⬡PipelineResearchFree
LightRAG
Fast retrieval-augmented generation framework that fuses knowledge-graph structure with vector retrieval.
LightRAG
LightRAG is a simple and fast retrieval-augmented generation framework from the HKU Data Intelligence Lab (EMNLP 2025). It combines automatically constructed knowledge-graph structure with vector similarity search, giving more context-aware and relational answers than plain vector RAG while staying lighter than heavyweight GraphRAG pipelines.
Key features
- Dual-level retrieval merging graph relationships and vector similarity
- Incremental knowledge-graph construction from ingested documents
- Multiple storage backends (JSON, PostgreSQL, Neo4j, Milvus, and more)
- Pluggable LLM and embedding backends (OpenAI, Ollama, Hugging Face)
- Fast, low-overhead indexing suitable for iterative research corpora
Usage note: install via pip, point it at your documents to build the graph + vector index, then query with graph, vector, or hybrid retrieval modes.
Curated mirror of the open-source LightRAG (MIT). Get it from the source.