In our last update of RAG, we introduced GraphRAG , originally described in as a two-step approach: (1) Chunking documents and using an LLM-based analysis of the chunks to create a knowledge graph; (2) retrieving relevant chunks at query time via embeddings while following edges in the knowledge graph to discover additional related chunks, which are then added to the augmented prompt. In many cases this approach enhances LLM-generated responses. We've observed similar benefits when using GenAI to understand legacy codebases, where we use structural information ¡ª such as abstract syntax trees and dependencies ¡ª to build the knowledge graph. The GraphRAG pattern has gained traction, with tools and frameworks like Neo4j's emerging to support it. We also see Graphiti as fitting a broader interpretation of GraphRAG as a pattern.
