An unofficial fetch based client for the Pinecone.io vector database with excellent TypeScript support.
Pinecone recently released a similar client. It's a great option if you aren't picky about fully typed metadata.
- 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
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;
// }[];
Warning: This package is native ESM and no longer provides a CommonJS export. If your project uses CommonJS, you will have to convert to ESM or use the dynamic import()
function. Please don't open issues for questions regarding CommonJS / ESM.
Runtimes
- Supported: Deno, Node v18+, Cloudflare Workers, browsers
- Unsupported: Anything without a native fetch implementation (Node<v17)
npm install pinecone-client
import { PineconeClient } from 'pinecone-client';
const pinecone = new PineconeClient({ /* ... */ });
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
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.
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.