Pinecone.io Client

A fetch based client for the Pinecone.io vector database with excellent TypeScript support.

Highlights

• Support for all vector operation endpoints
• Fully typed metadata with TypeScript generics
• Automatically remove null metadata values (Pinecone doesn't nulls)
• Supports modern fetch based runtimes (Cloudlflare workers, Deno, etc)
• In-editor documentation with IntelliSense/TS server
• Tiny package size. Less than 5kb gzipped
• Full e2e test coverage

Example Usage

import { PineconeClient } from 'pinecone-client';

// Specify the type of your metadata
type Metadata = { size: number, tags?: string[] | null };

// Instantiate a client
const pinecone = new PineconeClient<Metadata>({ namespace: 'test' });

// Upsert vectors with metadata.
await pinecone.upsert({
  vectors: [
    { id: '1', values: [1, 2, 3], metadata: { size: 3, tags: ['a', 'b', 'c'] } },
    { id: '2', values: [4, 5, 6], metadata: { size: 10, tags: null } },
  ],
});

// Query vectors with metadata filters.
const { matches } = await pinecone.query({
  topK: 2,
  id: '2',
  filter: { size: { $lt: 20 } },
  includeMetadata: true,
});

// typeof matches = {
//   id: string;
//   score: number;
//   metadata: Metadata;
// }[];

Install

Installation depends on runtime support for the Fetch API.

With Fetch

Runtime examples: Deno, Node v18+, Cloudflare Workers, browsers

npm install pinecone-client

import { PineconeClient } from 'pinecone-client';

const pinecone = new PineconeClient({ /* ... */ });

Without Fetch

For runtimes like Node.js v17 and below that don't support fetch, you will need to polyfill fetch. I suggest using cross-fetch.

npm install pinecone-client cross-fetch

import 'cross-fetch/polyfill';
import { PineconeClient } from 'pinecone-client';

const pinecone = new PineconeClient({ /* ... */ });

Setup

Once installed, you need to create an instance of the PineconeClient class to make API calls.

import { PineconeClient } from 'pinecone-client';

// A type representing your metadata
type Metadata = {};

const pinecone = new PineconeClient<Metadata>({
  apiKey: '<your api key>',
  baseUrl: '<your index url>'
  namespace: 'testing',
});

Both apiKey and baseUrl are optional and will be read from the following environment variables:

• process.env.PINECONE_API_KEY
• process.env.PINECONE_BASE_URL

API

The client supports all of the vector operations from the Pinecone API using the same method names and parameters. It also supports creating and deleting indexes.

For detailed documentation with links to the Pinecone docs, see the source code.

Supported methods:

• pinecone.delete()
• pinecone.describeIndexStats()
• pinecone.fetch()
• pinecone.query()
• pinecone.update()
• pinecone.upsert()
• pinecone.createIndex()
• pinecone.deleteIndex()

You can also find more example usage in the e2e tests.