⚙️ Session 9 Enterprise: Complete Enterprise Architecture & Security¶

# Enterprise integration framework

class EnterpriseRAGIntegrator:
    """Integration framework for enterprise data systems and workflows."""

    def __init__(self, integration_config: Dict[str, Any]):
        self.config = integration_config

        # Data source connectors
        self.data_connectors = {
            'sharepoint': SharePointConnector(integration_config.get('sharepoint', {})),
            'confluence': ConfluenceConnector(integration_config.get('confluence', {})),
            'database': DatabaseConnector(integration_config.get('database', {})),
            'file_system': FileSystemConnector(integration_config.get('file_system', {})),
            'api_endpoints': APIConnector(integration_config.get('api', {})),
            's3': S3Connector(integration_config.get('s3', {}))
        }

        # Authentication and authorization
        self.auth_manager = EnterpriseAuthManager(integration_config.get('auth', {}))

        # Data transformation pipeline
        self.data_transformer = DataTransformationPipeline()

        # Change detection and incremental updates
        self.change_detector = ChangeDetectionSystem(
            integration_config.get('change_detection', {})
        )

    async def setup_enterprise_integration(self, data_sources: List[str]) -> Dict[str, Any]:
        """Set up integration with specified enterprise data sources."""

        integration_results = {}

        for source_name in data_sources:
            if source_name in self.data_connectors:
                try:
                    # Initialize connector
                    connector = self.data_connectors[source_name]
                    connection_result = await connector.initialize_connection()

                    # Test connectivity and permissions
                    test_result = await connector.test_connection()

                    # Set up change monitoring
                    if self.config.get('enable_change_detection', True):
                        change_monitoring = await self.change_detector.setup_monitoring(
                            source_name, connector
                        )
                    else:
                        change_monitoring = {'enabled': False}

                    integration_results[source_name] = {
                        'status': 'connected',
                        'connection_result': connection_result,
                        'test_result': test_result,
                        'change_monitoring': change_monitoring
                    }

                except Exception as e:
                    integration_results[source_name] = {
                        'status': 'failed',
                        'error': str(e)
                    }

        return {
            'integration_results': integration_results,
            'successful_connections': len([r for r in integration_results.values()
                                         if r['status'] == 'connected']),
            'total_sources': len(data_sources),
            'change_detection_enabled': self.config.get('enable_change_detection', True)
        }

class SharePointConnector:
    """Enterprise SharePoint integration for document retrieval."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.site_url = config.get('site_url')
        self.client_id = config.get('client_id')
        self.client_secret = config.get('client_secret')
        self.tenant_id = config.get('tenant_id')

        # SharePoint client
        self.sp_client = None

    async def initialize_connection(self) -> Dict[str, Any]:
        """Initialize SharePoint connection with authentication."""

        try:
            # Initialize SharePoint client with OAuth
            from office365.sharepoint.client_context import ClientContext
            from office365.runtime.auth.client_credential import ClientCredential

            credentials = ClientCredential(self.client_id, self.client_secret)
            self.sp_client = ClientContext(self.site_url).with_credentials(credentials)

            # Test connection
            web = self.sp_client.web.get().execute_query()

            return {
                'success': True,
                'site_title': web.title,
                'site_url': web.url,
                'connection_time': time.time()
            }

        except Exception as e:
            return {
                'success': False,
                'error': str(e)
            }

    async def retrieve_documents(self, folder_path: str = None,
                               modified_since: datetime = None) -> List[Dict[str, Any]]:
        """Retrieve documents from SharePoint with optional filtering."""

        if not self.sp_client:
            raise RuntimeError("SharePoint client not initialized")

        documents = []

        try:
            # Get document library
            if folder_path:
                folder = self.sp_client.web.get_folder_by_server_relative_url(folder_path)
            else:
                folder = self.sp_client.web.default_document_library().root_folder

            # Get files
            files = folder.files.get().execute_query()

            for file in files:
                # Filter by modification date if specified
                if modified_since and file.time_last_modified < modified_since:
                    continue

                # Download file content
                file_content = file.get_content().execute_query()

                documents.append({
                    'id': file.unique_id,
                    'name': file.name,
                    'url': file.server_relative_url,
                    'content': file_content.value,
                    'modified': file.time_last_modified,
                    'size': file.length,
                    'content_type': file.content_type,
                    'metadata': {
                        'author': file.author.title if file.author else 'Unknown',
                        'created': file.time_created,
                        'version': file.ui_version_label
                    }
                })

            return documents

        except Exception as e:
            self.logger.error(f"SharePoint document retrieval error: {e}")
            return []

class EnterpriseAuthManager:
    """Enterprise authentication and authorization manager."""

    def __init__(self, auth_config: Dict[str, Any]):
        self.config = auth_config
        self.auth_providers = {}

        # Initialize authentication providers
        if 'active_directory' in auth_config:
            self.auth_providers['ad'] = ActiveDirectoryAuth(auth_config['active_directory'])
        if 'oauth2' in auth_config:
            self.auth_providers['oauth2'] = OAuth2Auth(auth_config['oauth2'])
        if 'saml' in auth_config:
            self.auth_providers['saml'] = SAMLAuth(auth_config['saml'])

        # Role-based access control
        self.rbac_manager = RBACManager(auth_config.get('rbac', {}))

    async def authenticate_user(self, credentials: Dict[str, Any]) -> Dict[str, Any]:
        """Authenticate user using configured providers."""

        auth_method = credentials.get('auth_method', 'oauth2')

        if auth_method not in self.auth_providers:
            return {
                'authenticated': False,
                'error': f'Authentication method {auth_method} not supported'
            }

        try:
            auth_result = await self.auth_providers[auth_method].authenticate(credentials)

            if auth_result['authenticated']:
                # Get user permissions
                user_permissions = await self.rbac_manager.get_user_permissions(
                    auth_result['user_info']
                )

                auth_result['permissions'] = user_permissions

                # Create session token
                session_token = self._create_session_token(auth_result['user_info'])
                auth_result['session_token'] = session_token

            return auth_result

        except Exception as e:
            return {
                'authenticated': False,
                'error': f'Authentication failed: {str(e)}'
            }

    async def authorize_request(self, session_token: str,
                              resource: str, action: str) -> Dict[str, Any]:
        """Authorize user request for specific resource and action."""

        try:
            # Validate session token
            user_info = self._validate_session_token(session_token)
            if not user_info:
                return {
                    'authorized': False,
                    'error': 'Invalid or expired session token'
                }

            # Check permissions
            authorized = await self.rbac_manager.check_permission(
                user_info, resource, action
            )

            return {
                'authorized': authorized,
                'user_id': user_info['user_id'],
                'permissions_checked': f'{resource}:{action}'
            }

        except Exception as e:
            return {
                'authorized': False,
                'error': f'Authorization failed: {str(e)}'
            }

class RBACManager:
    """Role-Based Access Control manager for RAG systems."""

    def __init__(self, rbac_config: Dict[str, Any]):
        self.config = rbac_config

        # Define roles and permissions
        self.roles = rbac_config.get('roles', {
            'admin': ['*'],  # Full access
            'power_user': ['rag:query', 'rag:upload', 'rag:view_sources'],
            'user': ['rag:query'],
            'readonly': ['rag:query:readonly']
        })

        # Resource-based permissions
        self.resources = rbac_config.get('resources', {
            'documents': ['read', 'write', 'delete'],
            'queries': ['execute', 'view_history'],
            'system': ['configure', 'monitor', 'admin']
        })

    async def get_user_permissions(self, user_info: Dict[str, Any]) -> List[str]:
        """Get all permissions for a user based on their roles."""

        user_roles = user_info.get('roles', [])
        permissions = set()

        for role in user_roles:
            if role in self.roles:
                role_permissions = self.roles[role]
                permissions.update(role_permissions)

        return list(permissions)

    async def check_permission(self, user_info: Dict[str, Any],
                             resource: str, action: str) -> bool:
        """Check if user has permission for specific resource and action."""

        user_permissions = await self.get_user_permissions(user_info)

        # Check for wildcard permission
        if '*' in user_permissions:
            return True

        # Check specific permission
        required_permission = f"{resource}:{action}"
        if required_permission in user_permissions:
            return True

        # Check resource-level permission
        resource_permission = f"{resource}:*"
        if resource_permission in user_permissions:
            return True

        return False

# Privacy and compliance framework

class PrivacyComplianceManager:
    """Comprehensive privacy and compliance manager for enterprise RAG systems."""

    def __init__(self, compliance_config: Dict[str, Any]):
        self.config = compliance_config

        # Initialize compliance framework handlers
        self.frameworks = {
            'gdpr': GDPRComplianceHandler(compliance_config.get('gdpr', {})),
            'hipaa': HIPAAComplianceHandler(compliance_config.get('hipaa', {})),
            'sox': SOXComplianceHandler(compliance_config.get('sox', {})),
            'ccpa': CCPAComplianceHandler(compliance_config.get('ccpa', {}))
        }

        # Set up data processing components
        self.data_classifier = DataClassifier()      # Classify data sensitivity
        self.pii_detector = PIIDetector()           # Detect personal information
        self.data_anonymizer = DataAnonymizer()     # Anonymize sensitive data

        # Initialize compliance audit system
        self.audit_logger = ComplianceAuditLogger(compliance_config.get('audit', {}))

    async def process_data_with_compliance(self, data: Dict[str, Any],
                                         compliance_requirements: List[str]) -> Dict[str, Any]:
        """Process data while ensuring compliance with specified requirements."""

        # Initialize processing result tracking
        processing_result = {
            'original_data_id': data.get('id'),
            'compliance_checks': {},
            'data_modifications': [],
            'audit_entries': []
        }

        # Step 1: Classify the data by sensitivity level
        data_classification = await self.data_classifier.classify_data(data)
        processing_result['data_classification'] = data_classification

        # Step 2: Detect personally identifiable information
        pii_detection = await self.pii_detector.detect_sensitive_data(data)
        processing_result['sensitive_data_detected'] = pii_detection

        # Process data through each compliance framework
        processed_data = data.copy()
        for framework in compliance_requirements:
            if framework in self.frameworks:
                compliance_result = await self.frameworks[framework].process_data(
                    processed_data, data_classification, pii_detection
                )

                processing_result['compliance_checks'][framework] = compliance_result

                # Apply modifications if not compliant
                if not compliance_result['compliant']:
                    processed_data = await self._apply_compliance_modifications(
                        processed_data, compliance_result['required_actions']
                    )
                    processing_result['data_modifications'].extend(
                        compliance_result['required_actions']
                    )

        # Log the compliance processing for audit trail
        audit_entry = await self.audit_logger.log_compliance_processing(
            data.get('id'), compliance_requirements, processing_result
        )
        processing_result['audit_entries'].append(audit_entry)

        return {
            'processed_data': processed_data,
            'compliance_result': processing_result,
            'compliant': all(
                check['compliant'] for check in processing_result['compliance_checks'].values()
            )
        }

class GDPRComplianceHandler:
    """GDPR compliance handler for RAG systems."""

    def __init__(self, gdpr_config: Dict[str, Any]):
        self.config = gdpr_config
        self.lawful_basis = gdpr_config.get('lawful_basis', 'legitimate_interest')
        self.data_retention_days = gdpr_config.get('retention_days', 365)

    async def process_data(self, data: Dict[str, Any],
                          classification: Dict[str, Any],
                          pii_detection: Dict[str, Any]) -> Dict[str, Any]:
        """Process data for GDPR compliance."""

        compliance_result = {
            'compliant': True,
            'required_actions': [],
            'gdpr_checks': {}
        }

        # Process only if personal data is detected
        if pii_detection.get('contains_pii', False):
            compliance_result['gdpr_checks']['personal_data_detected'] = True

            # Check 1: Verify lawful basis for processing
            consent_check = await self._check_consent(data, pii_detection)
            compliance_result['gdpr_checks']['consent'] = consent_check

            if not consent_check['valid']:
                compliance_result['compliant'] = False
                compliance_result['required_actions'].append({
                    'action': 'obtain_consent',
                    'reason': 'No valid consent for personal data processing'
                })

            # Check 2: Data minimization principle
            minimization_check = await self._check_data_minimization(data, classification)
            compliance_result['gdpr_checks']['data_minimization'] = minimization_check

            if not minimization_check['compliant']:
                compliance_result['required_actions'].append({
                    'action': 'minimize_data',
                    'fields_to_remove': minimization_check['excessive_fields']
                })

            # Check 3: Data retention limits
            retention_check = await self._check_retention_period(data)
            compliance_result['gdpr_checks']['retention'] = retention_check

            if not retention_check['compliant']:
                compliance_result['required_actions'].append({
                    'action': 'schedule_deletion',
                    'retention_expires': retention_check['expiry_date']
                })

        return compliance_result

    async def handle_data_subject_request(self, request_type: str,
                                        subject_id: str) -> Dict[str, Any]:
        """Handle GDPR data subject requests."""

        # Route request to appropriate handler
        if request_type == 'access':
            return await self._handle_access_request(subject_id)
        elif request_type == 'erasure':
            return await self._handle_erasure_request(subject_id)
        elif request_type == 'rectification':
            return await self._handle_rectification_request(subject_id)
        elif request_type == 'portability':
            return await self._handle_portability_request(subject_id)
        else:
            return {'error': f'Unsupported request type: {request_type}'}

# Real-time indexing and incremental update system

class IncrementalIndexingSystem:
    """Real-time incremental indexing system for dynamic knowledge bases."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config

        # Initialize change detection systems for different source types
        self.change_detectors = {
            'file_system': FileSystemChangeDetector(),
            'database': DatabaseChangeDetector(),
            'api_webhook': WebhookChangeDetector(),
            'message_queue': MessageQueueChangeDetector()
        }

        # Set up processing pipeline components
        self.incremental_processor = IncrementalDocumentProcessor()
        self.vector_store_updater = VectorStoreUpdater()
        self.knowledge_graph_updater = KnowledgeGraphUpdater()

        # Initialize change tracking system
        self.change_tracker = ChangeTracker()

        # Create processing queues with appropriate sizes
        self.update_queue = asyncio.Queue(maxsize=config.get('queue_size', 10000))
        self.deletion_queue = asyncio.Queue(maxsize=1000)

        # Start background processors for parallel processing
        self.processors = []
        for i in range(config.get('num_processors', 3)):
            processor = asyncio.create_task(self._incremental_update_processor(f"proc_{i}"))
            self.processors.append(processor)

    async def setup_change_detection(self, sources: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Set up change detection for specified data sources."""

        setup_results = {}

        # Configure each data source for monitoring
        for source in sources:
            source_type = source['type']
            source_config = source['config']
            source_id = source.get('id', f"{source_type}_{time.time()}")

            if source_type in self.change_detectors:
                try:
                    # Initialize the appropriate detector
                    detector = self.change_detectors[source_type]
                    setup_result = await detector.setup_monitoring(source_id, source_config)

                    # Connect change events to our processing pipeline
                    await detector.register_change_callback(
                        self._handle_change_event
                    )

                    setup_results[source_id] = {
                        'status': 'monitoring',
                        'detector_type': source_type,
                        'setup_result': setup_result
                    }

                except Exception as e:
                    setup_results[source_id] = {
                        'status': 'failed',
                        'error': str(e)
                    }

        return {
            'setup_results': setup_results,
            'monitoring_sources': len([r for r in setup_results.values()
                                     if r['status'] == 'monitoring']),
            'processors_active': len(self.processors)
        }

    async def _incremental_update_processor(self, processor_id: str):
        """Background processor for incremental updates."""

        while True:
            try:
                # Process document updates and creations
                if not self.update_queue.empty():
                    change_event = await self.update_queue.get()
                    await self._process_incremental_update(change_event, processor_id)
                    self.update_queue.task_done()

                # Process document deletions
                if not self.deletion_queue.empty():
                    deletion_event = await self.deletion_queue.get()
                    await self._process_deletion(deletion_event, processor_id)
                    self.deletion_queue.task_done()

                # Prevent busy waiting with small delay
                await asyncio.sleep(0.1)

            except Exception as e:
                self.logger.error(f"Processor {processor_id} error: {e}")
                await asyncio.sleep(1)

    async def _process_incremental_update(self, change_event: Dict[str, Any],
                                        processor_id: str):
        """Process individual incremental update."""

        start_time = time.time()

        try:
            # Extract change information from event
            source_id = change_event['source_id']
            document_id = change_event['document_id']
            change_type = change_event['type']  # 'create' or 'update'
            document_data = change_event['document_data']

            # Start tracking this change for monitoring
            tracking_id = await self.change_tracker.start_tracking(change_event)

            # Process the document through our pipeline
            processing_result = await self.incremental_processor.process_document(
                document_data, change_type
            )

            if processing_result['success']:
                # Update vector store with processed chunks
                vector_update_result = await self.vector_store_updater.update_document(
                    document_id, processing_result['chunks'], change_type
                )

                # Update knowledge graph if enabled
                if self.config.get('update_knowledge_graph', True):
                    kg_update_result = await self.knowledge_graph_updater.update_document(
                        document_id, processing_result['entities'],
                        processing_result['relationships'], change_type
                    )
                else:
                    kg_update_result = {'skipped': True}

                # Complete change tracking with results
                await self.change_tracker.complete_tracking(tracking_id, {
                    'processing_time': time.time() - start_time,
                    'vector_update': vector_update_result,
                    'kg_update': kg_update_result
                })

            else:
                await self.change_tracker.fail_tracking(tracking_id, processing_result['error'])

        except Exception as e:
            self.logger.error(f"Incremental update error: {e}")
            if 'tracking_id' in locals():
                await self.change_tracker.fail_tracking(tracking_id, str(e))

class FileSystemChangeDetector:
    """File system change detection using OS-level monitoring."""

    def __init__(self):
        self.watchers = {}  # Track active file system watchers
        self.change_callbacks = []  # Registered callback functions

    async def setup_monitoring(self, source_id: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Set up file system monitoring for specified paths."""

        import watchdog.observers
        from watchdog.events import FileSystemEventHandler

        watch_paths = config.get('paths', [])
        file_patterns = config.get('patterns', ['*'])

        # Create custom event handler for RAG system
        class RAGFileSystemEventHandler(FileSystemEventHandler):
            def __init__(self, detector_instance, source_id):
                self.detector = detector_instance
                self.source_id = source_id

            def on_modified(self, event):
                if not event.is_directory:
                    asyncio.create_task(self.detector._handle_file_change(
                        self.source_id, event.src_path, 'update'
                    ))

            def on_created(self, event):
                if not event.is_directory:
                    asyncio.create_task(self.detector._handle_file_change(
                        self.source_id, event.src_path, 'create'
                    ))

            def on_deleted(self, event):
                if not event.is_directory:
                    asyncio.create_task(self.detector._handle_file_change(
                        self.source_id, event.src_path, 'delete'
                    ))

        # Initialize file system observer
        observer = watchdog.observers.Observer()
        event_handler = RAGFileSystemEventHandler(self, source_id)

        # Set up monitoring for all specified paths
        for path in watch_paths:
            observer.schedule(event_handler, path, recursive=True)

        observer.start()
        self.watchers[source_id] = observer

        return {
            'monitoring_paths': watch_paths,
            'file_patterns': file_patterns,
            'watcher_active': True
        }

    async def _handle_file_change(self, source_id: str, file_path: str, change_type: str):
        """Handle detected file system change."""

        try:
            # Read file content for create/update operations
            if change_type in ['create', 'update']:
                with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                    content = f.read()
                document_data = {
                    'path': file_path,
                    'content': content,
                    'modified_time': os.path.getmtime(file_path)
                }
            else:
                # For deletions, no content is available
                document_data = None

            # Create standardized change event
            change_event = {
                'source_id': source_id,
                'document_id': file_path,
                'type': change_type,
                'document_data': document_data,
                'timestamp': time.time()
            }

            # Notify all registered callbacks
            for callback in self.change_callbacks:
                await callback(change_event)

        except Exception as e:
            self.logger.error(f"File change handling error: {e}")

# Production monitoring and observability system setup

import prometheus_client
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import structlog

class RAGMonitoringSystem:
    """Comprehensive monitoring and observability for production RAG systems."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config

        # Initialize core monitoring components
        self._setup_prometheus_metrics()
        self.logger = structlog.get_logger()

        # Initialize specialized monitoring components
        self.performance_tracker = RAGPerformanceTracker()
        self.alert_manager = RAGAlertManager(config.get('alerting', {}))
        self.analytics = RAGAnalytics(config.get('analytics', {}))
        self.health_checker = RAGHealthChecker()

    def _setup_prometheus_metrics(self):
        """Set up Prometheus metrics for RAG system monitoring."""

        # Request tracking metrics
        self.request_counter = Counter(
            'rag_requests_total',
            'Total number of RAG requests',
            ['method', 'endpoint', 'status']
        )

        self.request_duration = Histogram(
            'rag_request_duration_seconds',
            'RAG request duration in seconds',
            ['method', 'endpoint']
        )

        # System health metrics
        self.active_connections = Gauge(
            'rag_active_connections',
            'Number of active connections',
            ['service']
        )

        self.queue_size = Gauge(
            'rag_queue_size',
            'Size of processing queues',
            ['queue_type']
        )

        # Quality and accuracy metrics
        self.response_quality = Histogram(
            'rag_response_quality',
            'Response quality scores',
            ['query_type']
        )

        self.retrieval_accuracy = Histogram(
            'rag_retrieval_accuracy',
            'Retrieval accuracy scores',
            ['retrieval_method']
        )

        # Error tracking
        self.error_counter = Counter(
            'rag_errors_total',
            'Total number of errors',
            ['error_type', 'service']
        )

        # Start the Prometheus metrics server
        metrics_port = self.config.get('metrics_port', 8000)
        start_http_server(metrics_port)

    async def track_request(self, method: str, endpoint: str,
                          request_func: Callable) -> Dict[str, Any]:
        """Track RAG request with comprehensive monitoring."""

        start_time = time.time()

        # Use Prometheus histogram to automatically track duration
        with self.request_duration.labels(method=method, endpoint=endpoint).time():
            try:
                # Execute the RAG request function
                result = await request_func()

                # Record successful completion
                self.request_counter.labels(
                    method=method, endpoint=endpoint, status='success'
                ).inc()

                # Track quality metrics if available
                if 'quality_score' in result:
                    query_type = result.get('query_type', 'unknown')
                    self.response_quality.labels(query_type=query_type).observe(
                        result['quality_score']
                    )

                # Log structured information for observability
                self.logger.info(
                    "RAG request completed",
                    method=method,
                    endpoint=endpoint,
                    duration=time.time() - start_time,
                    quality_score=result.get('quality_score'),
                    sources_retrieved=result.get('sources_count', 0)
                )

                return result

            except Exception as e:
                # Record failed request metrics
                self.request_counter.labels(
                    method=method, endpoint=endpoint, status='error'
                ).inc()

                # Track error type for analysis
                error_type = type(e).__name__
                self.error_counter.labels(
                    error_type=error_type, service=endpoint
                ).inc()

                # Log detailed error information
                self.logger.error(
                    "RAG request failed",
                    method=method,
                    endpoint=endpoint,
                    error=str(e),
                    duration=time.time() - start_time
                )

                # Check if alert thresholds are exceeded
                await self.alert_manager.check_error_threshold(endpoint, error_type)

                # Re-raise the exception for proper error handling
                raise

class RAGAnalytics:
    """Advanced analytics for RAG system performance and usage."""

    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.analytics_data = {}

        # Initialize analytics components
        self.metrics_store = MetricsTimeSeriesStore()
        self.query_analyzer = QueryPatternAnalyzer()
        self.performance_predictor = PerformancePredictor()

    async def analyze_system_performance(self, time_window: str = '1h') -> Dict[str, Any]:
        """Analyze comprehensive system performance over time window."""

        # Retrieve metrics data for the specified time window
        metrics = await self.metrics_store.get_metrics_window(time_window)

        # Perform comprehensive performance analysis
        performance_analysis = {
            'request_volume': self._analyze_request_volume(metrics),
            'response_times': self._analyze_response_times(metrics),
            'quality_trends': self._analyze_quality_trends(metrics),
            'error_patterns': self._analyze_error_patterns(metrics),
            'resource_usage': self._analyze_resource_usage(metrics),
            'user_satisfaction': self._analyze_user_satisfaction(metrics)
        }

        # Identify potential performance issues
        performance_issues = self._identify_performance_issues(performance_analysis)

        # Generate actionable recommendations
        recommendations = self._generate_performance_recommendations(
            performance_analysis, performance_issues
        )

        return {
            'analysis_period': time_window,
            'performance_analysis': performance_analysis,
            'identified_issues': performance_issues,
            'recommendations': recommendations,
            'overall_health_score': self._calculate_health_score(performance_analysis)
        }

    def _generate_performance_recommendations(self, analysis: Dict[str, Any],
                                           issues: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Generate actionable performance recommendations."""

        recommendations = []

        # Check for high response time issues
        if analysis['response_times']['p95'] > 2.0:  # 2 second threshold
            recommendations.append({
                'type': 'performance',
                'priority': 'high',
                'issue': 'High response times detected',
                'recommendation': 'Scale retrieval services or optimize vector search indices',
                'expected_impact': 'Reduce P95 response time by 30-50%'
            })

        # Check for quality issues
        if analysis['quality_trends']['average_score'] < 0.7:
            recommendations.append({
                'type': 'quality',
                'priority': 'medium',
                'issue': 'Response quality below target',
                'recommendation': 'Review document chunking strategy, consider reranking',
                'expected_impact': 'Improve average quality score by 15-25%'
            })

        # Check for resource utilization issues
        if analysis['resource_usage']['cpu_utilization'] > 0.8:
            recommendations.append({
                'type': 'scaling',
                'priority': 'high',
                'issue': 'High CPU utilization detected',
                'recommendation': 'Enable auto-scaling or add more service instances',
                'expected_impact': 'Reduce CPU utilization to 60-70% range'
            })

        return recommendations

# Complete production RAG system deployment

class ProductionRAGDeployment:
    """Complete production RAG deployment with enterprise features."""

    def __init__(self, deployment_config: Dict[str, Any]):
        # Core system orchestration
        self.orchestrator = RAGServiceOrchestrator(deployment_config['services'])

        # Enterprise integration
        self.enterprise_integrator = EnterpriseRAGIntegrator(
            deployment_config['enterprise_integration']
        )

        # Security and compliance
        self.auth_manager = EnterpriseAuthManager(deployment_config['auth'])
        self.compliance_manager = PrivacyComplianceManager(
            deployment_config['compliance']
        )

        # Real-time indexing
        self.incremental_indexer = IncrementalIndexingSystem(
            deployment_config['incremental_indexing']
        )

        # Monitoring and analytics
        self.monitoring_system = RAGMonitoringSystem(deployment_config['monitoring'])

        # Auto-scaling
        self.auto_scaler = RAGAutoScaler(deployment_config['auto_scaling'])

    async def deploy_production_system(self) -> Dict[str, Any]:
        """Deploy complete production RAG system."""

        deployment_result = {
            'deployment_id': f"rag_prod_{int(time.time())}",
            'components': {},
            'status': 'deploying'
        }

        try:
            # 1. Start core services
            services_result = await self.orchestrator.start_services()
            deployment_result['components']['services'] = services_result

            # 2. Setup enterprise integration
            integration_result = await self.enterprise_integrator.setup_enterprise_integration(
                ['sharepoint', 'database', 'file_system']
            )
            deployment_result['components']['enterprise_integration'] = integration_result

            # 3. Initialize security
            security_result = await self._initialize_security()
            deployment_result['components']['security'] = security_result

            # 4. Setup incremental indexing
            indexing_result = await self.incremental_indexer.setup_change_detection([
                {'type': 'file_system', 'config': {'paths': ['/data/documents']}},
                {'type': 'database', 'config': {'connection_string': 'postgresql://...'}}
            ])
            deployment_result['components']['incremental_indexing'] = indexing_result

            # 5. Start monitoring
            monitoring_result = await self._start_monitoring()
            deployment_result['components']['monitoring'] = monitoring_result

            # 6. Configure auto-scaling
            scaling_result = await self._configure_auto_scaling()
            deployment_result['components']['auto_scaling'] = scaling_result

            deployment_result['status'] = 'deployed'
            deployment_result['deployment_time'] = time.time()

            return deployment_result

        except Exception as e:
            deployment_result['status'] = 'failed'
            deployment_result['error'] = str(e)
            return deployment_result

    async def health_check_production(self) -> Dict[str, Any]:
        """Comprehensive production health check."""

        return await self.monitoring_system.health_checker.comprehensive_health_check()

    async def get_production_metrics(self, time_window: str = '1h') -> Dict[str, Any]:
        """Get comprehensive production metrics."""

        return await self.monitoring_system.analytics.analyze_system_performance(time_window)