Medical Chatbot with RAG Architecture

A medical question-answering system that leverages Retrieval-Augmented Generation (RAG) to provide accurate and context-aware medical information. This application combines the power of large language models with vector search to deliver precise answers to medical queries.

🚀 Key Features

• Advanced RAG Pipeline: Implements a robust Retrieval-Augmented Generation system for accurate medical information retrieval
• Multi-document Support: Processes and indexes multiple PDF documents from the data/ directory
• Semantic Search: Utilizes Pinecone's vector database for efficient similarity search across medical documents
• State-of-the-Art LLM: Powered by Google's Gemini model through LangChain for high-quality response generation
• Web Interface: User-friendly Flask-based web interface for seamless interaction
• Scalable Architecture: Designed for easy extension and integration with additional data sources
• Customizable Prompts: Easily adjustable system prompts to tailor responses to medical domain requirements
• Efficient Chunking: Smart text splitting to maintain context while processing large documents

📊 Technical Architecture

The application follows a modern microservices architecture with the following components:

1. Frontend: Lightweight HTML/JS interface with responsive design
2. Backend: Flask web server handling API requests
3. Vector Database: Pinecone for efficient vector similarity search
4. Embedding Model: all-MiniLM-L6-v2 for creating document embeddings
5. LLM Integration: Google's Gemini model for generating human-like responses
6. Document Processing: Automated pipeline for PDF ingestion and text extraction

Project Structure

• app.py: Flask app and RAG pipeline
• store_index.py: Builds Pinecone index from PDFs in data/
• src/helper.py: Load PDF(s), split text, and create embeddings
• src/prompt.py: System prompt for the assistant
• templates/chat.html: Frontend chat page
• static/style.css: Simple styles

Prerequisites

• Python 3.10+
• A Pinecone account and API key
• A Google AI Studio API key (for Gemini)

1) Setup (Windows PowerShell)

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

2) Environment Variables

Create a .env file in the project root:

PINECONE_API_KEY="your_pinecone_api_key"
GOOGLE_API_KEY="your_google_api_key"

Notes:

• The code uses the Pinecone index name medical-catboot and expects embeddings of dimension 384 (all-MiniLM-L6-v2).
• Default serverless spec: cloud aws, region us-east-1.

3) Add/Update Your Data

Place your PDFs in the data/ folder (the repo includes data/Medical_book.pdf). Rebuild the index after you change files.

4) Build/Refresh the Pinecone Index

.\.venv\Scripts\python store_index.py

This will:

• Read PDFs from data/
• Split into chunks
• Create sentence-transformer embeddings (384 dims)
• Create or reuse Pinecone index medical-catboot
• Upsert embeddings

5) Run the App

.\.venv\Scripts\python app.py

Open http://localhost:8080 in your browser.

Troubleshooting

• Ensure .env contains valid PINECONE_API_KEY and GOOGLE_API_KEY.
• If you change PDFs, rerun store_index.py to refresh embeddings.
• If the index doesn’t exist, the script will create it (serverless us-east-1).
• On corporate networks, set proxy env vars for pip/downloads if needed.

Tech Stack

• Python, Flask
• LangChain (Retrieval chain)
• Sentence Transformers (all-MiniLM-L6-v2)
• Pinecone (Vector DB)
• Google Gemini (via langchain-google-genai)