""" Metrics Dashboard & Optimization System ==================================== Advanced metrics computation and dashboard system for context engineering, including real-time metrics, optimization recommendations, and performance monitoring. """ import asyncio import json import logging from datetime import datetime, timedelta from typing import Dict, List, Any, Optional, Set, Tuple, Union, Callable from dataclasses import dataclass, field, asdict from enum import Enum import numpy as np from collections import defaultdict, deque import threading from concurrent.futures import ThreadPoolExecutor import time import statistics from ai_agent_framework.core.context_engineering_agent import ( ContextElement, ContextModality, ContextDimension, ContextEngineeringAgent ) logger = logging.getLogger(__name__) class MetricType(Enum): """Types of metrics to compute.""" CONTEXT_RETENTION_ACCURACY = "context_retention_accuracy" CONTEXT_RELEVANCE_PRECISION = "context_relevance_precision" CONTEXT_ADAPTATION_SPEED = "context_adaptation_speed" CONTEXTUAL_REASONING_QUALITY = "contextual_reasoning_quality" USER_SATISFACTION = "user_satisfaction" CONTEXT_UTILIZATION_EFFICIENCY = "context_utilization_efficiency" PROCESSING_LATENCY = "processing_latency" MEMORY_USAGE = "memory_usage" ERROR_RATE = "error_rate" SYSTEM_THROUGHPUT = "system_throughput" class OptimizationTarget(Enum): """Optimization targets.""" PERFORMANCE = "performance" ACCURACY = "accuracy" EFFICIENCY = "efficiency" USER_EXPERIENCE = "user_experience" RESOURCE_USAGE = "resource_usage" ADAPTABILITY = "adaptability" class AlertLevel(Enum): """Alert severity levels.""" INFO = "info" WARNING = "warning" ERROR = "error" CRITICAL = "critical" @dataclass class MetricValue: """Individual metric value with metadata.""" metric_type: MetricType value: float timestamp: datetime confidence: float sample_size: int metadata: Dict[str, Any] def __post_init__(self): if not self.timestamp: self.timestamp = datetime.utcnow() if not self.metadata: self.metadata = {} @dataclass class MetricSeries: """Time series of metric values.""" metric_type: MetricType values: List[MetricValue] window_size: int last_updated: datetime aggregation_method: str def __post_init__(self): if not self.values: self.values = [] if not self.last_updated: self.last_updated = datetime.utcnow() if not self.aggregation_method: self.aggregation_method = "mean" @dataclass class OptimizationRecommendation: """System optimization recommendation.""" recommendation_id: str target: OptimizationTarget priority: int description: str expected_impact: float implementation_effort: str metrics_affected: List[MetricType] created_at: datetime def __post_init__(self): if not self.recommendation_id: self.recommendation_id = f"opt_rec_{int(time.time())}" if not self.created_at: self.created_at = datetime.utcnow() @dataclass class Alert: """System alert.""" alert_id: str level: AlertLevel title: str description: str affected_metrics: List[MetricType] threshold_value: float current_value: float created_at: datetime acknowledged: bool = False def __post_init__(self): if not self.alert_id: self.alert_id = f"alert_{int(time.time())}" if not self.created_at: self.created_at = datetime.utcnow() class MetricsCollector: """Advanced metrics collection and computation engine.""" def __init__(self, max_series_length: int = 1000): self.max_series_length = max_series_length self.metric_series = {} # MetricType -> MetricSeries self.real_time_metrics = {} # current metric values self.computation_functions = { MetricType.CONTEXT_RETENTION_ACCURACY: self._compute_context_retention_accuracy, MetricType.CONTEXT_RELEVANCE_PRECISION: self._compute_context_relevance_precision, MetricType.CONTEXT_ADAPTATION_SPEED: self._compute_context_adaptation_speed, MetricType.CONTEXTUAL_REASONING_QUALITY: self._compute_contextual_reasoning_quality, MetricType.USER_SATISFACTION: self._compute_user_satisfaction, MetricType.CONTEXT_UTILIZATION_EFFICIENCY: self._compute_context_utilization_efficiency, MetricType.PROCESSING_LATENCY: self._compute_processing_latency, MetricType.MEMORY_USAGE: self._compute_memory_usage, MetricType.ERROR_RATE: self._compute_error_rate, MetricType.SYSTEM_THROUGHPUT: self._compute_system_throughput } # Performance tracking self.performance_history = deque(maxlen=100) self.last_computation_time = {} # Thread safety self._lock = threading.RLock() async def compute_metric( self, metric_type: MetricType, context_data: Dict[str, Any], agent_data: Dict[str, Any] = None ) -> MetricValue: """Compute a specific metric.""" if agent_data is None: agent_data = {} try: start_time = time.time() # Get computation function computation_func = self.computation_functions.get(metric_type) if not computation_func: raise ValueError(f"No computation function for {metric_type}") # Compute metric result = await computation_func(context_data, agent_data) # Create metric value metric_value = MetricValue( metric_type=metric_type, value=result["value"], timestamp=datetime.utcnow(), confidence=result.get("confidence", 0.8), sample_size=result.get("sample_size", 1), metadata=result.get("metadata", {}) ) # Store in series await self._store_metric_value(metric_value) # Update real-time metrics self.real_time_metrics[metric_type] = metric_value # Record computation time computation_time = time.time() - start_time self.last_computation_time[metric_type] = computation_time return metric_value except Exception as e: logger.error(f"Failed to compute metric {metric_type}: {e}") # Return default metric value return MetricValue( metric_type=metric_type, value=0.0, timestamp=datetime.utcnow(), confidence=0.0, sample_size=0, metadata={"error": str(e)} ) async def compute_all_metrics( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] = None ) -> Dict[MetricType, MetricValue]: """Compute all available metrics.""" if agent_data is None: agent_data = {} results = {} # Compute metrics concurrently tasks = [] for metric_type in MetricType: task = self.compute_metric(metric_type, context_data, agent_data) tasks.append(task) computed_metrics = await asyncio.gather(*tasks, return_exceptions=True) for metric_type, result in zip(MetricType, computed_metrics): if isinstance(result, Exception): logger.error(f"Error computing {metric_type}: {result}") continue results[metric_type] = result return results async def get_metric_series( self, metric_type: MetricType, time_window: Optional[timedelta] = None, aggregation: str = "mean" ) -> List[MetricValue]: """Get metric time series.""" if metric_type not in self.metric_series: return [] series = self.metric_series[metric_type] if time_window: cutoff_time = datetime.utcnow() - time_window filtered_values = [ mv for mv in series.values if mv.timestamp >= cutoff_time ] return filtered_values return series.values async def get_real_time_metrics(self) -> Dict[MetricType, MetricValue]: """Get current real-time metrics.""" return self.real_time_metrics.copy() async def get_metric_statistics( self, metric_type: MetricType, time_window: timedelta = timedelta(hours=24) ) -> Dict[str, float]: """Get statistical summary of metric.""" series = await self.get_metric_series(metric_type, time_window) if not series: return {} values = [mv.value for mv in series] return { "count": len(values), "mean": np.mean(values), "median": np.median(values), "std": np.std(values), "min": np.min(values), "max": np.max(values), "p25": np.percentile(values, 25), "p75": np.percentile(values, 75), "p95": np.percentile(values, 95) } # Individual metric computation functions async def _compute_context_retention_accuracy( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute context retention accuracy.""" # Context retention measures how well important context is preserved contexts = context_data.get("contexts", []) if not contexts: return {"value": 0.0, "confidence": 0.0, "sample_size": 0} # Simplified retention calculation total_contexts = len(contexts) retained_contexts = sum(1 for ctx in contexts if ctx.get("retained", True)) accuracy = retained_contexts / max(total_contexts, 1) return { "value": accuracy, "confidence": min(1.0, total_contexts / 10), # Higher confidence with more data "sample_size": total_contexts, "metadata": { "total_contexts": total_contexts, "retained_contexts": retained_contexts } } async def _compute_context_relevance_precision( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute context relevance precision.""" contexts = context_data.get("contexts", []) if not contexts: return {"value": 0.0, "confidence": 0.0, "sample_size": 0} # Calculate precision based on relevance scores relevance_scores = [ctx.get("relevance_score", 0) for ctx in contexts] precision_scores = [ctx.get("precision_score", 0) for ctx in contexts] if not precision_scores: return {"value": 0.0, "confidence": 0.0, "sample_size": len(contexts)} # Weighted precision calculation total_precision = sum(precision_scores) max_possible_precision = len(contexts) precision = total_precision / max_possible_precision # Additional relevance factor relevance_factor = np.mean(relevance_scores) if relevance_scores else 0 combined_precision = (precision * 0.7) + (relevance_factor * 0.3) return { "value": combined_precision, "confidence": min(1.0, len(contexts) / 5), "sample_size": len(contexts), "metadata": { "avg_relevance": relevance_factor, "raw_precision": precision } } async def _compute_context_adaptation_speed( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute context adaptation speed.""" # Adaptation speed measures how quickly the system adapts to new contexts adaptations = context_data.get("adaptations", []) if not adaptations: return {"value": 0.5, "confidence": 0.0, "sample_size": 0} # Calculate adaptation times adaptation_times = [] for adaptation in adaptations: start_time = adaptation.get("start_time") end_time = adaptation.get("end_time") if start_time and end_time: duration = (end_time - start_time).total_seconds() adaptation_times.append(duration) if not adaptation_times: return {"value": 0.5, "confidence": 0.0, "sample_size": len(adaptations)} # Convert to speed score (lower time = higher speed) avg_duration = np.mean(adaptation_times) speed_score = 1.0 / (1.0 + avg_duration / 60) # Normalize by 60 seconds return { "value": speed_score, "confidence": min(1.0, len(adaptations) / 5), "sample_size": len(adaptations), "metadata": { "avg_duration_seconds": avg_duration, "min_duration": min(adaptation_times), "max_duration": max(adaptation_times) } } async def _compute_contextual_reasoning_quality( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute contextual reasoning quality.""" # Reasoning quality based on decision outcomes and context usage reasoning_decisions = context_data.get("reasoning_decisions", []) if not reasoning_decisions: return {"value": 0.5, "confidence": 0.0, "sample_size": 0} # Analyze reasoning quality factors successful_reasoning = 0 context_aware_reasoning = 0 for decision in reasoning_decisions: # Success factor if decision.get("successful", False): successful_reasoning += 1 # Context awareness factor if decision.get("context_aware", False): context_aware_reasoning += 1 # Calculate quality score success_rate = successful_reasoning / len(reasoning_decisions) context_awareness_rate = context_aware_reasoning / len(reasoning_decisions) quality_score = (success_rate * 0.6) + (context_awareness_rate * 0.4) return { "value": quality_score, "confidence": min(1.0, len(reasoning_decisions) / 10), "sample_size": len(reasoning_decisions), "metadata": { "success_rate": success_rate, "context_awareness_rate": context_awareness_rate } } async def _compute_user_satisfaction( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute user satisfaction metrics.""" # User satisfaction based on interaction outcomes user_interactions = context_data.get("user_interactions", []) if not user_interactions: return {"value": 0.5, "confidence": 0.0, "sample_size": 0} satisfaction_scores = [] completion_rates = [] for interaction in user_interactions: # Explicit satisfaction scores if "satisfaction_score" in interaction: satisfaction_scores.append(interaction["satisfaction_score"]) # Implicit satisfaction indicators completion_rate = interaction.get("completion_rate", 0.5) completion_rates.append(completion_rate) # Calculate overall satisfaction all_scores = satisfaction_scores + completion_rates if not all_scores: return {"value": 0.5, "confidence": 0.0, "sample_size": len(user_interactions)} avg_satisfaction = np.mean(all_scores) return { "value": avg_satisfaction, "confidence": min(1.0, len(all_scores) / 10), "sample_size": len(all_scores), "metadata": { "explicit_scores": len(satisfaction_scores), "implicit_scores": len(completion_rates), "satisfaction_variance": np.var(all_scores) if len(all_scores) > 1 else 0 } } async def _compute_context_utilization_efficiency( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute context utilization efficiency.""" # Efficiency measures how well available context is utilized contexts = context_data.get("contexts", []) if not contexts: return {"value": 0.0, "confidence": 0.0, "sample_size": 0} utilization_scores = [] for ctx in contexts: # Context usage frequency usage_count = ctx.get("usage_count", 0) available_count = ctx.get("available_count", 1) utilization = usage_count / max(available_count, 1) utilization_scores.append(min(1.0, utilization)) if not utilization_scores: return {"value": 0.0, "confidence": 0.0, "sample_size": len(contexts)} avg_utilization = np.mean(utilization_scores) return { "value": avg_utilization, "confidence": min(1.0, len(contexts) / 8), "sample_size": len(contexts), "metadata": { "total_contexts": len(contexts), "avg_utilization": avg_utilization, "underutilized_contexts": sum(1 for u in utilization_scores if u < 0.3) } } async def _compute_processing_latency( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute processing latency metrics.""" # Processing time analysis processing_times = context_data.get("processing_times", []) if not processing_times: return {"value": 0.5, "confidence": 0.0, "sample_size": 0} avg_processing_time = np.mean(processing_times) # Convert to performance score (lower latency = higher score) max_acceptable_latency = 1000 # 1 second latency_score = max(0.0, 1.0 - (avg_processing_time / max_acceptable_latency)) return { "value": latency_score, "confidence": min(1.0, len(processing_times) / 20), "sample_size": len(processing_times), "metadata": { "avg_processing_time_ms": avg_processing_time, "min_processing_time": min(processing_times), "max_processing_time": max(processing_times) } } async def _compute_memory_usage( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute memory usage efficiency.""" # Memory usage analysis memory_usage = context_data.get("memory_usage", {}) if not memory_usage: return {"value": 0.5, "confidence": 0.0, "sample_size": 0} current_usage = memory_usage.get("current_mb", 0) max_usage = memory_usage.get("max_mb", 1000) # Usage efficiency (lower usage = higher efficiency) usage_ratio = current_usage / max(max_usage, 1) efficiency_score = max(0.0, 1.0 - usage_ratio) return { "value": efficiency_score, "confidence": 0.8, # Memory metrics are generally reliable "sample_size": 1, "metadata": { "current_usage_mb": current_usage, "max_usage_mb": max_usage, "usage_ratio": usage_ratio } } async def _compute_error_rate( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute system error rate.""" # Error analysis total_operations = context_data.get("total_operations", 0) error_count = context_data.get("error_count", 0) if total_operations == 0: return {"value": 1.0, "confidence": 0.0, "sample_size": 0} error_rate = error_count / total_operations reliability_score = max(0.0, 1.0 - error_rate) return { "value": reliability_score, "confidence": min(1.0, total_operations / 50), "sample_size": total_operations, "metadata": { "total_operations": total_operations, "error_count": error_count, "raw_error_rate": error_rate } } async def _compute_system_throughput( self, context_data: Dict[str, Any], agent_data: Dict[str, Any] ) -> Dict[str, Any]: """Compute system throughput.""" # Throughput analysis operations_per_minute = context_data.get("operations_per_minute", 0) target_throughput = context_data.get("target_throughput", 100) # Throughput efficiency throughput_ratio = operations_per_minute / max(target_throughput, 1) efficiency_score = min(1.0, throughput_ratio) return { "value": efficiency_score, "confidence": 0.7, "sample_size": 1, "metadata": { "operations_per_minute": operations_per_minute, "target_throughput": target_throughput, "throughput_ratio": throughput_ratio } } # Helper methods async def _store_metric_value(self, metric_value: MetricValue) -> None: """Store metric value in time series.""" with self._lock: metric_type = metric_value.metric_type if metric_type not in self.metric_series: self.metric_series[metric_type] = MetricSeries( metric_type=metric_type, values=[], window_size=self.max_series_length, last_updated=datetime.utcnow(), aggregation_method="mean" ) series = self.metric_series[metric_type] series.values.append(metric_value) series.last_updated = datetime.utcnow() # Maintain window size if len(series.values) > self.max_series_length: series.values = series.values[-self.max_series_length:] class OptimizationEngine: """Advanced optimization engine for context engineering systems.""" def __init__(self): self.optimization_strategies = { OptimizationTarget.PERFORMANCE: self._optimize_performance, OptimizationTarget.ACCURACY: self._optimize_accuracy, OptimizationTarget.EFFICIENCY: self._optimize_efficiency, OptimizationTarget.USER_EXPERIENCE: self._optimize_user_experience, OptimizationTarget.RESOURCE_USAGE: self._optimize_resource_usage, OptimizationTarget.ADAPTABILITY: self._optimize_adaptability } self.current_recommendations = [] self.optimization_history = deque(maxlen=50) async def generate_optimization_recommendations( self, metrics: Dict[MetricType, MetricValue], context_data: Dict[str, Any] = None ) -> List[OptimizationRecommendation]: """Generate optimization recommendations based on current metrics.""" if context_data is None: context_data = {} recommendations = [] # Analyze each metric for optimization opportunities for metric_type, metric_value in metrics.items(): metric_recommendations = await self._analyze_metric_for_optimization( metric_type, metric_value, context_data ) recommendations.extend(metric_recommendations) # Cross-metric analysis cross_recommendations = await self._analyze_cross_metric_optimizations(metrics) recommendations.extend(cross_recommendations) # Rank and filter recommendations ranked_recommendations = await self._rank_recommendations(recommendations) self.current_recommendations = ranked_recommendations return ranked_recommendations async def apply_optimization( self, recommendation: OptimizationRecommendation, agent_system: Any ) -> Dict[str, Any]: """Apply an optimization recommendation.""" optimization_target = recommendation.target if optimization_target in self.optimization_strategies: optimization_func = self.optimization_strategies[optimization_target] result = await optimization_func(recommendation, agent_system) # Record optimization attempt self.optimization_history.append({ "recommendation_id": recommendation.recommendation_id, "timestamp": datetime.utcnow(), "result": result, "target": optimization_target.value }) return result return {"success": False, "reason": "unknown_optimization_target"} async def get_optimization_status(self) -> Dict[str, Any]: """Get current optimization status and history.""" recent_optimizations = list(self.optimization_history)[-10:] # Last 10 return { "current_recommendations": len(self.current_recommendations), "recent_optimizations": recent_optimizations, "total_optimization_attempts": len(self.optimization_history), "optimization_success_rate": self._calculate_success_rate() } def _calculate_success_rate(self) -> float: """Calculate optimization success rate.""" if not self.optimization_history: return 0.0 successful_attempts = sum( 1 for opt in self.optimization_history if opt.get("result", {}).get("success", False) ) return successful_attempts / len(self.optimization_history) # Individual optimization strategies async def _optimize_performance( self, recommendation: OptimizationRecommendation, agent_system: Any ) -> Dict[str, Any]: """Optimize for performance.""" # Performance optimization strategies strategies = { "cache_frequently_used_context": self._optimize_cache_strategy, "parallel_processing": self._optimize_parallel_processing, "reduce_computation_overhead": self._optimize_computation_overhead, "memory_pool_optimization": self._optimize_memory_pools } strategy_func = strategies.get(recommendation.description) if strategy_func: return await strategy_func(agent_system) return {"success": False, "reason": "unknown_performance_strategy"} async def _optimize_accuracy( self, recommendation: OptimizationRecommendation, agent_system: Any ) -> Dict[str, Any]: """Optimize for accuracy.""" # Accuracy optimization strategies strategies = { "enhance_context_validation": self._optimize_context_validation, "improve_relevance_scoring": self._optimize_relevance_scoring, "refine_reasoning_algorithms": self._optimize_reasoning_algorithms } strategy_func = strategies.get(recommendation.description) if strategy_func: return await strategy_func(agent_system) return {"success": False, "reason": "unknown_accuracy_strategy"} async def _optimize_efficiency( self, recommendation: OptimizationRecommendation, agent_system: Any ) -> Dict[str, Any]: """Optimize for efficiency.""" # Efficiency optimization strategies strategies = { "context_compression": self._optimize_context_compression, "resource_pooling": self._optimize_resource_pooling, "batch_processing": self._optimize_batch_processing } strategy_func = strategies.get(recommendation.description) if strategy_func: return await strategy_func(agent_system) return {"success": False, "reason": "unknown_efficiency_strategy"} async def _optimize_user_experience( self, recommendation: OptimizationRecommendation, agent_system: Any ) -> Dict[str, Any]: """Optimize for user experience.""" # User experience optimization strategies strategies = { "reduce_response_latency": self._optimize_response_latency, "improve_interaction_flow": self._optimize_interaction_flow, "enhance_feedback_mechanisms": self._optimize_feedback_mechanisms } strategy_func = strategies.get(recommendation.description) if strategy_func: return await strategy_func(agent_system) return {"success": False, "reason": "unknown_ux_strategy"} async def _optimize_resource_usage( self, recommendation: OptimizationRecommendation, agent_system: Any ) -> Dict[str, Any]: """Optimize for resource usage.""" # Resource optimization strategies strategies = { "memory_optimization": self._optimize_memory_usage, "cpu_optimization": self._optimize_cpu_usage, "storage_optimization": self._optimize_storage_usage } strategy_func = strategies.get(recommendation.description) if strategy_func: return await strategy_func(agent_system) return {"success": False, "reason": "unknown_resource_strategy"} async def _optimize_adaptability( self, recommendation: OptimizationRecommendation, agent_system: Any ) -> Dict[str, Any]: """Optimize for adaptability.""" # Adaptability optimization strategies strategies = { "improve_learning_rate": self._optimize_learning_rate, "enhance_pattern_recognition": self._optimize_pattern_recognition, "adaptive_parameter_tuning": self._optimize_parameter_tuning } strategy_func = strategies.get(recommendation.description) if strategy_func: return await strategy_func(agent_system) return {"success": False, "reason": "unknown_adaptability_strategy"} # Analysis methods async def _analyze_metric_for_optimization( self, metric_type: MetricType, metric_value: MetricValue, context_data: Dict[str, Any] ) -> List[OptimizationRecommendation]: """Analyze individual metric for optimization opportunities.""" recommendations = [] # Thresholds for optimization thresholds = { MetricType.CONTEXT_RETENTION_ACCURACY: {"poor": 0.6, "good": 0.8}, MetricType.CONTEXT_RELEVANCE_PRECISION: {"poor": 0.7, "good": 0.9}, MetricType.CONTEXT_ADAPTATION_SPEED: {"poor": 0.5, "good": 0.8}, MetricType.CONTEXTUAL_REASONING_QUALITY: {"poor": 0.6, "good": 0.85}, MetricType.USER_SATISFACTION: {"poor": 0.7, "good": 0.9}, MetricType.PROCESSING_LATENCY: {"poor": 0.4, "good": 0.7}, MetricType.ERROR_RATE: {"poor": 0.8, "good": 0.95} } threshold_data = thresholds.get(metric_type) if not threshold_data: return recommendations # Check if optimization is needed if metric_value.value < threshold_data["poor"]: # Generate optimization recommendation recommendation = await self._generate_metric_recommendation( metric_type, metric_value, threshold_data ) if recommendation: recommendations.append(recommendation) return recommendations async def _generate_metric_recommendation( self, metric_type: MetricType, metric_value: MetricValue, threshold_data: Dict[str, float] ) -> Optional[OptimizationRecommendation]: """Generate optimization recommendation for a metric.""" # Map metrics to optimization targets and strategies metric_mappings = { MetricType.CONTEXT_RETENTION_ACCURACY: { "target": OptimizationTarget.ACCURACY, "description": "enhance_context_validation", "priority": 8 }, MetricType.CONTEXT_RELEVANCE_PRECISION: { "target": OptimizationTarget.ACCURACY, "description": "improve_relevance_scoring", "priority": 7 }, MetricType.PROCESSING_LATENCY: { "target": OptimizationTarget.PERFORMANCE, "description": "cache_frequently_used_context", "priority": 9 }, MetricType.USER_SATISFACTION: { "target": OptimizationTarget.USER_EXPERIENCE, "description": "reduce_response_latency", "priority": 10 }, MetricType.ERROR_RATE: { "target": OptimizationTarget.ACCURACY, "description": "enhance_error_handling", "priority": 6 } } mapping = metric_mappings.get(metric_type) if not mapping: return None # Calculate expected impact impact = max(0.1, threshold_data["good"] - metric_value.value) return OptimizationRecommendation( recommendation_id=f"opt_{metric_type.value}_{int(time.time())}", target=mapping["target"], priority=mapping["priority"], description=mapping["description"], expected_impact=impact, implementation_effort="medium", metrics_affected=[metric_type], created_at=datetime.utcnow() ) async def _analyze_cross_metric_optimizations( self, metrics: Dict[MetricType, MetricValue] ) -> List[OptimizationRecommendation]: """Analyze cross-metric optimization opportunities.""" recommendations = [] # Performance-Accuracy trade-offs latency = metrics.get(MetricType.PROCESSING_LATUS) accuracy = metrics.get(MetricType.CONTEXT_RETENTION_ACCURACY) if latency and accuracy: if latency.value < 0.5 and accuracy.value > 0.8: # Good accuracy but poor performance - optimize for performance recommendation = OptimizationRecommendation( recommendation_id=f"perf_acc_opt_{int(time.time())}", target=OptimizationTarget.PERFORMANCE, priority=6, description="parallel_processing", expected_impact=0.3, implementation_effort="high", metrics_affected=[MetricType.PROCESSING_LATENCY], created_at=datetime.utcnow() ) recommendations.append(recommendation) # Efficiency-User Experience balance utilization = metrics.get(MetricType.CONTEXT_UTILIZATION_EFFICIENCY) satisfaction = metrics.get(MetricType.USER_SATISFACTION) if utilization and satisfaction: if utilization.value > 0.9 and satisfaction.value < 0.7: # High efficiency but low satisfaction - improve UX recommendation = OptimizationRecommendation( recommendation_id=f"eff_ux_opt_{int(time.time())}", target=OptimizationTarget.USER_EXPERIENCE, priority=7, description="improve_interaction_flow", expected_impact=0.4, implementation_effort="medium", metrics_affected=[MetricType.USER_SATISFACTION], created_at=datetime.utcnow() ) recommendations.append(recommendation) return recommendations async def _rank_recommendations( self, recommendations: List[OptimizationRecommendation] ) -> List[OptimizationRecommendation]: """Rank recommendations by priority and impact.""" # Score each recommendation scored_recommendations = [] for rec in recommendations: # Calculate composite score priority_score = rec.priority / 10.0 impact_score = rec.expected_impact effort_penalty = {"low": 0.0, "medium": -0.1, "high": -0.2}.get( rec.implementation_effort, -0.1 ) composite_score = priority_score * 0.4 + impact_score * 0.4 + effort_penalty scored_recommendations.append((composite_score, rec)) # Sort by score (highest first) scored_recommendations.sort(key=lambda x: x[0], reverse=True) return [rec for _, rec in scored_recommendations] # Placeholder optimization implementations async def _optimize_cache_strategy(self, agent_system: Any) -> Dict[str, Any]: """Optimize caching strategy.""" return {"success": True, "improvement": "cache_hit_rate_increased"} async def _optimize_parallel_processing(self, agent_system: Any) -> Dict[str, Any]: """Optimize parallel processing.""" return {"success": True, "improvement": "processing_speed_increased"} async def _optimize_computation_overhead(self, agent_system: Any) -> Dict[str, Any]: """Optimize computation overhead.""" return {"success": True, "improvement": "computation_overhead_reduced"} async def _optimize_memory_pools(self, agent_system: Any) -> Dict[str, Any]: """Optimize memory pools.""" return {"success": True, "improvement": "memory_efficiency_improved"} async def _optimize_context_validation(self, agent_system: Any) -> Dict[str, Any]: """Optimize context validation.""" return {"success": True, "improvement": "validation_accuracy_increased"} async def _optimize_relevance_scoring(self, agent_system: Any) -> Dict[str, Any]: """Optimize relevance scoring.""" return {"success": True, "improvement": "relevance_precision_increased"} async def _optimize_reasoning_algorithms(self, agent_system: Any) -> Dict[str, Any]: """Optimize reasoning algorithms.""" return {"success": True, "improvement": "reasoning_quality_increased"} async def _optimize_context_compression(self, agent_system: Any) -> Dict[str, Any]: """Optimize context compression.""" return {"success": True, "improvement": "compression_efficiency_increased"} async def _optimize_resource_pooling(self, agent_system: Any) -> Dict[str, Any]: """Optimize resource pooling.""" return {"success": True, "improvement": "resource_utilization_improved"} async def _optimize_batch_processing(self, agent_system: Any) -> Dict[str, Any]: """Optimize batch processing.""" return {"success": True, "improvement": "batch_efficiency_increased"} async def _optimize_response_latency(self, agent_system: Any) -> Dict[str, Any]: """Optimize response latency.""" return {"success": True, "improvement": "response_time_reduced"} async def _optimize_interaction_flow(self, agent_system: Any) -> Dict[str, Any]: """Optimize interaction flow.""" return {"success": True, "improvement": "interaction_ux_improved"} async def _optimize_feedback_mechanisms(self, agent_system: Any) -> Dict[str, Any]: """Optimize feedback mechanisms.""" return {"success": True, "improvement": "feedback_quality_increased"} async def _optimize_memory_usage(self, agent_system: Any) -> Dict[str, Any]: """Optimize memory usage.""" return {"success": True, "improvement": "memory_usage_optimized"} async def _optimize_cpu_usage(self, agent_system: Any) -> Dict[str, Any]: """Optimize CPU usage.""" return {"success": True, "improvement": "cpu_efficiency_improved"} async def _optimize_storage_usage(self, agent_system: Any) -> Dict[str, Any]: """Optimize storage usage.""" return {"success": True, "improvement": "storage_efficiency_increased"} async def _optimize_learning_rate(self, agent_system: Any) -> Dict[str, Any]: """Optimize learning rate.""" return {"success": True, "improvement": "learning_speed_increased"} async def _optimize_pattern_recognition(self, agent_system: Any) -> Dict[str, Any]: """Optimize pattern recognition.""" return {"success": True, "improvement": "pattern_recognition_improved"} async def _optimize_parameter_tuning(self, agent_system: Any) -> Dict[str, Any]: """Optimize parameter tuning.""" return {"success": True, "improvement": "parameter_optimization_increased"} class MetricsDashboard: """Comprehensive metrics dashboard and monitoring system.""" def __init__(self): self.metrics_collector = MetricsCollector() self.optimization_engine = OptimizationEngine() self.alerts = {} # Alert ID -> Alert self.dashboard_config = { "refresh_interval": 30, # seconds "alert_thresholds": { MetricType.ERROR_RATE: {"warning": 0.1, "critical": 0.2}, MetricType.PROCESSING_LATENCY: {"warning": 0.3, "critical": 0.2}, MetricType.USER_SATISFACTION: {"warning": 0.6, "critical": 0.4} }, "display_preferences": { "show_real_time_metrics": True, "show_historical_trends": True, "show_optimization_recommendations": True } } self._lock = threading.RLock() async def get_dashboard_data( self, include_recommendations: bool = True, include_alerts: bool = True, time_window: timedelta = timedelta(hours=24) ) -> Dict[str, Any]: """Get comprehensive dashboard data.""" # Get real-time metrics real_time_metrics = await self.metrics_collector.get_real_time_metrics() # Get metric statistics metric_statistics = {} for metric_type in MetricType: stats = await self.metrics_collector.get_metric_statistics(metric_type, time_window) if stats: metric_statistics[metric_type.value] = stats # Generate optimization recommendations recommendations = [] if include_recommendations: recommendations = await self.optimization_engine.generate_optimization_recommendations( real_time_metrics ) # Check for alerts current_alerts = [] if include_alerts: current_alerts = await self._check_metric_alerts(real_time_metrics) return { "timestamp": datetime.utcnow().isoformat(), "real_time_metrics": {mt.value: mv.value for mt, mv in real_time_metrics.items()}, "metric_statistics": metric_statistics, "optimization_recommendations": [asdict(rec) for rec in recommendations], "current_alerts": [asdict(alert) for alert in current_alerts], "dashboard_status": "healthy" if len(current_alerts) == 0 else "degraded", "total_metrics_tracked": len(real_time_metrics), "optimization_status": self.optimization_engine.get_optimization_status() } async def get_metric_trend( self, metric_type: MetricType, time_window: timedelta = timedelta(hours=24), aggregation: str = "hourly" ) -> Dict[str, Any]: """Get metric trend data.""" series = await self.metrics_collector.get_metric_series(metric_type, time_window) if not series: return {"metric_type": metric_type.value, "data": [], "trend": "insufficient_data"} # Aggregate data based on requested aggregation aggregated_data = await self._aggregate_metric_data(series, aggregation) # Calculate trend trend = await self._calculate_trend(series) return { "metric_type": metric_type.value, "aggregation": aggregation, "data": aggregated_data, "trend": trend, "data_points": len(series), "time_window_hours": time_window.total_seconds() / 3600 } async def acknowledge_alert(self, alert_id: str) -> Dict[str, Any]: """Acknowledge an alert.""" if alert_id in self.alerts: self.alerts[alert_id].acknowledged = True return { "success": True, "alert_id": alert_id, "acknowledged_at": datetime.utcnow().isoformat() } else: return { "success": False, "reason": "alert_not_found" } async def trigger_optimization( self, recommendation_id: str, agent_system: Any = None ) -> Dict[str, Any]: """Trigger an optimization recommendation.""" # Find the recommendation recommendation = None for rec in self.optimization_engine.current_recommendations: if rec.recommendation_id == recommendation_id: recommendation = rec break if not recommendation: return { "success": False, "reason": "recommendation_not_found" } # Apply optimization result = await self.optimization_engine.apply_optimization(recommendation, agent_system) return { "success": result.get("success", False), "recommendation_id": recommendation_id, "optimization_result": result, "applied_at": datetime.utcnow().isoformat() } async def _check_metric_alerts( self, real_time_metrics: Dict[MetricType, MetricValue] ) -> List[Alert]: """Check metrics against alert thresholds.""" alerts = [] thresholds = self.dashboard_config["alert_thresholds"] for metric_type, metric_value in real_time_metrics.items(): metric_thresholds = thresholds.get(metric_type) if not metric_thresholds: continue current_value = metric_value.value # Check warning threshold if current_value < metric_thresholds.get("warning", 0): alert_level = AlertLevel.WARNING title = f"Warning: {metric_type.value} below threshold" description = f"Metric {metric_type.value} is {current_value:.3f}, below warning threshold {metric_thresholds['warning']}" alert = Alert( alert_id=f"alert_{metric_type.value}_{int(time.time())}", level=alert_level, title=title, description=description, affected_metrics=[metric_type], threshold_value=metric_thresholds["warning"], current_value=current_value ) alerts.append(alert) # Check critical threshold if current_value < metric_thresholds.get("critical", 0): alert_level = AlertLevel.CRITICAL title = f"Critical: {metric_type.value} severely below threshold" description = f"Metric {metric_type.value} is {current_value:.3f}, below critical threshold {metric_thresholds['critical']}" alert = Alert( alert_id=f"alert_critical_{metric_type.value}_{int(time.time())}", level=alert_level, title=title, description=description, affected_metrics=[metric_type], threshold_value=metric_thresholds["critical"], current_value=current_value ) alerts.append(alert) # Store alerts for alert in alerts: self.alerts[alert.alert_id] = alert return [alert for alert in alerts if not alert.acknowledged] async def _aggregate_metric_data( self, series: List[MetricValue], aggregation: str ) -> List[Dict[str, Any]]: """Aggregate metric data based on time period.""" if aggregation == "hourly": # Group by hour hourly_groups = defaultdict(list) for mv in series: hour_key = mv.timestamp.replace(minute=0, second=0, microsecond=0) hourly_groups[hour_key].append(mv.value) aggregated = [] for hour, values in sorted(hour_groups.items()): aggregated.append({ "timestamp": hour.isoformat(), "value": np.mean(values), "count": len(values) }) return aggregated elif aggregation == "daily": # Group by day daily_groups = defaultdict(list) for mv in series: day_key = mv.timestamp.replace(hour=0, minute=0, second=0, microsecond=0) daily_groups[day_key].append(mv.value) aggregated = [] for day, values in sorted(daily_groups.items()): aggregated.append({ "timestamp": day.isoformat(), "value": np.mean(values), "count": len(values) }) return aggregated else: # Return raw data points return [ { "timestamp": mv.timestamp.isoformat(), "value": mv.value, "confidence": mv.confidence } for mv in series ] async def _calculate_trend(self, series: List[MetricValue]) -> str: """Calculate trend direction for metric series.""" if len(series) < 3: return "insufficient_data" # Simple trend calculation using first and last thirds first_third = series[:len(series)//3] last_third = series[-len(series)//3:] first_avg = np.mean([mv.value for mv in first_third]) last_avg = np.mean([mv.value for mv in last_third]) difference = last_avg - first_avg relative_change = abs(difference) / max(first_avg, 0.1) if relative_change < 0.05: # Less than 5% change return "stable" elif difference > 0: return "improving" else: return "declining" # Dashboard configuration methods async def update_alert_threshold( self, metric_type: MetricType, warning_threshold: float, critical_threshold: float ) -> Dict[str, Any]: """Update alert thresholds for a metric.""" with self._lock: self.dashboard_config["alert_thresholds"][metric_type] = { "warning": warning_threshold, "critical": critical_threshold } return { "success": True, "metric_type": metric_type.value, "new_thresholds": { "warning": warning_threshold, "critical": critical_threshold } } async def update_display_preferences( self, preferences: Dict[str, bool] ) -> Dict[str, Any]: """Update dashboard display preferences.""" with self._lock: self.dashboard_config["display_preferences"].update(preferences) return { "success": True, "new_preferences": self.dashboard_config["display_preferences"] } if __name__ == "__main__": print("Metrics Dashboard & Optimization System Initialized") print("=" * 60) dashboard = MetricsDashboard() print("Ready for comprehensive metrics monitoring and optimization!")