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Session 1 - Module A: Production Patterns

⚠️ ADVANCED OPTIONAL MODULE Prerequisites: Complete Session 1 core content first.

You've built a working RAG system in the main session. Now you'll learn what happens when that system faces production realities: unexpected errors, scaling demands, performance requirements, and the operational challenges that separate prototype demos from mission-critical infrastructure.

This module teaches you the engineering patterns that transform your RAG implementation from "works in development" to "runs reliably in production" - the resilience patterns, monitoring strategies, and operational practices that production systems demand.

Quick Start

# Test production RAG patterns
cd src/session1
python rag_system.py

# Run performance benchmarks
python test_rag_performance.py

# Interactive RAG exploration
python interactive_rag.py

Advanced Production Patterns

Pattern 1: Resilient Document Processing Pipeline

Production RAG systems must handle document processing failures gracefully without losing entire batches.

First, let's establish the foundation with our imports and data structures. These provide the building blocks for production-grade document processing:

# Production-grade document processor with circuit breaker pattern
import time
from typing import List, Dict, Any
from dataclasses import dataclass
from datetime import datetime, timedelta

The ProcessingResult dataclass encapsulates all the metrics and outcomes from document processing. This structured approach enables comprehensive monitoring and debugging in production environments:

```python @dataclass class ProcessingResult: success_count: int failure_count: int failed_documents: List

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