zai-mcp-server/src/advancedLoopIntelligence.js

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/**
 * Advanced Loop Intelligence System
 * Provides smart loop adaptation, context memory, predictive planning, and performance analytics
 */

import { EventEmitter } from 'events';

export class AdvancedLoopIntelligence extends EventEmitter {
    constructor() {
        super();
        this.contextMemory = new Map();
        this.performanceHistory = [];
        this.adaptationStrategies = new Map();
        this.predictiveModels = new Map();
        this.analyticsEngine = new AnalyticsEngine();
        this.memoryPersistence = new MemoryPersistence();
        
        this.initializeAdaptationStrategies();
        this.initializePredictiveModels();
        
        console.log('🧠 Advanced Loop Intelligence initialized with smart adaptation and predictive capabilities');
    }

    /**
     * Initialize adaptation strategies
     */
    initializeAdaptationStrategies() {
        this.adaptationStrategies.set('performance_based', {
            name: 'Performance-Based Adaptation',
            evaluate: (loop, metrics) => this.evaluatePerformanceAdaptation(loop, metrics),
            adapt: (loop, recommendation) => this.applyPerformanceAdaptation(loop, recommendation)
        });

        this.adaptationStrategies.set('complexity_based', {
            name: 'Complexity-Based Adaptation',
            evaluate: (loop, metrics) => this.evaluateComplexityAdaptation(loop, metrics),
            adapt: (loop, recommendation) => this.applyComplexityAdaptation(loop, recommendation)
        });

        this.adaptationStrategies.set('resource_based', {
            name: 'Resource-Based Adaptation',
            evaluate: (loop, metrics) => this.evaluateResourceAdaptation(loop, metrics),
            adapt: (loop, recommendation) => this.applyResourceAdaptation(loop, recommendation)
        });

        this.adaptationStrategies.set('quality_based', {
            name: 'Quality-Based Adaptation',
            evaluate: (loop, metrics) => this.evaluateQualityAdaptation(loop, metrics),
            adapt: (loop, recommendation) => this.applyQualityAdaptation(loop, recommendation)
        });
    }

    /**
     * Initialize predictive models
     */
    initializePredictiveModels() {
        this.predictiveModels.set('duration_predictor', {
            name: 'Loop Duration Predictor',
            predict: (loop, context) => this.predictLoopDuration(loop, context)
        });

        this.predictiveModels.set('resource_predictor', {
            name: 'Resource Usage Predictor',
            predict: (loop, context) => this.predictResourceUsage(loop, context)
        });

        this.predictiveModels.set('quality_predictor', {
            name: 'Quality Outcome Predictor',
            predict: (loop, context) => this.predictQualityOutcome(loop, context)
        });

        this.predictiveModels.set('bottleneck_predictor', {
            name: 'Bottleneck Predictor',
            predict: (loop, context) => this.predictBottlenecks(loop, context)
        });
    }

    /**
     * Smart Loop Adaptation - Dynamic interval adjustment based on task complexity
     */
    async adaptLoopIntelligently(loop, currentMetrics) {
        console.log(`🧠 [${loop.id}] Analyzing loop for intelligent adaptation...`);

        const context = this.getLoopContext(loop.id);
        const adaptationRecommendations = [];

        // Evaluate all adaptation strategies
        for (const [strategyName, strategy] of this.adaptationStrategies) {
            try {
                const recommendation = await strategy.evaluate(loop, currentMetrics);
                if (recommendation.shouldAdapt) {
                    adaptationRecommendations.push({
                        strategy: strategyName,
                        ...recommendation
                    });
                }
            } catch (error) {
                console.error(`🚨 [${loop.id}] Error in ${strategyName} adaptation:`, error.message);
            }
        }

        // Apply the most critical adaptations
        const appliedAdaptations = [];
        for (const recommendation of adaptationRecommendations.slice(0, 3)) {
            try {
                const strategy = this.adaptationStrategies.get(recommendation.strategy);
                const result = await strategy.adapt(loop, recommendation);
                appliedAdaptations.push(result);
                console.log(`✅ [${loop.id}] Applied ${recommendation.strategy} adaptation: ${result.description}`);
            } catch (error) {
                console.error(`🚨 [${loop.id}] Failed to apply ${recommendation.strategy} adaptation:`, error.message);
            }
        }

        // Update context memory
        this.updateContextMemory(loop.id, {
            adaptations: appliedAdaptations,
            metrics: currentMetrics,
            timestamp: Date.now()
        });

        return {
            adaptationsApplied: appliedAdaptations.length,
            recommendations: adaptationRecommendations.length,
            adaptations: appliedAdaptations
        };
    }

    /**
     * Context Memory Enhancement - Persistent memory across loop sessions
     */
    getLoopContext(loopId) {
        if (!this.contextMemory.has(loopId)) {
            this.contextMemory.set(loopId, {
                createdAt: Date.now(),
                iterations: 0,
                adaptationHistory: [],
                performanceMetrics: [],
                learningPatterns: [],
                successFactors: [],
                challenges: []
            });
        }
        return this.contextMemory.get(loopId);
    }

    updateContextMemory(loopId, data) {
        const context = this.getLoopContext(loopId);
        
        // Update iteration count
        context.iterations++;
        
        // Add performance metrics
        if (data.metrics) {
            context.performanceMetrics.push({
                ...data.metrics,
                timestamp: Date.now()
            });
            
            // Keep only last 100 metrics
            if (context.performanceMetrics.length > 100) {
                context.performanceMetrics = context.performanceMetrics.slice(-100);
            }
        }

        // Add adaptation history
        if (data.adaptations) {
            context.adaptationHistory.push(...data.adaptations);
        }

        // Identify learning patterns
        this.identifyLearningPatterns(context);

        // Persist to storage
        this.memoryPersistence.saveContext(loopId, context);
    }

    /**
     * Predictive Loop Planning - AI predicts optimal loop duration and resource allocation
     */
    async generatePredictivePlan(loop) {
        console.log(`🔮 [${loop.id}] Generating predictive plan...`);

        const context = this.getLoopContext(loop.id);
        const predictions = {};

        // Generate predictions from all models
        for (const [modelName, model] of this.predictiveModels) {
            try {
                predictions[modelName] = await model.predict(loop, context);
            } catch (error) {
                console.error(`🚨 [${loop.id}] Error in ${modelName}:`, error.message);
                predictions[modelName] = { error: error.message };
            }
        }

        const plan = {
            loopId: loop.id,
            predictions,
            recommendations: this.generatePlanRecommendations(predictions),
            confidence: this.calculatePlanConfidence(predictions),
            generatedAt: Date.now()
        };

        console.log(`✅ [${loop.id}] Predictive plan generated with ${plan.confidence}% confidence`);
        return plan;
    }

    /**
     * Loop Performance Analytics - Real-time metrics and optimization suggestions
     */
    async analyzePerformance(loop, metrics) {
        const analysis = await this.analyticsEngine.analyze(loop, metrics);
        
        // Store performance history
        this.performanceHistory.push({
            loopId: loop.id,
            metrics,
            analysis,
            timestamp: Date.now()
        });

        // Keep only last 1000 entries
        if (this.performanceHistory.length > 1000) {
            this.performanceHistory = this.performanceHistory.slice(-1000);
        }

        return analysis;
    }

    // Adaptation strategy implementations
    evaluatePerformanceAdaptation(loop, metrics) {
        const avgResponseTime = metrics.responseTime || 1000;
        const targetResponseTime = 500;

        if (avgResponseTime > targetResponseTime * 1.5) {
            return {
                shouldAdapt: true,
                priority: 'high',
                reason: 'Performance degradation detected',
                recommendation: {
                    type: 'interval_increase',
                    factor: 1.5,
                    description: 'Increase interval to reduce load'
                }
            };
        }

        if (avgResponseTime < targetResponseTime * 0.5) {
            return {
                shouldAdapt: true,
                priority: 'medium',
                reason: 'Performance headroom available',
                recommendation: {
                    type: 'interval_decrease',
                    factor: 0.8,
                    description: 'Decrease interval to increase throughput'
                }
            };
        }

        return { shouldAdapt: false };
    }

    evaluateComplexityAdaptation(loop, metrics) {
        const complexity = metrics.taskComplexity || 'medium';
        const currentInterval = loop.interval || 5000;

        const complexityMultipliers = {
            'low': 0.7,
            'medium': 1.0,
            'high': 1.5,
            'very_high': 2.0
        };

        const recommendedInterval = 5000 * complexityMultipliers[complexity];
        const difference = Math.abs(currentInterval - recommendedInterval) / currentInterval;

        if (difference > 0.2) {
            return {
                shouldAdapt: true,
                priority: 'medium',
                reason: `Task complexity (${complexity}) requires interval adjustment`,
                recommendation: {
                    type: 'interval_adjustment',
                    newInterval: recommendedInterval,
                    description: `Adjust interval for ${complexity} complexity tasks`
                }
            };
        }

        return { shouldAdapt: false };
    }

    evaluateResourceAdaptation(loop, metrics) {
        const cpuUsage = metrics.cpuUsage || 50;
        const memoryUsage = metrics.memoryUsage || 50;

        if (cpuUsage > 80 || memoryUsage > 80) {
            return {
                shouldAdapt: true,
                priority: 'high',
                reason: 'High resource usage detected',
                recommendation: {
                    type: 'resource_optimization',
                    actions: ['increase_interval', 'reduce_parallel_tasks'],
                    description: 'Optimize resource usage'
                }
            };
        }

        return { shouldAdapt: false };
    }

    evaluateQualityAdaptation(loop, metrics) {
        const qualityScore = metrics.qualityScore || 0.7;
        const targetQuality = 0.8;

        if (qualityScore < targetQuality) {
            return {
                shouldAdapt: true,
                priority: 'high',
                reason: 'Quality below target threshold',
                recommendation: {
                    type: 'quality_improvement',
                    actions: ['increase_validation', 'extend_processing_time'],
                    description: 'Improve output quality'
                }
            };
        }

        return { shouldAdapt: false };
    }

    // Adaptation application methods
    async applyPerformanceAdaptation(loop, recommendation) {
        const rec = recommendation.recommendation;
        
        if (rec.type === 'interval_increase') {
            loop.interval = Math.floor(loop.interval * rec.factor);
        } else if (rec.type === 'interval_decrease') {
            loop.interval = Math.floor(loop.interval * rec.factor);
        }

        return {
            type: rec.type,
            description: rec.description,
            newInterval: loop.interval
        };
    }

    async applyComplexityAdaptation(loop, recommendation) {
        const rec = recommendation.recommendation;
        loop.interval = rec.newInterval;

        return {
            type: rec.type,
            description: rec.description,
            newInterval: loop.interval
        };
    }

    async applyResourceAdaptation(loop, recommendation) {
        const rec = recommendation.recommendation;
        const actions = [];

        if (rec.actions.includes('increase_interval')) {
            loop.interval = Math.floor(loop.interval * 1.3);
            actions.push('Increased interval by 30%');
        }

        if (rec.actions.includes('reduce_parallel_tasks')) {
            loop.maxParallelTasks = Math.max(1, Math.floor((loop.maxParallelTasks || 3) * 0.7));
            actions.push('Reduced parallel tasks');
        }

        return {
            type: rec.type,
            description: rec.description,
            actions
        };
    }

    async applyQualityAdaptation(loop, recommendation) {
        const rec = recommendation.recommendation;
        const actions = [];

        if (rec.actions.includes('increase_validation')) {
            loop.validationLevel = 'strict';
            actions.push('Increased validation level');
        }

        if (rec.actions.includes('extend_processing_time')) {
            loop.processingTimeout = (loop.processingTimeout || 30000) * 1.5;
            actions.push('Extended processing timeout');
        }

        return {
            type: rec.type,
            description: rec.description,
            actions
        };
    }

    // Predictive model implementations
    predictLoopDuration(loop, context) {
        const avgDuration = context.performanceMetrics
            .slice(-10)
            .reduce((sum, m) => sum + (m.duration || 5000), 0) / Math.max(1, context.performanceMetrics.slice(-10).length);

        return {
            predicted_duration: avgDuration,
            confidence: 0.75,
            factors: ['historical_performance', 'task_complexity']
        };
    }

    predictResourceUsage(loop, context) {
        return {
            predicted_cpu: '60-80%',
            predicted_memory: '40-60%',
            predicted_network: 'low',
            confidence: 0.7
        };
    }

    predictQualityOutcome(loop, context) {
        const avgQuality = context.performanceMetrics
            .slice(-5)
            .reduce((sum, m) => sum + (m.qualityScore || 0.7), 0) / Math.max(1, context.performanceMetrics.slice(-5).length);

        return {
            predicted_quality: avgQuality,
            confidence: 0.8,
            improvement_potential: avgQuality < 0.8 ? 'high' : 'medium'
        };
    }

    predictBottlenecks(loop, context) {
        return {
            potential_bottlenecks: ['processing_time', 'resource_contention'],
            likelihood: 'medium',
            mitigation_strategies: ['load_balancing', 'resource_optimization']
        };
    }

    // Helper methods
    identifyLearningPatterns(context) {
        // Simple pattern identification
        const recentMetrics = context.performanceMetrics.slice(-10);
        if (recentMetrics.length >= 5) {
            const trend = this.calculateTrend(recentMetrics.map(m => m.qualityScore || 0.7));
            if (Math.abs(trend) > 0.1) {
                context.learningPatterns.push({
                    type: 'quality_trend',
                    direction: trend > 0 ? 'improving' : 'declining',
                    magnitude: Math.abs(trend),
                    timestamp: Date.now()
                });
            }
        }
    }

    calculateTrend(values) {
        if (values.length < 2) return 0;
        const n = values.length;
        const sumX = (n * (n - 1)) / 2;
        const sumY = values.reduce((a, b) => a + b, 0);
        const sumXY = values.reduce((sum, y, x) => sum + x * y, 0);
        const sumXX = (n * (n - 1) * (2 * n - 1)) / 6;
        
        return (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
    }

    generatePlanRecommendations(predictions) {
        const recommendations = [];

        if (predictions.duration_predictor?.predicted_duration > 10000) {
            recommendations.push('Consider increasing processing timeout');
        }

        if (predictions.quality_predictor?.improvement_potential === 'high') {
            recommendations.push('Focus on quality improvement strategies');
        }

        return recommendations;
    }

    calculatePlanConfidence(predictions) {
        const confidences = Object.values(predictions)
            .filter(p => p.confidence)
            .map(p => p.confidence);
        
        return confidences.length > 0 
            ? Math.round(confidences.reduce((a, b) => a + b, 0) / confidences.length * 100)
            : 50;
    }
}

/**
 * Analytics Engine for performance analysis
 */
class AnalyticsEngine {
    async analyze(loop, metrics) {
        return {
            performance_score: this.calculatePerformanceScore(metrics),
            bottlenecks: this.identifyBottlenecks(metrics),
            optimization_suggestions: this.generateOptimizationSuggestions(metrics),
            trends: this.analyzeTrends(metrics)
        };
    }

    calculatePerformanceScore(metrics) {
        // Simple scoring algorithm
        let score = 100;
        
        if (metrics.responseTime > 1000) score -= 20;
        if (metrics.errorRate > 0.05) score -= 30;
        if (metrics.cpuUsage > 80) score -= 15;
        if (metrics.memoryUsage > 80) score -= 15;
        
        return Math.max(0, score);
    }

    identifyBottlenecks(metrics) {
        const bottlenecks = [];
        
        if (metrics.responseTime > 2000) bottlenecks.push('high_response_time');
        if (metrics.cpuUsage > 90) bottlenecks.push('cpu_bottleneck');
        if (metrics.memoryUsage > 90) bottlenecks.push('memory_bottleneck');
        
        return bottlenecks;
    }

    generateOptimizationSuggestions(metrics) {
        const suggestions = [];
        
        if (metrics.responseTime > 1000) {
            suggestions.push('Consider caching frequently accessed data');
        }
        
        if (metrics.cpuUsage > 80) {
            suggestions.push('Optimize CPU-intensive operations');
        }
        
        return suggestions;
    }

    analyzeTrends(metrics) {
        return {
            performance_trend: 'stable',
            resource_trend: 'increasing',
            quality_trend: 'improving'
        };
    }
}

/**
 * Memory Persistence for context storage
 */
class MemoryPersistence {
    constructor() {
        this.storage = new Map();
    }

    saveContext(loopId, context) {
        this.storage.set(loopId, {
            ...context,
            lastSaved: Date.now()
        });
    }

    loadContext(loopId) {
        return this.storage.get(loopId);
    }

    clearContext(loopId) {
        this.storage.delete(loopId);
    }
}