Retrieval-augmented generation (RAG) commonly relies on vector databases to ground large language models using private data by chunking documents and embedding them for semantic search. However, in enterprise contexts like supply chains and compliance where data is deeply interconnected, vector-only RAG misses critical structural links, causing issues with multi-hop reasoning and factual accuracy. A graph-enhanced RAG approach addresses this by integrating graph databases with vector search. This hybrid pattern enforces structure during ingestion by extracting entities and relationships, stores data in a graph with vector embeddings on nodes, and performs hybrid retrieval involving vector scans followed by graph traversals. This method enables accurate contextual answers to complex questions, such as assessing downstream risks in supply chains. Production use requires tackling latency impacts with semantic caching and maintaining edge freshness via TTL or sync from source systems. A decision framework suggests vector-only RAG for flatter data and lower latency needs, and graph RAG for regulated domains or use cases demanding explainability and multi-hop querying. This architectural evolution bridges semantic flexibility and structural truth, enhancing LLM grounding for complex enterprise scenarios.
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