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 = false
Create a migration
diesel migration generate create_vector_extension
with up.sql:
CREATE EXTENSION vector
and down.sql:
DROP EXTENSION vector
Run the migration
diesel migration run
You can now use the vector type in future migrations
CREATE TABLE items ( id SERIAL PRIMARY KEY, embedding VECTOR(3) )
For models, use:
use pgvector::Vector; #[derive(Queryable)] #[diesel(table_name = items)] pub struct Item { pub id: i32, pub embedding: Option<Vector>, } #[derive(Insertable)] #[diesel(table_name = items)] pub struct NewItem { pub embedding: 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
CREATE INDEX my_index ON items USING hnsw (embedding vector_l2_ops) -- or CREATE INDEX my_index ON 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-features
To run an example:
cd examples/loading
createdb pgvector_example
cargo run