Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

Graph-enhanced RAG architectures combine vector search with graph databases to enable multi-hop reasoning over interconnected enterprise data, addressing critical limitations of vector-only systems in domains like supply chain and financial compliance where structural relationships are essential. Moving beyond semantic similarity alone, hybrid retrieval patterns extract and maintain entity relationships during ingestion, dramatically improving accuracy for complex business questions—though requiring mitigation strategies for latency (200-500ms vs. 50-100ms) through semantic caching and consistency management via TTL/CDC pipelines. IT organizations must evaluate Graph RAG adoption based on data interconnectedness and reasoning complexity requirements, as the architectural shift demands infrastructure investment in graph databases and entity extraction pipelines but delivers substantial ROI through reduced hallucination and precise risk identification in mission-critical systems.