HuggingFists is a low-code data flow tool designed for users to easily integrate and utilize Large Language Models (LLMs) and Hugging Face models. It offers a visual workflow builder, akin to a low-code Langchain, enabling rapid prototyping and deployment of AI-driven data processing pipelines without extensive coding.

How It Works

HuggingFists employs a visual, node-based workflow paradigm where operators (functions) are connected via input/output ports. This design allows for clear data flow visualization and modularity. Each operator has defined ports that expose data structure insights, similar to function signatures. The system supports complex logic through features like branch selection, function calls, and workflow variables, with integrated breakpoint debugging for troubleshooting.

Quick Start & Requirements

• Linux: Clone repository, navigate to sengee.community.linux, run bash install.sh. Test with curl http://localhost:38172. Access UI at http://serverIP:38172.
• Windows: Download Windows version from provided link.
• System Requirements: Linux requires at least 4 cores and 8GB RAM. Windows 11 recommended.
• Documentation: Video Link provides examples.

Highlighted Details

• Supports accessing Hugging Face models via Inference API and locally deployed models.
• Enables direct integration with Hugging Face datasets.
• Features breakpoint debugging for workflows.
• Allows expansion of connectors and operators.

Maintenance & Community

Community support is available via WeChat Group. Numerous Bilibili videos demonstrate usage and specific functionalities.

Licensing & Compatibility

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

Limitations & Caveats

Currently, HuggingFists does not support model training scenarios, though this is planned for future development. Manual downloading of all model files is required for local Hugging Face model deployment, which can be cumbersome.