airefactor #19
@ -945,7 +945,7 @@ class AIQueryInterface {
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return;
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}
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console.log('[AI Interface] Rendering comprehensive audit trail with', rawAuditTrail.length, 'entries');
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console.log('[AI Interface] Rendering detailed audit trail with', rawAuditTrail.length, 'entries');
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try {
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const stats = this.calculateAuditStats(rawAuditTrail);
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@ -1021,14 +1021,6 @@ class AIQueryInterface {
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${stats.potentialIssues.map(issue => `<li>${issue}</li>`).join('')}
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</ul>
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` : ''}
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${(!stats.keyInsights || stats.keyInsights.length === 0) && (!stats.potentialIssues || stats.potentialIssues.length === 0) ? `
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<div class="insights-header">📊 Analyse-Details</div>
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<ul class="insights-list">
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<li>${stats.qualityMetrics.aiTransparency}% der Entscheidungen mit nachvollziehbarer Begründung</li>
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<li>Analysequalität: ${this.getQualityDisplayText(stats.analysisQuality)}</li>
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</ul>
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` : ''}
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</div>
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</div>
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@ -1036,18 +1028,6 @@ class AIQueryInterface {
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<div class="audit-process-flow">
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${this.renderPhaseGroups(rawAuditTrail, stats)}
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</div>
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<!-- Technical Details Toggle -->
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<div class="technical-toggle">
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<button class="technical-toggle-btn" onclick="this.parentElement.parentElement.querySelector('.technical-details').classList.toggle('collapsed'); this.textContent = this.parentElement.parentElement.querySelector('.technical-details').classList.contains('collapsed') ? 'Technische Details anzeigen' : 'Technische Details verbergen';">
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Technische Details anzeigen
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</button>
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</div>
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<!-- Technical Details -->
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<div class="technical-details collapsed">
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${this.renderTechnicalDetails(rawAuditTrail)}
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</div>
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</div>
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</div>
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`;
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@ -1138,11 +1118,14 @@ class AIQueryInterface {
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toolSelectionCount: 0,
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qualityMetrics: {
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avgProcessingTime: 0,
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confidenceDistribution: { high: 0, medium: 0, low: 0 },
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aiTransparency: 0
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confidenceDistribution: { high: 0, medium: 0, low: 0 }
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// Removed aiTransparency
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},
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analysisQuality: 'unknown',
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keyInsights: [],
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analysisQuality: 'excellent',
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keyInsights: [
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'Vollständige Dokumentation aller Analyseschritte',
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'Transparente KI-Entscheidungsfindung'
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],
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potentialIssues: []
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};
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}
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@ -1183,10 +1166,6 @@ class AIQueryInterface {
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}
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});
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const avgProcessingTime = auditTrail.length > 0 ? totalTime / auditTrail.length : 0;
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const aiTransparency = auditTrail.length > 0 ?
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(auditTrail.filter(entry => entry.metadata?.aiPrompt || entry.metadata?.reasoning).length / auditTrail.length) * 100 : 0;
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let analysisQuality;
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if (avgConfidence >= 85 && lowConfidenceSteps === 0) {
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analysisQuality = 'excellent';
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@ -1204,34 +1183,43 @@ class AIQueryInterface {
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keyInsights.push('Semantische Suche wurde erfolgreich eingesetzt');
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}
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const toolSelectionEntries = auditTrail.filter(e => e.action === 'selection-decision');
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if (toolSelectionEntries.length > 0) {
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const avgSelectionConfidence = toolSelectionEntries.reduce((sum, e) => sum + (e.confidence || 0), 0) / toolSelectionEntries.length;
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if (avgSelectionConfidence >= 80) {
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keyInsights.push('Hohe Konfidenz bei der Tool-Auswahl');
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}
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}
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if (aiTransparency >= 90) {
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keyInsights.push('Sehr hohe Transparenz der KI-Entscheidungen');
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const aiDecisionsWithReasoning = auditTrail.filter(e =>
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e.action === 'ai-decision' && e.metadata?.reasoning
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).length;
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if (aiDecisionsWithReasoning > 0) {
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keyInsights.push(`${aiDecisionsWithReasoning} KI-Entscheidungen mit detaillierter Begründung`);
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}
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if (highConfidenceSteps > auditTrail.length * 0.7) {
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keyInsights.push('Mehrheit der Analyseschritte mit hoher Sicherheit');
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}
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// Calculate meaningful insights based on response quality
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const responseQualityEntries = auditTrail.filter(e =>
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e.metadata?.responseConfidence && e.metadata.finalConfidence
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);
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if (responseQualityEntries.length > 0) {
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const avgResponseQuality = responseQualityEntries.reduce((sum, e) =>
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sum + (e.metadata.responseConfidence || 0), 0
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) / responseQualityEntries.length;
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if (avgResponseQuality >= 70) {
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keyInsights.push(`Hohe AI-Antwortqualität (Ø ${Math.round(avgResponseQuality)}%)`);
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}
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}
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const potentialIssues = [];
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if (lowConfidenceSteps > 2) {
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potentialIssues.push(`${lowConfidenceSteps} Analyseschritte mit niedriger Konfidenz`);
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}
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if (aiTransparency < 50) {
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potentialIssues.push('Geringe Transparenz der KI-Entscheidungsfindung');
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}
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const failedAiDecisions = auditTrail.filter(e => e.action === 'ai-decision' && e.confidence < 50).length;
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if (failedAiDecisions > 0) {
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potentialIssues.push(`${failedAiDecisions} KI-Entscheidungen mit sehr niedriger Konfidenz`);
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// Check for truncated responses
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const truncatedResponses = auditTrail.filter(e =>
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e.output && typeof e.output === 'object' &&
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e.output.response && e.output.response.includes('...')
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).length;
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if (truncatedResponses > 0) {
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potentialIssues.push(`${truncatedResponses} möglicherweise unvollständige AI-Antworten`);
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}
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return {
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@ -1245,13 +1233,13 @@ class AIQueryInterface {
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embeddingsUsageCount,
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toolSelectionCount,
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qualityMetrics: {
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avgProcessingTime,
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avgProcessingTime: auditTrail.length > 0 ? totalTime / auditTrail.length : 0,
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confidenceDistribution: {
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high: highConfidenceSteps,
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medium: mediumConfidenceSteps,
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low: lowConfidenceSteps
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},
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aiTransparency: Math.round(aiTransparency)
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}
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// aiTransparency removed entirely
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},
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analysisQuality,
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keyInsights,
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@ -1311,11 +1299,19 @@ class AIQueryInterface {
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renderAuditEntry(entry) {
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const confidenceColor = getConfidenceColor(entry.confidence || 0);
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const processingTime = formatDuration(entry.processingTimeMs || 0);
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const decisionBasis = entry.metadata?.decisionBasis || 'unknown';
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return `
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<div class="audit-entry">
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<div class="entry-main">
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<div class="entry-action">${this.getActionDisplayName(entry.action)}</div>
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<div class="entry-action">
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<div style="display: flex; align-items: center; gap: 0.5rem;">
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<span>${this.getDetailedActionName(entry)}</span>
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<span class="decision-basis-badge" style="background-color: ${this.getDecisionBasisColor(decisionBasis)}; color: white; padding: 0.125rem 0.375rem; border-radius: 0.25rem; font-size: 0.625rem; font-weight: 500;">
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${this.getDecisionBasisText(decisionBasis)}
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</span>
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</div>
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</div>
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<div class="entry-meta">
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<div class="confidence-indicator" style="background-color: ${confidenceColor};"></div>
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<span class="confidence-value">${entry.confidence || 0}%</span>
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@ -1323,11 +1319,139 @@ class AIQueryInterface {
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</div>
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</div>
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${this.renderEntryDetails(entry)}
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${this.renderDetailedEntryInfo(entry)}
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</div>
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`;
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}
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getDecisionBasisColor(basis) {
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const colors = {
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'ai-analysis': 'var(--color-primary)',
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'semantic-search': 'var(--color-accent)',
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'hybrid': 'var(--color-warning)',
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'rule-based': 'var(--color-text-secondary)'
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};
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return colors[basis] || 'var(--color-text-secondary)';
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}
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getDecisionBasisText(basis) {
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const texts = {
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'ai-analysis': 'KI-Analyse',
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'semantic-search': 'Semantik',
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'hybrid': 'Hybrid',
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'rule-based': 'Regel-basiert'
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};
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return texts[basis] || 'Unbekannt';
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}
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renderDetailedEntryInfo(entry) {
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const details = [];
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const input = entry.input || {};
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const output = entry.output || {};
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const metadata = entry.metadata || {};
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// Show input summary
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if (metadata.inputSummary && metadata.inputSummary !== 'Empty') {
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details.push(`<div class="detail-item"><strong>Eingabe:</strong> ${escapeHtml(metadata.inputSummary)}</div>`);
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}
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// Show output summary
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if (metadata.outputSummary && metadata.outputSummary !== 'Empty') {
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details.push(`<div class="detail-item"><strong>Ausgabe:</strong> ${escapeHtml(metadata.outputSummary)}</div>`);
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}
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// Show reasoning
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if (metadata.reasoning) {
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details.push(`<div class="detail-item"><strong>Begründung:</strong> ${escapeHtml(metadata.reasoning)}</div>`);
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}
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// Show specific details based on action type
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if (entry.action === 'similarity-search' && metadata.similarityScores) {
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const topScores = Object.entries(metadata.similarityScores)
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.sort(([,a], [,b]) => b - a)
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.slice(0, 3)
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.map(([name, score]) => `${name} (${(score * 100).toFixed(1)}%)`)
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.join(', ');
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if (topScores) {
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details.push(`<div class="detail-item"><strong>Top Treffer:</strong> ${topScores}</div>`);
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}
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}
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if (entry.action === 'ai-decision' && metadata.aiModel) {
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details.push(`<div class="detail-item"><strong>KI-Modell:</strong> ${metadata.aiModel}</div>`);
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if (metadata.promptTokens && metadata.completionTokens) {
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details.push(`<div class="detail-item"><strong>Token-Nutzung:</strong> ${metadata.promptTokens} + ${metadata.completionTokens} = ${metadata.promptTokens + metadata.completionTokens}</div>`);
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}
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}
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if (entry.action === 'selection-decision' && metadata.selectionMethod) {
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details.push(`<div class="detail-item"><strong>Auswahlmethode:</strong> ${metadata.selectionMethod}</div>`);
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}
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if (entry.action === 'tool-confidence') {
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const confidence = entry.output || {};
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if (confidence.strengthIndicators?.length > 0) {
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details.push(`<div class="detail-item"><strong>Stärken:</strong> ${confidence.strengthIndicators.slice(0, 2).join(', ')}</div>`);
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}
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if (confidence.uncertaintyFactors?.length > 0) {
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details.push(`<div class="detail-item"><strong>Unsicherheiten:</strong> ${confidence.uncertaintyFactors.slice(0, 2).join(', ')}</div>`);
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}
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}
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if (details.length === 0) return '';
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return `
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<div class="entry-details">
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${details.join('')}
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</div>
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`;
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}
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getDetailedActionName(entry) {
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const action = entry.action;
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const metadata = entry.metadata || {};
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switch (action) {
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case 'selection-decision':
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return `Tool-Auswahl: ${metadata.selectedToolsCount || 0} von ${metadata.availableToolsCount || 0} Tools gewählt`;
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case 'ai-decision':
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if (metadata.microTaskType) {
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const taskTypes = {
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'scenario-analysis': 'Szenario-Analyse',
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'investigation-approach': 'Untersuchungsansatz',
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'critical-considerations': 'Kritische Überlegungen',
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'tool-evaluation': 'Tool-Bewertung',
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'background-knowledge': 'Hintergrundwissen-Auswahl',
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'final-recommendations': 'Abschließende Empfehlungen'
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};
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return `KI-Analyse: ${taskTypes[metadata.microTaskType] || metadata.microTaskType}`;
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}
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return 'KI-Entscheidung';
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case 'similarity-search':
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return `Semantische Suche: ${entry.output?.resultsCount || 0} ähnliche Items gefunden`;
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case 'phase-enhancement':
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return `Phasen-Vervollständigung: ${metadata.toolsAddedCount || 0} Tools für ${metadata.phaseId} hinzugefügt`;
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case 'tool-confidence':
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return `Vertrauenswertung: ${entry.input?.toolName || 'Tool'} bewertet`;
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case 'phase-tool-selection':
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return `Phasen-Tools: ${metadata.selectedToolsCount || 0} Tools für ${metadata.phaseId} ausgewählt`;
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case 'pipeline-start':
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return `Analyse gestartet (${entry.input?.mode || 'unknown'} Modus)`;
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case 'pipeline-end':
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return `Analyse abgeschlossen (${entry.input?.completedTasks || 0} erfolgreich, ${entry.input?.failedTasks || 0} fehlgeschlagen)`;
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default:
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return this.getActionDisplayName(action);
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}
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}
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renderEntryDetails(entry) {
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const details = [];
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@ -1362,24 +1486,6 @@ class AIQueryInterface {
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`;
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}
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renderTechnicalDetails(auditTrail) {
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return auditTrail.map(entry => `
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<div class="technical-entry">
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<div class="technical-header">
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<div class="technical-phase">${entry.phase}/${entry.action}</div>
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<div class="technical-time">${new Date(entry.timestamp).toLocaleTimeString('de-DE')}</div>
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</div>
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<div class="technical-content">
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${entry.metadata?.aiModel ? `<div class="technical-row">Model: ${entry.metadata.aiModel}</div>` : ''}
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${entry.metadata?.promptTokens ? `<div class="technical-row">Tokens: ${entry.metadata.promptTokens}/${entry.metadata.completionTokens}</div>` : ''}
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${entry.metadata?.microTaskType ? `<div class="technical-row">Task: ${entry.metadata.microTaskType}</div>` : ''}
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${entry.metadata?.selectionMethod ? `<div class="technical-row">Method: ${entry.metadata.selectionMethod}</div>` : ''}
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<div class="technical-row">Confidence: ${entry.confidence}% (${entry.processingTimeMs}ms)</div>
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</div>
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</div>
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`).join('');
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}
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|
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getPhaseIcon(phase) {
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const icons = {
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'initialization': '🚀',
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@ -656,17 +656,25 @@ class AIPipeline {
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this.addToContextHistory(context, `${isWorkflow ? 'Szenario' : 'Problem'}-Analyse: ${result.content.slice(0, 200)}...`);
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const confidence = auditService.calculateAIResponseConfidence(
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result.content,
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{ min: 50, max: 300 },
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'scenario-analysis'
|
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);
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auditService.addAIDecision(
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'contextual-analysis',
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prompt,
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result.content,
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80,
|
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`Generated ${isWorkflow ? 'scenario' : 'problem'} analysis for user query`,
|
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confidence,
|
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`Analysierte ${isWorkflow ? 'Szenario' : 'Problem'} basierend auf Nutzereingabe: "${context.userQuery.slice(0, 100)}..." - Identifizierte Kernaspekte und Herausforderungen für forensische Untersuchung`,
|
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taskStart,
|
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{
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microTaskType: 'scenario-analysis',
|
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analysisType: isWorkflow ? 'scenario' : 'problem',
|
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contentLength: result.content.length,
|
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decisionBasis: 'ai-analysis',
|
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aiModel: aiService.getConfig().model,
|
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...result.aiUsage
|
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}
|
||||
);
|
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@ -687,18 +695,26 @@ class AIPipeline {
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context.investigationApproach = result.content;
|
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this.addToContextHistory(context, `${isWorkflow ? 'Untersuchungs' : 'Lösungs'}ansatz: ${result.content.slice(0, 200)}...`);
|
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|
||||
const confidence = auditService.calculateAIResponseConfidence(
|
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result.content,
|
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{ min: 50, max: 300 },
|
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'investigation-approach'
|
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);
|
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|
||||
auditService.addAIDecision(
|
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'contextual-analysis',
|
||||
prompt,
|
||||
result.content,
|
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75,
|
||||
`Generated ${isWorkflow ? 'investigation' : 'solution'} approach`,
|
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confidence,
|
||||
`Entwickelte ${isWorkflow ? 'Untersuchungs' : 'Lösungs'}ansatz unter Berücksichtigung der Szenario-Analyse - Strukturierte Herangehensweise für forensische Methodik`,
|
||||
taskStart,
|
||||
{
|
||||
microTaskType: 'investigation-approach',
|
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approachType: isWorkflow ? 'investigation' : 'solution',
|
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contentLength: result.content.length,
|
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contextHistoryLength: context.contextHistory.length,
|
||||
decisionBasis: 'ai-analysis',
|
||||
aiModel: aiService.getConfig().model,
|
||||
...result.aiUsage
|
||||
}
|
||||
);
|
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@ -719,16 +735,24 @@ class AIPipeline {
|
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context.criticalConsiderations = result.content;
|
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this.addToContextHistory(context, `Kritische Überlegungen: ${result.content.slice(0, 200)}...`);
|
||||
|
||||
const confidence = auditService.calculateAIResponseConfidence(
|
||||
result.content,
|
||||
{ min: 40, max: 250 },
|
||||
'critical-considerations'
|
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);
|
||||
|
||||
auditService.addAIDecision(
|
||||
'contextual-analysis',
|
||||
prompt,
|
||||
result.content,
|
||||
70,
|
||||
'Generated critical considerations and constraints',
|
||||
confidence,
|
||||
'Identifizierte kritische Überlegungen für forensische Untersuchung - Berücksichtigung von Beweissicherung, Chain of Custody und methodischen Herausforderungen',
|
||||
taskStart,
|
||||
{
|
||||
microTaskType: 'critical-considerations',
|
||||
contentLength: result.content.length,
|
||||
decisionBasis: 'ai-analysis',
|
||||
aiModel: aiService.getConfig().model,
|
||||
...result.aiUsage
|
||||
}
|
||||
);
|
||||
@ -771,12 +795,22 @@ class AIPipeline {
|
||||
}
|
||||
}, 'evaluation', priority, evaluation.detailed_explanation, moderatedTaskRelevance, evaluation.limitations);
|
||||
|
||||
// Calculate confidence based on response quality and task relevance
|
||||
const responseConfidence = auditService.calculateAIResponseConfidence(
|
||||
result.content,
|
||||
{ min: 200, max: 800 },
|
||||
'tool-evaluation'
|
||||
);
|
||||
|
||||
// Use the higher of response quality confidence or moderated task relevance
|
||||
const finalConfidence = Math.max(responseConfidence, moderatedTaskRelevance);
|
||||
|
||||
auditService.addAIDecision(
|
||||
'tool-evaluation',
|
||||
prompt,
|
||||
result.content,
|
||||
moderatedTaskRelevance,
|
||||
`Evaluated tool ${tool.name} for ${context.mode} mode`,
|
||||
finalConfidence,
|
||||
`Bewertete Tool "${tool.name}" (Rang ${rank}) - Analysierte Eignung für spezifische Aufgabenstellung mit Fokus auf praktische Anwendbarkeit und methodische Integration`,
|
||||
taskStart,
|
||||
{
|
||||
microTaskType: 'tool-evaluation',
|
||||
@ -785,10 +819,14 @@ class AIPipeline {
|
||||
rank,
|
||||
originalTaskRelevance,
|
||||
moderatedTaskRelevance,
|
||||
responseConfidence,
|
||||
finalConfidence,
|
||||
moderationApplied: originalTaskRelevance !== moderatedTaskRelevance,
|
||||
evaluationParsed: !!evaluation.detailed_explanation,
|
||||
prosCount: evaluation.pros?.length || 0,
|
||||
limitationsCount: evaluation.limitations?.length || 0,
|
||||
decisionBasis: 'ai-analysis',
|
||||
aiModel: aiService.getConfig().model,
|
||||
...result.aiUsage
|
||||
}
|
||||
);
|
||||
@ -826,24 +864,39 @@ class AIPipeline {
|
||||
relevance: sel.relevance
|
||||
}));
|
||||
|
||||
const responseConfidence = auditService.calculateAIResponseConfidence(
|
||||
result.content,
|
||||
{ min: 100, max: 500 },
|
||||
'background-knowledge'
|
||||
);
|
||||
|
||||
const selectionBonus = context.backgroundKnowledge.length > 0 ? 15 : 0;
|
||||
const finalConfidence = Math.min(95, responseConfidence + selectionBonus);
|
||||
|
||||
auditService.addEntry(
|
||||
'knowledge-synthesis',
|
||||
'concept-selection',
|
||||
{
|
||||
availableConcepts: availableConcepts.map(c => c.name),
|
||||
selectedToolsContext: selectedToolNames
|
||||
selectedToolsContext: selectedToolNames,
|
||||
selectionCriteria: 'methodische Fundierung'
|
||||
},
|
||||
{
|
||||
selectedConcepts: context.backgroundKnowledge.map(bk => bk.concept.name),
|
||||
selectionReasonings: context.backgroundKnowledge.map(bk => bk.relevance)
|
||||
},
|
||||
context.backgroundKnowledge.length > 0 ? 75 : 50,
|
||||
finalConfidence,
|
||||
taskStart,
|
||||
{
|
||||
microTaskType: 'background-knowledge',
|
||||
availableConceptsCount: availableConcepts.length,
|
||||
selectedConceptsCount: context.backgroundKnowledge.length,
|
||||
selectionRatio: context.backgroundKnowledge.length / availableConcepts.length,
|
||||
responseConfidence,
|
||||
selectionBonus,
|
||||
decisionBasis: 'ai-analysis',
|
||||
reasoning: `Wählte ${context.backgroundKnowledge.length} von ${availableConcepts.length} verfügbaren Konzepten für methodische Fundierung der Empfehlungen`,
|
||||
aiModel: aiService.getConfig().model,
|
||||
...result.aiUsage
|
||||
}
|
||||
);
|
||||
@ -862,18 +915,31 @@ class AIPipeline {
|
||||
const result = await this.callMicroTaskAI(prompt, context, 350, 'final-recommendations');
|
||||
|
||||
if (result.success) {
|
||||
const confidence = auditService.calculateAIResponseConfidence(
|
||||
result.content,
|
||||
{ min: 60, max: 250 },
|
||||
'final-recommendations'
|
||||
);
|
||||
|
||||
const contextBonus = selectedToolNames.length >= 3 ? 10 : 0;
|
||||
const finalConfidence = Math.min(95, confidence + contextBonus);
|
||||
|
||||
auditService.addAIDecision(
|
||||
'synthesis',
|
||||
prompt,
|
||||
result.content,
|
||||
85,
|
||||
`Generated final ${context.mode} recommendations`,
|
||||
finalConfidence,
|
||||
`Generierte abschließende ${context.mode}-Empfehlungen basierend auf ausgewählten ${selectedToolNames.length} Tools - Synthese aller Analyseschritte zu kohärenter Handlungsempfehlung`,
|
||||
taskStart,
|
||||
{
|
||||
microTaskType: 'final-recommendations',
|
||||
mode: context.mode,
|
||||
selectedToolsCount: selectedToolNames.length,
|
||||
contentLength: result.content.length,
|
||||
responseConfidence: confidence,
|
||||
contextBonus,
|
||||
decisionBasis: 'ai-analysis',
|
||||
aiModel: aiService.getConfig().model,
|
||||
...result.aiUsage
|
||||
}
|
||||
);
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
// src/utils/auditService.ts - Enhanced for forensic-grade transparency
|
||||
// src/utils/auditService.ts - Always detailed, no compression modes
|
||||
import 'dotenv/config';
|
||||
|
||||
function env(key: string, fallback: string | undefined = undefined): string | undefined {
|
||||
@ -40,13 +40,15 @@ export interface AuditEntry {
|
||||
completionReasoning?: string;
|
||||
similarityScores?: Record<string, number>;
|
||||
contextLength?: number;
|
||||
decisionBasis?: 'ai-analysis' | 'semantic-search' | 'hybrid' | 'rule-based';
|
||||
inputSummary?: string;
|
||||
outputSummary?: string;
|
||||
[key: string]: any;
|
||||
};
|
||||
}
|
||||
|
||||
interface AuditConfig {
|
||||
enabled: boolean;
|
||||
detailLevel: 'minimal' | 'standard' | 'verbose';
|
||||
retentionHours: number;
|
||||
maxEntries: number;
|
||||
}
|
||||
@ -57,21 +59,16 @@ class AuditService {
|
||||
|
||||
constructor() {
|
||||
this.config = this.loadConfig();
|
||||
console.log('[AUDIT-SERVICE] Initialized:', {
|
||||
enabled: this.config.enabled,
|
||||
detailLevel: this.config.detailLevel
|
||||
});
|
||||
console.log('[AUDIT-SERVICE] Initialized with detailed logging enabled');
|
||||
}
|
||||
|
||||
private loadConfig(): AuditConfig {
|
||||
const enabledFlag = env('FORENSIC_AUDIT_ENABLED', 'false');
|
||||
const detailLevel = env('FORENSIC_AUDIT_DETAIL_LEVEL', 'standard') as 'minimal' | 'standard' | 'verbose';
|
||||
const enabled = env('FORENSIC_AUDIT_ENABLED', 'true') === 'true';
|
||||
const retentionHours = parseInt(env('FORENSIC_AUDIT_RETENTION_HOURS', '72') || '72', 10);
|
||||
const maxEntries = parseInt(env('FORENSIC_AUDIT_MAX_ENTRIES', '50') || '50', 10);
|
||||
|
||||
return {
|
||||
enabled: enabledFlag === 'true',
|
||||
detailLevel,
|
||||
enabled,
|
||||
retentionHours,
|
||||
maxEntries
|
||||
};
|
||||
@ -88,15 +85,24 @@ class AuditService {
|
||||
): void {
|
||||
if (!this.config.enabled) return;
|
||||
|
||||
// Always store full details with meaningful summaries
|
||||
const enhancedMetadata = {
|
||||
...metadata,
|
||||
inputSummary: this.createMeaningfulSummary(input, 'input'),
|
||||
outputSummary: this.createMeaningfulSummary(output, 'output'),
|
||||
decisionBasis: metadata.decisionBasis || this.inferDecisionBasis(metadata),
|
||||
reasoning: metadata.reasoning || this.extractReasoning(action, input, output, metadata)
|
||||
};
|
||||
|
||||
const entry: AuditEntry = {
|
||||
timestamp: Date.now(),
|
||||
phase,
|
||||
action,
|
||||
input: this.compressData(input),
|
||||
output: this.compressData(output),
|
||||
input: input, // Store full input
|
||||
output: output, // Store full output
|
||||
confidence: Math.round(confidence),
|
||||
processingTimeMs: Date.now() - startTime,
|
||||
metadata
|
||||
metadata: enhancedMetadata
|
||||
};
|
||||
|
||||
this.activeAuditTrail.push(entry);
|
||||
@ -105,7 +111,7 @@ class AuditService {
|
||||
this.activeAuditTrail.shift();
|
||||
}
|
||||
|
||||
console.log(`[AUDIT-SERVICE] ${phase}/${action}: ${confidence}% confidence, ${entry.processingTimeMs}ms`);
|
||||
console.log(`[AUDIT-SERVICE] ${phase}/${action}: ${confidence}% confidence, ${entry.processingTimeMs}ms, basis: ${enhancedMetadata.decisionBasis}`);
|
||||
}
|
||||
|
||||
addAIDecision(
|
||||
@ -120,15 +126,16 @@ class AuditService {
|
||||
this.addEntry(
|
||||
phase,
|
||||
'ai-decision',
|
||||
{ prompt: this.truncateForAudit(aiPrompt) },
|
||||
{ response: this.truncateForAudit(aiResponse) },
|
||||
{ prompt: aiPrompt },
|
||||
{ response: aiResponse },
|
||||
confidence,
|
||||
startTime,
|
||||
{
|
||||
...metadata,
|
||||
reasoning,
|
||||
aiPrompt: this.config.detailLevel === 'verbose' ? aiPrompt : this.truncateForAudit(aiPrompt),
|
||||
aiResponse: this.config.detailLevel === 'verbose' ? aiResponse : this.truncateForAudit(aiResponse)
|
||||
aiPrompt: aiPrompt,
|
||||
aiResponse: aiResponse,
|
||||
decisionBasis: 'ai-analysis'
|
||||
}
|
||||
);
|
||||
}
|
||||
@ -144,8 +151,15 @@ class AuditService {
|
||||
this.addEntry(
|
||||
'tool-selection',
|
||||
'selection-decision',
|
||||
{ availableTools: availableTools.length > 10 ? availableTools.slice(0, 10) : availableTools },
|
||||
{ selectedTools },
|
||||
{
|
||||
availableTools: availableTools,
|
||||
selectionMethod: selectionMethod,
|
||||
candidateCount: availableTools.length
|
||||
},
|
||||
{
|
||||
selectedTools: selectedTools,
|
||||
selectionRatio: selectedTools.length / availableTools.length
|
||||
},
|
||||
confidence,
|
||||
startTime,
|
||||
{
|
||||
@ -153,7 +167,9 @@ class AuditService {
|
||||
selectionMethod,
|
||||
availableToolsCount: availableTools.length,
|
||||
selectedToolsCount: selectedTools.length,
|
||||
toolSelectionCriteria: `${selectionMethod} selection from ${availableTools.length} available tools`
|
||||
toolSelectionCriteria: `${selectionMethod} selection from ${availableTools.length} available tools`,
|
||||
decisionBasis: selectionMethod.includes('embeddings') ? 'semantic-search' : 'ai-analysis',
|
||||
reasoning: `Selected ${selectedTools.length} tools out of ${availableTools.length} candidates using ${selectionMethod}`
|
||||
}
|
||||
);
|
||||
}
|
||||
@ -169,11 +185,12 @@ class AuditService {
|
||||
'phase-completion',
|
||||
'phase-enhancement',
|
||||
{
|
||||
phaseId,
|
||||
addedTools,
|
||||
reasoning: reasoning.slice(0, 200)
|
||||
phaseId: phaseId,
|
||||
completionReason: 'underrepresented-phase',
|
||||
semanticQuery: `forensic ${phaseId} tools methods`
|
||||
},
|
||||
{
|
||||
addedTools: addedTools,
|
||||
toolsAddedCount: addedTools.length,
|
||||
enhancementMethod: 'semantic-search-with-ai-reasoning'
|
||||
},
|
||||
@ -181,6 +198,8 @@ class AuditService {
|
||||
startTime,
|
||||
{
|
||||
...metadata,
|
||||
reasoning: reasoning,
|
||||
decisionBasis: 'hybrid',
|
||||
phaseCompletionMethod: 'sophisticated-ai-reasoning'
|
||||
}
|
||||
);
|
||||
@ -201,8 +220,17 @@ class AuditService {
|
||||
this.addEntry(
|
||||
'embeddings',
|
||||
'similarity-search',
|
||||
{ query: this.truncateForAudit(query), threshold },
|
||||
{ resultsCount: similarResults.length, topResults: similarResults.slice(0, 5).map(r => r.name) },
|
||||
{
|
||||
query: query,
|
||||
threshold: threshold,
|
||||
searchType: 'semantic-embeddings'
|
||||
},
|
||||
{
|
||||
resultsCount: similarResults.length,
|
||||
topResults: similarResults.slice(0, 10),
|
||||
averageSimilarity: similarResults.length > 0 ?
|
||||
similarResults.reduce((sum, r) => sum + r.similarity, 0) / similarResults.length : 0
|
||||
},
|
||||
similarResults.length > 0 ? 85 : 50,
|
||||
startTime,
|
||||
{
|
||||
@ -210,7 +238,9 @@ class AuditService {
|
||||
embeddingsUsed: true,
|
||||
similarityScores,
|
||||
searchThreshold: threshold,
|
||||
totalMatches: similarResults.length
|
||||
totalMatches: similarResults.length,
|
||||
decisionBasis: 'semantic-search',
|
||||
reasoning: `Semantic search found ${similarResults.length} items with similarity above ${threshold}`
|
||||
}
|
||||
);
|
||||
}
|
||||
@ -225,26 +255,92 @@ class AuditService {
|
||||
'confidence-scoring',
|
||||
'tool-confidence',
|
||||
{
|
||||
toolName,
|
||||
confidence: {
|
||||
overall: confidence.overall,
|
||||
semantic: confidence.semanticRelevance,
|
||||
task: confidence.taskSuitability
|
||||
}
|
||||
toolName: toolName,
|
||||
semanticSimilarity: confidence.semanticRelevance,
|
||||
taskRelevance: confidence.taskSuitability
|
||||
},
|
||||
{
|
||||
uncertaintyFactorsCount: confidence.uncertaintyFactors?.length || 0,
|
||||
strengthIndicatorsCount: confidence.strengthIndicators?.length || 0
|
||||
overallConfidence: confidence.overall,
|
||||
strengthIndicators: confidence.strengthIndicators || [],
|
||||
uncertaintyFactors: confidence.uncertaintyFactors || []
|
||||
},
|
||||
confidence.overall,
|
||||
startTime,
|
||||
{
|
||||
...metadata,
|
||||
confidenceCalculation: true
|
||||
confidenceCalculation: true,
|
||||
decisionBasis: 'ai-analysis',
|
||||
reasoning: `Calculated confidence: ${confidence.overall}% (semantic: ${confidence.semanticRelevance}%, task: ${confidence.taskSuitability}%)`
|
||||
}
|
||||
);
|
||||
}
|
||||
|
||||
private createMeaningfulSummary(data: any, type: 'input' | 'output'): string {
|
||||
if (!data) return 'Empty';
|
||||
|
||||
if (typeof data === 'string') {
|
||||
return data.length > 150 ? data.slice(0, 150) + '...' : data;
|
||||
}
|
||||
|
||||
if (Array.isArray(data)) {
|
||||
if (data.length === 0) return 'Empty array';
|
||||
if (data.length <= 3) return data.join(', ');
|
||||
return `${data.slice(0, 3).join(', ')} and ${data.length - 3} more items`;
|
||||
}
|
||||
|
||||
if (typeof data === 'object') {
|
||||
const keys = Object.keys(data);
|
||||
if (keys.length === 0) return 'Empty object';
|
||||
|
||||
// Create meaningful summaries based on common patterns
|
||||
if (data.prompt) return `AI Prompt: ${data.prompt.slice(0, 100)}...`;
|
||||
if (data.response) return `AI Response: ${data.response.slice(0, 100)}...`;
|
||||
if (data.selectedTools) return `Selected: ${data.selectedTools.join(', ')}`;
|
||||
if (data.availableTools) return `${data.availableTools.length} tools available`;
|
||||
if (data.query) return `Query: ${data.query}`;
|
||||
|
||||
return `Object with ${keys.length} properties: ${keys.slice(0, 3).join(', ')}${keys.length > 3 ? '...' : ''}`;
|
||||
}
|
||||
|
||||
return String(data);
|
||||
}
|
||||
|
||||
private inferDecisionBasis(metadata: Record<string, any>): string {
|
||||
if (metadata.embeddingsUsed) return 'semantic-search';
|
||||
if (metadata.aiPrompt || metadata.microTaskType) return 'ai-analysis';
|
||||
if (metadata.selectionMethod?.includes('embeddings')) return 'semantic-search';
|
||||
if (metadata.selectionMethod?.includes('full')) return 'ai-analysis';
|
||||
return 'rule-based';
|
||||
}
|
||||
|
||||
private extractReasoning(action: string, input: any, output: any, metadata: Record<string, any>): string {
|
||||
if (metadata.reasoning) return metadata.reasoning;
|
||||
|
||||
// Generate meaningful reasoning based on action type
|
||||
switch (action) {
|
||||
case 'selection-decision':
|
||||
const selectionRatio = metadata.selectedToolsCount / metadata.availableToolsCount;
|
||||
return `Selected ${metadata.selectedToolsCount} tools (${Math.round(selectionRatio * 100)}%) using ${metadata.selectionMethod}`;
|
||||
|
||||
case 'similarity-search':
|
||||
return `Found ${output?.resultsCount || 0} similar items above threshold ${input?.threshold || 0}`;
|
||||
|
||||
case 'ai-decision':
|
||||
return metadata.microTaskType ?
|
||||
`AI analysis for ${metadata.microTaskType}` :
|
||||
'AI decision based on prompt analysis';
|
||||
|
||||
case 'tool-confidence':
|
||||
return `Confidence scored based on semantic similarity and task relevance`;
|
||||
|
||||
case 'phase-enhancement':
|
||||
return `Enhanced ${metadata.phaseId} phase with ${metadata.toolsAddedCount} additional tools`;
|
||||
|
||||
default:
|
||||
return `${action} completed with ${Math.round(metadata.confidence || 0)}% confidence`;
|
||||
}
|
||||
}
|
||||
|
||||
getCurrentAuditTrail(): AuditEntry[] {
|
||||
return [...this.activeAuditTrail];
|
||||
}
|
||||
@ -263,42 +359,6 @@ class AuditService {
|
||||
return finalTrail;
|
||||
}
|
||||
|
||||
private compressData(data: any): any {
|
||||
if (this.config.detailLevel === 'verbose') {
|
||||
return data;
|
||||
} else if (this.config.detailLevel === 'standard') {
|
||||
return this.summarizeForStorage(data);
|
||||
} else {
|
||||
return this.minimalSummary(data);
|
||||
}
|
||||
}
|
||||
|
||||
private summarizeForStorage(data: any): any {
|
||||
if (typeof data === 'string' && data.length > 500) {
|
||||
return data.slice(0, 500) + '...[truncated]';
|
||||
}
|
||||
if (Array.isArray(data) && data.length > 10) {
|
||||
return [...data.slice(0, 10), `...[${data.length - 10} more items]`];
|
||||
}
|
||||
return data;
|
||||
}
|
||||
|
||||
private minimalSummary(data: any): any {
|
||||
if (typeof data === 'string' && data.length > 100) {
|
||||
return data.slice(0, 100) + '...[truncated]';
|
||||
}
|
||||
if (Array.isArray(data) && data.length > 3) {
|
||||
return [...data.slice(0, 3), `...[${data.length - 3} more items]`];
|
||||
}
|
||||
return data;
|
||||
}
|
||||
|
||||
private truncateForAudit(text: string, maxLength: number = 300): string {
|
||||
if (typeof text !== 'string') return String(text);
|
||||
if (text.length <= maxLength) return text;
|
||||
return text.slice(0, maxLength) + '...[truncated for audit]';
|
||||
}
|
||||
|
||||
isEnabled(): boolean {
|
||||
return this.config.enabled;
|
||||
}
|
||||
@ -320,7 +380,6 @@ class AuditService {
|
||||
qualityMetrics: {
|
||||
avgProcessingTime: number;
|
||||
confidenceDistribution: { high: number; medium: number; low: number };
|
||||
aiTransparency: number;
|
||||
};
|
||||
} {
|
||||
if (!auditTrail || auditTrail.length === 0) {
|
||||
@ -336,8 +395,7 @@ class AuditService {
|
||||
toolSelectionCount: 0,
|
||||
qualityMetrics: {
|
||||
avgProcessingTime: 0,
|
||||
confidenceDistribution: { high: 0, medium: 0, low: 0 },
|
||||
aiTransparency: 0
|
||||
confidenceDistribution: { high: 0, medium: 0, low: 0 }
|
||||
}
|
||||
};
|
||||
}
|
||||
@ -380,8 +438,6 @@ class AuditService {
|
||||
});
|
||||
|
||||
const avgProcessingTime = auditTrail.length > 0 ? totalTime / auditTrail.length : 0;
|
||||
const aiTransparency = auditTrail.length > 0 ?
|
||||
(auditTrail.filter(entry => entry.metadata?.aiPrompt || entry.metadata?.reasoning).length / auditTrail.length) * 100 : 0;
|
||||
|
||||
return {
|
||||
totalTime,
|
||||
@ -399,12 +455,72 @@ class AuditService {
|
||||
high: highConfidenceSteps,
|
||||
medium: mediumConfidenceSteps,
|
||||
low: lowConfidenceSteps
|
||||
},
|
||||
aiTransparency: Math.round(aiTransparency)
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
calculateAIResponseConfidence(
|
||||
response: string,
|
||||
expectedLength: { min: number; max: number },
|
||||
taskType: string
|
||||
): number {
|
||||
let confidence = 50; // Base confidence
|
||||
|
||||
// Response length indicates completeness
|
||||
if (response.length >= expectedLength.min) {
|
||||
confidence += 20;
|
||||
if (response.length <= expectedLength.max) {
|
||||
confidence += 10; // Optimal length
|
||||
}
|
||||
} else {
|
||||
confidence -= 20; // Too short
|
||||
}
|
||||
|
||||
// Response quality indicators
|
||||
if (response.includes('...') || response.endsWith('...')) {
|
||||
confidence -= 10; // Truncated response
|
||||
}
|
||||
|
||||
// Task-specific quality checks
|
||||
switch (taskType) {
|
||||
case 'scenario-analysis':
|
||||
case 'investigation-approach':
|
||||
case 'critical-considerations':
|
||||
// Should contain forensic methodology terms
|
||||
const forensicTerms = ['forensisch', 'beweis', 'evidence', 'analyse', 'untersuchung', 'methodik'];
|
||||
const termsFound = forensicTerms.filter(term =>
|
||||
response.toLowerCase().includes(term)
|
||||
).length;
|
||||
confidence += Math.min(15, termsFound * 3);
|
||||
break;
|
||||
|
||||
case 'tool-evaluation':
|
||||
// Should be structured and comprehensive
|
||||
if (response.includes('detailed_explanation') || response.includes('implementation_approach')) {
|
||||
confidence += 15;
|
||||
}
|
||||
if (response.includes('pros') && response.includes('limitations')) {
|
||||
confidence += 10;
|
||||
}
|
||||
break;
|
||||
|
||||
case 'background-knowledge':
|
||||
// Should be valid JSON array
|
||||
try {
|
||||
const parsed = JSON.parse(response);
|
||||
if (Array.isArray(parsed) && parsed.length > 0) {
|
||||
confidence += 20;
|
||||
}
|
||||
} catch {
|
||||
confidence -= 20;
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
return Math.min(95, Math.max(25, confidence));
|
||||
}
|
||||
|
||||
validateAuditTrail(auditTrail: AuditEntry[]): {
|
||||
isValid: boolean;
|
||||
issues: string[];
|
||||
@ -435,14 +551,6 @@ class AuditService {
|
||||
}
|
||||
});
|
||||
|
||||
if (entry.action === 'ai-decision' && !entry.metadata?.aiPrompt && !entry.metadata?.reasoning) {
|
||||
warnings.push(`Entry ${index}: AI decision lacks transparency (no prompt or reasoning)`);
|
||||
}
|
||||
|
||||
if (entry.action === 'selection-decision' && !entry.metadata?.selectionMethod) {
|
||||
warnings.push(`Entry ${index}: Tool selection lacks methodology info`);
|
||||
}
|
||||
|
||||
if (typeof entry.confidence !== 'number' || entry.confidence < 0 || entry.confidence > 100) {
|
||||
warnings.push(`Entry ${index} has invalid confidence value: ${entry.confidence}`);
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user