Standard RAG retrieves text chunks. That works for many factual questions, but it struggles when the answer depends on relationships: which service depends on which database, which team owns which API, which incidents share the same root cause, or which people worked on two different projects.

GraphRAG adds a graph layer to retrieval. Instead of treating information only as isolated text chunks, the system represents entities and relationships as a structured network. The graph lets retrieval traverse connections, aggregate related facts, and gather context that vector similarity alone may miss.

This chapter covers knowledge graph basics, Microsoft's GraphRAG approach, hybrid vector-plus-graph retrieval, and the trade-offs that decide whether a graph is worth the operational cost.