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# Recommended layered architecture
class DataKnowledgeArchitecture:
    """
    Layer 1: Data Ingestion & Storage
    Layer 2: Processing & Transformation  
    Layer 3: Knowledge Management
    Layer 4: Retrieval & Serving
    Layer 5: Application Interface
    """
    
    def __init__(self):
        # Layer 1: Data Foundation
        self.storage_layer = {
            'vector_store': 'Primary semantic search',
            'graph_db': 'Relationship modeling',
            'document_store': 'Full-text search',
            'time_series': 'Temporal data',
            'cache': 'Performance optimization'
        }
        
        # Layer 2: Processing
        self.processing_layer = {
            'ingestion_pipeline': 'Data intake and validation',
            'transformation_engine': 'ETL/ELT operations',
            'quality_assurance': 'Data validation and cleaning'
        }
        
        # Layer 3: Intelligence
        self.knowledge_layer = {
            'extraction_engine': 'Automated knowledge discovery',
            'validation_system': 'Fact checking and verification',
            'curation_pipeline': 'Quality control and organization'
        }
        
        # Layer 4: Retrieval
        self.retrieval_layer = {
            'rag_engine': 'Retrieval-augmented generation',
            'search_optimization': 'Query processing and ranking',
            'context_management': 'Response optimization'
        }

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# Microservices architecture pattern
class ServiceRegistry:
    """Break functionality into focused, maintainable services"""
    
    services = {
        'knowledge_extraction_service': {
            'responsibility': 'Extract structured knowledge from raw data',
            'dependencies': ['nlp_models', 'computer_vision', 'audio_processing'],
            'scaling_strategy': 'horizontal_cpu',
            'sla': {'latency_p99': '2s', 'availability': '99.9%'}
        },
        
        'vector_search_service': {
            'responsibility': 'Semantic similarity search',
            'dependencies': ['vector_db', 'embedding_models'],
            'scaling_strategy': 'horizontal_gpu',
            'sla': {'latency_p99': '100ms', 'availability': '99.99%'}
        },
        
        'knowledge_validation_service': {
            'responsibility': 'Validate and verify extracted knowledge',
            'dependencies': ['fact_checking_apis', 'consistency_models'],
            'scaling_strategy': 'vertical',
            'sla': {'latency_p99': '5s', 'availability': '99.5%'}
        },
        
        'model_serving_service': {
            'responsibility': 'Serve ML models with optimization',
            'dependencies': ['model_registry', 'inference_engine'],
            'scaling_strategy': 'auto_scaling_gpu',
            'sla': {'latency_p99': '50ms', 'availability': '99.99%'}
        }
    }

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class DataQualityFramework:
    """Comprehensive data quality management"""
    
    def __init__(self):
        self.quality_dimensions = {
            'completeness': self._check_completeness,
            'accuracy': self._check_accuracy,
            'consistency': self._check_consistency,
            'timeliness': self._check_timeliness,
            'validity': self._check_validity,
            'uniqueness': self._check_uniqueness
        }
        
    async def validate_data_batch(self, data_batch: List[Dict]) -> Dict[str, Any]:
        """Validate data batch against all quality dimensions"""
        
        quality_scores = {}
        quality_issues = []
        
        for dimension, check_func in self.quality_dimensions.items():
            try:
                score, issues = await check_func(data_batch)
                quality_scores[dimension] = score
                
                if issues:
                    quality_issues.extend([
                        {'dimension': dimension, 'issue': issue} 
                        for issue in issues
                    ])
                    
            except Exception as e:
                logger.error(f"Quality check failed for {dimension}: {e}")
                quality_scores[dimension] = 0.0
                
        # Calculate overall quality score
        overall_score = sum(quality_scores.values()) / len(quality_scores)
        
        return {
            'overall_score': overall_score,
            'dimension_scores': quality_scores,
            'issues': quality_issues,
            'passed_threshold': overall_score >= 0.8,  # Configurable
            'recommendations': self._generate_quality_recommendations(quality_issues)
        }
        
    def _generate_quality_recommendations(self, issues: List[Dict]) -> List[str]:
        """Generate actionable recommendations for quality issues"""
        
        recommendations = []
        issue_counts = {}
        
        # Count issues by dimension
        for issue in issues:
            dimension = issue['dimension']
            issue_counts[dimension] = issue_counts.get(dimension, 0) + 1
            
        # Generate specific recommendations
        if issue_counts.get('completeness', 0) > 10:
            recommendations.append(
                "High number of completeness issues detected. "
                "Review data source connectivity and extraction logic."
            )
            
        if issue_counts.get('accuracy', 0) > 5:
            recommendations.append(
                "Accuracy issues detected. Implement additional validation "
                "rules and cross-reference with authoritative sources."
            )
            
        if issue_counts.get('consistency', 0) > 5:
            recommendations.append(
                "Data consistency issues found. Review data transformation "
                "logic and implement standardization rules."
            )
            
        return recommendations

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class SchemaEvolutionManager:
    """Manage schema changes across the knowledge layer"""
    
    def __init__(self):
        self.schema_versions = {}
        self.migration_strategies = {
            'backward_compatible': self._backward_compatible_migration,
            'forward_compatible': self._forward_compatible_migration,
            'breaking_change': self._breaking_change_migration
        }
        
    async def evolve_schema(self, schema_name: str, 
                          new_schema: Dict,
                          migration_strategy: str = 'backward_compatible') -> bool:
        """Evolve schema with proper versioning and migration"""
        
        current_schema = self.schema_versions.get(schema_name)
        
        if not current_schema:
            # First schema version
            await self._register_schema(schema_name, new_schema, version='1.0.0')
            return True
            
        # Analyze schema changes
        changes = self._analyze_schema_changes(current_schema['schema'], new_schema)
        
        # Determine migration strategy
        required_strategy = self._determine_migration_strategy(changes)
        
        if required_strategy != migration_strategy:
            raise ValueError(
                f"Schema changes require '{required_strategy}' migration, "
                f"but '{migration_strategy}' was specified"
            )
            
        # Execute migration
        migration_func = self.migration_strategies[migration_strategy]
        success = await migration_func(schema_name, current_schema, new_schema, changes)
        
        if success:
            # Update schema version
            new_version = self._increment_version(
                current_schema['version'], required_strategy
            )
            await self._register_schema(schema_name, new_schema, new_version)
            
        return success
        
    def _analyze_schema_changes(self, old_schema: Dict, new_schema: Dict) -> Dict:
        """Analyze differences between schema versions"""
        
        changes = {
            'added_fields': [],
            'removed_fields': [],
            'modified_fields': [],
            'renamed_fields': []
        }
        
        old_fields = set(old_schema.get('fields', {}).keys())
        new_fields = set(new_schema.get('fields', {}).keys())
        
        # Added fields
        changes['added_fields'] = list(new_fields - old_fields)
        
        # Removed fields
        changes['removed_fields'] = list(old_fields - new_fields)
        
        # Modified fields
        common_fields = old_fields & new_fields
        for field in common_fields:
            old_field_def = old_schema['fields'][field]
            new_field_def = new_schema['fields'][field]
            
            if old_field_def != new_field_def:
                changes['modified_fields'].append({
                    'field': field,
                    'old_definition': old_field_def,
                    'new_definition': new_field_def
                })
                
        return changes

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class MultilevelCachingStrategy:
    """Implement intelligent caching across all layers"""
    
    def __init__(self):
        self.cache_layers = {
            'l1_memory': {
                'type': 'in_memory',
                'size_limit': '2GB',
                'ttl': 300,  # 5 minutes
                'eviction_policy': 'LRU'
            },
            'l2_redis': {
                'type': 'redis',
                'size_limit': '10GB', 
                'ttl': 3600,  # 1 hour
                'eviction_policy': 'LFU'
            },
            'l3_persistent': {
                'type': 'disk',
                'size_limit': '100GB',
                'ttl': 86400,  # 24 hours
                'eviction_policy': 'FIFO'
            }
        }
        
        self.cache_strategies = {
            'knowledge_entities': {
                'layers': ['l1_memory', 'l2_redis'],
                'key_pattern': 'entity:{entity_id}',
                'invalidation_triggers': ['entity_update', 'schema_change']
            },
            'search_results': {
                'layers': ['l1_memory', 'l2_redis', 'l3_persistent'],
                'key_pattern': 'search:{query_hash}:{params_hash}',
                'invalidation_triggers': ['knowledge_update', 'model_update']
            },
            'model_predictions': {
                'layers': ['l1_memory'],
                'key_pattern': 'prediction:{model_id}:{input_hash}',
                'invalidation_triggers': ['model_retrain', 'model_update']
            }
        }
        
    async def get_cached_result(self, cache_type: str, key: str) -> Optional[Any]:
        """Get result from appropriate cache layers"""
        
        strategy = self.cache_strategies.get(cache_type)
        if not strategy:
            return None
            
        # Check cache layers in order (L1 -> L2 -> L3)
        for layer_name in strategy['layers']:
            layer_config = self.cache_layers[layer_name]
            
            try:
                result = await self._get_from_cache_layer(layer_name, key)
                
                if result is not None:
                    # Promote to higher cache levels
                    await self._promote_to_higher_levels(
                        key, result, layer_name, strategy['layers']
                    )
                    return result
                    
            except Exception as e:
                logger.warning(f"Cache layer {layer_name} failed: {e}")
                continue
                
        return None
        
    async def cache_result(self, cache_type: str, key: str, 
                          value: Any, ttl_override: Optional[int] = None):
        """Cache result in appropriate layers"""
        
        strategy = self.cache_strategies.get(cache_type)
        if not strategy:
            return
            
        # Store in all configured layers
        for layer_name in strategy['layers']:
            layer_config = self.cache_layers[layer_name]
            ttl = ttl_override or layer_config['ttl']
            
            try:
                await self._store_in_cache_layer(layer_name, key, value, ttl)
            except Exception as e:
                logger.warning(f"Failed to cache in {layer_name}: {e}")

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class QueryOptimizationEngine:
    """Optimize queries across all data sources"""
    
    def __init__(self):
        self.query_patterns = {}
        self.optimization_rules = [
            self._optimize_vector_queries,
            self._optimize_graph_traversals,
            self._optimize_full_text_search,
            self._optimize_hybrid_queries
        ]
        
        self.query_cache = QueryCache()
        self.performance_monitor = QueryPerformanceMonitor()
        
    async def optimize_query(self, query_request: Dict[str, Any]) -> Dict[str, Any]:
        """Apply comprehensive query optimization"""
        
        # Analyze query pattern
        query_analysis = await self._analyze_query_pattern(query_request)
        
        # Check for cached optimization
        cache_key = self._generate_optimization_cache_key(query_request)
        cached_optimization = await self.query_cache.get(cache_key)
        
        if cached_optimization:
            return cached_optimization
            
        # Apply optimization rules
        optimized_query = query_request.copy()
        
        for optimization_rule in self.optimization_rules:
            try:
                optimized_query = await optimization_rule(
                    optimized_query, query_analysis
                )
            except Exception as e:
                logger.warning(f"Optimization rule failed: {e}")
                continue
                
        # Cache optimization
        await self.query_cache.set(cache_key, optimized_query)
        
        # Track optimization performance
        await self.performance_monitor.track_optimization(
            original_query=query_request,
            optimized_query=optimized_query,
            analysis=query_analysis
        )
        
        return optimized_query
        
    async def _optimize_vector_queries(self, query: Dict, analysis: Dict) -> Dict:
        """Optimize vector similarity queries"""
        
        optimizations = {}
        
        # Adjust similarity threshold based on query complexity
        if analysis.get('complexity_score', 0) > 0.7:
            optimizations['similarity_threshold'] = 0.6  # Lower for complex queries
        else:
            optimizations['similarity_threshold'] = 0.8  # Higher for simple queries
            
        # Optimize top_k based on historical performance
        historical_performance = await self.performance_monitor.get_query_performance(
            query.get('query_text', '')
        )
        
        if historical_performance:
            optimal_k = historical_performance.get('optimal_k', query.get('top_k', 10))
            optimizations['top_k'] = min(optimal_k, 50)  # Cap at 50
            
        # Add search strategy optimization
        if analysis.get('has_entities', False):
            optimizations['search_strategies'] = ['semantic', 'graph']
        elif analysis.get('has_temporal_elements', False):
            optimizations['search_strategies'] = ['semantic', 'temporal']
        else:
            optimizations['search_strategies'] = ['semantic', 'keyword']
            
        return {**query, **optimizations}

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class DataProtectionFramework:
    """Comprehensive data protection and privacy management"""
    
    def __init__(self):
        self.encryption_manager = EncryptionManager()
        self.access_controller = AccessController()
        self.audit_logger = AuditLogger()
        self.privacy_engine = PrivacyEngine()
        
    async def protect_sensitive_data(self, data: Dict[str, Any], 
                                   protection_level: str) -> Dict[str, Any]:
        """Apply appropriate protection based on data sensitivity"""
        
        # Classify data sensitivity
        sensitivity_analysis = await self._classify_data_sensitivity(data)
        
        protected_data = data.copy()
        
        if sensitivity_analysis['pii_detected']:
            # Apply PII protection
            protected_data = await self._protect_pii(protected_data, protection_level)
            
        if sensitivity_analysis['confidential_detected']:
            # Apply encryption
            protected_data = await self.encryption_manager.encrypt_confidential_fields(
                protected_data
            )
            
        if sensitivity_analysis['regulated_data_detected']:
            # Apply regulatory compliance measures
            protected_data = await self._apply_regulatory_protection(
                protected_data, sensitivity_analysis['regulations']
            )
            
        # Log data protection actions
        await self.audit_logger.log_protection_applied(
            data_id=data.get('id', 'unknown'),
            protections_applied=list(protected_data.keys()),
            sensitivity_level=sensitivity_analysis['max_level']
        )
        
        return protected_data
        
    async def _protect_pii(self, data: Dict, protection_level: str) -> Dict:
        """Apply PII-specific protection measures"""
        
        pii_fields = await self._identify_pii_fields(data)
        protected_data = data.copy()
        
        for field_name, pii_type in pii_fields.items():
            if protection_level == 'anonymize':
                protected_data[field_name] = await self._anonymize_field(
                    data[field_name], pii_type
                )
            elif protection_level == 'pseudonymize':
                protected_data[field_name] = await self._pseudonymize_field(
                    data[field_name], pii_type
                )
            elif protection_level == 'encrypt':
                protected_data[field_name] = await self.encryption_manager.encrypt_field(
                    data[field_name]
                )
            elif protection_level == 'redact':
                protected_data[field_name] = '[REDACTED]'
                
        return protected_data

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class AdvancedAccessControl:
    """Multi-layer access control system"""
    
    def __init__(self):
        self.rbac_manager = RoleBasedAccessControl()
        self.abac_engine = AttributeBasedAccessControl()
        self.session_manager = SecureSessionManager()
        
    async def authorize_request(self, user_context: Dict, 
                              resource: str, 
                              action: str,
                              additional_context: Optional[Dict] = None) -> bool:
        """Comprehensive authorization using multiple access control models"""
        
        # Basic RBAC check
        rbac_allowed = await self.rbac_manager.check_permission(
            user_context['roles'], resource, action
        )
        
        if not rbac_allowed:
            return False
            
        # Advanced ABAC evaluation
        abac_context = {
            'user': user_context,
            'resource': resource,
            'action': action,
            'environment': additional_context or {},
            'time': datetime.utcnow(),
            'location': additional_context.get('location', 'unknown')
        }
        
        abac_allowed = await self.abac_engine.evaluate_policy(abac_context)
        
        if not abac_allowed:
            return False
            
        # Session validation
        session_valid = await self.session_manager.validate_session(
            user_context.get('session_id')
        )
        
        # Log authorization decision
        await self._log_authorization_decision(
            user_context, resource, action, 
            rbac_allowed and abac_allowed and session_valid
        )
        
        return rbac_allowed and abac_allowed and session_valid
        
    async def implement_data_governance(self, data_classification: Dict) -> Dict[str, Any]:
        """Implement data governance policies based on classification"""
        
        governance_rules = {
            'retention_policy': self._determine_retention_policy(data_classification),
            'access_restrictions': self._determine_access_restrictions(data_classification),
            'encryption_requirements': self._determine_encryption_requirements(data_classification),
            'audit_requirements': self._determine_audit_requirements(data_classification)
        }
        
        return governance_rules

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class ObservabilityStack:
    """Complete observability solution for the knowledge layer"""
    
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.log_aggregator = LogAggregator()
        self.trace_collector = TraceCollector()
        self.alert_manager = AlertManager()
        
        # Initialize monitoring components
        self.setup_core_metrics()
        self.setup_custom_dashboards()
        self.setup_alert_rules()
        
    def setup_core_metrics(self):
        """Define core metrics for monitoring"""
        
        self.core_metrics = {
            # Performance metrics
            'query_latency': Histogram(
                'knowledge_query_latency_seconds',
                'Query processing latency',
                ['query_type', 'data_source']
            ),
            'throughput': Counter(
                'knowledge_operations_total', 
                'Total operations processed',
                ['operation_type', 'status']
            ),
            
            # Quality metrics
            'data_quality_score': Gauge(
                'data_quality_score',
                'Data quality score by dimension',
                ['data_source', 'quality_dimension']
            ),
            'knowledge_accuracy': Gauge(
                'knowledge_extraction_accuracy',
                'Knowledge extraction accuracy',
                ['extraction_type', 'model_version']
            ),
            
            # Resource metrics
            'memory_usage': Gauge(
                'memory_usage_bytes',
                'Memory usage by component',
                ['component', 'instance']
            ),
            'cpu_utilization': Gauge(
                'cpu_utilization_percent',
                'CPU utilization',
                ['component', 'instance']
            ),
            
            # Business metrics
            'knowledge_coverage': Gauge(
                'knowledge_base_coverage_ratio',
                'Knowledge base coverage ratio',
                ['domain', 'topic']
            ),
            'user_satisfaction': Gauge(
                'user_satisfaction_score',
                'User satisfaction with responses',
                ['interface', 'use_case']
            )
        }
        
    async def setup_alert_rules(self):
        """Configure intelligent alerting rules"""
        
        alert_rules = {
            'high_latency': {
                'condition': 'query_latency_p99 > 2.0',
                'severity': 'warning',
                'notification_channels': ['email', 'slack'],
                'cooldown_minutes': 15
            },
            
            'low_accuracy': {
                'condition': 'knowledge_accuracy < 0.8',
                'severity': 'critical',
                'notification_channels': ['email', 'slack', 'pagerduty'],
                'cooldown_minutes': 5
            },
            
            'data_quality_degradation': {
                'condition': 'data_quality_score < 0.7',
                'severity': 'warning',
                'notification_channels': ['email'],
                'cooldown_minutes': 30
            },
            
            'resource_exhaustion': {
                'condition': 'memory_usage > 0.9 OR cpu_utilization > 0.9',
                'severity': 'critical',
                'notification_channels': ['email', 'slack', 'pagerduty'],
                'cooldown_minutes': 5
            }
        }
        
        for rule_name, rule_config in alert_rules.items():
            await self.alert_manager.create_rule(rule_name, rule_config)

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class HealthCheckFramework:
    """Comprehensive health monitoring system"""
    
    def __init__(self):
        self.health_checks = {
            'database_connectivity': self._check_database_health,
            'model_availability': self._check_model_health,
            'data_pipeline_status': self._check_pipeline_health,
            'api_endpoint_status': self._check_api_health,
            'resource_availability': self._check_resource_health
        }
        
        self.health_status = {}
        self.check_scheduler = HealthCheckScheduler()
        
    async def run_comprehensive_health_check(self) -> Dict[str, Any]:
        """Execute all health checks and generate comprehensive report"""
        
        health_results = {}
        overall_health = True
        
        # Execute all health checks concurrently
        check_tasks = [
            self._execute_health_check(check_name, check_func)
            for check_name, check_func in self.health_checks.items()
        ]
        
        check_results = await asyncio.gather(*check_tasks, return_exceptions=True)
        
        # Process results
        for i, (check_name, result) in enumerate(zip(self.health_checks.keys(), check_results)):
            if isinstance(result, Exception):
                health_results[check_name] = {
                    'status': 'error',
                    'healthy': False,
                    'error': str(result),
                    'timestamp': datetime.utcnow().isoformat()
                }
                overall_health = False
            else:
                health_results[check_name] = result
                if not result.get('healthy', False):
                    overall_health = False
                    
        # Generate health summary
        health_summary = {
            'overall_healthy': overall_health,
            'timestamp': datetime.utcnow().isoformat(),
            'checks': health_results,
            'summary': self._generate_health_summary(health_results),
            'recommendations': self._generate_health_recommendations(health_results)
        }
        
        # Store health status
        self.health_status = health_summary
        
        # Trigger alerts if unhealthy
        if not overall_health:
            await self._trigger_health_alerts(health_summary)
            
        return health_summary
        
    async def _check_database_health(self) -> Dict[str, Any]:
        """Check health of all database connections"""
        
        db_health = {
            'healthy': True,
            'checks': {},
            'response_times': {},
            'error_rates': {}
        }
        
        databases = ['vector_db', 'graph_db', 'document_store', 'cache']
        
        for db_name in databases:
            try:
                # Test basic connectivity
                start_time = time.time()
                connection_ok = await self._test_db_connection(db_name)
                response_time = time.time() - start_time
                
                # Test basic operations
                read_ok = await self._test_db_read(db_name)
                write_ok = await self._test_db_write(db_name)
                
                db_health['checks'][db_name] = {
                    'connection': connection_ok,
                    'read_operations': read_ok,
                    'write_operations': write_ok
                }
                
                db_health['response_times'][db_name] = response_time
                
                if not (connection_ok and read_ok and write_ok):
                    db_health['healthy'] = False
                    
            except Exception as e:
                db_health['healthy'] = False
                db_health['checks'][db_name] = {
                    'error': str(e),
                    'connection': False,
                    'read_operations': False,
                    'write_operations': False
                }
                
        return db_health

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class KnowledgeLayerTestSuite:
    """Complete testing framework for the knowledge layer"""
    
    def __init__(self):
        self.unit_tests = UnitTestRunner()
        self.integration_tests = IntegrationTestRunner()
        self.performance_tests = PerformanceTestRunner()
        self.security_tests = SecurityTestRunner()
        self.data_quality_tests = DataQualityTestRunner()
        
    async def run_full_test_suite(self) -> Dict[str, Any]:
        """Execute comprehensive test suite"""
        
        test_results = {
            'unit_tests': await self.unit_tests.run_all(),
            'integration_tests': await self.integration_tests.run_all(),
            'performance_tests': await self.performance_tests.run_all(),
            'security_tests': await self.security_tests.run_all(),
            'data_quality_tests': await self.data_quality_tests.run_all()
        }
        
        # Generate overall test report
        overall_success = all(
            result.get('success', False) 
            for result in test_results.values()
        )
        
        return {
            'overall_success': overall_success,
            'detailed_results': test_results,
            'coverage_report': await self._generate_coverage_report(),
            'performance_benchmarks': await self._generate_performance_benchmarks(),
            'recommendations': self._generate_test_recommendations(test_results)
        }
        
    async def setup_continuous_testing(self):
        """Set up continuous testing pipeline"""
        
        testing_pipeline = {
            'pre_commit_hooks': {
                'tests': ['unit_tests', 'linting', 'type_checking'],
                'required_coverage': 80
            },
            
            'ci_pipeline': {
                'on_pull_request': ['unit_tests', 'integration_tests', 'security_tests'],
                'on_merge': ['full_test_suite', 'performance_regression_tests'],
                'nightly': ['data_quality_tests', 'end_to_end_tests']
            },
            
            'production_monitoring': {
                'smoke_tests': 'every_5_minutes',
                'health_checks': 'every_minute',
                'performance_tests': 'every_hour',
                'security_scans': 'daily'
            }
        }
        
        return testing_pipeline

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# production-deployment.yml
apiVersion: v1
kind: ConfigMap
metadata:
  name: aimatrix-deployment-config
data:
  deployment_strategy.yaml: |
    # Blue-Green Deployment Configuration
    blue_green_deployment:
      enabled: true
      health_check_path: "/health"
      readiness_timeout: "300s"
      traffic_split_strategy: "gradual"
      rollback_on_failure: true
    
    # Resource Requirements
    resources:
      vector_db:
        requests:
          memory: "8Gi"
          cpu: "2"
        limits:
          memory: "16Gi" 
          cpu: "4"
      
      knowledge_extraction:
        requests:
          memory: "4Gi"
          cpu: "2"
          nvidia.com/gpu: "1"
        limits:
          memory: "8Gi"
          cpu: "4"
          nvidia.com/gpu: "1"
    
    # Scaling Configuration
    autoscaling:
      enabled: true
      min_replicas: 2
      max_replicas: 20
      target_cpu_utilization: 70
      target_memory_utilization: 80
      
    # Backup and Recovery
    backup:
      schedule: "0 2 * * *"  # Daily at 2 AM
      retention_days: 30
      backup_destinations:
        - "s3://aimatrix-backups/knowledge-layer/"
        - "gs://aimatrix-backups/knowledge-layer/"

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class EnvironmentManager:
    """Manage different deployment environments"""
    
    def __init__(self):
        self.environments = {
            'development': {
                'resource_limits': 'minimal',
                'data_sources': 'test_data',
                'monitoring': 'basic',
                'security': 'relaxed'
            },
            'staging': {
                'resource_limits': 'moderate',
                'data_sources': 'sanitized_production_subset',
                'monitoring': 'comprehensive',
                'security': 'production_like'
            },
            'production': {
                'resource_limits': 'full',
                'data_sources': 'production_data',
                'monitoring': 'full_observability',
                'security': 'maximum'
            }
        }
        
    async def deploy_to_environment(self, environment: str, 
                                  deployment_config: Dict) -> bool:
        """Deploy to specific environment with appropriate configuration"""
        
        env_config = self.environments.get(environment)
        if not env_config:
            raise ValueError(f"Unknown environment: {environment}")
            
        # Customize deployment for environment
        customized_config = await self._customize_for_environment(
            deployment_config, env_config
        )
        
        # Validate deployment configuration
        validation_passed = await self._validate_deployment_config(
            customized_config, environment
        )
        
        if not validation_passed:
            raise ValueError(f"Deployment configuration invalid for {environment}")
            
        # Execute deployment
        deployment_success = await self._execute_deployment(
            environment, customized_config
        )
        
        if deployment_success:
            await self._post_deployment_validation(environment)
            
        return deployment_success