pgvector-ruby

pgvector support for Ruby

Supports pg and Sequel

For Rails, check out Neighbor

Add this line to your application’s Gemfile:

gem "pgvector"

And follow the instructions for your database library:

• pg
• Sequel

Or check out some examples:

• Embeddings with OpenAI
• Binary embeddings with Cohere
• Sentence embeddings with Informers
• Hybrid search with Informers (Reciprocal Rank Fusion)
• Sparse search with Transformers.rb
• Morgan fingerprints with RDKit.rb
• Topic modeling with tomoto.rb
• User-based recommendations with Disco
• Item-based recommendations with Disco
• Horizontal scaling with Citus
• Bulk loading with COPY

Enable the extension

conn.exec("CREATE EXTENSION IF NOT EXISTS vector")

Optionally enable type casting for results

registry = PG::BasicTypeRegistry.new.define_default_types
Pgvector::PG.register_vector(registry)
conn.type_map_for_results = PG::BasicTypeMapForResults.new(conn, registry: registry)

Create a table

conn.exec("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))")

Insert a vector

embedding = [1, 2, 3]
conn.exec_params("INSERT INTO items (embedding) VALUES ($1)", [embedding])

Get the nearest neighbors to a vector

conn.exec_params("SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5", [embedding]).to_a

Add an approximate index

conn.exec("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")
# or
conn.exec("CREATE 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

Enable the extension

DB.run("CREATE EXTENSION IF NOT EXISTS vector")

Create a table

DB.create_table :items do
  primary_key :id
  column :embedding, "vector(3)"
end

Add the plugin to your model

class Item < Sequel::Model
  plugin :pgvector, :embedding
end

Insert a vector

Item.create(embedding: [1, 1, 1])

Get the nearest neighbors to a record

item.nearest_neighbors(:embedding, distance: "euclidean").limit(5)

Also supports inner_product, cosine, taxicab, hamming, and jaccard distance

Get the nearest neighbors to a vector

Item.nearest_neighbors(:embedding, [1, 1, 1], distance: "euclidean").limit(5)

Add an approximate index

DB.add_index :items, :embedding, type: "hnsw", opclass: "vector_l2_ops"

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

Create a sparse vector from an array

vec = Pgvector::SparseVector.new([1, 0, 2, 0, 3, 0])

Or a hash of non-zero elements

vec = Pgvector::SparseVector.new({0 => 1, 2 => 2, 4 => 3}, 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 an array

arr = vec.to_a

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-ruby.git
cd pgvector-ruby
createdb pgvector_ruby_test
bundle install
bundle exec rake test

To run an example:

cd examples/loading
bundle install
createdb pgvector_example
bundle exec ruby example.rb