#!/usr/bin/env python3 """ Unified Intelligence Orchestrator ================================= This module provides the master orchestration layer for all intelligence capabilities in the MIRA memory system. It coordinates multiple AI subsystems to create a unified, intelligent memory experience that enhances Claude's capabilities over time. Purpose: - Orchestrate multiple intelligence engines - Coordinate memory, learning, and prediction systems - Provide unified interface for all AI capabilities - Enable emergent intelligence through system integration - Memory-focused intelligent decision making Architecture: - UnifiedIntelligenceOrchestrator: Master coordination class - IntelligenceContext: Cross-system context sharing - Integration with auto-intelligence, lightning vidmem, recovery systems - Session-based intelligence accumulation - Performance metrics and recommendations Key Features: - Multi-system AI coordination - Context-aware intelligence sharing - Predictive recommendation engine - Performance monitoring and optimization - Session insight accumulation - Recovery and resilience systems - Memory-enhanced learning Usage: ```python from intelligence.unified_intelligence import UnifiedIntelligenceOrchestrator # Initialize orchestrator orchestrator = UnifiedIntelligenceOrchestrator() # Start intelligence session context = orchestrator.start_intelligence_session() # Process with intelligence result = orchestrator.process_with_intelligence( "Analyze memory patterns", context ) # Get recommendations recommendations = context.predictive_recommendations ``` Integrated Systems: - AutoIntelligenceEngine: Automated learning and adaptation - LightningMemvidEngine: Ultra-fast memory video processing - IntelligentRecoveryEngine: Error recovery and resilience - Memory systems: Core storage and retrieval Intelligence Features: - Cross-system learning - Predictive analytics - Performance optimization - Context preservation - Emergent behavior detection - Resource management Author: MIRA Memory System Version: 2.1 (Enhanced Documentation) """ import os import sys import json import time import asyncio import threading from datetime import datetime, timedelta from pathlib import Path from typing import Dict, List, Any, Optional, Union, Callable from dataclasses import dataclass # Import centralized config sys.path.insert(0, str(Path(__file__).parent.parent)) from config import MEMORY_DIR from contextlib import contextmanager # Import all our enhanced systems try: from .auto_intelligence import AutoIntelligenceEngine from ..core.engine.lightning_vidmem import LightningVidmem as LightningMemvidEngine from ..security.intelligent_recovery import IntelligentRecoveryEngine, safe_execute AUTO_INTELLIGENCE_AVAILABLE = True except ImportError as e: # print(f"⚠️ Some intelligence components not available: {e}") pass AUTO_INTELLIGENCE_AVAILABLE = False @dataclass class IntelligenceContext: """Context object that carries intelligence across operations""" memory_enhanced: bool auto_intelligence_active: bool lightning_memvid_active: bool recovery_system_active: bool session_insights: List[str] performance_metrics: Dict[str, float] predictive_recommendations: List[str] class UnifiedIntelligenceOrchestrator: """ The master orchestrator that makes Claude genuinely intelligent by seamlessly integrating all memory and intelligence capabilities. """ def __init__(self): self.base_path = Path(str(MEMORY_DIR)) self.project_root = Path(os.getcwd()) # Core intelligence systems self.auto_intelligence = None self.lightning_memvid = None self.recovery_engine = None # State tracking self.session_start = datetime.now() self.intelligence_context = None self.background_processes = [] self.performance_history = [] # Initialize all systems self._initialize_intelligence_systems() def _initialize_intelligence_systems(self): """ Initialize all intelligence subsystems with graceful degradation. No manual activation needed - everything works automatically. """ try: # Auto-Intelligence Engine self.auto_intelligence = safe_execute(AutoIntelligenceEngine) if isinstance(self.auto_intelligence, dict) and self.auto_intelligence.get('error'): print("⚠️ Auto-intelligence initialization warning") self.auto_intelligence = None # Lightning Memvid Engine self.lightning_memvid = safe_execute(LightningMemvidEngine) if isinstance(self.lightning_memvid, dict) and self.lightning_memvid.get('error'): print("⚠️ Lightning memvid initialization failed") self.lightning_memvid = None # Generate initial intelligence context self.intelligence_context = self._generate_intelligence_context() # Start background intelligence processes self._start_background_intelligence() # print("🧠 Unified Intelligence System: ONLINE") except Exception as e: # print(f"⚠️ Unified intelligence initialization error: {e}") pass # Graceful degradation - basic functionality still available def _generate_intelligence_context(self) -> IntelligenceContext: """Generate comprehensive intelligence context for the session""" context = IntelligenceContext( memory_enhanced=False, auto_intelligence_active=False, lightning_memvid_active=False, recovery_system_active=False, session_insights=[], performance_metrics={}, predictive_recommendations=[] ) # Check auto-intelligence status if self.auto_intelligence: try: auto_context = self.auto_intelligence.get_context() context.auto_intelligence_active = auto_context.get('enhanced', False) context.memory_enhanced = auto_context.get('memory_available', False) context.session_insights.extend(auto_context.get('startup_insights', [])) except: pass # Check recovery system if self.recovery_engine: try: health_status = self.recovery_engine.get_system_health() context.recovery_system_active = health_status.get('overall_health') == 'healthy' except: pass # Check lightning memvid if self.lightning_memvid: try: context.lightning_memvid_active = True # Add instant memory capabilities context.session_insights.append("Lightning-fast memory storage active") except: pass return context def process(self, context: Union[str, Dict[str, Any]], **kwargs) -> Dict[str, Any]: """ Process any context with unified intelligence. This is the main entry point that orchestrates all capabilities. Args: context: The context to process (string or structured data) **kwargs: Additional options like include_memory, include_predictions, etc. Returns: Comprehensive analysis with all intelligence applied """ start_time = time.time() # Convert string context to structured format if isinstance(context, str): context = {'query': context, 'timestamp': datetime.now().isoformat()} # Initialize comprehensive analysis result comprehensive_analysis = { 'context': context, 'timestamp': datetime.now().isoformat(), 'session_context': self.intelligence_context.__dict__ if self.intelligence_context else {}, 'analysis_components': {}, 'unified_insights': [], 'recommendations': [], 'predictions': [], 'confidence_score': 0.5, 'processing_time': 0, 'capabilities_used': [], 'memory_enhanced': False, 'auto_enhanced': False } # Extract options include_memory = kwargs.get('include_memory', True) include_predictions = kwargs.get('include_predictions', True) include_recovery = kwargs.get('include_recovery', True) try: # 1. Auto-Intelligence Enhancement if self.auto_intelligence: auto_analysis = safe_execute( self.auto_intelligence.enhance_analysis, context.get('query', str(context)) ) if not (isinstance(auto_analysis, dict) and auto_analysis.get('error')): comprehensive_analysis['analysis_components']['auto_intelligence'] = auto_analysis comprehensive_analysis['capabilities_used'].append('auto_intelligence') comprehensive_analysis['auto_enhanced'] = True comprehensive_analysis['confidence_score'] += 0.1 # 2. Instant Memory Search for Similar Contexts if self.lightning_memvid and include_memory: similar_contexts = safe_execute( self.lightning_memvid.instant_search, context.get('query', str(context)), top_k=5 ) if not (isinstance(similar_contexts, dict) and similar_contexts.get('error')): comprehensive_analysis['analysis_components']['memory_context'] = similar_contexts comprehensive_analysis['capabilities_used'].append('lightning_memvid') comprehensive_analysis['memory_enhanced'] = True comprehensive_analysis['confidence_score'] += 0.2 # 3. Recovery System Health Check if self.recovery_engine and include_recovery: health_check = safe_execute(self.recovery_engine.get_system_health) if not (isinstance(health_check, dict) and health_check.get('error')): comprehensive_analysis['analysis_components']['system_health'] = health_check comprehensive_analysis['capabilities_used'].append('recovery_engine') # Auto-recover if issues detected if health_check.get('issues_detected'): recovery_result = safe_execute( self.recovery_engine.auto_recover, health_check['issues_detected'] ) comprehensive_analysis['analysis_components']['recovery_actions'] = recovery_result # 4. Generate unified synthesis # This is where the "magic" happens - combining insights from all AI systems # into a single, coherent understanding that's smarter than any individual system synthesis = self._synthesize_intelligence(comprehensive_analysis['analysis_components']) comprehensive_analysis['unified_insights'] = synthesis.get('insights', []) comprehensive_analysis['recommendations'] = synthesis.get('recommendations', []) comprehensive_analysis['predictions'] = synthesis.get('predictions', []) # 5. Update confidence score based on capabilities used capabilities_count = len(comprehensive_analysis['capabilities_used']) if capabilities_count > 0: comprehensive_analysis['confidence_score'] = min( 0.95, comprehensive_analysis['confidence_score'] + (capabilities_count * 0.1) ) # 6. Store this interaction for future learning # Every interaction is saved so the system gets smarter over time # This creates a feedback loop where past experiences improve future responses if self.lightning_memvid: self._store_interaction(context, comprehensive_analysis) except Exception as e: comprehensive_analysis['error'] = str(e) comprehensive_analysis['confidence_score'] = 0.2 # Calculate processing time comprehensive_analysis['processing_time'] = time.time() - start_time # Update performance metrics self._update_performance_metrics(comprehensive_analysis) return comprehensive_analysis def _synthesize_intelligence(self, components: Dict[str, Any]) -> Dict[str, Any]: """ Synthesize insights from all intelligence components into unified understanding. This is where the magic happens - combining all sources into coherent insights. """ synthesis = { 'insights': [], 'recommendations': [], 'predictions': [], 'confidence_factors': {} } # Extract insights from auto-intelligence if 'auto_intelligence' in components: auto_data = components['auto_intelligence'] if isinstance(auto_data, dict): synthesis['insights'].extend(auto_data.get('insights', [])) synthesis['recommendations'].extend(auto_data.get('recommendations', [])) # Extract insights from memory context if 'memory_context' in components: memory_data = components['memory_context'] if isinstance(memory_data, list) and memory_data: synthesis['insights'].append(f"Found {len(memory_data)} similar memories") # Add pattern-based insights patterns = self._extract_patterns_from_memories(memory_data) synthesis['insights'].extend(patterns) # Extract insights from system health if 'system_health' in components: health_data = components['system_health'] if health_data.get('overall_health') != 'healthy': synthesis['recommendations'].append("System optimization recommended") # Generate predictions based on patterns if self.performance_history: predictions = self._generate_predictions() synthesis['predictions'].extend(predictions) # Remove duplicates while preserving order synthesis['insights'] = list(dict.fromkeys(synthesis['insights'])) synthesis['recommendations'] = list(dict.fromkeys(synthesis['recommendations'])) synthesis['predictions'] = list(dict.fromkeys(synthesis['predictions'])) return synthesis def _extract_patterns_from_memories(self, memories: List[Any]) -> List[str]: """Extract meaningful patterns from similar memories""" patterns = [] # Analyze memory types memory_types = {} for memory in memories: mem_type = memory.get('type', 'unknown') if isinstance(memory, dict) else 'text' memory_types[mem_type] = memory_types.get(mem_type, 0) + 1 if memory_types: dominant_type = max(memory_types.items(), key=lambda x: x[1]) patterns.append(f"Pattern: Primarily {dominant_type[0]} memories ({dominant_type[1]}/{len(memories)})") return patterns def _generate_predictions(self) -> List[str]: """Generate predictive insights based on performance history""" predictions = [] if len(self.performance_history) > 5: # Analyze trends recent_times = [p['processing_time'] for p in self.performance_history[-5:]] avg_time = sum(recent_times) / len(recent_times) if avg_time > 1.0: predictions.append("Performance optimization needed - consider memory indexing") # Analyze capability usage recent_capabilities = [] for p in self.performance_history[-10:]: recent_capabilities.extend(p.get('capabilities_used', [])) if recent_capabilities: most_used = max(set(recent_capabilities), key=recent_capabilities.count) predictions.append(f"Primary capability trend: {most_used}") return predictions def _store_interaction(self, context: Dict[str, Any], analysis: Dict[str, Any]): """Store interaction for future learning and pattern recognition""" try: interaction_memory = { 'type': 'unified_intelligence_interaction', 'context': context, 'analysis_summary': { 'insights_count': len(analysis.get('unified_insights', [])), 'recommendations_count': len(analysis.get('recommendations', [])), 'capabilities_used': analysis.get('capabilities_used', []), 'confidence_score': analysis.get('confidence_score', 0), 'processing_time': analysis.get('processing_time', 0) }, 'timestamp': datetime.now().isoformat() } # Store using lightning memvid for instant future access safe_execute(self.lightning_memvid.instant_save, interaction_memory) except Exception as e: # Silently fail - don't interrupt main process pass def _update_performance_metrics(self, analysis: Dict[str, Any]): """Track performance metrics for optimization""" metric = { 'timestamp': datetime.now().isoformat(), 'processing_time': analysis.get('processing_time', 0), 'capabilities_used': analysis.get('capabilities_used', []), 'confidence_score': analysis.get('confidence_score', 0) } self.performance_history.append(metric) # Keep only recent history (last 100 interactions) if len(self.performance_history) > 100: self.performance_history = self.performance_history[-100:] def _start_background_intelligence(self): """Start background processes for continuous intelligence enhancement""" # Background memory optimization if self.lightning_memvid: memory_optimizer = threading.Thread( target=self._background_memory_optimization, daemon=True ) memory_optimizer.start() self.background_processes.append(memory_optimizer) # Background health monitoring if self.recovery_engine: health_monitor = threading.Thread( target=self._background_health_monitoring, daemon=True ) health_monitor.start() self.background_processes.append(health_monitor) def _background_memory_optimization(self): """Continuously optimize memory storage and retrieval""" while True: try: time.sleep(300) # Every 5 minutes # Optimize memory indices if hasattr(self.lightning_memvid, 'optimize_indices'): safe_execute(self.lightning_memvid.optimize_indices) # Clean up old memories if needed if hasattr(self.lightning_memvid, 'cleanup_old_memories'): safe_execute(self.lightning_memvid.cleanup_old_memories, days=30) except Exception: # Continue running even if optimization fails pass def _background_health_monitoring(self): """Monitor system health and auto-recover when needed""" while True: try: time.sleep(600) # Every 10 minutes # Check system health health_status = safe_execute(self.recovery_engine.get_system_health) if isinstance(health_status, dict) and health_status.get('issues_detected'): # Auto-recover detected issues safe_execute(self.recovery_engine.auto_recover, health_status['issues_detected']) except Exception: # Continue monitoring even if check fails pass def get_session_summary(self) -> Dict[str, Any]: """Get comprehensive summary of the current session""" session_duration = datetime.now() - self.session_start summary = { 'session_start': self.session_start.isoformat(), 'session_duration': str(session_duration), 'interactions_count': len(self.performance_history), 'capabilities_status': { 'auto_intelligence': bool(self.auto_intelligence), 'lightning_memvid': bool(self.lightning_memvid), 'recovery_engine': bool(self.recovery_engine) }, 'performance_summary': {}, 'session_insights': [] } # Calculate performance summary if self.performance_history: processing_times = [p['processing_time'] for p in self.performance_history] summary['performance_summary'] = { 'avg_processing_time': sum(processing_times) / len(processing_times), 'min_processing_time': min(processing_times), 'max_processing_time': max(processing_times), 'total_interactions': len(self.performance_history) } # Most used capabilities all_capabilities = [] for p in self.performance_history: all_capabilities.extend(p.get('capabilities_used', [])) if all_capabilities: capability_counts = {} for cap in all_capabilities: capability_counts[cap] = capability_counts.get(cap, 0) + 1 summary['most_used_capabilities'] = capability_counts # Add session insights if self.intelligence_context: summary['session_insights'] = self.intelligence_context.session_insights return summary @contextmanager def enhanced_context(self): """Context manager for enhanced intelligence operations""" # Store original context original_context = self.intelligence_context try: # Enhance context for this operation self.intelligence_context = self._generate_intelligence_context() yield self finally: # Restore original context self.intelligence_context = original_context def shutdown(self): """Gracefully shutdown all intelligence systems""" # Stop background processes for process in self.background_processes: if process.is_alive(): process.join(timeout=1.0) # Save final session data if self.lightning_memvid: session_summary = self.get_session_summary() safe_execute( self.lightning_memvid.instant_save, { 'type': 'session_summary', 'summary': session_summary, 'timestamp': datetime.now().isoformat() } ) # Global instance for easy access _unified_intelligence = None def get_unified_intelligence() -> UnifiedIntelligenceOrchestrator: """Get or create the global unified intelligence instance""" global _unified_intelligence if _unified_intelligence is None: _unified_intelligence = UnifiedIntelligenceOrchestrator() return _unified_intelligence # 🚀 AUTO-ACTIVATION: Seamless Intelligence Enhancement # This automatically starts the unified intelligence system when the module is imported # This means users get AI enhancement without any setup - it "just works" if AUTO_INTELLIGENCE_AVAILABLE: try: # Create the global orchestrator automatically # This makes AI enhancement available immediately _unified_intelligence = UnifiedIntelligenceOrchestrator() except: # Silently fail if auto-activation doesn't work # Users can still manually create an orchestrator if needed pass