Files
payload/test/database/postgres-vector.int.spec.ts
Alessio Gravili 583a733334 feat(drizzle): support half-precision, binary, and sparse vectors column types (#12491)
Adds support for `halfvec` and `sparsevec` and `bit` (binary vector)
column types. This is required for supporting indexing of embeddings >
2000 dimensions on postgres using the pg-vector extension.
2025-07-02 19:24:53 +03:00

361 lines
8.3 KiB
TypeScript

/* eslint-disable jest/no-conditional-in-test */
/* eslint-disable jest/expect-expect */
/* eslint-disable jest/require-top-level-describe */
import type { PostgresAdapter } from '@payloadcms/db-postgres'
import type { PostgresDB } from '@payloadcms/drizzle'
import { cosineDistance, desc, gt, jaccardDistance, l2Distance, lt, sql } from 'drizzle-orm'
import path from 'path'
import { BasePayload, buildConfig, type DatabaseAdapterObj } from 'payload'
import { fileURLToPath } from 'url'
const filename = fileURLToPath(import.meta.url)
const dirname = path.dirname(filename)
const describeToUse = process.env.PAYLOAD_DATABASE?.startsWith('postgres')
? describe
: describe.skip
describeToUse('postgres vector custom column', () => {
const vectorColumnQueryTest = async (vectorType: string) => {
const {
databaseAdapter,
}: {
databaseAdapter: DatabaseAdapterObj<PostgresAdapter>
} = await import(path.resolve(dirname, '../databaseAdapter.js'))
const init = databaseAdapter.init
// set options
databaseAdapter.init = ({ payload }) => {
const adapter = init({ payload })
adapter.extensions = {
vector: true,
}
adapter.beforeSchemaInit = [
({ schema, adapter }) => {
if (adapter?.rawTables?.posts?.columns) {
adapter.rawTables.posts.columns.embedding = {
type: vectorType,
dimensions: 5,
name: 'embedding',
}
}
return schema
},
]
return adapter
}
const config = await buildConfig({
db: databaseAdapter,
secret: 'secret',
collections: [
{
slug: 'users',
auth: true,
fields: [],
},
{
slug: 'posts',
fields: [
{
type: 'json',
name: 'embedding',
},
{
name: 'title',
type: 'text',
},
],
},
],
})
// do not use getPayload to avoid caching and re-using payload instance from previous tests
const payload = await new BasePayload().init({ config })
const catEmbedding = [1.5, -0.4, 7.2, 19.6, 20.2]
await payload.create({
collection: 'posts',
data: {
embedding: [-5.2, 3.1, 0.2, 8.1, 3.5],
title: 'apple',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: catEmbedding,
title: 'cat',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: [-5.1, 2.9, 0.8, 7.9, 3.1],
title: 'fruit',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: [1.7, -0.3, 6.9, 19.1, 21.1],
title: 'dog',
},
})
const similarity = sql<number>`1 - (${cosineDistance(payload.db.tables.posts.embedding, catEmbedding)})`
const db = payload.db.drizzle as PostgresDB
const res = await db
.select()
.from(payload.db.tables.posts)
.where(gt(similarity, 0.9))
.orderBy(desc(similarity))
// Only cat and dog
expect(res).toHaveLength(2)
// similarity sort
expect(res?.[0]?.title).toBe('cat')
expect(res?.[1]?.title).toBe('dog')
}
it('should add a vector column and query it', async () => {
await vectorColumnQueryTest('vector')
})
it('should add a halfvec column and query it', async () => {
await vectorColumnQueryTest('halfvec')
})
it('should add a sparsevec column and query it', async () => {
const {
databaseAdapter,
}: {
databaseAdapter: DatabaseAdapterObj<PostgresAdapter>
} = await import(path.resolve(dirname, '../databaseAdapter.js'))
const init = databaseAdapter.init
databaseAdapter.init = ({ payload }) => {
const adapter = init({ payload })
adapter.extensions = {
vector: true,
}
adapter.beforeSchemaInit = [
({ schema, adapter }) => {
if (adapter?.rawTables?.posts?.columns) {
adapter.rawTables.posts.columns.embedding = {
type: 'sparsevec',
dimensions: 5,
name: 'embedding',
}
}
return schema
},
]
return adapter
}
const config = await buildConfig({
db: databaseAdapter,
secret: 'secret',
collections: [
{
slug: 'users',
auth: true,
fields: [],
},
{
slug: 'posts',
fields: [
{
name: 'embedding',
type: 'text',
},
{
name: 'title',
type: 'text',
},
],
},
],
})
const payload = await new BasePayload().init({ config })
// sparse-vector format: '{index:value,...}/dims'
const catEmbedding = '{1:1,3:2,5:3}/5'
await payload.create({
collection: 'posts',
data: {
embedding: '{2:1,4:2}/5',
title: 'apple',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: catEmbedding,
title: 'cat',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: '{2:4,4:6}/5',
title: 'fruit',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: '{1:1,3:2,5:2}/5',
title: 'dog',
},
})
const distance = sql<number>`(${l2Distance(payload.db.tables.posts.embedding, catEmbedding)})`
const db = payload.db.drizzle as PostgresDB
const res = await db
.select()
.from(payload.db.tables.posts)
.where(lt(distance, 1.1))
.orderBy(distance)
.execute()
// should return cat (distance 0) then dog
expect(res).toHaveLength(2)
expect(res?.[0]?.title).toBe('cat')
expect(res?.[1]?.title).toBe('dog')
})
it('should add a binaryvec column and query it', async () => {
const {
databaseAdapter,
}: {
databaseAdapter: DatabaseAdapterObj<PostgresAdapter>
} = await import(path.resolve(dirname, '../databaseAdapter.js'))
const init = databaseAdapter.init
// set options
databaseAdapter.init = ({ payload }) => {
const adapter = init({ payload })
adapter.extensions = {
vector: true,
}
adapter.beforeSchemaInit = [
({ schema, adapter }) => {
if (adapter?.rawTables?.posts?.columns) {
adapter.rawTables.posts.columns.embedding = {
type: 'bit',
dimensions: 5,
name: 'embedding',
}
}
return schema
},
]
return adapter
}
const config = await buildConfig({
db: databaseAdapter,
secret: 'secret',
collections: [
{
slug: 'users',
auth: true,
fields: [],
},
{
slug: 'posts',
fields: [
{
type: 'text',
name: 'embedding',
},
{
name: 'title',
type: 'text',
},
],
},
],
})
// do not use getPayload to avoid caching and re-using payload instance from previous tests
const payload = await new BasePayload().init({ config })
const catEmbedding = '10101'
await payload.create({
collection: 'posts',
data: {
embedding: '01010',
title: 'apple',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: '10101',
title: 'cat',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: '11111',
title: 'fruit',
},
})
await payload.create({
collection: 'posts',
data: {
embedding: '10100',
title: 'dog',
},
})
const similarity = sql<number>`1 - (${jaccardDistance(payload.db.tables.posts.embedding, catEmbedding)})`
const db = payload.db.drizzle as PostgresDB
const res = await db
.select()
.from(payload.db.tables.posts)
.where(gt(similarity, 0.6))
.orderBy(desc(similarity))
// Only cat and dog
expect(res).toHaveLength(2)
// similarity sort
expect(res?.[0]?.title).toBe('cat')
expect(res?.[1]?.title).toBe('dog')
})
})