@molecule/api-ai-rag

Auto-generated, AI-first package reference for the molecule.dev ecosystem. It is written to be read by coding agents as much as by people, and is generated from this package's source — edit src/index.ts JSDoc, not this file.

@molecule/api-ai-rag — the Retrieval-Augmented Generation contract.

Defines the AIRagProvider interface (ingest / query / remove) plus its input/result types, and the bond accessor (setProvider / getProvider / requireProvider / …). It ships NO implementation — bond a concrete provider such as @molecule/api-ai-rag-llm, which composes @molecule/api-semantic-search (retrieval) with the bonded @molecule/api-ai chat provider (generation) to answer questions grounded in your own documents.

Everything underneath is swappable via bond() — different embeddings, vector store, chat model, or RAG strategy, with no consumer changes.

Quick Start

import { setProvider as setEmbeddings } from '@molecule/api-ai-embeddings'
import { provider as embeddings } from '@molecule/api-ai-embeddings-openai'
import { setProvider as setVectorStore } from '@molecule/api-ai-vector-store'
import { provider as vectorStore } from '@molecule/api-ai-vector-store-memory'
import { bond } from '@molecule/api-bond'
import { provider as ai } from '@molecule/api-ai-anthropic'
import { provider as rag } from '@molecule/api-ai-rag-llm'
import { requireProvider } from '@molecule/api-ai-rag'

// Wire the retrieval + generation dependencies first, then RAG itself.
// ai-embeddings and ai-vector-store keep their OWN singletons — wire them with
// their packages' setProvider(); a generic bond('ai-embeddings', …) does NOT
// reach them and query()/ingest() would throw "not configured" at runtime.
setEmbeddings(embeddings)
setVectorStore(vectorStore)
bond('ai', ai)
bond('ai-rag', rag)

// Ingest a corpus.
await requireProvider().ingest({
  collection: 'handbook',
  documents: [
    { id: 'pto', text: 'Employees accrue 15 PTO days per year.' },
    { id: 'wfh', text: 'Remote work is allowed up to 3 days per week.' },
  ],
})

// Ask a grounded question.
const { answer, sources, usage } = await requireProvider().query({
  collection: 'handbook',
  query: 'How many PTO days do I get?',
  topK: 5,
})
// answer: "You accrue 15 PTO days per year [1]."  sources: [{ id: 'pto', … }]

Type

core

Installation

npm install @molecule/api-ai-rag @molecule/api-ai @molecule/api-ai-vector-store @molecule/api-bond @molecule/api-i18n @molecule/api-semantic-search

API

Interfaces

AIRagConfig

Config options for an AI RAG bond.

interface AIRagConfig {
  [key: string]: unknown
}

AIRagProvider

Retrieval-Augmented Generation contract.

A provider ingests a document corpus, then answers questions grounded in the most relevant retrieved chunks. The default provider composes @molecule/api-semantic-search (retrieval) with the bonded @molecule/api-ai chat provider (generation); swap either underlying bond without touching consumers.

interface AIRagProvider {
  /** Human-readable provider name (the default composed provider is `'default'`). */
  readonly name: string
  /**
   * Embed and index a corpus of documents into a collection.
   *
   * @param input - The collection, documents, and optional embedding model.
   * @returns The number of documents indexed and the embedding dimensionality.
   */
  ingest(input: IngestInput): Promise<IngestResult>
  /**
   * Retrieve the most relevant chunks for a question, then generate an answer
   * grounded in (and citing) them.
   *
   * @param input - The collection, question, and optional retrieval/generation overrides.
   * @returns The grounded answer, the retrieved sources, and token usage.
   */
  query(input: RagQueryInput): Promise<RagQueryResult>
  /**
   * Remove previously-ingested documents from a collection by their ids.
   *
   * @param input - The collection and the ids of the documents to remove.
   * @returns A promise that resolves once the documents have been deleted.
   */
  remove(input: RemoveInput): Promise<void>
}

IngestInput

Parameters for {@link AIRagProvider.ingest}.

interface IngestInput {
  /** The collection/namespace to index the documents into. */
  collection: string
  /** The documents to embed and upsert. An empty array is a no-op. */
  documents: RagDocument[]
  /** Embedding model override (provider-specific). Falls back to the provider default. */
  model?: string
}

IngestResult

Result of {@link AIRagProvider.ingest}.

interface IngestResult {
  /** Number of documents embedded and upserted. */
  indexed: number
  /** Dimensionality of the embedding vectors (0 when no documents were indexed). */
  dimension: number
}

RagDocument

A single document to ingest into a RAG collection.

interface RagDocument {
  /** Stable unique identifier for this document (used as the vector record id). */
  id: string
  /** The document's text — embedded, stored, and returned as a source on retrieval. */
  text: string
  /** Arbitrary metadata stored alongside the vector and usable as a query filter. */
  metadata?: Record<string, unknown>
}

RagQueryInput

Parameters for {@link AIRagProvider.query}.

interface RagQueryInput {
  /** The collection/namespace to retrieve context from. */
  collection: string
  /** The natural-language question to answer. */
  query: string
  /** Number of chunks to retrieve and ground the answer on (default 5). */
  topK?: number
  /** Optional metadata filters to narrow retrieval before scoring. */
  filter?: MetadataFilter[]
  /** Minimum similarity score threshold — retrieved chunks below this are excluded. */
  minScore?: number
  /** Extra system guidance appended to the grounding instructions for the answer. */
  system?: string
  /** AI chat model override (provider-specific). Falls back to the provider default. */
  model?: string
  /** Named AI provider to answer with. Omit to use the bonded singleton AI provider. */
  provider?: string
  /** Abort signal forwarded to the AI chat call to cancel in-flight generation. */
  signal?: AbortSignal
}

RagQueryResult

Result of {@link AIRagProvider.query}.

interface RagQueryResult {
  /** The generated answer, grounded in and citing the retrieved sources. */
  answer: string
  /** The retrieved chunks the answer was grounded on, ranked most-similar first. */
  sources: SearchHit[]
  /** Token usage reported by the AI provider for the answer generation, if any. */
  usage?: TokenUsage
}

RemoveInput

Parameters for {@link AIRagProvider.remove}.

interface RemoveInput {
  /** The collection/namespace to remove documents from. */
  collection: string
  /** Ids of the documents to remove. */
  ids: string[]
}

Functions

getAllProviders()

Retrieves all named AI RAG providers as a Map keyed by provider name.

function getAllProviders(): Map<string, AIRagProvider>

Returns: Map of provider name → AIRagProvider.

getProvider()

Retrieves the singleton AI RAG provider, or null if none is bonded.

Falls back to a single named provider when no singleton is bonded. When multiple named providers are bonded the fallback declines (returns null) because the choice is ambiguous — those call sites must use getProviderByName(name) explicitly.

function getProvider(): AIRagProvider | null

Returns: The bonded AI RAG provider, or null.

getProviderByName(name)

Retrieves a named AI RAG provider, or null if not bonded.

function getProviderByName(name: string): AIRagProvider | null

• name — The provider name.

Returns: The named AI RAG provider, or null.

hasProvider(name)

Checks whether an AI RAG provider is currently bonded.

function hasProvider(name?: string): boolean

• name — Optional provider name. If omitted, checks the singleton.

Returns: true if the provider is bonded.

requireProvider()

Retrieves the bonded AI RAG provider, throwing if none is bonded.

Routes through getProvider() so the same single-named-bond fallback applies.

function requireProvider(): AIRagProvider

Returns: The bonded AI RAG provider.

setProvider(provider)

Registers the default AI RAG provider in singleton mode.

function setProvider(provider: AIRagProvider): void

• provider — The default provider implementation for this process.

Available Providers

Provider	Package
Ai Rag	@molecule/api-ai-rag-llm

Injection Notes

Requirements

Peer dependencies:

• @molecule/api-ai ^1.0.1
• @molecule/api-ai-vector-store ^1.0.1
• @molecule/api-bond ^1.0.1
• @molecule/api-i18n ^1.0.1
• @molecule/api-semantic-search ^1.0.1

Runtime Dependencies

• @molecule/api-ai
• @molecule/api-ai-vector-store
• @molecule/api-bond
• @molecule/api-i18n
• @molecule/api-semantic-search

A RAG provider is not built in — bond one (e.g. @molecule/api-ai-rag-llm). The llm provider needs THREE dependencies present at runtime: an ai chat provider (generation) plus the ai-embeddings and ai-vector-store providers (retrieval, via @molecule/api-semantic-search). Wire each through its own core's registration API: bond('ai', …) works for the chat provider, but ai-embeddings and ai-vector-store are local-singleton cores — they are wired ONLY via their packages' setProvider() (a generic bond('ai-embeddings', …) is silently ignored by their accessors). Wire all three before calling query/ingest, or the underlying accessors throw. The whole capability is swappable: bond('ai-rag', myProvider) replaces the default with your own AIRagProvider.

E2E Tests

Integration checklist — drive the real UI (live preview, no mocks), adapt each item to this app's actual screens/flows, and check every box off one by one. A box you can't check is an integration bug to fix — not a skip:

• After ingesting a known document set, ask a question whose answer is IN the corpus: the returned answer USES the retrieved content — it states the specific fact from the source doc (with the [n] citation query() returns), NOT the base model's generic prior. If it's right only because the model already knew the fact, retrieval isn't actually wired.
• Retrieval genuinely runs — the answer tracks the corpus. Remove the source doc (remove({ collection, ids })) or ingest a corrected version, then re-ask: the answer changes or disappears; it must NOT keep reciting a fact whose document is gone.
• An out-of-corpus question is DECLINED ("I don't have information on that" / "not in the documents"), not answered from the model's own prior. This is the key RAG failure to catch — a confident, well-formed answer to a question no ingested document supports is a hallucination and fails the box.
• A newly ingested document is answerable immediately: ingest() one more doc, then ask about its content in the same session — it's retrieved with no rebuild or redeploy.
• Every source query() returns points to a really-ingested document (its id/text matches a RagDocument you actually ingested), and each [n] citation in the answer maps to one of those returned sources — no fabricated ids and no dangling [n] with no matching source.
• Retrieval is SCOPED to the caller's own data: a query resolves only the authenticated user's/tenant's collection (or metadata filter) and can NOT surface another tenant's private documents in answer or sources. Confirm by ingesting two tenants' docs and querying as one — the other's content never appears.
• The RAG call is server-side only — ingest/query run in an API route, and the embeddings/AI provider key is never shipped to or readable in the browser.