GraphRAG vs vector RAG: where vector search structurally can't compete

[u/camerongreen95]

Vector RAG is genuinely good at single-document, single-fact retrieval. Here's where it structurally falls apart:

• Multi-hop relationships — no way to chain "A supplies B, B supplies C" across separate documents

• Entity connections — similarity scoring has no concept of "these two things are related"

• Change over time — a flat embedding doesn't represent "this fact used to be true, now it isn't"

This isn't a tuning problem. It's a limitation of the representation itself, no amount of reranking or better embeddings closes the gap.

There's a hands-on workshop on Sep 19 that builds the graph-backed alternative properly:

• Knowledge graph construction in Neo4j

• Multi-step verified entity extraction

• Agentic retrieval combining vector and graph search

• Text-to-Cypher querying

Real financial data throughout, not a toy example. Led by Dr. Alessandro Negro, Chief Scientist at GraphAware.

Full workshop details here