Welcome to the RAG University repository! This repository contains code implementations for Retrieval-Augmented Generation (RAG) models, specifically designed for Language Model (LM) tasks. RAG models combine the strengths of both retrieval and generation approaches, enhancing the capabilities of LLMs (Large Language Models) in various natural language processing tasks.
Retrieval-Augmented Generation is a powerful paradigm that leverages pre-existing knowledge through retrieval mechanisms and generates contextually relevant responses. This repository focuses on providing a comprehensive set of codes and examples for implementing RAG models within the context of Large Language Models.
- Retrieval Augmented Generation: Implement state-of-the-art retrieval-augmented generation techniques for enhancing the performance of language models.
- Compatibility: Codebase is compatible with popular language models, allowing easy integration into existing projects or workflows.
- Example Implementations: Explore example implementations and use cases to understand how RAG models can be applied to various language tasks.
Follow these steps to get started with RAG University:
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Clone the Repository:
git clone https://github.com/1zuu/RAG-University.git
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Install Dependencies:
cd RAG-University pip install -r requirements.txt -
Explore Examples: Dive into the
examplesdirectory to find sample implementations and notebooks demonstrating the usage of RAG models. -
Run Your Model: Use the provided code snippets and documentation to integrate RAG models into your language tasks.
We welcome contributions from the community! If you have ideas, bug reports, or improvements, feel free to open an issue or submit a pull request. Check out our contribution guidelines for more details.
This project is licensed under the MIT License - see the LICENSE file for details.
Happy coding with RAG University! If you find this repository helpful, consider giving it a star and spreading the word. Thank you for your interest and contribution!
