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da0a356eec
Revolutionary transformation from hardcoded Python intelligence to hot-reloadable YAML patterns, enabling dynamic configuration without code changes. ## Phase 1: Foundation Intelligence Complete ### YAML Intelligence Patterns (6 files) - intelligence_patterns.yaml: Multi-dimensional pattern recognition with adaptive learning - mcp_orchestration.yaml: Server selection decision trees with load balancing - hook_coordination.yaml: Parallel execution patterns with dependency resolution - performance_intelligence.yaml: Resource zones and auto-optimization triggers - validation_intelligence.yaml: Health scoring and proactive diagnostic patterns - user_experience.yaml: Project detection and smart UX adaptations ### Python Infrastructure Enhanced (4 components) - intelligence_engine.py: Generic YAML pattern interpreter with hot-reload - learning_engine.py: Enhanced with YAML intelligence integration - yaml_loader.py: Added intelligence configuration helper methods - validate_system.py: New YAML-driven validation with health scoring ### Key Features Implemented - Hot-reload intelligence: Update patterns without code changes or restarts - Declarative configuration: All intelligence logic expressed in YAML - Graceful fallbacks: System works correctly even with missing YAML files - Multi-pattern coordination: Intelligent recommendations from multiple sources - Health scoring: Component-weighted validation with predictive diagnostics - Generic architecture: Single engine consumes all intelligence pattern types ### Testing Results ✅ All components integrate correctly ✅ Hot-reload mechanism functional ✅ Graceful error handling verified ✅ YAML-driven validation operational ✅ Health scoring system working (detected real system issues) This enables users to modify intelligence behavior by editing YAML files, add new pattern types without coding, and hot-reload improvements in real-time. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
299 lines
10 KiB
YAML
299 lines
10 KiB
YAML
# Performance Intelligence Configuration
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# Adaptive performance patterns, auto-optimization, and resource management
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# Enables intelligent performance monitoring and self-optimization
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# Metadata
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version: "1.0.0"
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last_updated: "2025-01-06"
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description: "Performance intelligence and auto-optimization patterns"
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# Adaptive Performance Targets
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adaptive_targets:
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baseline_management:
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# Dynamic baseline adjustment based on system performance
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adjustment_strategy: "rolling_average"
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adjustment_window: 50 # operations
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adjustment_sensitivity: 0.15 # 15% threshold for adjustment
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min_samples: 10 # minimum samples before adjustment
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baseline_metrics:
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response_time:
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initial_target: 500 # ms
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acceptable_variance: 0.3
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improvement_threshold: 0.1
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resource_usage:
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initial_target: 0.7 # 70%
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acceptable_variance: 0.2
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critical_threshold: 0.9
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error_rate:
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initial_target: 0.02 # 2%
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acceptable_variance: 0.01
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critical_threshold: 0.1
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target_adaptation:
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# How targets adapt to system capabilities
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adaptation_triggers:
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- condition: {performance_improvement: ">20%", duration: ">7_days"}
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action: "tighten_targets"
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adjustment: 0.15
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- condition: {performance_degradation: ">15%", duration: ">3_days"}
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action: "relax_targets"
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adjustment: 0.2
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- condition: {system_upgrade_detected: true}
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action: "recalibrate_baselines"
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reset_period: "24_hours"
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adaptation_limits:
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max_target_tightening: 0.5 # Don't make targets too aggressive
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max_target_relaxation: 2.0 # Don't make targets too loose
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adaptation_cooldown: 3600 # seconds between major adjustments
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# Auto-Optimization Engine
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auto_optimization:
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optimization_triggers:
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# Automatic optimization triggers
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performance_triggers:
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- name: "response_time_degradation"
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condition: {avg_response_time: ">target*1.3", samples: ">10"}
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urgency: "high"
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actions: ["enable_aggressive_caching", "reduce_analysis_depth", "parallel_processing"]
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- name: "memory_pressure"
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condition: {memory_usage: ">0.85", duration: ">300_seconds"}
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urgency: "critical"
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actions: ["garbage_collection", "cache_cleanup", "reduce_context_size"]
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- name: "cpu_saturation"
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condition: {cpu_usage: ">0.9", duration: ">60_seconds"}
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urgency: "high"
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actions: ["reduce_concurrent_operations", "defer_non_critical", "enable_throttling"]
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- name: "error_rate_spike"
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condition: {error_rate: ">0.1", recent_window: "5_minutes"}
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urgency: "critical"
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actions: ["enable_fallback_mode", "increase_timeouts", "reduce_complexity"]
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optimization_strategies:
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# Available optimization strategies
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aggressive_caching:
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description: "Enable aggressive caching of results and computations"
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performance_impact: 0.3 # Expected improvement
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resource_cost: 0.1 # Memory cost
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duration: 1800 # seconds
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parallel_processing:
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description: "Increase parallelization where possible"
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performance_impact: 0.25
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resource_cost: 0.2
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duration: 3600
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reduce_analysis_depth:
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description: "Reduce depth of analysis to improve speed"
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performance_impact: 0.4
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quality_impact: -0.1 # Slight quality reduction
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duration: 1800
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intelligent_batching:
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description: "Batch similar operations for efficiency"
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performance_impact: 0.2
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resource_cost: -0.05 # Reduces resource usage
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duration: 3600
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# Resource Management Intelligence
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resource_management:
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resource_zones:
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# Performance zones with different strategies
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green_zone:
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threshold: 0.60 # Below 60% resource usage
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strategy: "optimal_performance"
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features_enabled: ["full_analysis", "comprehensive_caching", "background_optimization"]
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yellow_zone:
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threshold: 0.75 # 60-75% resource usage
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strategy: "balanced_optimization"
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features_enabled: ["standard_analysis", "selective_caching", "reduced_background"]
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optimizations: ["defer_non_critical", "reduce_verbosity"]
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orange_zone:
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threshold: 0.85 # 75-85% resource usage
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strategy: "performance_preservation"
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features_enabled: ["essential_analysis", "minimal_caching"]
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optimizations: ["aggressive_caching", "parallel_where_safe", "reduce_context"]
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red_zone:
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threshold: 0.95 # 85-95% resource usage
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strategy: "resource_conservation"
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features_enabled: ["critical_only"]
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optimizations: ["emergency_cleanup", "minimal_processing", "fail_fast"]
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critical_zone:
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threshold: 1.0 # Above 95% resource usage
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strategy: "emergency_mode"
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features_enabled: []
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optimizations: ["immediate_cleanup", "operation_rejection", "system_protection"]
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dynamic_allocation:
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# Intelligent resource allocation
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allocation_strategies:
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workload_based:
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description: "Allocate based on current workload patterns"
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factors: ["operation_complexity", "expected_duration", "priority"]
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predictive:
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description: "Allocate based on predicted resource needs"
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factors: ["historical_patterns", "operation_type", "context_size"]
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adaptive:
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description: "Adapt allocation based on real-time performance"
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factors: ["current_performance", "resource_availability", "optimization_goals"]
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# Performance Regression Detection
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regression_detection:
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detection_algorithms:
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# Algorithms for detecting performance regression
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statistical_analysis:
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algorithm: "t_test"
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confidence_level: 0.95
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minimum_samples: 20
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window_size: 100 # operations
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trend_analysis:
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algorithm: "linear_regression"
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trend_threshold: 0.1 # 10% degradation trend
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analysis_window: 168 # hours (1 week)
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anomaly_detection:
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algorithm: "isolation_forest"
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contamination: 0.1 # Expected anomaly rate
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sensitivity: 0.8
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regression_patterns:
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# Common regression patterns to detect
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gradual_degradation:
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pattern: {performance_trend: "decreasing", duration: ">5_days"}
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severity: "medium"
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investigation: "check_for_memory_leaks"
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sudden_degradation:
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pattern: {performance_drop: ">30%", timeframe: "<1_hour"}
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severity: "high"
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investigation: "check_recent_changes"
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periodic_degradation:
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pattern: {performance_cycles: "detected", frequency: "regular"}
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severity: "low"
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investigation: "analyze_periodic_patterns"
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# Intelligent Resource Optimization
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intelligent_optimization:
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predictive_optimization:
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# Predict and prevent performance issues
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prediction_models:
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resource_exhaustion:
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model_type: "time_series"
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prediction_horizon: 3600 # seconds
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accuracy_threshold: 0.8
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performance_degradation:
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model_type: "pattern_matching"
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pattern_library: "historical_degradations"
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confidence_threshold: 0.7
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proactive_actions:
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- prediction: "memory_exhaustion"
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lead_time: 1800 # seconds
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actions: ["preemptive_cleanup", "cache_optimization", "context_reduction"]
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- prediction: "cpu_saturation"
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lead_time: 600 # seconds
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actions: ["reduce_parallelism", "defer_background_tasks", "enable_throttling"]
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optimization_recommendations:
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# Generate optimization recommendations
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recommendation_engine:
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analysis_depth: "comprehensive"
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recommendation_confidence: 0.8
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implementation_difficulty: "user_friendly"
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recommendation_types:
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configuration_tuning:
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description: "Suggest configuration changes for better performance"
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impact_assessment: "quantified"
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resource_allocation:
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description: "Recommend better resource allocation strategies"
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cost_benefit_analysis: true
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workflow_optimization:
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description: "Suggest workflow improvements"
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user_experience_impact: "minimal"
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# Performance Monitoring Intelligence
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monitoring_intelligence:
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intelligent_metrics:
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# Smart metric collection and analysis
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adaptive_sampling:
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base_sampling_rate: 1.0 # Sample every operation
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high_load_rate: 0.5 # Reduce sampling under load
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critical_load_rate: 0.1 # Minimal sampling in critical situations
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contextual_metrics:
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# Collect different metrics based on context
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ui_operations:
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focus_metrics: ["response_time", "render_time", "user_interaction_delay"]
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analysis_operations:
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focus_metrics: ["processing_time", "memory_usage", "accuracy_score"]
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batch_operations:
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focus_metrics: ["throughput", "resource_efficiency", "completion_rate"]
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performance_insights:
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# Generate performance insights
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insight_generation:
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pattern_recognition: true
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correlation_analysis: true
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root_cause_analysis: true
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improvement_suggestions: true
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insight_types:
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bottleneck_identification:
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description: "Identify performance bottlenecks"
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priority: "high"
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optimization_opportunities:
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description: "Find optimization opportunities"
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priority: "medium"
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capacity_planning:
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description: "Predict capacity requirements"
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priority: "low"
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# Performance Validation
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performance_validation:
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validation_framework:
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# Validate performance improvements
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a_b_testing:
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enable_automatic_testing: true
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test_duration: 3600 # seconds
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statistical_significance: 0.95
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performance_benchmarking:
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benchmark_frequency: "weekly"
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regression_threshold: 0.05 # 5% regression tolerance
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continuous_improvement:
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# Continuous performance improvement
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improvement_tracking:
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track_optimization_effectiveness: true
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measure_user_satisfaction: true
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monitor_system_health: true
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feedback_loops:
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performance_feedback: "real_time"
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user_feedback_integration: true
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system_learning_integration: true |