/**
 * Neural Import - Atomic Age AI-Powered Data Understanding System
 * 
 * 🧠 Leveraging the brain-in-jar to understand and automatically structure data
 * ⚛️ Complete with confidence scoring and relationship weight calculation
 */

import { Brainy } from '../brainy.js'
import { NounType, VerbType } from '../types/graphTypes.js'
import * as fs from '../universal/fs.js'
import * as path from '../universal/path.js'
// @ts-ignore
import chalk from 'chalk'
// @ts-ignore
import ora from 'ora'
// @ts-ignore
import boxen from 'boxen'
// @ts-ignore
import Table from 'cli-table3'
// @ts-ignore
import prompts from 'prompts'

// Neural Import Types
export interface NeuralAnalysisResult {
  detectedEntities: DetectedEntity[]
  detectedRelationships: DetectedRelationship[]
  confidence: number
  insights: NeuralInsight[]
  preview: ProcessedData[]
}

export interface DetectedEntity {
  originalData: any
  nounType: string
  confidence: number
  suggestedId: string
  reasoning: string
  alternativeTypes: Array<{ type: string, confidence: number }>
}

export interface DetectedRelationship {
  sourceId: string
  targetId: string
  verbType: string
  confidence: number
  weight: number
  reasoning: string
  context: string
  metadata?: Record<string, any>
}

export interface NeuralInsight {
  type: 'hierarchy' | 'cluster' | 'pattern' | 'anomaly' | 'opportunity'
  description: string
  confidence: number
  affectedEntities: string[]
  recommendation?: string
}

export interface ProcessedData {
  id: string
  nounType: string
  data: any
  relationships: Array<{
    target: string
    verbType: string
    weight: number
    confidence: number
  }>
}

export interface NeuralImportOptions {
  confidenceThreshold: number
  autoApply: boolean
  enableWeights: boolean
  previewOnly: boolean
  validateOnly: boolean
  categoryFilter?: string[]
  skipDuplicates: boolean
}

/**
 * Neural Import Engine - The Brain Behind the Analysis
 */
export class NeuralImport {
  private brainy: Brainy
  private colors = {
    primary: chalk.hex('#3A5F4A'),
    success: chalk.hex('#2D4A3A'),
    warning: chalk.hex('#D67441'),
    error: chalk.hex('#B85C35'),
    info: chalk.hex('#4A6B5A'),
    dim: chalk.hex('#8A9B8A'),
    highlight: chalk.hex('#E88B5A'),
    accent: chalk.hex('#F5E6D3'),
    brain: chalk.hex('#E88B5A')
  }
  
  private emojis = {
    brain: '🧠',
    atom: '⚛️',
    lab: '🔬',
    data: '🎛️',
    magic: '⚡',
    check: '✅',
    warning: '⚠️',
    sparkle: '✨',
    rocket: '🚀',
    gear: '⚙️'
  }

  constructor(brainy: Brainy) {
    this.brainy = brainy
  }

  /**
   * Main Neural Import Function - The Master Controller
   */
  async neuralImport(filePath: string, options: Partial<NeuralImportOptions> = {}): Promise<NeuralAnalysisResult> {
    const opts: NeuralImportOptions = {
      confidenceThreshold: 0.7,
      autoApply: false,
      enableWeights: true,
      previewOnly: false,
      validateOnly: false,
      skipDuplicates: true,
      ...options
    }

    console.log(boxen(
      `${this.emojis.brain} ${this.colors.brain('NEURAL IMPORT INITIATED')} ${this.emojis.atom}\n\n` +
      `${this.colors.accent('◆')} ${this.colors.dim('Activating atomic age AI analysis')}\n` +
      `${this.colors.accent('◆')} ${this.colors.dim('File:')} ${this.colors.highlight(filePath)}\n` +
      `${this.colors.accent('◆')} ${this.colors.dim('Confidence Threshold:')} ${this.colors.highlight(opts.confidenceThreshold.toString())}`,
      { padding: 1, borderStyle: 'round', borderColor: '#E88B5A' }
    ))

    const spinner = ora(`${this.emojis.brain} Initializing neural analysis...`).start()

    try {
      // Phase 1: Data Parsing
      spinner.text = `${this.emojis.lab} Parsing data structure...`
      const rawData = await this.parseFile(filePath)
      
      // Phase 2: Neural Entity Detection
      spinner.text = `${this.emojis.atom} Analyzing ${Object.keys(NounType).length} entity types...`
      const detectedEntities = await this.detectEntitiesWithNeuralAnalysis(rawData, opts)
      
      // Phase 3: Neural Relationship Detection  
      spinner.text = `${this.emojis.data} Testing ${Object.keys(VerbType).length} relationship patterns...`
      const detectedRelationships = await this.detectRelationshipsWithNeuralAnalysis(detectedEntities, rawData, opts)
      
      // Phase 4: Neural Insights Generation
      spinner.text = `${this.emojis.magic} Computing neural insights...`
      const insights = await this.generateNeuralInsights(detectedEntities, detectedRelationships)
      
      // Phase 5: Confidence Scoring
      const overallConfidence = this.calculateOverallConfidence(detectedEntities, detectedRelationships)
      
      spinner.stop()

      const result: NeuralAnalysisResult = {
        detectedEntities,
        detectedRelationships,
        confidence: overallConfidence,
        insights,
        preview: await this.generatePreview(detectedEntities, detectedRelationships)
      }

      // Display results
      await this.displayNeuralAnalysisResults(result, opts)

      // Handle execution based on options
      if (opts.previewOnly || opts.validateOnly) {
        return result
      }

      if (!opts.autoApply) {
        const shouldExecute = await this.confirmNeuralImport(result)
        if (!shouldExecute) {
          console.log(this.colors.dim('Neural import cancelled'))
          return result
        }
      }

      // Execute the import
      await this.executeNeuralImport(result, opts)
      
      return result

    } catch (error) {
      spinner.fail('Neural analysis failed')
      throw error
    }
  }

  /**
   * Parse file based on extension
   */
  private async parseFile(filePath: string): Promise<any[]> {
    const ext = path.extname(filePath).toLowerCase()
    const content = await fs.readFile(filePath, 'utf8')

    switch (ext) {
      case '.json':
        const jsonData = JSON.parse(content)
        return Array.isArray(jsonData) ? jsonData : [jsonData]
      
      case '.csv':
        return this.parseCSV(content)
      
      case '.yaml':
      case '.yml':
        // For now, basic YAML support - in full implementation would use yaml parser
        return JSON.parse(content) // Placeholder
      
      default:
        throw new Error(`Unsupported file format: ${ext}`)
    }
  }

  /**
   * Basic CSV parser
   */
  private parseCSV(content: string): any[] {
    const lines = content.split('\n').filter(line => line.trim())
    if (lines.length < 2) return []

    const headers = lines[0].split(',').map(h => h.trim().replace(/"/g, ''))
    const data: any[] = []

    for (let i = 1; i < lines.length; i++) {
      const values = lines[i].split(',').map(v => v.trim().replace(/"/g, ''))
      const row: any = {}
      
      headers.forEach((header, index) => {
        row[header] = values[index] || ''
      })
      
      data.push(row)
    }

    return data
  }

  /**
   * Neural Entity Detection - The Core AI Engine
   */
  private async detectEntitiesWithNeuralAnalysis(rawData: any[], options: NeuralImportOptions): Promise<DetectedEntity[]> {
    const entities: DetectedEntity[] = []
    const nounTypes = Object.values(NounType)

    for (const [index, dataItem] of rawData.entries()) {
      const mainText = this.extractMainText(dataItem)
      const detections: Array<{ type: string, confidence: number, reasoning: string }> = []

      // Test against all noun types using semantic similarity
      for (const nounType of nounTypes) {
        const confidence = await this.calculateEntityTypeConfidence(mainText, dataItem, nounType)
        if (confidence >= options.confidenceThreshold - 0.2) { // Allow slightly lower for alternatives
          const reasoning = await this.generateEntityReasoning(mainText, dataItem, nounType)
          detections.push({ type: nounType, confidence, reasoning })
        }
      }

      if (detections.length > 0) {
        // Sort by confidence
        detections.sort((a, b) => b.confidence - a.confidence)
        const primaryType = detections[0]
        const alternatives = detections.slice(1, 3) // Top 2 alternatives

        entities.push({
          originalData: dataItem,
          nounType: primaryType.type,
          confidence: primaryType.confidence,
          suggestedId: this.generateSmartId(dataItem, primaryType.type, index),
          reasoning: primaryType.reasoning,
          alternativeTypes: alternatives
        })
      }
    }

    return entities
  }

  /**
   * Calculate entity type confidence using AI
   */
  private async calculateEntityTypeConfidence(text: string, data: any, nounType: string): Promise<number> {
    // Base semantic similarity using search instead of similarity method
    const searchResults = await this.brainy.find(text + ' ' + nounType)
    const textSimilarity = searchResults.length > 0 ? searchResults[0].score : 0.5
    
    // Field-based confidence boost
    const fieldBoost = this.calculateFieldBasedConfidence(data, nounType)
    
    // Pattern-based confidence boost  
    const patternBoost = this.calculatePatternBasedConfidence(text, data, nounType)
    
    // Combine confidences with weights
    const combined = (textSimilarity * 0.5) + (fieldBoost * 0.3) + (patternBoost * 0.2)
    
    return Math.min(combined, 1.0)
  }

  /**
   * Field-based confidence calculation
   */
  private calculateFieldBasedConfidence(data: any, nounType: string): number {
    const fields = Object.keys(data)
    let boost = 0

    // Field patterns that boost confidence for specific noun types
    const fieldPatterns: Record<string, string[]> = {
      [NounType.Person]: ['name', 'email', 'phone', 'age', 'firstname', 'lastname', 'employee'],
      [NounType.Organization]: ['company', 'organization', 'corp', 'inc', 'ltd', 'department', 'team'],
      [NounType.Project]: ['project', 'task', 'deadline', 'status', 'milestone', 'deliverable'],
      [NounType.Location]: ['address', 'city', 'country', 'state', 'zip', 'location', 'coordinates'],
      [NounType.Product]: ['product', 'price', 'sku', 'inventory', 'category', 'brand'],
      [NounType.Event]: ['date', 'time', 'venue', 'event', 'meeting', 'conference', 'schedule']
    }

    const relevantPatterns = fieldPatterns[nounType] || []
    for (const field of fields) {
      for (const pattern of relevantPatterns) {
        if (field.toLowerCase().includes(pattern)) {
          boost += 0.1
        }
      }
    }

    return Math.min(boost, 0.5)
  }

  /**
   * Pattern-based confidence calculation
   */
  private calculatePatternBasedConfidence(text: string, data: any, nounType: string): number {
    let boost = 0

    // Content patterns that indicate entity types
    const patterns: Record<string, RegExp[]> = {
      [NounType.Person]: [
        /@.*\.com/i, // Email pattern
        /\b[A-Z][a-z]+ [A-Z][a-z]+\b/, // Name pattern
        /Mr\.|Mrs\.|Dr\.|Prof\./i // Title pattern
      ],
      [NounType.Organization]: [
        /\bInc\.|Corp\.|LLC\.|Ltd\./i, // Corporate suffixes
        /Company|Corporation|Enterprise/i
      ],
      [NounType.Location]: [
        /\b\d{5}(-\d{4})?\b/, // ZIP code
        /Street|Ave|Road|Blvd/i
      ]
    }

    const relevantPatterns = patterns[nounType] || []
    for (const pattern of relevantPatterns) {
      if (pattern.test(text)) {
        boost += 0.15
      }
    }

    return Math.min(boost, 0.3)
  }

  /**
   * Generate reasoning for entity type selection
   */
  private async generateEntityReasoning(text: string, data: any, nounType: string): Promise<string> {
    const reasons: string[] = []

    // Semantic similarity reason using search
    const searchResults = await this.brainy.find(text + ' ' + nounType)
    const similarity = searchResults.length > 0 ? searchResults[0].score : 0.5
    if (similarity > 0.7) {
      reasons.push(`High semantic similarity (${(similarity * 100).toFixed(1)}%)`)
    }

    // Field-based reasons
    const relevantFields = this.getRelevantFields(data, nounType)
    if (relevantFields.length > 0) {
      reasons.push(`Contains ${nounType}-specific fields: ${relevantFields.join(', ')}`)
    }

    // Pattern-based reasons
    const matchedPatterns = this.getMatchedPatterns(text, data, nounType)
    if (matchedPatterns.length > 0) {
      reasons.push(`Matches ${nounType} patterns: ${matchedPatterns.join(', ')}`)
    }

    return reasons.length > 0 ? reasons.join('; ') : 'General semantic match'
  }

  /**
   * Neural Relationship Detection
   */
  private async detectRelationshipsWithNeuralAnalysis(
    entities: DetectedEntity[], 
    rawData: any[], 
    options: NeuralImportOptions
  ): Promise<DetectedRelationship[]> {
    const relationships: DetectedRelationship[] = []
    const verbTypes = Object.values(VerbType)

    // For each pair of entities, test relationship possibilities
    for (let i = 0; i < entities.length; i++) {
      for (let j = i + 1; j < entities.length; j++) {
        const sourceEntity = entities[i]
        const targetEntity = entities[j]

        // Extract context for relationship detection
        const context = this.extractRelationshipContext(sourceEntity.originalData, targetEntity.originalData, rawData)

        // Test all verb types
        for (const verbType of verbTypes) {
          const confidence = await this.calculateRelationshipConfidence(
            sourceEntity, targetEntity, verbType, context
          )

          if (confidence >= options.confidenceThreshold - 0.1) { // Slightly lower threshold for relationships
            const weight = options.enableWeights ? 
              this.calculateRelationshipWeight(sourceEntity, targetEntity, verbType, context) : 
              0.5

            const reasoning = await this.generateRelationshipReasoning(sourceEntity, targetEntity, verbType, context)

            relationships.push({
              sourceId: sourceEntity.suggestedId,
              targetId: targetEntity.suggestedId,
              verbType,
              confidence,
              weight,
              reasoning,
              context,
              metadata: this.extractRelationshipMetadata(sourceEntity.originalData, targetEntity.originalData, verbType)
            })
          }
        }
      }
    }

    // Sort by confidence and remove duplicates/conflicts
    return this.pruneRelationships(relationships)
  }

  /**
   * Calculate relationship confidence
   */
  private async calculateRelationshipConfidence(
    source: DetectedEntity, 
    target: DetectedEntity, 
    verbType: string, 
    context: string
  ): Promise<number> {
    // Semantic similarity between entities and verb type using search
    const relationshipText = `${this.extractMainText(source.originalData)} ${verbType} ${this.extractMainText(target.originalData)}`
    const directResults = await this.brainy.find(relationshipText)
    const directScore = directResults.length > 0 ? directResults[0].score : 0.4
    
    const contextResults = await this.brainy.find(context + ' ' + verbType)
    const contextSimilarity = contextResults.length > 0 ? contextResults[0].score : 0.5
    
    // Entity type compatibility
    const typeCompatibility = this.calculateTypeCompatibility(source.nounType, target.nounType, verbType)
    
    // Combine with weights
    return (directScore * 0.4) + (contextSimilarity * 0.4) + (typeCompatibility * 0.2)
  }

  /**
   * Calculate relationship weight/strength
   */
  private calculateRelationshipWeight(
    source: DetectedEntity, 
    target: DetectedEntity, 
    verbType: string, 
    context: string
  ): number {
    let weight = 0.5 // Base weight

    // Context richness (more descriptive = stronger)
    const contextWords = context.split(' ').length
    weight += Math.min(contextWords / 20, 0.2)

    // Entity importance (higher confidence entities = stronger relationships)
    const avgEntityConfidence = (source.confidence + target.confidence) / 2
    weight += avgEntityConfidence * 0.2

    // Verb type specificity (more specific verbs = stronger)
    const verbSpecificity = this.getVerbSpecificity(verbType)
    weight += verbSpecificity * 0.1

    return Math.min(weight, 1.0)
  }

  /**
   * Generate Neural Insights - The Intelligence Layer
   */
  private async generateNeuralInsights(entities: DetectedEntity[], relationships: DetectedRelationship[]): Promise<NeuralInsight[]> {
    const insights: NeuralInsight[] = []

    // Detect hierarchies
    const hierarchies = this.detectHierarchies(relationships)
    hierarchies.forEach(hierarchy => {
      insights.push({
        type: 'hierarchy',
        description: `Detected ${hierarchy.type} hierarchy with ${hierarchy.levels} levels`,
        confidence: hierarchy.confidence,
        affectedEntities: hierarchy.entities,
        recommendation: `Consider visualizing the ${hierarchy.type} structure`
      })
    })

    // Detect clusters  
    const clusters = this.detectClusters(entities, relationships)
    clusters.forEach(cluster => {
      insights.push({
        type: 'cluster',
        description: `Found cluster of ${cluster.size} ${cluster.primaryType} entities`,
        confidence: cluster.confidence,
        affectedEntities: cluster.entities,
        recommendation: `These ${cluster.primaryType}s might form a natural grouping`
      })
    })

    // Detect patterns
    const patterns = this.detectPatterns(relationships)
    patterns.forEach(pattern => {
      insights.push({
        type: 'pattern',
        description: `Common relationship pattern: ${pattern.description}`,
        confidence: pattern.confidence,
        affectedEntities: pattern.entities,
        recommendation: pattern.recommendation
      })
    })

    return insights
  }

  /**
   * Display Neural Analysis Results
   */
  private async displayNeuralAnalysisResults(result: NeuralAnalysisResult, options: NeuralImportOptions): Promise<void> {
    // Entity summary
    const entityTable = new Table({
      head: [this.colors.brain('Entity Type'), this.colors.brain('Count'), this.colors.brain('Avg Confidence')],
      colWidths: [20, 10, 15]
    })

    const entitySummary = this.summarizeEntities(result.detectedEntities)
    Object.entries(entitySummary).forEach(([type, stats]) => {
      entityTable.push([
        this.colors.highlight(type),
        this.colors.primary(stats.count.toString()),
        this.colors.success(`${(stats.avgConfidence * 100).toFixed(1)}%`)
      ])
    })

    // Relationship summary
    const relationshipTable = new Table({
      head: [this.colors.brain('Relationship Type'), this.colors.brain('Count'), this.colors.brain('Avg Weight'), this.colors.brain('Avg Confidence')],
      colWidths: [20, 10, 12, 15]
    })

    const relationshipSummary = this.summarizeRelationships(result.detectedRelationships)
    Object.entries(relationshipSummary).forEach(([type, stats]) => {
      relationshipTable.push([
        this.colors.highlight(type),
        this.colors.primary(stats.count.toString()),
        this.colors.warning(`${stats.avgWeight.toFixed(2)}`),
        this.colors.success(`${(stats.avgConfidence * 100).toFixed(1)}%`)
      ])
    })

    console.log(boxen(
      `${this.emojis.atom} ${this.colors.brain('NEURAL CLASSIFICATION RESULTS')}\n\n` +
      entityTable.toString(),
      { padding: 1, borderStyle: 'round', borderColor: '#D67441' }
    ))

    console.log(boxen(
      `${this.emojis.data} ${this.colors.brain('NEURAL RELATIONSHIP MAPPING')}\n\n` +
      relationshipTable.toString(),
      { padding: 1, borderStyle: 'round', borderColor: '#D67441' }
    ))

    // Display insights
    if (result.insights.length > 0) {
      const insightsText = result.insights.map(insight => 
        `${this.colors.accent('◆')} ${insight.description} (${(insight.confidence * 100).toFixed(1)}% confidence)`
      ).join('\n')

      console.log(boxen(
        `${this.emojis.magic} ${this.colors.brain('NEURAL INSIGHTS')}\n\n` +
        insightsText,
        { padding: 1, borderStyle: 'round', borderColor: '#E88B5A' }
      ))
    }
  }

  /**
   * Helper methods for the neural system
   */

  private extractMainText(data: any): string {
    // Extract the most relevant text from a data object
    const textFields = ['name', 'title', 'description', 'content', 'text', 'label']
    
    for (const field of textFields) {
      if (data[field] && typeof data[field] === 'string') {
        return data[field]
      }
    }
    
    // Fallback: concatenate all string values
    return Object.values(data)
      .filter(v => typeof v === 'string')
      .join(' ')
      .substring(0, 200) // Limit length
  }

  private generateSmartId(data: any, nounType: string, index: number): string {
    const mainText = this.extractMainText(data)
    const cleanText = mainText.toLowerCase().replace(/[^a-z0-9]/g, '_').substring(0, 20)
    return `${nounType}_${cleanText}_${index}`
  }

  private extractRelationshipContext(source: any, target: any, allData: any[]): string {
    // Extract context for relationship detection
    return [
      this.extractMainText(source),
      this.extractMainText(target),
      // Add more contextual information
    ].join(' ')
  }

  private calculateTypeCompatibility(sourceType: string, targetType: string, verbType: string): number {
    // Define type compatibility matrix for relationships
    const compatibilityMatrix: Record<string, Record<string, string[]>> = {
      [NounType.Person]: {
        [NounType.Organization]: [VerbType.MemberOf, VerbType.WorksWith],
        [NounType.Project]: [VerbType.WorksWith, VerbType.Creates],
        [NounType.Person]: [VerbType.WorksWith, VerbType.Mentors, VerbType.ReportsTo]
      }
      // Add more compatibility rules
    }

    const sourceCompatibility = compatibilityMatrix[sourceType]
    if (sourceCompatibility && sourceCompatibility[targetType]) {
      return sourceCompatibility[targetType].includes(verbType) ? 1.0 : 0.3
    }

    return 0.5 // Default compatibility
  }

  private getVerbSpecificity(verbType: string): number {
    // More specific verbs get higher scores
    const specificityScores: Record<string, number> = {
      [VerbType.RelatedTo]: 0.1,       // Very generic
      [VerbType.WorksWith]: 0.7,       // Specific
      [VerbType.Mentors]: 0.9,         // Very specific
      [VerbType.ReportsTo]: 0.9,       // Very specific
      [VerbType.Supervises]: 0.9       // Very specific
    }

    return specificityScores[verbType] || 0.5
  }

  private getRelevantFields(data: any, nounType: string): string[] {
    // Implementation for finding relevant fields
    return []
  }

  private getMatchedPatterns(text: string, data: any, nounType: string): string[] {
    // Implementation for finding matched patterns
    return []
  }

  private pruneRelationships(relationships: DetectedRelationship[]): DetectedRelationship[] {
    // Remove duplicates and low-confidence relationships
    return relationships
      .sort((a, b) => b.confidence - a.confidence)
      .slice(0, 1000) // Limit to top 1000 relationships
  }

  private detectHierarchies(relationships: DetectedRelationship[]): any[] {
    // Detect hierarchical structures
    return []
  }

  private detectClusters(entities: DetectedEntity[], relationships: DetectedRelationship[]): any[] {
    // Detect entity clusters
    return []
  }

  private detectPatterns(relationships: DetectedRelationship[]): any[] {
    // Detect relationship patterns
    return []
  }

  private summarizeEntities(entities: DetectedEntity[]): Record<string, any> {
    const summary: Record<string, any> = {}
    
    entities.forEach(entity => {
      if (!summary[entity.nounType]) {
        summary[entity.nounType] = { count: 0, totalConfidence: 0 }
      }
      summary[entity.nounType].count++
      summary[entity.nounType].totalConfidence += entity.confidence
    })

    Object.keys(summary).forEach(type => {
      summary[type].avgConfidence = summary[type].totalConfidence / summary[type].count
    })

    return summary
  }

  private summarizeRelationships(relationships: DetectedRelationship[]): Record<string, any> {
    const summary: Record<string, any> = {}
    
    relationships.forEach(rel => {
      if (!summary[rel.verbType]) {
        summary[rel.verbType] = { count: 0, totalWeight: 0, totalConfidence: 0 }
      }
      summary[rel.verbType].count++
      summary[rel.verbType].totalWeight += rel.weight
      summary[rel.verbType].totalConfidence += rel.confidence
    })

    Object.keys(summary).forEach(type => {
      const stats = summary[type]
      stats.avgWeight = stats.totalWeight / stats.count
      stats.avgConfidence = stats.totalConfidence / stats.count
    })

    return summary
  }

  private calculateOverallConfidence(entities: DetectedEntity[], relationships: DetectedRelationship[]): number {
    const entityConfidence = entities.reduce((sum, e) => sum + e.confidence, 0) / entities.length
    const relationshipConfidence = relationships.reduce((sum, r) => sum + r.confidence, 0) / relationships.length
    return (entityConfidence + relationshipConfidence) / 2
  }

  private async generatePreview(entities: DetectedEntity[], relationships: DetectedRelationship[]): Promise<ProcessedData[]> {
    return entities.slice(0, 5).map(entity => ({
      id: entity.suggestedId,
      nounType: entity.nounType,
      data: entity.originalData,
      relationships: relationships
        .filter(r => r.sourceId === entity.suggestedId)
        .slice(0, 3)
        .map(r => ({
          target: r.targetId,
          verbType: r.verbType,
          weight: r.weight,
          confidence: r.confidence
        }))
    }))
  }

  private async confirmNeuralImport(result: NeuralAnalysisResult): Promise<boolean> {
    const { confirm } = await prompts({
      type: 'confirm',
      name: 'confirm',
      message: `${this.emojis.rocket} Execute neural import?`,
      initial: true
    })
    return confirm
  }

  private async executeNeuralImport(result: NeuralAnalysisResult, options: NeuralImportOptions): Promise<void> {
    const spinner = ora(`${this.emojis.gear} Executing neural import...`).start()
    
    try {
      // Add entities to Brainy
      for (const entity of result.detectedEntities) {
        await this.brainy.add({
          data: this.extractMainText(entity.originalData),
          type: entity.nounType as NounType,
          metadata: {
            ...entity.originalData,
            confidence: entity.confidence,
            id: entity.suggestedId
          }
        })
      }

      // Add relationships to Brainy
      for (const relationship of result.detectedRelationships) {
        await this.brainy.relate({
          from: relationship.sourceId,
          to: relationship.targetId,
          type: relationship.verbType as VerbType,
          weight: relationship.weight,
          metadata: {
            confidence: relationship.confidence,
              context: relationship.context,
              ...relationship.metadata
            }
          }
        )
      }

      spinner.succeed(this.colors.success(
        `${this.emojis.check} Neural import complete! ` +
        `${result.detectedEntities.length} entities and ` +
        `${result.detectedRelationships.length} relationships imported.`
      ))

    } catch (error) {
      spinner.fail('Neural import failed')
      throw error
    }
  }

  private async generateRelationshipReasoning(
    source: DetectedEntity, 
    target: DetectedEntity, 
    verbType: string, 
    context: string
  ): Promise<string> {
    return `Neural analysis detected ${verbType} relationship based on semantic context`
  }

  private extractRelationshipMetadata(sourceData: any, targetData: any, verbType: string): Record<string, any> {
    return {
      sourceType: typeof sourceData,
      targetType: typeof targetData,
      detectedBy: 'neural-import',
      timestamp: new Date().toISOString()
    }
  }
}