Graph RAG vs Vector RAG

Vector RAG retrieves information based on semantic similarity (finding chunks that "sound like" the query), while Graph RAG traverses structured relationships between entities (finding information that is "connected to" the query) — and understanding when each approach excels determines the quality of your agent's knowledge retrieval. Vector RAG works well for open-ended questions where the answer lives in a specific passage ("what does this error mean?"), but struggles when the answer requires connecting multiple pieces of information across a knowledge base ("what are all the services that depend on this database?"). Graph RAG excels at relational queries, multi-hop reasoning, and providing comprehensive coverage of a topic by following explicit entity relationships, but requires significantly more upfront investment in knowledge graph construction and maintenance. The emerging best practice is hybrid retrieval: using vector search for initial discovery and graph traversal for structured exploration, combining the strengths of both approaches. This concept connects to vector databases and knowledge graphs for the underlying infrastructure, RAG patterns for the retrieval strategies built on top of both, and embedding models for the representation layer that vector RAG depends on.