GraphRAG at Scale When Relationships Beat Embeddings

Embeddings are great at semantics and bad at relationships. GraphRAG captures the relationships embeddings average away — and at scale, that's often the gap that matters.

Vector RAG retrieves similar chunks. It doesn't retrieve related entities — the people, organizations, and concepts connected across your corpus. GraphRAG builds a graph of those relationships and retrieves along it, which is the right tool when the question is relational.

GraphRAG wins when the query is about relationships, not similarity:

• "Who works with whom on what?" — relational, graph wins.
• "What entities are connected to this event?" — relational, graph wins.
• "Find documents similar to this one" — semantic, vectors win.
• "Summarize this document" — semantic, vectors win.

The pattern: graph for relations, vectors for similarity, hybrid for both.

The production pattern is rarely graph-or-vectors. It's graph-plus-vectors: a graph for the relational retrieval, a vector index for the semantic, and a fusion step that uses both. The graph captures the structure; the vectors capture the meaning; the answer uses both.

• Build a graph of entities and relations.
• Keep a vector index for semantic retrieval.
• Fuse the two at query time.

• Entity extraction quality — the graph is only as good as the extraction.
• Graph construction cost — building the graph is more expensive than indexing vectors.
• Maintenance — the graph has to be updated as the corpus changes.

• GraphRAG before you've shown vectors are insufficient.
• GraphRAG without an eval that compares it to the vector baseline.
• GraphRAG when the queries are semantic, not relational.

GraphRAG at scale is the right tool when the queries are relational, not semantic. The production pattern is graph-plus-vectors, fused — and the decision is made on an eval, not on the appeal of the architecture.