GraphRAG in Production: When Vector Search Fails at Multi-Hop Reasoning
Your RAG pipeline returns confident, well-formatted answers. The embeddings are tuned, the chunk size is optimized, and retrieval scores look great. Then a user asks "Which suppliers affected by the port strike also have contracts expiring this quarter?" and the system returns irrelevant fragments about port logistics and contract management — separately, never connecting them. This is the multi-hop reasoning gap, and it's where vector search quietly fails.
The failure isn't a tuning problem — it's architectural. Vector similarity finds documents that look like the query but cannot traverse relationships between entities scattered across different documents. GraphRAG — retrieval augmented generation backed by knowledge graphs — addresses this by making entity relationships first-class retrieval objects. But shipping it to production is harder than the demos suggest.
