@molecule/api-ai-rag-llm

@molecule/api-ai-rag-llm — the default LLM-composed ai-rag provider.

Implements @molecule/api-ai-rag's AIRagProvider contract by composing two existing molecule capabilities rather than reimplementing them:

• Retrieval — @molecule/api-semantic-search (indexDocuments / search / removeDocuments), which composes the bonded ai-embeddings
  • ai-vector-store providers to embed a corpus and similarity-search it.
• Generation — the bonded @molecule/api-ai chat provider, prompted to answer using ONLY the retrieved context and to cite sources as [n].

Bond it like any other capability, then ingest(...) a corpus and query(...) it. Everything underneath is swappable via bond() — different embeddings, vector store, or chat model, with no consumer changes. It is the interchangeable default for the ai-rag core; swap bond('ai-rag', myProvider) to replace it.

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.

Quick Start

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

// Bond the retrieval + generation dependencies first, then RAG itself.
bond('ai-embeddings', embeddings)
bond('ai-vector-store', 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

provider

Installation

npm install @molecule/api-ai-rag-llm @molecule/api-ai @molecule/api-ai-rag @molecule/api-i18n @molecule/api-semantic-search

API

Constants

provider

Default, batteries-included Retrieval-Augmented-Generation provider.

Composes @molecule/api-semantic-search for retrieval with the bonded @molecule/api-ai chat provider for generation. Bond it with bond('ai-rag', provider) after bonding an ai provider plus the ai-embeddings + ai-vector-store providers that semantic-search needs.

const provider: AIRagProvider

Core Interface

Implements @molecule/api-ai-rag interface.

Bond Wiring

Setup function to register this provider with the core interface:

import { setProvider } from '@molecule/api-ai-rag'
import { provider } from '@molecule/api-ai-rag-llm'

export function setupAiRagLlm(): void {
  setProvider(provider)
}

Injection Notes

Requirements

Peer dependencies:

• @molecule/api-ai ^1.0.1
• @molecule/api-ai-rag ^1.0.1
• @molecule/api-i18n ^1.0.1
• @molecule/api-semantic-search ^1.0.1

Runtime Dependencies

• @molecule/api-ai
• @molecule/api-ai-rag
• @molecule/api-i18n
• @molecule/api-semantic-search

This provider needs THREE bonds present at runtime: a ai chat provider (generation) plus the ai-embeddings and ai-vector-store providers (retrieval, via @molecule/api-semantic-search). Bond those before calling query/ingest, or the underlying accessors throw. query still calls the model when retrieval returns zero chunks, but instructs it to say it has no information rather than hallucinate. The whole capability is swappable: bond('ai-rag', myProvider) replaces this composed 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.