pgvector support for Rust
Supports Rust-Postgres, SQLx, and Diesel
Follow the instructions for your database library:
Or check out some examples:
- Embeddings with OpenAI
- Binary embeddings with Cohere
- Sentence embeddings with Candle
- Hybrid search with Candle (Reciprocal Rank Fusion)
- Recommendations with Disco
- Bulk loading with
COPY
Add this line to your application’s Cargo.toml under [dependencies]:
pgvector ={version = "0.4", features = ["postgres"] }Enable the extension
client.execute("CREATE EXTENSION IF NOT EXISTS vector",&[])?;Create a table
client.execute("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))",&[])?;Create a vector from a Vec<f32>
use pgvector::Vector;let embedding = Vector::from(vec![1.0,2.0,3.0]);Insert a vector
client.execute("INSERT INTO items (embedding) VALUES ($1)",&[&embedding])?;Get the nearest neighbor
let row = client.query_one("SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 1",&[&embedding],)?;Retrieve a vector
let row = client.query_one("SELECT embedding FROM items LIMIT 1",&[])?;let embedding:Vector = row.get(0);Use Option if the value could be NULL
let embedding:Option<Vector> = row.get(0);Add this line to your application’s Cargo.toml under [dependencies]:
pgvector ={version = "0.4", features = ["sqlx"] }For SQLx < 0.8, use version = "0.3" and this readme.
Enable the extension
sqlx::query("CREATE EXTENSION IF NOT EXISTS vector").execute(&pool).await?;Create a table
sqlx::query("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))").execute(&pool).await?;Create a vector from a Vec<f32>
use pgvector::Vector;let embedding = Vector::from(vec![1.0,2.0,3.0]);Insert a vector
sqlx::query("INSERT INTO items (embedding) VALUES ($1)").bind(embedding).execute(&pool).await?;Get the nearest neighbors
let rows = sqlx::query("SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 1").bind(embedding).fetch_all(&pool).await?;Retrieve a vector
let row = sqlx::query("SELECT embedding FROM items LIMIT 1").fetch_one(&pool).await?;let embedding:Vector = row.try_get("embedding")?;Add this line to your application’s Cargo.toml under [dependencies]:
pgvector ={version = "0.4", features = ["diesel"] }And update your application’s diesel.toml under [print_schema]:
import_types = ["diesel::sql_types::*", "pgvector::sql_types::*"] generate_missing_sql_type_definitions = falseCreate a migration
diesel migration generate create_vector_extensionwith up.sql:
CREATE EXTENSION vectorand down.sql:
DROP EXTENSION vectorRun the migration
diesel migration runYou can now use the vector type in future migrations
CREATETABLEitems ( id SERIALPRIMARY KEY, embedding VECTOR(3) )For models, use:
use pgvector::Vector;#[derive(Queryable)]#[diesel(table_name = items)]pubstructItem{pubid:i32,pubembedding:Option<Vector>,}#[derive(Insertable)]#[diesel(table_name = items)]pubstructNewItem{pubembedding:Option<Vector>,}Create a vector from a Vec<f32>
let embedding = Vector::from(vec![1.0,2.0,3.0]);Insert a vector
let new_item = NewItem{embedding:Some(embedding)}; diesel::insert_into(items::table).values(&new_item).get_result::<Item>(&mut conn)?;Get the nearest neighbors
use pgvector::VectorExpressionMethods;let neighbors = items::table .order(items::embedding.l2_distance(embedding)).limit(5).load::<Item>(&mut conn)?;Also supports max_inner_product, cosine_distance, l1_distance, hamming_distance, and jaccard_distance
Get the distances
let distances = items::table .select(items::embedding.l2_distance(embedding)).load::<Option<f64>>(&mut conn)?;Add an approximate index in a migration
CREATEINDEXmy_indexON items USING hnsw (embedding vector_l2_ops) -- orCREATEINDEXmy_indexON items USING ivfflat (embedding vector_l2_ops) WITH (lists =100)Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
Use the serde feature to enable serialization
Use the halfvec feature to enable half vectors
Convert a vector to a Vec<f32>
let f32_vec:Vec<f32> = vec.into();Get a slice
let slice = vec.as_slice();View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/pgvector/pgvector-rust.git cd pgvector-rust createdb pgvector_rust_test cargo test --all-featuresTo run an example:
cd examples/loading createdb pgvector_example cargo run