[ktyptorio]

There are two steps:

Vector search (HNSW): Find top-k similar entities/text units from the query embedding

Graph traversal (BFS): From those seed entities, traverse relationships (up to 2 hops by default) to find connected entities

This catches both semantically similar entities AND structurally related ones that might not match the query text.

Implementation: https://github.com/gibram-io/gibram/blob/main/pkg/engine/eng...

[kordlessagain]

This is how I did it a few years back while working for a set store company. It works well.