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At a glance

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The philosophical difference

Vector databases (such as Pinecone, Qdrant, or pgvector) answer “what is semantically similar?” They index embeddings and return the nearest neighbors. That is a useful primitive, and it is one signal among three in Worlds’ hybrid search. Worlds stores facts as RDF triples with typed relationships. Retrieval can walk the graph, filter by relationship, and fuse results with vector and full-text signals. When an agent asks “Ethan’s manager”, a vector store returns the most similar text, which can be another person’s manager. Worlds resolves the exact entity, then lets SPARQL traverse the actual relationship.

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When Worlds fits

  • Answers depend on relationships, not just similarity: ownership, reporting lines, dependencies, provenance.
  • You need exact answers you can verify with a deterministic query.
  • Facts change and stale chunks must not contradict current state.

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When Worlds does not fit

  • Your workload is pure similarity: deduplication, recommendations, anomaly detection over dense data.
  • You have no relational structure to model.
  • You are already on a vector store and similarity-only recall is sufficient.

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Coexistence

You can keep a vector index for fuzzy recall and add Worlds for the facts that must be exact. Hybrid search inside Worlds already uses vector similarity as one signal, so moving from a vector-only store to Worlds keeps the semantic layer while adding graph precision.