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pgvector/pgvector-node: pgvector support for Node.js, Deno, and Bun (and TypeScript)

pgvector support for Node.js, Deno, and Bun (and TypeScript)

Supports node-postgres, Knex.js, Objection.js, Kysely, Sequelize, pg-promise, Prisma, Postgres.js, Slonik, TypeORM, MikroORM, Drizzle ORM, and Bun SQL

Run:

And follow the instructions for your database library:

Or check out some examples:

Embeddings with OpenAI
Binary embeddings with Cohere
Sentence embeddings with Transformers.js
Hybrid search with Transformers.js
Sparse search with Text Embeddings Inference
Morgan fingerprints with RDKit.js
Recommendations with Disco
Horizontal scaling with Citus
WebAssembly with PGLite
Bulk loading with COPY

Enable the extension

await client.query('CREATE EXTENSION IF NOT EXISTS vector');

import pgvector from 'pgvector/pg';

await pgvector.registerTypes(client);

or a pool

pool.on('connect', async function (client) {
  await pgvector.registerTypes(client);
});

Create a table

await client.query('CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))');

Insert a vector

await client.query('INSERT INTO items (embedding) VALUES ($1)', [pgvector.toSql([1, 2, 3])]);

Get the nearest neighbors to a vector

const result = await client.query('SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5', [pgvector.toSql([1, 2, 3])]);

Add an approximate index

await client.query('CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)');
// or
await client.query('CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)');

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Import the library

import pgvector from 'pgvector/knex';

Enable the extension

await knex.schema.createExtensionIfNotExists('vector');

Create a table

await knex.schema.createTable('items', (table) => {
  table.increments('id');
  table.vector('embedding', 3);
});

Insert vectors

const newItems = [
  {embedding: pgvector.toSql([1, 2, 3])},
  {embedding: pgvector.toSql([4, 5, 6])}
];
await knex('items').insert(newItems);

Get the nearest neighbors to a vector

const items = await knex('items')
  .orderBy(knex.l2Distance('embedding', [1, 2, 3]))
  .limit(5);

Also supports maxInnerProduct, cosineDistance, l1Distance, hammingDistance, and jaccardDistance

Add an approximate index

await knex.schema.alterTable('items', function (table) {
  table.index(knex.raw('embedding vector_l2_ops'), 'index_name', 'hnsw');
});

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Import the library

import pgvector from 'pgvector/objection';

Enable the extension

await knex.schema.createExtensionIfNotExists('vector');

Create a table

await knex.schema.createTable('items', (table) => {
  table.increments('id');
  table.vector('embedding', 3);
});

Insert vectors

const newItems = [
  {embedding: pgvector.toSql([1, 2, 3])},
  {embedding: pgvector.toSql([4, 5, 6])}
];
await Item.query().insert(newItems);

Get the nearest neighbors to a vector

import { l2Distance } from 'pgvector/objection';

const items = await Item.query()
  .orderBy(l2Distance('embedding', [1, 2, 3]))
  .limit(5);

Also supports maxInnerProduct, cosineDistance, l1Distance, hammingDistance, and jaccardDistance

Add an approximate index

await knex.schema.alterTable('items', function (table) {
  table.index(knex.raw('embedding vector_l2_ops'), 'index_name', 'hnsw');
});

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Enable the extension

await sql`CREATE EXTENSION IF NOT EXISTS vector`.execute(db);

Create a table

await db.schema.createTable('items')
  .addColumn('id', 'serial', (cb) => cb.primaryKey())
  .addColumn('embedding', sql`vector(3)`)
  .execute();

Insert vectors

import pgvector from 'pgvector/kysely';

const newItems = [
  {embedding: pgvector.toSql([1, 2, 3])},
  {embedding: pgvector.toSql([4, 5, 6])}
];
await db.insertInto('items').values(newItems).execute();

Get the nearest neighbors to a vector

import { l2Distance } from 'pgvector/kysely';

const items = await db.selectFrom('items')
  .selectAll()
  .orderBy(l2Distance('embedding', [1, 2, 3]))
  .limit(5)
  .execute();

Also supports maxInnerProduct, cosineDistance, l1Distance, hammingDistance, and jaccardDistance

Get items within a certain distance

const items = await db.selectFrom('items')
  .selectAll()
  .where(l2Distance('embedding', [1, 2, 3]), '<', 5)
  .execute();

Add an approximate index

await db.schema.createIndex('index_name')
  .on('items')
  .using('hnsw')
  .expression(sql`embedding vector_l2_ops`)
  .execute();

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Enable the extension

await sequelize.query('CREATE EXTENSION IF NOT EXISTS vector');

import { Sequelize } from 'sequelize';
import pgvector from 'pgvector/sequelize';

pgvector.registerTypes(Sequelize);

Add a vector field

const Item = sequelize.define('Item', {
  embedding: {
    type: DataTypes.VECTOR(3)
  }
}, ...);

Insert a vector

await Item.create({embedding: [1, 2, 3]});

Get the nearest neighbors to a vector

import { l2Distance } from 'pgvector/sequelize';

const items = await Item.findAll({
  order: l2Distance('embedding', [1, 1, 1], sequelize),
  limit: 5
});

Also supports maxInnerProduct, cosineDistance, l1Distance, hammingDistance, and jaccardDistance

Add an approximate index

const Item = sequelize.define('Item', ..., {
  indexes: [
    {
      fields: ['embedding'],
      using: 'hnsw',
      operator: 'vector_l2_ops'
    }
  ]
});

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Enable the extension

await db.none('CREATE EXTENSION IF NOT EXISTS vector');

import pgpromise from 'pg-promise';
import pgvector from 'pgvector/pg-promise';

const initOptions = {
  async connect(e) {
    await pgvector.registerTypes(e.client);
  }
};
const pgp = pgpromise(initOptions);

Create a table

await db.none('CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))');

Insert a vector

await db.none('INSERT INTO items (embedding) VALUES ($1)', [pgvector.toSql([1, 2, 3])]);

Get the nearest neighbors to a vector

const result = await db.any('SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5', [pgvector.toSql([1, 2, 3])]);

Add an approximate index

await db.none('CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)');
// or
await db.none('CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)');

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Note: prisma migrate dev does not support pgvector indexes

Import the library

import pgvector from 'pgvector';

Add the extension to the schema

generator client {
  provider        = "prisma-client-js"
  previewFeatures = ["postgresqlExtensions"]
}

datasource db {
  provider   = "postgresql"
  url        = env("DATABASE_URL")
  extensions = [vector]
}

Add a vector column to the schema

model Item {
  id        Int                       @id @default(autoincrement())
  embedding Unsupported("vector(3)")?
}

Insert a vector

const embedding = pgvector.toSql([1, 2, 3])
await prisma.$executeRaw`INSERT INTO items (embedding) VALUES (${embedding}::vector)`

Get the nearest neighbors to a vector

const embedding = pgvector.toSql([1, 2, 3])
const items = await prisma.$queryRaw`SELECT id, embedding::text FROM items ORDER BY embedding <-> ${embedding}::vector LIMIT 5`

See a full example (and the schema)

Import the library

import pgvector from 'pgvector';

Enable the extension

await sql`CREATE EXTENSION IF NOT EXISTS vector`;

Create a table

await sql`CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))`;

Insert vectors

const newItems = [
  {embedding: pgvector.toSql([1, 2, 3])},
  {embedding: pgvector.toSql([4, 5, 6])}
];
await sql`INSERT INTO items ${ sql(newItems, 'embedding') }`;

Get the nearest neighbors to a vector

const embedding = pgvector.toSql([1, 2, 3]);
const items = await sql`SELECT * FROM items ORDER BY embedding <-> ${ embedding } LIMIT 5`;

Add an approximate index

await sql`CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)`;
// or
await sql`CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)`;

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Import the library

import pgvector from 'pgvector';

Enable the extension

await pool.query(sql.unsafe`CREATE EXTENSION IF NOT EXISTS vector`);

Create a table

await pool.query(sql.unsafe`CREATE TABLE items (id serial PRIMARY KEY, embedding vector(3))`);

Insert a vector

const embedding = pgvector.toSql([1, 2, 3]);
await pool.query(sql.unsafe`INSERT INTO items (embedding) VALUES (${embedding})`);

Get the nearest neighbors to a vector

const embedding = pgvector.toSql([1, 2, 3]);
const items = await pool.query(sql.unsafe`SELECT * FROM items ORDER BY embedding <-> ${embedding} LIMIT 5`);

Add an approximate index

await pool.query(sql.unsafe`CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)`);
// or
await pool.query(sql.unsafe`CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)`);

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Import the library

import pgvector from 'pgvector';

Enable the extension

await AppDataSource.query('CREATE EXTENSION IF NOT EXISTS vector');

Create a table

await AppDataSource.query('CREATE TABLE item (id bigserial PRIMARY KEY, embedding vector(3))');

Define an entity

@Entity()
class Item {
  @PrimaryGeneratedColumn()
  id: number

  @Column()
  embedding: string
}

Insert a vector

const itemRepository = AppDataSource.getRepository(Item);
await itemRepository.save({embedding: pgvector.toSql([1, 2, 3])});

Get the nearest neighbors to a vector

const items = await itemRepository
  .createQueryBuilder('item')
  .orderBy('embedding <-> :embedding')
  .setParameters({embedding: pgvector.toSql([1, 2, 3])})
  .limit(5)
  .getMany();

See a full example

Enable the extension

await em.execute('CREATE EXTENSION IF NOT EXISTS vector');

Define an entity

import { VectorType } from 'pgvector/mikro-orm';

@Entity()
class Item {
  @PrimaryKey()
  id: number;

  @Property({type: VectorType})
  embedding: number[];
}

Insert a vector

em.create(Item, {embedding: [1, 2, 3]});

Get the nearest neighbors to a vector

import { l2Distance } from 'pgvector/mikro-orm';

const items = await em.createQueryBuilder(Item)
  .orderBy({[l2Distance('embedding', [1, 2, 3])]: 'ASC'})
  .limit(5)
  .getResult();

Also supports maxInnerProduct, cosineDistance, l1Distance, hammingDistance, and jaccardDistance

See a full example

Drizzle ORM 0.31.0+ has built-in support for pgvector 🎉

Enable the extension

await client`CREATE EXTENSION IF NOT EXISTS vector`;

Add a vector field

import { vector } from 'drizzle-orm/pg-core';

const items = pgTable('items', {
  id: serial('id').primaryKey(),
  embedding: vector('embedding', {dimensions: 3})
});

Also supports halfvec, bit, and sparsevec

Insert vectors

const newItems = [
  {embedding: [1, 2, 3]},
  {embedding: [4, 5, 6]}
];
await db.insert(items).values(newItems);

Get the nearest neighbors to a vector

import { l2Distance } from 'drizzle-orm';

const allItems = await db.select()
  .from(items)
  .orderBy(l2Distance(items.embedding, [1, 2, 3]))
  .limit(5);

Also supports innerProduct, cosineDistance, l1Distance, hammingDistance, and jaccardDistance

See a full example

Import the library

import pgvector from 'pgvector';

Enable the extension

await sql`CREATE EXTENSION IF NOT EXISTS vector`;

Create a table

await sql`CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))`;

Insert vectors

const newItems = [
  {embedding: pgvector.toSql([1, 2, 3])},
  {embedding: pgvector.toSql([4, 5, 6])}
];
await sql`INSERT INTO items ${sql(newItems)}`;

Get the nearest neighbors to a vector

const embedding = pgvector.toSql([1, 2, 3]);
const items = await sql`SELECT * FROM items ORDER BY embedding <-> ${embedding} LIMIT 5`.values();

Add an approximate index

await sql`CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)`;
// or
await sql`CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)`;

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

See a full example

Create a sparse vector from an array

const vec = new SparseVector([1, 0, 2, 0, 3, 0]);

Or a map of non-zero elements

const vec = new SparseVector({0: 1, 2: 2, 4: 3}, 6);
// or
const map = new Map();
map.set(0, 1);
map.set(2, 2);
map.set(4, 3);
const vec = new SparseVector(map, 6);

Note: Indices start at 0

Get the number of dimensions

const dim = vec.dimensions;

Get the indices of non-zero elements

const indices = vec.indices;

Get the values of non-zero elements

const values = vec.values;

Get an array

const arr = vec.toArray();

View the changelog

Everyone is encouraged to help improve this project. Here are a few ways you can help:

Report bugs
Fix bugs and submit pull requests
Write, clarify, or fix documentation
Suggest or add new features

To get started with development:

git clone https://github.com/pgvector/pgvector-node.git
cd pgvector-node
npm install
createdb pgvector_node_test
npx prisma migrate dev
npm test

To run an example:

cd examples/loading
npm install
createdb pgvector_example
node example.js

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