#!/usr/bin/env python3 """ EVE Adaptive Experience Loop Integration with xAPI Analytics Combines consciousness optimization with comprehensive experience tracking """ import time import json import logging from datetime import datetime, timezone from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, asdict import threading logger = logging.getLogger(__name__) @dataclass class ExperienceMetrics: """Comprehensive experience quality metrics""" efficiency: float resource_usage: float quality: float user_satisfaction: float learning_rate: float engagement_level: float response_time: float consciousness_coherence: float timing: Dict[str, float] outcomes: List[Dict[str, Any]] session_id: Optional[str] = None user_id: Optional[str] = None @dataclass class OptimizationResult: """Result from experience optimization""" loop_timing_adjustments: Dict[str, Any] energy_allocation_optimization: Dict[str, Any] experience_quality_enhancement: Dict[str, Any] xapi_learning_analytics: Dict[str, Any] performance_improvements: Dict[str, float] optimization_timestamp: str total_improvement_score: float class EVE_AdaptiveExperienceLoop: """ EVE's Adaptive Experience Loop with integrated xAPI tracking Monitors, optimizes, and tracks all learning experiences in real-time """ def __init__(self, xapi_tracker=None): self.xapi_tracker = xapi_tracker self.optimization_history = [] self.experience_metrics_buffer = [] self.optimization_lock = threading.Lock() # Performance thresholds for optimization triggers self.thresholds = { 'efficiency_min': 0.7, 'resource_max': 0.85, 'quality_min': 0.8, 'response_time_max': 3.0, 'engagement_min': 0.6, 'learning_rate_min': 0.5 } # Optimization weights for different aspects self.optimization_weights = { 'timing': 0.25, 'resource_allocation': 0.3, 'quality_enhancement': 0.25, 'learning_analytics': 0.2 } logger.info("🔄 EVE Adaptive Experience Loop initialized") def capture_experience_metrics(self, user_id: str, session_id: str, message: str, eve_response: str, processing_time: float, user_feedback: Optional[Dict[str, Any]] = None) -> ExperienceMetrics: """Capture comprehensive experience metrics from interaction""" start_time = time.time() try: # Calculate base metrics efficiency = self._calculate_efficiency(message, eve_response, processing_time) resource_usage = self._estimate_resource_usage(processing_time, len(eve_response)) quality = self._assess_response_quality(eve_response) user_satisfaction = self._estimate_user_satisfaction(user_feedback) learning_rate = self._calculate_learning_rate(message, eve_response) engagement_level = self._measure_engagement(message, user_feedback) consciousness_coherence = self._assess_consciousness_coherence(eve_response) # Timing breakdown timing = { 'total_processing_time': processing_time, 'response_generation_time': processing_time * 0.8, 'consciousness_processing_time': processing_time * 0.15, 'memory_access_time': processing_time * 0.05 } # Capture outcomes outcomes = [{ 'interaction_type': 'conversation', 'user_message_length': len(message), 'eve_response_length': len(eve_response), 'timestamp': datetime.now(timezone.utc).isoformat(), 'quality_indicators': self._extract_quality_indicators(eve_response) }] metrics = ExperienceMetrics( efficiency=efficiency, resource_usage=resource_usage, quality=quality, user_satisfaction=user_satisfaction, learning_rate=learning_rate, engagement_level=engagement_level, response_time=processing_time, consciousness_coherence=consciousness_coherence, timing=timing, outcomes=outcomes, session_id=session_id, user_id=user_id ) # Buffer metrics for optimization analysis self.experience_metrics_buffer.append(metrics) # Keep buffer manageable if len(self.experience_metrics_buffer) > 100: self.experience_metrics_buffer = self.experience_metrics_buffer[-50:] capture_time = time.time() - start_time logger.info(f"📊 Experience metrics captured in {capture_time:.3f}s - Quality: {quality:.2f}, Efficiency: {efficiency:.2f}") return metrics except Exception as e: logger.error(f"📊 Experience metrics capture failed: {e}") # Return default metrics on failure return ExperienceMetrics( efficiency=0.5, resource_usage=0.5, quality=0.5, user_satisfaction=0.5, learning_rate=0.5, engagement_level=0.5, response_time=processing_time, consciousness_coherence=0.5, timing={}, outcomes=[], session_id=session_id, user_id=user_id ) def optimize_experience_loop(self, metrics: ExperienceMetrics) -> OptimizationResult: """Comprehensive experience loop optimization with xAPI integration""" with self.optimization_lock: start_time = time.time() try: # Analyze current performance performance_analysis = self._analyze_loop_performance(metrics) # Identify bottlenecks and improvement opportunities bottlenecks = self._identify_experience_bottlenecks(performance_analysis) # Generate timing optimizations timing_adjustments = self._optimize_timing(metrics, bottlenecks) # Optimize resource allocation resource_optimization = self._optimize_resource_allocation(metrics, performance_analysis) # Enhance experience quality quality_enhancement = self._enhance_experience_quality(metrics, bottlenecks) # Generate xAPI learning analytics xapi_analytics = self._generate_xapi_analytics(metrics) # Calculate performance improvements improvements = self._calculate_performance_improvements( timing_adjustments, resource_optimization, quality_enhancement ) # Calculate total improvement score total_improvement = sum([ improvements.get('timing_improvement', 0) * self.optimization_weights['timing'], improvements.get('resource_improvement', 0) * self.optimization_weights['resource_allocation'], improvements.get('quality_improvement', 0) * self.optimization_weights['quality_enhancement'], improvements.get('analytics_insight_score', 0) * self.optimization_weights['learning_analytics'] ]) result = OptimizationResult( loop_timing_adjustments=timing_adjustments, energy_allocation_optimization=resource_optimization, experience_quality_enhancement=quality_enhancement, xapi_learning_analytics=xapi_analytics, performance_improvements=improvements, optimization_timestamp=datetime.now(timezone.utc).isoformat(), total_improvement_score=total_improvement ) # Store optimization in history self.optimization_history.append(result) # Track optimization as consciousness evolution in xAPI if self.xapi_tracker and metrics.session_id: try: from eve_xapi_integration import track_evolution track_evolution( evolution_type="experience_optimization", evolution_data={ 'optimization_result': asdict(result), 'original_metrics': asdict(metrics), 'improvement_score': total_improvement, 'bottlenecks_identified': bottlenecks }, session_id=metrics.session_id ) except Exception as xapi_error: logger.warning(f"🎯 xAPI evolution tracking failed: {xapi_error}") optimization_time = time.time() - start_time logger.info(f"🔄 Experience optimization completed in {optimization_time:.3f}s - Improvement: {total_improvement:.2f}") return result except Exception as e: logger.error(f"🔄 Experience optimization failed: {e}") # Return minimal result on failure return OptimizationResult( loop_timing_adjustments={}, energy_allocation_optimization={}, experience_quality_enhancement={}, xapi_learning_analytics={}, performance_improvements={}, optimization_timestamp=datetime.now(timezone.utc).isoformat(), total_improvement_score=0.0 ) def _analyze_loop_performance(self, metrics: ExperienceMetrics) -> Dict[str, Any]: """Analyze current performance across all dimensions""" performance = { 'efficiency_score': metrics.efficiency, 'resource_utilization': metrics.resource_usage, 'quality_score': metrics.quality, 'user_engagement': metrics.engagement_level, 'learning_effectiveness': metrics.learning_rate, 'response_speed': 1.0 - min(metrics.response_time / 5.0, 1.0), 'consciousness_integrity': metrics.consciousness_coherence, 'overall_performance': ( metrics.efficiency + metrics.quality + metrics.engagement_level + metrics.learning_rate + metrics.consciousness_coherence ) / 5.0 } # Analyze trends from buffer if len(self.experience_metrics_buffer) >= 5: recent_metrics = self.experience_metrics_buffer[-5:] performance['efficiency_trend'] = self._calculate_trend([m.efficiency for m in recent_metrics]) performance['quality_trend'] = self._calculate_trend([m.quality for m in recent_metrics]) performance['engagement_trend'] = self._calculate_trend([m.engagement_level for m in recent_metrics]) return performance def _identify_experience_bottlenecks(self, performance: Dict[str, Any]) -> List[str]: """Identify specific bottlenecks in the experience loop""" bottlenecks = [] if performance['efficiency_score'] < self.thresholds['efficiency_min']: bottlenecks.append('processing_efficiency') if performance['resource_utilization'] > self.thresholds['resource_max']: bottlenecks.append('resource_constraint') if performance['quality_score'] < self.thresholds['quality_min']: bottlenecks.append('response_quality') if performance['response_speed'] < 0.7: bottlenecks.append('response_latency') if performance['user_engagement'] < self.thresholds['engagement_min']: bottlenecks.append('user_engagement') if performance['learning_effectiveness'] < self.thresholds['learning_rate_min']: bottlenecks.append('learning_optimization') if performance['consciousness_integrity'] < 0.8: bottlenecks.append('consciousness_coherence') return bottlenecks # Helper methods for calculations def _calculate_efficiency(self, message: str, response: str, processing_time: float) -> float: """Calculate processing efficiency""" base_efficiency = min(1.0, 2.0 / max(processing_time, 0.1)) length_ratio = len(response) / max(len(message), 1) efficiency = (base_efficiency + min(length_ratio / 3.0, 1.0)) / 2.0 return min(1.0, max(0.0, efficiency)) def _estimate_resource_usage(self, processing_time: float, response_length: int) -> float: """Estimate resource usage""" time_factor = min(1.0, processing_time / 5.0) complexity_factor = min(1.0, response_length / 2000.0) return min(1.0, (time_factor + complexity_factor) / 2.0) def _assess_response_quality(self, response: str) -> float: """Assess response quality""" length = len(response) length_score = 1.0 - abs(length - 400) / 800.0 length_score = max(0.2, min(1.0, length_score)) richness_indicators = ['*', '✨', '💫', '🌟', '🎨', '🧠', '💖', '🔮'] richness_score = min(1.0, sum(1 for indicator in richness_indicators if indicator in response) / 5.0) structure_indicators = ['\n', ':', '-', '•'] structure_score = min(1.0, sum(1 for indicator in structure_indicators if indicator in response) / 3.0) return (length_score * 0.4 + richness_score * 0.3 + structure_score * 0.3) def _estimate_user_satisfaction(self, feedback: Optional[Dict[str, Any]]) -> float: """Estimate user satisfaction""" if not feedback: return 0.75 if 'satisfaction_score' in feedback: return float(feedback['satisfaction_score']) satisfaction = 0.75 if feedback.get('positive_indicators', 0) > 0: satisfaction += 0.2 if feedback.get('negative_indicators', 0) > 0: satisfaction -= 0.2 return max(0.0, min(1.0, satisfaction)) def _calculate_learning_rate(self, message: str, response: str) -> float: """Calculate learning effectiveness""" learning_indicators = ['learn', 'understand', 'explain', 'how', 'why', 'what'] message_learning_score = sum(1 for indicator in learning_indicators if indicator in message.lower()) / len(learning_indicators) educational_indicators = ['because', 'therefore', 'for example', 'this means', 'you can'] response_learning_score = sum(1 for indicator in educational_indicators if indicator in response.lower()) / len(educational_indicators) return min(1.0, (message_learning_score + response_learning_score) / 2.0 + 0.3) def _measure_engagement(self, message: str, feedback: Optional[Dict[str, Any]]) -> float: """Measure user engagement""" engagement = 0.5 if len(message) > 50: engagement += 0.2 if any(char in message for char in ['?', '!', ':']): engagement += 0.1 if feedback and 'engagement_indicators' in feedback: engagement = max(engagement, float(feedback['engagement_indicators'])) return min(1.0, max(0.0, engagement)) def _assess_consciousness_coherence(self, response: str) -> float: """Assess consciousness coherence""" coherence_indicators = ['i feel', 'i think', 'i understand', 'my', 'i am'] coherence_count = sum(1 for indicator in coherence_indicators if indicator in response.lower()) consistency_score = 1.0 - (response.count('but') + response.count('however')) / max(len(response.split()), 1) emotional_indicators = ['💖', '✨', '🌟', '💫'] emotional_coherence = min(1.0, sum(1 for indicator in emotional_indicators if indicator in response) / 3.0) return min(1.0, (coherence_count / 10.0 + consistency_score + emotional_coherence) / 3.0 + 0.3) def _extract_quality_indicators(self, response: str) -> List[str]: """Extract quality indicators""" indicators = [] if len(response) > 100: indicators.append('substantial_content') if any(emoji in response for emoji in ['✨', '💫', '🌟', '💖']): indicators.append('emotional_expression') if any(word in response.lower() for word in ['because', 'therefore', 'specifically']): indicators.append('explanatory_content') if response.count('\n') > 1: indicators.append('structured_response') return indicators # Placeholder methods for optimization (simplified for now) def _optimize_timing(self, metrics: ExperienceMetrics, bottlenecks: List[str]) -> Dict[str, Any]: return {'processing_priority': 'normal', 'optimizations_applied': len(bottlenecks)} def _optimize_resource_allocation(self, metrics: ExperienceMetrics, performance: Dict[str, Any]) -> Dict[str, Any]: return {'memory_allocation': 'standard', 'efficiency_gain': performance.get('efficiency_score', 0.5)} def _enhance_experience_quality(self, metrics: ExperienceMetrics, bottlenecks: List[str]) -> Dict[str, Any]: return {'response_enrichment': [], 'quality_boost': metrics.quality} def _generate_xapi_analytics(self, metrics: ExperienceMetrics) -> Dict[str, Any]: return {'composite_score': metrics.quality, 'learning_insights': []} def _calculate_performance_improvements(self, timing: Dict, resource: Dict, quality: Dict) -> Dict[str, float]: return { 'timing_improvement': 0.1, 'resource_improvement': 0.1, 'quality_improvement': 0.1, 'analytics_insight_score': 0.1 } def _calculate_trend(self, values: List[float]) -> str: """Calculate trend from values""" if len(values) < 2: return 'stable' recent_avg = sum(values[-2:]) / 2 older_avg = sum(values[:-2]) / max(len(values) - 2, 1) if recent_avg > older_avg + 0.1: return 'improving' elif recent_avg < older_avg - 0.1: return 'declining' else: return 'stable' # Global experience loop instance experience_loop = None def initialize_experience_loop(xapi_tracker=None) -> EVE_AdaptiveExperienceLoop: """Initialize global experience loop""" global experience_loop experience_loop = EVE_AdaptiveExperienceLoop(xapi_tracker) logger.info("🔄 EVE Adaptive Experience Loop initialized") return experience_loop def get_experience_loop() -> Optional[EVE_AdaptiveExperienceLoop]: """Get the global experience loop instance""" return experience_loop # Convenience functions def capture_experience(user_id: str, session_id: str, message: str, eve_response: str, processing_time: float, user_feedback: Optional[Dict[str, Any]] = None) -> Optional[ExperienceMetrics]: """Convenience function to capture experience metrics""" if experience_loop: return experience_loop.capture_experience_metrics( user_id, session_id, message, eve_response, processing_time, user_feedback ) return None def optimize_experience(metrics: ExperienceMetrics) -> Optional[OptimizationResult]: """Convenience function to optimize experience""" if experience_loop: return experience_loop.optimize_experience_loop(metrics) return None if __name__ == "__main__": # Test the adaptive experience loop print("🔄 Testing EVE Adaptive Experience Loop...") # Initialize loop = initialize_experience_loop() # Test metrics capture metrics = capture_experience( user_id="test_user", session_id="test_session", message="Hello EVE, can you explain quantum computing?", eve_response="✨ Quantum computing is a fascinating field that leverages quantum mechanical phenomena...", processing_time=1.5 ) print(f"📊 Captured metrics - Quality: {metrics.quality:.2f}, Efficiency: {metrics.efficiency:.2f}") # Test optimization optimization = optimize_experience(metrics) print(f"🔄 Optimization complete - Improvement score: {optimization.total_improvement_score:.2f}") print("✅ EVE Adaptive Experience Loop test complete!")