pgvector support for Elixir
Add this line to your application’s mix.exs under deps:
{:pgvector, "~> 0.4.0"}
And follow the instructions for your database library:
Or check out some examples:
- Embeddings with OpenAI
- Binary embeddings with Cohere
- Sentence embeddings with Bumblebee
- Hybrid search with Bumblebee (Reciprocal Rank Fusion)
- Sparse search with Bumblebee
- Horizontal scaling with Citus
- Bulk loading with
COPY
Create lib/postgrex_types.ex with:
Postgrex.Types.define(MyApp.PostgrexTypes, Pgvector.extensions() ++ 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
Also supports :halfvec, :bit, and :sparsevec
Update the model
schema "items" do field :embedding, Pgvector.Ecto.Vector end
Also supports Pgvector.Ecto.HalfVector, Pgvector.Ecto.Bit, and Pgvector.Ecto.SparseVector
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, ^Pgvector.new([1, 2, 3])), limit: 5)
Also supports max_inner_product, cosine_distance, l1_distance, hamming_distance, and jaccard_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: :hnsw) # or create index("items", ["embedding vector_l2_ops"], using: :ivfflat, options: "lists = 100")
Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
Register the extension
Postgrex.Types.define(MyApp.PostgrexTypes, Pgvector.extensions(), [])
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 hnsw (embedding vector_l2_ops)", []) # or Postgrex.query!(pid, "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
Create a vector from a list
vec = Pgvector.new([1, 2, 3])
Or an Nx tensor
vec = Nx.tensor([1.0, 2.0, 3.0]) |> Pgvector.new()
Get a list
list = vec |> Pgvector.to_list()
Get an Nx tensor
tensor = vec |> Pgvector.to_tensor()
Create a half vector from a list
vec = Pgvector.HalfVector.new([1, 2, 3])
Or an Nx tensor
vec = Nx.tensor([1.0, 2.0, 3.0], type: :f16) |> Pgvector.HalfVector.new()
Get a list
list = vec |> Pgvector.to_list()
Get an Nx tensor
tensor = vec |> Pgvector.to_tensor()
Create a sparse vector from a list
vec = Pgvector.SparseVector.new([1, 2, 3])
Or an Nx tensor
vec = Nx.tensor([1.0, 2.0, 3.0]) |> Pgvector.SparseVector.new()
Or a map of non-zero elements
elements = %{0 => 1.0, 2 => 2.0, 4 => 3.0} vec = Pgvector.SparseVector.new(elements, 6)
Note: Indices start at 0
Get the number of dimensions
dim = vec |> Pgvector.SparseVector.dimensions()
Get the indices of non-zero elements
indices = vec |> Pgvector.SparseVector.indices()
Get the values of non-zero elements
values = vec |> Pgvector.SparseVector.values()
Get a list
list = vec |> Pgvector.to_list()
Get an Nx tensor
tensor = vec |> Pgvector.to_tensor()
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-elixir.git cd pgvector-elixir mix deps.get createdb pgvector_elixir_test mix test
To run an example:
cd examples/loading
mix deps.get
createdb pgvector_example
mix run example.exs