fix(go.sum): update ResolveSpec dependency to v1.0.87
This commit is contained in:
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# pgvector-go
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[pgvector](https://github.com/pgvector/pgvector) support for Go
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Supports [pgx](https://github.com/jackc/pgx), [pg](https://github.com/go-pg/pg), [Bun](https://github.com/uptrace/bun), [Ent](https://github.com/ent/ent), [GORM](https://github.com/go-gorm/gorm), and [sqlx](https://github.com/jmoiron/sqlx)
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[](https://github.com/pgvector/pgvector-go/actions)
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## Getting Started
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Run:
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```sh
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go get github.com/pgvector/pgvector-go
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```
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And follow the instructions for your database library:
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- [pgx](#pgx)
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- [pg](#pg)
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- [Bun](#bun)
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- [Ent](#ent)
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- [GORM](#gorm)
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- [sqlx](#sqlx)
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Or check out some examples:
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- [Embeddings](examples/openai/main.go) with OpenAI
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- [Binary embeddings](examples/cohere/main.go) with Cohere
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- [Hybrid search](examples/hybrid/main.go) with Ollama (Reciprocal Rank Fusion)
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- [Sparse search](examples/sparse/main.go) with Text Embeddings Inference
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- [Recommendations](examples/disco/main.go) with Disco
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- [Horizontal scaling](examples/citus/main.go) with Citus
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- [Bulk loading](examples/loading/main.go) with `COPY`
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## pgx
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Import the packages
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```go
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import (
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"github.com/pgvector/pgvector-go"
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pgxvec "github.com/pgvector/pgvector-go/pgx"
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)
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```
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Enable the extension
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```go
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_, err := conn.Exec(ctx, "CREATE EXTENSION IF NOT EXISTS vector")
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```
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Register the types with the connection
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```go
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err := pgxvec.RegisterTypes(ctx, conn)
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```
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or the pool
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```go
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config.AfterConnect = func(ctx context.Context, conn *pgx.Conn) error {
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return pgxvec.RegisterTypes(ctx, conn)
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}
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```
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Create a table
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```go
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_, err := conn.Exec(ctx, "CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))")
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```
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Insert a vector
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```go
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_, err := conn.Exec(ctx, "INSERT INTO items (embedding) VALUES ($1)", pgvector.NewVector([]float32{1, 2, 3}))
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```
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Get the nearest neighbors to a vector
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```go
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rows, err := conn.Query(ctx, "SELECT id FROM items ORDER BY embedding <-> $1 LIMIT 5", pgvector.NewVector([]float32{1, 2, 3}))
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```
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Add an approximate index
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```go
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_, err := conn.Exec(ctx, "CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")
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// or
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_, err := conn.Exec(ctx, "CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")
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```
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Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance
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See a [full example](pgx_test.go)
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## pg
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Import the package
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```go
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import "github.com/pgvector/pgvector-go"
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```
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Enable the extension
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```go
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_, err := db.Exec("CREATE EXTENSION IF NOT EXISTS vector")
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```
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Add a vector column
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```go
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type Item struct {
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Embedding pgvector.Vector `pg:"type:vector(3)"`
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}
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```
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Insert a vector
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```go
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item := Item{
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Embedding: pgvector.NewVector([]float32{1, 2, 3}),
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}
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_, err := db.Model(&item).Insert()
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```
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Get the nearest neighbors to a vector
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```go
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var items []Item
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err := db.Model(&items).
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OrderExpr("embedding <-> ?", pgvector.NewVector([]float32{1, 2, 3})).
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Limit(5).
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Select()
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```
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Add an approximate index
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```go
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_, err := conn.Exec(ctx, "CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")
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// or
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_, err := conn.Exec(ctx, "CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")
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```
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Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance
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See a [full example](pg_test.go)
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## Bun
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Import the package
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```go
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import "github.com/pgvector/pgvector-go"
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```
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Enable the extension
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```go
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_, err := db.Exec("CREATE EXTENSION IF NOT EXISTS vector")
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```
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Add a vector column
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```go
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type Item struct {
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Embedding pgvector.Vector `bun:"type:vector(3)"`
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}
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```
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Insert a vector
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```go
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item := Item{
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Embedding: pgvector.NewVector([]float32{1, 2, 3}),
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}
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_, err := db.NewInsert().Model(&item).Exec(ctx)
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```
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Get the nearest neighbors to a vector
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```go
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var items []Item
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err := db.NewSelect().
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Model(&items).
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OrderExpr("embedding <-> ?", pgvector.NewVector([]float32{1, 2, 3})).
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Limit(5).
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Scan(ctx)
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```
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Add an approximate index
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```go
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var _ bun.AfterCreateTableHook = (*Item)(nil)
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func (*Item) AfterCreateTable(ctx context.Context, query *bun.CreateTableQuery) error {
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_, err := query.DB().NewCreateIndex().
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Model((*Item)(nil)).
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Index("items_embedding_idx").
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ColumnExpr("embedding vector_l2_ops").
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Using("hnsw").
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Exec(ctx)
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return err
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}
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```
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Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance
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See a [full example](bun_test.go)
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## Ent
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Import the package
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```go
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import "github.com/pgvector/pgvector-go"
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import entvec "github.com/pgvector/pgvector-go/ent"
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```
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Enable the extension (requires the [sql/execquery](https://entgo.io/docs/feature-flags/#sql-raw-api) feature)
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```go
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_, err := client.ExecContext(ctx, "CREATE EXTENSION IF NOT EXISTS vector")
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```
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Add a vector column
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```go
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func (Item) Fields() []ent.Field {
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return []ent.Field{
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field.Other("embedding", pgvector.Vector{}).
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SchemaType(map[string]string{
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dialect.Postgres: "vector(3)",
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}),
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}
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}
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```
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Insert a vector
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```go
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_, err := client.Item.
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Create().
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SetEmbedding(pgvector.NewVector([]float32{1, 2, 3})).
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Save(ctx)
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```
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Get the nearest neighbors to a vector
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```go
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items, err := client.Item.
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Query().
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Order(func(s *sql.Selector) {
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s.OrderExpr(entvec.L2Distance("embedding", pgvector.NewVector([]float32{1, 2, 3})))
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}).
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Limit(5).
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All(ctx)
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```
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Also supports `MaxInnerProduct`, `CosineDistance`, `L1Distance`, `HammingDistance`, and `JaccardDistance`
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Add an approximate index
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```go
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func (Item) Indexes() []ent.Index {
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return []ent.Index{
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index.Fields("embedding").
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Annotations(
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entsql.IndexType("hnsw"),
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entsql.OpClass("vector_l2_ops"),
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),
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}
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}
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```
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Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance
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See a [full example](ent_test.go)
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## GORM
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Import the package
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```go
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import "github.com/pgvector/pgvector-go"
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```
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Enable the extension
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```go
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db.Exec("CREATE EXTENSION IF NOT EXISTS vector")
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```
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Add a vector column
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```go
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type Item struct {
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Embedding pgvector.Vector `gorm:"type:vector(3)"`
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}
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```
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Insert a vector
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```go
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item := Item{
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Embedding: pgvector.NewVector([]float32{1, 2, 3}),
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}
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result := db.Create(&item)
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```
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Get the nearest neighbors to a vector
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```go
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var items []Item
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db.Clauses(clause.OrderBy{
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Expression: clause.Expr{SQL: "embedding <-> ?", Vars: []interface{}{pgvector.NewVector([]float32{1, 1, 1})}},
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}).Limit(5).Find(&items)
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```
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Add an approximate index
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```go
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db.Exec("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")
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// or
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db.Exec("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")
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```
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Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance
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See a [full example](gorm_test.go)
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## sqlx
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Import the package
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```go
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import "github.com/pgvector/pgvector-go"
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```
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Enable the extension
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```go
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db.MustExec("CREATE EXTENSION IF NOT EXISTS vector")
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```
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Add a vector column
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```go
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type Item struct {
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Embedding pgvector.Vector
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}
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```
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Insert a vector
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```go
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item := Item{
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Embedding: pgvector.NewVector([]float32{1, 2, 3}),
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}
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_, err := db.NamedExec(`INSERT INTO items (embedding) VALUES (:embedding)`, item)
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```
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Get the nearest neighbors to a vector
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```go
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var items []Item
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db.Select(&items, "SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5", pgvector.NewVector([]float32{1, 1, 1}))
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```
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Add an approximate index
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```go
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db.MustExec("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")
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// or
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db.MustExec("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")
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```
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Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance
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See a [full example](sqlx_test.go)
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## Reference
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### Vectors
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Create a vector from a slice
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```go
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vec := pgvector.NewVector([]float32{1, 2, 3})
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```
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Get a slice
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```go
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slice := vec.Slice()
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```
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### Half Vectors
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Create a half vector from a slice
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```go
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vec := pgvector.NewHalfVector([]float32{1, 2, 3})
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```
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Get a slice
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```go
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slice := vec.Slice()
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```
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### Sparse Vectors
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Create a sparse vector from a slice
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```go
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vec := pgvector.NewSparseVector([]float32{1, 0, 2, 0, 3, 0})
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```
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Or a map of non-zero elements
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```go
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elements := map[int32]float32{0: 1, 2: 2, 4: 3}
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vec := pgvector.NewSparseVectorFromMap(elements, 6)
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```
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Note: Indices start at 0
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Get the number of dimensions
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```go
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dim := vec.Dimensions()
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```
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Get the indices of non-zero elements
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```go
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indices := vec.Indices()
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```
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Get the values of non-zero elements
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```go
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values := vec.Values()
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```
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Get a slice
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```go
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slice := vec.Slice()
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```
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## History
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View the [changelog](https://github.com/pgvector/pgvector-go/blob/master/CHANGELOG.md)
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## Contributing
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Everyone is encouraged to help improve this project. Here are a few ways you can help:
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- [Report bugs](https://github.com/pgvector/pgvector-go/issues)
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- Fix bugs and [submit pull requests](https://github.com/pgvector/pgvector-go/pulls)
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- Write, clarify, or fix documentation
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- Suggest or add new features
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To get started with development:
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```sh
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git clone https://github.com/pgvector/pgvector-go.git
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cd pgvector-go
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go mod tidy
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createdb pgvector_go_test
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go generate ./test/ent
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go test -v
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```
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To run an example:
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```sh
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createdb pgvector_example
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go run ./examples/loading
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```
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