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

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.

Pinecone vector store provider for molecule.dev.

Maps molecule collections to Pinecone serverless indexes, providing similarity search, metadata filtering, and batch upsert operations.

Quick Start

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

setProvider(provider) // at startup — lazy; reads PINECONE_API_KEY on first use
// or pass explicit config: setProvider(createProvider({ apiKey }))

Type

provider

Installation

npm install @molecule/api-ai-vector-store-pinecone @molecule/api-ai-vector-store @pinecone-database/pinecone

API

Interfaces

`PineconeConfig`

Configuration for the Pinecone vector store provider.

interface PineconeConfig {
  /** Pinecone API key. Falls back to `PINECONE_API_KEY` env var. */
  apiKey?: string
  /** Cloud provider for serverless indexes. Defaults to `'aws'`. */
  cloud?: 'aws' | 'gcp' | 'azure'
  /** Cloud region for serverless indexes. Defaults to `'us-east-1'`. */
  region?: string
  /** Prefix for Pinecone index names (collections map to indexes). Defaults to `'mol-'`. */
  indexPrefix?: string
  /** Default distance metric for new collections. Defaults to `'cosine'`. */
  defaultMetric?: DistanceMetric
  /** Whether to wait for index readiness after creation. Defaults to `true`. */
  waitUntilReady?: boolean
}

Functions

`createProvider(config)`

Creates a Pinecone vector store provider instance.

function createProvider(config?: PineconeConfig): AIVectorStoreProvider

config — Pinecone configuration.

Returns: An AIVectorStoreProvider backed by Pinecone.

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-pinecone'

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

Injection Notes

Requirements

Peer dependencies:

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

Environment Variables

PINECONE_API_KEY (required) — Pinecone API key
- Setup: Create an API key in the Pinecone console (API Keys page).
- Get it here: https://app.pinecone.io/
- Example: pcsk_...

Runtime Dependencies

@molecule/api-ai-vector-store
@pinecone-database/pinecone
Config: PINECONE_API_KEY (required, SERVER-side only) — the Pinecone SDK throws at construction time when it is missing. The exported provider is a lazy proxy, so this fires on first use, NOT at import time; createProvider() throws eagerly.
Collections are serverless indexes created on demand (name prefix mol-) in config.cloud/config.region (defaults aws/us-east-1 — set these for other regions; existing indexes are never moved). With waitUntilReady (default true) createCollection blocks until the index is live, which can take ~a minute — create collections at startup/provisioning time, not inside request handlers.

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).

`@molecule/api-ai-vector-store-pinecone`

How it works

Reference