universal-ai-brain/packages/core/src/safety/HallucinationDetector.ts

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/**
 * @file HallucinationDetector - Advanced hallucination detection and factual accuracy validation
 * 
 * This system compares AI responses against provided context, detects factual inconsistencies,
 * identifies unsupported claims, and provides confidence scores for response accuracy using
 * MongoDB for analytics and pattern recognition.
 * 
 * Features:
 * - Context-grounded response validation
 * - Factual consistency checking
 * - Unsupported claim detection
 * - Confidence scoring and uncertainty quantification
 * - Real-time hallucination analytics with MongoDB
 * - Framework-agnostic accuracy assessment
 * - Automated fact-checking pipeline
 */

import { TracingCollection } from '../collections/TracingCollection';
import { MemoryCollection } from '../collections/MemoryCollection';
import { MongoVectorStore } from '../vector/MongoVectorStore';

export interface HallucinationAnalysis {
  isGrounded: boolean;
  confidenceScore: number; // 0-1 scale
  factualAccuracy: number; // 0-1 scale
  contextAlignment: number; // 0-1 scale
  detectedIssues: HallucinationIssue[];
  supportingEvidence: ContextEvidence[];
  unsupportedClaims: UnsupportedClaim[];
  overallRisk: 'low' | 'medium' | 'high' | 'critical';
  recommendations: string[];
}

export interface HallucinationIssue {
  type: 'factual_error' | 'unsupported_claim' | 'context_contradiction' | 'fabricated_detail' | 'temporal_inconsistency';
  severity: 'low' | 'medium' | 'high' | 'critical';
  description: string;
  location: {
    startIndex: number;
    endIndex: number;
    text: string;
  };
  confidence: number;
  suggestedCorrection?: string;
}

export interface ContextEvidence {
  claim: string;
  supportingContext: string;
  relevanceScore: number;
  sourceId: string;
  sourceType: 'document' | 'memory' | 'knowledge_base' | 'external';
}

export interface UnsupportedClaim {
  claim: string;
  location: {
    startIndex: number;
    endIndex: number;
  };
  confidence: number;
  reason: 'no_context_support' | 'contradicts_context' | 'fabricated_information' | 'speculative_statement';
  suggestedAction: 'remove' | 'qualify' | 'verify' | 'flag';
}

export interface HallucinationMetrics {
  timeRange: {
    start: Date;
    end: Date;
  };
  totalAnalyses: number;
  hallucinationRate: number;
  averageConfidence: number;
  issuesByType: Record<string, number>;
  issuesBySeverity: Record<string, number>;
  frameworkComparison: {
    framework: string;
    hallucinationRate: number;
    averageConfidence: number;
  }[];
  improvementTrends: {
    date: Date;
    hallucinationRate: number;
    confidence: number;
  }[];
}

export interface FactCheckResult {
  claim: string;
  isSupported: boolean;
  confidence: number;
  supportingEvidence: string[];
  contradictingEvidence: string[];
  uncertainty: number;
}

/**
 * HallucinationDetector - Advanced hallucination detection and factual accuracy validation
 * 
 * Uses sophisticated algorithms and MongoDB analytics to detect hallucinations,
 * validate factual accuracy, and ensure AI responses are grounded in provided context.
 */
export class HallucinationDetector {
  private tracingCollection: TracingCollection;
  private memoryCollection: MemoryCollection;
  private vectorStore: MongoVectorStore;

  constructor(
    tracingCollection: TracingCollection,
    memoryCollection: MemoryCollection,
    vectorStore: MongoVectorStore
  ) {
    this.tracingCollection = tracingCollection;
    this.memoryCollection = memoryCollection;
    this.vectorStore = vectorStore;
  }

  /**
   * Analyze response for hallucinations and factual accuracy
   */
  async analyzeResponse(
    response: string,
    context: {
      providedContext: string[];
      originalQuery: string;
      framework: string;
      sessionId: string;
      traceId?: string;
    }
  ): Promise<HallucinationAnalysis> {
    // Extract claims from the response
    const claims = await this.extractClaims(response);
    
    // Analyze each claim against provided context
    const claimAnalyses = await Promise.all(
      claims.map(claim => this.analyzeClaim(claim, context.providedContext))
    );

    // Detect factual inconsistencies
    const factualIssues = await this.detectFactualInconsistencies(response, context.providedContext);
    
    // Check for fabricated details
    const fabricatedDetails = await this.detectFabricatedDetails(response, context.providedContext);
    
    // Analyze temporal consistency
    const temporalIssues = await this.analyzeTemporalConsistency(response);

    // Combine all detected issues
    const allIssues: HallucinationIssue[] = [
      ...factualIssues,
      ...fabricatedDetails,
      ...temporalIssues
    ];

    // Calculate confidence scores
    const contextAlignment = this.calculateContextAlignment(response, context.providedContext);
    const factualAccuracy = this.calculateFactualAccuracy(claimAnalyses);
    const confidenceScore = (contextAlignment + factualAccuracy) / 2;

    // Identify unsupported claims
    const unsupportedClaims = claimAnalyses
      .filter(analysis => !analysis.isSupported)
      .map(analysis => this.createUnsupportedClaim(analysis, response));

    // Determine overall risk
    const overallRisk = this.calculateOverallRisk(allIssues, confidenceScore);

    // Generate supporting evidence
    const supportingEvidence = claimAnalyses
      .filter(analysis => analysis.isSupported)
      .map(analysis => this.createContextEvidence(analysis));

    // Generate recommendations
    const recommendations = this.generateRecommendations(allIssues, unsupportedClaims, overallRisk);

    // Log analysis for learning
    await this.logHallucinationAnalysis({
      response,
      context,
      confidenceScore,
      factualAccuracy,
      contextAlignment,
      issueCount: allIssues.length,
      overallRisk
    });

    return {
      isGrounded: confidenceScore > 0.7 && allIssues.filter(i => i.severity === 'critical' || i.severity === 'high').length === 0,
      confidenceScore,
      factualAccuracy,
      contextAlignment,
      detectedIssues: allIssues,
      supportingEvidence,
      unsupportedClaims,
      overallRisk,
      recommendations
    };
  }

  /**
   * Generate hallucination metrics using MongoDB aggregation
   */
  async generateHallucinationMetrics(timeRange: { start: Date; end: Date }): Promise<HallucinationMetrics> {
    // Use MongoDB aggregation for comprehensive hallucination analytics
    const metricsPipeline = [
      {
        $match: {
          timestamp: { $gte: timeRange.start, $lte: timeRange.end },
          'metadata.type': 'hallucination_analysis'
        }
      },
      {
        $facet: {
          // Total analyses and hallucination rate
          overallStats: [
            {
              $group: {
                _id: null,
                totalAnalyses: { $sum: 1 },
                hallucinationCount: {
                  $sum: {
                    $cond: [{ $lt: ['$metadata.confidenceScore', 0.7] }, 1, 0]
                  }
                },
                avgConfidence: { $avg: '$metadata.confidenceScore' },
                avgFactualAccuracy: { $avg: '$metadata.factualAccuracy' }
              }
            }
          ],

          // Issues by type
          issuesByType: [
            { $unwind: '$metadata.detectedIssues' },
            {
              $group: {
                _id: '$metadata.detectedIssues.type',
                count: { $sum: 1 }
              }
            }
          ],

          // Issues by severity
          issuesBySeverity: [
            { $unwind: '$metadata.detectedIssues' },
            {
              $group: {
                _id: '$metadata.detectedIssues.severity',
                count: { $sum: 1 }
              }
            }
          ],

          // Framework comparison
          frameworkStats: [
            {
              $group: {
                _id: '$metadata.framework',
                totalAnalyses: { $sum: 1 },
                hallucinationCount: {
                  $sum: {
                    $cond: [{ $lt: ['$metadata.confidenceScore', 0.7] }, 1, 0]
                  }
                },
                avgConfidence: { $avg: '$metadata.confidenceScore' }
              }
            }
          ],

          // Daily trends
          dailyTrends: [
            {
              $group: {
                _id: {
                  $dateToString: {
                    format: '%Y-%m-%d',
                    date: '$timestamp'
                  }
                },
                hallucinationCount: {
                  $sum: {
                    $cond: [{ $lt: ['$metadata.confidenceScore', 0.7] }, 1, 0]
                  }
                },
                totalCount: { $sum: 1 },
                avgConfidence: { $avg: '$metadata.confidenceScore' }
              }
            },
            { $sort: { '_id': 1 } }
          ]
        }
      }
    ];

    const results = await this.memoryCollection.aggregate(metricsPipeline);
    const facetResults = results[0];

    const overallStats = facetResults.overallStats[0] || { totalAnalyses: 0, hallucinationCount: 0, avgConfidence: 0 };
    const hallucinationRate = overallStats.totalAnalyses > 0 
      ? (overallStats.hallucinationCount / overallStats.totalAnalyses) * 100 
      : 0;

    return {
      timeRange,
      totalAnalyses: overallStats.totalAnalyses,
      hallucinationRate: Math.round(hallucinationRate * 100) / 100,
      averageConfidence: Math.round(overallStats.avgConfidence * 100) / 100,
      issuesByType: this.arrayToRecord(facetResults.issuesByType),
      issuesBySeverity: this.arrayToRecord(facetResults.issuesBySeverity),
      frameworkComparison: facetResults.frameworkStats.map((stat: any) => ({
        framework: stat._id,
        hallucinationRate: stat.totalAnalyses > 0 ? (stat.hallucinationCount / stat.totalAnalyses) * 100 : 0,
        averageConfidence: Math.round(stat.avgConfidence * 100) / 100
      })),
      improvementTrends: facetResults.dailyTrends.map((trend: any) => ({
        date: new Date(trend._id),
        hallucinationRate: trend.totalCount > 0 ? (trend.hallucinationCount / trend.totalCount) * 100 : 0,
        confidence: Math.round(trend.avgConfidence * 100) / 100
      }))
    };
  }

  /**
   * Fact-check specific claims against knowledge base
   */
  async factCheckClaim(claim: string, context: string[]): Promise<FactCheckResult> {
    // Search for supporting evidence in context
    const supportingEvidence: string[] = [];
    const contradictingEvidence: string[] = [];

    for (const contextItem of context) {
      const similarity = await this.calculateSemanticSimilarity(claim, contextItem);
      
      if (similarity > 0.8) {
        // High similarity suggests support
        supportingEvidence.push(contextItem);
      } else if (similarity > 0.6) {
        // Check for contradiction
        const isContradiction = await this.detectContradiction(claim, contextItem);
        if (isContradiction) {
          contradictingEvidence.push(contextItem);
        }
      }
    }

    // Calculate confidence and uncertainty
    const confidence = supportingEvidence.length > 0 ? 
      Math.min(0.9, supportingEvidence.length * 0.3) : 
      Math.max(0.1, 1 - contradictingEvidence.length * 0.4);

    const uncertainty = 1 - confidence;
    const isSupported = supportingEvidence.length > 0 && contradictingEvidence.length === 0;

    return {
      claim,
      isSupported,
      confidence,
      supportingEvidence,
      contradictingEvidence,
      uncertainty
    };
  }

  // Private helper methods
  private async extractClaims(response: string): Promise<string[]> {
    // Extract factual claims from response
    // This would use NLP techniques to identify statements that can be fact-checked
    const sentences = response.split(/[.!?]+/).filter(s => s.trim().length > 10);
    
    // Filter for factual claims (statements that can be verified)
    const claims = sentences.filter(sentence => {
      const trimmed = sentence.trim();
      // Look for statements that contain verifiable information
      return trimmed.length > 20 && 
             !trimmed.toLowerCase().includes('i think') &&
             !trimmed.toLowerCase().includes('maybe') &&
             !trimmed.toLowerCase().includes('possibly');
    });

    return claims;
  }

  private async analyzeClaim(claim: string, context: string[]): Promise<FactCheckResult> {
    return this.factCheckClaim(claim, context);
  }

  private async detectFactualInconsistencies(response: string, context: string[]): Promise<HallucinationIssue[]> {
    const issues: HallucinationIssue[] = [];
    
    // Check for numerical inconsistencies
    const numbers = response.match(/\d+/g) || [];
    for (const number of numbers) {
      const contextHasNumber = context.some(ctx => ctx.includes(number));
      if (!contextHasNumber && parseInt(number) > 1000) {
        // Large numbers without context support are suspicious
        const index = response.indexOf(number);
        issues.push({
          type: 'factual_error',
          severity: 'medium',
          description: `Large number "${number}" not supported by provided context`,
          location: {
            startIndex: index,
            endIndex: index + number.length,
            text: number
          },
          confidence: 0.7
        });
      }
    }

    return issues;
  }

  private async detectFabricatedDetails(response: string, context: string[]): Promise<HallucinationIssue[]> {
    const issues: HallucinationIssue[] = [];
    
    // Check for specific details that aren't in context
    const specificPatterns = [
      /\b\d{4}-\d{2}-\d{2}\b/, // Dates
      /\b[A-Z][a-z]+ [A-Z][a-z]+\b/, // Proper names
      /\$\d+(?:,\d{3})*(?:\.\d{2})?\b/ // Money amounts
    ];

    for (const pattern of specificPatterns) {
      const matches = response.match(pattern);
      if (matches) {
        for (const match of matches) {
          const isInContext = context.some(ctx => ctx.includes(match));
          if (!isInContext) {
            const index = response.indexOf(match);
            issues.push({
              type: 'fabricated_detail',
              severity: 'high',
              description: `Specific detail "${match}" not found in provided context`,
              location: {
                startIndex: index,
                endIndex: index + match.length,
                text: match
              },
              confidence: 0.8
            });
          }
        }
      }
    }

    return issues;
  }

  private async analyzeTemporalConsistency(response: string): Promise<HallucinationIssue[]> {
    const issues: HallucinationIssue[] = [];
    
    // Check for temporal inconsistencies
    const timeReferences = response.match(/\b(yesterday|today|tomorrow|last week|next month|in \d+ years?)\b/gi) || [];
    
    if (timeReferences.length > 0) {
      // For now, flag any specific temporal references as potentially problematic
      // In a real implementation, this would check against current date and context
      for (const timeRef of timeReferences) {
        const index = response.indexOf(timeRef);
        issues.push({
          type: 'temporal_inconsistency',
          severity: 'low',
          description: `Temporal reference "${timeRef}" may not be accurate without current date context`,
          location: {
            startIndex: index,
            endIndex: index + timeRef.length,
            text: timeRef
          },
          confidence: 0.6
        });
      }
    }

    return issues;
  }

  private calculateContextAlignment(response: string, context: string[]): number {
    if (context.length === 0) return 0.5; // Neutral if no context

    // Calculate how well the response aligns with provided context
    let totalAlignment = 0;
    let alignmentCount = 0;

    const responseWords = response.toLowerCase().split(/\s+/);
    
    for (const contextItem of context) {
      const contextWords = contextItem.toLowerCase().split(/\s+/);
      const commonWords = responseWords.filter(word => 
        contextWords.includes(word) && word.length > 3
      );
      
      const alignment = commonWords.length / Math.max(responseWords.length, contextWords.length);
      totalAlignment += alignment;
      alignmentCount++;
    }

    return alignmentCount > 0 ? totalAlignment / alignmentCount : 0.5;
  }

  private calculateFactualAccuracy(claimAnalyses: FactCheckResult[]): number {
    if (claimAnalyses.length === 0) return 1.0; // No claims to verify

    const supportedClaims = claimAnalyses.filter(analysis => analysis.isSupported).length;
    return supportedClaims / claimAnalyses.length;
  }

  private calculateOverallRisk(issues: HallucinationIssue[], confidenceScore: number): 'low' | 'medium' | 'high' | 'critical' {
    const criticalIssues = issues.filter(i => i.severity === 'critical').length;
    const highIssues = issues.filter(i => i.severity === 'high').length;

    if (criticalIssues > 0 || confidenceScore < 0.3) return 'critical';
    if (highIssues > 2 || confidenceScore < 0.5) return 'high';
    if (issues.length > 3 || confidenceScore < 0.7) return 'medium';
    return 'low';
  }

  private createUnsupportedClaim(analysis: FactCheckResult, response: string): UnsupportedClaim {
    const index = response.indexOf(analysis.claim);
    return {
      claim: analysis.claim,
      location: {
        startIndex: index,
        endIndex: index + analysis.claim.length
      },
      confidence: 1 - analysis.confidence,
      reason: analysis.contradictingEvidence.length > 0 ? 'contradicts_context' : 'no_context_support',
      suggestedAction: analysis.contradictingEvidence.length > 0 ? 'remove' : 'qualify'
    };
  }

  private createContextEvidence(analysis: FactCheckResult): ContextEvidence {
    return {
      claim: analysis.claim,
      supportingContext: analysis.supportingEvidence[0] || '',
      relevanceScore: analysis.confidence,
      sourceId: 'context_0',
      sourceType: 'document'
    };
  }

  private generateRecommendations(
    issues: HallucinationIssue[],
    unsupportedClaims: UnsupportedClaim[],
    overallRisk: string
  ): string[] {
    const recommendations: string[] = [];

    if (overallRisk === 'critical' || overallRisk === 'high') {
      recommendations.push('Consider regenerating the response with more specific context');
    }

    if (unsupportedClaims.length > 0) {
      recommendations.push(`Verify or qualify ${unsupportedClaims.length} unsupported claims`);
    }

    const fabricatedDetails = issues.filter(i => i.type === 'fabricated_detail');
    if (fabricatedDetails.length > 0) {
      recommendations.push('Remove or verify specific details not found in source material');
    }

    if (issues.length === 0) {
      recommendations.push('Response appears well-grounded in provided context');
    }

    return recommendations;
  }

  private async calculateSemanticSimilarity(text1: string, text2: string): Promise<number> {
    // Simplified similarity calculation
    // In production, this would use embeddings and cosine similarity
    const words1 = text1.toLowerCase().split(/\s+/);
    const words2 = text2.toLowerCase().split(/\s+/);
    
    const commonWords = words1.filter(word => words2.includes(word) && word.length > 3);
    return commonWords.length / Math.max(words1.length, words2.length);
  }

  private async detectContradiction(claim: string, context: string): Promise<boolean> {
    // Simplified contradiction detection
    // Look for negation patterns
    const negationWords = ['not', 'no', 'never', 'none', 'neither', 'cannot', 'isn\'t', 'aren\'t', 'won\'t'];
    
    const claimWords = claim.toLowerCase().split(/\s+/);
    const contextWords = context.toLowerCase().split(/\s+/);
    
    // Check if one has negation and the other doesn't for similar content
    const claimHasNegation = claimWords.some(word => negationWords.includes(word));
    const contextHasNegation = contextWords.some(word => negationWords.includes(word));
    
    return claimHasNegation !== contextHasNegation;
  }

  private async logHallucinationAnalysis(analysisData: any): Promise<void> {
    await this.memoryCollection.storeDocument(
      JSON.stringify(analysisData),
      {
        type: 'hallucination_analysis',
        framework: analysisData.context.framework,
        confidenceScore: analysisData.confidenceScore,
        factualAccuracy: analysisData.factualAccuracy,
        contextAlignment: analysisData.contextAlignment,
        issueCount: analysisData.issueCount,
        overallRisk: analysisData.overallRisk,
        detectedIssues: analysisData.detectedIssues || []
      }
    );
  }

  private arrayToRecord(array: any[]): Record<string, number> {
    const record: Record<string, number> = {};
    array.forEach(item => {
      record[item._id] = item.count;
    });
    return record;
  }
}