LightRAG is a new retrieval-augmented generation (RAG) technique that leverages knowledge graphs to enhance retrieval quality. It offers better answers for different question types, smarter focus on what matters, and easy updates with new information. The extraction process involves three stages: cleaning up the mess, breaking content into digestible chunks, and letting LLM identify the important entities and how they connect. LightRAG stores this information both as a knowledge graph and in a vector database, understanding meaning and explaining its reasoning. It makes use of Neo4j as a graph store and uses Cypher queries to insert or update entities and relationships. The technique provides a hybrid retrieval model similar to GraphRAG, unlocking powerful capabilities for semantic search and graph-powered reasoning.