statembed-py

Fast, lightweight static text embeddings.

Python bindings for the statembed Rust crate, built with PyO3.

statembed-py loads a static embedding model (a model.safetensors file) and produces mean-pooled, optionally L2-normalized embeddings from token IDs. It does not tokenize text itself — pair it with a tokenizer library such as tokenizers.

Installation

# with uv
uv add statembed-py
# with pip
pip install statembed-py

Building from source

Requires Rust and maturin:

pip install maturin
maturin develop --release

Usage

from functools import lru_cache
from tokenizers import Tokenizer
from statembed_py import StaticEmbedding

@lru_cache(maxsize=1)
def get_embedding_model() -> StaticEmbedding:
    return StaticEmbedding(model_dir="./my-model")  # must contain model.safetensors

@lru_cache(maxsize=1)
def get_tokenizer() -> Tokenizer:
    return Tokenizer.from_file("./my-model/tokenizer.json")

def embed(text: str) -> list[float]:
    tokens = get_tokenizer().encode(text).ids
    embedding = get_embedding_model().embed_tokens(tokens)
    return embedding

API

`StaticEmbedding(model_dir, normalize=True)`

Loads a model from a local directory containing model.safetensors. The tensor is loaded lazily on the first embed_tokens call.

model_dir: str — path to the model directory.
normalize: bool — if True (default), output embeddings are L2-normalized.

`embed_tokens(tokens: Sequence[int]) -> list[float]`

Mean-pools the embedding rows for the given token IDs into a single fixed-length vector, applying normalization if enabled.

Type stubs (statembed_py.pyi) are bundled for editor and type-checker support.

Development

maturin develop — build and install the extension into the active virtualenv.
cargo run --bin stub_gen — regenerate statembed_py.pyi after changing the PyO3 bindings.

License

MIT

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This release

0.1.0 This release

Aug 14, 2026 42 files

statembed-py 0.1.0

statembed-py

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Building from source

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StaticEmbedding(model_dir, normalize=True)

embed_tokens(tokens: Sequence[int]) -> list[float]

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`StaticEmbedding(model_dir, normalize=True)`

`embed_tokens(tokens: Sequence[int]) -> list[float]`