RAGio is your go-to toolkit for effortlessly launching Retrieval-Augmented Generation (RAG) models using Gradio interfaces. This project is designed for both novices and seasoned developers seeking to integrate cutting-edge Natural Language Processing (NLP) capabilities into their applications.

Key Features

• Beginner Friendly: Launch and run your RAG models locally or seamlessly host them on HuggingFace Spaces. The toolkit currently supports .pdf documents, making it simpler than ever to get started.
• Interactive User Interface: Experience engaging interactions with your models and data through a dynamic Gradio interface, allowing for real-time experimentation and feedback.
• Integration with HuggingFace and OpenAI: Leverage the powerful capabilities of HuggingFace and OpenAI APIs to enrich your NLP applications.
• Efficient Vector Storage: Utilize LanceDB for efficient storage and management of embedding vectors, enhancing your data handling capabilities.
• Upcoming Chunking Strategies: Stay tuned for the introduction of multiple chunking strategies to further optimize your RAG deployments.

Getting Started

To get started with RAGio, simply clone the repository and install the necessary dependencies. After setting up your environment, you can easily run the Gradio application to begin exploring your RAG models.

Example Code Snippet

# Clone the repository
git clone https://github.com/your-username/RAGio.git
cd RAGio
# Install requirements
pip install -r requirements.txt

Ensure to configure your environment by updating the .template.env file with your HuggingFace and OpenAI credentials, and run the app:

source ./.env  # Apply environment variables
gradio app.py  # Run Gradio app

Then, access the application in your browser at http://127.0.0.1:7860.

Contributing

Contributions are welcome! If you have suggestions or wish to enhance RAGio, feel free to fork the repository, implement changes, and submit a pull request. Join us in improving RAGio and making NLP accessible to all!