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

   - Complete implementation guide from ground up
   - RAG in 10 lines of code
   - Understanding embeddings and similarity
   - Basic requirements setup
   

Basic Implementation & Evaluation¶

1. Basic RAG Implementation

   - Basic server implementation
   - Jupyter notebook tutorials
   - Performance evaluation notebooks
   - Environment setup guide
   

2. BM25 RAG

   - BM25 algorithm implementation
   - Application setup
   - Interactive notebook examples
   

3. Data Ingestion

   - Data chunking strategies
   - Embedding generation
   - Batch processing examples
   - Data parsing techniques
   

4. RAG Evaluation

   - RAGAS metrics implementation
   - Deepeval integration
   - TruLens evaluation
   - Test dataset examples
   

5. RAG Observability

   - System monitoring setup
   - Performance tracking
   - Debug tools integration
   

Advanced Techniques¶

1. ReRanker RAG

   - Result re-ranking implementation
   - Evaluation metrics
   - Performance optimization
   

2. Hybrid RAG

   - Qdrant hybrid search implementation
   - Multiple retrieval method integration
   

3. Sentence Window RAG

   - Context window optimization
   - Sentence-level retrieval
   

4. Auto Merging RAG

   - Automatic content merging
   - Redundancy elimination
   

5. 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

🛠️ 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.