Added Kysely example

README.md

@@ -2,7 +2,7 @@
 
 [pgvector](https:/pgvector/pgvector) support for Node.js and Bun (and TypeScript)
 
-Supports [node-postgres](https:/brianc/node-postgres), [Knex.js](https:/knex/knex), [Objection.js](https:/vincit/objection.js), [Sequelize](https:/sequelize/sequelize), [pg-promise](https:/vitaly-t/pg-promise), [Prisma](https:/prisma/prisma), [Postgres.js](https:/porsager/postgres), [TypeORM](https:/typeorm/typeorm), [MikroORM](https:/mikro-orm/mikro-orm), and [Drizzle ORM](https:/drizzle-team/drizzle-orm)
+Supports [node-postgres](https:/brianc/node-postgres), [Knex.js](https:/knex/knex), [Objection.js](https:/vincit/objection.js), [Kysely](https:/kysely-org/kysely), [Sequelize](https:/sequelize/sequelize), [pg-promise](https:/vitaly-t/pg-promise), [Prisma](https:/prisma/prisma), [Postgres.js](https:/porsager/postgres), [TypeORM](https:/typeorm/typeorm), [MikroORM](https:/mikro-orm/mikro-orm), and [Drizzle ORM](https:/drizzle-team/drizzle-orm)
 
 [![Build Status](https:/pgvector/pgvector-node/workflows/build/badge.svg?branch=master)](https:/pgvector/pgvector-node/actions)
 
@@ -19,6 +19,7 @@ And follow the instructions for your database library:
 - [node-postgres](#node-postgres)
 - [Knex.js](#knexjs)
 - [Objection.js](#objectionjs)
+- [Kysely](#kysely)
 - [Sequelize](#sequelize)
 - [pg-promise](#pg-promise)
 - [Prisma](#prisma)
@@ -198,6 +199,65 @@ Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distanc
 
 See a [full example](tests/objection/index.test.mjs)
 
+## Kysely
+
+Import the library
+
+```javascript
+import pgvector from 'pgvector/utils';
+```
+
+Enable the extension
+
+```javascript
+await sql`CREATE EXTENSION IF NOT EXISTS vector`.execute(db);
+```
+
+Create a table
+
+```javascript
+await db.schema.createTable('items')
+ .addColumn('id', 'serial', (cb) => cb.primaryKey())
+ .addColumn('embedding', 'vector(3)')
+ .execute();
+```
+
+Insert vectors
+
+```javascript
+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
+
+```javascript
+const items = await db.selectFrom('items')
+ .selectAll()
+ .orderBy(sql`embedding <-> '[1,1,1]'`)
+ .limit(5)
+ .execute();
+```
+
+Also supports `maxInnerProduct` and `cosineDistance`
+
+Add an approximate index
+
+```javascript
+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](tests/kysely/index.test.mjs)
+
 ## Sequelize
 
 Enable the extension

package.json

@@ -84,6 +84,7 @@
  "drizzle-orm": "^0.29.1",
  "jest": "^29.5.0",
  "knex": "^3.1.0",
+ "kysely": "^0.26.3",
  "objection": "^3.1.3",
  "pg": "^8.6.0",
  "pg-promise": "^11.4.3",

tests/kysely/index.test.mjs

@@ -0,0 +1,53 @@
+import pg from 'pg';
+import { Kysely, PostgresDialect, sql } from 'kysely';
+import pgvector from 'pgvector/utils';
+
+test('example', async () => {
+ const dialect = new PostgresDialect({
+ pool: new pg.Pool({
+ database: 'pgvector_node_test'
+ })
+ });
+
+ const db = new Kysely({
+ dialect
+ });
+
+ await sql`CREATE EXTENSION IF NOT EXISTS vector`.execute(db);
+
+ await db.schema.dropTable('kysely_items')
+ .ifExists()
+ .execute();
+
+ await db.schema.createTable('kysely_items')
+ .addColumn('id', 'serial', (cb) => cb.primaryKey())
+ .addColumn('embedding', 'vector(3)')
+ .execute();
+
+ const newItems = [
+ {embedding: pgvector.toSql([1, 1, 1])},
+ {embedding: pgvector.toSql([2, 2, 2])},
+ {embedding: pgvector.toSql([1, 1, 2])}
+ ];
+ await db.insertInto('kysely_items')
+ .values(newItems)
+ .execute();
+
+ const items = await db.selectFrom('kysely_items')
+ .selectAll()
+ .orderBy(sql`embedding <-> '[1,1,1]'`)
+ .limit(5)
+ .execute();
+ expect(items.map(v => v.id)).toStrictEqual([1, 3, 2]);
+ expect(pgvector.fromSql(items[0].embedding)).toStrictEqual([1, 1, 1]);
+ expect(pgvector.fromSql(items[1].embedding)).toStrictEqual([1, 1, 2]);
+ expect(pgvector.fromSql(items[2].embedding)).toStrictEqual([2, 2, 2]);
+
+ await db.schema.createIndex('kysely_items_embedding_idx')
+ .on('kysely_items')
+ .using('hnsw')
+ .expression(sql`embedding vector_l2_ops`)
+ .execute();
+
+ db.destroy();
+});