RAG-X

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Advanced Video Graph RAG using SAM2,CLIP,BLIP,Qwen2-VL,YOLO-World ,Neo4j, WebGPU, local LLM

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# RAG-X: Advanced Video Retrieval-Augmented Generation (RAG) Framework 🚧
https://github.com/yangchris11/samurai
RAG-X is a cutting-edge AI framework designed to revolutionize video content analysis, retrieval, and understanding by integrating Retrieval-Augmented Generation (RAG) techniques with knowledge graph capabilities. This framework deconstructs complex video data into structured, meaningful components and maps them in an interconnected graph, enhancing semantic search, contextual analysis, and information retrieval.

> 🚧 **Note**: RAG-X is currently under active development. We are continuously building and refining its features, so stay tuned for updates! Contributions, feedback, and collaboration are welcome!

## Planned Workflow

The diagram below outlines the planned workflow for the RAG-X framework:

![Planned Workflow](https://raw.githubusercontent.com/admineral/RAG-X/main/public/image1.webp)

## Key Components

1. **Video Upload and Extraction**
   - The first step involves uploading the video and extracting its key components, such as frames and audio transcripts, for further analysis.
   
   ![Video Upload and Extraction](https://raw.githubusercontent.com/admineral/RAG-X/main/public/image2.webp)

2. **Video Processing Pipeline**
   - Breaks down long videos into manageable segments for focused content analysis. This includes frame extraction, similarity search, semantic/context analysis, and scene clustering.
   
   ![Video Processing Pipeline](https://raw.githubusercontent.com/admineral/RAG-X/main/public/image3.webp)

3. **Captioning Pipeline**
   - Generates high-precision captions and metadata for video clips using advanced AI models like Qwen2-VL, BLIP2, SAM2, and more.
   
   ![Captioning Pipeline](https://raw.githubusercontent.com/admineral/RAG-X/main/public/image4.webp)

4. **Knowledge Base Structuring**
   - Constructs a comprehensive knowledge graph to represent relationships between scenes, segments, and entities, allowing for advanced querying, semantic search, and contextual analysis.
   
   ![Knowledge Base Structuring](https://raw.githubusercontent.com/admineral/RAG-X/main/public/image5.webp)

## Future Enhancements

- **Enhanced Video Understanding**: Leveraging more advanced models for better scene understanding and narrative creation.
- **Real-Time Processing**: Optimizing the pipeline for faster, real-time video processing and retrieval.
- **User Interface**: Developing an intuitive UI for easy navigation and interaction with the knowledge graph.

## How to Contribute

We welcome contributions from the community to help us improve and expand RAG-X. If you have ideas, suggestions, or improvements, feel free to submit a pull request or open an issue.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contact

For any inquiries or feedback, please reach out via Discord

Stay tuned for more updates as we build the future of AI-driven video content retrieval!

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*This README is dynamically generated and subject to change as the project progresses.*

RAG-X

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