LlamaIndex KnowledgeGraphIndex
FreeBuild and query knowledge graphs from your documents for LLM-powered RAG
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About LlamaIndex KnowledgeGraphIndex
LlamaIndex KnowledgeGraphIndex is a specialized module within the LlamaIndex framework for building knowledge graphs from unstructured data and using them to enhance LLM-powered retrieval and generation. It constructs a structured graph representation of entities and relationships extracted from documents, enabling more sophisticated querying and context-aware responses. The module integrates seamlessly with LlamaIndex's data connectors, indexes, and query engines to support complex RAG workflows.
Key Features
Constructs knowledge graphs from unstructured text documents
Extracts entities and relationships to create a structured graph
Supports graph-based retrieval for RAG pipelines
Integrates with LlamaIndex data connectors and query engines
Enables advanced query patterns like multi-hop reasoning over entities
Pros & Cons
Pros
- Enables more context-rich retrieval than flat vector indexes
- Seamless integration with the broader LlamaIndex ecosystem
- Open source and free to use
- Supports complex multi-hop queries over entities
- Well-documented with examples
Cons
- Constructing knowledge graphs can be computationally intensive
- Requires careful tuning of entity extraction
- Graph-based retrieval may have higher latency than simple vector search
- Documentation specific to KnowledgeGraphIndex is separate from main LlamaIndex docs
Best For
Building domain-specific knowledge bases from PDFs, slide decks, and APIsEnhancing question-answering with entity-relationship contextPowering research assistants that need to reason across connected factsCreating interactive knowledge graphs for data exploration
FAQ
What is the KnowledgeGraphIndex in LlamaIndex?
It is a module that builds a knowledge graph from indexed documents, extracting entities and relationships to enable graph-based retrieval for LLM applications.
How does KnowledgeGraphIndex differ from a vector index?
KnowledgeGraphIndex stores structured entity-relationship graphs, allowing for multi-hop reasoning and relationship-aware queries, whereas vector indexes rely on embedding similarity.
Is LlamaIndex free and open source?
Yes, LlamaIndex is an open-source framework with an MIT license, and it is free to use.