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pgvector support for Elixir

Supports Ecto and Postgrex

installation

Installation

Add this line to your application’s mix.exs under deps:

{:pgvector, "~> 0.2.0"}

And follow the instructions for your database library:

• Ecto
• Postgrex

Or check out an example:

• Embeddings with OpenAI

ecto

Ecto

Create lib/postgrex_types.ex with:

Postgrex.Types.define(MyApp.PostgrexTypes, [Pgvector.Extensions.Vector] ++ Ecto.Adapters.Postgres.extensions(), [])

And add to config/config.exs:

config :my_app, MyApp.Repo, types: MyApp.PostgrexTypes

Create a migration

mix ecto.gen.migration create_vector_extension

with:

defmodule MyApp.Repo.Migrations.CreateVectorExtension do
  use Ecto.Migration

  def up do
    execute "CREATE EXTENSION IF NOT EXISTS vector"
  end

  def down do
    execute "DROP EXTENSION vector"
  end
end

Run the migration

mix ecto.migrate

You can now use the vector type in future migrations

create table(:items) do
  add :embedding, :vector, size: 3
end

Update the model

schema "items" do
  field :embedding, Pgvector.Ecto.Vector
end

Insert a vector

alias MyApp.{Repo, Item}

Repo.insert(%Item{embedding: [1, 2, 3]})

Get the nearest neighbors

import Ecto.Query
import Pgvector.Ecto.Query

Repo.all(from i in Item, order_by: l2_distance(i.embedding, [1, 2, 3]), limit: 5)

Also supports max_inner_product and cosine_distance

Convert a vector to a list or Nx tensor

item.embedding |> Pgvector.to_list()
item.embedding |> Pgvector.to_tensor()

Add an approximate index in a migration

create index("items", ["embedding vector_l2_ops"], using: :ivfflat)
# or
create index("items", ["embedding vector_l2_ops"], using: :hnsw)

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

postgrex

Postgrex

Register the extension

Postgrex.Types.define(MyApp.PostgrexTypes, [Pgvector.Extensions.Vector], [])

And pass it to start_link

{:ok, pid} = Postgrex.start_link(types: MyApp.PostgrexTypes)

Enable the extension

Postgrex.query!(pid, "CREATE EXTENSION IF NOT EXISTS vector", [])

Create a table

Postgrex.query!(pid, "CREATE TABLE items (embedding vector(3))", [])

Insert a vector

Postgrex.query!(pid, "INSERT INTO items (embedding) VALUES ($1)", [[1, 2, 3]])

Get the nearest neighbors

Postgrex.query!(pid, "SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5", [[1, 2, 3]])

Convert a vector to a list or Nx tensor

vector |> Pgvector.to_list()
vector |> Pgvector.to_tensor()

Add an approximate index

Postgrex.query!(pid, "CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)", [])
# or
Postgrex.query!(pid, "CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)", [])

Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance

upgrading

Upgrading

0-2-0

0.2.0

Vectors are now returned as Pgvector structs instead of lists. Get a list with:

vector |> Pgvector.to_list()

or an Nx tensor with:

vector |> Pgvector.to_tensor()

history

History

View the changelog

contributing

Contributing

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-elixir.git
cd pgvector-elixir
mix deps.get
createdb pgvector_elixir_test
mix test