An advanced RAG architecture that uses knowledge graphs to improve retrieval accuracy and multi-hop reasoning.
GraphRAG is an advanced variant of Retrieval-Augmented Generation that uses knowledge graphs to enhance retrieval quality and enable multi-hop reasoning. While standard RAG retrieves isolated document chunks, GraphRAG retrieves connected subgraphs of related information, enabling AI systems to answer complex questions that require synthesizing information across multiple related entities and relationships. Microsoft Research introduced GraphRAG in 2024, demonstrating significant improvements over standard RAG on complex reasoning tasks.
GraphRAG has become the architecture of choice for enterprise AI applications that require complex, multi-step reasoning over large document corpora. Standard RAG fails on questions like 'How do the risk factors mentioned by Apple, Microsoft, and Google in their 2025 annual reports compare?' — because answering requires synthesizing information across multiple documents and entities. GraphRAG's knowledge graph backbone enables this type of global reasoning.
GraphRAG works by first building a knowledge graph from the document corpus — extracting entities, relationships, and communities using LLMs. At query time, it identifies the relevant subgraph (using both vector similarity and graph traversal), retrieves the connected information, and generates a response grounded in the graph structure. Community summaries — pre-computed summaries of entity clusters — enable efficient global reasoning without retrieving every document.
A consulting firm uses GraphRAG over 10,000 client engagement documents. When asked 'What are the common failure patterns in digital transformation projects in the retail sector?', GraphRAG traverses the knowledge graph to identify all retail engagements, extracts failure patterns across them, synthesizes community-level insights, and generates a comprehensive answer that would require reading hundreds of documents manually.