Vector search

Vector search embeds Things and their parts and finds their nearest neighbors. It powers meaning-based search, duplicate detection, matching and Memory.

ContractCopy Anchor Link

• A Noun declares which fields embed. Long text fields embed in chunks.
• An embedding is keyed by the hash of its source. Changing the source re-embeds it; nothing else does.
• The embedding model is a Setting. Changing it rebuilds the index, and queries answer from the old index until the new one is complete.
• Vectors never leave the namespace that made them.
• similar returns the nearest Things with a score. resolve returns an existing Thing above a threshold or creates one, so an import never makes a duplicate.
• An Agent recalls its Memory by similarity, scoped to its own seat.
• Embeddings run through the AI gateway, so their Cost meters like any model call.

VerbsCopy Anchor Link

None.

InterfacesCopy Anchor Link

• API
• SDK
• CLI
• MCP

ExampleCopy Anchor Link

// benchtalent.co places contract designers. Before it adds a résumé, it looks for the same person.
import { $ } from 'startup-builder'

const lookalikes = await $.search('Ana Ruiz, product designer, ana@ruiz.design', { limit: 5 })