04-Complex-Docs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60ec6048-44e4-4118-b16a-9c4c9cc78a3b",
   "metadata": {},
   "source": [
    "# How to deal with complex/large Documents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9281ac79-47cd-49d4-bdd4-7f5c173a947d",
   "metadata": {},
   "source": [
    "In the previous notebook, we developed a solution for various types of files and data formats commonly found in organizations, and this covers big majority of the use cases. However, you will find that there are issues when dealing with questions that require answers from complex files. The complexity of these files arises from their length and the way information is distributed within them. Large documents are always a challenge for Search Engines.\n",
    "\n",
    "One example of such complex files is Technical Specification Guides or Product Manuals, which can span hundreds of pages and contain information in the form of images, tables, forms, and more. Books are also complex due to their length and the presence of images or tables.\n",
    "\n",
    "These files are typically in PDF format. To better handle these PDFs, we need a smarter parsing method that treats each document as a special source and processes them page by page (1 page = 1 chunk). The objective is to obtain more accurate and faster answers from our system. Fortunately, there are usually not many of these types of documents in an organization, allowing us to make exceptions and treat them differently.\n",
    "\n",
    "If your use case is just PDFs, for example, you can just use [PyPDF library](https://pypi.org/project/pypdf/) or [Azure AI Document Intelligence SDK (former Form Recognizer)](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview?view=doc-intel-3.0.0), vectorize using OpenAI API and push the content to a vector-based index. And this is problably the simplest and fastest way to go.  However if your use case entails connecting to a datalake, or Sharepoint libraries or any other document data source with thousands of documents with multiple file types and that can change dynamically, then you would want to use the Ingestion and Document Cracking and AI-Enrichment capabilities of Azure Search engine, Notebooks 1-3, and avoid a lot of painful custom code. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "15f6044e-463f-4988-bc46-a3c3d641c15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import time\n",
    "import requests\n",
    "import random\n",
    "from collections import OrderedDict\n",
    "import urllib.request\n",
    "from tqdm import tqdm\n",
    "from typing import List\n",
    "\n",
    "from langchain_openai import AzureOpenAIEmbeddings\n",
    "from langchain_openai import AzureChatOpenAI\n",
    "from langchain_core.retrievers import BaseRetriever\n",
    "from langchain_core.callbacks import CallbackManagerForRetrieverRun\n",
    "from langchain_core.documents import Document\n",
    "from langchain_core.messages import HumanMessage\n",
    "from langchain_core.runnables import ConfigurableField\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from operator import itemgetter\n",
    "\n",
    "\n",
    "from common.utils import parse_pdf, read_pdf_files, text_to_base64\n",
    "from common.prompts import DOCSEARCH_PROMPT\n",
    "from common.utils import CustomAzureSearchRetriever\n",
    "\n",
    "\n",
    "from IPython.display import Markdown, HTML, display  \n",
    "\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv(\"credentials.env\")\n",
    "\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "    \n",
    "os.makedirs(\"data/books/\",exist_ok=True)\n",
    "    \n",
    "\n",
    "BLOB_CONTAINER_NAME = \"books\"\n",
    "BASE_CONTAINER_URL = \"https://datasetsgptsmartsearch.blob.core.windows.net/\" + BLOB_CONTAINER_NAME + \"/\"\n",
    "LOCAL_FOLDER = \"./data/books/\"\n",
    "\n",
    "os.makedirs(LOCAL_FOLDER,exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "331692ba-b68e-4b99-9bae-5057da9a389d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the ENV variables that Langchain needs to connect to Azure OpenAI\n",
    "os.environ[\"OPENAI_API_VERSION\"] = os.environ[\"AZURE_OPENAI_API_VERSION\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "594ff0d4-56e3-4bed-843d-28c7a092069b",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 75\n",
    "embedder = AzureOpenAIEmbeddings(deployment=os.environ[\"EMBEDDING_DEPLOYMENT_NAME\"], chunk_size=batch_size, \n",
    "                                 max_retries=2, \n",
    "                                 retry_min_seconds= 60,\n",
    "                                 retry_max_seconds= 70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb87c647-158c-4f85-b569-5b9462f06c83",
   "metadata": {},
   "source": [
    "## 1 - Manual Document Cracking with Push to Vector-based Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75551868-1546-421b-a14e-e42618d88e61",
   "metadata": {},
   "source": [
    "Within our demo storage account, we have a container named `books`, which holds 5 books of different lengths, languages, and complexities. Let's create a `cogsrch-index-books-vector` and load it with the pages of all these books.\n",
    "\n",
    "We begin by downloading these books to our local machine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0999e24b-6a75-4fa1-9a5f-426cf0f0bdba",
   "metadata": {},
   "outputs": [],
   "source": [
    "books = [\"Azure_Cognitive_Search_Documentation.pdf\", \n",
    "         \"Boundaries_When_to_Say_Yes_How_to_Say_No_to_Take_Control_of_Your_Life.pdf\",\n",
    "         \"Fundamentals_of_Physics_Textbook.pdf\",\n",
    "         \"Made_To_Stick.pdf\",\n",
    "         \"Pere_Riche_Pere_Pauvre.pdf\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd867b2f-b5a1-443c-aa0a-ce914a66b3c9",
   "metadata": {},
   "source": [
    "Let's download the files to the local `./data/` folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3554f0b7-fee8-4446-a155-5d22dc0f0888",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:24<00:00,  4.97s/it]\n"
     ]
    }
   ],
   "source": [
    "for book in tqdm(books):\n",
    "    book_url = BASE_CONTAINER_URL + book + os.environ['BLOB_SAS_TOKEN']\n",
    "    urllib.request.urlretrieve(book_url, LOCAL_FOLDER+ book)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788cc0db-9dae-45f2-8943-2b6fa32fcc75",
   "metadata": {},
   "source": [
    "### What to use: pyPDF or AI Documment Intelligence API (Form Recognizer)?\n",
    "\n",
    "In `utils.py` there is a **parse_pdf()** function. This utility function can parse local files using PyPDF library and can also parse local or from_url PDFs files using Azure AI Document Intelligence (Former Form Recognizer).\n",
    "\n",
    "If `form_recognizer=False`, the function will parse the PDF using the python pyPDF library, which 75% of the time does a good job.<br>\n",
    "\n",
    "Setting `form_recognizer=True`, is the best (and slower) parsing method using AI Documment Intelligence API (former known as Form Recognizer). You can specify the prebuilt model to use, the default is `model=\"prebuilt-document\"`. However, if you have a complex document with tables, charts and figures , you can try\n",
    "`model=\"prebuilt-layout\"`, and it will capture all of the nuances of each page (it takes longer of course).\n",
    "\n",
    "**Note: Many PDFs are scanned images. For example, any signed contract that was scanned and saved as PDF will NOT be parsed by pyPDF. Only AI Documment Intelligence API will work.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1c63a2f-7a53-4346-8a1f-483cfd159d34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Text from Azure_Cognitive_Search_Documentation.pdf ...\n",
      "Extracting text using PyPDF\n",
      "Parsing took: 37.697261 seconds\n",
      "Azure_Cognitive_Search_Documentation.pdf contained 1947 pages\n",
      "\n",
      "Extracting Text from Boundaries_When_to_Say_Yes_How_to_Say_No_to_Take_Control_of_Your_Life.pdf ...\n",
      "Extracting text using PyPDF\n",
      "Parsing took: 2.074029 seconds\n",
      "Boundaries_When_to_Say_Yes_How_to_Say_No_to_Take_Control_of_Your_Life.pdf contained 357 pages\n",
      "\n",
      "Extracting Text from Fundamentals_of_Physics_Textbook.pdf ...\n",
      "Extracting text using PyPDF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing took: 112.681871 seconds\n",
      "Fundamentals_of_Physics_Textbook.pdf contained 1450 pages\n",
      "\n",
      "Extracting Text from Made_To_Stick.pdf ...\n",
      "Extracting text using PyPDF\n",
      "Parsing took: 8.713429 seconds\n",
      "Made_To_Stick.pdf contained 225 pages\n",
      "\n",
      "Extracting Text from Pere_Riche_Pere_Pauvre.pdf ...\n",
      "Extracting text using PyPDF\n",
      "Parsing took: 1.317080 seconds\n",
      "Pere_Riche_Pere_Pauvre.pdf contained 225 pages\n",
      "\n"
     ]
    }
   ],
   "source": [
    "book_pages_map = dict()\n",
    "for book in books:\n",
    "    print(\"Extracting Text from\",book,\"...\")\n",
    "    \n",
    "    # Capture the start time\n",
    "    start_time = time.time()\n",
    "    \n",
    "    # Parse the PDF\n",
    "    book_path = LOCAL_FOLDER+book\n",
    "    book_map = parse_pdf(file=book_path, form_recognizer=False, verbose=True)\n",
    "    book_pages_map[book]= book_map\n",
    "    \n",
    "    # Capture the end time and Calculate the elapsed time\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "\n",
    "    print(f\"Parsing took: {elapsed_time:.6f} seconds\")\n",
    "    print(f\"{book} contained {len(book_map)} pages\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de0a722-ae0c-4b57-802a-518f5d4d93fd",
   "metadata": {},
   "source": [
    "Now let's check a random page of each book to make sure the parsing was done correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2a5d62f-b664-4662-a6c9-a1eb2a3c5e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure_Cognitive_Search_Documentation.pdf \n",
      " chunk text: Service update announcements  for Azure Cognitive Search can be found on the Azure\n",
      "web site.\n",
      " ...\n",
      "\n",
      "Boundaries_When_to_Say_Yes_How_to_Say_No_to_Take_Control_of_Your_Life.pdf \n",
      " chunk text: 33\n",
      "the “edges” that help identify you. “I don’t like it when you yell\n",
      "at me!” gives people a clear message about how you ...\n",
      "\n",
      "Fundamentals_of_Physics_Textbook.pdf \n",
      " chunk text: 51-2TIME\n",
      "Additional examples, video, and practice available at WileyPLUS1-2TIMELearning ObjectivesAfter reading this mod ...\n",
      "\n",
      "Made_To_Stick.pdf \n",
      " chunk text: UNEXPECTED  \n",
      "By FAA edict, a flight attendant must make a safety  announce- \n",
      "ment before a passenger plane takes off. We ...\n",
      "\n",
      "Pere_Riche_Pere_Pauvre.pdf \n",
      " chunk text: ~~~~~~~~~~~\n",
      "~~~~~~~~~~~~~~~~~~~~~~~\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for bookname,bookmap in book_pages_map.items():\n",
    "    print(bookname,\"\\n\",\"chunk text:\",bookmap[random.randint(10, 50)][2][:120],\"...\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcdc1ee-71fc-49d2-8e7c-0964bc3a4370",
   "metadata": {},
   "source": [
    "As we can see above, all books were parsed except `Pere_Riche_Pere_Pauvre.pdf` (this book is \"Rich Dad, Poor Dad\" written in French), why? Well, as we mentioned above, this book was scanned, so each page is an image and with a very unique font. We need a good PDF parser with good OCR capabilities in order to extract the content of this PDF. \n",
    "Let's try to parse this book again, but this time using Azure Document Intelligence API (former Form Recognizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "801c6bc2-467c-4418-aa7e-ef89a1e20e1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting text using Azure Document Intelligence\n",
      "CPU times: user 12.5 s, sys: 244 ms, total: 12.7 s\n",
      "Wall time: 1min 16s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "book = \"Pere_Riche_Pere_Pauvre.pdf\"\n",
    "book_path = LOCAL_FOLDER+book\n",
    "book_map = parse_pdf(file=book_path, form_recognizer=True, model=\"prebuilt-document\",from_url=False, verbose=True)\n",
    "book_pages_map[book]= book_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97f9c5bb-c44b-4a4d-9780-591f9f8d128a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pere_Riche_Pere_Pauvre.pdf \n",
      " chunk text: - Dieu du ciel, non!» dit papa riche. « Le gouvernement prend toujours sa part e ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(book,\"\\n\",\"chunk text:\",book_map[random.randint(10, 50)][2][:80],\"...\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c279dfb-4fed-41b8-89e1-0ca2cefbcdc9",
   "metadata": {},
   "source": [
    "As demonstrated above, Azure Document Intelligence proves to be superior to pyPDF. **For production scenarios, we strongly recommend using Azure Document Intelligence consistently**. When doing so, it's important to make a wise choice between the available models, such as \"prebuilt-document,\" \"prebuilt-layout,\" or others. You can find more information on model selection [HERE](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/choose-model-feature?view=doc-intel-3.0.0).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5f9b7d-99e6-426d-a47e-343c7e8b492e",
   "metadata": {},
   "source": [
    "## Create Vector-based index\n",
    "\n",
    "\n",
    "Now that we have the content of the book's chunks (each page of each book) in the dictionary `book_pages_map`, let's create the Vector index in our Azure Search Engine where this content is going to land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d46e7c5-49c4-40f3-bb2d-79a9afeab4b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "book_index_name = \"srch-index-books\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b07e84b-d306-4bc9-9124-e64f252dd7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create Azure Search Vector-based Index\n",
    "# Setup the Payloads header\n",
    "headers = {'Content-Type': 'application/json','api-key': os.environ['AZURE_SEARCH_KEY']}\n",
    "params = {'api-version': os.environ['AZURE_SEARCH_API_VERSION']}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2faab899-977b-40d0-b36e-26f75ac07e54",
   "metadata": {},
   "source": [
    "\n",
    "Please note the following points regarding the index:\n",
    "\n",
    "- The ParentKey field is absent.\n",
    "- The page_num field is present.\n",
    "\n",
    "The absence of the ParentKey field is due to the utilization of a PUSH method, rather than a PULL method. This approach indicates that we are not leveraging the integrated indexing provided by the Azure AI Search engine. Instead, we are engaging in the process of parsing, performing OCR, and manually creating and pushing the content along with its vectors.\n",
    "\n",
    "This manual parsing process involves the use of either, the pyPDF library, or the Azure Document Intelligence API. These APIs allow for the segmentation of content by page rather than by a specified number of characters, which is the method employed by the Azure AI search indexer. Consequently, this enables the inclusion of page_num as a field in our index."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d63e68-69a5-4b3b-8eb0-86da02cb7230",
   "metadata": {},
   "source": [
    "REST API version 2023-10-01-Preview supports external and internal vectorization. This Notebook assumes an external vectorization strategy. This API also supports:\n",
    "    \n",
    "- vectorSearch algorithms, hnsw and exhaustiveKnn nearest neighbors, with parameters for indexing and scoring.\n",
    "- vectorProfiles for multiple combinations of algorithm configurations.\n",
    "\n",
    "Vector search algorithms include **exhaustive k-nearest neighbors (KNN)** and **Hierarchical Navigable Small World (HNSW)**. Exhaustive KNN performs a brute-force search that scans the entire vector space. HNSW performs an approximate nearest neighbor (ANN) search. While KNN provides exact nearest neighbor search results with high accuracy, its computational cost and poor scalability make it impractical for large datasets or real-time applications. HNSW, on the other hand, offers a highly efficient and scalable solution for nearest neighbor searches by finding approximate nearest neighbors quickly, making it more suitable for large-scale and high-dimensional data applications.\n",
    "\n",
    "\n",
    "check [HERE](https://learn.microsoft.com/en-us/azure/search/vector-search-how-to-create-index?tabs=config-2023-10-01-Preview%2Crest-2023-11-01%2Cpush%2Cportal-check-index) for the details of the vector configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2df4db6b-969b-4b91-963f-9334e17a4e3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "201\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "index_payload = {\n",
    "    \"name\": book_index_name,\n",
    "    \"vectorSearch\": {\n",
    "        \"algorithms\": [  # We are showing here 3 types of search algorithms configurations that you can do\n",
    "             {\n",
    "                 \"name\": \"my-hnsw-config-1\",\n",
    "                 \"kind\": \"hnsw\",\n",
    "                 \"hnswParameters\": {\n",
    "                     \"m\": 4,\n",
    "                     \"efConstruction\": 400,\n",
    "                     \"efSearch\": 500,\n",
    "                     \"metric\": \"cosine\"\n",
    "                 }\n",
    "             },\n",
    "             {\n",
    "                 \"name\": \"my-hnsw-config-2\",\n",
    "                 \"kind\": \"hnsw\",\n",
    "                 \"hnswParameters\": {\n",
    "                     \"m\": 8,\n",
    "                     \"efConstruction\": 800,\n",
    "                     \"efSearch\": 800,\n",
    "                     \"metric\": \"cosine\"\n",
    "                 }\n",
    "             },\n",
    "             {\n",
    "                 \"name\": \"my-eknn-config\",\n",
    "                 \"kind\": \"exhaustiveKnn\",\n",
    "                 \"exhaustiveKnnParameters\": {\n",
    "                     \"metric\": \"cosine\"\n",
    "                 }\n",
    "             }\n",
    "        ],\n",
    "        \"vectorizers\": [\n",
    "            {\n",
    "                \"name\": \"openai\",\n",
    "                \"kind\": \"azureOpenAI\",\n",
    "                \"azureOpenAIParameters\":\n",
    "                {\n",
    "                    \"resourceUri\" : os.environ['AZURE_OPENAI_ENDPOINT'],\n",
    "                    \"apiKey\" : os.environ['AZURE_OPENAI_API_KEY'],\n",
    "                    \"deploymentId\" : os.environ['EMBEDDING_DEPLOYMENT_NAME'],\n",
    "                    \"modelName\" : os.environ['EMBEDDING_DEPLOYMENT_NAME']\n",
    "                }\n",
    "            }\n",
    "        ],\n",
    "        \"profiles\": [  # profiles is the diferent kind of combinations of algos and vectorizers\n",
    "            {\n",
    "             \"name\": \"my-vector-profile-1\",\n",
    "             \"algorithm\": \"my-hnsw-config-1\",\n",
    "             \"vectorizer\":\"openai\"\n",
    "            },\n",
    "            {\n",
    "             \"name\": \"my-vector-profile-2\",\n",
    "             \"algorithm\": \"my-hnsw-config-2\",\n",
    "             \"vectorizer\":\"openai\"\n",
    "            },\n",
    "            {\n",
    "             \"name\": \"my-vector-profile-3\",\n",
    "             \"algorithm\": \"my-eknn-config\",\n",
    "             \"vectorizer\":\"openai\"\n",
    "            }\n",
    "        ]\n",
    "    },\n",
    "    \"semantic\": {\n",
    "        \"configurations\": [\n",
    "            {\n",
    "                \"name\": \"my-semantic-config\",\n",
    "                \"prioritizedFields\": {\n",
    "                    \"titleField\": {\n",
    "                        \"fieldName\": \"title\"\n",
    "                    },\n",
    "                    \"prioritizedContentFields\": [\n",
    "                        {\n",
    "                            \"fieldName\": \"chunk\"\n",
    "                        }\n",
    "                    ],\n",
    "                    \"prioritizedKeywordsFields\": []\n",
    "                }\n",
    "            }\n",
    "        ]\n",
    "    },\n",
    "    \"fields\": [\n",
    "        {\"name\": \"id\", \"type\": \"Edm.String\", \"key\": \"true\", \"filterable\": \"true\" },\n",
    "        {\"name\": \"title\",\"type\": \"Edm.String\",\"searchable\": \"true\",\"retrievable\": \"true\"},\n",
    "        {\"name\": \"chunk\",\"type\": \"Edm.String\",\"searchable\": \"true\",\"retrievable\": \"true\"},\n",
    "        {\"name\": \"name\", \"type\": \"Edm.String\", \"searchable\": \"true\", \"retrievable\": \"true\", \"sortable\": \"false\", \"filterable\": \"false\", \"facetable\": \"false\"},\n",
    "        {\"name\": \"location\", \"type\": \"Edm.String\", \"searchable\": \"false\", \"retrievable\": \"true\", \"sortable\": \"false\", \"filterable\": \"false\", \"facetable\": \"false\"},\n",
    "        {\"name\": \"page_num\",\"type\": \"Edm.Int32\",\"searchable\": \"false\",\"retrievable\": \"true\"},\n",
    "        {\n",
    "            \"name\": \"chunkVector\",\n",
    "            \"type\": \"Collection(Edm.Single)\",\n",
    "            \"dimensions\": 1536,\n",
    "            \"vectorSearchProfile\": \"my-vector-profile-3\", # we picked profile 3 to show that this index uses eKNN vs HNSW (on prior notebooks)\n",
    "            \"searchable\": \"true\",\n",
    "            \"retrievable\": \"true\",\n",
    "            \"filterable\": \"false\",\n",
    "            \"sortable\": \"false\",\n",
    "            \"facetable\": \"false\"\n",
    "        }\n",
    "        \n",
    "    ],\n",
    "}\n",
    "\n",
    "r = requests.put(os.environ['AZURE_SEARCH_ENDPOINT'] + \"/indexes/\" + book_index_name,\n",
    "                 data=json.dumps(index_payload), headers=headers, params=params)\n",
    "print(r.status_code)\n",
    "print(r.ok)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36691ff0-c4c8-49d0-bfa8-3e076ece0ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to debug errors\n",
    "# r.text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc7dda9-4725-410e-9465-54f0298fc758",
   "metadata": {},
   "source": [
    "## Upload the Document chunks and its vectors to the Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d73e7600-7902-48d4-b199-9d9dc0a17aa0",
   "metadata": {},
   "source": [
    "The following code will iterate over each chunk of each book and use the Azure Search Rest API upload method to insert each document with its corresponding vector (using OpenAI embedding model) to the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a94911cf-c95f-4306-8574-b56296f29b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to process a batch of pages\n",
    "def process_batch(bookname, pages):\n",
    "    try:\n",
    "        contents = [page[2] for page in pages]\n",
    "        chunk_vectors = embedder.embed_documents(contents)\n",
    "        \n",
    "        upload_payload = {\"value\": []}\n",
    "        for i, page in enumerate(pages):\n",
    "            page_num = page[0] + 1\n",
    "            content = page[2]\n",
    "            book_url = BASE_CONTAINER_URL + bookname\n",
    "            \n",
    "            payload = {\n",
    "                \"@search.action\": \"upload\",\n",
    "                \"id\": text_to_base64(bookname + str(page_num)),\n",
    "                \"title\": f\"{bookname}_page_{str(page_num)}\",\n",
    "                \"chunk\": content,\n",
    "                \"chunkVector\": chunk_vectors[i],\n",
    "                \"name\": bookname,\n",
    "                \"location\": book_url,\n",
    "                \"page_num\": page_num\n",
    "            }\n",
    "            upload_payload[\"value\"].append(payload)\n",
    "        \n",
    "        r = requests.post(os.environ['AZURE_SEARCH_ENDPOINT'] + \"/indexes/\" + book_index_name + \"/docs/index\",\n",
    "                          data=json.dumps(upload_payload), headers=headers, params=params)\n",
    "        if r.status_code != 200:\n",
    "            print(f\"Failed to upload batch of pages from {bookname}: {r.status_code}\")\n",
    "            print(r.text)\n",
    "    except Exception as e:\n",
    "        print(f\"Exception processing batch of pages from {bookname}: {e}\")\n",
    "        time.sleep(10)  # Wait before retrying\n",
    "        process_batch(bookname, pages)  # Retry the same batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "793a3171-f8f0-4070-8a54-8a540828333c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading chunks from Azure_Cognitive_Search_Documentation.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 26/26 [02:30<00:00,  5.80s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading chunks from Boundaries_When_to_Say_Yes_How_to_Say_No_to_Take_Control_of_Your_Life.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:28<00:00,  5.75s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading chunks from Fundamentals_of_Physics_Textbook.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [05:11<00:00, 15.59s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading chunks from Made_To_Stick.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:27<00:00,  9.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading chunks from Pere_Riche_Pere_Pauvre.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:33<00:00, 11.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34.4 s, sys: 227 ms, total: 34.6 s\n",
      "Wall time: 9min 12s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for bookname, bookmap in book_pages_map.items():\n",
    "        print(\"Uploading chunks from\", bookname)\n",
    "        # Split bookmap into chunks of size chunk_size\n",
    "        for i in tqdm(range(0, len(bookmap), batch_size)):\n",
    "            batch = bookmap[i:i + batch_size]\n",
    "            process_batch(bookname, batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715cddcf-af7b-4006-a047-853fc7a66be3",
   "metadata": {},
   "source": [
    "## Query the Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b408798-5527-44ca-9dba-cad2ee726aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "QUESTION = \"what normally rich dad do that is different from poor dad?\"\n",
    "# QUESTION = \"Tell me a summary of the book Boundaries\"\n",
    "# QUESTION = \"Dime que significa la radiacion del cuerpo negro\"\n",
    "# QUESTION = \"what is the acronym of the main point of Made to Stick book\"\n",
    "# QUESTION = \"Tell me a python example of how do I push documents with vectors to an index using the python SDK?\"\n",
    "# QUESTION = \"who won the soccer worldcup in 1994?\" # this question should have no answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1b182ade-0ddd-47a1-b1eb-2cbf435c317f",
   "metadata": {},
   "outputs": [],
   "source": [
    "indexes = [book_index_name]\n",
    "k=20 # in this index k corresponds to the top pages as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d50eecb2-ce26-4127-a62b-79735b937046",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = CustomAzureSearchRetriever(indexes=[book_index_name], topK=k, reranker_threshold=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd2f3f2-2d66-4bd4-b90b-d30970b71af4",
   "metadata": {},
   "source": [
    "**Note**: that we are picking a larger k=20 since these chunks are NOT of 5000 chars each like prior notebooks, but instead each page is a chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "410ff796-dab1-4817-a3a5-82eeff6c0c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "COMPLETION_TOKENS = 2500\n",
    "llm = AzureChatOpenAI(deployment_name=os.environ[\"GPT35_DEPLOYMENT_NAME\"], temperature=0.5, max_tokens=COMPLETION_TOKENS).configurable_alternatives(\n",
    "    ConfigurableField(id=\"model\"),\n",
    "    default_key=\"gpt35\",\n",
    "    gpt4=AzureChatOpenAI(deployment_name=os.environ[\"GPT4_DEPLOYMENT_NAME\"], temperature=0, max_tokens=COMPLETION_TOKENS),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c33c2851-08c5-4e7c-ba7b-4655a6021e1e",
   "metadata": {},
   "source": [
    "In `utils.py` we created the **CustomAzureSearchRetriever** class that we will use going forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "26f47c69-44d8-48e3-974e-7989b4a8b7c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = (\n",
    "    {\n",
    "        \"context\": itemgetter(\"question\") | retriever, # Passes the question to the retriever and the results are assign to context\n",
    "        \"question\": itemgetter(\"question\")\n",
    "    }\n",
    "    | DOCSEARCH_PROMPT  # Passes the 4 variables above to the prompt template\n",
    "    | llm   # Passes the finished prompt to the LLM\n",
    "    | StrOutputParser()  # converts the output (Runnable object) to the desired output (string)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765df250-af7f-46c9-8d7a-15c0522969ec",
   "metadata": {},
   "source": [
    "#### With GPT 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "73f34192-519d-45b9-a0e2-a8b2de51ee1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rich dad and poor dad have distinct approaches and mindsets when it comes to money and financial decisions:\n",
      "\n",
      "1. **Financial Education**:\n",
      "   - Poor dad believed in traditional education, focusing on getting good grades, finding a secure job, and working for a paycheck. He emphasized academic qualifications and a stable career path.\n",
      "   - Rich dad, on the other hand, encouraged financial education. He believed in understanding how money works, investing wisely, and making money work for you. Rich dad valued learning about money management, investing, and entrepreneurship to build wealth.\n",
      "\n",
      "2. **Work and Money**:\n",
      "   - Poor dad believed in working for money. He saw a job as the primary source of income and security.\n",
      "   - Rich dad believed in making money work for him. He emphasized investing, creating passive income streams, and building assets that generate wealth over time.\n",
      "\n",
      "3. **Attitude Towards Money**:\n",
      "   - Poor dad had a cautious approach towards money, often focused on saving and budgeting to make ends meet.\n",
      "   - Rich dad had a more entrepreneurial mindset, focusing on taking calculated risks, investing in opportunities, and leveraging money to create wealth.\n",
      "\n",
      "4. **View on Assets and Liabilities**:\n",
      "   - Poor dad viewed his house as an asset and often accumulated liabilities like mortgages, car loans, and credit card debt.\n",
      "   - Rich dad understood the difference between assets and liabilities. He focused on acquiring income-generating assets like real estate, stocks, and businesses while minimizing liabilities.\n",
      "\n",
      "5. **Risk-Taking**:\n",
      "   - Poor dad tended to avoid risks and preferred the security of a steady job and predictable income.\n",
      "   - Rich dad was willing to take calculated risks to achieve financial growth. He understood that risks are inherent in building wealth and was open to exploring new opportunities.\n",
      "\n",
      "6. **Mindset Towards Wealth**:\n",
      "   - Poor dad saw money as a means to an end, primarily for meeting basic needs and ensuring financial stability.\n",
      "   - Rich dad viewed money as a tool for creating opportunities, achieving financial freedom, and building wealth to support a desired lifestyle.\n",
      "\n",
      "These differences in mindset and approach to money highlight the contrasting philosophies of a rich dad and a poor dad when it comes to financial decisions and wealth creation. Ultimately, the rich dad's focus on financial education, investment, and leveraging money distinguishes his approach from that of the poor dad. [[7]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf?query_parameters)"
     ]
    }
   ],
   "source": [
    "for chunk in chain.with_config(configurable={\"model\": \"gpt35\"}).stream({\"question\": QUESTION}):\n",
    "    print(chunk, end=\"\", flush=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a8761d-2c1e-4369-b7c4-c3571a0793e9",
   "metadata": {},
   "source": [
    "#### With GPT 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "14b77511-b178-4c9b-9fa5-fdddb0d3e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The book \"Père Riche, Père Pauvre\" by Robert T. Kiyosaki outlines several key differences in the mindset and actions between what he terms \"rich dad\" and \"poor dad.\" These differences fundamentally revolve around how each views and manages money, education, and opportunities.\n",
      "\n",
      "1. **Approach to Money:**\n",
      "   - Poor Dad: Views being poor as a permanent state and is not particularly interested in money, saying things like \"I'm not interested in money\" or \"money doesn't matter to me\"[[2]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "   - Rich Dad: Believes that money is power and emphasizes that \"the rich do not work for money\" but rather, they make money work for them. This is a fundamental principle that distinguishes the rich dad's approach to financial growth and independence[[5]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "\n",
      "2. **Education and Learning:**\n",
      "   - Poor Dad: Encourages traditional education as the path to success, urging the pursuit of a good job through obtaining degrees and professional qualifications[[2]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "   - Rich Dad: Stresses financial education and learning how money works as essential. He advocates for understanding how to acquire assets and investing in one's mind to make informed financial decisions. Rich dad's focus is on learning to manage and grow wealth[[5]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "\n",
      "3. **Dealing with Fear and Taking Risks:**\n",
      "   - Poor Dad: Often lets fear dictate actions, particularly the fear of losing money, which leads to avoiding risks and potentially lucrative opportunities[[3]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "   - Rich Dad: Recognizes fear but chooses to manage it constructively. He understands that taking calculated risks is part of wealth-building and emphasizes the importance of starting to save and invest early in life to benefit from compound interest[[3]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "\n",
      "4. **Financial Management:**\n",
      "   - Poor Dad: Tends to work for money, living paycheck to paycheck, and often finds himself in financial difficulties due to poor money management habits[[4]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "   - Rich Dad: Focuses on generating passive income through investments and assets that cover expenses, leading to financial growth and stability. He pays himself first, prioritizing investment in assets over settling liabilities[[14]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "\n",
      "5. **Perception of Assets and Liabilities:**\n",
      "   - Poor Dad: Considers a primary residence the most important asset, not recognizing that it can also be a liability if it doesn't generate income[[7]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "   - Rich Dad: Understands that true assets generate income and increase one's wealth. He distinguishes between assets and liabilities based on their ability to put money in one's pocket[[7]](https://datasetsgptsmartsearch.blob.core.windows.net/books/Pere_Riche_Pere_Pauvre.pdf).\n",
      "\n",
      "These distinctions highlight the importance of mindset, financial education, and the proactive management of money in achieving financial independence and wealth, as advocated by Kiyosaki's \"rich dad.\""
     ]
    }
   ],
   "source": [
    "for chunk in chain.with_config(configurable={\"model\": \"gpt4\"}).stream(\n",
    "    {\"question\": QUESTION, \"language\": \"English\"}):\n",
    "    print(chunk, end=\"\", flush=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3941796c-7655-4888-a358-8a62e380bd7e",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "In this notebook we learned how to deal with complex and large Documents and make them available for Q&A over them using [Hybrid Search](https://learn.microsoft.com/en-us/azure/search/search-get-started-vector#hybrid-search) (text + vector search).\n",
    "\n",
    "We also learned the power of Azure Document Inteligence API and why it is recommended for production scenarios where manual Document parsing (instead of Azure Search Indexer Document Cracking) is necessary.\n",
    "\n",
    "Using Azure AI Search with its Vector capabilities and hybrid search features eliminates the need for other vector databases such as Weaviate, Qdrant, Milvus, Pinecone, and so on.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d9a7d1-f029-416b-8eb2-00a8afb9151d",
   "metadata": {},
   "source": [
    "# NEXT\n",
    "So far we have learned how to use OpenAI vectors and completion APIs in order to get an excelent answer from our documents stored in Azure AI Search. This is the backbone for a GPT Smart Search Engine.\n",
    "\n",
    "However, we are missing something: **How to have a conversation with this engine?**\n",
    "\n",
    "On the next Notebook, we are going to understand the concept of **memory**. This is necessary in order to have a chatbot that can establish a conversation with the user. Without memory, there is no real conversation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ed733fb-dec4-4a8f-bff0-c7cbbcc0328e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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