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

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via cosine similarity — is effective for unstructured semantic search. However, for enterprise domains characterized by highly interconnected data (supply chain, financial compliance, fraud detection), vector-only RAG often fails.