Vector-only retrieval-augmented generation hits a hard wall in enterprise settings. The standard approach of chunking documents, embedding them into vectors, and retrieving similar results works fine for semantic search. But it fails when data contains meaningful relationships that vectors cannot capture.

The problem surfaces in complex domains like supply chain, financial compliance, and fraud detection. These fields deal with highly interconnected data where structure matters as much as content. A vector database can find semantically similar information but cannot answer questions requiring multi-hop reasoning across relationships. A query like "How will the delay in Component X impact our Q3 deliverable for Client Y?" requires understanding that Component X belongs to Client Y's deliverable. Vector embeddings alone cannot represent this connection.

Graph-enhanced RAG addresses this gap by combining vector search with graph databases. Instead of treating documents as isolated chunks, graph approaches map entities and their relationships explicitly. Component X becomes a node connected to Client Y's deliverable node through a "belongs-to" edge. Multi-hop queries then traverse these connections to find answers that pure similarity matching would miss.

The architectural shift involves three key changes. First, data ingestion extracts entities and relationships from documents, building a knowledge graph alongside embeddings. Second, retrieval combines vector search with graph traversal. A query triggers both similarity matching and relationship-based path finding. Third, the LLM receives not just similar chunks but structured context showing how retrieved information connects.

This hybrid approach trades simplicity for accuracy. Vector-only RAG remains easy to implement and suits unstructured domains well. Graph-enhanced RAG requires careful data modeling and adds operational complexity. The payoff arrives in domains where relationships define the answer. Supply chain questions need to trace component dependencies. Fraud detection requires following transaction chains. Compliance queries often demand tracing decision paths across interconnected policies.

Enterprise teams building production RAG systems face a choice. Vector search