@molecule/api-ai-vector-store-pgvector

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.

PostgreSQL pgvector vector store provider for molecule.dev.

Stores each molecule collection as its own Postgres table (default prefix mol_vectors_) with HNSW indexes, using the pgvector extension.

Quick Start

import { setProvider, requireProvider } from '@molecule/api-ai-vector-store'
import { provider } from '@molecule/api-ai-vector-store-pgvector'

setProvider(provider) // at startup — lazy; reads DATABASE_URL / opens its pool on first use
// or pass explicit config: setProvider(createProvider({ connectionString }))

Type

provider

Installation

npm install @molecule/api-ai-vector-store-pgvector @molecule/api-ai-vector-store pg pgvector
npm install -D @types/pg

API

Interfaces

PgvectorConfig

Configuration for the pgvector vector store provider.

interface PgvectorConfig {
  /** PostgreSQL connection string (e.g. `postgresql://user:pass@host:5432/db`). Falls back to `DATABASE_URL` env var. */
  connectionString?: string
  /** Schema to use for vector store tables. Defaults to `'public'`. */
  schema?: string
  /** Table name prefix for vector store tables. Defaults to `'mol_vectors_'`. */
  tablePrefix?: string
  /** Default distance metric for new collections. Defaults to `'cosine'`. */
  defaultMetric?: DistanceMetric
  /** Connection pool size. Defaults to 5. */
  poolSize?: number
}

Functions

clampTopK(value)

[M7-2] Coerce a requested topK into a bounded positive integer for SAFE interpolation into the LIMIT clause (it is not a bound $N parameter). topK is typed number, but a caller forwarding an untrusted value (e.g. req.body.topK cast to any) could otherwise inject SQL or request an unbounded scan. Non-finite / < 1 falls back to the default 10; capped at 10_000.

function clampTopK(value: unknown): number

• value — The caller-supplied topK (untyped at runtime).

Returns: A safe integer in [1, 10000].

createProvider(config)

Creates a pgvector vector store provider instance.

function createProvider(config?: PgvectorConfig): PgvectorProvider

• config — PostgreSQL + pgvector configuration.

Returns: An AIVectorStoreProvider backed by PostgreSQL with pgvector.

Constants

provider

The provider implementation.

const provider: AIVectorStoreProvider

Core Interface

Implements @molecule/api-ai-vector-store interface.

Bond Wiring

Setup function to register this provider with the core interface:

import { setProvider } from '@molecule/api-ai-vector-store'
import { provider } from '@molecule/api-ai-vector-store-pgvector'

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

Injection Notes

Requirements

Peer dependencies:

• @molecule/api-ai-vector-store >=1.0.1

Runtime Dependencies

• @molecule/api-ai-vector-store

• pg

• pgvector

• Owns its own pg.Pool — it does NOT go through the @molecule/api-database bond. Connection comes from config.connectionString or the DATABASE_URL env var; poolSize (default 5) is independent of the app's database pool.

• Requires the pgvector extension. On first use it runs CREATE EXTENSION IF NOT EXISTS vector — the connecting role must be allowed to create extensions, otherwise install vector up front via a migration/DBA.

• Auto-creates its tables: a mol_vectors_collections registry plus one mol_vectors_<collection> table (+ HNSW index) per collection — no migration needed beyond the extension. topK is clamped to [1, 10000] before hitting SQL.

E2E Tests

Integration checklist — drive the real flow (no mocks): upsert real vectors, run real query calls, and verify through the FEATURE this store powers (semantic search / RAG retrieval / related-items) plus direct property checks on the returned VectorSearchResult[]. Adapt each item to this app's actual corpus/screens and check every box off. A box you can't check is an integration bug to fix — not a skip:

• upsert records (each with a stable id + metadata), then query with an embedding: results come back ranked by score (highest first), the semantically closest stored item is result #1 and unrelated items rank lower — the whole point. score is a sane similarity (bounded, ~0–1, higher = closer) and each hit's record.id / record.metadata come back intact.
• topK is honored: a query with topK: k returns AT MOST k results, best-first — never more, never unordered.
• Metadata filter works: a query carrying a MetadataFilter (e.g. { field: 'userId', operator: 'eq', value }) returns only records matching the filter and never leaks non-matching ones.
• Collection/namespace ISOLATION: a query scoped to one collection never returns another collection's vectors — the multi-tenant boundary that keeps one user's private docs out of another's results. Confirm with two collections (or two owner ids) that a scoped query returns only its own.
• delete removes a record: after delete({ collection, ids }) the vector stops appearing in query results (and fetch omits it).
• The feature built on the store returns MEANING-ranked results end-to-end in the UI — a semantic-search / RAG / related-items query surfaces the relevant items first, not a keyword or insertion-order match. This store does NOT embed text itself, so confirm it composes with @molecule/api-ai-embeddings (query text → embedding → query).
• Every upsert / query runs SERVER-SIDE — the provider/store key stays on the server and never ships in the browser bundle (the package is server-only; a client import throws by design).