Practical GraphRAG - Making LLMs smarter with Knowledge Graphs by Michael Hunger

GraphRAG combines traditional RAG (Retrieval Augmented Generation) with knowledge graphs to provide more context and better grounding for LLM responses

Knowledge graphs help overcome limitations of basic vector search by capturing rich, complex relationships between entities and enabling cross-document connections

Key advantages of GraphRAG include:

• Better explainability by tracing information sources
• Enhanced context through relationship traversal
• Ability to detect patterns and clusters across documents
• More accurate responses through structured data validation

Documents can be automatically transformed into knowledge graphs using LLMs to extract entities and relationships, creating connected networks of information

Multiple retrieval methods can be combined:

• Vector search for semantic similarity
• Graph traversal for relationship context
• Hybrid approaches using both
• Community detection for related document clusters

Integration options exist for major LLM frameworks like LangChain, LlamaIndex, and Spring AI through Neo4j connectors

Advanced RAG patterns enabled by knowledge graphs include:

• Query rewriting
• Context augmentation
• Relationship discovery
• Cross-document summarization

Challenge: LLMs are trained more for helpfulness than factuality, making grounding through knowledge graphs important for accuracy

Practical implementation requires:

• Document chunking and embedding
• Entity and relationship extraction
• Graph schema definition
• Hybrid retrieval strategies

The approach allows building domain-specific applications that leverage both company knowledge and LLM capabilities while maintaining explainability