import { logger } from '../utils/logger.js'

/**
 * Auto-Discovery Augmentation - Helps users understand their Brainy data automatically\n * Provides intelligent insights about data patterns, relationships, and optimization suggestions\n */\nexport class AutoDiscoveryAugmentation {\n  constructor(options = {}) {\n    this.type = 'COGNITION'\n    this.priority = 4\n    this.options = {\n      analysisInterval: options.analysisInterval || 300000, // 5 minutes\n      minDataPoints: options.minDataPoints || 10,\n      enablePatternDetection: options.enablePatternDetection !== false,\n      enableOptimizationSuggestions: options.enableOptimizationSuggestions !== false,\n      enableDataQualityAnalysis: options.enableDataQualityAnalysis !== false,\n      ...options\n    }\n    \n    this.db = null\n    this.analysisTimer = null\n    this.lastAnalysis = null\n    this.insights = {\n      patterns: [],\n      suggestions: [],\n      dataQuality: {},\n      statistics: {}\n    }\n  }\n\n  async augment(brainyData, context) {\n    this.db = brainyData\n    \n    try {\n      // Run initial analysis\n      await this.runAnalysis()\n      \n      // Schedule periodic analysis\n      if (this.options.analysisInterval > 0) {\n        this.scheduleAnalysis()\n      }\n      \n      logger.info('Auto-Discovery augmentation initialized', {\n        features: {\n          patternDetection: this.options.enablePatternDetection,\n          optimizationSuggestions: this.options.enableOptimizationSuggestions,\n          dataQualityAnalysis: this.options.enableDataQualityAnalysis\n        },\n        analysisInterval: this.options.analysisInterval\n      })\n      \n    } catch (error) {\n      logger.error('Failed to initialize Auto-Discovery augmentation:', error)\n      throw error\n    }\n  }\n\n  async runAnalysis() {\n    try {\n      logger.debug('Running auto-discovery analysis...')\n      \n      const stats = await this.gatherStatistics()\n      \n      // Only run analysis if we have enough data\n      if (stats.totalEntities < this.options.minDataPoints) {\n        logger.debug('Not enough data for meaningful analysis', {\n          entities: stats.totalEntities,\n          required: this.options.minDataPoints\n        })\n        return\n      }\n      \n      this.insights.statistics = stats\n      \n      // Run different types of analysis\n      if (this.options.enablePatternDetection) {\n        this.insights.patterns = await this.detectPatterns(stats)\n      }\n      \n      if (this.options.enableOptimizationSuggestions) {\n        this.insights.suggestions = await this.generateOptimizationSuggestions(stats)\n      }\n      \n      if (this.options.enableDataQualityAnalysis) {\n        this.insights.dataQuality = await this.analyzeDataQuality(stats)\n      }\n      \n      this.lastAnalysis = new Date().toISOString()\n      \n      logger.info('Auto-discovery analysis completed', {\n        patterns: this.insights.patterns.length,\n        suggestions: this.insights.suggestions.length,\n        dataQualityScore: this.insights.dataQuality.overallScore || 'N/A'\n      })\n      \n    } catch (error) {\n      logger.error('Auto-discovery analysis failed:', error)\n    }\n  }\n\n  async gatherStatistics() {\n    const stats = {\n      totalEntities: 0,\n      totalRelationships: 0,\n      averageConnectionsPerEntity: 0,\n      mostCommonRelationshipTypes: [],\n      dataTypes: {},\n      metadataFields: {},\n      embedding: {\n        averageSimilarity: 0,\n        clusters: []\n      }\n    }\n    \n    try {\n      // Get basic statistics from Brainy\n      const brainyStats = await this.db.getStatistics()\n      \n      if (brainyStats) {\n        stats.totalEntities = brainyStats.nounCount || 0\n        stats.totalRelationships = brainyStats.verbCount || 0\n        \n        if (stats.totalEntities > 0 && stats.totalRelationships > 0) {\n          stats.averageConnectionsPerEntity = stats.totalRelationships / stats.totalEntities\n        }\n      }\n      \n      // Analyze data types and metadata (sample-based for performance)\n      const sampleSize = Math.min(100, stats.totalEntities)\n      if (sampleSize > 0) {\n        const sampleEntities = await this.sampleEntities(sampleSize)\n        this.analyzeEntitySample(sampleEntities, stats)\n      }\n      \n    } catch (error) {\n      logger.warn('Failed to gather some statistics:', error.message)\n    }\n    \n    return stats\n  }\n\n  async sampleEntities(count) {\n    // This would need to be implemented based on Brainy's API\n    // For now, return empty array\n    return []\n  }\n\n  analyzeEntitySample(entities, stats) {\n    entities.forEach(entity => {\n      // Analyze data types\n      const dataType = this.detectDataType(entity.data)\n      stats.dataTypes[dataType] = (stats.dataTypes[dataType] || 0) + 1\n      \n      // Analyze metadata fields\n      if (entity.metadata && typeof entity.metadata === 'object') {\n        Object.keys(entity.metadata).forEach(field => {\n          stats.metadataFields[field] = (stats.metadataFields[field] || 0) + 1\n        })\n      }\n    })\n  }\n\n  detectDataType(data) {\n    if (typeof data === 'string') {\n      if (data.length > 500) return 'longText'\n      if (data.includes('http://') || data.includes('https://')) return 'url'\n      if (data.includes('@') && data.includes('.')) return 'email'\n      if (/^\\d+$/.test(data)) return 'numericString'\n      return 'text'\n    }\n    \n    if (typeof data === 'number') return 'number'\n    if (typeof data === 'boolean') return 'boolean'\n    if (Array.isArray(data)) return 'array'\n    if (typeof data === 'object') return 'object'\n    \n    return 'unknown'\n  }\n\n  async detectPatterns(stats) {\n    const patterns = []\n    \n    // Connection density patterns\n    if (stats.averageConnectionsPerEntity > 0) {\n      if (stats.averageConnectionsPerEntity > 10) {\n        patterns.push({\n          type: 'highConnectivity',\n          description: 'Your data shows high connectivity between entities',\n          details: `Average ${stats.averageConnectionsPerEntity.toFixed(2)} connections per entity`,\n          impact: 'positive',\n          actionable: false\n        })\n      } else if (stats.averageConnectionsPerEntity < 2) {\n        patterns.push({\n          type: 'lowConnectivity',\n          description: 'Your data has relatively few connections between entities',\n          details: `Average ${stats.averageConnectionsPerEntity.toFixed(2)} connections per entity`,\n          impact: 'neutral',\n          actionable: true,\n          suggestion: 'Consider adding more relationships to improve semantic search quality'\n        })\n      }\n    }\n    \n    // Data type diversity patterns\n    const dataTypeCount = Object.keys(stats.dataTypes).length\n    if (dataTypeCount > 1) {\n      patterns.push({\n        type: 'dataTypeDiversity',\n        description: 'Your dataset contains diverse data types',\n        details: `${dataTypeCount} different data types detected: ${Object.keys(stats.dataTypes).join(', ')}`,\n        impact: 'positive',\n        actionable: false\n      })\n    }\n    \n    // Metadata usage patterns\n    const metadataFieldCount = Object.keys(stats.metadataFields).length\n    if (metadataFieldCount > 5) {\n      patterns.push({\n        type: 'richMetadata',\n        description: 'Your entities have rich metadata',\n        details: `${metadataFieldCount} metadata fields in use`,\n        impact: 'positive',\n        actionable: false\n      })\n    } else if (metadataFieldCount < 2) {\n      patterns.push({\n        type: 'sparseMetadata',\n        description: 'Limited metadata usage detected',\n        details: `Only ${metadataFieldCount} metadata fields found`,\n        impact: 'neutral',\n        actionable: true,\n        suggestion: 'Adding more metadata fields can improve search and organization'\n      })\n    }\n    \n    return patterns\n  }\n\n  async generateOptimizationSuggestions(stats) {\n    const suggestions = []\n    \n    // Storage optimization\n    if (stats.totalEntities > 10000) {\n      suggestions.push({\n        type: 'storage',\n        priority: 'medium',\n        title: 'Consider upgrading storage configuration',\n        description: `With ${stats.totalEntities} entities, you might benefit from persistent storage`,\n        action: 'Configure filesystem or S3 storage for better performance',\n        impact: 'performance'\n      })\n    }\n    \n    // Caching optimization\n    if (stats.totalEntities > 1000) {\n      suggestions.push({\n        type: 'caching',\n        priority: 'low',\n        title: 'Optimize caching settings',\n        description: 'Your dataset size could benefit from cache tuning',\n        action: 'Enable adaptive caching or increase cache size',\n        impact: 'performance'\n      })\n    }\n    \n    // Relationship suggestions\n    if (stats.averageConnectionsPerEntity < 1) {\n      suggestions.push({\n        type: 'relationships',\n        priority: 'high',\n        title: 'Add more relationships between entities',\n        description: 'Low connectivity reduces semantic search effectiveness',\n        action: 'Use intelligent verb scoring or manually add relationships',\n        impact: 'accuracy'\n      })\n    }\n    \n    // Intelligent verb scoring suggestion\n    if (stats.totalRelationships > 10 && !this.isIntelligentScoringEnabled()) {\n      suggestions.push({\n        type: 'features',\n        priority: 'medium',\n        title: 'Enable intelligent verb scoring',\n        description: 'Automatic relationship weighting can improve search quality',\n        action: 'Enable intelligent verb scoring in configuration',\n        impact: 'accuracy'\n      })\n    }\n    \n    return suggestions\n  }\n\n  async analyzeDataQuality(stats) {\n    const quality = {\n      overallScore: 0,\n      metrics: {},\n      issues: [],\n      recommendations: []\n    }\n    \n    let scoreComponents = 0\n    let totalScore = 0\n    \n    // Connectivity quality\n    if (stats.averageConnectionsPerEntity > 0) {\n      const connectivityScore = Math.min(100, (stats.averageConnectionsPerEntity / 5) * 100)\n      quality.metrics.connectivity = {\n        score: connectivityScore,\n        description: 'How well entities are connected to each other'\n      }\n      totalScore += connectivityScore\n      scoreComponents++\n    }\n    \n    // Metadata richness\n    const metadataCount = Object.keys(stats.metadataFields).length\n    const metadataScore = Math.min(100, (metadataCount / 10) * 100)\n    quality.metrics.metadataRichness = {\n      score: metadataScore,\n      description: 'Richness and diversity of metadata'\n    }\n    totalScore += metadataScore\n    scoreComponents++\n    \n    // Data type diversity\n    const dataTypeCount = Object.keys(stats.dataTypes).length\n    const diversityScore = Math.min(100, (dataTypeCount / 5) * 100)\n    quality.metrics.dataTypeDiversity = {\n      score: diversityScore,\n      description: 'Variety of data types in the dataset'\n    }\n    totalScore += diversityScore\n    scoreComponents++\n    \n    // Calculate overall score\n    quality.overallScore = scoreComponents > 0 ? Math.round(totalScore / scoreComponents) : 0\n    \n    // Identify issues\n    if (quality.metrics.connectivity?.score < 50) {\n      quality.issues.push('Low entity connectivity may affect search quality')\n      quality.recommendations.push('Add more relationships between related entities')\n    }\n    \n    if (quality.metrics.metadataRichness?.score < 30) {\n      quality.issues.push('Limited metadata usage detected')\n      quality.recommendations.push('Add descriptive metadata to entities for better organization')\n    }\n    \n    return quality\n  }\n\n  isIntelligentScoringEnabled() {\n    // Check if intelligent verb scoring is enabled\n    return this.db.intelligentVerbScoring?.enabled || false\n  }\n\n  scheduleAnalysis() {\n    this.analysisTimer = setInterval(() => {\n      this.runAnalysis().catch(error => {\n        logger.error('Scheduled analysis failed:', error)\n      })\n    }, this.options.analysisInterval)\n  }\n\n  // Public API methods\n  getInsights() {\n    return {\n      ...this.insights,\n      lastAnalysis: this.lastAnalysis,\n      nextAnalysis: this.analysisTimer ? \n        new Date(Date.now() + this.options.analysisInterval).toISOString() : \n        null\n    }\n  }\n\n  async forceAnalysis() {\n    await this.runAnalysis()\n    return this.getInsights()\n  }\n\n  async cleanup() {\n    if (this.analysisTimer) {\n      clearInterval(this.analysisTimer)\n      this.analysisTimer = null\n    }\n    \n    logger.info('Auto-Discovery augmentation cleaned up')\n  }\n}