HKUDS / LightRAG

What it does

• Deep Contextual Understanding. Through graph-structured indexing, LightRAG captures complex semantic dependencies between entities, overcoming the fragmented context limitations typical of traditional chunk-based retrieval methods. Its gene
• Exceptional Comprehensiveness & Diversity. LightRAG’s dual-level retrieval mechanism allows it to integrate detailed facts and abstract concepts concurrently. This enables the system to achieve remarkable performance in query result compreh
• Extreme Retrieval Efficiency & Low Cost. LightRAG does not rely on inefficient community reports or multi-hop reasoning for complex queries. This drastically reduces the number of LLM calls required during both the indexing and querying pha
• Incremental Updates & Selective Deletion. LightRAG addresses the challenges of incrementally updating and selectively deleting content from graph-based knowledge bases, keeping them current in dynamic data environments. When a document is d
• Multiple Document Parsing Engines. LightRAG's document processing pipeline supports MinerU, Docling, and Native and can be extended with third-party parsers. LightRAG's Native engine efficiently parses images, tables, and formulas in Word a