🏥 MedAI Backend – AI Powered Healthcare Assistant

A scalable RAG-based AI healthcare backend that provides intelligent medical assistance using LLMs, vector databases, and real-time APIs.

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🔗 Project Links

• 🌐 Frontend Repository: 👉 https://github.com/Anmol-pi/MedAI-Frontend

• 🚀 Backend Deployment (Live API): 👉 https://medai-backend-production-a830.up.railway.app

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📌 Overview

MedAI is an AI-powered healthcare assistant designed to deliver accurate, context-aware medical responses using a Retrieval-Augmented Generation (RAG) pipeline.

The backend integrates advanced AI models with a vector database to ensure responses are relevant, factual, and domain-specific, making it suitable for real-world healthcare support systems.

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⚙️ Key Features

• 🧠 RAG-based AI System Combines LLMs with vector search for accurate answers

• 🔍 Semantic Search with Vector DB Uses embeddings to retrieve relevant medical context

• 🤖 LLM Integration Generates human-like, contextual responses

• 📂 Document Processing Pipeline Supports ingestion of medical datasets / knowledge sources

• ⚡ FastAPI Backend High-performance API endpoints for frontend integration

• 🔐 Scalable Architecture Designed for production deployment (Railway)

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🏗️ Tech Stack

🚀 Backend

• Python
• FastAPI

🧠 AI / ML

• LLM APIs (Groq / LLaMA / etc.)
• Embeddings

🗄️ Database

• Vector Database (Qdrant)

🔗 Integration

• REST APIs
• Frontend (React / TypeScript)

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📊 System Architecture

User Query
   ↓
Frontend (React)
   ↓
FastAPI Backend
   ↓
Embedding Generation
   ↓
Vector Search (Qdrant)
   ↓
Context Retrieval
   ↓
LLM (Groq / LLaMA)
   ↓
Final AI Response

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📁 Project Structure

MedAI-backend/
│── app/
│   ├── routes/          # API endpoints
│   ├── services/        # Business logic
│   ├── rag/             # RAG pipeline
│   ├── db/              # Database connections
│   └── utils/           # Helper functions
│
│── data/                # Documents / embeddings
│── main.py              # Entry point
│── requirements.txt     # Dependencies
│── .env                 # Environment variables

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🚀 Getting Started

🔧 Prerequisites

• Python 3.9+
• API Keys (Groq / Cohere / etc.)
• Qdrant setup

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📥 Installation

git clone <your-repo-url>
cd MedAI-backend
pip install -r requirements.txt

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🔑 Environment Setup

Create a .env file:

GROQ_API_KEY=your_key
QDRANT_URL=your_url
QDRANT_API_KEY=your_key

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▶️ Run the Server

uvicorn main:app --reload

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📡 API Endpoints (Example)

Method	Endpoint	Description
GET	/	Health check
POST	/query	Ask medical questions

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🧠 How It Works

The system follows a Retrieval-Augmented Generation pipeline:

1. User sends a query
2. Query is converted into embeddings
3. Relevant documents are retrieved from vector DB
4. Context is passed to LLM
5. LLM generates final response

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🌟 Use Cases

• AI Medical Chatbot
• Health Assistance Platforms
• Clinical Decision Support (basic level)
• Educational Medical Tools

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⚠️ Disclaimer

This project is for educational purposes only and should not be used as a substitute for professional medical advice.

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👨‍💻 Contributors

• Anmol Kumar Jindal
• HariOm

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🤝 Contributing

Contributions are welcome!

fork → clone → create branch → commit → push → PR

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📄 License

This project is licensed under the MIT License

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⭐ Support

If you like this project:

• ⭐ Star the repo
• 🍴 Fork it
• 🧠 Share ideas