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RAG Sessions Python Code

This directory contains the extracted and organized Python code from Sessions 7, 8, and 9 of the RAG course module.

Directory Structure

src/
├── session7/           # Agentic RAG Systems
├── session8/           # Multi-Modal Advanced RAG
├── session9/           # Production RAG & Enterprise Integration
└── README.md          # This file

Session 7: Agentic RAG Systems

Core Concepts: Reasoning-driven retrieval, chain-of-thought integration, self-correcting systems

Files Created:

  • reasoning_augmented_retrieval.py - RAG system where reasoning frameworks guide retrieval strategies
  • retrieval_augmented_reasoning.py - System that uses retrieved information to enhance reasoning capabilities
  • chain_of_thought_rag.py - RAG system with integrated chain-of-thought reasoning capabilities
  • node_rag_reasoning_bridge.py - Bridge between NodeRAG structured knowledge and reasoning capabilities
  • reasoning_driven_query_planner.py - Advanced agentic RAG with query planning and cognitive orchestration
  • self_correcting_rag.py - Reasoning-based self-correcting RAG with logical validation and cognitive correction
  • requirements.txt - Dependencies for Session 7

Key Features:

  • Reasoning-Guided Retrieval: Systems that use logical frameworks to guide what information to retrieve
  • Chain-of-Thought Integration: Step-by-step reasoning processes integrated with RAG
  • Self-Correction: Autonomous validation and improvement of responses using logical reasoning
  • Query Planning: Intelligent decomposition and planning of complex queries
  • Cognitive Orchestration: Advanced planning and execution of reasoning-driven RAG workflows

Session 8: Multi-Modal Advanced RAG

Core Concepts: Multi-modal content processing, MRAG evolution (1.0 → 2.0 → 3.0), cross-modal fusion

Files Created:

  • multimodal_rag_system.py - Comprehensive multi-modal RAG system with content processing
  • mrag_evolution.py - MRAG Evolution System demonstrating 1.0 → 2.0 → 3.0 progression
  • multimodal_vector_store.py - Multi-modal vector storage and retrieval system
  • multimodal_rag_fusion.py - MRAG 3.0 Autonomous Multimodal RAG-Fusion implementation
  • ensemble_rag.py - Ensemble RAG system combining multiple models and strategies
  • domain_rag_systems.py - Domain-specific RAG systems for legal and medical applications
  • requirements.txt - Dependencies for Session 8

Key Features:

  • MRAG 1.0: Text-centric approach with lossy conversion (demonstrates limitations)
  • MRAG 2.0: True multimodal processing with semantic integrity preservation
  • MRAG 3.0: Autonomous multimodal intelligence with dynamic reasoning
  • Cross-Modal Fusion: Advanced fusion strategies for combining different modalities
  • Domain Specialization: Legal and medical RAG systems with safety and accuracy focus

Session 9: Production RAG & Enterprise Integration

Core Concepts: Scalable deployment, enterprise integration, security, monitoring, compliance

Files Created:

  • production_rag_orchestrator.py - Production-ready containerized RAG system orchestrator
  • load_balancer_autoscaler.py - Load balancing and auto-scaling for RAG services
  • enterprise_integration.py - Enterprise integration framework for data systems and workflows
  • privacy_compliance.py - Privacy and compliance framework (GDPR, HIPAA, etc.)
  • incremental_indexing.py - Real-time indexing and incremental update system
  • monitoring_analytics.py - Production monitoring and observability system
  • production_deployment.py - Complete production RAG deployment system
  • requirements.txt - Dependencies for Session 9

Key Features:

  • Microservices Architecture: Containerized services with orchestration and load balancing
  • Enterprise Integration: SharePoint, databases, file systems, APIs
  • Security & Compliance: RBAC, GDPR/HIPAA compliance, audit logging
  • Real-Time Processing: Change detection, incremental updates, event-driven architecture
  • Production Monitoring: Metrics, alerting, analytics, health checks
  • Auto-Scaling: Dynamic scaling based on load and performance metrics

Usage Instructions

Installation

Each session has its own requirements file. Install dependencies for the session you want to work with:

# For Session 7
cd src/session7
pip install -r requirements.txt

# For Session 8
cd src/session8
pip install -r requirements.txt

# For Session 9
cd src/session9
pip install -r requirements.txt

Running the Code

The code is designed to be modular and extensible. Each file contains classes and functions that can be imported and used:

# Example: Using Session 7 reasoning systems
from session7.reasoning_augmented_retrieval import ReasoningAugmentedRetrieval
from session7.chain_of_thought_rag import ChainOfThoughtRAG

# Example: Using Session 8 multimodal systems
from session8.multimodal_rag_system import MultiModalProcessor
from session8.mrag_evolution import MRAG_3_0_AutonomousSystem

# Example: Using Session 9 production systems
from session9.production_deployment import ProductionRAGDeployment
from session9.monitoring_analytics import RAGMonitoringSystem

Configuration

Most systems accept configuration dictionaries for customization:

# Example configuration for production deployment
config = {
    'services': {...},
    'enterprise_integration': {...},
    'monitoring': {...},
    'auto_scaling': {...}
}

deployment = ProductionRAGDeployment(config)
await deployment.deploy_production_system()

Implementation Notes

Code Quality

  • All code follows modern Python practices with type hints
  • Comprehensive error handling and logging
  • Async/await patterns for scalable performance
  • Modular design for easy testing and extension

Production Readiness

  • Session 9 code includes production-grade patterns:
  • Health checks and monitoring
  • Graceful shutdown procedures
  • Security and compliance frameworks
  • Auto-scaling and load balancing

Extensibility

  • Plugin architecture for adding new components
  • Configuration-driven behavior
  • Clear interfaces for integration with existing systems

Advanced Features Demonstrated

Session 7 (Agentic RAG)

  • Logical reasoning integration with retrieval
  • Self-correcting and self-improving systems
  • Complex query planning and decomposition
  • Cognitive validation and error correction

Session 8 (Multi-Modal RAG)

  • Native multimodal processing (vs lossy text conversion)
  • Cross-modal semantic understanding
  • Autonomous multimodal intelligence
  • Domain-specific safety and accuracy measures

Session 9 (Production RAG)

  • Enterprise-scale architecture patterns
  • Comprehensive security and compliance
  • Real-time data processing and updates
  • Advanced monitoring and analytics

Getting Started

  1. Choose your session based on your interests:
  2. Session 7: Advanced reasoning and agentic capabilities
  3. Session 8: Multi-modal content processing

  4. Session 9: Production deployment and enterprise features

  5. Install dependencies using the session's requirements.txt

  6. Study the code structure - each file contains detailed docstrings and comments

  7. Run examples - many classes include example usage and demonstration methods

  8. Extend and customize - the modular design makes it easy to adapt for your needs

This codebase represents state-of-the-art RAG system implementations covering the full spectrum from advanced reasoning to production deployment.