intextus-embed 0.1.4

A lightweight, zero-PyTorch ONNX encoder for generic ColBERT models.

intextus

ColBERT embedding and MaxSim scoring without PyTorch. Uses a native C++ extension (ONNX Runtime + tokenizers-cpp) so you don't need to pull in 2 GB of deep learning dependencies just to encode some text.

Install

pip install intextus-embed

Only runtime deps are numpy and huggingface-hub. The C++ bits (ONNX Runtime, tokenizer) are compiled into the wheel.

Usage

from intextus import LateInteractionEncoder, compute_maxsim

model = LateInteractionEncoder()  # downloads intextus/mxbai-edge-colbert-v0-17m-onnx

q = model.encode_queries("What is late interaction?")
d = model.encode_docs("ColBERT computes token-level similarity.")

score = compute_maxsim(q[0], d[0])
print(score)

You can also point it at a local directory with model.onnx and tokenizer.json:

model = LateInteractionEncoder("./my-model/")

Models

Alias	Repo	Size	Dim	Notes
mxbai-edge-colbert-v0-17m	intextus/mxbai-edge-colbert-v0-17m-onnx	66 MB	48	Default
mxbai-edge-colbert-v0-32m	intextus/mxbai-edge-colbert-v0-32m-onnx	124 MB	64
lateon	intextus/lateon-onnx	580 MB	128	Case-sensitive: use do_lower_case=False

Any ColBERT ONNX model should work if you put model.onnx and tokenizer.json in a folder and pass the path.

How it works

• Tokenization and inference run in C++ via a nanobind extension
• GIL is released during encode and MaxSim calls, so you can run multiple threads
• Punctuation tokens are masked out of document embeddings (standard ColBERT behavior)
• Embeddings are L2-normalized by default
• CPU only for now

License

MIT. See LICENSE.