🚀 RAG (Retrieval Augmented Generation) System Implementation Guide

Welcome to the comprehensive guide for implementing RAG systems! This repository provides a structured approach to building and optimizing Retrieval Augmented Generation systems, from basic implementations to advanced techniques.

📚 Repository Structure

Core Modules

Fundamentals

1. RAG from Scratch
2. Complete implementation guide from ground up
3. RAG in 10 lines of code
4. Understanding embeddings and similarity
5. Basic requirements setup

Basic Implementation & Evaluation

1. Basic RAG Implementation

2. Basic server implementation

3. Jupyter notebook tutorials
4. Performance evaluation notebooks

5. Environment setup guide

6. BM25 RAG

7. BM25 algorithm implementation

8. Application setup

9. Interactive notebook examples

10. Data Ingestion

11. Data chunking strategies

12. Embedding generation
13. Batch processing examples

14. Data parsing techniques

15. RAG Evaluation

16. RAGAS metrics implementation

17. Deepeval integration
18. TruLens evaluation

19. Test dataset examples

20. RAG Observability

21. System monitoring setup
22. Performance tracking
23. Debug tools integration

Advanced Techniques

1. ReRanker RAG

2. Result re-ranking implementation

3. Evaluation metrics

4. Performance optimization

5. Hybrid RAG

6. Qdrant hybrid search implementation

7. Multiple retrieval method integration

8. Sentence Window RAG

9. Context window optimization

10. Sentence-level retrieval

11. Auto Merging RAG

    • Automatic content merging
    • Redundancy elimination

12. Advanced Query Processing

    • HyDE (Hypothetical Document Embeddings)
    • Query transformation techniques
    • Query optimization strategies

Specialized Implementations

1. Self Query RAG

   • Self-querying mechanisms
   • Query refinement techniques

2. RAG Fusion

   • Multiple RAG model integration
   • Result fusion strategies

3. RAPTOR

   • Advanced reasoning implementation
   • Performance optimization

4. ColBERT RAG

   • ColBERT model integration
   • Ragatouille retriever implementation

5. Graph RAG

   • Graph-based retrieval
   • Knowledge graph integration

6. Agnetic RAG

   • Multi-document agent system
   • Domain-specific implementations

7. Vision RAG

   • GPT-4V integration
   • Multi-modal retrieval implementation

📂 Data Resources

Located in the data/ directory:

• Markdown Documents (md/): Processed markdown versions of papers
• PDF Documents (pdf/): Original research papers and documentation
• Sample Database (sample-lancedb/): Example database implementation

🎯 Implementation Techniques

✅ Implemented Features

1. Simple RAG with vector store integration
2. Context enrichment algorithms
3. Multi-faceted filtering systems
4. Fusion retrieval mechanisms
5. Intelligent reranking
6. Query transformation
7. Hierarchical indexing
8. HyDE implementation
9. Dynamic chunk sizing
10. Semantic chunking
11. Context compression
12. Explainable retrieval
13. Graph RAG implementation
14. RAPTOR integration

🚧 Upcoming Features

1. Retrieval with feedback loops
2. Adaptive retrieval systems
3. Iterative retrieval mechanisms
4. Ensemble retrieval implementation
5. Multi-modal integration
6. Self RAG optimization
7. Corrective RAG systems

🛠️ Tech Stack

• 🦙 RAG Orchestration: Llama-index
• 🔍 Vector Database: Qdrant
• 👁️ Observability: Arize Phoenix
• 📊 Evaluation: RAGAS & Deepeval

🤝 Contributing

We welcome contributions! Please see our contributing guidelines for more information.

📚 References

This project builds upon research and implementations from various sources. See our acknowledgments section for detailed credits.

📝 License

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

---

Made with ❤️ for the RAG community