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

Standard chunk-and-embed RAG struggles with structured enterprise problems because it can lose important relationships in connected data.

The article proposes a three-layer hybrid architecture: extract entities and links at ingestion, store them in a graph database with embeddings, then combine vector search with graph traversal at retrieval.

This graph-enhanced approach improves multi-hop reasoning and helps answer impact, dependency, and risk questions more reliably in production.

By preserving relationships, the design can reduce hallucinations and perform better on use cases like supply chain risk analysis.