Why not only vector search?
Standard vector search retrieves semantically similar text, but agents require factual precision. If an agent searches for “Ethan’s manager”, a pure vector query might return “Gregory’s manager” because their text embeddings are nearly identical in vector space.Why not just RDF and SPARQL?
While SPARQL supports structured logic and keyword search, it cannot perform semantic vector retrieval. Worlds provides open-source infrastructure for indexed RDF stores built specifically for autonomous agents.Hybrid retrieval
Rather than relying on a single index, Worlds fuses three complementary signals to guarantee retrieval accuracy. By layering keyword precision (BM25) and structural graph filters on top of vector embeddings, hybrid search ensures the agent disambiguates the exact item it needs before executing strict symbolic logic.Signals
| Signal | Technique | What it captures |
|---|---|---|
| Semantic | Vector embeddings, 1536-dim | Conceptual meaning |
| Keyword | FTS5 / BM25 | Exact term matches |
| Graph context | RDF relationship filters | Structural relationships |