Introduction
LightRAG is a production-oriented lightweight RAG framework that integrates document indexing, retrieval, reranking and generation. It supports both Server (Web UI + REST API) and Core (embedded library) modes, suitable for large-scale document retrieval and knowledge-graph-enhanced applications.
Key features
- Support multiple storage backends (local files, Postgres, Redis, Milvus, Qdrant, etc.) for flexible deployment.
- Integrated graph extraction and entity-relation management to build knowledge graphs for improved retrieval.
- Provides both Server and Core modes for easy integration into existing systems.
- Extensible model and reranker plugins, compatible with Ollama, Hugging Face and OpenAI models.
Use cases
- Enterprise document search and question-answering systems.
- Multimodal RAG and knowledge-graph-augmented retrieval pipelines.
- Academic and industrial evaluations, and rapid prototyping.
Technical highlights
- Core logic implemented in Python, front-end Web UI implemented with TypeScript/JS.
- Flexible storage and indexing strategies for large-scale vector search and distributed deployments.
- Modular architecture to swap embedding, reranker and storage implementations.