Easy RAG

A simple and efficient RAG (Retrieval-Augmented Generation) API built with FastAPI, Qdrant, and LangChain. Upload documents, index them using semantic embeddings, and query them using natural language.

📚 Full Documentation: https://easy-rag.readthedocs.io/en/latest/

Features

• 📄 Document Upload: Upload PDF and text files for indexing
• ⚡ Batch Processing: Efficiently handles large documents (3500+ pages) with batch processing
• 🔍 Semantic Search: Query documents using natural language with relevance scoring
• 📑 Page Tracking: Results include page numbers for easy reference
• 🚀 gRPC Communication: Fast communication with Qdrant using gRPC protocol
• 🐳 Docker Support: Easy deployment with Docker and Docker Compose
• 📖 RESTful API: Clean REST API with automatic OpenAPI documentation

Quick Start

Using Docker Compose (Recommended)

# Clone the repository
git clone <repository-url>
cd easy-rag

# Optionally create a .env file to customize configuration
cp .env.example .env  # if available

# Build and start services
docker compose up --build

# Run in background
docker compose up -d --build

The API will be available at http://localhost:8000 and Qdrant at http://localhost:6333.

Local Installation

# Clone the repository
git clone <repository-url>
cd easy-rag

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start Qdrant (using Docker)
docker compose up -d qdrant

# Create .env file (optional)
cp .env.example .env

# Run the application
uvicorn src.easyrag.main:app --host 0.0.0.0 --port 8000

Configuration

All configuration options can be set via environment variables or a .env file. See .env.example for all available options:

• Qdrant: QDRANT_HOST, QDRANT_GRPC_PORT, COLLECTION_NAME
• Embedding Model: EMBED_MODEL
• Document Processing: CHUNK_SIZE, CHUNK_OVERLAP, BATCH_SIZE
• Retrieval: DEFAULT_K, MAX_K
• Server: HOST, PORT

For detailed configuration documentation, see the Installation Guide.

Usage Examples

Upload a Document

curl -X POST "http://localhost:8000/api/v1/upload" \
     -F "file=@your-document.pdf"

Query Documents

curl -X POST "http://localhost:8000/api/v1/ask" \
     -H "Content-Type: application/json" \
     -d '{"query": "What is an Amazon EC2 instance?"}'

Health Check

curl http://localhost:8000/health

API Documentation

Once the server is running, access the interactive API documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Key Technologies

• FastAPI: Modern, fast web framework for building APIs
• Qdrant: Vector database for storing embeddings
• LangChain: Framework for building LLM applications
• PyMuPDF: PDF processing with better structure preservation
• HuggingFace: Embedding models for semantic search

Documentation

📚 Full documentation is available at: https://easy-rag.readthedocs.io/en/latest/

The documentation includes:

• Installation Guide - Detailed setup instructions
• Usage Guide - Examples and best practices
• API Reference - Complete API documentation

Requirements

• Python 3.11 or higher
• Qdrant vector database (can be run via Docker)
• 4GB+ RAM recommended for large documents
• Docker and Docker Compose (for containerized deployment)

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues, questions, or contributions, please open an issue on GitHub.

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Easy RAG - A simple RAG API using Qdrant and LangChain