TaskX-Nano

Overview

TaskX-Nano is an open-source, lightweight tool that maps natural language processing (NLP) prompts to programming functions, identifying the best-matching functions and their optimal parameters. Unlike traditional LLM-based systems, FunctionX-Nano operates without requiring a large language model, making it fast, resource-efficient, and ideal for developers seeking to integrate NLP-driven automation into their workflows.

Features

• NLP-to-Function Mapping: Interprets natural language prompts and matches them to relevant programming functions.
• Parameter Optimization: Extracts and optimizes function parameters from the prompt context.
• Lightweight Design: Built for speed and efficiency, requiring minimal computational resources (no LLM dependency).
• Extensible: Easily adaptable to support custom functions and various use cases.
• Open Source: Freely available for community contributions and improvements.

Installation

1. Clone the repository:

   git clone https://github.com/rodyrahi/Taskx-nano.git
   cd Taskx-nano
   

2. Install dependencies:

   pip install -r requirements.txt
   

   Note: Dependencies (e.g., spacy, scikit-learn) will be listed in requirements.txt. No large-scale NLP models like BERT or GPT are required.

3. Set up the environment:

   • Ensure Python 3.8+ is installed.
   • Configure any additional settings (e.g., function library, model weights) as specified in config.yaml (to be added).

Usage

1. Write a Prompt: Create a natural language prompt describing the desired function, e.g., "Calculate the square of a number."
2. Run the Model:

   python main.py
   

3. Output: The model returns the matched function (e.g., square(num)) and optimized parameters (e.g., num=5).

How It Works

FunctionX-Nano uses lightweight NLP techniques (e.g., rule-based parsing, keyword matching, or small-scale embeddings) to analyze user prompts. It matches prompts to a predefined or user-defined function library using semantic analysis and similarity scoring. Parameters are extracted and optimized based on entities or constraints in the prompt. Unlike MCP systems reliant on LLMs, FunctionX-Nano achieves similar functionality with minimal resource overhead, making it suitable for low-resource environments.

Note: Detailed architecture and implementation details will be documented as the project evolves.

Why No LLM?

FunctionX-Nano is designed to avoid the computational and memory demands of large language models. By leveraging efficient NLP techniques and a focused function-matching approach, it delivers fast, reliable results without the need for heavy model dependencies, making it ideal for edge devices, small-scale applications, or rapid prototyping.

Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature
   

3. Commit your changes:

   git commit -m "Add your feature"
   

4. Push to the branch:

   git push origin feature/your-feature
   

5. Open a pull request.

Please follow the project's coding standards and include tests where applicable.

Roadmap

• Support for multiple programming languages (e.g., Python, JavaScript).
• Integration of lightweight NLP libraries for enhanced prompt understanding.
• Release of a pre-built function library for common tasks.
• Documentation for adding custom functions and extending the model.
• Support for batch processing of prompts.

License

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

Contact

For questions or feedback, create a GitHub Issue or contact the maintainer at [email protected].