LlamaIndex RAG Techniques

🚧 Work in Progress: This repository is actively under development. New techniques, implementations, and documentation are being added regularly.

Overview

This repository provides a comprehensive collection of advanced RAG (Retrieval Augmented Generation) techniques, with a focus on practical implementations using LlamaIndex. Each technique is thoroughly documented with both theoretical explanations and working code examples.

Currently Implemented Techniques

✅ Vector Store Index

• Complete implementation and documentation
• Best practices and optimization strategies
• Performance considerations
• Documentation
• Implementation

✅ Reranking

• Multiple reranking strategies
• Integration patterns
• Performance optimization
• Documentation
• Implementation

✅ Deep Memory

• Training process implementation
• Integration with vector stores
• Performance optimization guides
• Documentation
• Implementation

Coming Soon

🔄 In Development

• Hybrid Search Techniques
• Query Decomposition
• Contextual Compression
• Advanced Chunking Strategies
• Evaluation Frameworks

📋 Planned Features

• Interactive Examples
• Performance Benchmarks
• Integration Guides
• Troubleshooting Guides
• Best Practices Documentation

Getting Started

git clone https://github.com/yourusername/llamaindex-rag-techniques.git
cd llamaindex-rag-techniques
pip install -r requirements.txt

Prerequisites

• Python 3.8+
• LlamaIndex
• Additional requirements listed in requirements.txt

Project Structure

llamaindex-rag-techniques/
├── docs/                  # Detailed documentation for each technique
├── src/                   # Source code implementations
├── examples/              # Example usage and notebooks
├── tests/                 # Test suites
└── requirements.txt       # Project dependencies

Contributing

Contributions are welcome! Please read our Contributing Guidelines before submitting PRs.

Areas Where Help is Needed

• Additional RAG techniques implementation
• Documentation improvements
• Example notebooks
• Testing and validation
• Performance optimization

Resources

Official Documentation

• LlamaIndex Documentation
• DeepLake Documentation

Additional Reading

• Links to relevant articles and papers (Coming Soon)
• Performance benchmarks (Coming Soon)
• Integration guides (Coming Soon)

License

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

Acknowledgments

• LlamaIndex team for the excellent framework
• ActiveLoop for Deep Lake integration
• Contributors and community members

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📢 Want to Contribute?
This repository is actively seeking contributions! Check the Issues tab for current tasks or propose new improvements.