Master LightRAG with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make LightRAG powerful for knowledge & documents workflows.
Combines knowledge graph traversal with vector similarity search for context-rich answers that understand entity relationships, using a dual-level retrieval paradigm that operates at both specific and abstract levels.
Answering 'Which departments collaborate on compliance projects?' from organizational documents by traversing entity relationships rather than matching keywords.
Efficient LLM-based extraction of entities and relationships during indexing with lower compute cost than full GraphRAG — typically 2-3x source token count versus 5-10x for GraphRAG.
Indexing a 10,000-page technical documentation set with manageable LLM costs that a small team can afford.
Naive (vector-only), local (graph-focused), and hybrid (combined) modes let you trade off speed vs. relationship awareness depending on the query type.
Using hybrid mode for complex relational queries like 'how do these regulations interact?' and naive mode for simple factual lookups.
New documents can be added to the index without re-processing the entire collection, and the graph structure updates automatically with new entities and relationships.
Adding daily news articles to a knowledge base without re-indexing the full corpus each time.
Full support for local LLMs through Ollama for both entity extraction during indexing and query-time processing, enabling zero-cost operation on private infrastructure.
Running a HIPAA-compliant medical document Q&A system on-premise with no external API dependencies.
Support for Neo4j, NetworkX, OpenSearch (new in 2026), and built-in lightweight stores for both graph and vector data, with OpenSearch providing unified storage across all four LightRAG storage types.
Starting with built-in storage for prototyping and migrating to Neo4j + OpenSearch for production-scale deployments.
LightRAG is significantly lighter and cheaper to run. GraphRAG builds more comprehensive community summaries and handles global queries better, but costs 5-10x in indexing tokens. LightRAG is ideal when you want graph-enhanced retrieval without the heavy infrastructure and cost overhead.
Yes. LightRAG supports Ollama and other local LLM providers for both entity extraction during indexing and query-time processing. This means you can run the entire pipeline on-premise with zero API costs.
Higher than plain vector RAG because entity extraction requires LLM calls during indexing. Typically 2-3x the token count of source material for LightRAG vs near-zero LLM cost for basic vector RAG. With local models via Ollama, the monetary cost is essentially zero.
Yes. New documents can be added without re-indexing the entire collection. The knowledge graph is updated incrementally with new entities and relationships, though periodic full re-indexing can improve graph quality over time.
LightRAG supports Neo4j for production graph storage, NetworkX for lightweight in-memory graphs, OpenSearch as a unified backend for all four storage types (added in March 2026), and built-in lightweight stores for quick prototyping.
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Tutorial updated March 2026