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
- Recommendations with Disco
Add this line to your application’s Cargo.toml under [dependencies]:
pgvector = { version = "0.2", 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>
let embedding = pgvector::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: pgvector::Vector = row.get(0);Use Option if the value could be NULL
let embedding: Option<pgvector::Vector> = row.get(0);Add this line to your application’s Cargo.toml under [dependencies]:
pgvector = { version = "0.2", features = ["sqlx"] }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>
let embedding = pgvector::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: pgvector::Vector = row.try_get("embedding")?;Add this line to your application’s Cargo.toml under [dependencies]:
pgvector = { version = "0.2", features = ["diesel"] }And add this line to your application’s diesel.toml under [print_schema]:
import_types = ["diesel::sql_types::*", "pgvector::sql_types::*"]Create 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
CREATE TABLE items ( embedding VECTOR(3) )For models, use:
pub struct Item { pub embedding: Option<pgvector::Vector> }Create a vector from a Vec<f32>
let embedding = pgvector::Vector::from(vec![1.0, 2.0, 3.0]);Insert a vector
let new_item = Item { 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 and cosine_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
CREATE INDEX my_index ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100) -- or CREATE INDEX my_index ON items USING hnsw (embedding vector_l2_ops)Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
Convert a vector to a Vec<f32>
let f32_vec: Vec<f32> = vec.into();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-features