brainy/src/neural/naturalLanguageProcessorStatic.ts

/**
 * 🧠 Natural Language Query Processor - STATIC VERSION
 * No runtime initialization, no memory leaks, patterns pre-built at compile time
 * 
 * Uses static pattern matching with 220 pre-built patterns
 */

import { Vector } from '../coreTypes.js'
import { TripleQuery } from '../triple/TripleIntelligence.js'
import { patternMatchQuery, PATTERN_STATS } from './staticPatternMatcher.js'

export interface NaturalQueryIntent {
  type: 'vector' | 'field' | 'graph' | 'combined'
  confidence: number
  extractedTerms: {
    entities?: string[]
    fields?: string[]
    relationships?: string[]
    modifiers?: string[]
  }
}

export class NaturalLanguageProcessor {
  private queryHistory: Array<{ query: string; result: TripleQuery; success: boolean }>
  
  constructor() {
    this.queryHistory = []
    // Patterns are static - no initialization needed!
  }
  
  /**
   * No initialization needed - patterns are pre-built!
   */
  async init(): Promise<void> {
    // Nothing to do - patterns are compiled into the code
    return Promise.resolve()
  }
  
  /**
   * Process natural language query into structured Triple Intelligence query
   * @param naturalQuery The natural language query string
   * @param queryEmbedding Pre-computed embedding from Brainy (passed in to avoid circular dependency)
   */
  async processNaturalQuery(naturalQuery: string, queryEmbedding?: Vector): Promise<TripleQuery> {
    // Use static pattern matcher (no async, no memory allocation!)
    const structuredQuery = patternMatchQuery(naturalQuery, queryEmbedding)
    
    // Step 3: Enhance with intent analysis if needed
    if (!structuredQuery.where && !structuredQuery.connected) {
      const intent = await this.analyzeIntent(naturalQuery)
      
      // Add metadata based on intent
      if (intent.type === 'field' && intent.extractedTerms.fields) {
        structuredQuery.where = this.buildFieldConstraints(intent.extractedTerms.fields)
      }
    }
    
    // Track for learning (but don't create new Brainy!)
    this.queryHistory.push({
      query: naturalQuery,
      result: structuredQuery,
      success: false // Will be updated based on user interaction
    })
    
    // Keep history limited to prevent memory growth
    if (this.queryHistory.length > 100) {
      this.queryHistory.shift()
    }
    
    return structuredQuery
  }
  
  /**
   * Analyze query intent using keywords
   */
  private async analyzeIntent(query: string): Promise<NaturalQueryIntent> {
    const lowerQuery = query.toLowerCase()
    
    // Check for field-specific keywords
    const fieldKeywords = ['where', 'filter', 'with', 'has', 'contains', 'equals', 'greater', 'less', 'between']
    const hasFieldIntent = fieldKeywords.some(kw => lowerQuery.includes(kw))
    
    // Check for graph keywords
    const graphKeywords = ['related', 'connected', 'linked', 'associated', 'references']
    const hasGraphIntent = graphKeywords.some(kw => lowerQuery.includes(kw))
    
    // Determine type
    let type: NaturalQueryIntent['type'] = 'vector'
    if (hasFieldIntent && hasGraphIntent) {
      type = 'combined'
    } else if (hasFieldIntent) {
      type = 'field'
    } else if (hasGraphIntent) {
      type = 'graph'
    }
    
    return {
      type,
      confidence: 0.8,
      extractedTerms: {
        fields: hasFieldIntent ? this.extractFieldTerms(query) : undefined,
        relationships: hasGraphIntent ? this.extractRelationshipTerms(query) : undefined
      }
    }
  }
  
  /**
   * Extract field terms from query
   */
  private extractFieldTerms(query: string): string[] {
    const terms: string[] = []
    
    // Simple extraction of potential field names
    const words = query.split(/\s+/)
    const fieldIndicators = ['year', 'date', 'author', 'type', 'category', 'status', 'price']
    
    for (const word of words) {
      if (fieldIndicators.includes(word.toLowerCase())) {
        terms.push(word.toLowerCase())
      }
    }
    
    return terms
  }
  
  /**
   * Extract relationship terms
   */
  private extractRelationshipTerms(query: string): string[] {
    const terms: string[] = []
    const relationshipWords = ['related', 'connected', 'linked', 'references', 'cites']
    
    const words = query.toLowerCase().split(/\s+/)
    for (const word of words) {
      if (relationshipWords.includes(word)) {
        terms.push(word)
      }
    }
    
    return terms
  }
  
  /**
   * Build field constraints from extracted terms
   */
  private buildFieldConstraints(fields: string[]): Record<string, any> {
    const constraints: Record<string, any> = {}
    
    // Simple mapping for common fields
    for (const field of fields) {
      // This would be enhanced with actual value extraction
      constraints[field] = { exists: true }
    }
    
    return constraints
  }
  
  /**
   * Find similar queries from history (without using Brainy)
   * NOTE: Currently unused - reserved for future query caching optimization
   */
  private findSimilarQueries(embedding: Vector): Array<{
    query: string
    result: TripleQuery
    similarity: number
  }> {
    // Not implemented - not required for core functionality
    // Would implement cosine similarity against queryHistory if needed
    return []
  }
  
  /**
   * Adapt a previous query for new input
   */
  private adaptQuery(newQuery: string, previousResult: TripleQuery): TripleQuery {
    return previousResult
  }
  
  /**
   * Extract entities from query
   */
  private async extractEntities(query: string): Promise<string[]> {
    // Could use the Entity Registry here if available
    return []
  }
  
  /**
   * Build query from components
   */
  private buildQuery(
    query: string,
    intent: NaturalQueryIntent,
    entities: string[]
  ): TripleQuery {
    return {
      like: query,
      limit: 10
    }
  }
}