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pgvector support for Go

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pgvector/pgvector-go

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pgvector support for Go

Supportspgx,pg,Bun,Ent,GORM, andsqlx

Build Status

Getting Started

Run:

go get github.com/pgvector/pgvector-go

And follow the instructions for your database library:

Or check out some examples:

pgx

Import the packages

import ("github.com/pgvector/pgvector-go"    pgxvec"github.com/pgvector/pgvector-go/pgx")

Enable the extension

_,err:=conn.Exec(ctx,"CREATE EXTENSION IF NOT EXISTS vector")

Register the types with the connection

err:=pgxvec.RegisterTypes(ctx,conn)

or the pool

config.AfterConnect=func(ctx context.Context,conn*pgx.Conn)error {returnpgxvec.RegisterTypes(ctx,conn)}

Create a table

_,err:=conn.Exec(ctx,"CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))")

Insert a vector

_,err:=conn.Exec(ctx,"INSERT INTO items (embedding) VALUES ($1)",pgvector.NewVector([]float32{1,2,3}))

Get the nearest neighbors to a vector

rows,err:=conn.Query(ctx,"SELECT id FROM items ORDER BY embedding <-> $1 LIMIT 5",pgvector.NewVector([]float32{1,2,3}))

Add an approximate index

_,err:=conn.Exec(ctx,"CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")// or_,err:=conn.Exec(ctx,"CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")

Usevector_ip_ops for inner product andvector_cosine_ops for cosine distance

See afull example

pg

Import the package

import"github.com/pgvector/pgvector-go"

Enable the extension

_,err:=db.Exec("CREATE EXTENSION IF NOT EXISTS vector")

Add a vector column

typeItemstruct {Embedding pgvector.Vector`pg:"type:vector(3)"`}

Insert a vector

item:=Item{Embedding:pgvector.NewVector([]float32{1,2,3}),}_,err:=db.Model(&item).Insert()

Get the nearest neighbors to a vector

varitems []Itemerr:=db.Model(&items).OrderExpr("embedding <-> ?",pgvector.NewVector([]float32{1,2,3})).Limit(5).Select()

Add an approximate index

_,err:=conn.Exec(ctx,"CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")// or_,err:=conn.Exec(ctx,"CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")

Usevector_ip_ops for inner product andvector_cosine_ops for cosine distance

See afull example

Bun

Import the package

import"github.com/pgvector/pgvector-go"

Enable the extension

_,err:=db.Exec("CREATE EXTENSION IF NOT EXISTS vector")

Add a vector column

typeItemstruct {Embedding pgvector.Vector`bun:"type:vector(3)"`}

Insert a vector

item:=Item{Embedding:pgvector.NewVector([]float32{1,2,3}),}_,err:=db.NewInsert().Model(&item).Exec(ctx)

Get the nearest neighbors to a vector

varitems []Itemerr:=db.NewSelect().Model(&items).OrderExpr("embedding <-> ?",pgvector.NewVector([]float32{1,2,3})).Limit(5).Scan(ctx)

Add an approximate index

var_ bun.AfterCreateTableHook= (*Item)(nil)func (*Item)AfterCreateTable(ctx context.Context,query*bun.CreateTableQuery)error {_,err:=query.DB().NewCreateIndex().Model((*Item)(nil)).Index("items_embedding_idx").ColumnExpr("embedding vector_l2_ops").Using("hnsw").Exec(ctx)returnerr}

Usevector_ip_ops for inner product andvector_cosine_ops for cosine distance

See afull example

Ent

Import the package

import ("github.com/pgvector/pgvector-go"    entvec"github.com/pgvector/pgvector-go/ent")

Enable the extension (requires thesql/execquery feature)

_,err:=client.ExecContext(ctx,"CREATE EXTENSION IF NOT EXISTS vector")

Add a vector column

func (Item)Fields() []ent.Field {return []ent.Field{field.Other("embedding", pgvector.Vector{}).SchemaType(map[string]string{dialect.Postgres:"vector(3)",            }),    }}

Insert a vector

_,err:=client.Item.Create().SetEmbedding(pgvector.NewVector([]float32{1,2,3})).Save(ctx)

Get the nearest neighbors to a vector

items,err:=client.Item.Query().Order(func(s*sql.Selector) {s.OrderExpr(entvec.L2Distance("embedding",pgvector.NewVector([]float32{1,2,3})))    }).Limit(5).All(ctx)

Also supportsMaxInnerProduct,CosineDistance,L1Distance,HammingDistance, andJaccardDistance

Add an approximate index

func (Item)Indexes() []ent.Index {return []ent.Index{index.Fields("embedding").Annotations(entsql.IndexType("hnsw"),entsql.OpClass("vector_l2_ops"),            ),    }}

Usevector_ip_ops for inner product andvector_cosine_ops for cosine distance

See afull example

GORM

Import the package

import"github.com/pgvector/pgvector-go"

Enable the extension

db.Exec("CREATE EXTENSION IF NOT EXISTS vector")

Add a vector column

typeItemstruct {Embedding pgvector.Vector`gorm:"type:vector(3)"`}

Insert a vector

item:=Item{Embedding:pgvector.NewVector([]float32{1,2,3}),}result:=db.Create(&item)

Get the nearest neighbors to a vector

varitems []Itemdb.Clauses(clause.OrderBy{Expression: clause.Expr{SQL:"embedding <-> ?",Vars: []interface{}{pgvector.NewVector([]float32{1,1,1})}},}).Limit(5).Find(&items)

Add an approximate index

db.Exec("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")// ordb.Exec("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")

Usevector_ip_ops for inner product andvector_cosine_ops for cosine distance

See afull example

sqlx

Import the package

import"github.com/pgvector/pgvector-go"

Enable the extension

db.MustExec("CREATE EXTENSION IF NOT EXISTS vector")

Add a vector column

typeItemstruct {Embedding pgvector.Vector}

Insert a vector

item:=Item{Embedding:pgvector.NewVector([]float32{1,2,3}),}_,err:=db.NamedExec(`INSERT INTO items (embedding) VALUES (:embedding)`,item)

Get the nearest neighbors to a vector

varitems []Itemdb.Select(&items,"SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5",pgvector.NewVector([]float32{1,1,1}))

Add an approximate index

db.MustExec("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")// ordb.MustExec("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")

Usevector_ip_ops for inner product andvector_cosine_ops for cosine distance

See afull example

Reference

Vectors

Create a vector from a slice

vec:=pgvector.NewVector([]float32{1,2,3})

Get a slice

slice:=vec.Slice()

Half Vectors

Create a half vector from a slice

vec:=pgvector.NewHalfVector([]float32{1,2,3})

Get a slice

slice:=vec.Slice()

Sparse Vectors

Create a sparse vector from a slice

vec:=pgvector.NewSparseVector([]float32{1,0,2,0,3,0})

Or a map of non-zero elements

elements:=map[int32]float32{0:1,2:2,4:3}vec:=pgvector.NewSparseVectorFromMap(elements,6)

Note: Indices start at 0

Get the number of dimensions

dim:=vec.Dimensions()

Get the indices of non-zero elements

indices:=vec.Indices()

Get the values of non-zero elements

values:=vec.Values()

Get a slice

slice:=vec.Slice()

History

View thechangelog

Contributing

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

To get started with development:

git clone https://github.com/pgvector/pgvector-go.gitcd pgvector-gogo mod tidycreatedb pgvector_go_testgo generate ./test/entgotest -v

To run an example:

createdb pgvector_examplego run ./examples/loading

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