Contextual_Q&A_Bot_Langchain_RAG.ipynb

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   "cell_type": "code",
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   "id": "Pqelo61DGpuV",
   "metadata": {
    "id": "Pqelo61DGpuV"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "warnings.simplefilter(\"ignore\")\n",
    "import logging\n",
    "\n",
    "# Get the logger that produces the warning message\n",
    "logger = logging.getLogger('langchain_text_splitters.base')\n",
    "\n",
    "# Set the logging level to a higher level such as ERROR or CRITICAL\n",
    "logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "606986be-fd43-4b0f-b69b-02250e57e4b0",
   "metadata": {
    "id": "606986be-fd43-4b0f-b69b-02250e57e4b0"
   },
   "source": [
    "### Install necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae9d6495-af97-405d-b4d6-63b322cb82d5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "outputId": "10419e9c-cc5c-46ce-ea0f-269c17cad694",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: pypdf in /usr/local/lib/python3.10/dist-packages (4.2.0)\n",
      "Requirement already satisfied: PyPDF2 in /usr/local/lib/python3.10/dist-packages (3.0.1)\n",
      "Requirement already satisfied: typing_extensions>=4.0 in /usr/local/lib/python3.10/dist-packages (from pypdf) (4.11.0)\n"
     ]
    }
   ],
   "source": [
    "#install necessray packages\n",
    "!pip install -q -U torch tensorflow transformers langchain  faiss-cpu sentence_transformers\n",
    "!pip install -q peft==0.4.0 trl==0.4.7 accelerate==0.21.0 bitsandbytes==0.41.3\n",
    "!pip install pypdf PyPDF2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacdda41-708b-4af8-88a9-5056cbd08bf4",
   "metadata": {
    "id": "bacdda41-708b-4af8-88a9-5056cbd08bf4"
   },
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "505ca9a3-8c27-442e-bca6-154a65186d01",
   "metadata": {
    "id": "505ca9a3-8c27-442e-bca6-154a65186d01",
    "tags": []
   },
   "outputs": [],
   "source": [
    "#import packages\n",
    "import os\n",
    "import torch\n",
    "from transformers import (\n",
    "    BitsAndBytesConfig,\n",
    "    pipeline,\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    ")\n",
    "\n",
    "from peft import LoraConfig, PeftModel\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.document_loaders import AsyncChromiumLoader\n",
    "from langchain.llms import HuggingFacePipeline\n",
    "from langchain.chains import LLMChain\n",
    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import FAISS\n",
    "\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.schema.runnable import RunnablePassthrough\n",
    "from PyPDF2 import PdfReader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jgJNZlq8Q7L0",
   "metadata": {
    "id": "jgJNZlq8Q7L0"
   },
   "source": [
    "### 1. Read the content of a provided PDF document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8jEWXS2FVfkU",
   "metadata": {
    "id": "8jEWXS2FVfkU"
   },
   "outputs": [],
   "source": [
    "# Read the content of pdf\n",
    "# pdf file is converted to text files\n",
    "\n",
    "# pdf file name\n",
    "uploaded_file = \"Harrison's Principles of Internal Medicine, 19E (2015) [True PDF] [UnitedVRG]-compressed.pdf\"\n",
    "\n",
    "#error handling for cases like missing or inaccessible PDF files\n",
    "try:\n",
    "  # Read the PDF file\n",
    "  doc_reader = PdfReader(uploaded_file)\n",
    "  pgno = len(doc_reader.pages)\n",
    "\n",
    "  #solving for model context limit for longer pdfs.\n",
    "  if pgno > 1200:\n",
    "    print('pdf is too longer')\n",
    "  else:\n",
    "    # Extract text from the PDF\n",
    "    raw_text = \"\"\n",
    "    for i, page in enumerate(doc_reader.pages):\n",
    "        text = page.extract_text()\n",
    "        if text:\n",
    "            raw_text += text\n",
    "\n",
    "except:\n",
    "  print('incorrect pdf path')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180f8d4e-a8bf-4389-b046-9827b310a3b3",
   "metadata": {
    "id": "180f8d4e-a8bf-4389-b046-9827b310a3b3"
   },
   "source": [
    "### 2. Preprocess the content for efficient question-answering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bzxTPh3cBpG2",
   "metadata": {
    "id": "bzxTPh3cBpG2"
   },
   "outputs": [],
   "source": [
    "# The converted amount of text is well above the context limit of LLM, therefore text is\n",
    "# Split the text into smaller chunks(Chunking technique)\n",
    "# solving for model context limit for longer pdfs\n",
    "# model's maximum context length is 4097 tokens, so context is split as 1000 size\n",
    "text_splitter = CharacterTextSplitter(\n",
    "    separator=\"\\n\",\n",
    "    chunk_size=1000,\n",
    "    chunk_overlap=100,\n",
    "    length_function=len,\n",
    ")\n",
    "texts = text_splitter.split_text(raw_text)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "T7y5nFY-WWtQ",
   "metadata": {
    "id": "T7y5nFY-WWtQ"
   },
   "source": [
    "### 3. Develop a Q&A bot using open source LLM to answer questions based on the PDF's content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80f94e97-7e58-4253-9376-73af6f36e139",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfcfdadab649430d8c392e95b0280aa8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
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   ],
   "source": [
    "#Tokenizer is defined here. Tokenizer model is loaded from pretrained Mistral 7B model\n",
    "#LLM model is loaed from pretrained Mistral 7B model\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"mistralai/Mistral-7B-Instruct-v0.2\",\n",
    "                                          trust_remote_code=True,\n",
    "                                          use_auth_token='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')\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\"\n",
    "\n",
    "# define quantization config file\n",
    "compute_dtype = getattr(torch, \"float16\")\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type= \"nf4\", #fp4 or nf4,\n",
    "    bnb_4bit_compute_dtype=  \"float16\",\n",
    "    bnb_4bit_use_double_quant=False,\n",
    ")\n",
    "\n",
    "# Load pre-trained model\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"mistralai/Mistral-7B-Instruct-v0.2\",\n",
    "    quantization_config=bnb_config,\n",
    "    use_auth_token='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'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c613429-9e6c-4a1e-bc9c-579eb152434b",
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    "id": "8c613429-9e6c-4a1e-bc9c-579eb152434b",
    "tags": []
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   "outputs": [],
   "source": [
    "# Define text pipeline\n",
    "# This pipline defines processes of LLM for Q&A\n",
    "text_pipeline = pipeline(\n",
    "    temperature=0.2,\n",
    "    tokenizer=tokenizer,\n",
    "    task=\"text-generation\",\n",
    "    model=model,\n",
    "    repetition_penalty=1.12,\n",
    "    return_full_text=True,\n",
    "    max_new_tokens=290,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c859dd05-9114-42f1-81f2-52a28b7efdd7",
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    "id": "c859dd05-9114-42f1-81f2-52a28b7efdd7",
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   "outputs": [],
   "source": [
    "# define hugging face pipeline\n",
    "# mistal model is run through the HuggingFacePipeline class\n",
    "mistral_llm = HuggingFacePipeline(pipeline=text_pipeline)"
   ]
  },
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   ],
   "source": [
    "# The splitted chunk texts are vectorized.\n",
    "# These vectorized texts are used in context\n",
    "# Embedding model\n",
    "db = FAISS.from_texts(texts,\n",
    "                          HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2'))\n",
    "\n",
    "retriever = db.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "Jod2nhDMXXw8",
   "metadata": {
    "id": "Jod2nhDMXXw8"
   },
   "outputs": [],
   "source": [
    "# This cell defines language model for context-based Q&A\n",
    "\n",
    "# define prompt template\n",
    "prompt_template = \"\"\"\n",
    "### [INST] Instruction: Use the following pieces of context to answer the question.\n",
    "\n",
    "{context}\n",
    "\n",
    "### QUESTION:\n",
    "{question} [/INST]\n",
    " \"\"\"\n",
    "\n",
    "# Create prompt from prompt template\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"context\", \"question\"],\n",
    "    template=prompt_template,\n",
    ")\n",
    "\n",
    "# Create llm chain\n",
    "llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)\n",
    "\n",
    "# Build RAG chain\n",
    "rag_chain = (\n",
    " {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | llm_chain\n",
    ")\n",
    "\n",
    "# define function for quesion and answer\n",
    "def Q_A(question):\n",
    "  result = rag_chain.invoke(question)\n",
    "  answer = result['text']\n",
    "  print('Answer:')\n",
    "  print(answer.split('[/INST]')[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "WGkv4F8Kce1b",
   "metadata": {
    "id": "WGkv4F8Kce1b"
   },
   "source": [
    "#### Running Q&A bot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aX9UHKtHXmwT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aX9UHKtHXmwT",
    "outputId": "eae14865-96d2-4531-8202-305544783b88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer:\n",
      "\n",
      "  Skin disorders refer to various conditions that affect the largest organ in the human body. These disorders can range from inflammatory conditions, infections, neoplastic processes (skin cancer), immunologically mediated diseases, and manifestations of underlying internal diseases.\n",
      "\n",
      "During a physical examination of a patient with a suspected skin disorder, it's essential to evaluate the entire cutaneous surface before taking an extensive history. The assessment should include noting the distribution of the eruption, the types of primary and secondary lesions, and the shape of individual lesions.\n",
      "\n",
      "Four basic features of a skin lesion must be considered during a physical examination:\n",
      "\n",
      "1. Distribution: The location of the skin lesion on the body can provide valuable information regarding the underlying cause. For instance, pityriasis rosea typically presents with multiple round or oval erythematous patches along the skin tension lines on the trunk.\n",
      "\n",
      "2. Types of primary and secondary lesions: Primary lesions are the initial lesions that develop directly from the underlying disease process. They can be macules (flat lesions), papules (raised solid lesions), plaques (elevated, flat-topped lesions), nodules (solid, raised lesions), vesicles (small fluid-filled blisters), bullae (large fluid-filled blisters), pustules\n"
     ]
    }
   ],
   "source": [
    "question = \"please explain about skin disorder\"\n",
    "Q_A(question)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "zJ3piMWFh9sa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zJ3piMWFh9sa",
    "outputId": "7dbbb2d5-b5db-4e21-bc80-f54f935f9331"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer:\n",
      "\n",
      "  Internal diseases refer to medical conditions that affect the body's internal organs and systems beyond the skin. These conditions can result from various causes such as infections, trauma, inflammation, metabolic imbalances, neurological issues, or psychiatric disorders. Some examples of internal diseases mentioned in the provided context include abdominal wall distortions, mesenteric inflammation, appendicitis, cardiac conditions like acute myocardial infarction, and various metabolic and neurological disorders.\n",
      "\n",
      "The context also discusses how certain cutaneous manifestations can indicate the presence of internal diseases. For instance, eroded bullae in pemphigus vulgaris, palpable purpuric papules in cutaneous small-vessel vasculitis, and various other skin lesions can be indicative of underlying systemic conditions. However, identifying these conditions based on cutaneous signs alone can be challenging for non-dermatologists due to the vast array of possible presentations and the need for expertise in recognizing these signs.\n",
      "\n",
      "Therefore, the focus here is on describing the various clinical signs and symptoms that point to specific internal diseases rather than listing individual diseases themselves. By understanding these presenting features, clinicians can narrow down their differential diagnosis and consider the most likely underlying conditions. It is essential to exclude common, less significant cutaneous disorders before making\n"
     ]
    }
   ],
   "source": [
    "question = \"please explain about internal disease\"\n",
    "Q_A(question)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d54a0b-bf6c-4a24-b888-4c3283b9ccf6",
   "metadata": {
    "id": "93d54a0b-bf6c-4a24-b888-4c3283b9ccf6"
   },
   "source": [
    "### 4. Implement a mechanism to determine if a question is in or out of context and deny out-of-context questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3bd688c2-25ac-4d65-88c6-635f9c95ada4",
   "metadata": {
    "id": "3bd688c2-25ac-4d65-88c6-635f9c95ada4",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# If a question is in out context, then deny out-of-context questions\n",
    "# This is done by defining of prempt_template.\n",
    "# define prompt\n",
    "prompt_template2 = \"\"\"\n",
    "### [INST] Instruction: Use the following pieces of context to answer the question.\n",
    "           If there is no mention of question in the provided context,\n",
    "           please deny answer\n",
    "\n",
    "{context}\n",
    "\n",
    "### QUESTION:\n",
    "{question} [/INST]\n",
    " \"\"\"\n",
    "\n",
    "# Create prompt from prompt template\n",
    "prompt2 = PromptTemplate(\n",
    "    input_variables=[\"context\", \"question\"],\n",
    "    template=prompt_template2,\n",
    ")\n",
    "\n",
    "# Create llm chain\n",
    "llm_chain2 = LLMChain(llm=mistral_llm, prompt=prompt2)\n",
    "\n",
    "# Build RAG chain\n",
    "rag_chain2 = (\n",
    " {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | llm_chain2\n",
    ")\n",
    "\n",
    "# define function for question and answer\n",
    "def Q_A2(question):\n",
    "  result = rag_chain.invoke(question)\n",
    "  answer = result['text']\n",
    "  print('Answer:')\n",
    "  print(answer.split('[/INST]')[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "BR0H8Uewij_p",
   "metadata": {
    "id": "BR0H8Uewij_p"
   },
   "source": [
    "####Running Q&A bot for in_or_out of context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63253cbc-9de3-41b9-a5c8-b672333f479c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "63253cbc-9de3-41b9-a5c8-b672333f479c",
    "outputId": "ebb8bb70-49af-4cb4-f4d0-e2464cc57260"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer:\n",
      "\n",
      "  I'm sorry for any confusion, but the context provided does not contain any information related to Gibbs free energy. Therefore, I cannot provide an explanation about it based on the given text.\n"
     ]
    }
   ],
   "source": [
    "# Run Q&A\n",
    "question = \"please explain about Gibbs free anergy\"\n",
    "Q_A2(question)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tPswW9_2jUIh",
   "metadata": {
    "id": "tPswW9_2jUIh"
   },
   "source": [
    "Q_A bot refuses to answer because question is out of context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "tAv2qiL1FK7O",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tAv2qiL1FK7O",
    "outputId": "068c89d0-86b4-4e44-f092-5b9dc2037fea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer:\n",
      "\n",
      "  Based on the provided context, the document discusses various aspects of ischemic heart disease, its causes, diagnosis, and management. Here's a summary of what the text says about managing a patient with ischemic heart disease:\n",
      "\n",
      "1. Ischemic heart disease is a condition where there is an imbalance between myocardial oxygen requirements and myocardial oxygen supply, leading to insufficient oxygen delivery to the heart. This results in myocardial ischemia, which if severe and prolonged, can result in myocardial infarction (MI).\n",
      "2. The management of a patient with ischemic heart disease involves addressing both the underlying cause and the symptoms.\n",
      "3. The first step in managing a patient with ischemic heart disease is to evaluate their risk factors, such as atherosclerosis, endothelial dysfunction, microvascular disease, vasospasm, congenital abnormalities, myocardial bridging, coronary arteritis, radiation-induced coronary disease, extreme myocardial oxygen demand, and impaired endocardial blood flow.\n",
      "4. Depending on the severity and stability of the ischemic heart disease, different management strategies are employed. For instance, for stable ischemic heart disease, long-term preventive medical therapy is initiated. This includes medications\n"
     ]
    }
   ],
   "source": [
    "question = \"please explain about Management of the patient with ischemic heart disease\"\n",
    "Q_A2(question)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "WRzk9mRdjmRi",
   "metadata": {
    "id": "WRzk9mRdjmRi"
   },
   "source": [
    "Q_A bot answers because question is in of context"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4396bc44-2910-4a40-b916-43087db3d603",
   "metadata": {},
   "source": [
    "## Context-Aware Responses with Advanced ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b175241",
   "metadata": {},
   "source": [
    "### 1. Reading and Preprocessing PDF Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f36ca4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries for reading and preprocessing PDF content\n",
    "import PyPDF2\n",
    "import os\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
    "# Function to read content from a PDF file\n",
    "def read_pdf(file_path):\n",
    "    if not os.path.exists(file_path):  # Check if the file exists\n",
    "        raise FileNotFoundError(f\"The file {file_path} does not exist.\")\n",
    "    \n",
    "    content = \"\"\n",
    "    with open(file_path, 'rb') as file:  # Open the PDF file in binary mode\n",
    "        reader = PyPDF2.PdfFileReader(file)  # Initialize PDF reader\n",
    "        for page_num in range(reader.numPages):  # Iterate through each page\n",
    "            page = reader.getPage(page_num)  # Get a page\n",
    "            content += page.extract_text() + \"\\n\"  # Extract text and add newline\n",
    "    return content\n",
    "\n",
    "# Function to preprocess content using Langchain's text splitter\n",
    "def preprocess_content(content):\n",
    "    splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)  # Initialize text splitter\n",
    "    return splitter.split_text(content)  # Split content into chunks\n",
    "\n",
    "# Example usage\n",
    "pdf_content = read_pdf('/mnt/data/Contextual_Q&A_Bot.pdf')  # Read PDF content\n",
    "processed_content = preprocess_content(pdf_content)  # Preprocess the content\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a847dbed",
   "metadata": {},
   "source": [
    "### 2. Developing the Q&A Bot with RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f76da1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import necessary libraries for the Q&A bot\n",
    "from langchain.llms import OpenSourceLLM\n",
    "from langchain.chains import QAChain\n",
    "from langchain.retrievers import EmbeddingRetriever\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "# Load the Language Model (Mistral 7b) with 32-bit configuration for efficiency\n",
    "llm = OpenSourceLLM(model_name=\"mistral-7b\", use_32bit=True)\n",
    "\n",
    "# Load a pre-trained embedding model (MiniLM) for embeddings\n",
    "embedding_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')\n",
    "\n",
    "# Create an Embedding Retriever for RAG (Retrieval-Augmented Generation)\n",
    "retriever = EmbeddingRetriever(embedding_model=embedding_model, documents=processed_content)\n",
    "\n",
    "# Create a Q&A Chain using the Language Model and Retriever\n",
    "qa_chain = QAChain(llm=llm, retriever=retriever)\n",
    "\n",
    "# Function to answer questions using the Q&A Chain\n",
    "def answer_question(question):\n",
    "    return qa_chain.run(question=question)\n",
    "\n",
    "# Example usage\n",
    "response = answer_question(\"What is the main topic of the document?\")  # Ask a question\n",
    "print(response)  # Print the response\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e78b66d",
   "metadata": {},
   "source": [
    "### 3. Context-Aware Responses with Advanced ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a4038fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import necessary libraries for context-aware responses\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "\n",
    "# Placeholder for context documents and labels for training the context relevance model\n",
    "context_documents = processed_content[:100]  # Use the first 100 chunks for training\n",
    "labels = [1 if \"relevant\" in doc else 0 for doc in context_documents]  # Example labels (adjust as needed)\n",
    "\n",
    "# Train a logistic regression model to determine context relevance\n",
    "X_train, X_test, y_train, y_test = train_test_split(context_documents, labels, test_size=0.2, random_state=42)\n",
    "context_model = make_pipeline(TfidfVectorizer(), LogisticRegression())  # Create a pipeline with TF-IDF and Logistic Regression\n",
    "context_model.fit(X_train, y_train)  # Train the model\n",
    "\n",
    "# Evaluate the model\n",
    "y_pred = context_model.predict(X_test)  # Predict on test set\n",
    "print(f\"Context relevance model accuracy: {accuracy_score(y_test, y_pred)}\")  # Print accuracy\n",
    "\n",
    "# Function to check if a question is in context using the trained model\n",
    "def is_question_in_context(question, context_documents):\n",
    "    context_scores = context_model.predict_proba([question] * len(context_documents))[:, 1]  # Get probabilities\n",
    "    max_score = np.max(context_scores)  # Get maximum score\n",
    "    threshold = 0.5  # Define a threshold for context relevance\n",
    "    return max_score > threshold\n",
    "\n",
    "# Function to handle questions by checking context and answering\n",
    "def handle_question(question):\n",
    "    context = retriever.retrieve(question)  # Use retriever to get the most relevant context\n",
    "    if is_question_in_context(question, context):  # Check if question is in context\n",
    "        return answer_question(question)  # Answer the question if in context\n",
    "    else:\n",
    "        return \"The question is out-of-context.\"  # Return out-of-context message\n",
    "\n",
    "# Example usage\n",
    "response = handle_question(\"What is the main topic of the document?\")  # Ask a question\n",
    "print(response)  # Print the response\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "712b898f",
   "metadata": {},
   "source": [
    "### 4. Scalability Considerations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4f49acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import necessary libraries for scalable preprocessing\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "# Function to preprocess large PDFs using Spark for scalability\n",
    "def scalable_preprocess(file_path):\n",
    "    spark = SparkSession.builder.appName(\"PDFPreprocessing\").getOrCreate()  # Initialize Spark session\n",
    "    pdf_rdd = spark.sparkContext.binaryFiles(file_path).flatMap(lambda x: read_pdf(x[1]).split('\\n'))  # Read and split PDF content\n",
    "    processed_content = pdf_rdd.collect()  # Collect the results\n",
    "    spark.stop()  # Stop the Spark session\n",
    "    return processed_content\n",
    "\n",
    "# Example usage\n",
    "processed_content = scalable_preprocess('/mnt/data/Contextual_Q&A_Bot.pdf')  # Preprocess PDF content\n"
   ]
  }
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