Welcome to Easy RAG’s documentation!
Easy RAG is a simple and efficient RAG (Retrieval-Augmented Generation) API built with FastAPI, Qdrant, and LangChain. It enables you to upload documents (PDFs and text files), index them using semantic embeddings, and query them using natural language.
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
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
Contents: