Hey!
Semantic search is finally here! Read more in Semantic search with Torus guide.
Shortly - it allows you to generate embeddings using a configurable (and chainable) adapters and use them to compare against the ones stored in your database.
Supported adapters (for now):
-
Torus.Embeddings.OpenAI- uses OpenAI’s API to generate embeddings. -
Torus.Embeddings.HuggingFace- uses HuggingFace’s API to generate embeddings. -
Torus.Embeddings.LocalNxServing- generate embeddings on your local machine using a variety of models available on Hugging Face -
Torus.Embeddings.PostgresML- uses PostgreSQL PostgresML extension to generate embeddings -
Torus.Embeddings.Batcher- a long‑running GenServer that collects individual embedding calls, groups them into a single batch, and forwards the batch to the configuredembedding_module(any from the above or your custom one). -
Torus.Embeddings.NebulexCache- a wrapper around Nebulex cache, allowing you to cache the embedding calls in memory, so you save the resources/cost of calling the embedding module multiple times for the same input.
And you can easily create your own adapter by implementing the Torus.Embedding behaviour.
So after you’ll pick your favorite embedding adapter, you can add semantic search:
def search(term) do
search_vector = Torus.to_vector(term)
Post
|> Torus.semantic([p], p.embedding, search_vector, distance: :l2_distance, pre_filter: 0.7)
|> Repo.all()
end
Future plans:
- Release
torus_exampleapp that would allow experimenting with options and different search types to pick the best one - Add support for highlighting search results. (Base off of a
ts_headlinefunction) - Extend similarity search to support
fuzzystrmatchextension distance options.
