# Create RAN AgentDB workspace
mkdir -p ran-agentdb/{adapters,coordinators,optimizers,cache}
cd ran-agentdb

# Initialize AgentDB for RAN systems
npx agentdb@latest init ./.agentdb/ran-agentdb.db --dimension 1536

# Install AgentDB and RAN packages
npm init -y
npm install agentdb @tensorflow/tfjs-node
npm install quic-protocol
npm install vector-search

import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';

class RANAgentDBAdapter {
  private agentDB: AgentDBAdapter;
  private cache: Map<string, CachedPattern>;
  private quantizationConfig: QuantizationConfig;

  async initialize() {
    this.agentDB = await createAgentDBAdapter({
      dbPath: '.agentdb/ran-agentdb.db',
      enableQUICSync: true,
      enableLearning: true,
      enableReasoning: true,
      cacheSize: 3000,
      quantizationType: 'scalar', // 32x memory reduction
      compression: true,
      hnswM: 16,
      hnswEf: 100
    });

    this.cache = new Map();
    this.quantizationConfig = {
      type: 'scalar',
      bits: 8,
      blockSize: 32
    };

    await this.setupCacheWarmer();
    await this.initializeIndexOptimization();
  }

  async storeRANPattern(
    patternType: string,
    ranData: RANData,
    metadata?: RANMetadata
  ): Promise<string> {
    const startTime = Date.now();

    // Create embedding from RAN data
    const embedding = await this.createRANEmbedding(ranData);

    // Create pattern with RAN-specific structure
    const pattern: RANPattern = {
      id: this.generatePatternId(),
      type: patternType,
      domain: this.classifyRANDomain(ranData),
      ranData,
      metadata: metadata || {},
      embedding,
      confidence: this.calculatePatternConfidence(ranData),
      usage_count: 0,
      success_count: 0,
      created_at: Date.now(),
      last_used: Date.now(),
      performance_metrics: this.extractPerformanceMetrics(ranData)
    };

    // Store in AgentDB with quantization
    await this.agentDB.insertPattern({
      id: pattern.id,
      type: pattern.type,
      domain: pattern.domain,
      pattern_data: JSON.stringify({
        embedding,
        pattern: {
          ranData: pattern.ranData,
          metadata: pattern.metadata,
          performance_metrics: pattern.performance_metrics
        }
      }),
      confidence: pattern.confidence,
      usage_count: pattern.usage_count,
      success_count: pattern.success_count,
      created_at: pattern.created_at,
      last_used: pattern.last_used,
    });

    // Cache for ultra-fast access
    this.cache.set(pattern.id, {
      pattern,
      timestamp: Date.now()
    });

    const storageTime = Date.now() - startTime;
    console.log(`Stored ${patternType} pattern in ${storageTime}ms`);

    return pattern.id;
  }

  async retrieveSimilarRANPatterns(
    queryRANData: RANData,
    options: RANSearchOptions = {}
  ): Promise<RANSearchResult> {
    const startTime = Date.now();

    // Create query embedding
    const queryEmbedding = await this.createRANEmbedding(queryRANData);

    // Check cache first for ultra-fast response
    const cacheKey = this.generateCacheKey(queryEmbedding, options);
    const cached = this.cache.get(cacheKey);
    if (cached && (Date.now() - cached.timestamp) < 60000) { // 1 minute cache
      return this.formatCachedResult(cached.pattern, options);
    }

    // Search AgentDB with RAN-optimized parameters
    const agentDBResult = await this.agentDB.retrieveWithReasoning(queryEmbedding, {
      domain: options.domain,
      k: options.k || 10,
      useMMR: options.useMMR !== false,
      synthesizeContext: options.synthesizeContext !== false,
      filters: this.buildRANFilters(options.filters),
      hybridWeights: options.hybridWeights,
      optimizeMemory: options.optimizeMemory !== false
    });

    // Post-process results with RAN-specific logic
    const processedResults = await this.processRANResults(agentDBResult, queryRANData, options);

    // Cache results
    this.cache.set(cacheKey, {
      pattern: processedResults,
      timestamp: Date.now()
    });

    const searchTime = Date.now() - startTime;
    console.log(`RAN pattern search completed in ${searchTime}ms - found ${processedResults.memories.length} results`);

    return processedResults;
  }

  private async createRANEmbedding(ranData: RANData): Promise<number[]> {
    // RAN-specific embedding creation
    const features = [
      // Performance metrics (normalized)
      ranData.throughput / 1000,
      ranData.latency / 100,
      ranData.packetLoss,
      ranData.signalStrength / 100,
      ranData.interference,
      ranData.energyConsumption / 200,

      // Network state
      ranData.userCount / 100,
      ranData.mobilityIndex / 100,
      ranData.coverageHoleCount / 50,
      ranData.handoverCount / 20,

      // Temporal features
      this.getTimeOfDayFeature(),
      this.getTrafficPatternFeature(ranData),
      this.getEnvironmentalFeature(ranData),

      // Advanced features for RAN optimization
      this.calculateSignalToInterferenceRatio(ranData),
      this.calculateChannelQuality(ranData),
      this.calculateLoadBalance(ranData),
      this.calculateMobilityComplexity(ranData)
    ];

    // Generate embedding using model or fallback
    try {
      return await this.generateEmbedding(features);
    } catch (error) {
      console.warn('Embedding generation failed, using fallback:', error);
      return this.createFallbackEmbedding(features);
    }
  }

  private async generateEmbedding(features: number[]): Promise<number[]> {
    // Would use a trained embedding model
    // For now, return features as embedding
    return features;
  }

  private createFallbackEmbedding(features: number[]): number[] {
    // Simple fallback embedding with RAN-specific transformations
    const embedding = features.map((feature, index) => {
      // Apply different transformations based on feature type
      switch (index) {
        case 0: case 1: case 2: // Performance metrics
          return this.normalizeFeature(feature, 0, 1);
        case 3: case 4: case 5: // Signal metrics
          return Math.tanh(feature);
        case 6: case 7: case 8: // Network state
          return this.applyPolynomialTransformation(feature);
        default:
          return feature;
      }
    });

    // Pad or truncate to standard size
    const standardSize = 1536;
    while (embedding.length < standardSize) {
      embedding.push(...this.generatePaddingFeatures(embedding.length));
    }
    return embedding.slice(0, standardSize);
  }

  private normalizeFeature(value: number, min: number, max: number): number {
    return (value - min) / (max - min);
  }

  private applyPolynomialTransformation(value: number): number {
    // Apply polynomial transformation for better distribution
    return Math.tanh(value + Math.pow(value, 2) * 0.1);
  }

  private generatePaddingFeatures(currentLength: number): number[] {
    // Generate padding features based on current embedding
    const seed = currentLength % 10;
    return [
      Math.sin(seed) * 0.1,
      Math.cos(seed) * 0.1,
      Math.tan(seed * 0.1) * 0.05,
      Math.sin(seed * 2) * 0.05,
      Math.cos(seed * 3) * 0.03
    ];
  }

  private getTimeOfDayFeature(): number {
    const hour = new Date().getHours();
    return Math.sin((hour / 24) * 2 * Math.PI);
  }

  private getTrafficPatternFeature(ranData: RANData): number {
    // Traffic pattern based on user count and time
    const userLoad = ranData.userCount / 100;
    const timeFactor = this.getTimeOfDayFeature();
    return (userLoad + timeFactor) / 2;
  }

  private getEnvironmentalFeature(ranData: RANData): number {
    // Environmental factors affecting RAN performance
    return (ranData.interference + ranData.mobilityIndex / 100) / 2;
  }

  private calculateSignalToInterferenceRatio(ranData: RANData): number {
    const sinr = ranData.signalStrength - (ranData.interference * 50);
    return Math.max(0, Math.min(1, sinr / 50));
  }

  private calculateChannelQuality(ranData: RANData): number {
    // Channel quality indicator
    const signalQuality = Math.max(0, (ranData.signalStrength + 50) / 50);
    const interferencePenalty = ranData.interference;
    return Math.max(0, signalQuality - interferencePenalty);
  }

  private calculateLoadBalance(ranData: RANData): number {
    // Load balance quality
    const optimalLoad = 50;
    const deviation = Math.abs(ranData.userCount - optimalLoad) / optimalLoad;
    return Math.max(0, 1 - deviation);
  }

  private calculateMobilityComplexity(ranData: RANData): number {
    // Mobility complexity factor
    return Math.min(1, (ranData.mobilityIndex + ranData.handoverCount * 2) / 100);
  }

  private classifyRANDomain(ranData: RANData): string {
    // Classify RAN domain for better organization
    if (ranData.energyConsumption > 150) return 'energy-optimization';
    if (ranData.mobilityIndex > 70) return 'mobility-optimization';
    if (ranData.coverageHoleCount > 10) return 'coverage-optimization';
    if (ranData.throughput < 500) return 'capacity-optimization';
    if (ranData.latency > 50) return 'latency-optimization';
    return 'general-optimization';
  }

  private calculatePatternConfidence(ranData: RANData): number {
    // Calculate confidence based on data quality and completeness
    let confidence = 0.5; // Base confidence

    // Data completeness bonus
    const requiredFields = ['throughput', 'latency', 'signalStrength', 'userCount'];
    const completeness = requiredFields.filter(field => ranData[field] !== undefined).length / requiredFields.length;
    confidence += completeness * 0.2;

    // Data quality bonus
    if (ranData.signalStrength > -80) confidence += 0.1;
    if (ranData.latency < 50) confidence += 0.1;
    if (ranData.packetLoss < 0.02) confidence += 0.1;

    return Math.min(confidence, 1.0);
  }

  private extractPerformanceMetrics(ranData: RANData): RANPerformanceMetrics {
    return {
      throughput_score: Math.min(1, ranData.throughput / 1000),
      latency_score: Math.max(0, 1 - ranData.latency / 100),
      signal_score: Math.max(0, (ranData.signalStrength + 50) / 50),
      energy_efficiency: Math.min(1, ranData.throughput / (ranData.energyConsumption * 10)),
      coverage_score: Math.max(0, 1 - ranData.coverageHoleCount / 20),
      mobility_score: Math.min(1, ranData.mobilityIndex / 100)
    };
  }

  private generatePatternId(): string {
    return `ran-pattern-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`;
  }

  private generateCacheKey(embedding: number[], options: RANSearchOptions): string {
    // Generate cache key from embedding hash and options
    const embeddingHash = this.hashArray(embedding.slice(0, 10)); // Hash first 10 elements
    const optionsHash = this.hashString(JSON.stringify(options));
    return `${embeddingHash}-${optionsHash}`;
  }

  private hashArray(array: number[]): string {
    return array.reduce((hash, num) => (hash * 31 + Math.floor(num * 1000)).toString(36), '').substr(0, 8);
  }

  private hashString(str: string): string {
    return str.split('').reduce((hash, char) => (hash * 31 + char.charCodeAt(0)).toString(36), '').substr(0, 8);
  }

  private formatCachedResult(pattern: any, options: RANSearchOptions): RANSearchResult {
    // Format cached result to match expected structure
    return {
      memories: [pattern],
      context: pattern.synthesizedContext || '',
      patterns: this.extractPatterns(pattern),
      optimization: pattern.memoryOptimization || null
    };
  }

  private buildRANFilters(filters?: RANFilters): any {
    if (!filters) return {};

    const agentDBFilters: any = {};

    // Convert RAN filters to AgentDB filters
    if (filters.domain) agentDBFilters.domain = filters.domain;
    if (filters.confidence) agentDBFilters.confidence = { $gte: filters.confidence };
    if (filters.timestamp) {
      if (filters.timestamp.after) agentDBFilters.timestamp = { $gte: filters.timestamp.after };
      if (filters.timestamp.before) agentDBFilters.timestamp = { ...agentDBFilters.timestamp, $lte: filters.timestamp.before };
    }
    if (filters.performanceThreshold) {
      agentDBFilters['performance_metrics.throughput_score'] = { $gte: filters.performanceThreshold };
    }

    return agentDBFilters;
  }

  private async processRANResults(
    agentDBResult: any,
    queryRANData: RANData,
    options: RANSearchOptions
  ): Promise<RANSearchResult> {
    // Process and enhance results with RAN-specific logic
    const enhancedMemories = await Promise.all(
      agentDBResult.memories.map(async (memory: any) => {
        const enhancedMemory = { ...memory };

        // Add RAN-specific similarity calculations
        enhancedMemory.ranSimilarity = this.calculateRANSimilarity(queryRANData, memory.pattern.ranData);

        // Add performance comparison
        enhancedMemory.performanceComparison = this.comparePerformance(
          queryRANData,
          memory.pattern.ranData
        );

        // Add recommendation
        enhancedMemory.recommendation = this.generateRANRecommendation(
          queryRANData,
          memory.pattern.ranData,
          enhancedMemory.ranSimilarity
        );

        return enhancedMemory;
      })
    );

    return {
      memories: enhancedMemories,
      context: agentDBResult.context || '',
      patterns: this.extractPatternsFromResults(enhancedMemories),
      optimization: agentDBResult.optimization || null
    };
  }

  private calculateRANSimilarity(queryData: RANData, storedData: RANData): number {
    // Calculate RAN-specific similarity
    const similarities = [
      this.compareThroughput(queryData, storedData),
      this.compareLatency(queryData, storedData),
      this.compareSignal(queryData, storedData),
      this.compareEnergy(queryData, storedData),
      this.compareMobility(queryData, storedData)
    ];

    // Weighted average
    const weights = [0.3, 0.2, 0.2, 0.15, 0.15];
    return similarities.reduce((sum, sim, i) => sum + sim * weights[i], 0);
  }

  private compareThroughput(query: RANData, stored: RANData): number {
    const diff = Math.abs(query.throughput - stored.throughput);
    const maxThroughput = Math.max(query.throughput, stored.throughput);
    return maxThroughput > 0 ? 1 - (diff / maxThroughput) : 1;
  }

  private compareLatency(query: RANData, stored: RANData): number {
    const diff = Math.abs(query.latency - stored.latency);
    const maxLatency = Math.max(query.latency, stored.latency);
    return maxLatency > 0 ? 1 - (diff / maxLatency) : 1;
  }

  private compareSignal(query: RANData, stored: RANData): number {
    const diff = Math.abs(query.signalStrength - stored.signalStrength);
    return Math.max(0, 1 - (diff / 50)); // 50dB range
  }

  private compareEnergy(query: RANData, stored: RANData): number {
    const diff = Math.abs(query.energyConsumption - stored.energyConsumption);
    const maxEnergy = Math.max(query.energyConsumption, stored.energyConsumption);
    return maxEnergy > 0 ? 1 - (diff / maxEnergy) : 1;
  }

  private compareMobility(query: RANData, stored: RANData): number {
    const diff = Math.abs(query.mobilityIndex - stored.mobilityIndex);
    return Math.max(0, 1 - (diff / 100)); // 0-100 range
  }

  private comparePerformance(query: RANData, stored: RANData): RANPerformanceComparison {
    const queryMetrics = this.extractPerformanceMetrics(query);
    const storedMetrics = this.extractPerformanceMetrics(stored);

    return {
      throughput: this.compareMetric(queryMetrics.throughput_score, storedMetrics.throughput_score),
      latency: this.compareMetric(queryMetrics.latency_score, storedMetrics.latency_score),
      signal: this.compareMetric(queryMetrics.signal_score, storedMetrics.signal_score),
      energy: this.compareMetric(queryMetrics.energy_efficiency, storedMetrics.energy_efficiency),
      coverage: this.compareMetric(queryMetrics.coverage_score, storedMetrics.coverage_score),
      mobility: this.compareMetric(queryMetrics.mobility_score, storedMetrics.mobility_score)
    };
  }

  private compareMetric(query: number, stored: number): number {
    return query >= stored ? 1 : query / stored;
  }

  private generateRANRecommendation(
    queryData: RANData,
    storedData: RANData,
    similarity: number
  ): string {
    if (similarity < 0.5) return 'Low similarity - use with caution';

    const improvements = this.identifyPotentialImprovements(queryData, storedData);
    if (improvements.length > 0) {
      return `Potential improvements: ${improvements.join(', ')}`;
    }

    return 'Similar conditions - recommended approach';
  }

  private identifyPotentialImprovements(query: RANData, stored: RANData): string[] {
    const improvements: string[] = [];

    if (stored.throughput > query.throughput * 1.1) {
      improvements.push('throughput increase');
    }
    if (stored.latency < query.latency * 0.9) {
      improvements.push('latency reduction');
    }
    if (stored.energyConsumption < query.energyConsumption * 0.9) {
      improvements.push('energy efficiency');
    }
    if (stored.signalStrength > query.signalStrength + 3) {
      improvements.push('signal improvement');
    }

    return improvements;
  }

  private extractPatterns(memory: any): string[] {
    // Extract patterns from stored memory
    const patterns: string[] = [];

    if (memory.pattern.ranData) {
      const data = memory.pattern.ranData;

      // Identify performance patterns
      if (data.throughput > 800) patterns.push('high-throughput');
      if (data.latency < 30) patterns.push('low-latency');
      if (data.energyConsumption < 80) patterns.push('energy-efficient');
      if (data.mobilityIndex > 70) patterns.push('high-mobility');
      if (data.coverageHoleCount < 5) patterns.push('good-coverage');
    }

    return patterns;
  }

  private extractPatternsFromResults(memories: any[]): string[] {
    const allPatterns = new Set<string>();

    memories.forEach(memory => {
      const patterns = this.extractPatterns(memory);
      patterns.forEach(pattern => allPatterns.add(pattern));
    });

    return Array.from(allPatterns);
  }

  private async setupCacheWarmer() {
    // Pre-warm cache with common RAN patterns
    const commonPatterns = [
      { throughput: 500, latency: 50, signalStrength: -75, userCount: 50 },
      { throughput: 800, latency: 30, signalStrength: -70, userCount: 80 },
      { throughput: 300, latency: 80, signalStrength: -85, userCount: 30 }
    ];

    for (const pattern of commonPatterns) {
      await this.storeRANPattern('cache-warmer', pattern as RANData);
    }

    console.log('Cache warmed with common RAN patterns');
  }

  private async initializeIndexOptimization() {
    // Optimize HNSW index for RAN workloads
    console.log('Optimizing AgentDB indexes for RAN workloads');

    // Would run actual index optimization here
    // For now, just log that it's initialized
  }
}

// RAN-specific data structures
interface RANData {
  throughput: number;          // Mbps
  latency: number;            // ms
  packetLoss: number;         // 0-1
  signalStrength: number;     // dBm
  interference: number;       // 0-1
  energyConsumption: number;  // Watts
  userCount: number;         // Number of active users
  mobilityIndex: number;     // 0-100
  coverageHoleCount: number;  // Number of coverage holes
  handoverCount?: number;     // Handover frequency
  [key: string]: any;
}

interface RANMetadata {
  location?: string;
  timeOfDay?: string;
  cellId?: string;
  technology?: string;       // 4G, 5G, etc.
  weather?: string;
  event?: string;
}

interface RANPattern {
  id: string;
  type: string;
  domain: string;
  ranData: RANData;
  metadata: RANMetadata;
  embedding: number[];
  confidence: number;
  usage_count: number;
  success_count: number;
  created_at: number;
  last_used: number;
  performance_metrics: RANPerformanceMetrics;
}

interface RANPerformanceMetrics {
  throughput_score: number;
  latency_score: number;
  signal_score: number;
  energy_efficiency: number;
  coverage_score: number;
  mobility_score: number;
}

interface RANSearchOptions {
  domain?: string;
  k?: number;
  useMMR?: boolean;
  synthesizeContext?: boolean;
  filters?: RANFilters;
  hybridWeights?: {
    vectorSimilarity: number;
    metadataScore: number;
  };
  optimizeMemory?: boolean;
}

interface RANFilters {
  domain?: string;
  confidence?: number;
  timestamp?: {
    after?: number;
    before?: number;
  };
  performanceThreshold?: number;
}

interface RANSearchResult {
  memories: Array<{
    similarity?: number;
    ranSimilarity?: number;
    performanceComparison?: RANPerformanceComparison;
    recommendation?: string;
    pattern: RANPattern;
  }>;
  context: string;
  patterns: string[];
  optimization: any;
}

interface RANPerformanceComparison {
  throughput: number;
  latency: number;
  signal: number;
  energy: number;
  coverage: number;
  mobility: number;
}

interface QuantizationConfig {
  type: 'binary' | 'scalar' | 'product' | 'none';
  bits: number;
  blockSize: number;
}

interface CachedPattern {
  pattern: any;
  timestamp: number;
}

class RANFastPatternSearch {
  private adapter: RANAgentDBAdapter;
  private searchIndex: Map<string, number[]>; // Quick lookup index
  private recentSearches: Map<string, RANSearchResult>;

  async initialize() {
    this.adapter = new RANAgentDBAdapter();
    await this.adapter.initialize();
    this.searchIndex = new Map();
    this.recentSearches = new Map();
    await this.buildSearchIndex();
  }

  async buildSearchIndex() {
    // Build fast search index for common RAN patterns
    const domains = ['energy-optimization', 'mobility-optimization', 'coverage-optimization', 'capacity-optimization'];

    for (const domain of domains) {
      const embedding = await this.createDomainEmbedding(domain);
      this.searchIndex.set(domain, embedding);
    }

    console.log('Fast search index built for RAN domains');
  }

  async ultraFastSearch(queryRANData: RANData, options: RANSearchOptions = {}): Promise<RANSearchResult> {
    const startTime = Date.now();

    // Check recent searches cache
    const searchKey = this.generateSearchKey(queryRANData, options);
    const cached = this.recentSearches.get(searchKey);

    if (cached && (Date.now() - this.getLastAccessTime(searchKey)) < 30000) { // 30 second cache
      console.log(`Ultra-fast cache hit in ${Date.now() - startTime}ms`);
      return cached;
    }

    // Use domain-based optimization
    const domain = this.classifyRANDomain(queryRANData);
    const optimizedOptions = this.optimizeSearchOptions(domain, options);

    // Perform fast search
    const result = await this.adapter.retrieveSimilarRANPatterns(queryRANData, optimizedOptions);

    // Cache result
    this.recentSearches.set(searchKey, result);
    this.updateLastAccessTime(searchKey);

    const searchTime = Date.now() - startTime;
    console.log(`Ultra-fast search completed in ${searchTime}ms for domain: ${domain}`);

    return result;
  }

  private optimizeSearchOptions(domain: string, options: RANSearchOptions): RANSearchOptions {
    // Optimize search parameters based on domain
    const domainOptimizations: { [domain: string]: Partial<RANSearchOptions> } = {
      'energy-optimization': {
        k: 5,
        hybridWeights: { vectorSimilarity: 0.6, metadataScore: 0.4 },
        filters: { performanceThreshold: 0.7 }
      },
      'mobility-optimization': {
        k: 8,
        useMMR: true,
        synthesizeContext: true
      },
      'coverage-optimization': {
        k: 10,
        hybridWeights: { vectorSimilarity: 0.7, metadataScore: 0.3 }
      },
      'capacity-optimization': {
        k: 6,
        filters: { confidence: 0.8 }
      }
    };

    const domainDefaults = domainOptimizations[domain] || {};
    return { ...domainDefaults, ...options };
  }

  private classifyRANDomain(ranData: RANData): string {
    if (ranData.energyConsumption > 150) return 'energy-optimization';
    if (ranData.mobilityIndex > 70) return 'mobility-optimization';
    if (ranData.coverageHoleCount > 10) return 'coverage-optimization';
    if (ranData.throughput < 500) return 'capacity-optimization';
    return 'general-optimization';
  }

  private generateSearchKey(queryRANData: RANData, options: RANSearchOptions): string {
    const dataHash = this.hashRANData(queryRANData);
    const optionsHash = this.hashOptions(options);
    return `${dataHash}-${optionsHash}`;
  }

  private hashRANData(ranData: RANData): string {
    const keyFeatures = [
      Math.floor(ranData.throughput / 100),
      Math.floor(ranData.latency / 10),
      Math.floor(ranData.signalStrength / 10),
      Math.floor(ranData.userCount / 10)
    ].join('-');

    return this.simpleHash(keyFeatures);
  }

  private hashOptions(options: RANSearchOptions): string {
    const keyOptions = [
      options.domain || 'any',
      options.k || 10,
      options.useMMR ? 'mmr' : 'no-mmr'
    ].join('-');

    return this.simpleHash(keyOptions);
  }

  private simpleHash(input: string): string {
    let hash = 0;
    for (let i = 0; i < input.length; i++) {
      const char = input.charCodeAt(i);
      hash = ((hash << 5) - hash) + char;
      hash = hash & hash; // Convert to 32-bit integer
    }
    return Math.abs(hash).toString(36);
  }

  private async createDomainEmbedding(domain: string): Promise<number[]> {
    // Create embedding for domain classification
    const domainFeatures = {
      'energy-optimization': [1, 0, 0, 0],
      'mobility-optimization': [0, 1, 0, 0],
      'coverage-optimization': [0, 0, 1, 0],
      'capacity-optimization': [0, 0, 0, 1]
    };

    const features = domainFeatures[domain] || [0.25, 0.25, 0.25, 0.25];

    // Pad to standard size
    while (features.length < 1536) {
      features.push(0);
    }

    return features.slice(0, 1536);
  }

  private getLastAccessTime(key: string): number {
    // Would store actual access times
    return Date.now() - 60000; // Assume 1 minute ago
  }

  private updateLastAccessTime(key: string) {
    // Would update actual access times
    // For now, this is a placeholder
  }
}

class RANDistributedTrainingCoordinator {
  private agentDB: AgentDBAdapter;
  private nodeCoordinator: QUICNodeCoordinator;
  private trainingNodes: Map<string, TrainingNode>;
  private syncIntervals: Map<string, NodeJS.Timeout>;

  async initialize() {
    this.agentDB = await createAgentDBAdapter({
      dbPath: '.agentdb/ran-distributed.db',
      enableQUICSync: true,
      enableLearning: true,
      enableReasoning: true,
      cacheSize: 5000,
      syncPort: 4433,
      syncPeers: [], // Will be populated dynamically
      syncInterval: 1000,  // 1 second sync
      syncBatchSize: 100,
      compression: true
    });

    this.nodeCoordinator = new QUICNodeCoordinator();
    this.trainingNodes = new Map();
    this.syncIntervals = new Map();

    await this.initializeNodeCoordinator();
    await this.startTrainingCoordination();
  }

  private async initializeNodeCoordinator() {
    await this.nodeCoordinator.initialize({
      nodeId: this.getNodeId(),
      port: 4433,
      maxPeers: 10,
      heartbeatInterval: 5000,
      enableCompression: true,
      enableEncryption: true
    });

    // Set up peer discovery
    this.nodeCoordinator.on('peerConnected', (peerId: string) => {
      console.log(`Training node connected: ${peerId}`);
      this.setupPeerSync(peerId);
    });

    this.nodeCoordinator.on('peerDisconnected', (peerId: string) => {
      console.log(`Training node disconnected: ${peerId}`);
      this.cleanupPeerSync(peerId);
    });
  }

  private async startTrainingCoordination() {
    // Start periodic coordination tasks
    setInterval(async () => {
      await this.coordinateTrainingProgress();
    }, 10000); // Every 10 seconds

    setInterval(async () => {
      await this.syncPerformanceMetrics();
    }, 30000); // Every 30 seconds

    setInterval(async () => {
      await this.balanceTrainingLoad();
    }, 60000); // Every minute
  }

  async registerTrainingNode(nodeConfig: TrainingNodeConfig): Promise<string> {
    const nodeId = nodeConfig.id || this.generateNodeId();

    const trainingNode: TrainingNode = {
      id: nodeId,
      ...nodeConfig,
      status: 'active',
      lastSeen: Date.now(),
      trainingProgress: 0,
      performanceMetrics: {},
      connectedPeers: new Set()
    };

    this.trainingNodes.set(nodeId, trainingNode);

    // Set up sync for this node
    await this.setupNodeSync(nodeId);

    // Announce node to network
    await this.announceNodeToNetwork(trainingNode);

    console.log(`Training node registered: ${nodeId}`);
    return nodeId;
  }

  private async setupNodeSync(nodeId: string) {
    // Clear existing sync interval if exists
    const existingInterval = this.syncIntervals.get(nodeId);
    if (existingInterval) {
      clearInterval(existingInterval);
    }

    // Set up new sync interval
    const syncInterval = setInterval(async () => {
      await this.syncWithNode(nodeId);
    }, 2000); // Sync every 2 seconds

    this.syncIntervals.set(nodeId, syncInterval);
  }

  private async syncWithNode(nodeId: string) {
    const node = this.trainingNodes.get(nodeId);
    if (!node || node.status !== 'active') return;

    try {
      // Get recent training experiences to sync
      const recentExperiences = await this.getRecentTrainingExperiences(nodeId);

      if (recentExperiences.length > 0) {
        // Package experiences for transmission
        const syncPackage = {
          nodeId: this.getNodeId(),
          experiences: recentExperiences,
          timestamp: Date.now(),
          compression: true
        };

        // Send via QUIC
        await this.nodeCoordinator.send(nodeId, syncPackage);

        // Mark experiences as synced
        await this.markExperiencesSynced(recentExperiences);

        node.lastSeen = Date.now();
      }

    } catch (error) {
      console.error(`Failed to sync with node ${nodeId}:`, error);
      node.status = 'error';
    }
  }

  private async getRecentTrainingExperiences(nodeId: string): Promise<Array<TrainingExperience>> {
    // Get experiences that haven't been synced to this node
    const embedding = await computeEmbedding(`training-experiences-${nodeId}`);

    const result = await this.agentDB.retrieveWithReasoning(embedding, {
      domain: 'ran-training-experiences',
      k: 50,
      filters: {
        synced_to_nodes: { $ne: nodeId },
        created_at: { $gte: Date.now() - 60000 } // Last minute
      }
    });

    return result.memories.map(m => m.pattern);
  }

  private async markExperiencesSynced(experiences: Array<TrainingExperience>) {
    for (const experience of experiences) {
      // Update experience to mark as synced
      await this.agentDB.updatePattern(experience.id, {
        $addToSet: { synced_to_nodes: experience.targetNodeId || 'all' }
      });
    }
  }

  private async coordinateTrainingProgress() {
    // Collect training progress from all nodes
    const progressReport: DistributedTrainingReport = {
      timestamp: Date.now(),
      nodeId: this.getNodeId(),
      totalNodes: this.trainingNodes.size,
      activeNodes: Array.from(this.trainingNodes.values()).filter(n => n.status === 'active').length,
      totalExperiences: 0,
      averageProgress: 0,
      globalPerformance: {}
    };

    let totalProgress = 0;
    let totalExperiences = 0;

    for (const node of this.trainingNodes.values()) {
      if (node.status === 'active') {
        totalProgress += node.trainingProgress;
        totalExperiences += node.experienceCount || 0;
      }
    }

    progressReport.totalExperiences = totalExperiences;
    progressReport.averageProgress = totalExperiences > 0 ? totalProgress / this.trainingNodes.size : 0;

    // Calculate global performance metrics
    progressReport.globalPerformance = await this.calculateGlobalPerformance();

    // Store coordination report
    await this.storeCoordinationReport(progressReport);

    // Broadcast to all nodes
    await this.broadcastCoordinationReport(progressReport);
  }

  private async calculateGlobalPerformance(): Promise<GlobalPerformanceMetrics> {
    const allMetrics = Array.from(this.trainingNodes.values())
      .filter(node => node.status === 'active')
      .map(node => node.performanceMetrics);

    if (allMetrics.length === 0) {
      return {
        avgLoss: 0,
        avgAccuracy: 0,
        avgReward: 0,
        convergenceRate: 0
      };
    }

    const totalLoss = allMetrics.reduce((sum, m) => sum + (m.loss || 0), 0);
    const totalAccuracy = allMetrics.reduce((sum, m) => sum + (m.accuracy || 0), 0);
    const totalReward = allMetrics.reduce((sum, m) => sum + (m.reward || 0), 0);
    const convergedNodes = allMetrics.filter(m => m.converged).length;

    return {
      avgLoss: totalLoss / allMetrics.length,
      avgAccuracy: totalAccuracy / allMetrics.length,
      avgReward: totalReward / allMetrics.length,
      convergenceRate: convergedNodes / allMetrics.length
    };
  }

  private async syncPerformanceMetrics() {
    // Sync performance metrics across all nodes
    const currentMetrics = await this.getCurrentNodeMetrics();

    const metricsPackage = {
      type: 'performance-metrics',
      nodeId: this.getNodeId(),
      metrics: currentMetrics,
      timestamp: Date.now()
    };

    // Broadcast to all active nodes
    for (const node of this.trainingNodes.values()) {
      if (node.status === 'active' && node.id !== this.getNodeId()) {
        await this.nodeCoordinator.send(node.id, metricsPackage);
      }
    }
  }

  private async getCurrentNodeMetrics(): Promise<NodePerformanceMetrics> {
    // Get current node's performance metrics
    const embedding = await computeEmbedding(`performance-metrics-${this.getNodeId()}`);

    const result = await this.agentDB.retrieveWithReasoning(embedding, {
      domain: 'ran-performance-metrics',
      k: 10,
      filters: {
        nodeId: this.getNodeId(),
        timestamp: { $gte: Date.now() - 300000 } // Last 5 minutes
      }
    });

    const metrics = result.memories.map(m => m.pattern);

    // Calculate aggregate metrics
    if (metrics.length === 0) {
      return {
        loss: 0,
        accuracy: 0,
        reward: 0,
        experiences_processed: 0,
        cpu_usage: 0,
        memory_usage: 0,
        converged: false
      };
    }

    return {
      loss: metrics.reduce((sum, m) => sum + m.loss, 0) / metrics.length,
      accuracy: metrics.reduce((sum, m) => sum + m.accuracy, 0) / metrics.length,
      reward: metrics.reduce((sum, m) => sum + m.reward, 0) / metrics.length,
      experiences_processed: metrics.reduce((sum, m) => sum + m.experiences_processed, 0),
      cpu_usage: metrics[metrics.length - 1]?.cpu_usage || 0,
      memory_usage: metrics[metrics.length - 1]?.memory_usage || 0,
      converged: metrics[metrics.length - 1]?.converged || false
    };
  }

  private async balanceTrainingLoad() {
    // Analyze load across nodes and rebalance if necessary
    const nodeLoads = await this.analyzeNodeLoads();

    const overloadedNodes = nodeLoads.filter(n => n.load > 0.8);
    const underloadedNodes = nodeLoads.filter(n => n.load < 0.4);

    if (overloadedNodes.length > 0 && underloadedNodes.length > 0) {
      await this.rebalanceTrainingLoad(overloadedNodes, underloadedNodes);
    }
  }

  private async analyzeNodeLoads(): Promise<Array<NodeLoadInfo>> {
    const loadInfos: Array<NodeLoadInfo> = [];

    for (const node of this.trainingNodes.values()) {
      if (node.status === 'active') {
        const load = await this.calculateNodeLoad(node);
        loadInfos.push({
          nodeId: node.id,
          load,
          cpu_usage: node.performanceMetrics.cpu_usage || 0,
          memory_usage: node.performanceMetrics.memory_usage || 0,
          experience_count: node.experienceCount || 0
        });
      }
    }

    return loadInfos;
  }

  private async calculateNodeLoad(node: TrainingNode): Promise<number> {
    // Calculate load based on CPU, memory, and experience processing
    const cpuWeight = 0.4;
    const memoryWeight = 0.3;
    const experienceWeight = 0.3;

    const cpuLoad = (node.performanceMetrics.cpu_usage || 0) / 100;
    const memoryLoad = (node.performanceMetrics.memory_usage || 0) / 100;
    const experienceLoad = Math.min(1, (node.experienceCount || 0) / 1000);

    return cpuLoad * cpuWeight + memoryLoad * memoryWeight + experienceLoad * experienceWeight;
  }

  private async rebalanceTrainingLoad(
    overloadedNodes: Array<NodeLoadInfo>,
    underloadedNodes: Array<NodeLoadInfo>
  ) {
    // Suggest workload redistribution
    const rebalanceSuggestions: Array<RebalanceSuggestion> = [];

    for (const overloaded of overloadedNodes) {
      // Find least loaded underloaded node
      const target = underloadedNodes.reduce((min, current) =>
        current.load < min.load ? current : min
      );

      if (target) {
        rebalanceSuggestions.push({
          sourceNodeId: overloaded.nodeId,
          targetNodeId: target.nodeId,
          suggestedTransfer: Math.floor((overloaded.load - target.load) * 100),
          reason: 'load_balancing'
        });
      }
    }

    // Store rebalance suggestions
    await this.storeRebalanceSuggestions(rebalanceSuggestions);

    // Notify nodes about rebalancing
    for (const suggestion of rebalanceSuggestions) {
      await this.notifyNodeRebalance(suggestion);
    }
  }

  private getNodeId(): string {
    // Get or generate node ID
    if (typeof process !== 'undefined' && process.env.NODE_ID) {
      return process.env.NODE_ID;
    }

    // Generate persistent node ID
    const persistentId = this.getPersistentNodeId();
    return persistentId || `ran-node-${Math.random().toString(36).substr(2, 9)}`;
  }

  private getPersistentNodeId(): string | null {
    // Would load from persistent storage
    return null; // Placeholder
  }

  private generateNodeId(): string {
    return `ran-node-${Date.now()}-${Math.random().toString(36).substr(2, 6)}`;
  }
}

// Supporting classes and interfaces
class QUICNodeCoordinator {
  private nodeId: string;
  private peers: Map<string, PeerConnection>;
  private config: NodeCoordinatorConfig;

  async initialize(config: NodeCoordinatorConfig) {
    this.config = config;
    this.nodeId = config.nodeId;
    this.peers = new Map();

    // Initialize QUIC server
    await this.startQUICServer();

    // Connect to known peers
    await this.connectToPeers(config.knownPeers || []);
  }

  private async startQUICServer() {
    // Would initialize QUIC server
    console.log(`QUIC server started for node ${this.nodeId} on port ${this.config.port}`);
  }

  private async connectToPeers(peerAddresses: string[]) {
    for (const address of peerAddresses) {
      try {
        await this.connectToPeer(address);
      } catch (error) {
        console.error(`Failed to connect to peer ${address}:`, error);
      }
    }
  }

  private async connectToPeer(address: string) {
    // Would establish QUIC connection to peer
    console.log(`Connecting to peer: ${address}`);
  }

  async send(peerId: string, data: any): Promise<void> {
    const peer = this.peers.get(peerId);
    if (!peer) {
      throw new Error(`Peer not connected: ${peerId}`);
    }

    // Send data via QUIC
    await peer.send(data);
  }

  on(event: string, callback: (data: any) => void) {
    // Set up event handlers
  }

  private cleanupPeerSync(peerId: string) {
    const peer = this.peers.get(peerId);
    if (peer) {
      peer.close();
      this.peers.delete(peerId);
    }
  }
}

interface TrainingNodeConfig {
  id?: string;
  capabilities: string[];
  maxBatchSize?: number;
  learningRate?: number;
  architecture?: string;
}

interface TrainingNode {
  id: string;
  capabilities: string[];
  maxBatchSize: number;
  learningRate: number;
  architecture: string;
  status: 'active' | 'inactive' | 'error';
  lastSeen: number;
  trainingProgress: number;
  performanceMetrics: NodePerformanceMetrics;
  connectedPeers: Set<string>;
  experienceCount?: number;
}

interface NodePerformanceMetrics {
  loss: number;
  accuracy: number;
  reward: number;
  experiences_processed: number;
  cpu_usage: number;
  memory_usage: number;
  converged: boolean;
}

interface NodeCoordinatorConfig {
  nodeId: string;
  port: number;
  maxPeers: number;
  heartbeatInterval: number;
  enableCompression: boolean;
  enableEncryption: boolean;
  knownPeers?: string[];
}

interface PeerConnection {
  send(data: any): Promise<void>;
  close(): void;
}

interface TrainingExperience {
  id: string;
  nodeId: string;
  targetNodeId?: string;
  experience: any;
  timestamp: number;
  synced_to_nodes?: string[];
}

interface DistributedTrainingReport {
  timestamp: number;
  nodeId: string;
  totalNodes: number;
  activeNodes: number;
  totalExperiences: number;
  averageProgress: number;
  globalPerformance: GlobalPerformanceMetrics;
}

interface GlobalPerformanceMetrics {
  avgLoss: number;
  avgAccuracy: number;
  avgReward: number;
  convergenceRate: number;
}

interface NodeLoadInfo {
  nodeId: string;
  load: number;
  cpu_usage: number;
  memory_usage: number;
  experience_count: number;
}

interface RebalanceSuggestion {
  sourceNodeId: string;
  targetNodeId: string;
  suggestedTransfer: number;
  reason: string;
}

class RANPatternRecognition {
  private agentDB: AgentDBAdapter;
  private patternModels: Map<string, PatternModel>;
  private recognitionCache: Map<string, PatternRecognitionResult>;

  async initialize() {
    this.agentDB = await createAgentDBAdapter({
      dbPath: '.agentdb/ran-patterns.db',
      enableLearning: true,
      enableReasoning: true,
      cacheSize: 4000,
      quantizationType: 'scalar'
    });

    this.patternModels = new Map();
    this.recognitionCache = new Map();
    await this.initializePatternModels();
  }

  private async initializePatternModels() {
    // Initialize different pattern recognition models
    const modelTypes = [
      'performance-degradation',
      'signal-fluctuation',
      'traffic-anomaly',
      'mobility-pattern',
      'energy-inefficiency'
    ];

    for (const modelType of modelTypes) {
      const model = await this.createPatternModel(modelType);
      this.patternModels.set(modelType, model);
    }

    console.log(`Initialized ${modelTypes.length} pattern recognition models`);
  }

  private async createPatternModel(modelType: string): Promise<PatternModel> {
    // Create pattern model based on type
    const modelConfig = this.getModelConfig(modelType);

    return {
      type: modelType,
      features: modelConfig.features,
      thresholds: modelConfig.thresholds,
      weights: modelConfig.weights,
      algorithm: modelConfig.algorithm,
      trained: false,
      accuracy: 0,
      lastTrained: null
    };
  }

  private getModelConfig(modelType: string): PatternModelConfig {
    const configs: { [type: string]: PatternModelConfig } = {
      'performance-degradation': {
        features: ['throughput', 'latency', 'packetLoss', 'signalStrength'],
        thresholds: { throughput_degradation: 0.3, latency_increase: 0.5, packetLoss_increase: 0.02 },
        weights: { throughput: 0.4, latency: 0.3, packetLoss: 0.2, signalStrength: 0.1 },
        algorithm: 'isolation-forest'
      },
      'signal-fluctuation': {
        features: ['signalStrength', 'interference', 'snr', 'rsrq'],
        thresholds: { fluctuation_amplitude: 10, frequency_threshold: 0.1 },
        weights: { signalStrength: 0.4, interference: 0.3, snr: 0.2, rsrq: 0.1 },
        algorithm: 'fft-analysis'
      },
      'traffic-anomaly': {
        features: ['userCount', 'throughput', 'mobilityIndex', 'handoverCount'],
        thresholds: { user_spike: 2.0, throughput_anomaly: 0.5, mobility_surge: 0.7 },
        weights: { userCount: 0.3, throughput: 0.3, mobilityIndex: 0.2, handoverCount: 0.2 },
        algorithm: 'statistical-outlier'
      },
      'mobility-pattern': {
        features: ['mobilityIndex', 'handoverCount', 'trajectory_length', 'velocity_changes'],
        thresholds: { high_mobility: 0.8, frequent_handovers: 0.3, irregular_movement: 0.6 },
        weights: { mobilityIndex: 0.4, handoverCount: 0.3, trajectory_length: 0.2, velocity_changes: 0.1 },
        algorithm: 'sequential-pattern'
      },
      'energy-inefficiency': {
        features: ['energyConsumption', 'throughput', 'userCount', 'signalStrength'],
        thresholds: { high_energy: 150, low_efficiency: 0.1, power_waste: 0.3 },
        weights: { energyConsumption: 0.4, throughput: 0.3, userCount: 0.2, signalStrength: 0.1 },
        algorithm: 'regression-analysis'
      }
    };

    return configs[modelType] || configs['performance-degradation'];
  }

  async recognizePatterns(ranData: RANData, timeWindow: number = 300000): Promise<PatternRecognitionResult> {
    const startTime = Date.now();

    // Check cache first
    const cacheKey = this.generatePatternCacheKey(ranData, timeWindow);
    const cached = this.recognitionCache.get(cacheKey);

    if (cached && (Date.now() - cached.timestamp) < 60000) { // 1 minute cache
      return cached.result;
    }

    // Get time series data for pattern analysis
    const timeSeriesData = await this.getTimeSeriesData(ranData, timeWindow);

    const recognizedPatterns: Array<RecognizedPattern> = [];

    // Run each pattern model
    for (const [modelType, model] of this.patternModels) {
      try {
        const pattern = await this.runPatternModel(model, timeSeriesData);
        if (pattern.confidence > 0.5) {
          recognizedPatterns.push(pattern);
        }
      } catch (error) {
        console.error(`Pattern model ${modelType} failed:`, error);
      }
    }

    // Analyze pattern combinations
    const patternCombinations = await this.analyzePatternCombinations(recognizedPatterns);

    // Generate insights and recommendations
    const insights = await this.generatePatternInsights(recognizedPatterns, ranData);
    const recommendations = await this.generateRecommendations(recognizedPatterns, insights);

    const result: PatternRecognitionResult = {
      patterns: recognizedPatterns,
      combinations: patternCombinations,
      insights,
      recommendations,
      confidence: this.calculateOverallConfidence(recognizedPatterns),
      timestamp: Date.now(),
      processingTime: Date.now() - startTime
    };

    // Cache result
    this.recognitionCache.set(cacheKey, {
      result,
      timestamp: Date.now()
    });

    // Store recognition result for learning
    await this.storeRecognitionResult(result);

    console.log(`Pattern recognition completed in ${result.processingTime}ms - found ${recognizedPatterns.length} patterns`);

    return result;
  }

  private async getTimeSeriesData(ranData: RANData, timeWindow: number): Promise<TimeSeriesData> {
    // Get historical data for the same location/cell
    const embedding = await computeEmbedding(`timeseries-${ranData.cellId || 'unknown'}`);

    const result = await this.agentDB.retrieveWithReasoning(embedding, {
      domain: 'ran-time-series',
      k: 100,
      filters: {
        timestamp: { $gte: Date.now() - timeWindow },
        cellId: { $eq: ranData.cellId || 'unknown' }
      },
      sort: { timestamp: 1 }
    });

    const dataPoints = result.memories.map(m => m.pattern);

    return {
      dataPoints,
      timeWindow,
      samplingRate: this.calculateSamplingRate(dataPoints),
      completeness: this.calculateDataCompleteness(dataPoints, timeWindow)
    };
  }

  private async runPatternModel(model: PatternModel, timeSeriesData: TimeSeriesData): Promise<RecognizedPattern> {
    // Extract features for this model
    const features = this.extractFeatures(model.features, timeSeriesData);

    // Run pattern recognition algorithm
    let confidence: number;
    let details: any = {};

    switch (model.algorithm) {
      case 'isolation-forest':
        ({ confidence, details } = await this.runIsolationForest(features, model.thresholds));
        break;
      case 'fft-analysis':
        ({ confidence, details } = await this.runFFTAnalysis(features, model.thresholds));
        break;
      case 'statistical-outlier':
        ({ confidence, details } = await this.runStatisticalOutlier(features, model.thresholds));
        break;
      case 'sequential-pattern':
        ({ confidence, details } = await this.runSequentialPattern(features, model.thresholds));
        break;
      case 'regression-analysis':
        ({ confidence, details } = await this.runRegressionAnalysis(features, model.thresholds));
        break;
      default:
        confidence = 0;
        details = {};
    }

    // Apply model weights
    const weightedConfidence = this.applyModelWeights(confidence, features, model.weights);

    return {
      type: model.type,
      confidence: weightedConfidence,
      severity: this.calculateSeverity(weightedConfidence, details),
      details,
      features: features,
      modelAccuracy: model.accuracy
    };
  }

  private extractFeatures(featureNames: string[], timeSeriesData: TimeSeriesData): FeatureVector {
    const features: FeatureVector = {};

    for (const featureName of featureNames) {
      const values = timeSeriesData.dataPoints.map(point => point[featureName] || 0);

      features[featureName] = {
        values,
        mean: this.calculateMean(values),
        std: this.calculateStd(values),
        min: Math.min(...values),
        max: Math.max(...values),
        trend: this.calculateTrend(values),
        variance: this.calculateVariance(values)
      };
    }

    return features;
  }

  private async runIsolationForest(features: FeatureVector, thresholds: any): Promise<{ confidence: number, details: any }> {
    // Simplified isolation forest implementation
    const anomalyScores = Object.values(features).map(feature => {
      const zScore = Math.abs(feature.mean - feature.std) / (feature.std + 1e-8);
      return Math.min(zScore / 3, 1); // Normalize to 0-1
    });

    const maxScore = Math.max(...anomalyScores);
    const threshold = 0.5;

    return {
      confidence: maxScore > threshold ? maxScore : 0,
      details: {
        anomalyScores,
        maxScore,
        threshold,
        anomalousFeatures: Object.keys(features).filter((key, index) => anomalyScores[index] > threshold)
      }
    };
  }

  private async runFFTAnalysis(features: FeatureVector, thresholds: any): Promise<{ confidence: number, details: any }> {
    // Simplified FFT analysis for signal fluctuation patterns
    const signalFeatures = features.signalStrength || features.throughput;

    if (!signalFeatures) {
      return { confidence: 0, details: {} };
    }

    // Calculate dominant frequency components
    const frequencies = this.calculateFrequencyComponents(signalFeatures.values);

    // Find high-frequency components indicating fluctuation
    const highFreqPower = frequencies
      .filter((freq, index) => index > frequencies.length / 2)
      .reduce((sum, freq) => sum + freq.power, 0);

    const totalPower = frequencies.reduce((sum, freq) => sum + freq.power, 0);
    const highFreqRatio = totalPower > 0 ? highFreqPower / totalPower : 0;

    return {
      confidence: Math.min(highFreqRatio * 2, 1),
      details: {
        frequencies,
        highFreqPower,
        totalPower,
        highFreqRatio,
        dominantFrequency: frequencies.reduce((max, freq) => freq.power > max.power ? freq : max).frequency
      }
    };
  }

  private async runStatisticalOutlier(features: FeatureVector, thresholds: any): Promise<{ confidence: number, details: any }> {
    // Z-score based outlier detection
    const outlierScores = Object.entries(features).map(([name, feature]) => {
      const currentZScore = Math.abs((feature.values[feature.values.length - 1] - feature.mean) / (feature.std + 1e-8));
      return { name, score: currentZScore };
    });

    const maxScore = Math.max(...outlierScores.map(o => o.score));
    const threshold = 2.0; // 2 standard deviations

    return {
      confidence: Math.min(maxScore / threshold, 1),
      details: {
        outlierScores,
        maxScore,
        threshold,
        outliers: outlierScores.filter(o => o.score > threshold).map(o => o.name)
      }
    };
  }

  private async runSequentialPattern(features: FeatureVector, thresholds: any): Promise<{ confidence: number, details: any }> {
    // Pattern detection in sequential data
    const sequenceFeatures = this.extractSequentialFeatures(features);

    // Look for repeating patterns
    const patterns = this.detectRepeatingPatterns(sequenceFeatures);

    const maxPattern = patterns.reduce((max, pattern) =>
      pattern.confidence > max.confidence ? pattern : max,
      { confidence: 0, pattern: '', frequency: 0 }
    );

    return {
      confidence: maxPattern.confidence,
      details: {
        patterns,
        maxPattern: maxPattern.pattern,
        frequency: maxPattern.frequency
      }
    };
  }

  private async runRegressionAnalysis(features: FeatureVector, thresholds: any): Promise<{ confidence: number, details: any }> {
    // Simple linear regression to detect trends
    const regressionScores = Object.entries(features).map(([name, feature]) => {
      const slope = feature.trend || 0;
      const rSquared = this.calculateRSquared(feature.values);
      return { name, slope, rSquared };
    });

    // Find strongest trend
    const strongestTrend = regressionScores.reduce((max, current) =>
      Math.abs(current.slope) > Math.abs(max.slope) ? current : max,
      { name: '', slope: 0, rSquared: 0 }
    );

    return {
      confidence: Math.abs(strongestTrend.slope) * strongestTrend.rSquared,
      details: {
        regressionScores,
        strongestTrend,
        trend: strongestTrend.slope,
        rSquared: strongestTrend.rSquared
      }
    };
  }

  private calculateMean(values: number[]): number {
    return values.reduce((sum, val) => sum + val, 0) / values.length;
  }

  private calculateStd(values: number[]): number {
    const mean = this.calculateMean(values);
    const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;
    return Math.sqrt(variance);
  }

  private calculateVariance(values: number[]): number {
    const mean = this.calculateMean(values);
    return values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;
  }

  private calculateTrend(values: number[]): number {
    if (values.length < 2) return 0;

    const n = values.length;
    const x = Array.from({ length: n }, (_, i) => i);
    const sumX = x.reduce((sum, val) => sum + val, 0);
    const sumY = values.reduce((sum, val) => sum + val, 0);
    const sumXY = x.reduce((sum, val, i) => sum + val * values[i], 0);
    const sumXX = x.reduce((sum, val) => sum + val * val, 0);

    const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
    return slope;
  }

  private calculateRSquared(values: number[]): number {
    if (values.length < 2) return 0;

    const trend = this.calculateTrend(values);
    const mean = this.calculateMean(values);

    const ssTotal = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0);
    const ssResidual = values.reduce((sum, val, i) => {
      const predicted = mean + trend * i;
      return sum + Math.pow(val - predicted, 2);
    }, 0);

    return ssTotal > 0 ? 1 - (ssResidual / ssTotal) : 0;
  }

  private calculateFrequencyComponents(values: number[]): Array<{ frequency: number, power: number }> {
    // Simplified FFT implementation
    const n = values.length;
    const frequencies: Array<{ frequency: number, power: number }> = [];

    for (let k = 0; k < n / 2; k++) {
      let real = 0;
      let imag = 0;

      for (let i = 0; i < n; i++) {
        const angle = -2 * Math.PI * k * i / n;
        real += values[i] * Math.cos(angle);
        imag += values[i] * Math.sin(angle);
      }

      const power = Math.sqrt(real * real + imag * imag) / n;
      frequencies.push({
        frequency: k,
        power: power
      });
    }

    return frequencies;
  }

  private extractSequentialFeatures(features: FeatureVector): SequentialFeatures {
    return {
      values: Object.values(features).map(f => f.values),
      changes: Object.values(features).map(f => this.calculateChanges(f.values)),
      slopes: Object.values(features).map(f => f.trend)
    };
  }

  private calculateChanges(values: number[]): number[] {
    const changes = [];
    for (let i = 1; i < values.length; i++) {
      changes.push(values[i] - values[i - 1]);
    }
    return changes;
  }

  private detectRepeatingPatterns(sequence: SequentialFeatures): Array<{ pattern: string, confidence: number, frequency: number }> {
    const patterns: Array<{ pattern: string, confidence: number, frequency: number }> = [];

    // Simple pattern detection - look for repeating value sequences
    const maxPatternLength = 5;

    for (let patternLength = 2; patternLength <= maxPatternLength; patternLength++) {
      for (let startIndex = 0; startIndex <= sequence.values[0].length - patternLength * 2; startIndex++) {
        const pattern = sequence.values[0].slice(startIndex, startIndex + patternLength);
        const patternString = pattern.map(v => v.toFixed(2)).join(',');

        // Count occurrences
        let occurrences = 0;
        for (let i = startIndex; i <= sequence.values[0].length - patternLength; i++) {
          const currentPattern = sequence.values[0].slice(i, i + patternLength);
          const currentString = currentPattern.map(v => v.toFixed(2)).join(',');

          if (currentString === patternString) {
            occurrences++;
          }
        }

        if (occurrences > 1) {
          const confidence = occurrences / (sequence.values[0].length / patternLength);
          patterns.push({
            pattern: patternString,
            confidence,
            frequency: occurrences
          });
        }
      }
    }

    return patterns;
  }

  private applyModelWeights(confidence: number, features: FeatureVector, weights: any): number {
    let weightedScore = 0;
    let totalWeight = 0;

    for (const [featureName, weight] of Object.entries(weights)) {
      if (features[featureName]) {
        const featureWeight = this.calculateFeatureWeight(features[featureName], weight);
        weightedScore += featureWeight;
        totalWeight += weight;
      }
    }

    return totalWeight > 0 ? (confidence * weightedScore) / totalWeight : confidence;
  }

  private calculateFeatureWeight(feature: any, weight: number): number {
    // Calculate feature weight based on its quality
    let qualityScore = 1;

    // Penalize features with high variance (unstable)
    if (feature.variance > feature.std * 2) {
      qualityScore *= 0.8;
    }

    // Reward features with clear trends
    if (Math.abs(feature.trend) > feature.std) {
      qualityScore *= 1.2;
    }

    return weight * qualityScore;
  }

  private calculateSeverity(confidence: number, details: any): 'low' | 'medium' | 'high' | 'critical' {
    if (confidence > 0.9) return 'critical';
    if (confidence > 0.7) return 'high';
    if (confidence > 0.5) return 'medium';
    return 'low';
  }

  private async analyzePatternCombinations(patterns: Array<RecognizedPattern>): Promise<PatternCombination[]> {
    const combinations: PatternCombination[] = [];

    // Analyze patterns that occur together
    for (let i = 0; i < patterns.length; i++) {
      for (let j = i + 1; j < patterns.length; j++) {
        const pattern1 = patterns[i];
        const pattern2 = patterns[j];

        const combination = this.analyzePatternPair(pattern1, pattern2);
        if (combination.correlation > 0.5) {
          combinations.push(combination);
        }
      }
    }

    return combinations;
  }

  private analyzePatternPair(pattern1: RecognizedPattern, pattern2: RecognizedPattern): PatternCombination {
    // Calculate correlation between patterns
    const correlation = this.calculatePatternCorrelation(pattern1, pattern2);

    return {
      patterns: [pattern1.type, pattern2.type],
      correlation,
      combinedConfidence: (pattern1.confidence + pattern2.confidence) / 2,
      combinedSeverity: this.combineSeverity(pattern1.severity, pattern2.severity),
      interaction: this.determineInteraction(pattern1, pattern2)
    };
  }

  private calculatePatternCorrelation(pattern1: RecognizedPattern, pattern2: RecognizedPattern): number {
    // Simplified correlation calculation
    // In practice, would use more sophisticated methods

    // Check if patterns are commonly related in RAN systems
    const relatedPairs: { [string, string]: number } = {
      ['performance-degradation', 'energy-inefficiency']: 0.8,
      ['signal-fluctuation', 'mobility-pattern']: 0.7,
      ['traffic-anomaly', 'performance-degradation']: 0.9,
      ['mobility-pattern', 'handover-frequency']: 0.6
    };

    const key = [pattern1.type, pattern2.type].sort().join(',') as keyof typeof relatedPairs;
    return relatedPairs[key] || 0.1;
  }

  private combineSeverity(severity1: string, severity2: string): string {
    const severityLevels = { low: 1, medium: 2, high: 3, critical: 4 };

    const level1 = severityLevels[severity1 as keyof typeof severityLevels];
    const level2 = severityLevels[severity2 as keyof typeof severityLevels];

    const combinedLevel = Math.max(level1, level2);

    return Object.keys(severityLevels).find(key => severityLevels[key as keyof typeof severityLevels] === combinedLevel) || 'medium';
  }

  private determineInteraction(pattern1: RecognizedPattern, pattern2: RecognizedPattern): 'causal' | 'correlated' | 'coincidental' {
    if (pattern1.confidence > 0.8 && pattern2.confidence > 0.8) {
      return 'causal';
    } else if (this.calculatePatternCorrelation(pattern1, pattern2) > 0.7) {
      return 'correlated';
    }
    return 'coincidental';
  }

  private async generatePatternInsights(patterns: Array<RecognizedPattern>, currentData: RANData): Promise<string[]> {
    const insights: string[] = [];

    // High-level insights
    if (patterns.length === 0) {
      insights.push('No significant patterns detected - system operating normally');
    } else {
      insights.push(`Detected ${patterns.length} significant patterns requiring attention`);
    }

    // Severity-based insights
    const criticalPatterns = patterns.filter(p => p.severity === 'critical');
    if (criticalPatterns.length > 0) {
      insights.push(`${criticalPatterns.length} critical patterns detected - immediate action required`);
    }

    // Pattern-specific insights
    for (const pattern of patterns) {
      switch (pattern.type) {
        case 'performance-degradation':
          insights.push(this.generatePerformanceInsight(pattern, currentData));
          break;
        case 'energy-inefficiency':
          insights.push(this.generateEnergyInsight(pattern, currentData));
          break;
        case 'mobility-pattern':
          insights.push(this.generateMobilityInsight(pattern, currentData));
          break;
        case 'signal-fluctuation':
          insights.push(this.generateSignalInsight(pattern, currentData));
          break;
        case 'traffic-anomaly':
          insights.push(this.generateTrafficInsight(pattern, currentData));
          break;
      }
    }

    return insights;
  }

  private generatePerformanceInsight(pattern: RecognizedPattern, currentData: RANData): string {
    const degradation = pattern.details.anomalousFeatures || [];

    if (degradation.includes('throughput')) {
      return `Throughput degradation detected (${(pattern.confidence * 100).toFixed(1)}% confidence) - likely caused by ${this.identifyLikelyCause('throughput', currentData)}`;
    }

    if (degradation.includes('latency')) {
      return `Latency increase detected (${(pattern.confidence * 100).toFixed(1)}% confidence) - investigate ${this.identifyLikelyCause('latency', currentData)}`;
    }

    return `Performance degradation pattern detected (${(pattern.confidence * 100).toFixed(1)}% confidence)`;
  }

  private generateEnergyInsight(pattern: RecognizedPattern, currentData: RANData): string {
    return `Energy inefficiency pattern detected (${(pattern.confidence * 100).toFixed(1)}% confidence) - potential savings: ${this.estimateEnergySavings(currentData)}%`;
  }

  private generateMobilityInsight(pattern: RecognizedPattern, currentData: RANData): string {
    return `Mobility pattern anomaly detected (${(pattern.confidence * 100).toFixed(1)}% confidence) - handover optimization may be required`;
  }

  private generateSignalInsight(pattern: RecognizedPattern, currentData: RANData): string {
    return `Signal fluctuation pattern detected (${(pattern.confidence * 100).toFixed(1)}% confidence) - antenna optimization recommended`;
  }

  private generateTrafficInsight(pattern: RecognizedPattern, currentData: RANData): string {
    return `Traffic anomaly detected (${(pattern.confidence * 100).toFixed(1)}% confidence) - capacity planning may be required`;
  }

  private identifyLikelyCause(metric: string, data: RANData): string {
    const causes: { [metric: string]: string[] } = {
      'throughput': ['interference', 'user overload', 'resource congestion'],
      'latency': ['bufferbloat', 'processing delays', 'backhaul congestion'],
      'energy': ['idle capacity', 'over-provisioning', 'inefficient scheduling']
    };

    const metricCauses = causes[metric] || [];
    return metricCauses[Math.floor(Math.random() * metricCauses.length)] || 'unknown factors';
  }

  private estimateEnergySavings(data: RANData): number {
    const baselineEfficiency = data.throughput / (data.energyConsumption || 1);
    const potentialEfficiency = baselineEfficiency * 1.2; // Assume 20% improvement possible

    return Math.min(30, Math.max(5, ((potentialEfficiency - baselineEfficiency) / baselineEfficiency) * 100));
  }

  private async generateRecommendations(patterns: Array<RecognizedPattern>, insights: string[]): Promise<string[]> {
    const recommendations: string[] = [];

    // Pattern-specific recommendations
    for (const pattern of patterns) {
      const patternRecs = this.generatePatternRecommendations(pattern);
      recommendations.push(...patternRecs);
    }

    // Insight-based recommendations
    for (const insight of insights) {
      const insightRecs = this.generateInsightRecommendations(insight);
      recommendations.push(...insightRecs);
    }

    // Remove duplicates and prioritize
    const uniqueRecommendations = [...new Set(recommendations)];
    return uniqueRecommendations.slice(0, 10); // Top 10 recommendations
  }

  private generatePatternRecommendations(pattern: RecognizedPattern): string[] {
    const recommendations: string[] = [];

    switch (pattern.type) {
      case 'performance-degradation':
        recommendations.push('Review and optimize radio resource allocation');
        recommendations.push('Check for interference sources and mitigate');
        recommendations.push('Consider load balancing across cells');
        break;
      case 'energy-inefficiency':
        recommendations.push('Implement energy-saving features');
        recommendations.push('Optimize transmission power levels');
        recommendations.push('Consider carrier activation/deactivation');
        break;
      case 'mobility-pattern':
        recommendations.push('Optimize handover parameters');
        recommendations.push('Review mobility robustness settings');
        recommendations.push('Consider beamforming optimization');
        break;
      case 'signal-fluctuation':
        recommendations.push('Check antenna alignment and configuration');
        recommendations.push('Investigate environmental factors');
        recommendations.push('Consider antenna tilt adjustments');
        break;
      case 'traffic-anomaly':
        recommendations.push('Review capacity planning');
        recommendations.push('Implement traffic prediction models');
        recommendations.push('Consider dynamic resource allocation');
        break;
    }

    return recommendations;
  }

  private generateInsightRecommendations(insight: string): string[] {
    const recommendations: string[] = [];

    if (insight.includes('critical')) {
      recommendations.push('Immediate intervention required - consult network operations team');
      recommendations.push('Implement emergency mitigation procedures');
    }

    if (insight.includes('performance')) {
      recommendations.push('Monitor KPI trends closely');
      recommendations.push('Prepare capacity expansion plans');
    }

    if (insight.includes('energy')) {
      recommendations.push('Evaluate energy optimization strategies');
      recommendations.push('Consider green network initiatives');
    }

    return recommendations;
  }

  private calculateOverallConfidence(patterns: Array<RecognizedPattern>): number {
    if (patterns.length === 0) return 0;

    // Weighted average confidence
    const weights = {
      'critical': 1.0,
      'high': 0.8,
      'medium': 0.6,
      'low': 0.4
    };

    const weightedSum = patterns.reduce((sum, pattern) => {
      return sum + pattern.confidence * weights[pattern.severity];
    }, 0);

    const totalWeight = patterns.reduce((sum, pattern) => {
      return sum + weights[pattern.severity];
    }, 0);

    return totalWeight > 0 ? weightedSum / totalWeight : 0;
  }

  private generatePatternCacheKey(ranData: RANData, timeWindow: number): string {
    const keyData = [
      ranData.cellId || 'unknown',
      Math.floor(timeWindow / 60000), // Minute resolution
      Math.floor(ranData.throughput / 100), // 100 Mbps resolution
      Math.floor(ranData.latency / 10) // 10 ms resolution
    ].join('-');

    return this.simpleHash(keyData);
  }

  private simpleHash(input: string): string {
    let hash = 0;
    for (let i = 0; i < input.length; i++) {
      const char = input.charCodeAt(i);
      hash = ((hash << 5) - hash) + char);
      hash = hash & hash;
    }
    return Math.abs(hash).toString(36);
  }

  private calculateSamplingRate(dataPoints: Array<any>): number {
    if (dataPoints.length < 2) return 0;

    const timeSpan = dataPoints[dataPoints.length - 1].timestamp - dataPoints[0].timestamp;
    return timeSpan > 0 ? (dataPoints.length * 1000) / timeSpan : 0; // Points per second
  }

  private calculateDataCompleteness(dataPoints: Array<any>, timeWindow: number): number {
    const expectedPoints = Math.floor(timeWindow / 10000); // Expect point every 10 seconds
    return Math.min(1, dataPoints.length / expectedPoints);
  }

  private async storeRecognitionResult(result: PatternRecognitionResult) {
    const embedding = await computeEmbedding(JSON.stringify(result));

    await this.agentDB.insertPattern({
      id: `pattern-recognition-${Date.now()}`,
      type: 'pattern-recognition-result',
      domain: 'ran-pattern-recognition',
      pattern_data: JSON.stringify({ embedding, pattern: result }),
      confidence: result.confidence,
      usage_count: 1,
      success_count: result.patterns.length > 0 ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }
}

// Supporting interfaces
interface PatternModel {
  type: string;
  features: string[];
  thresholds: any;
  weights: any;
  algorithm: string;
  trained: boolean;
  accuracy: number;
  lastTrained: number | null;
}

interface PatternModelConfig {
  features: string[];
  thresholds: any;
  weights: any;
  algorithm: string;
}

interface FeatureVector {
  [featureName: string]: {
    values: number[];
    mean: number;
    std: number;
    min: number;
    max: number;
    trend: number;
    variance: number;
  };
}

interface TimeSeriesData {
  dataPoints: Array<any>;
  timeWindow: number;
  samplingRate: number;
  completeness: number;
}

interface SequentialFeatures {
  values: number[][];
  changes: number[][];
  slopes: number[];
}

interface RecognizedPattern {
  type: string;
  confidence: number;
  severity: 'low' | 'medium' | 'high' | 'critical';
  details: any;
  features: FeatureVector;
  modelAccuracy: number;
}

interface PatternCombination {
  patterns: string[];
  correlation: number;
  combinedConfidence: number;
  combinedSeverity: string;
  interaction: 'causal' | 'correlated' | 'coincidental';
}

interface PatternRecognitionResult {
  patterns: Array<RecognizedPattern>;
  combinations: Array<PatternCombination>;
  insights: string[];
  recommendations: string[];
  confidence: number;
  timestamp: number;
  processingTime: number;
}

interface CachedPatternResult {
  result: PatternRecognitionResult;
  timestamp: number;
}

class ProductionRANAgentDBSystem {
  private adapter: RANAgentDBAdapter;
  private coordinator: RANDistributedTrainingCoordinator;
  fastSearch: RANFastPatternSearch;
  private patternRecognition: RANPatternRecognition;
  private performanceMonitor: AgentDBPerformanceMonitor;
  private optimizationEngine: AgentDBOptimizationEngine;

  async initialize() {
    await Promise.all([
      this.adapter?.initialize(),
      this.coordinator.initialize(),
      this.fastSearch?.initialize(),
      this.patternRecognition?.initialize(),
      this.performanceMonitor?.initialize(),
      this.optimizationEngine?.initialize()
    ]);

    console.log('RAN AgentDB Production System initialized');
  }

  async runProductionRANIntegration(ranData: RANData, operation: 'store' | 'search' | 'recognize' | 'coordinate'): Promise<RANProductionResult> {
    const startTime = Date.now();
    const operationId = this.generateOperationId();

    try {
      let result: any;

      switch (operation) {
        case 'store':
          result = await this.executeStoreOperation(ranData);
          break;
        case 'search':
          result = await this.executeSearchOperation(ranData);
          break;
        case 'recognize':
          result = await this.executeRecognizeOperation(ranData);
          break;
        case 'coordinate':
          result = await this.executeCoordinateOperation(ranData);
          break;
        default:
          throw new Error(`Unknown operation: ${operation}`);
      }

      const processingTime = Date.now() - startTime;

      const productionResult: RANProductionResult = {
        operationId,
        operation,
        inputData: ranData,
        result,
        processingTime,
        systemMetrics: await this.collectSystemMetrics(),
        performanceMetrics: this.calculateOperationMetrics(operation, result, processingTime),
        timestamp: Date.now()
      };

      // Store production result
      await this.storeProductionResult(productionResult);

      // Update performance monitoring
      this.performanceMonitor.recordOperation(productionResult);

      return productionResult;

    } catch (error) {
      console.error(`RAN AgentDB production operation failed:`, error);
      return this.generateFallbackResult(ranData, operation, operationId, startTime, error);
    }
  }

  private async executeStoreOperation(ranData: RANData): Promise<StoreOperationResult> {
    const startTime = Date.now();

    // Store RAN pattern with metadata
    const patternId = await this.adapter.storeRANPattern('production', ranData, {
      operation: 'store',
      source: 'ran-system',
      priority: this.calculatePriority(ranData),
      environment: this.detectEnvironment(ranData)
    });

    // Optimize indices after storage
    await this.optimizationEngine.optimizeAfterStorage(patternId);

    // Distribute to training nodes
    await this.coordinator.distributeTrainingData({
      type: 'ran-pattern',
      id: patternId,
      data: ranData,
      timestamp: Date.now()
    });

    return {
      success: true,
      patternId,
      storageTime: Date.now() - startTime,
      optimizationMetrics: await this.optimizationEngine.getLastOptimizationMetrics(),
      distributionStatus: await this.coordinator.getDistributionStatus()
    };
  }

  private async executeSearchOperation(ranData: RANData): Promise<SearchOperationResult> {
    const startTime = Date.now();

    // Use ultra-fast search
    const searchResult = await this.fastSearch.ultraFastSearch(ranData, {
      k: 20,
      domain: this.classifyRANDomain(ranData),
      useMMR: true,
      synthesizeContext: true,
      optimizeMemory: true
    });

    // Analyze search results
    const analysis = await this.analyzeSearchResults(searchResult, ranData);

    // Update search statistics
    await this.updateSearchStatistics(ranData, searchResult);

    return {
      success: true,
      results: searchResult,
      analysis,
      searchTime: Date.now() - startTime,
      cacheHit: searchResult.cacheHit || false,
      resultCount: searchResult.memories.length,
      relevanceScore: analysis.averageRelevance
    };
  }

  private async executeRecognizeOperation(ranData: RANData): Promise<RecognizeOperationResult> {
    const startTime = Date.now();

    // Run pattern recognition
    const recognitionResult = await this.patternRecognition.recognizePatterns(ranData, 600000); // 10 minutes

    // Analyze recognition results
    const impact = await this.analyzeRecognitionImpact(recognitionResult, ranData);

    // Store recognition insights for future learning
    await this.storeRecognitionInsights(recognitionResult, ranData);

    return {
      success: true,
      patterns: recognitionResult.patterns,
      insights: recognitionResult.insights,
      recommendations: recognitionResult.recommendations,
      recognitionTime: Date.now() - startTime,
      patternCount: recognitionResult.patterns.length,
      confidence: recognitionResult.confidence,
      impact
    };
  }

  private async executeCoordinateOperation(ranData: RANData): Promise<CoordinateOperationResult> {
    const startTime = Date.now();

    // Coordinate across distributed nodes
    const coordinationResult = await this.coordinator.coordinateTrainingOperation({
      operationType: 'ran-optimization',
      data: ranData,
      nodeId: this.getCoordinatorNodeId(),
      timestamp: Date.now(),
      priority: this.calculateCoordinationPriority(ranData)
    });

    // Wait for coordination completion
    const completedOperation = await this.waitForCoordinationCompletion(coordinationResult.operationId);

    return {
      success: true,
      operationId: coordinationResult.operationId,
      participatingNodes: completedOperation.participatingNodes,
      coordinationTime: Date.now() - startTime,
      consensus: completedOperation.consensus,
      distributedResults: completedOperation.results,
      globalMetrics: completedOperation.globalMetrics
    };
  }

  private async analyzeSearchResults(searchResult: RANSearchResult, queryData: RANData): Promise<SearchAnalysis> {
    const analysis: SearchAnalysis = {
      resultQuality: this.assessResultQuality(searchResult),
      relevanceDistribution: this.calculateRelevanceDistribution(searchResult),
      diversityScore: this.calculateDiversityScore(searchResult),
      performanceComparison: this.comparePerformanceWithQuery(searchResult, queryData),
      optimizationOpportunities: this.identifyOptimizationOpportunities(searchResult, queryData)
    };

    return analysis;
  }

  private assessResultQuality(searchResult: RANSearchResult): number {
    if (searchResult.memories.length === 0) return 0;

    // Calculate quality based on similarity scores and result relevance
    const avgSimilarity = searchResult.memories.reduce((sum, mem) =>
      sum + (mem.ranSimilarity || mem.similarity || 0), 0) / searchResult.memories.length;

    const contextQuality = searchResult.context ? searchResult.context.length / 500 : 0;
    const patternQuality = Math.min(1, searchResult.patterns.length / 10);

    return (avgSimilarity * 0.5 + contextQuality * 0.3 + patternQuality * 0.2);
  }

  private calculateRelevanceDistribution(searchResult: RANSearchResult): Array<{ range: string, count: number, percentage: number }> {
    const distribution = [
      { range: '0.0-0.2', count: 0, percentage: 0 },
      { range: '0.2-0.4', count: 0, percentage: 0 },
      { range: '0.4-0.6', count: 0, percentage: 0 },
      { range: '0.6-0.8', count: 0, percentage: 0 },
      { range: '0.8-1.0', count: 0, percentage: 0 }
    ];

    searchResult.memories.forEach(mem => {
      const similarity = mem.ranSimilarity || mem.similarity || 0;
      const rangeIndex = Math.min(4, Math.floor(similarity * 5));
      distribution[rangeIndex].count++;
    });

    const total = searchResult.memories.length;
    distribution.forEach(dist => {
      dist.percentage = (dist.count / total) * 100;
    });

    return distribution;
  }

  private calculateDiversityScore(searchResult: RANSearchResult): number {
    if (searchResult.memories.length < 2) return 1;

    // Calculate diversity based on pattern variety
    const patterns = new Set(searchResult.patterns);
    return Math.min(1, patterns.size / searchResult.memories.length);
  }

  private comparePerformanceWithQuery(searchResult: RANSearchResult, queryData: RANData): PerformanceComparison {
    const queryMetrics = this.extractPerformanceMetrics(queryData);
    const comparisons: Array<{ metric: string, improvement: number, status: 'better' | 'worse' | 'same' }> = [];

    for (const memory of searchResult.memories) {
      const storedMetrics = memory.pattern.performance_metrics;

      for (const [metric, queryValue] of Object.entries(queryMetrics)) {
        const storedValue = storedMetrics[metric] || 0;
        const improvement = (storedValue - queryValue) / queryValue;

        let status: 'better' | 'worse' | 'same';
        if (Math.abs(improvement) < 0.05) status = 'same';
        else if (improvement > 0) status = 'better';
        else status = 'worse';

        comparisons.push({ metric, improvement, status });
      }
    }

    // Aggregate comparisons
    const avgImprovement = comparisons.reduce((sum, comp) => sum + comp.improvement, 0) / comparisons.length;
    const improvementCounts = {
      better: comparisons.filter(c => c.status === 'better').length,
      worse: comparisons.filter(c => c.status === 'worse').length,
      same: comparisons.filter(c => c.status === 'same').length
    };

    return {
      averageImprovement,
      improvementCounts,
      detailedComparisons: comparisons
    };
  }

  private identifyOptimizationOpportunities(searchResult: RANSearchResult, queryData: RANData): Array<OptimizationOpportunity> {
    const opportunities: Array<OptimizationOpportunity> = [];

    // Find patterns with better performance
    const betterPerforming = searchResult.memories.filter(mem => {
      const memMetrics = mem.pattern.performance_metrics;
      const queryMetrics = this.extractPerformanceMetrics(queryData);

      return (memMetrics.throughput_score > queryMetrics.throughput_score * 1.1) ||
             (memMetrics.latency_score < queryMetrics.latency_score * 0.9) ||
             (memMetrics.energy_efficiency > queryMetrics.energy_efficiency * 1.1);
    });

    for (const memory of betterPerforming.slice(0, 5)) {
      const improvement = this.calculatePotentialImprovement(queryData, memory.pattern.ranData);

      opportunities.push({
        type: this.classifyOptimizationType(memory.pattern.ranData, queryData),
        potentialImprovement: improvement,
        confidence: memory.ranSimilarity || memory.similarity || 0.5,
        sourcePattern: memory.pattern,
        recommendedActions: this.generateOptimizationActions(memory.pattern.ranData, queryData)
      });
    }

    return opportunities;
  }

  private classifyOptimizationType(storedData: RANData, queryData: RANData): string {
    const throughputImprovement = (storedData.throughput - queryData.throughput) / queryData.throughput;
    const latencyImprovement = (queryData.latency - storedData.latency) / queryData.latency;
    const energyImprovement = (queryData.energyConsumption - storedData.energyConsumption) / queryData.energyConsumption;

    if (throughputImprovement > 0.1) return 'capacity-optimization';
    if (latencyImprovement > 0.1) return 'latency-optimization';
    if (energyImprovement > 0.1) return 'energy-optimization';
    return 'general-optimization';
  }

  private calculatePotentialImprovement(queryData: RANData, storedData: RANData): number {
    const improvements = [
      (storedData.throughput - queryData.throughput) / queryData.throughput,
      (queryData.latency - storedData.latency) / queryData.latency,
      (queryData.energyConsumption - storedData.energyConsumption) / queryData.energyConsumption
    ];

    return improvements.reduce((sum, imp) => sum + Math.max(0, imp), 0);
  }

  private generateOptimizationActions(storedData: RANData, queryData: RANData): string[] {
    const actions: string[] = [];

    if (storedData.throughput > queryData.throughput * 1.1) {
      actions.push('Apply throughput optimization parameters');
    }

    if (storedData.latency < queryData.latency * 0.9) {
      actions.push('Reduce latency using stored configuration');
    }

    if (storedData.energyConsumption < queryData.energyConsumption * 0.9) {
      actions.push('Implement energy-saving configuration');
    }

    return actions;
  }

  private async analyzeRecognitionImpact(recognitionResult: PatternRecognitionResult, ranData: RANData): Promise<RecognitionImpact> {
    const impact: RecognitionImpact = {
      severityLevel: this.calculateSeverityLevel(recognitionResult.patterns),
      businessImpact: this.estimateBusinessImpact(recognitionResult.patterns, ranData),
      operationalImpact: this.estimateOperationalImpact(recognitionResult.patterns),
      urgency: this.calculateUrgency(recognitionResult.patterns),
      affectedKPIs: this.identifyAffectedKPIs(recognitionResult.patterns),
      estimatedResolutionTime: this.estimateResolutionTime(recognitionResult.patterns)
    };

    return impact;
  }

  private calculateSeverityLevel(patterns: Array<RecognizedPattern>): 'low' | 'medium' | 'high' | 'critical' {
    if (patterns.length === 0) return 'low';

    const criticalCount = patterns.filter(p => p.severity === 'critical').length;
    const highCount = patterns.filter(p => p.severity === 'high').length;

    if (criticalCount > 0) return 'critical';
    if (highCount > 2) return 'high';
    if (patterns.length > 3) return 'medium';
    return 'low';
  }

  private estimateBusinessImpact(patterns: Array<RecognizedPattern>, ranData: RANData): number {
    // Estimate potential business impact in percentage
    let impact = 0;

    for (const pattern of patterns) {
      switch (pattern.type) {
        case 'performance-degradation':
          impact += pattern.confidence * 0.15; // 15% potential impact
          break;
        case 'energy-inefficiency':
          impact += pattern.confidence * 0.12; // 12% potential cost savings
          break;
        case 'mobility-pattern':
          impact += pattern.confidence * 0.08; // 8% QoE improvement
          break;
        case 'signal-fluctuation':
          impact += pattern.confidence * 0.10; // 10% coverage improvement
          break;
        case 'traffic-anomaly':
          impact += pattern.confidence * 0.20; // 20% capacity utilization
          break;
      }
    }

    return Math.min(0.5, impact); // Cap at 50% impact
  }

  private estimateOperationalImpact(patterns: Array<RecognizedPattern>): string[] {
    const impacts: string[] = [];

    for (const pattern of patterns) {
      switch (pattern.type) {
        case 'performance-degradation':
          impacts.push('Performance degradation may affect user experience');
          impacts.push('Increased customer complaints likely');
          break;
        case 'energy-inefficiency':
          impacts.push('Higher operational costs');
          impacts.push('Environmental impact concerns');
          break;
        case 'mobility-pattern':
          impacts.push('Handover failures may increase');
          impacts.push('Mobility robustness degradation');
          break;
        case 'signal-fluctuation':
          impacts.push('Service quality inconsistencies');
          impacts.push('Coverage holes may develop');
          break;
        case 'traffic-anomaly':
          impacts.push('Capacity planning challenges');
          impacts.push('Resource utilization inefficiency');
          break;
      }
    }

    return [...new Set(impacts)];
  }

  private calculateUrgency(patterns: Array<RecognizedPattern>): 'low' | 'medium' | 'high' | 'critical' {
    const severity = this.calculateSeverityLevel(patterns);

    // Add urgency based on pattern combination
    const patternCount = patterns.length;

    if (severity === 'critical' && patternCount > 2) return 'critical';
    if (severity === 'high' && patternCount > 1) return 'high';
    if (severity === 'medium' && patternCount > 3) return 'medium';
    return 'low';
  }

  private identifyAffectedKPIs(patterns: Array<RecognizedPattern>): string[] {
    const affectedKPIs = new Set<string>();

    for (const pattern of patterns) {
      switch (pattern.type) {
        case 'performance-degradation':
          affectedKPIs.add('throughput');
          affectedKPIs.add('latency');
          affectedKPIs.add('packetLoss');
          break;
        case 'energy-ineefficiency':
          affectedKPIs.add('energyConsumption');
          affectedKPIs.add('cost-per-GB');
          break;
        case 'mobility-pattern':
          affectedKPIs.add('handoverSuccessRate');
          affectedKPIs.add('mobilityRobustness');
          affectedKPIs.add('call-drop-rate');
          break;
        case 'signal-fluctuation':
          affectedKPIs.add('signalStrength');
          affectedKPIs.add('RSRP');
          affectedKPs.add('SINR');
          affectedKPIs.add('coverage-area');
          break;
        case 'traffic-anomaly':
          affectedKPIs.add('cell-utilization');
          affectedKPIs.add('capacity-utilization');
          affectedKPIs.add('QoE-index');
          break;
      }
    }

    return Array.from(affectedKPIs);
  }

  private estimateResolutionTime(patterns: Array<RecognizedPattern>): number {
    // Estimate resolution time in minutes
    let baseTime = 30; // 30 minutes base resolution time

    for (const pattern of patterns) {
      switch (pattern.severity) {
        case 'critical':
          baseTime = 5; // 5 minutes for critical
          break;
        case 'high':
          baseTime = 15; // 15 minutes for high severity
          break;
        case 'medium':
          baseTime = 30; // 30 minutes for medium
          break;
        case 'low':
          baseTime = 60; // 1 hour for low priority
          break;
      }
    }

    return baseTime;
  }

  private async updateSearchStatistics(ranData: RANData, searchResult: RANSearchResult) {
    const statistics = {
      timestamp: Date.now(),
      queryDomain: this.classifyRANDomain(ranData),
      resultCount: searchResult.memories.length,
      averageRelevance: searchResult.memories.reduce((sum, mem) => sum + (mem.ranSimilarity || mem.similarity || 0), 0) / searchResult.memories.length,
      cacheHit: searchResult.cacheHit || false,
      processingTime: searchResult.processingTime
    };

    const embedding = await computeEmbedding(JSON.stringify(statistics));

    await this.agentDB.insertPattern({
      id: `search-stats-${Date.now()}`,
      type: 'search-statistics',
      domain: 'ran-search-analytics',
      pattern_data: JSON.stringify({ embedding, pattern: statistics }),
      confidence: 1.0,
      usage_count: 1,
      success_count: 1,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }

  private calculateOperationMetrics(operation: string, result: any, processingTime: number): OperationMetrics {
    return {
      operation,
      processingTime,
      success: result.success || false,
      dataSize: JSON.stringify(result).length,
      systemLoad: this.getCurrentSystemLoad(),
      cacheHitRate: this.getCurrentCacheHitRate(),
      distributionNodes: this.getCurrentNodeCount()
    };
  }

  private async collectSystemMetrics(): Promise<SystemMetrics> {
    const memoryUsage = await this.agentDB.getMemoryUsage();
    const databaseStats = await this.agentDB.getStats();

    return {
      memoryUsage: {
        used: memoryUsage.used,
        available: memoryUsage.available,
        percentage: memoryUsage.percentage,
        totalPatterns: databaseStats.totalPatterns
      },
      networkStats: await this.coordinator.getNetworkStats(),
      performanceStats: this.performanceMonitor.getCurrentMetrics(),
      optimizationStats: await this.optimizationEngine.getOptimizationStats()
    };
  }

  private getCurrentSystemLoad(): number {
    // Simplified system load calculation
    return 0.65; // Placeholder
  }

  private getCurrentCacheHitRate(): number {
    // Would calculate actual cache hit rate
    return 0.85; // Placeholder
  }

  private getCurrentNodeCount(): number {
    return this.trainingNodes ? this.trainingNodes.size : 1;
  }

  private classifyRANDomain(ranData: RANData): string {
    // Reuse domain classification from adapter
    if (ranData.energyConsumption > 150) return 'energy-optimization';
    if (ranData.mobilityIndex > 70) return 'mobility-optimization';
    if (ranData.coverageHoleCount > 10) return 'coverage-optimization';
    if (ranData.throughput < 500) return 'capacity-optimization';
    return 'general-optimization';
  }

  private calculatePriority(ranData: RANData): number {
    // Calculate priority based on RAN state
    let priority = 0.5; // Base priority

    // Higher priority for poor performance
    if (ranData.latency > 100) priority += 0.2;
    if (ranData.signalStrength < -90) priority += 0.3;
    if (ranData.packetLoss > 0.05) priority += 0.2;

    // Higher priority for high-value users
    if (ranData.userCount > 100) priority += 0.1;

    return Math.min(1.0, priority);
  }

  private detectEnvironment(ranData: RANData): string {
    // Detect environmental factors
    if (ranData.interference > 0.2) return 'high-interference';
    if (ranData.mobilityIndex > 80) return 'high-mobility';
    if (ranData.userCount > 150) return 'high-traffic';
    return 'normal';
  }

  private generateOperationId(): string {
    return `ran-agentdb-${Date.now()}-${Math.random().toString(36).substr(2, 8)}`;
  }

  private getCoordinatorNodeId(): string {
    return 'ran-coordinator-primary';
  }

  private calculateCoordinationPriority(ranData: RANData): number {
    return this.calculatePriority(ranData);
  }

  private async waitForCoordinationCompletion(operationId: string): Promise<CompletedCoordination> {
    // Wait for distributed coordination to complete
    // In practice, would use actual coordination mechanism

    await new Promise(resolve => setTimeout(resolve, 5000)); // 5 second timeout

    return {
      operationId,
      participatingNodes: 3, // Placeholder
      consensus: true,
      results: [],
      globalMetrics: {
        avgConfidence: 0.85,
        totalNodes: 3,
        activeNodes: 3
      }
    };
  }

  private async storeRecognitionInsights(result: PatternRecognitionResult, ranData: RANData) {
    const insightsData = {
      patterns: result.patterns,
      insights: result.insights,
      recommendations: result.recommendations,
      timestamp: Date.now(),
      ranData: ranData
    };

    const embedding = await computeEmbedding(JSON.stringify(insightsData));

    await this.agentDB.insertPattern({
      id: `recognition-insights-${Date.now()}`,
      type: 'recognition-insights',
      domain: 'ran-pattern-insights',
      pattern_data: JSON.stringify({ embedding, pattern: insightsData }),
      confidence: result.confidence,
      usage_count: 1,
      success_count: result.patterns.length > 0 ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }

  private async storeProductionResult(result: RANProductionResult) {
    const embedding = await computeEmbedding(JSON.stringify(result));

    await this.agentDB.insertPattern({
      id: result.operationId,
      type: 'production-result',
      domain: 'ran-agentdb-production',
      pattern_data: JSON.stringify({ embedding, pattern: result }),
      confidence: result.performanceMetrics.success ? 0.9 : 0.5,
      usage_count: 1,
      success_count: result.performanceMetrics.success ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }

  private generateFallbackResult(ranData: RANData, operation: string, operationId: string, startTime: number, error: any): RANProductionResult {
    return {
      operationId,
      operation,
      inputData: ranData,
      result: {
        success: false,
        error: error.message || 'Unknown error'
      },
      processingTime: Date.now() - startTime,
      systemMetrics: {},
      performanceMetrics: {
        operation,
        processingTime: Date.now() - startTime,
        success: false,
        dataSize: 0,
        systemLoad: 0,
        cacheHitRate: 0,
        distributionNodes: 0
      },
      timestamp: Date.now()
    };
  }

  async generateSystemPerformanceReport(): Promise<string> {
    const metrics = await this.collectSystemMetrics();
    const recentOperations = await this.performanceMonitor.getRecentOperations(100);
    const optimizationStats = await this.optimizationEngine.getOptimizationStats();

    return `
RAN AgentDB System Performance Report
===================================

Report Generated: ${new Date().toISOString()}

Memory Performance:
- Used: ${(metrics.memoryUsage.percentage * 100).toFixed(1)}%
- Available: ${((100 - metrics.memoryUsage.percentage) * 100).toFixed(1)}%
- Total Patterns: ${metrics.memoryUsage.totalPatterns.toLocaleString()}
- Memory Optimization: ${metrics.optimizationStats?.compressionRatio ? (metrics.optimizationStats.compressionRatio * 100).toFixed(1) + '%' : 'N/A'}

Distributed Network:
- Active Nodes: ${metrics.networkStats?.activeNodes || 0}
- Total Nodes: ${metrics.networkStats?.totalNodes || 0}
- Sync Status: ${metrics.networkStats?.syncStatus || 'unknown'}
- Message Rate: ${metrics.networkStats?.messageRate || 0} msg/s

Performance Metrics:
- Average Processing Time: ${recentOperations.length > 0 ? (recentOperations.reduce((sum, op) => sum + op.processingTime, 0) / recentOperations.length).toFixed(1) : 0}ms
- Success Rate: ${recentOperations.length > 0 ? ((recentOperations.filter(op => op.success).length / recentOperations.length) * 100).toFixed(1) : 0}%
- Cache Hit Rate: ${this.getCurrentCacheHitRate().toFixed(1)}%
- System Load: ${(this.getCurrentSystemLoad() * 100).toFixed(1)}%

Recent Operations Summary:
${this.summarizeRecentOperations(recentOperations)}

Optimization Status:
${this.summarizeOptimizationStats(optimizationStats)}

Health Indicators:
${this.getHealthIndicators(metrics)}

Recommendations:
${this.generateSystemRecommendations(metrics)}

Next Maintenance: ${new Date(Date.now() + 3600000).toISOString()}
    `.trim();
  }

  private summarizeRecentOperations(operations: Array<any>): string {
    if (operations.length === 0) return 'No recent operations';

    const operationCounts = operations.reduce((counts, op) => {
      counts[op.operation] = (counts[op.operation] || 0) + 1;
      return counts;
    }, {});

    return Object.entries(operationCounts)
      .map(([op, count]) => `  ${op}: ${count} operations`)
      .join('\n');
  }

  private summarizeOptimizationStats(stats: any): string {
    if (!stats) return 'No optimization data available';

    return `  Compression: ${stats.compressionRatio ? (stats.compressionRatio * 100).toFixed(1) + '%' : 'N/A'}
  - Index Updates: ${stats.indexUpdates || 0}
  - Memory Consolidations: ${stats.memoryConsolidations || 0}
  - Cache Evictions: ${stats.cacheEvictions || 0}`;
  }

  private getHealthIndicators(metrics: SystemMetrics): string[] {
    const indicators: string[] = [];

    if (metrics.memoryUsage.percentage < 80) {
      indicators.push('✅ Memory usage healthy');
    } else if (metrics.memoryUsage.percentage < 90) {
      indicators.push('⚠️ Memory usage moderate');
    } else {
      indicators.push('❌ Memory usage high - consider cleanup');
    }

    if (metrics.networkStats?.syncStatus === 'healthy') {
      indicators.push('✅ Distributed network healthy');
    } else {
      indicators.push('⚠️ Network sync issues detected');
    }

    if (this.getCurrentSystemLoad() < 0.7) {
      indicators.push('✅ System load normal');
    } else {
      indicators.push('⚠️ High system load detected');
    }

    return indicators;
  }

  private generateSystemRecommendations(metrics: SystemMetrics): string[] {
    const recommendations: string[] = [];

    if (metrics.memoryUsage.percentage > 85) {
      recommendations.push('Consider running memory optimization and consolidation');
      recommendations.push('Review cache eviction policies');
    }

    if (metrics.networkStats?.activeNodes < metrics.networkStats?.totalNodes * 0.8) {
      recommendations.push('Check network connectivity and node health');
    }

    if (this.getCurrentCacheHitRate() < 0.7) {
      recommendations.push('Consider cache warming strategies');
    }

    if (metrics.optimizationStats?.indexUpdates < 10) {
      recommendations.push('Run index optimization for better query performance');
    }

    return recommendations;
  }
}

// Supporting interfaces for production system
interface RANProductionResult {
  operationId: string;
  operation: string;
  inputData: RANData;
  result: any;
  processingTime: number;
  systemMetrics: SystemMetrics;
  performanceMetrics: OperationMetrics;
  timestamp: number;
}

interface StoreOperationResult {
  success: boolean;
  patternId: string;
  storageTime: number;
  optimizationMetrics: any;
  distributionStatus: any;
}

interface SearchOperationResult {
  success: boolean;
  results: RANSearchResult;
  analysis: SearchAnalysis;
  searchTime: number;
  cacheHit: boolean;
  resultCount: number;
  relevanceScore: number;
}

interface RecognizeOperationResult {
  success: boolean;
  patterns: Array<RecognizedPattern>;
  insights: string[];
  recommendations: string[];
  recognitionTime: number;
  patternCount: number;
  confidence: number;
  impact: RecognitionImpact;
}

interface CoordinateOperationResult {
  success: boolean;
  operationId: string;
  participatingNodes: number;
  coordinationTime: number;
  consensus: boolean;
  distributedResults: any[];
  globalMetrics: any;
}

interface SystemMetrics {
  memoryUsage: {
    used: number;
    available: number;
    percentage: number;
    totalPatterns: number;
  };
  networkStats?: {
    activeNodes: number;
    totalNodes: number;
    syncStatus: string;
    messageRate: number;
  };
  performanceStats?: any;
  optimizationStats?: any;
}

interface OperationMetrics {
  operation: string;
  processingTime: number;
  success: boolean;
  dataSize: number;
  systemLoad: number;
  cacheHitRate: number;
  distributionNodes: number;
}

interface SearchAnalysis {
  resultQuality: number;
  relevanceDistribution: Array<{ range: string, count: number, percentage: number }>;
  diversityScore: number;
  performanceComparison: PerformanceComparison;
  optimizationOpportunities: Array<OptimizationOpportunity>;
}

interface PerformanceComparison {
  averageImprovement: number;
  improvementCounts: {
    better: number;
    worse: number;
    same: number;
  };
  detailedComparisons: Array<{ metric: string, improvement: number, status: 'better' | 'worse' | 'same' }>;
}

interface OptimizationOpportunity {
  type: string;
  potentialImprovement: number;
  confidence: number;
  sourcePattern: any;
  recommendedActions: string[];
}

interface RecognitionImpact {
  severityLevel: 'low' | 'medium' | 'high' | 'critical';
  businessImpact: number;
  operationalImpact: string[];
  urgency: 'low' | 'medium' | 'high' | 'critical';
  affectedKPIs: string[];
  estimatedResolutionTime: number;
}

interface CompletedCoordination {
  operationId: string;
  participatingNodes: number;
  consensus: boolean;
  results: any[];
  globalMetrics: {
    avgConfidence: number;
    totalNodes: number;
    activeNodes: number;
  };
}

// Classes that would be imported from their respective modules
class AgentDBPerformanceMonitor {
  async initialize() {}
  recordOperation(result: RANProductionResult) {}
  getCurrentMetrics(): any { return {}; }
  getRecentOperations(count: number): Array<any> { return []; }
}

class AgentDBOptimizationEngine {
  async initialize() {}
  async optimizeAfterStorage(patternId: string) {}
  async getLastOptimizationMetrics(): Promise<any> { return {}; }
  getOptimizationStats(): Promise<any> { return {}; }
}

class QUICNodeCoordinator {
  async initialize(config: NodeCoordinatorConfig) {}
  on(event: string, callback: (data: any) => void) {}
  async send(peerId: string, data: any): Promise<void> {}
}

class AgentDBAdapter {
  async initialize() {}
  async insertPattern(pattern: any) {}
  async retrieveWithReasoning(embedding: number[], options: any): Promise<any> { return {}; }
  async updatePattern(id: string, update: any): Promise<void> {}
  async getMemoryUsage(): Promise<{ used: number, available: number, percentage: number, totalPatterns: number }> {
    return { used: 0, available: 0, percentage: 0, totalPatterns: 0 };
  }
  async getStats(): Promise<any> { return {}; }
}

const agentDBSystem = new ProductionRANAgentDBSystem();
await agentDBSystem.initialize();

const ranData = {
  throughput: 750,
  latency: 45,
  packetLoss: 0.03,
  signalStrength: -75,
  interference: 0.12,
  energyConsumption: 95,
  userCount: 65,
  mobilityIndex: 45,
  coverageHoleCount: 8,
  cellId: 'Cell_001',
  timestamp: Date.now()
};

const result = await agentDBSystem.runProductionRANIntegration(ranData, 'store');
console.log(`Pattern stored with ID: ${result.result.patternId}`);
console.log(`Storage time: ${result.processingTime}ms`);
console.log(`Distribution status: ${result.result.distributionStatus}`);

const queryData = { /* RAN state */ };
const searchResult = await agentDBSystem.runProductionRANIntegration(queryData, 'search');

console.log(`Found ${searchResult.result.resultCount} similar patterns`);
console.log(`Search time: ${searchResult.result.searchTime}ms`);
console.log(`Cache hit: ${searchResult.result.cacheHit ? 'Yes' : 'No'}`);
console.log(`Average relevance: ${(searchResult.result.analysis.relevanceScore * 100).toFixed(1)}%`);

if (searchResult.result.analysis.optimizationOpportunities.length > 0) {
  console.log('Optimization opportunities:');
  searchResult.result.analysis.optimizationOpportunities.forEach((opp, i) => {
    console.log(`  ${i + 1}. ${opp.type}: ${(opp.potentialImprovement * 100).toFixed(1)}% improvement`);
    console.log(`     Confidence: ${(opp.confidence * 100).toFixed(1)}%`);
  });
}

const recognitionResult = await agentDBSystem.runProductionRANIntegration(ranData, 'recognize');

console.log(`Recognized ${recognitionResult.result.patternCount} patterns`);
console.log(`Recognition time: ${recognitionResult.result.recognitionTime}ms`);
console.log(`Overall confidence: ${(recognitionResult.result.confidence * 100).toFixed(1)}%`);

if (recognitionResult.result.insights.length > 0) {
  console.log('Key insights:');
  recognitionResult.result.insights.forEach((insight, i) => {
    console.log(`  ${i + 1}. ${insight}`);
  });
}

if (recognitionResult.result.recommendations.length > 0) {
  console.log('Recommendations:');
  recognitionResult.result.recommendations.forEach((rec, i) => {
    console.log(`  ${i + 1}. ${rec}`);
  });
}

const coordinationResult = await agentDBSystem.runProductionRANIntegration(ranData, 'coordinate');

console.log(`Coordination completed in ${coordinationResult.processingTime}ms`);
console.log(`Participating nodes: ${coordination.result.result.participatingNodes}`);
console.log(`Consensus achieved: ${coordination.result.result.consensus ? 'Yes' : 'No'}`);
console.log(`Global metrics: ${JSON.stringify(coordination.result.result.globalMetrics, null, 2)}`);

const performanceReport = await agentDBSystem.generateSystemPerformanceReport();
console.log(performanceReport);

# RAN AgentDB Configuration
export RAN_AGENTDB_DB_PATH=.agentdb/ran-agentdb.db
export RAN_AGENTDB_MODEL_PATH=./models
export RAN_AGENTDB_LOG_LEVEL=info

# QUIC Configuration
export RAN_QUIC_SYNC_PORT=4433
export RAN_QUIC_PEERS=node1:4433,node2:4433,node3:4433
export RAN_QUIC_SYNC_INTERVAL=1000
export RAN_QUIC_BATCH_SIZE=100

# AgentDB Configuration
export AGENTDB_ENABLED=true
export AGENTDB_QUANTIZATION=scalar
export AGENTDB_CACHE_SIZE=5000
export AGENTDB_COMPRESSION=true
export AGENTDB_HNSW_M=16
export AGENTDB_HNSW_EF=100

# Performance Optimization
export RAN_AGENTDB_ENABLE_CACHING=true
export RAN_AGENTDB_ENABLE_QUIC_SYNC=true
export RAN_AGENTDB_ENABLE_DISTRIBUTED_TRAINING=true
export RAN_AGENTDB_PARALLEL_PROCESSING=true
export RAN_AGENTDB_MEMORY_OPTIMIZATION=true

# Check network connectivity
telnet node1 4433
ping node2 4433

# Check firewall allows UDP port 4433
sudo ufw allow 4433/udp

# Monitor QUIC logs
DEBUG=agentdb:quic node server.js

// Pre-warm cache with common patterns
await adapter.setupCacheWarmer();

// Increase cache size
await agentDB.initialize({
  cacheSize: 8000 // Increase from default
});

// Enable memory optimization
await agentDB.optimizeMemory({
  consolidationThreshold: 0.9,
  compressionEnabled: true,
  quantizationType: 'scalar'
});

class EricssonAgentDBIntegration {
  private agentDBSystem: ProductionRANAgentDBSystem;

  async integrateWithEricssonRAN() {
    // Continuous integration loop
    setInterval(async () => {
      const currentKPIs = await this.getEricssonRANKPIs();

      // Store RAN data in AgentDB
      await this.agentDBSystem.runProductionRANIntegration(currentKPIs, 'store');

      // Run pattern recognition
      const recognition = await this.agentDBSystem.runProductionRANIntegration(currentKPIs, 'recognize');

      // Apply recommendations if patterns detected
      if (recognition.result.recommendations.length > 0) {
        await this.applyOptimizations(recognition.result.recommendations);
      }

      // Coordinate distributed training
      await this.agentDBSystem.runProductionRANIntegration(currentKPIs, 'coordinate');

    }, 45000); // Every 45 seconds
  }

  private async getEricssonRANKPIs(): Promise<RANData> {
    // Fetch from Ericsson RAN monitoring APIs
    const response = await fetch('/api/ericsson/ran/kpis/current');
    const data = await response.json();
    return this.convertEricssonData(data);
  }

  private async applyOptimizations(recommendations: string[]) {
    for (const recommendation of recommendations) {
      await fetch('/api/ericsson/ran/optimize', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ recommendation })
      });
    }
  }
}