@juspay/neurolink
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The pipe layer of an AI nervous system: one interface connecting provider neurons to your application, across three inference types — generate, stream and decide. `decide` returns typed, calibrated judgements from a non-generative model (~400ms, ~$0.00002
30 lines (29 loc) • 1.54 kB
JavaScript
/**
* Knowledge grounding — lexical-first host-supplied knowledge retrieval.
*
* A host application owns a structured *knowledge registry*: versioned entries
* describing concepts, configuration, taxonomies, procedures, and operating
* rules. NeuroLink owns the
* generic engine that ingests that registry, builds an in-memory lexical
* index once per instance, and — independently of tool routing — retrieves a
* small, token-bounded set of relevant entries before each main model call.
* The selected entries are attached as *ephemeral context*: visible to the
* current generation call, never persisted into conversation memory.
*
* This is retrieval-augmented generation WITHOUT vector retrieval. The first
* release ranks with exact identifiers, reviewed aliases, metadata filters,
* and weighted field-aware BM25 only. No embedding model, embedding service,
* or vector store is initialized. An LLM reranker and semantic retrieval are
* deferred behind an explicit evaluation gate.
*
* Ownership boundary: NeuroLink defines the contracts below and the engine;
* it never contains host product content. The host supplies content and
* per-request scope. The engine stays provider-neutral.
*
* Type names are globally prefixed `Knowledge*` to satisfy the repo's
* unique-type-name rule and to avoid collision with the pre-existing
* web-search `grounding.ts` vocabulary (`EnhancedGroundingMetadata` et al.),
* which is a different concept (Google search grounding, not product
* knowledge).
*/
export {};