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, tr

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/engine.go#L662-L778 https://github.com/gibram-io/gibram/blob/main/pkg/engine/eng...