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...
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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...