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5WswlWbO4Da",
        "outputId": "ddf65c9d-ef12-4b07-a000-b8c1e6c1319e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Question 1: What are the most common adverse reactions of Iwilfin?\n",
            "Question 2: What is the recommended dosage of Iwilfin on body surface area between 0.5 m2 and 0.75 m2?\n",
            "Question 3: I need a succinct summary of the compound name, indication, route of administration, and mechanism of action of Iwilfin.\n",
            "\n",
            "Pick which question you want to see (1,2,3):  3\n",
            "Do you want to see the references as well? References are long and noisy (y/n): n\n",
            "\n",
            "\n",
            "\n",
            "| gcp |\n",
            "\n",
            "\n",
            "Compound Name: eflornithine hydrochloride ([0], [1], [2]) (IWILFIN ([1])™)\n",
            "\n",
            "Indication: used to reduce the risk of relapse in adult and paediatric patients with high-risk neuroblastoma (HRNB) ([1], [3]), who have responded at least partially to prior multiagent, multimodality therapy. ([1], [3], [4])\n",
            "\n",
            "Route of Administration: IWILFIN™ tablets ([1], [3], [4]) are taken orally twice daily ([3], [4]), with doses ranging from 192 to 768 mg based on body surface area. ([3], [4])\n",
            "\n",
            "Mechanism of Action: IWILFIN™ is an ornithine decarboxylase inhibitor. ([0], [2])\n",
            "\n",
            "\n",
            "\n",
            "| aws |\n",
            "\n",
            "\n",
            "Compound Name: eflornithine ([0], [1], [2], [3]) (IWILFIN ([0])™)\n",
            "\n",
            "Indication: used to reduce the risk of relapse ([0], [3]) in adults ([0], [3]) and paediatric patients ([0], [3]) with high-risk neuroblastoma (HRNB) ([0], [3]) who have responded to prior therapies. ([0], [3], [4])\n",
            "\n",
            "Route of Administration: Oral ([2], [4])\n",
            "\n",
            "Mechanism of Action: IWILFIN is an ornithine decarboxylase inhibitor. ([1])\n",
            "\n",
            "\n",
            "| unstructured-io |\n",
            "\n",
            "\n",
            "Compound Name: Iwilfin ([1], [2], [3], [4]) (eflornithine) ([0], [2], [3], [4])\n",
            "\n",
            "Indication: Iwilfin is indicated to reduce the risk of relapse ([1], [3]) in adult and paediatric patients ([1], [3]) with high-risk neuroblastoma (HRNB) ([1], [3]), who have responded to prior anti-GD2 ([1]) immunotherapy ([1], [4]) and multi-modality therapy. ([1])\n",
            "\n",
            "Route of Administration: Oral ([0], [3])\n",
            "\n",
            "Mechanism of Action: Iwilfin is an ornithine decarboxylase inhibitor. ([1], [2], [3], [4])\n",
            "\n",
            "\n",
            "| llamaparse-text |\n",
            "\n",
            "\n",
            "Compound Name: IWILFIN ([2], [3]) (eflornithine) ([3])\n",
            "\n",
            "Indication: IWILFIN is used to reduce the risk of relapse ([1], [2], [3]) in adult and paediatric patients ([1], [2], [3]) with high-risk neuroblastoma (HRNB) ([1], [2], [3]), who have responded at least partially to certain prior therapies. ([2], [3])\n",
            "\n",
            "Route of Administration: IWILFIN is administered as a tablet. ([2])\n",
            "\n",
            "Mechanism of Action: IWILFIN is an ornithine decarboxylase inhibitor. ([0], [1], [4])\n",
            "\n",
            "\n",
            "| llamaparse-markdown |\n",
            "\n",
            "\n",
            "Compound Name: IWILFIN ([1], [2]) (eflornithine) ([1])\n",
            "\n",
            "Indication: IWILFIN is indicated to reduce the risk of relapse ([1], [2]) in adult and paediatric patients ([1], [2]) with high-risk neuroblastoma (HRNB) ([1], [2]), who have responded at least partially ([1], [2], [3]) to prior anti-GD2 immunotherapy ([1], [2]) and multiagent, multimodality therapy. ([1], [2], [3])\n",
            "\n",
            "Route of Administration: Oral ([0], [1], [3], [4])\n",
            "\n",
            "Mechanism of Action: IWILFIN acts as an ornithine decarboxylase inhibitor. ([1])\n",
            "\n",
            "\n",
            "| pytesseract |\n",
            "\n",
            "\n",
            "Compound Name: IWILFIN™ ([0], [2]) (eflornithine) ([0], [2])\n",
            "\n",
            "Indication: IWILFIN is indicated to reduce the risk of relapse ([0], [2]) in adult and paediatric patients ([0], [2]) with high-risk neuroblastoma (HRNB) ([0], [2]), who have responded positively to prior anti-GD2 immunotherapy and multiagent, multimodality therapy. ([0], [2], [4])\n",
            "\n",
            "Route of Administration: IWILFIN is administered orally ([0], [1], [3], [4]), in the form of a tablet. ([1])\n",
            " \n",
            "Mechanism of Action: IWILFIN acts as an ornithine decarboxylase inhibitor. ([0])\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "question = input(\"\"\"\n",
        "Question 1: What are the most common adverse reactions of Iwilfin?\n",
        "Question 2: What is the recommended dosage of Iwilfin on body surface area between 0.5 m2 and 0.75 m2?\n",
        "Question 3: I need a succinct summary of the compound name, indication, route of administration, and mechanism of action of Iwilfin.\n",
        "\n",
        "Pick which question you want to see (1,2,3):  \"\"\")\n",
        "references = input(\"Do you want to see the references as well? References are long and noisy (y/n): \")\n",
        "print(\"\\n\\n\")\n",
        "\n",
        "index = {\"1\": 0, \"2\": 3, \"3\": 6}[question]\n",
        "\n",
        "for src in [\"gcp\", \"aws\", \"unstructured-io\", \"llamaparse-text\", \"llamaparse-markdown\", \"pytesseract\"]:\n",
        "  print(\"| {} |\".format(src))\n",
        "  print(\"\\n\")\n",
        "  print(results[src][index])\n",
        "  if references == \"y\":\n",
        "    print(\"\\n\")\n",
        "    print(\"References:\")\n",
        "    print(results[src][index+1])\n",
        "  print(\"\\n\")"
      ]
    }
  ],
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