Upstash Vector Node.js Client ·

Note

This project is in GA Stage.

The Upstash Professional Support fully covers this project. It receives regular updates, and bug fixes. The Upstash team is committed to maintaining and improving its functionality.

@upstash/vector is an HTTP/REST based client for Typescript, built on top of Upstash REST API.

It is the only connectionless (HTTP based) Vector client and designed for:

• Serverless functions (AWS Lambda ...)
• Cloudflare Workers
• Next.js, Jamstack ...
• Client side web/mobile applications
• WebAssembly
• and other environments where HTTP is preferred over TCP.

See the list of APIs supported.

Quick Start

Install

Node.js

npm install @upstash/vector

Create Index

Create a new index on Upstash

Basic Usage:

import { Index } from "@upstash/vector";

type Metadata = {
  title: string,
  genre: 'sci-fi' | 'fantasy' | 'horror' | 'action'
  category: "classic" | "modern"
}

const index = new Index<Metadata>({
  url: "<UPSTASH_VECTOR_REST_URL>",
  token: "<UPSTASH_VECTOR_REST_TOKEN>",
});

//Upsert data
await index.upsert([{
  id: 'upstash-rocks',
  vector: [
    .... // embedding values
  ],
  metadata: {
    title: 'Lord of The Rings',
    genre: 'fantasy',
    category: 'classic'
  }
}])

// Upsert data as plain text.
await index.upsert([{
  id: 'tokyo',
  data: "Tokyo is the capital of Japan.",
}])

//Upsert data alongside with your embedding
await index.upsert([{
  id: 'tokyo',
  data: "Tokyo is the capital of Japan.",
  vector: [......]
}])


//Query data
const results = await index.query<Metadata>(
  {
    vector: [
      ... // query embedding
    ],
    includeVectors: true,
    includeMetadata: true,
    topK: 1,
    filter: "genre = 'fantasy' and title = 'Lord of the Rings'"
  },
  {
    namespace: "example-namespace"
  }
)

//Query with your data
const results = await index.query(
  {
    data: "Where is the capital of Japan",
    topK: 1,
  },
)

//Query with your data
const results = await index.query(
  {
    vector: [
      ... // query embedding
    ],
    includeData: true, // Returns data associated with your vector.
    topK: 1,
  },
)

// If you wanna learn more about filtering check: [Metadata Filtering](https://upstash.com/docs/vector/features/filtering)

//Update data
await index.upsert(
  {
    id: "upstash-rocks",
    metadata: {
      title: 'Star Wars',
      genre: 'sci-fi',
      category: 'classic'
    }
  },
  {
    namespace: "namespace"
  }
);

//Delete record
await index.delete("upstash-rocks", {namespace: "example-namespace"});

//Delete many by id
await index.delete(["id-1", "id-2", "id-3"]);

//Fetch records by their IDs
await index.fetch(["id-1", "id-2"], {namespace: "example-namespace"});

//Fetch records by their IDs
await index.fetch(["id-1", "id-2"], {namespace: "example-namespace", includeData:true});

//Fetch records with range
await index.range(
  {
    cursor: 0,
    limit: 5,
    includeVectors: true,
  },
  {
    namespace: "example-namespace"
  }
);

//Reset index
await index.reset();

//Info about index
await index.info();

//Random vector based on stored vectors
await index.random({namespace: "example-namespace"});

//List existing namesapces
await index.listNamespaces();

//Delete a namespace
await index.deleteNamespace("namespace-to-be-deleted");

Namespaces

Upstash Vector allows you to partition a single index into multiple isolated namespaces. Each namespace functions as a self-contained subset of the index, in which read and write requests are only limited to one namespace. To learn more about it, see Namespaces

Example

import { Index } from "@upstash/vector";

type Metadata = {
  title: string;
  genre: "sci-fi" | "fantasy" | "horror" | "action";
  category: "classic" | "modern";
};

const index = new Index<Metadata>({
  url: "<UPSTASH_VECTOR_REST_URL>",
  token: "<UPSTASH_VECTOR_REST_TOKEN>",
});

const namespace = index.namespace("example-namespace");

//Upsert Data
await namespace.upsert([{
  id: 'upstash-rocks',
  vector: [
    .... // embedding values
  ],
  metadata: {
    title: 'Lord of The Rings',
    genre: 'fantasy',
    category: 'classic'
  }
}])

//Query Vector
const results = await namespace.query<Metadata>(
  {
    vector: [
      ... // query embedding
    ],
    includeVectors: true,
    includeMetadata: true,
    topK: 1,
    filter: "genre = 'fantasy' and title = 'Lord of the Rings'"
  },
)

//Delete Record
await namespace.delete("upstash-rocks");

//Fetch records by their IDs
await namespace.fetch(["id-1", "id-2"]);

Metadata Filtering

If you wanna learn more about filtering check: Metadata Filtering

Troubleshooting

We have a Discord for common problems. If you can't find a solution, please open an issue.

Docs

See the documentation for details.

Contributing

Install Bun

Vector Database

Create a new index on Upstash and copy the url and token.

Running tests

bun run test

Building

bun run build

Contributing

Make sure you have Bun.js installed and have those relevant keys with specific vector dimensions:

## Vector dimension should be 3842
UPSTASH_VECTOR_REST_URL="XXXXX"
UPSTASH_VECTOR_REST_TOKEN="XXXXX"