⚙️ Session 1 Production: Enterprise RAG Deployment & Optimization¶

# src/domain/legal_rag.py
from typing import List, Dict, Any, Optional
from src.rag_system import ProductionRAGSystem
from src.config import RAGConfig
from langchain.prompts import PromptTemplate
import re
from datetime import datetime

class LegalDocumentRAG(ProductionRAGSystem):
    """Specialized RAG system for legal document analysis."""

    def __init__(self, config: RAGConfig):
        super().__init__(config)
        self.legal_config = self._setup_legal_config()
        self.prompt_template = self._create_legal_prompt()
        self.citation_patterns = self._init_citation_patterns()

    def _setup_legal_config(self) -> Dict[str, Any]:
        """Configure legal-specific settings for high accuracy requirements."""
        return {
            'chunk_strategy': 'semantic',      # Preserve legal structure
            'quality_threshold': 0.85,        # Higher accuracy for legal
            'source_validation': True,        # Essential for legal citations
            'jurisdiction_filtering': True,   # Filter by legal jurisdiction
            'date_relevance_weighting': True, # Newer precedents matter more
            'citation_extraction': True,     # Extract legal citations
            'terminology_boost': [
                'precedent', 'statute', 'regulation', 'case law',
                'jurisdiction', 'ruling', 'opinion', 'brief'
            ]
        }

    def _create_legal_prompt(self) -> PromptTemplate:
        """Create legal-specialized prompt with citation requirements."""
        template = """You are a legal research assistant providing accurate legal information based on retrieved documents.

CRITICAL LEGAL GUIDELINES:
- Provide ONLY information directly supported by the cited legal sources
- Always include proper legal citations for all information
- Clearly distinguish between binding precedent and persuasive authority
- Note the jurisdiction and date of each cited source
- Express uncertainty when sources are insufficient or conflicting
- Never provide legal advice - only factual legal information

CITATION FORMAT:
- Case citations: [Case Name], [Volume] [Reporter] [Page] ([Court] [Year])
- Statute citations: [Code] § [Section] ([Year])
- Regulation citations: [CFR Title] C.F.R. § [Section] ([Year])

Context Sources (with jurisdiction and dates):
{context}

Legal Question: {question}

Instructions: Analyze the provided legal sources and provide accurate information with proper citations. Indicate jurisdiction and relevance of each source.

Legal Analysis:"""

        return PromptTemplate(
            input_variables=["context", "question"],
            template=template
        )

    def process_legal_query(self, question: str, jurisdiction: Optional[str] = None) -> Dict[str, Any]:
        """Legal-specific query processing with enhanced validation."""
        # Add legal preprocessing
        enhanced_query = self._preprocess_legal_query(question, jurisdiction)

        # Apply jurisdiction filtering if specified
        filters = {}
        if jurisdiction:
            filters['jurisdiction'] = jurisdiction

        # Use higher quality threshold for legal queries
        result = self.process_query(
            enhanced_query,
            filters=filters,
            score_threshold=self.legal_config['quality_threshold']
        )

        # Post-process for legal-specific features
        if result['status'] == 'success':
            result = self._enhance_legal_response(result, question)

        return result

    def _enhance_legal_response(self, result: Dict[str, Any], original_question: str) -> Dict[str, Any]:
        """Enhance response with legal-specific metadata and validation."""
        # Extract citations from response
        citations = self._extract_citations(result['answer'])

        # Validate citation format
        valid_citations = self._validate_citations(citations)

        # Add jurisdiction analysis
        jurisdictions = self._analyze_jurisdictions(result['sources'])

        # Add legal-specific metadata
        result['legal_metadata'] = {
            'citations_found': len(valid_citations),
            'valid_citations': valid_citations,
            'jurisdictions_covered': jurisdictions,
            'precedent_strength': self._assess_precedent_strength(result['sources']),
            'currency_assessment': self._assess_legal_currency(result['sources'])
        }

        return result

# src/domain/technical_rag.py
class TechnicalDocumentationRAG(ProductionRAGSystem):
    """Specialized RAG system for technical documentation and API references."""

    def __init__(self, config: RAGConfig):
        super().__init__(config)
        self.tech_config = self._setup_tech_config()
        self.prompt_template = self._create_tech_prompt()
        self.code_analyzer = CodeAnalyzer()

    def _setup_tech_config(self) -> Dict[str, Any]:
        """Configure technical documentation specific settings."""
        return {
            'chunk_strategy': 'hierarchical',  # Preserve code structure
            'quality_threshold': 0.75,        # Good balance of accuracy/coverage
            'code_context_preservation': True, # Keep code examples intact
            'version_tracking': True,         # Track API/library versions
            'syntax_highlighting': True,      # Format code in responses
            'deprecation_warnings': True,     # Warn about deprecated features
            'terminology_boost': [
                'function', 'method', 'class', 'parameter', 'return',
                'exception', 'API', 'endpoint', 'authentication', 'configuration'
            ]
        }

    def _create_tech_prompt(self) -> PromptTemplate:
        """Create technical documentation specialized prompt."""
        template = """You are a technical documentation assistant providing accurate programming and API information.

TECHNICAL GUIDELINES:
- Provide code examples when relevant and available in the context
- Include version information for APIs, libraries, and frameworks
- Warn about deprecated features or methods
- Explain technical concepts clearly with appropriate detail level
- Include error handling and best practices when applicable
- Format code snippets with proper syntax highlighting markers

Context Sources (with versions and dates):
{context}

Technical Question: {question}

Instructions: Analyze the technical documentation and provide accurate, practical information with working code examples where appropriate.

Technical Response:"""

        return PromptTemplate(
            input_variables=["context", "question"],
            template=template
        )

# src/domain/medical_rag.py
class MedicalLiteratureRAG(ProductionRAGSystem):
    """Specialized RAG system for medical literature and clinical guidelines."""

    def __init__(self, config: RAGConfig):
        super().__init__(config)
        self.medical_config = self._setup_medical_config()
        self.prompt_template = self._create_medical_prompt()
        self.evidence_grader = MedicalEvidenceGrader()

    def _setup_medical_config(self) -> Dict[str, Any]:
        """Configure medical literature specific settings."""
        return {
            'chunk_strategy': 'semantic',        # Preserve medical context
            'quality_threshold': 0.90,          # Highest accuracy for medical
            'source_credibility_check': True,   # Verify medical source quality
            'recency_weighting': True,          # Newer research preferred
            'evidence_level_tracking': True,    # Track evidence strength
            'peer_review_validation': True,     # Prefer peer-reviewed sources
            'terminology_boost': [
                'clinical', 'diagnosis', 'treatment', 'prognosis', 'pathology',
                'therapy', 'medication', 'dosage', 'contraindication', 'adverse'
            ]
        }

    def _create_medical_prompt(self) -> PromptTemplate:
        """Create medical literature specialized prompt with safety emphasis."""
        template = """You are a medical literature research assistant providing information from peer-reviewed medical sources.

CRITICAL MEDICAL GUIDELINES:
- This information is for research purposes only - NOT medical advice
- Always include evidence level and source credibility indicators
- Note publication dates and study methodologies
- Highlight any conflicting evidence or uncertainty
- Include sample sizes and study limitations when available
- Emphasize when clinical consultation is recommended

IMPORTANT DISCLAIMER: This information should not replace professional medical advice, diagnosis, or treatment.

Context Sources (with evidence levels and dates):
{context}

Medical Research Question: {question}

Instructions: Provide research-based information with evidence levels, noting limitations and recommending clinical consultation for medical decisions.

Medical Literature Analysis:"""

        return PromptTemplate(
            input_variables=["context", "question"],
            template=template
        )

# src/exercises/rag_challenge.py
from typing import Dict, List, Any, Optional
from abc import ABC, abstractmethod
import time
import json

class RAGImplementationChallenge:
    """Comprehensive RAG implementation challenge framework."""

    def __init__(self, scenario: str):
        self.scenario = scenario
        self.challenges = self._create_scenario_challenges(scenario)
        self.student_progress = {
            'completed_challenges': [],
            'total_score': 0,
            'time_spent': 0,
            'skills_demonstrated': set()
        }

    def _create_scenario_challenges(self, scenario: str) -> List[Dict]:
        """Create comprehensive challenges for the chosen scenario."""
        scenario_map = {
            'legal': self._create_legal_challenges(),
            'technical': self._create_technical_challenges(),
            'medical': self._create_medical_challenges()
        }
        return scenario_map.get(scenario, self._create_general_challenges())

    def _create_legal_challenges(self) -> List[Dict]:
        """Create legal-specific RAG challenges."""
        return [
            {
                'id': 'legal_001',
                'title': 'Case Law Research System',
                'description': 'Build a RAG system that can research precedent cases for a given legal issue',
                'requirements': [
                    'Handle legal citation formats correctly',
                    'Filter by jurisdiction when specified',
                    'Rank results by precedent strength',
                    'Provide proper legal disclaimers'
                ],
                'test_queries': [
                    'Find precedent cases for software licensing disputes in California',
                    'What are the key cases establishing fair use in copyright law?'
                ],
                'success_criteria': {
                    'citation_accuracy': 0.9,
                    'jurisdiction_filtering': True,
                    'response_accuracy': 0.85,
                    'disclaimer_present': True
                },
                'max_points': 100,
                'estimated_time': 120  # minutes
            }
        ]

    def run_interactive_challenge(self, challenge_id: str) -> Dict[str, Any]:
        """Execute an interactive challenge with real-time feedback."""
        challenge = self._get_challenge(challenge_id)
        if not challenge:
            return {'status': 'error', 'message': 'Challenge not found'}

        print(f"\n🚀 Starting Challenge: {challenge['title']}")
        print("=" * 60)
        print(f"Description: {challenge['description']}")
        print(f"Estimated Time: {challenge['estimated_time']} minutes")
        print("Requirements:")
        for req in challenge['requirements']:
            print(f"  ✓ {req}")

        start_time = time.time()

        # Challenge execution framework
        results = {
            'challenge_id': challenge_id,
            'start_time': start_time,
            'test_results': [],
            'skill_validations': {},
            'feedback': []
        }

        # Execute test queries
        for query in challenge['test_queries']:
            print(f"\n🔍 Testing Query: {query}")
            query_result = self._execute_challenge_query(query, challenge)
            results['test_results'].append(query_result)

        # Validate success criteria
        validation_results = self._validate_success_criteria(results, challenge)
        results['skill_validations'] = validation_results

        # Calculate score
        final_score = self._calculate_challenge_score(results, challenge)
        results['final_score'] = final_score

        end_time = time.time()
        results['completion_time'] = end_time - start_time

        # Update student progress
        self._update_progress(challenge_id, results)

        return results

class DomainSpecificExercises:
    """Advanced exercises for domain-specific RAG systems."""

    @staticmethod
    def legal_document_analysis_exercise() -> Dict[str, Any]:
        """Legal document analysis with citation verification."""
        return {
            'exercise_type': 'legal_analysis',
            'scenario': 'Contract Dispute Research',
            'documents': [
                'sample_contracts/software_license.pdf',
                'case_law/precedent_cases.json',
                'statutes/intellectual_property_law.txt'
            ],
            'tasks': [
                {
                    'task': 'Analyze potential breach of contract',
                    'query': 'What constitutes breach of software licensing agreement?',
                    'expected_citations': ['UCC § 2-106', 'ProCD v. Zeidenberg'],
                    'validation_criteria': {
                        'citation_present': True,
                        'jurisdiction_noted': True,
                        'precedent_strength_assessed': True
                    }
                }
            ],
            'success_metrics': {
                'citation_accuracy': 0.95,
                'legal_reasoning_quality': 0.85,
                'source_credibility': 0.90
            }
        }

    @staticmethod
    def technical_api_documentation_exercise() -> Dict[str, Any]:
        """Technical documentation with code generation."""
        return {
            'exercise_type': 'technical_documentation',
            'scenario': 'API Integration Guide',
            'documents': [
                'api_docs/rest_api_reference.md',
                'code_examples/authentication_samples.py',
                'troubleshooting/common_errors.json'
            ],
            'tasks': [
                {
                    'task': 'Generate authentication code example',
                    'query': 'How do I implement OAuth2 authentication for this API?',
                    'expected_elements': ['code_example', 'error_handling', 'best_practices'],
                    'validation_criteria': {
                        'code_syntax_correct': True,
                        'error_handling_included': True,
                        'version_compatibility_noted': True
                    }
                }
            ],
            'success_metrics': {
                'code_accuracy': 0.90,
                'completeness': 0.85,
                'best_practices_included': 0.80
            }
        }

# src/testing/production_readiness.py
class ProductionReadinessFramework:
    """Comprehensive framework for validating RAG system production readiness."""

    def __init__(self, rag_system):
        self.rag_system = rag_system
        self.test_categories = {
            'functionality': FunctionalityTests(),
            'performance': PerformanceTests(),
            'reliability': ReliabilityTests(),
            'security': SecurityTests(),
            'scalability': ScalabilityTests(),
            'compliance': ComplianceTests()
        }

    def run_complete_validation(self) -> Dict[str, Any]:
        """Execute comprehensive production readiness validation."""
        print("🔍 Starting Production Readiness Assessment...")
        print("=" * 60)

        results = {
            'overall_status': 'unknown',
            'readiness_score': 0.0,
            'category_results': {},
            'critical_issues': [],
            'recommendations': [],
            'certification_status': {}
        }

        # Execute all test categories
        for category, test_suite in self.test_categories.items():
            print(f"\n📋 Testing {category.title()}...")
            category_result = test_suite.execute_tests(self.rag_system)
            results['category_results'][category] = category_result

            # Collect critical issues
            if category_result.get('critical_failures'):
                results['critical_issues'].extend(category_result['critical_failures'])

        # Calculate overall readiness score
        results['readiness_score'] = self._calculate_readiness_score(results['category_results'])

        # Determine overall status
        results['overall_status'] = self._determine_overall_status(
            results['readiness_score'],
            results['critical_issues']
        )

        # Generate recommendations
        results['recommendations'] = self._generate_recommendations(results)

        return results

class AdvancedPerformanceBenchmarking:
    """Advanced performance benchmarking for enterprise RAG systems."""

    def __init__(self, rag_system):
        self.rag_system = rag_system
        self.benchmark_config = {
            'concurrent_users': [1, 5, 10, 25, 50, 100],
            'document_volumes': [100, 1000, 10000, 50000],
            'query_complexities': ['simple', 'medium', 'complex'],
            'test_duration_seconds': 300
        }

    def run_performance_benchmarks(self) -> Dict[str, Any]:
        """Execute comprehensive performance benchmarking."""
        results = {
            'load_testing': self._run_load_tests(),
            'scalability_testing': self._run_scalability_tests(),
            'stress_testing': self._run_stress_tests(),
            'endurance_testing': self._run_endurance_tests(),
            'resource_utilization': self._monitor_resource_usage()
        }

        # Generate performance report
        results['performance_summary'] = self._generate_performance_summary(results)
        results['optimization_recommendations'] = self._generate_optimization_recommendations(results)

        return results

    def _run_load_tests(self) -> Dict[str, Any]:
        """Execute load testing with varying user concurrency."""
        load_results = {}

        for user_count in self.benchmark_config['concurrent_users']:
            print(f"Testing with {user_count} concurrent users...")

            # Simulate concurrent user load
            load_result = self._simulate_concurrent_load(user_count)
            load_results[f'{user_count}_users'] = load_result

            # Check for performance degradation
            if load_result['avg_response_time'] > 5.0:  # 5 second threshold
                load_results[f'{user_count}_users']['warning'] = 'Performance degradation detected'

        return load_results

class SecurityValidationFramework:
    """Comprehensive security validation for production RAG systems."""

    def __init__(self, rag_system):
        self.rag_system = rag_system
        self.security_checks = [
            'api_key_security',
            'input_sanitization',
            'output_filtering',
            'data_privacy',
            'access_control',
            'audit_logging'
        ]

    def validate_security_posture(self) -> Dict[str, Any]:
        """Execute comprehensive security validation."""
        security_results = {
            'overall_security_score': 0.0,
            'check_results': {},
            'vulnerabilities': [],
            'compliance_status': {},
            'recommendations': []
        }

        for check in self.security_checks:
            print(f"🔒 Running security check: {check}")
            check_result = self._execute_security_check(check)
            security_results['check_results'][check] = check_result

            if check_result['status'] == 'fail':
                security_results['vulnerabilities'].append({
                    'check': check,
                    'severity': check_result.get('severity', 'medium'),
                    'description': check_result.get('description', ''),
                    'remediation': check_result.get('remediation', '')
                })

        # Calculate overall security score
        security_results['overall_security_score'] = self._calculate_security_score(
            security_results['check_results']
        )

        return security_results

# src/monitoring/production_monitoring.py
from typing import Dict, Any, List
import time
import logging
from dataclasses import dataclass
from datetime import datetime, timedelta

@dataclass
class SystemMetrics:
    """System performance and health metrics."""
    timestamp: datetime
    response_time_ms: float
    query_count: int
    error_rate: float
    memory_usage_mb: float
    cpu_usage_percent: float
    active_connections: int
    cache_hit_rate: float

class ProductionMonitoringSystem:
    """Advanced monitoring system for production RAG deployments."""

    def __init__(self, rag_system):
        self.rag_system = rag_system
        self.metrics_buffer = []
        self.alert_thresholds = {
            'response_time_ms': 3000,      # 3 second threshold
            'error_rate_percent': 5.0,     # 5% error rate threshold
            'memory_usage_mb': 1024,       # 1GB memory threshold
            'cpu_usage_percent': 80.0      # 80% CPU threshold
        }
        self.monitoring_active = False

    def start_monitoring(self, collection_interval: int = 60):
        """Start continuous system monitoring."""
        print("📊 Starting production monitoring system...")
        self.monitoring_active = True

        while self.monitoring_active:
            # Collect current metrics
            current_metrics = self._collect_system_metrics()
            self.metrics_buffer.append(current_metrics)

            # Check for alerts
            self._check_alert_conditions(current_metrics)

            # Prune old metrics (keep last 24 hours)
            self._prune_metrics_buffer()

            time.sleep(collection_interval)

    def _collect_system_metrics(self) -> SystemMetrics:
        """Collect comprehensive system metrics."""
        # In production, this would integrate with system monitoring tools
        return SystemMetrics(
            timestamp=datetime.now(),
            response_time_ms=self._get_avg_response_time(),
            query_count=self._get_query_count_last_minute(),
            error_rate=self._calculate_error_rate(),
            memory_usage_mb=self._get_memory_usage(),
            cpu_usage_percent=self._get_cpu_usage(),
            active_connections=self._get_active_connections(),
            cache_hit_rate=self._get_cache_hit_rate()
        )

class PerformanceOptimizationEngine:
    """Intelligent performance optimization for RAG systems."""

    def __init__(self, rag_system, monitoring_system):
        self.rag_system = rag_system
        self.monitoring_system = monitoring_system
        self.optimization_strategies = [
            'query_caching',
            'embedding_caching',
            'chunk_size_optimization',
            'retrieval_optimization',
            'batch_processing_tuning'
        ]

    def analyze_and_optimize(self) -> Dict[str, Any]:
        """Analyze system performance and apply optimizations."""
        print("🔧 Analyzing system performance for optimization...")

        # Collect performance data
        performance_analysis = self._analyze_performance_patterns()

        # Identify optimization opportunities
        opportunities = self._identify_optimization_opportunities(performance_analysis)

        # Apply safe optimizations
        applied_optimizations = self._apply_optimizations(opportunities)

        # Measure improvement
        improvement_metrics = self._measure_optimization_impact(applied_optimizations)

        return {
            'analysis': performance_analysis,
            'opportunities': opportunities,
            'applied_optimizations': applied_optimizations,
            'improvement_metrics': improvement_metrics,
            'recommendations': self._generate_optimization_recommendations(improvement_metrics)
        }

class EnterpriseScalabilityArchitecture:
    """Scalable architecture patterns for enterprise RAG deployment."""

    def __init__(self):
        self.architecture_components = {
            'load_balancer': LoadBalancerConfig(),
            'api_gateway': APIGatewayConfig(),
            'caching_layer': CachingLayerConfig(),
            'database_cluster': DatabaseClusterConfig(),
            'monitoring_stack': MonitoringStackConfig()
        }

    def design_scalable_deployment(self, expected_load: Dict[str, int]) -> Dict[str, Any]:
        """Design scalable deployment architecture based on expected load."""
        deployment_plan = {
            'infrastructure': self._design_infrastructure(expected_load),
            'scaling_policies': self._create_scaling_policies(expected_load),
            'monitoring_setup': self._setup_monitoring_stack(),
            'disaster_recovery': self._plan_disaster_recovery(),
            'cost_optimization': self._optimize_costs(expected_load)
        }

        return deployment_plan

def run_final_mastery_assessment() -> Dict[str, Any]:
    """Comprehensive assessment of RAG implementation mastery."""

    assessment_components = [
        'theoretical_knowledge_test',
        'practical_implementation_challenge',
        'domain_specialization_task',
        'production_deployment_simulation',
        'performance_optimization_exercise',
        'security_and_compliance_validation'
    ]

    mastery_results = {
        'overall_mastery_level': 'unknown',
        'component_scores': {},
        'strengths': [],
        'improvement_areas': [],
        'certification_eligibility': False,
        'recommended_next_steps': []
    }

    # Execute each assessment component
    for component in assessment_components:
        component_result = execute_assessment_component(component)
        mastery_results['component_scores'][component] = component_result

    # Calculate overall mastery level
    mastery_results['overall_mastery_level'] = calculate_mastery_level(
        mastery_results['component_scores']
    )

    # Determine certification eligibility
    mastery_results['certification_eligibility'] = assess_certification_eligibility(
        mastery_results['component_scores']
    )

    return mastery_results