A simple RAG Library

Introduction The RAG (Retrieval-Augmented Generation) Library is a sophisticated tool designed to optimize the performance of large language models by integrating external data sources. This approach significantly enhances the model's ability to generate accurate, relevant, and contextually rich responses.

Features

Retrieval-Augmented Generation: Leverages external data to enhance response quality of language models. Customizable Data Sources: Flexibility to integrate various external data sources like websites, databases, and text documents. Efficient Data Chunking: Optimizes data retrieval with effective data segmentation. Advanced Prompt Engineering: Crafts detailed and contextual prompts for superior text generation. Broad Application Spectrum: Ideal for complex tasks like legal research, scientific reviews, and sophisticated customer service. Getting Started Follow these steps to set up and use the RAG Library:

git clone https://github.com/talda/Ragger.git
cd Ragger
pip install -r requirements.txt

Usage Example code snippet to demonstrate the basic usage of the RAG Library:

import ragger
# Example usage code here

Documentation

For detailed information, visit our Documentation Page.

Applications

RAG is particularly powerful in scenarios requiring multi-step reasoning or synthesis of information from various sources, such as:

• Text Summarization
• Question-Answering Systems
• Content Generation

Contributing

Contributions are welcome! Please read our Contributing Guidelines.

Support

For issues or questions, file an issue on the GitHub Issues Page.

License

RAGger is released under the MIT License.

Acknowledgments

Thanks to all contributors and supporters of this project!