Medical AI Chatbot 🩺

A production-ready, RAG-enhanced (Retrieval-Augmented Generation) medical assistant designed to provide accurate, context-aware information from clinical datasets.

🌟 Key Features

• Intelligent Retrieval: Hybrid search combining BM25 (lexical) and MMR (semantic) dense retrieval.
• Multi-turn Conversation: Context-aware chat history management with pronoun resolution.
• Clinical Safety: Integrated safety triggers and disclaimers for sensitive medical queries.
• RAGAS Evaluated: High-performance metrics for answer relevancy and faithfulness.
• High Performance: Optimized with LLM rotation and connection pooling to handle rate limits.

🛠️ Tech Stack

• Backend: Flask (Python)
• Orchestration: LangChain
• LLM: Groq (Llama 3.3 70B)
• Vector Database: Pinecone
• Embeddings: BGE-Small-EN-V1.5 (HuggingFace)
• Testing: Pytest with environmental isolation mocking

🚀 Quick Start

1. Prerequisites

• Python 3.11+
• Groq API Key
• Pinecone API Key

2. Installation

git clone https://github.com/kinza7124/medical-chatbot-ai.git
cd medical-chatbot-ai
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Environment Setup

Create a .env file in the root:

PINECONE_API_KEY=your_pinecone_key
GROQ_API_KEY=your_groq_key
FLASK_SECRET_KEY=your_secret

4. Running the Application

python app.py

The app will be available at http://127.0.0.1:5000.

🧪 Testing

The project includes a robust test suite of 150+ cases.

pytest tests/ -v

📂 Project Structure

• app.py: Main Flask application and RAG pipeline.
• src/: Core logic for embeddings and data processing.
• tests/: Comprehensive unit, functional, and integration tests.
• docs/: Detailed SRS, Project Summary, and Testing documentation.

⚖️ Disclaimer

This chatbot is for informational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician.