universal-ai-brain/packages/core/src/intelligence/AnalogicalMappingSystem.ts

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/**
 * @file AnalogicalMappingSystem - Advanced analogical reasoning using MongoDB Atlas Vector Search
 * 
 * This system demonstrates MongoDB Atlas EXCLUSIVE Vector Search capabilities for analogical reasoning.
 * Based on official MongoDB Atlas documentation: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
 * 
 * CRITICAL: This uses MongoDB Atlas EXCLUSIVE features:
 * - $vectorSearch aggregation stage (Atlas ONLY)
 * - Atlas Vector Search indexes (Atlas ONLY)
 * - Vector similarity search with embeddings (Atlas ONLY)
 * - Semantic search capabilities (Atlas ONLY)
 * 
 * Features:
 * - $vectorSearch for semantic analogical similarity
 * - Vector embeddings for analogical reasoning
 * - Structural and semantic analogy detection
 * - Multi-dimensional analogical mapping
 * - Analogical inference and projection
 */

import { Db } from 'mongodb';
import { AnalogicalMappingCollection, AnalogicalMapping } from '../collections/AnalogicalMappingCollection';

export interface AnalogicalReasoningRequest {
  agentId: string;
  scenario: {
    description: string;
    context: Record<string, any>;
    domain: string;
    complexity?: number;
  };
  
  // Source domain (base for analogy)
  source: {
    id: string;
    name: string;
    description: string;
    domain: string;
    type: 'concept' | 'situation' | 'process' | 'structure' | 'relationship';
    embedding?: number[]; // Vector embedding for Atlas Vector Search
    structure?: Record<string, any>;
    semantics?: Record<string, any>;
  };
  
  // Target domain (to find analogies for)
  target?: {
    id: string;
    name: string;
    description: string;
    domain: string;
    type: 'concept' | 'situation' | 'process' | 'structure' | 'relationship';
    embedding?: number[]; // Vector embedding for Atlas Vector Search
    structure?: Record<string, any>;
    semantics?: Record<string, any>;
  };
  
  // Search parameters
  parameters: {
    searchType: 'similarity' | 'structure' | 'function' | 'causal' | 'hybrid';
    maxResults: number;
    minSimilarity: number;
    domains?: string[]; // Restrict search to specific domains
    vectorSearchIndex: string; // Atlas Vector Search index name
  };
}

export interface AnalogicalReasoningResult {
  request: AnalogicalReasoningRequest;
  
  // Found analogical mappings
  analogies: Array<{
    mapping: AnalogicalMapping;
    similarity: number;
    confidence: number;
    type: 'structural' | 'semantic' | 'functional' | 'causal' | 'surface';
    
    // Correspondence details
    correspondences: Array<{
      sourceElement: string;
      targetElement: string;
      type: 'object' | 'relation' | 'attribute' | 'function';
      strength: number;
      justification: string;
    }>;
    
    // Quality assessment
    quality: {
      systematicity: number; // Coherent system of relations
      oneToOne: number; // One-to-one correspondence
      semantic: number; // Semantic similarity
      pragmatic: number; // Practical utility
      overall: number; // Overall quality
    };
  }>;
  
  // Analogical inferences
  inferences: Array<{
    type: 'prediction' | 'explanation' | 'hypothesis' | 'generalization';
    content: string;
    confidence: number;
    basedOn: string[]; // Which analogies support this inference
    testable: boolean;
  }>;
  
  // Novel insights
  insights: Array<{
    insight: string;
    novelty: number; // 0-1 how novel this insight is
    plausibility: number; // 0-1 how plausible
    implications: string[];
    evidence: string[];
  }>;
  
  // Metadata
  metadata: {
    searchTime: number;
    analogiesExplored: number;
    vectorSearchUsed: boolean;
    embeddingModel?: string;
    qualityThreshold: number;
  };
}

export interface AnalogicalLearningRequest {
  agentId: string;
  examples: Array<{
    source: Record<string, any>;
    target: Record<string, any>;
    mapping: Record<string, any>;
    quality: number; // 0-1
    feedback?: string;
  }>;
  
  // Learning parameters
  parameters: {
    method: 'similarity_learning' | 'structure_mapping' | 'case_based' | 'neural_analogy';
    iterations: number;
    learningRate: number;
    generalizationLevel: number; // 0-1
  };
}

/**
 * AnalogicalMappingSystem - Advanced analogical reasoning using MongoDB Atlas Vector Search
 * 
 * This system demonstrates MongoDB Atlas EXCLUSIVE capabilities:
 * - $vectorSearch aggregation stage for semantic similarity
 * - Atlas Vector Search indexes for vector embeddings
 * - Similarity scoring and ranking with Atlas
 * - Multi-dimensional analogical reasoning with vectors
 * 
 * CRITICAL: Requires MongoDB Atlas (not local MongoDB)
 */
export class AnalogicalMappingSystem {
  private analogicalCollection: AnalogicalMappingCollection;
  private isInitialized = false;

  constructor(private db: Db) {
    this.analogicalCollection = new AnalogicalMappingCollection(db);
  }

  /**
   * Initialize the analogical mapping system
   */
  async initialize(): Promise<void> {
    try {
      await this.analogicalCollection.createIndexes();
      this.isInitialized = true;
      console.log('AnalogicalMappingSystem initialized successfully');
      console.log('📝 Note: Atlas Vector Search indexes must be created separately in Atlas UI or API');
    } catch (error) {
      console.error('Failed to initialize AnalogicalMappingSystem:', error);
      throw error;
    }
  }

  /**
   * Perform analogical reasoning using Atlas Vector Search
   */
  async performAnalogicalReasoning(request: AnalogicalReasoningRequest): Promise<AnalogicalReasoningResult> {
    if (!this.isInitialized) {
      throw new Error('AnalogicalMappingSystem not initialized');
    }

    const startTime = Date.now();

    try {
      let analogies: any[] = [];
      let vectorSearchUsed = false;

      // Use Atlas Vector Search if embedding is provided
      if (request.source.embedding && request.source.embedding.length > 0) {
        try {
          const vectorResults = await this.analogicalCollection.findSimilarAnalogies(
            request.source.embedding,
            {
              indexName: request.parameters.vectorSearchIndex,
              limit: request.parameters.maxResults,
              numCandidates: request.parameters.maxResults * 10,
              minScore: request.parameters.minSimilarity,
              filter: {
                agentId: request.agentId,
                ...(request.parameters.domains && {
                  'mapping.target.domain': { $in: request.parameters.domains }
                })
              }
            }
          );
          
          analogies = vectorResults;
          vectorSearchUsed = true;
          console.log(`🔍 Atlas Vector Search found ${analogies.length} similar analogies`);
        } catch (error) {
          console.warn('Atlas Vector Search failed, falling back to traditional search:', error);
          vectorSearchUsed = false;
        }
      }

      // Fallback to traditional search if vector search not available
      if (!vectorSearchUsed) {
        const traditionalResults = await this.analogicalCollection.getAgentAnalogicalMappings(
          request.agentId,
          {
            ...(request.parameters.domains && {
              'mapping.target.domain': { $in: request.parameters.domains }
            })
          }
        );
        
        analogies = traditionalResults.slice(0, request.parameters.maxResults).map(mapping => ({
          mapping,
          similarity: 0.5, // Default similarity
          score: 0.5
        }));
      }

      // Process analogies into structured format
      const processedAnalogies = analogies.map(analogy => ({
        mapping: analogy.mapping,
        similarity: analogy.similarity,
        confidence: analogy.mapping.mapping.confidence,
        type: analogy.mapping.mapping.type,
        correspondences: this.extractCorrespondences(analogy.mapping),
        quality: analogy.mapping.mapping.quality
      }));

      // Generate analogical inferences
      const inferences = this.generateInferences(processedAnalogies, request);

      // Generate novel insights
      const insights = this.generateInsights(processedAnalogies, request);

      const searchTime = Date.now() - startTime;

      return {
        request,
        analogies: processedAnalogies,
        inferences,
        insights,
        metadata: {
          searchTime,
          analogiesExplored: analogies.length,
          vectorSearchUsed,
          embeddingModel: vectorSearchUsed ? 'atlas-vector-search' : undefined,
          qualityThreshold: request.parameters.minSimilarity
        }
      };
    } catch (error) {
      console.error('Analogical reasoning failed:', error);
      throw error;
    }
  }

  /**
   * Learn analogical patterns from examples
   */
  async learnAnalogicalPatterns(request: AnalogicalLearningRequest): Promise<{
    learnedPatterns: Array<{
      pattern: string;
      strength: number;
      generality: number;
      examples: number;
    }>;
    performance: {
      accuracy: number;
      generalization: number;
      efficiency: number;
    };
    insights: string[];
  }> {
    if (!this.isInitialized) {
      throw new Error('AnalogicalMappingSystem not initialized');
    }

    try {
      // For demonstration, we'll implement a simple pattern learning algorithm
      const patterns = [];
      const insights = [];

      // Analyze examples to find common patterns
      for (let i = 0; i < request.examples.length; i++) {
        const example = request.examples[i];
        
        // Extract structural patterns
        const structuralPattern = this.extractStructuralPattern(example.source, example.target);
        if (structuralPattern) {
          patterns.push({
            pattern: `Structural: ${structuralPattern}`,
            strength: example.quality,
            generality: 0.7,
            examples: 1
          });
        }

        // Extract semantic patterns
        const semanticPattern = this.extractSemanticPattern(example.source, example.target);
        if (semanticPattern) {
          patterns.push({
            pattern: `Semantic: ${semanticPattern}`,
            strength: example.quality,
            generality: 0.6,
            examples: 1
          });
        }
      }

      // Generate insights from learned patterns
      if (patterns.length > 0) {
        insights.push(`Learned ${patterns.length} analogical patterns from ${request.examples.length} examples`);
        
        const avgStrength = patterns.reduce((sum, p) => sum + p.strength, 0) / patterns.length;
        insights.push(`Average pattern strength: ${avgStrength.toFixed(2)}`);
        
        if (avgStrength > 0.8) {
          insights.push('High-quality analogical patterns detected - strong systematic correspondences');
        }
      }

      // Store learned patterns as analogical mappings
      for (const pattern of patterns) {
        await this.storeLearnedPattern(request.agentId, pattern, request.examples);
      }

      return {
        learnedPatterns: patterns,
        performance: {
          accuracy: Math.min(patterns.reduce((sum, p) => sum + p.strength, 0) / patterns.length || 0, 1),
          generalization: Math.min(patterns.reduce((sum, p) => sum + p.generality, 0) / patterns.length || 0, 1),
          efficiency: Math.min(patterns.length / request.examples.length, 1)
        },
        insights
      };
    } catch (error) {
      console.error('Analogical learning failed:', error);
      throw error;
    }
  }

  /**
   * Get analogical patterns for an agent
   */
  async getAnalogicalPatterns(agentId: string): Promise<{
    commonMappings: Array<{ type: string; frequency: number; averageStrength: number }>;
    domainPairs: Array<{ source: string; target: string; frequency: number; averageQuality: number }>;
    reasoningMethods: Array<{ method: string; frequency: number; averageAccuracy: number }>;
    qualityMetrics: Array<{ metric: string; average: number; distribution: Record<string, number> }>;
  }> {
    if (!this.isInitialized) {
      throw new Error('AnalogicalMappingSystem not initialized');
    }

    return await this.analogicalCollection.findAnalogicalPatterns(agentId);
  }

  /**
   * Extract correspondences from an analogical mapping
   */
  private extractCorrespondences(mapping: AnalogicalMapping): Array<{
    sourceElement: string;
    targetElement: string;
    type: 'object' | 'relation' | 'attribute' | 'function';
    strength: number;
    justification: string;
  }> {
    return mapping.mapping.correspondences.map(corr => ({
      sourceElement: corr.sourceEntity,
      targetElement: corr.targetEntity,
      type: corr.type as 'object' | 'relation' | 'attribute' | 'function',
      strength: corr.strength,
      justification: corr.justification
    }));
  }

  /**
   * Generate analogical inferences
   */
  private generateInferences(analogies: any[], request: AnalogicalReasoningRequest): Array<{
    type: 'prediction' | 'explanation' | 'hypothesis' | 'generalization';
    content: string;
    confidence: number;
    basedOn: string[];
    testable: boolean;
  }> {
    const inferences = [];

    for (const analogy of analogies) {
      // Generate predictions based on analogical mapping
      if (analogy.similarity > 0.7) {
        inferences.push({
          type: 'prediction',
          content: `Based on analogy with ${analogy.mapping.mapping.source.name}, we predict similar behavior in ${request.source.name}`,
          confidence: analogy.similarity,
          basedOn: [analogy.mapping.mapping.id],
          testable: true
        });
      }

      // Generate explanations
      if (analogy.quality.systematicity > 0.8) {
        inferences.push({
          type: 'explanation',
          content: `The systematic correspondence between ${analogy.mapping.mapping.source.domain} and ${analogy.mapping.mapping.target.domain} explains the observed patterns`,
          confidence: analogy.quality.systematicity,
          basedOn: [analogy.mapping.mapping.id],
          testable: false
        });
      }
    }

    return inferences;
  }

  /**
   * Generate novel insights from analogical mappings
   */
  private generateInsights(analogies: any[], request: AnalogicalReasoningRequest): Array<{
    insight: string;
    novelty: number;
    plausibility: number;
    implications: string[];
    evidence: string[];
  }> {
    const insights = [];

    // Cross-domain insights
    const domains = new Set(analogies.map(a => a.mapping.mapping.target.domain));
    if (domains.size > 1) {
      insights.push({
        insight: `Cross-domain patterns detected across ${domains.size} different domains`,
        novelty: 0.8,
        plausibility: 0.9,
        implications: ['Universal principles may apply', 'Transfer learning opportunities'],
        evidence: Array.from(domains)
      });
    }

    // High-quality mapping insights
    const highQualityMappings = analogies.filter(a => a.quality.overall > 0.8);
    if (highQualityMappings.length > 0) {
      insights.push({
        insight: `${highQualityMappings.length} high-quality analogical mappings suggest strong structural similarities`,
        novelty: 0.6,
        plausibility: 0.95,
        implications: ['Reliable analogical reasoning possible', 'Strong predictive power'],
        evidence: highQualityMappings.map(m => m.mapping.mapping.id)
      });
    }

    return insights;
  }

  /**
   * Extract structural pattern from source-target pair
   */
  private extractStructuralPattern(source: any, target: any): string | null {
    // Simple pattern extraction - in real implementation, this would be more sophisticated
    if (source.type && target.type && source.type === target.type) {
      return `Same type: ${source.type}`;
    }
    return null;
  }

  /**
   * Extract semantic pattern from source-target pair
   */
  private extractSemanticPattern(source: any, target: any): string | null {
    // Simple pattern extraction - in real implementation, this would use semantic analysis
    if (source.domain && target.domain) {
      return `Domain mapping: ${source.domain} -> ${target.domain}`;
    }
    return null;
  }

  /**
   * Store a learned pattern as an analogical mapping
   */
  private async storeLearnedPattern(agentId: string, pattern: any, examples: any[]): Promise<void> {
    // Create a synthetic analogical mapping from the learned pattern
    const mapping: Omit<AnalogicalMapping, '_id' | 'createdAt' | 'updatedAt'> = {
      agentId,
      timestamp: new Date(),
      mapping: {
        id: `learned_pattern_${Date.now()}`,
        type: 'structural',
        category: 'conceptual',
        strength: pattern.strength,
        confidence: pattern.generality,
        source: {
          id: 'learned_source',
          name: 'Learned Source Pattern',
          description: pattern.pattern,
          domain: 'learned',
          type: 'structure',
          embedding: [], // Would be populated with actual embeddings
          structure: { entities: [], relationships: [], patterns: [] },
          semantics: { concepts: [], attributes: {}, functions: [], goals: [], constraints: [] },
          context: { domain: 'learned', subdomain: 'pattern', complexity: 0.5, abstractness: 0.7, familiarity: 0.6, cultural: {}, temporal: {} }
        },
        target: {
          id: 'learned_target',
          name: 'Learned Target Pattern',
          description: pattern.pattern,
          domain: 'learned',
          type: 'structure',
          embedding: [], // Would be populated with actual embeddings
          structure: { entities: [], relationships: [], patterns: [] },
          semantics: { concepts: [], attributes: {}, functions: [], goals: [], constraints: [] },
          context: { domain: 'learned', subdomain: 'pattern', complexity: 0.5, abstractness: 0.7, familiarity: 0.6, cultural: {}, temporal: {} }
        },
        correspondences: [],
        quality: {
          systematicity: pattern.generality,
          oneToOne: 0.8,
          semantic: 0.7,
          pragmatic: 0.9,
          overall: pattern.strength
        }
      },
      reasoning: {
        discovery: {
          method: 'similarity',
          trigger: 'learning',
          searchSpace: [],
          candidates: [],
          selection: { criteria: [], winner: '', justification: '' }
        },
        alignment: {
          strategy: 'incremental',
          iterations: 1,
          convergence: 1.0,
          conflicts: []
        },
        evaluation: {
          criteria: ['quality', 'generality'],
          scores: { quality: pattern.strength, generality: pattern.generality },
          strengths: ['learned from examples'],
          weaknesses: [],
          alternatives: []
        },
        projection: {
          predictions: [],
          inferences: [],
          hypotheses: []
        }
      },
      learning: {
        usage: [],
        generalization: {
          abstractions: [],
          schemas: []
        },
        performance: {
          accuracy: pattern.strength,
          utility: 0.8,
          efficiency: 0.7,
          robustness: 0.6,
          transferability: pattern.generality
        }
      },
      metadata: {
        framework: 'analogical-mapping-system',
        version: '1.0.0',
        source: 'learned',
        reliability: pattern.strength,
        lastValidated: new Date(),
        quality: {
          completeness: 0.8,
          consistency: 0.9,
          coherence: 0.8,
          novelty: 0.7
        },
        vectorSearch: {
          indexName: 'analogical_mappings_vector_index',
          embeddingModel: 'atlas-vector-search',
          dimensions: 384,
          similarity: 'cosine',
          lastIndexed: new Date()
        }
      }
    };

    await this.analogicalCollection.storeAnalogicalMapping(mapping);
  }

  /**
   * Cleanup resources
   */
  async cleanup(): Promise<void> {
    // Cleanup any resources if needed
  }
}