🩺 Medical Chatbot

An intelligent medical assistant chatbot powered by AI that provides accurate medical information using Retrieval-Augmented Generation (RAG) with LangChain, Pinecone, and Groq.

📋 Table of Contents

• Overview
• Features
• Tech Stack
• Architecture
• Installation
• Configuration
• Usage
• Project Structure
• API Endpoints

🎯 Overview

This Medical Chatbot uses state-of-the-art natural language processing to answer medical questions by retrieving relevant information from a curated knowledge base of medical documents. The system combines semantic search with large language models to provide accurate, context-aware responses.

✨ Features

• 🤖 AI-Powered Responses: Uses Groq's Llama 3.3 70B model for intelligent answers
• 🔍 Semantic Search: Leverages Pinecone vector database for fast, accurate document retrieval
• 📚 PDF Knowledge Base: Processes medical PDFs to build a comprehensive knowledge base
• 💬 Interactive Web Interface: User-friendly chat interface built with Flask
• 🎯 Context-Aware: Retrieves top 3 most relevant documents for each query
• ⚡ Fast Response Time: Optimized for quick inference with Groq API

🛠️ Tech Stack

Backend

• Python 3.10+
• Flask: Web framework
• LangChain: LLM orchestration framework
• LangChain Groq: Groq integration for LLM inference
• LangChain Pinecone: Vector store integration
• LangChain HuggingFace: Embeddings model

AI/ML

• Groq API: Fast LLM inference (Llama 3.3 70B Versatile)
• HuggingFace: Embeddings (sentence-transformers/all-MiniLM-L6-v2)
• Pinecone: Vector database for semantic search

Data Processing

• PyPDF: PDF parsing
• RecursiveCharacterTextSplitter: Text chunking

🏗️ Architecture

User Query
    ↓
Flask Web App
    ↓
LangChain RAG Pipeline
    ↓
    ├─→ HuggingFace Embeddings (384-dim vectors)
    ↓
Pinecone Vector Store (Similarity Search)
    ↓
Top 3 Relevant Documents
    ↓
Groq LLM (Llama 3.3 70B)
    ↓
Generated Response
    ↓
User Interface

📦 Installation

Prerequisites

• Python 3.10 or higher
• Anaconda/Miniconda (recommended)
• Pinecone account
• Groq API account

Step 1: Clone the Repository

git clone https://github.com/nadamankai/Medical-Chatbot.git
cd Medical-Chatbot

Step 2: Create Virtual Environment

conda create -n medibot python=3.10 -y
conda activate medibot

Step 3: Install Dependencies

pip install -r requirements.txt

⚙️ Configuration

Step 1: Create Environment File

Create a .env file in the root directory:

PINECONE_API_KEY=your_pinecone_api_key_here
GROQ_API_KEY=your_groq_api_key_here

Step 2: Get API Keys

Pinecone API Key

1. Go to https://www.pinecone.io/
2. Sign up for a free account
3. Navigate to API Keys section
4. Copy your API key

Groq API Key

1. Go to https://console.groq.com/
2. Sign up for a free account
3. Navigate to API Keys section
4. Create a new API key
5. Copy your API key (starts with gsk_)

Step 3: Prepare Your Data

1. Place your medical PDF files in the data/ directory
2. Run the indexing script to create embeddings:

python store_index.py

This will:

• Load all PDFs from the data/ directory
• Split documents into chunks (500 chars with 20 char overlap)
• Generate embeddings using HuggingFace model
• Store vectors in Pinecone index named medical-bot

🚀 Usage

Start the Application

python app.py

The application will start on http://localhost:8080

Using the Chatbot

1. Open your browser and navigate to http://localhost:8080
2. Type your medical question in the chat interface
3. Press Enter or click Send
4. Wait for the AI-generated response

Example Queries

• "What is diabetes?"
• "What are the symptoms of hypertension?"
• "How is acne treated?"
• "What causes anemia?"

📁 Project Structure

Medical-Chatbot/
├── app.py                      # Main Flask application
├── store_index.py              # Script to create Pinecone index
├── requirements.txt            # Python dependencies
├── setup.py                    # Package setup file
├── .env                        # Environment variables (not in repo)
├── README.md                   # Project documentation
│
├── data/                       # Medical PDF documents
│   └── *.pdf
│
├── src/                        # Source code modules
│   ├── __init__.py
│   ├── helper.py               # Helper functions for data processing
│   └── prompt_template.py     # System prompt for the chatbot
│
├── templates/                  # HTML templates
│   └── chat.html               # Chat interface
│
└── research/                   # Jupyter notebooks for experimentation
    └── trials.ipynb

🔌 API Endpoints

GET /

Renders the main chat interface.

Response: HTML page

POST /get

Handles chat messages and returns bot responses.

Request:

{
  "msg": "What is diabetes?"
}

Response:

Diabetes is a chronic condition characterized by high blood sugar levels...