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import { pgTable, vector } from "drizzle-orm/pg-core";
 
const Products = pgTable("products", {
	embedding: vector("embedding", { dimensions: 1024 }),
});

Adding an index to your vector column is just as straightforward:

export const Products = pgTable(
	"products",
	{
		embedding: vector("embedding", { dimensions: 1024 }),
	},
	(t) => [
		index("products_embedding_index").using(
			"hnsw",
			t.embedding.

import { innerProduct } from "drizzle-orm";
 
const products = await this.db
	.select({
		similarity: innerProduct(Products.embedding, embedding),
	})
	.from(Products)
	.orderBy((t) => asc(t.similarity))
	.limit(5);

import { pgTable, serial, text } from "drizzle-orm/pg-core";
import { drizzle } from "drizzle-orm/node-postgres";
import { Client } from "pg";
 
const client = new Client({
	connectionString: "postgres://user:password@host:port/db",
});
 
export const users = pgTable("users", {
	id: serial("id").primaryKey(),
	fullName: text("full_name"),
});
 
await client.connect();
const db = drizzle(client, { users });

// Adding vector field
import { vector } from 'pgvector/drizzle-orm';
 
export const users = pgTable('users', {
  id: serial('id').primaryKey(),
  fullName: text('full_name'),
  embedding: vector('embedding', { dimensions: 256 })
});
 
// Finding nearest neighbors to a vector
import { l2Distance } from 'pgvector/drizzle-orm';
 
const nearest = await db.select()
  .from(users)
  .orderBy(l2Distance(users.embedding, [1, 2, 3, ..., 256]))
  .limit(5);

import { customType } from "drizzle-orm/pg-core";
 
export const vector = customType<{
	data: number[];
	driverData: string;
	config: { size: number };
}>({
	dataType(config) {
		const dt =
			!!config && typeof config.size === "number"
				? `vector(${config.size})`
				: "vector";
		return dt;
	},
	fromDriver(value) {
		return JSON.parse(value);
	},
	toDriver(value) {
		return JSON.stringify(value);
	},
});
 
// Adding it to schema
export const users = pgTable("users", {
	id: serial("id").primaryKey(),
	fullName: text("full_name"),
	embedding: vector("embedding", { size: 256 }),
});
 
// Querying nearest users (cosine similarity)
const nearest = await db
	.select({
		id: users.id,
		distance: sql
			.raw(`1 - (${users.embedding.name} <=> '[${vector}]')`)
			.as<number>("distance"),
	})
	.from(user)
	.orderBy(sql`distance DESC`)
	.limit(5);

Online

Storing Vector in Postgres with Drizzle ORM

Storing Vector in Postgres with Drizzle ORM

Vectors and Vector DBs

pgvector and Drizzle ORM

Starting with pgvector

Combining the two

pgvector-node

Creative way