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, and Drizzle ORM
Run:
npm install pgvector
And follow the instructions for your database library:
- node-postgres
- Knex.js
- Objection.js
- Kysely
- Sequelize
- pg-promise
- Prisma
- Postgres.js
- Slonik
- TypeORM
- MikroORM
- Drizzle ORM
Or check out some examples:
- Embeddings with OpenAI
- Binary embeddings with Cohere
- Sentence embeddings with Transformers.js
- Hybrid search with Transformers.js
- 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');
Register the types for a client
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');
Register the types
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
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');
Register the types
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
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