RAG with FastAPI & LangChain

A modern document Q&A system built with FastAPI, LangChain, and Next.js that allows users to upload documents and ask questions about their content using Retrieval-Augmented Generation (RAG).

🚀 Features

• Document Upload: Support for PDF and TXT files
• Smart Search: Document-specific search with global fallback capabilities
• Real-time Streaming: Server-Sent Events for live query responses
• Contextual Compression: Automatic result sanitization and deduplication
• Modern UI: Beautiful, responsive interface built with Next.js and Tailwind CSS
• Vector Database: ChromaDB for efficient document storage and retrieval
• OpenAI Integration: Human-readable answer formatting

📸 Screenshots

🎥 Demo Video

Watch Demo

🏗️ Architecture

• Backend: FastAPI with LangChain for RAG implementation
• Frontend: Next.js 15 with TypeScript and Tailwind CSS
• Vector Database: ChromaDB for document embeddings
• AI Provider: OpenAI for text generation and embeddings

📋 Prerequisites

• Python 3.8+
• Node.js 18+
• Docker and Docker Compose
• OpenAI API key

🛠️ Installation & Setup

1. Clone the Repository

git clone <your-repo-url>
cd ask-docs

2. Start ChromaDB

First, start the ChromaDB vector database using Docker:

cd server
docker-compose up -d

This will start ChromaDB on port 8001.

3. Set Up the Backend

cd server
pip install -r requirements.txt

Create a .env file in the server directory:

OPENAI_API_KEY=your_openai_api_key_here

4. Set Up the Frontend

cd client
npm install

🚀 Running the Application

1. Start the Server

cd server
python main.py

The FastAPI server will start on http://localhost:8000

2. Start the Client

cd client
npm run dev

The Next.js application will start on http://localhost:3000

🔧 API Endpoints

• POST /api/documents/upload - Upload a document
• POST /api/documents/query - Query documents
• POST /api/documents/query/stream - Stream query responses
• GET /api/documents/stats - Get document statistics
• GET /api/documents/health - Health check
• GET /docs - API documentation (Swagger UI)

🏃‍♂️ Quick Start

1. Start ChromaDB: docker-compose up -d
2. Start Server: python main.py (from server directory)
3. Start Client: npm run dev (from client directory)
4. Open Browser: Navigate to http://localhost:3000
5. Upload Document: Click the upload button and select a PDF or TXT file
6. Ask Questions: Start asking questions about your uploaded document!

🛠️ Development

Backend Development

The backend is built with FastAPI and includes:

• Document processing with LangChain
• Vector embeddings with OpenAI
• ChromaDB integration for document storage
• Streaming responses with Server-Sent Events

Frontend Development

The frontend is built with Next.js 15 and includes:

• Modern React with TypeScript
• Tailwind CSS for styling
• Real-time chat interface
• File upload functionality
• Responsive design

📁 Project Structure

ask-docs/
├── client/                 # Next.js frontend
│   ├── src/app/           # App router components
│   ├── public/            # Static assets
│   └── package.json       # Frontend dependencies
├── server/                # FastAPI backend
│   ├── controller/        # API controllers
│   ├── service/           # Business logic
│   ├── config/            # Configuration files
│   ├── main.py           # FastAPI application
│   └── requirements.txt   # Python dependencies
├── assets/               # Media files
│   ├── photo.png         # Screenshot
│   └── recording.mov     # Demo video
└── README.md             # This file

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

This project is created for learning purposes and is open source. Feel free to use, modify, and learn from this code for educational purposes.

Note: This is a learning project demonstrating RAG (Retrieval-Augmented Generation) implementation with FastAPI, LangChain, and Next.js. The code is provided as-is for educational use.

🙏 Acknowledgments

• LangChain for RAG implementation
• FastAPI for the backend framework
• Next.js for the frontend framework
• ChromaDB for vector storage
• OpenAI for AI capabilities