GraphRAG Q&A

GraphRAG is the next-gen Retrieval-Augmented Generation (RAG) architecture, leveraging a knowledge graph structure for superior multi-hop reasoning and verifiable Q&A over complex, private datasets.

This is a significant upgrade from vector-only RAG. GraphRAG uses a Large Language Model (LLM) to automatically build a rich knowledge graph (nodes and edges) from your unstructured text corpus, indexing complex relationships that traditional semantic search simply misses. This graph structure enables the system to perform multi-hop reasoning, connecting disparate data points to answer complex, 'global' queries with high accuracy. The key advantage: explainability. You get verifiable outputs by tracing the answer directly back through the graph's connections. We see the results: production studies report up to a 77.6% improvement in retrieval accuracy (MRR) compared to conventional embedding-based methods.

https://graphrag.github.io/