Vector search finds semantically similar content but misses structurally connected information. Graph databases solve this by revealing relationships between entities. Combining both in hybrid queries enables GraphRAG, an evolution of RAG that retrieves both semantically similar and relationally connected context for LLMs. Two hybrid patterns are covered: vector-first-then-graph (e.g., product recommendations) and graph-first-then-vector (e.g., friend-of-friend ranking). AWS services like Amazon Neptune Analytics support unified graph and vector storage for these hybrid queries. Real-world applications include fraud detection, cybersecurity, healthcare, retail, and enterprise knowledge management.
