Graph-Enabled RAG: The Architecture That Comes After Vector Search
Vector search made RAG usable. Structured, human-maintained knowledge makes it truly capable of reasoning. Here’s why SILKLEARN skipped the graph extraction race entirely.
Where We Are: The Vector Search Ceiling
Vector search has become the default approach for retrieval-augmented generation. Fast, scalable, and surprisingly effective — embedding documents into high-dimensional space and retrieving the nearest neighbors works well enough for many use cases. The ecosystem has rallied around it: every major cloud provider offers a vector database, and the pattern has become the baseline assumption behind most RAG architectures.
But the field is hitting its ceiling.
Similarity is not the same as relevance. And reasoning over relationships requires more than nearest-neighbor search. When a user asks a question that spans multiple documents, requires understanding causal chains, or demands a synthesized view of a whole topic area, vector retrieval hands back a bag of chunks and hopes the language model figures it out.
It often doesn't.
What GraphRAG Added
Microsoft's GraphRAG (2024) was a meaningful step forward. The approach extracts a knowledge graph from source documents — identifying entities, relationships, and communities of related concepts — and then runs queries over those communities rather than over raw chunks.
The performance difference on global reasoning questions is significant. "What are the main themes in this corpus?" is essentially impossible for vector search to answer well; it requires synthesis across all documents. GraphRAG handles this by operating at the level of entity communities rather than individual passages. The query can reason about structure, not just similarity.
This matters. It's the difference between retrieval and understanding.
