intextus-embed 0.1.0

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

🕸️ intextus

intextus (Latin for "woven into the text") is an ultra-lightweight, 100% PyTorch-free, and production-grade Python library designed to encode late-interaction ColBERT multi-vectors.

By replacing massive deep learning libraries with highly optimized, compiled C++/Rust backends, intextus delivers full ColBERT MaxSim embeddings in under 65MB of RAM with zero PyTorch or Transformers dependencies. It is optimized for edge devices, serverless functions (AWS Lambda, Cloudflare Workers), and resource-constrained environments.

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⚡ Key Features

• No PyTorch or Transformers: Fully decoupled from the heavy standard library pipeline. A simple pip install completes in seconds.
• Micro Memory Footprint: Executes multi-vector graphs inside ONNX Runtime, drawing less than 65MB of RAM during inference.
• Fast Rust Tokenization: Uses Hugging Face's raw Rust tokenization backend directly.
• Dynamic Punctuation Skiplist: Dynamically parses tokenizer.json at initialization, creating a zero-overhead mask to discard punctuation vectors, matching ColBERT index-saving behaviors.
• Standardized Late Interaction: Exposes native NumPy-based MaxSim calculations.

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📦 Installation

Install the library directly via pip:

pip install intextus-embed

[!NOTE] intextus currently defaults to highly optimized CPU inference. Full hardware acceleration and GPU execution support are planned for a future release.

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🚀 Quick Start

Here is how to load a model, extract multi-vector embeddings, and compute late-interaction cross-similarity scores entirely in NumPy:

from intextus import IntextusEncoder, compute_maxsim

# Initialize the encoder (defaults to intextus/mxbai-edge-colbert-v0-17m-onnx)
model = IntextusEncoder()

# Or initialize from a local directory containing 'model.onnx' and 'tokenizer.json'
# model = IntextusEncoder("./my_model_directory")

# Extract query and document embeddings (Batch_Size, Sequence_Length, Dimension)
query_embeddings = model.encode_queries("What is ultra-low latency?")
doc_embeddings = model.encode_docs("ONNX runtime bypasses the PyTorch layer completely.")

# Compute the cross-similarity score via NumPy (using the first item in the batch)
score = compute_maxsim(query_embeddings[0], doc_embeddings[0])
print(f"Relevance Score (MaxSim): {score:.4f}")

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🎯 Supported & Tested Models

intextus is designed for ultra-fast, edge-compatible ColBERT execution. The primary officially supported and fully validated models are:

• intextus/mxbai-edge-colbert-v0-17m-onnx (Alias: mxbai-edge-colbert-v0-17m) — A highly-optimized, single-file ONNX representation of ModernBERT-backed mxbai-edge-colbert-v0-17m (66 MB, 48-dimensional late-interaction embeddings). (Default Model)
• intextus/mxbai-edge-colbert-v0-32m-onnx (Alias: mxbai-edge-colbert-v0-32m) — A larger, higher-capacity ONNX representation of ModernBERT-backed mxbai-edge-colbert-v0-32m (124 MB, 64-dimensional late-interaction embeddings).
• intextus/lateon-onnx (Alias: lateon) — A high-capacity base ModernBERT-backed model (580 MB, 128-dimensional late-interaction embeddings). Note: LateOn is case-sensitive, so load it with IntextusEncoder("lateon", do_lower_case=False).

[!NOTE] Any ColBERT model exported via standard Hugging Face/PyLate workflows can be loaded locally by providing the path to its model.onnx and tokenizer.json.

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⚖️ License

This project is licensed under the MIT License. See the LICENSE file for details.