GraphRAG

GraphRAG integrates knowledge graphs with Retrieval-Augmented Generation (RAG) to enable multi-hop reasoning and deliver context-rich, verifiable LLM responses.

GraphRAG is a superior RAG architecture: it moves beyond simple vector-based semantic search. The system constructs a knowledge graph (KG) from unstructured data, extracting entities and relationships (nodes and edges). This structure allows the Large Language Model (LLM) to perform complex, multi-hop reasoning, a task where baseline RAG systems often fail. By leveraging the KG's relational context instead of isolated text chunks, GraphRAG significantly improves answer accuracy, reduces hallucination, and provides a clear, traceable provenance for the generated response. It is a critical upgrade for enterprise GenAI applications demanding high-trust, explainable results.

https://graphrag.com