tu-zhenzhao/data-cashing-rag

Advanced RAG for Data Caching

Overview

Advanced RAG for Data Caching is a pioneering database project designed to bridge the gap between Long Language Models (LLMs) and sophisticated data analysis and visualization. Our goal is to provide an extensive collection of LLM prompt templates, particularly for Python plotting and graphing datasets, enabling users to tackle complex data analysis tasks effortlessly.

Features

• Comprehensive Prompt Templates: A wide array of LLM prompt templates for various data visualization and machine learning tasks.
• Advanced RAG System: Utilizes an Advanced Retrieval-Augmented Generation system for efficient template and instruction retrieval.
• Dynamic Template Updating: Allows the community to contribute new and improved templates, constantly enhancing the system's capabilities.
• User-Friendly Interface: Easy to navigate and use, ensuring users can quickly find and apply methods or models to their datasets.

Getting Started

Prerequisites

• Python 3.8+
• Specific Python libraries: pandasai, pandas, matplotlib, scikit-learn (See requirements.txt for more details)

Installation

Wait for the official release of the project to install it.

Usage

Note: The Detailed Usage section will be updated once the project is officially released.

1. Search for the desired analysis or visualization template using the system interface.
2. Input your dataset details as per the template's instructions.
3. Generate the LLM prompt and retrieve the custom code template.
4. Edit and execute the generated code as needed for your analysis.

Contributing

We welcome contributions from the community! Whether it's adding new templates, improving existing ones, or reporting issues, your input helps make Advanced RAG for Data Caching better for everyone. Please read CONTRIBUTING.md for more details on how to contribute.

License

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

Acknowledgments

• Special thanks to all contributors who help enhance the project.
• Acknowledge any organizations or individuals whose tools or data you've used.