""" Phase 7 Integration Module for Cyber-LLM Integrates all continuous intelligence and evolution components into a cohesive system. Author: Muzan Sano """ import asyncio import json import logging from datetime import datetime, timedelta from typing import Dict, List, Optional, Tuple, Any, Union from dataclasses import dataclass, asdict from enum import Enum import yaml from pathlib import Path # Import Phase 7 components from .online_learning import OnlineLearningManager, LearningEvent, LearningEventType from .federated_learning import FederatedLearningCoordinator, FederatedLearningParticipant from .meta_learning import MetaLearningManager, MetaLearningStrategy from .research_collaboration import ResearchCollaborationManager, CollaborationType from .constitutional_ai import ConstitutionalAIManager from ..utils.logging_system import CyberLLMLogger # Configure logging logger = CyberLLMLogger(__name__).get_logger() class ContinuousIntelligenceMode(Enum): """Modes of continuous intelligence operation""" CONSERVATIVE = "conservative" # Minimal learning, high safety BALANCED = "balanced" # Balanced learning and safety AGGRESSIVE = "aggressive" # Maximum learning, calculated risks RESEARCH = "research" # Research-focused with collaboration PRODUCTION = "production" # Production-optimized stability @dataclass class ContinuousIntelligenceConfig: """Configuration for continuous intelligence system""" # General settings mode: ContinuousIntelligenceMode = ContinuousIntelligenceMode.BALANCED organization_name: str = "CyberLLM-Org" enable_online_learning: bool = True enable_federated_learning: bool = True enable_meta_learning: bool = True enable_research_collaboration: bool = True enable_constitutional_ai: bool = True # Learning parameters learning_rate_multiplier: float = 1.0 adaptation_threshold: float = 0.8 meta_learning_batch_size: int = 4 collaboration_sensitivity_level: str = "consortium" # Safety parameters constitutional_strict_mode: bool = True human_oversight_threshold: float = 0.8 max_autonomous_adaptations: int = 10 # Performance parameters update_frequency_minutes: int = 60 batch_processing_size: int = 100 max_memory_usage_gb: float = 8.0 def to_dict(self) -> Dict[str, Any]: """Convert to dictionary""" return asdict(self) class ContinuousIntelligenceOrchestrator: """Main orchestrator for all continuous intelligence components""" def __init__(self, config: ContinuousIntelligenceConfig, model, tokenizer): self.config = config self.model = model self.tokenizer = tokenizer # Initialize components based on configuration self.components = {} self._initialize_components() # Operational state self.is_running = False self.last_update_time = None self.adaptation_count = 0 self.performance_metrics = { 'total_learning_events': 0, 'successful_adaptations': 0, 'constitutional_violations': 0, 'collaboration_insights': 0, 'meta_learning_episodes': 0 } logger.info(f"ContinuousIntelligenceOrchestrator initialized in {config.mode.value} mode") def _initialize_components(self): """Initialize continuous intelligence components""" try: # Online Learning Manager if self.config.enable_online_learning: self.components['online_learning'] = OnlineLearningManager( model=self.model, tokenizer=self.tokenizer, learning_rate=0.001 * self.config.learning_rate_multiplier, batch_size=self.config.batch_processing_size ) logger.info("Initialized OnlineLearningManager") # Federated Learning Coordinator if self.config.enable_federated_learning: self.components['federated_learning'] = FederatedLearningCoordinator( coordinator_id=f"{self.config.organization_name}_coordinator", model=self.model ) logger.info("Initialized FederatedLearningCoordinator") # Meta Learning Manager if self.config.enable_meta_learning: self.components['meta_learning'] = MetaLearningManager( model=self.model, tokenizer=self.tokenizer, strategy=MetaLearningStrategy.MAML, meta_batch_size=self.config.meta_learning_batch_size ) logger.info("Initialized MetaLearningManager") # Research Collaboration Manager if self.config.enable_research_collaboration: self.components['research_collaboration'] = ResearchCollaborationManager( organization_name=self.config.organization_name ) logger.info("Initialized ResearchCollaborationManager") # Constitutional AI Manager if self.config.enable_constitutional_ai: self.components['constitutional_ai'] = ConstitutionalAIManager() logger.info("Initialized ConstitutionalAIManager") except Exception as e: logger.error(f"Failed to initialize components: {str(e)}") raise async def start_continuous_intelligence(self): """Start the continuous intelligence system""" if self.is_running: logger.warning("Continuous intelligence system is already running") return self.is_running = True self.last_update_time = datetime.now() logger.info("Starting continuous intelligence system") # Start background tasks tasks = [] if 'online_learning' in self.components: tasks.append(self._run_online_learning_loop()) if 'meta_learning' in self.components: tasks.append(self._run_meta_learning_loop()) if 'federated_learning' in self.components: tasks.append(self._run_federated_learning_loop()) # Start monitoring task tasks.append(self._run_monitoring_loop()) # Run all tasks concurrently try: await asyncio.gather(*tasks) except Exception as e: logger.error(f"Error in continuous intelligence system: {str(e)}") self.is_running = False async def stop_continuous_intelligence(self): """Stop the continuous intelligence system""" self.is_running = False logger.info("Stopping continuous intelligence system") # Save current state await self._save_system_state() async def process_learning_event(self, event: LearningEvent) -> Dict[str, Any]: """Process a single learning event through all applicable components""" results = {} try: # Constitutional AI evaluation first if 'constitutional_ai' in self.components: constitutional_result = await self.components['constitutional_ai'].evaluate_and_enforce( content=json.dumps(event.context), context={'event_type': event.event_type.value} ) if not constitutional_result['allowed']: logger.warning(f"Learning event blocked by constitutional AI: {event.event_id}") self.performance_metrics['constitutional_violations'] += 1 return {'blocked': True, 'reason': 'constitutional_violation'} results['constitutional_check'] = constitutional_result # Process through online learning if 'online_learning' in self.components: online_result = await self.components['online_learning'].process_learning_event(event) results['online_learning'] = online_result # Add to meta-learning episodes if 'meta_learning' in self.components: episodes_created = await self.components['meta_learning'].add_learning_episodes([event]) results['meta_learning_episodes'] = episodes_created self.performance_metrics['meta_learning_episodes'] += episodes_created # Update performance metrics self.performance_metrics['total_learning_events'] += 1 logger.debug(f"Processed learning event: {event.event_id}") except Exception as e: logger.error(f"Error processing learning event {event.event_id}: {str(e)}") results['error'] = str(e) return results async def trigger_adaptation_cycle(self) -> Dict[str, Any]: """Trigger a complete adaptation cycle across all components""" if self.adaptation_count >= self.config.max_autonomous_adaptations: logger.warning("Maximum autonomous adaptations reached, requiring human oversight") return {'blocked': True, 'reason': 'max_adaptations_reached'} adaptation_results = {} try: # Online learning adaptation if 'online_learning' in self.components: online_result = await self.components['online_learning'].apply_accumulated_updates() adaptation_results['online_learning'] = online_result # Meta-learning adaptation if 'meta_learning' in self.components: meta_result = await self.components['meta_learning'].meta_train_step() adaptation_results['meta_learning'] = meta_result # Update adaptation count if any(result.get('success') for result in adaptation_results.values()): self.adaptation_count += 1 self.performance_metrics['successful_adaptations'] += 1 logger.info(f"Completed adaptation cycle {self.adaptation_count}") except Exception as e: logger.error(f"Error in adaptation cycle: {str(e)}") adaptation_results['error'] = str(e) return adaptation_results async def _run_online_learning_loop(self): """Background loop for online learning""" logger.info("Starting online learning loop") while self.is_running: try: # Check if adaptation threshold is met if 'online_learning' in self.components: stats = self.components['online_learning'].get_learning_statistics() if stats['pending_updates'] > 0 and stats['confidence_score'] >= self.config.adaptation_threshold: await self.trigger_adaptation_cycle() await asyncio.sleep(self.config.update_frequency_minutes * 60) except Exception as e: logger.error(f"Error in online learning loop: {str(e)}") await asyncio.sleep(300) # Wait 5 minutes on error async def _run_meta_learning_loop(self): """Background loop for meta-learning""" logger.info("Starting meta-learning loop") while self.is_running: try: if 'meta_learning' in self.components: # Perform meta-training if enough episodes available await self.components['meta_learning'].meta_train_step() await asyncio.sleep(self.config.update_frequency_minutes * 60 * 2) # Less frequent except Exception as e: logger.error(f"Error in meta-learning loop: {str(e)}") await asyncio.sleep(600) # Wait 10 minutes on error async def _run_federated_learning_loop(self): """Background loop for federated learning""" logger.info("Starting federated learning loop") while self.is_running: try: if 'federated_learning' in self.components: # Check for federated learning opportunities await self.components['federated_learning'].coordinate_federated_round() await asyncio.sleep(self.config.update_frequency_minutes * 60 * 4) # Even less frequent except Exception as e: logger.error(f"Error in federated learning loop: {str(e)}") await asyncio.sleep(900) # Wait 15 minutes on error async def _run_monitoring_loop(self): """Background loop for system monitoring""" logger.info("Starting monitoring loop") while self.is_running: try: # Log system status status = await self.get_system_status() # Check for any critical issues if status['memory_usage_gb'] > self.config.max_memory_usage_gb: logger.warning(f"High memory usage: {status['memory_usage_gb']:.2f}GB") if status['constitutional_violations_rate'] > 0.1: logger.warning(f"High constitutional violation rate: {status['constitutional_violations_rate']:.2f}") await asyncio.sleep(300) # Monitor every 5 minutes except Exception as e: logger.error(f"Error in monitoring loop: {str(e)}") await asyncio.sleep(300) async def get_system_status(self) -> Dict[str, Any]: """Get comprehensive system status""" status = { 'is_running': self.is_running, 'mode': self.config.mode.value, 'uptime_hours': (datetime.now() - (self.last_update_time or datetime.now())).total_seconds() / 3600, 'adaptation_count': self.adaptation_count, 'performance_metrics': self.performance_metrics.copy() } # Component-specific status component_status = {} if 'online_learning' in self.components: component_status['online_learning'] = self.components['online_learning'].get_learning_statistics() if 'meta_learning' in self.components: component_status['meta_learning'] = self.components['meta_learning'].get_meta_learning_statistics() if 'constitutional_ai' in self.components: component_status['constitutional_ai'] = self.components['constitutional_ai'].get_constitutional_statistics() if 'research_collaboration' in self.components: component_status['research_collaboration'] = self.components['research_collaboration'].get_collaboration_statistics() status['components'] = component_status # Calculate derived metrics total_events = self.performance_metrics['total_learning_events'] if total_events > 0: status['constitutional_violations_rate'] = self.performance_metrics['constitutional_violations'] / total_events status['adaptation_success_rate'] = self.performance_metrics['successful_adaptations'] / self.adaptation_count if self.adaptation_count > 0 else 0.0 else: status['constitutional_violations_rate'] = 0.0 status['adaptation_success_rate'] = 0.0 # Estimate memory usage (simplified) status['memory_usage_gb'] = 2.0 # Base estimate, would use actual monitoring in production return status async def _save_system_state(self): """Save current system state to disk""" try: state_data = { 'config': self.config.to_dict(), 'performance_metrics': self.performance_metrics, 'adaptation_count': self.adaptation_count, 'last_update_time': self.last_update_time.isoformat() if self.last_update_time else None, 'timestamp': datetime.now().isoformat() } state_file = Path("data/system_state/continuous_intelligence_state.json") state_file.parent.mkdir(parents=True, exist_ok=True) with open(state_file, 'w') as f: json.dump(state_data, f, indent=2) logger.info(f"Saved system state to {state_file}") except Exception as e: logger.error(f"Failed to save system state: {str(e)}") async def load_system_state(self, state_file: Optional[str] = None) -> bool: """Load system state from disk""" try: if state_file is None: state_file = "data/system_state/continuous_intelligence_state.json" state_path = Path(state_file) if not state_path.exists(): logger.info("No previous system state found") return False with open(state_path, 'r') as f: state_data = json.load(f) # Restore state self.performance_metrics = state_data.get('performance_metrics', {}) self.adaptation_count = state_data.get('adaptation_count', 0) if state_data.get('last_update_time'): self.last_update_time = datetime.fromisoformat(state_data['last_update_time']) logger.info(f"Loaded system state from {state_path}") return True except Exception as e: logger.error(f"Failed to load system state: {str(e)}") return False def create_learning_event(self, event_type: LearningEventType, source: str, context: Dict[str, Any], confidence: float = 1.0, priority: int = 1) -> LearningEvent: """Helper method to create learning events""" return LearningEvent( event_id=f"evt_{datetime.now().timestamp()}", event_type=event_type, source=source, timestamp=datetime.now(), context=context, confidence=confidence, priority=priority ) # Factory functions def create_continuous_intelligence_config(mode: ContinuousIntelligenceMode = ContinuousIntelligenceMode.BALANCED, **kwargs) -> ContinuousIntelligenceConfig: """Create continuous intelligence configuration""" # Mode-specific defaults mode_defaults = { ContinuousIntelligenceMode.CONSERVATIVE: { 'learning_rate_multiplier': 0.5, 'constitutional_strict_mode': True, 'human_oversight_threshold': 0.6, 'max_autonomous_adaptations': 5, 'update_frequency_minutes': 120 }, ContinuousIntelligenceMode.BALANCED: { 'learning_rate_multiplier': 1.0, 'constitutional_strict_mode': True, 'human_oversight_threshold': 0.8, 'max_autonomous_adaptations': 10, 'update_frequency_minutes': 60 }, ContinuousIntelligenceMode.AGGRESSIVE: { 'learning_rate_multiplier': 2.0, 'constitutional_strict_mode': False, 'human_oversight_threshold': 0.9, 'max_autonomous_adaptations': 20, 'update_frequency_minutes': 30 }, ContinuousIntelligenceMode.RESEARCH: { 'enable_research_collaboration': True, 'learning_rate_multiplier': 1.5, 'constitutional_strict_mode': False, 'collaboration_sensitivity_level': 'consortium', 'update_frequency_minutes': 45 }, ContinuousIntelligenceMode.PRODUCTION: { 'learning_rate_multiplier': 0.8, 'constitutional_strict_mode': True, 'human_oversight_threshold': 0.7, 'max_autonomous_adaptations': 8, 'update_frequency_minutes': 90 } } # Start with mode defaults config_dict = mode_defaults.get(mode, {}) # Override with user-provided kwargs config_dict.update(kwargs) config_dict['mode'] = mode return ContinuousIntelligenceConfig(**config_dict) def create_continuous_intelligence_orchestrator(model, tokenizer, config: Optional[ContinuousIntelligenceConfig] = None, **kwargs) -> ContinuousIntelligenceOrchestrator: """Create continuous intelligence orchestrator""" if config is None: config = create_continuous_intelligence_config(**kwargs) return ContinuousIntelligenceOrchestrator(config, model, tokenizer)