CrossModalRetrieval-RAG

Most of the content in this project is still AI-generated placeholders, and currently only embbeding.py and processors.py in the models directory are meaningful. Other features will be gradually improved and completed.

本项目大多数内容还是AI生成的placeholder，目前只有models里的embbeding.py和processors.py是有意义的。其他功能会陆续完善。

A RAG system with true cross-modal retrieval capabilities, enabling seamless integration of visual and textual information.

Key Features | Installation | Quick Start | Documentation | Contributing

🌟 Introduction

CrossModalRetrieval-RAG revolutionizes traditional RAG systems by implementing genuine cross-modal retrieval and response generation. While conventional multimodal systems process different data types in isolation, our approach focuses on understanding and leveraging the intricate relationships between text and visual content during both retrieval and generation phases.

✨ Key Features

Cross-Modal Retrieval

• Joint Embedding Space: Unified representation for both text and images
• Context-Aware Matching: Considers relationships between visual and textual elements
• Semantic Cross-Reference: Enables bidirectional search between modalities
• Unified Ranking: Single ranking system considering both visual and textual relevance

Advanced Response Generation

• Coherent Multimodal Responses: Generates responses that naturally combine text and images
• Context-Aware Image Selection: Intelligently chooses relevant visual content
• Cross-Modal Understanding: Maintains semantic connections across modalities

🔍 What Sets Us Apart

Feature	Traditional RAG	Multimodal RAG	CrossModalRetrieval-RAG
Text Processing	✅	✅	✅
Image Processing	❌	✅	✅
Cross-Modal Search	❌	❌	✅
Joint Embedding	❌	❌	✅
Unified Ranking	❌	❌	✅
Visual-Textual Response	❌	Partial	✅

🚀 Quick Start

# Install the package
pip install crossmodal-rag

# Basic usage
from crossmodal_rag import CrossModalRAG

# Initialize the system
rag = CrossModalRAG()

# Add your documents and images
rag.add_documents("path/to/documents")
rag.add_images("path/to/images")

# Query with text or image
response = rag.query("Your query here")

## 📖 Documentation

For detailed documentation, visit our [Documentation Page](docs/README.md).

### Basic Components

```python
from crossmodal_rag import (
    CrossModalRetriever,
    MultimodalGenerator,
    DocumentStore
)

Example Usage

# Initialize components
retriever = CrossModalRetriever()
generator = MultimodalGenerator()
doc_store = DocumentStore()

# Create a custom pipeline
rag = CrossModalRAG(
    retriever=retriever,
    generator=generator,
    doc_store=doc_store
)

🛠️ Installation

# Basic installation
pip install crossmodal-rag

# With all optional dependencies
pip install crossmodal-rag[all]

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• CLIP team for their groundbreaking work in cross-modal understanding
• Langchain community for RAG implementation insights
• All our contributors and supporters

📚 Citation

If you use CrossModalRetrieval-RAG in your research, please cite:

@software{crossmodal_rag2024,
  title = {CrossModalRetrieval-RAG: A Cross-Modal Retrieval Enhanced RAG System},
  author = {Your Name},
  year = {2024},
  url = {https://github.com/yourusername/CrossModalRetrieval-RAG}
}

📬 Contact

• GitHub Issues: For bug reports and feature requests
• Email: your.email@example.com
• Twitter: @YourHandle

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