https://github.com/machinelearningzuu/ragdynamics
Welcome to RAGDynamics, a repository dedicated to exploring and understanding the dynamics of Retrieval-Augmented Generation (RAG) models in the year 2024. We mainly focus on new data parsers, upgraded tools and techniques
https://github.com/machinelearningzuu/ragdynamics
documentation large-language-models llama-index llamacpp openai
Last synced: 5 months ago
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Welcome to RAGDynamics, a repository dedicated to exploring and understanding the dynamics of Retrieval-Augmented Generation (RAG) models in the year 2024. We mainly focus on new data parsers, upgraded tools and techniques
- Host: GitHub
- URL: https://github.com/machinelearningzuu/ragdynamics
- Owner: machinelearningzuu
- Created: 2024-03-16T03:52:09.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-09T02:31:32.000Z (about 2 years ago)
- Last Synced: 2024-12-22T06:40:11.440Z (over 1 year ago)
- Topics: documentation, large-language-models, llama-index, llamacpp, openai
- Language: Jupyter Notebook
- Homepage:
- Size: 4.59 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RAGDynamics
Welcome to RAGDynamics, a repository dedicated to exploring and understanding the dynamics of Retrieval-Augmented Generation (RAG) models in the year 2024. We mainly focus on new data parsers, upgraded tools and techniques
## About
RAGDynamics serves as a collaborative platform for researchers, developers, and enthusiasts interested in unraveling the capabilities, nuances, and applications of Retrieval-Augmented Generation models. This repository is designed to facilitate knowledge sharing, code implementations, dataset curation, and insights related to RAG models.
## Features
- **Research Papers**: Curated collection of seminal papers and recent advancements in the field of Retrieval-Augmented Generation.
- **Code Implementations**: Open-source implementations of state-of-the-art RAG architectures, fine-tuning methods, and evaluation utilities.
- **Datasets**: Relevant datasets for training and evaluating Retrieval-Augmented Generation models, accompanied by preprocessing utilities and guidelines.
- **Tutorials and Guides**: Step-by-step tutorials, guides, and best practices for working with RAG models.
- **Community Contributions**: Opportunities for community members to contribute research findings, code enhancements, dataset annotations, and more.
## Repository Structure
- `papers/`: Repository of research papers focusing on Retrieval-Augmented Generation models.
- `code/`: Implementations of RAG architectures, fine-tuning scripts, and evaluation tools.
- `datasets/`: Datasets utilized for training and assessing Retrieval-Augmented Generation models.
- `tutorials/`: Comprehensive tutorials and guides for leveraging RAG models effectively.
- `contributing.md`: Guidelines for contributing to RAGDynamics.
- `license.md`: Information on the licensing terms governing the repository content.
## Get Involved
We invite contributions from researchers, developers, and enthusiasts passionate about Retrieval-Augmented Generation models. Whether you're eager to share your research insights, contribute code implementations, enhance existing tutorials, or propose innovative ideas, your participation is highly valued.
To get started, please refer to our [Contribution Guidelines](contributing.md) for detailed instructions.
## Contact
For inquiries, feedback, or suggestions concerning RAGDynamics, don't hesitate to contact the repository maintainer:
- **GitHub Username**: [@machinelearningzuu](https://github.com/machinelearningzuu)
## License
This repository is licensed under the [MIT License](license.md), allowing unrestricted use, modification, and distribution of the content for academic and non-commercial purposes.