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Vector Search allows you to find information in your world based on its meaning, rather than exact keyword matches.

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How it Works

  1. Ingestion: When you insert triples into your world, the API automatically generates embeddings for the subject, predicate, and object.
  2. Indexing: These embeddings are stored in a vector index.
  3. Querying: When you perform a search, your query is embedded, and the API finds the semantically closest triples in the index.

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Use Cases

  • RAG (Retrieval Augmented Generation): Retrieve relevant context for an LLM based on a user’s prompt.
  • Fuzzy Finding: Find entities even if the user doesn’t know their exact URI or name.