Towhee is a Python framework designed to simplify and accelerate neural data processing pipelines for unstructured data like text, images, audio, and video. It targets developers and researchers who need to extract insights from diverse data types, enabling the creation of LLM-based pipelines with state-of-the-art deep learning models and efficient deployment.

How It Works

Towhee utilizes a modular approach with "Operators" (pre-built models and functions) that are chained together to form "Pipelines." These pipelines are defined using a Pythonic, method-chaining API, allowing for intuitive prototyping and complex DAG construction. The framework supports multi-modal data and LLM orchestration, including local model hosting and prompt management. For high-performance execution, it leverages the Triton Inference Server, enabling efficient model serving on CPU/GPU via backends like TensorRT and ONNX.

Quick Start & Requirements

• Install via pip: pip install towhee
• Requires Python 3.7+.
• Official docs: https://towhee.readthedocs.io/en/latest/
• Examples: https://github.com/towhee-io/examples

Highlighted Details

• Supports over 140 SOTA models across CV, NLP, multimodal, audio, and medical domains.
• Offers pre-built ETL pipelines for tasks like RAG, text-image search, and video deduplication.
• Enables transformation of Python pipelines into high-performance Docker containers.
• Integrates with vector databases like Milvus for embedding storage.

Maintenance & Community

• Active community contributions are encouraged via issues, documentation, and code.
• Special thanks to contributors listed in the README.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project's license is not clearly stated in the README, which may pose a risk for commercial adoption or integration into closed-source projects.