RAG Techniques Handbook

🎯 Status: Production-Ready | 📚 Complete Techniques: 6 | 🔗 Integrated Docs: 100%

Overview

This repository provides a comprehensive collection of advanced RAG (Retrieval Augmented Generation) techniques with production-ready implementations and seamlessly integrated documentation. Each technique includes both theoretical explanations and working Python code that you can immediately use in your projects.

Key Features:

• ✅ Production-Ready Code: All implementations are tested and optimized
• 📖 Integrated Documentation: Theory and code seamlessly connected
• 🚀 Quick Start Guides: Get running in 3 minutes with any technique
• 🔧 Configurable: Extensive configuration options for customization
• 📊 Evaluation Framework: Built-in metrics and performance assessment
• 🤖 Multiple LLM Support: OpenAI, Cohere, and more

Currently Implemented Techniques

✅ RAG Metrics & Evaluation

Complete evaluation framework with LlamaIndex and RAGAS integration

• Features: Retrieval metrics (MRR, Hit Rate), Generation quality (Faithfulness, Relevancy), RAGAS comprehensive metrics
• Classes: RAGEvaluationFramework, EvaluationConfig
• Documentation: RAG Metrics & Evaluation Guide
• Implementation: rag_metrics_evaluation_module.py

✅ Vector Store Index Implementation

Optimized vector store operations with DeepLake integration

• Features: Deep Memory optimization, Performance evaluation, Cohere reranking, Async operations
• Classes: VectorStoreManager, VectorStoreConfig
• Documentation: Vector Store Implementation Guide
• Implementation: vector_store_index_implementation.py

✅ Advanced Reranking Systems

Multiple reranking strategies with ensemble capabilities

• Features: Cohere API, SentenceTransformer, LLM-based, Ensemble reranking, Performance caching
• Classes: RerankerManager, RerankerConfig, EnsembleReranker
• Documentation: Reranking Systems Guide
• Implementation: reranking_rag_systems.py

✅ Advanced RAG Techniques

LlamaIndex-based advanced query processing and retrieval

• Features: Sub-question decomposition, Query transformation, Hierarchical retrieval, Streaming support
• Classes: AdvancedRAGEngine
• Documentation: Advanced RAG Techniques Guide
• Implementation: Advanced_RAG_tenchniques_LLamaIndex.py

✅ RAG Agent System

Multi-source agent-based RAG with tool integration

• Features: Multi-document querying, Tool integration, OpenAI agents, DeepLake support
• Classes: RAGAgent
• Documentation: RAG Agent Guide
• Implementation: LlamaIndex_rag_agent.py

✅ LangSmith Integration

Complete monitoring and evaluation with LangSmith

• Features: Tracing, Prompt management, Evaluation pipelines, Chain monitoring
• Classes: LangSmithManager
• Documentation: LangSmith Integration Guide
• Implementation: Langsmith.py

Quick Start

Installation

# Clone the repository
git clone https://github.com/sosanzma/rag-techniques-handbook.git
cd rag-techniques-handbook

# Install dependencies
pip install llama-index==0.9.14.post3 deeplake==3.8.12 openai==1.3.8 cohere==4.37 ragas==0.0.22 pandas

# Set up environment variables
export OPENAI_API_KEY="your_openai_key"
export ACTIVELOOP_TOKEN="your_deeplake_token"
export COHERE_API_KEY="your_cohere_key"

3-Minute Example: Complete RAG Evaluation

from src.rag_metrics_evaluation_module import RAGEvaluationFramework, EvaluationConfig
import asyncio

# Configure and run comprehensive evaluation
config = EvaluationConfig(llm_model="gpt-3.5-turbo", enable_ragas=True)
evaluator = RAGEvaluationFramework(config)

# Evaluate your documents
results = asyncio.run(
    evaluator.comprehensive_evaluation(documents_path="your/docs/path")
)

print(f"📊 Retrieval MRR: {results['retrieval_metrics']['mrr']:.3f}")
print(f"✅ Faithfulness: {results['generation_metrics']['faithfulness']:.3f}")
print(f"🎯 Relevancy: {results['generation_metrics']['relevancy']:.3f}")

Project Structure

rag-techniques-handbook/
├── 📁 docs/                           # Comprehensive guides (theory + implementation)
│   ├── 📋 rag_metrics_evaluation_guide.md
│   ├── 📋 vector_store_index_implementation_guide.md
│   ├── 📋 reranking_rag_systems.md
│   ├── 📋 Advanced_RAg_techniques_LLamaIndex.md
│   ├── 📋 LlamaIndex_rag_agent.md
│   └── 📋 Langsmith.md
├── 🐍 src/                            # Production-ready implementations
│   ├── 🔬 rag_metrics_evaluation_module.py
│   ├── 🗄️ vector_store_index_implementation.py
│   ├── 🔄 reranking_rag_systems.py
│   ├── 🚀 Advanced_RAG_tenchniques_LLamaIndex.py
│   ├── 🤖 LlamaIndex_rag_agent.py
│   └── 📈 Langsmith.py
├── 📓 src/Module_04_RAG_Metrics&Evaluation.ipynb  # Jupyter notebook example
├── 📖 CLAUDE.md                       # Development documentation
└── 🗺️ README.md                       # This file

Prerequisites

• Python: 3.8+
• API Keys: OpenAI (required), Cohere (optional), ActiveLoop (for DeepLake)
• Memory: 8GB+ RAM recommended for large document processing
• Storage: Vector databases may require significant disk space

Contributing

We welcome contributions! The repository is designed for easy extension:

Current Areas for Contribution

• Additional evaluation metrics
• New vector store integrations
• Performance optimizations
• Documentation improvements
• Example notebooks

How to Contribute

1. Fork the repository
2. Create a feature branch
3. Follow the existing patterns (see any src/ file for reference)
4. Update both implementation and documentation
5. Submit a pull request

Resources

Documentation

• CLAUDE.md: Complete technical documentation and development guide
• Planned Features: Upcoming techniques and improvements

External Resources

• LlamaIndex Documentation
• RAGAS Documentation
• DeepLake Documentation
• OpenAI API Documentation

License

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

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🚀 Ready to get started? Pick any technique from the list above and follow its Quick Start guide. Each implementation is production-ready and includes comprehensive documentation to get you running in minutes!