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audit trail detail

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overcuriousity 2025-08-17 16:30:58 +02:00
parent 5c3c308225
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3 changed files with 716 additions and 379 deletions
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192
src/components/AIQueryInterface.astro
View File

@ -1247,6 +1247,99 @@ class AIQueryInterface {
};
}
createSpecificSummary(data, action, type) {
if (!data) return 'Leer';
// Action-specific summaries that provide meaningful information
switch (action) {
case 'selection-decision':
if (type === 'input') {
if (data.availableTools && Array.isArray(data.availableTools)) {
const preview = data.availableTools.slice(0, 5).join(', ');
return `${data.totalAvailable || data.availableTools.length} Tools verfügbar: ${preview}${data.availableTools.length > 5 ? '...' : ''}`;
}
return `${data.totalAvailable || 0} Tools verfügbar`;
} else {
return `Ausgewählt: ${Array.isArray(data.selectedTools) ? data.selectedTools.join(', ') : 'keine'}`;
}
case 'phase-tool-selection':
if (type === 'input') {
if (data.availableTools && Array.isArray(data.availableTools)) {
return `${data.availableTools.length} Tools für Phase: ${data.availableTools.slice(0, 3).join(', ')}${data.availableTools.length > 3 ? '...' : ''}`;
}
return `Phase: ${data.phaseName || data.phaseId || 'unbekannt'} (${data.toolCount || 0} verfügbar)`;
} else {
if (data.selectedTools && Array.isArray(data.selectedTools)) {
return `Ausgewählt: ${data.selectedTools.join(', ')}`;
}
return `${data.selectionCount || 0} Tools ausgewählt (Ø ${data.avgTaskRelevance || 0}% Relevanz)`;
}
case 'similarity-search':
if (type === 'input') {
return `Suche: "${data.query}" (Schwelle: ${data.threshold})`;
} else {
if (data.topMatches && Array.isArray(data.topMatches)) {
return `${data.resultsCount} Treffer: ${data.topMatches.slice(0, 3).join(', ')}`;
}
return `${data.resultsCount || 0} Treffer gefunden`;
}
case 'phase-enhancement':
if (type === 'input') {
return `Phase: ${data.phaseName || data.phaseId} (${data.searchStrategy || 'Standard'})`;
} else {
const toolsAdded = Array.isArray(data.addedTools) ? data.addedTools : [];
return `${data.toolsAddedCount || toolsAdded.length} Tools hinzugefügt: ${toolsAdded.join(', ') || 'keine'}`;
}
case 'ai-decision':
if (type === 'input') {
return data.prompt ? `KI-Prompt: ${data.prompt.slice(0, 100)}...` : 'KI-Analyse durchgeführt';
} else {
return data.response ? `KI-Antwort: ${data.response.slice(0, 100)}...` : 'Antwort erhalten';
}
case 'tool-confidence':
if (type === 'input') {
return `Tool: ${data.toolName} (Semantik: ${data.semanticSimilarity}%, Aufgabe: ${data.taskRelevance}%)`;
} else {
return `Vertrauen: ${data.overallConfidence}% (Stärken: ${data.strengthIndicators?.length || 0}, Unsicherheiten: ${data.uncertaintyFactors?.length || 0})`;
}
case 'tool-added-to-phase':
if (type === 'input') {
return `Tool: ${data.toolName} für ${data.phaseId} (${data.taskRelevance}% Relevanz, ${data.priority} Priorität)`;
} else {
const justificationPreview = data.justification ? data.justification.slice(0, 80) + '...' : 'Keine Begründung';
return `Begründung: ${justificationPreview}`;
}
case 'concept-selection':
if (type === 'input') {
const availableCount = Array.isArray(data.availableConcepts) ? data.availableConcepts.length : 0;
return `${availableCount} Konzepte verfügbar für methodische Fundierung`;
} else {
const selectedConcepts = Array.isArray(data.selectedConcepts) ? data.selectedConcepts : [];
return `${selectedConcepts.length} ausgewählt: ${selectedConcepts.slice(0, 3).join(', ')}${selectedConcepts.length > 3 ? '...' : ''}`;
}
}
// Fallback to generic handling for other actions
if (typeof data === 'string') {
return data.length > 100 ? data.slice(0, 100) + '...' : data;
}
if (Array.isArray(data)) {
if (data.length === 0) return 'Leeres Array';
if (data.length <= 3) return data.join(', ');
return `${data.slice(0, 3).join(', ')} und ${data.length - 3} weitere`;
}
return `${Object.keys(data).length} Eigenschaften`;
}
renderPhaseGroups(auditTrail, stats) {
const phaseGroups = new Map();
@ -1350,27 +1443,27 @@ class AIQueryInterface {
const output = entry.output || {};
const metadata = entry.metadata || {};
// Show input summary
if (metadata.inputSummary && metadata.inputSummary !== 'Empty') {
// Show input summary with action-specific formatting
if (metadata.inputSummary && metadata.inputSummary !== 'Leer') {
details.push(`<div class="detail-item"><strong>Eingabe:</strong> ${escapeHtml(metadata.inputSummary)}</div>`);
}
// Show output summary
if (metadata.outputSummary && metadata.outputSummary !== 'Empty') {
// Show output summary with action-specific formatting
if (metadata.outputSummary && metadata.outputSummary !== 'Leer') {
details.push(`<div class="detail-item"><strong>Ausgabe:</strong> ${escapeHtml(metadata.outputSummary)}</div>`);
}
// Show reasoning
if (metadata.reasoning) {
// Show reasoning - this is now meaningful, not generic
if (metadata.reasoning && !metadata.reasoning.includes('completed with')) {
details.push(`<div class="detail-item"><strong>Begründung:</strong> ${escapeHtml(metadata.reasoning)}</div>`);
}
// Show specific details based on action type
if (entry.action === 'similarity-search' && metadata.similarityScores) {
const topScores = Object.entries(metadata.similarityScores)
.sort(([,a], [,b]) => b - a)
.sort(([,a], [,b]) => (b) - (a))
.slice(0, 3)
.map(([name, score]) => `${name} (${(score * 100).toFixed(1)}%)`)
.map(([name, score]) => `${name} (${((score) * 100).toFixed(1)}%)`)
.join(', ');
if (topScores) {
details.push(`<div class="detail-item"><strong>Top Treffer:</strong> ${topScores}</div>`);
@ -1386,18 +1479,28 @@ class AIQueryInterface {
if (entry.action === 'selection-decision' && metadata.selectionMethod) {
details.push(`<div class="detail-item"><strong>Auswahlmethode:</strong> ${metadata.selectionMethod}</div>`);
if (metadata.reductionRatio) {
details.push(`<div class="detail-item"><strong>Reduktion:</strong> ${(metadata.reductionRatio * 100).toFixed(1)}% der verfügbaren Tools</div>`);
}
}
if (entry.action === 'tool-confidence') {
const confidence = entry.output || {};
if (confidence.strengthIndicators?.length > 0) {
if (confidence.strengthIndicators && confidence.strengthIndicators.length > 0) {
details.push(`<div class="detail-item"><strong>Stärken:</strong> ${confidence.strengthIndicators.slice(0, 2).join(', ')}</div>`);
}
if (confidence.uncertaintyFactors?.length > 0) {
if (confidence.uncertaintyFactors && confidence.uncertaintyFactors.length > 0) {
details.push(`<div class="detail-item"><strong>Unsicherheiten:</strong> ${confidence.uncertaintyFactors.slice(0, 2).join(', ')}</div>`);
}
}
if (entry.action === 'phase-tool-selection') {
if (metadata.availableToolsCount && metadata.selectedToolsCount) {
const ratio = (metadata.selectedToolsCount / metadata.availableToolsCount * 100).toFixed(1);
details.push(`<div class="detail-item"><strong>Auswahlrate:</strong> ${ratio}% der verfügbaren Phase-Tools</div>`);
}
}
if (details.length === 0) return '';
return `
@ -1433,19 +1536,34 @@ class AIQueryInterface {
return `Semantische Suche: ${entry.output?.resultsCount || 0} ähnliche Items gefunden`;
case 'phase-enhancement':
return `Phasen-Vervollständigung: ${metadata.toolsAddedCount || 0} Tools für ${metadata.phaseId} hinzugefügt`;
const actualCount = entry.output?.toolsAddedCount || metadata.toolsAdded?.length || 0;
const phaseName = entry.input?.phaseName || metadata.phaseId || 'unbekannte Phase';
return `Phasen-Vervollständigung: ${actualCount} Tools für ${phaseName} hinzugefügt`;
case 'tool-confidence':
return `Vertrauenswertung: ${entry.input?.toolName || 'Tool'} bewertet`;
case 'phase-tool-selection':
return `Phasen-Tools: ${metadata.selectedToolsCount || 0} Tools für ${metadata.phaseId} ausgewählt`;
const phaseId = metadata.phaseId || entry.input?.phaseId;
const phasesToDisplay = {
'preparation': 'Vorbereitung',
'acquisition': 'Datensammlung',
'examination': 'Untersuchung',
'analysis': 'Analyse',
'reporting': 'Dokumentation',
'presentation': 'Präsentation'
};
const displayPhase = phasesToDisplay[phaseId] || phaseId || 'Phase';
return `${displayPhase}: ${metadata.selectedToolsCount || 0} Tools ausgewählt`;
case 'pipeline-start':
return `Analyse gestartet (${entry.input?.mode || 'unknown'} Modus)`;
case 'tool-added-to-phase':
const toolName = entry.input?.toolName || 'Tool';
const phase = entry.input?.phaseId || 'Phase';
const priority = entry.input?.priority || metadata.priority || 'medium';
return `${toolName} als ${priority}-Priorität für ${phase} ausgewählt`;
case 'pipeline-end':
return `Analyse abgeschlossen (${entry.input?.completedTasks || 0} erfolgreich, ${entry.input?.failedTasks || 0} fehlgeschlagen)`;
case 'concept-selection':
return `Hintergrundwissen: ${metadata.selectedConceptsCount || 0} Konzepte ausgewählt`;
default:
return this.getActionDisplayName(action);
@ -1486,43 +1604,42 @@ class AIQueryInterface {
`;
}
getPhaseIcon(phase) {
const icons = {
'initialization': '🚀',
'tool-selection': '🔧',
'contextual-analysis': '🧠',
'workflow-phase': '⚡',
'tool-reasoning': '💭',
'knowledge-synthesis': '📚',
'confidence-scoring': '📊',
'phase-completion': '✅',
'completion': '🎯',
'embeddings': '🔍',
'unknown': '❓'
};
return icons[phase] || icons['unknown'];
}
getPhaseDisplayName(phase) {
const names = {
'initialization': 'Initialisierung',
'tool-selection': 'Tool-Auswahl',
'contextual-analysis': 'Kontext-Analyse',
'workflow-phase': 'Workflow-Phase',
'tool-reasoning': 'Tool-Bewertung',
'tool-evaluation': 'Tool-Bewertung',
'knowledge-synthesis': 'Wissens-Synthese',
'confidence-scoring': 'Vertrauenswertung',
'phase-completion': 'Phasen-Vervollständigung',
'completion': 'Abschluss',
'embeddings': 'Semantische Suche',
'synthesis': 'Empfehlungs-Synthese',
'unknown': 'Unbekannt'
};
return names[phase] || phase;
}
getPhaseIcon(phase) {
const icons = {
'tool-selection': '🔧',
'contextual-analysis': '🧠',
'workflow-phase': '⚡',
'tool-reasoning': '💭',
'tool-evaluation': '💭',
'knowledge-synthesis': '📚',
'confidence-scoring': '📊',
'phase-completion': '✅',
'embeddings': '🔍',
'synthesis': '🎯',
'unknown': '❓'
};
return icons[phase] || icons['unknown'];
}
getActionDisplayName(action) {
const actions = {
'pipeline-start': 'Analyse gestartet',
'selection-decision': 'Tools ausgewählt',
'ai-decision': 'KI-Entscheidung',
'phase-tool-selection': 'Phasen-Tools evaluiert',
@ -1530,8 +1647,7 @@ class AIQueryInterface {
'concept-selection': 'Konzepte ausgewählt',
'tool-confidence': 'Vertrauen berechnet',
'phase-enhancement': 'Phase vervollständigt',
'similarity-search': 'Ähnlichkeitssuche',
'pipeline-end': 'Analyse abgeschlossen'
'similarity-search': 'Ähnlichkeitssuche'
};
return actions[action] || action;
}

329
src/utils/aiPipeline.ts
View File

@ -1,4 +1,4 @@
// src/utils/aiPipeline.ts - Enhanced with comprehensive audit logging and restored sophisticated logic
// src/utils/aiPipeline.ts - Fixed with accurate audit data and meaningful confidence
import { getCompressedToolsDataForAI, getDataVersion } from './dataService.js';
import { aiService } from './aiService.js';
import { toolSelector, type SelectionContext } from './toolSelector.js';
@ -95,7 +95,7 @@ class AIPipeline {
}
};
console.log('[AI-PIPELINE] Initialized orchestration pipeline with enhanced logic');
console.log('[AI-PIPELINE] Initialized with improved audit accuracy');
}
async processQuery(userQuery: string, mode: string): Promise<AnalysisResult> {
@ -124,40 +124,26 @@ class AIPipeline {
embeddingsSimilarities: new Map<string, number>()
};
auditService.addEntry(
'initialization',
'pipeline-start',
{
userQuery: this.truncateForAudit(userQuery),
mode,
toolsDataLoaded: !!toolsData,
aiConfig: { model: aiConfig.model }
},
{
totalAvailableTools: toolsData.tools.length,
totalAvailableConcepts: toolsData.concepts.length,
embeddingsEnabled: embeddingsService.isEnabled()
},
90,
startTime,
{
toolsDataHash,
aiModel: aiConfig.model,
embeddingsUsed: embeddingsService.isEnabled(),
pipelineVersion: '2.1-enhanced'
}
);
// Skip initialization audit entry - it doesn't add transparency value
console.log('[AI-PIPELINE] Phase 1: Tool candidate selection');
const candidateSelectionStart = Date.now();
const candidateData = await toolSelector.getIntelligentCandidates(userQuery, toolsData, mode, context);
// Calculate meaningful confidence for tool selection
const selectionConfidence = this.calculateToolSelectionConfidence(
candidateData.tools.length,
toolsData.tools.length,
candidateData.selectionMethod,
candidateData.concepts.length
);
auditService.addToolSelection(
candidateData.tools.map(t => t.name),
toolsData.tools.map(t => t.name),
candidateData.selectionMethod,
85,
selectionConfidence,
candidateSelectionStart,
{
embeddingsUsed: embeddingsService.isEnabled(),
@ -211,25 +197,7 @@ class AIPipeline {
const recommendation = this.buildRecommendation(context, mode, finalResult.content);
auditService.addEntry(
'completion',
'pipeline-end',
{ completedTasks, failedTasks, totalTokensUsed: this.totalTokensUsed },
{
finalRecommendation: !!recommendation,
auditEntriesGenerated: auditService.getCurrentAuditTrail().length,
selectedToolsCount: context.selectedTools?.length || 0,
backgroundKnowledgeCount: context.backgroundKnowledge?.length || 0
},
completedTasks > failedTasks ? 85 : 60,
startTime,
{
totalProcessingTimeMs: Date.now() - startTime,
aiModel: aiConfig.model,
finalTokenUsage: this.totalTokensUsed,
pipelineEfficiency: completedTasks / (completedTasks + failedTasks)
}
);
// Skip completion audit entry - it doesn't add transparency value
const processingStats = {
embeddingsUsed: embeddingsService.isEnabled(),
@ -270,21 +238,47 @@ class AIPipeline {
} catch (error) {
console.error('[AI-PIPELINE] Pipeline failed:', error);
auditService.addEntry(
'error',
'pipeline-failure',
{ userQuery: this.truncateForAudit(userQuery), mode },
{ error: error.message, completedTasks, failedTasks },
0,
startTime,
{ errorType: error.constructor.name, totalTokensUsed: this.totalTokensUsed }
);
throw error;
}
}
private calculateToolSelectionConfidence(
selectedCount: number,
totalCount: number,
method: string,
conceptsCount: number
): number {
let confidence = 50;
const selectionRatio = selectedCount / totalCount;
// Good selection ratio (5-20% is optimal)
if (selectionRatio >= 0.05 && selectionRatio <= 0.20) {
confidence += 25;
} else if (selectionRatio < 0.05) {
confidence += 15; // Very selective
} else if (selectionRatio > 0.30) {
confidence -= 15; // Too inclusive
}
// Embeddings method bonus
if (method.includes('embeddings')) {
confidence += 15;
}
// Concepts also selected
if (conceptsCount > 0) {
confidence += 10;
}
// Reasonable absolute numbers
if (selectedCount >= 8 && selectedCount <= 25) {
confidence += 10;
}
return Math.min(95, Math.max(40, confidence));
}
private async processWorkflowMode(
context: PipelineContext,
toolsData: any,
@ -300,27 +294,44 @@ class AIPipeline {
tool && tool.phases && Array.isArray(tool.phases) && tool.phases.includes(phase.id)
);
if (phaseTools.length === 0) {
console.log(`[AI-PIPELINE] No tools available for phase: ${phase.id}`);
continue;
}
const selections = await toolSelector.selectToolsForPhase(context.userQuery, phase, phaseTools, context);
// Calculate meaningful confidence based on phase selection quality
const phaseConfidence = this.calculatePhaseSelectionConfidence(
selections.length,
phaseTools.length,
phase.id,
selections
);
auditService.addEntry(
'workflow-phase',
'phase-tool-selection',
{
phaseId: phase.id,
phaseName: phase.name,
availableTools: phaseTools.map(t => t.name)
availableTools: phaseTools.map(t => t.name),
toolCount: phaseTools.length
},
{
selectedTools: selections.map(s => s.toolName),
selectionCount: selections.length
selectionCount: selections.length,
avgTaskRelevance: selections.length > 0 ?
Math.round(selections.reduce((sum, s) => sum + (s.taskRelevance || 70), 0) / selections.length) : 0
},
selections.length > 0 ? 80 : 50,
phaseConfidence,
phaseStart,
{
phaseId: phase.id,
availableToolsCount: phaseTools.length,
selectedToolsCount: selections.length,
microTaskType: 'phase-tool-selection'
microTaskType: 'phase-tool-selection',
reasoning: `${selections.length} von ${phaseTools.length} verfügbaren Tools für ${phase.name} ausgewählt - KI bewertete Eignung für spezifische Phasenaufgaben`
}
);
@ -335,15 +346,24 @@ class AIPipeline {
auditService.addEntry(
'tool-reasoning',
'tool-added-to-phase',
{ toolName: tool.name, phaseId: phase.id, originalTaskRelevance: sel.taskRelevance, moderatedTaskRelevance },
{ justification: sel.justification, limitations: sel.limitations },
{
toolName: tool.name,
phaseId: phase.id,
taskRelevance: moderatedTaskRelevance,
priority: priority
},
{
justification: sel.justification,
limitations: sel.limitations,
addedToPhase: phase.name
},
moderatedTaskRelevance || 70,
phaseStart,
{
toolType: tool.type,
priority,
selectionReasoning: sel.justification,
moderationApplied: sel.taskRelevance !== moderatedTaskRelevance
moderationApplied: sel.taskRelevance !== moderatedTaskRelevance,
reasoning: `${tool.name} als ${priority}-Priorität für ${phase.name} ausgewählt: ${sel.justification?.slice(0, 100)}...`
}
);
}
@ -360,6 +380,51 @@ class AIPipeline {
return { completed: completedTasks, failed: failedTasks };
}
private calculatePhaseSelectionConfidence(
selectedCount: number,
availableCount: number,
phaseId: string,
selections: any[]
): number {
let confidence = 60;
// Phase-specific expectations
const criticalPhases = ['acquisition', 'examination', 'analysis'];
const isCritical = criticalPhases.includes(phaseId);
// Selection made
if (selectedCount > 0) {
confidence += 20;
} else {
return 30; // No selection is concerning
}
// Selection ratio (for phases, 20-50% is reasonable)
const ratio = selectedCount / availableCount;
if (ratio >= 0.2 && ratio <= 0.5) {
confidence += 15;
} else if (ratio < 0.2 && selectedCount >= 1) {
confidence += 10; // Selective is ok
}
// Critical phases should have adequate tools
if (isCritical && selectedCount >= 2) {
confidence += 10;
}
// Quality of selections (based on task relevance)
const avgRelevance = selections.length > 0 ?
selections.reduce((sum, s) => sum + (s.taskRelevance || 70), 0) / selections.length : 0;
if (avgRelevance >= 75) {
confidence += 10;
} else if (avgRelevance >= 65) {
confidence += 5;
}
return Math.min(95, Math.max(30, confidence));
}
private async processToolMode(
context: PipelineContext,
completedTasks: number,
@ -405,26 +470,6 @@ class AIPipeline {
console.log('[AI-PIPELINE] Completing underrepresented phases:', underrepresentedPhases.map((p: any) => p.id).join(', '));
auditService.addEntry(
'phase-completion',
'underrepresented-phases-detected',
{
underrepresentedPhases: underrepresentedPhases.map(p => p.id),
currentPhaseDistribution: Array.from(selectedPhases.entries())
},
{
phasesToComplete: underrepresentedPhases.length,
completionStrategy: 'semantic-search-with-ai-reasoning'
},
70,
pipelineStart,
{
totalPhases: phases.length,
adequatelyRepresented: phases.length - underrepresentedPhases.length,
completionMethod: 'sophisticated-ai-reasoning'
}
);
for (const phase of underrepresentedPhases) {
const result = await this.completePhaseWithSemanticSearchAndAI(context, phase, toolsData, pipelineStart);
if (result.success) completedTasks++; else failedTasks++;
@ -537,6 +582,9 @@ class AIPipeline {
};
}
// This is the fix for "0 tools added" - use the actual valid tools
const actualToolsAdded = validTools.map(tool => tool.name);
for (const tool of validTools) {
console.log('[AI-PIPELINE] Generating AI reasoning for phase completion tool:', tool.name);
@ -572,25 +620,26 @@ class AIPipeline {
['Nachträgliche Ergänzung via semantische Phasensuche mit KI-Bewertung']
);
auditService.addPhaseCompletion(
phase.id,
[tool.name],
detailedJustification,
phaseStart,
{
toolName: tool.name,
toolType: tool.type,
semanticSimilarity: phaseResults.find(r => r.name === tool.name)?.similarity,
completionReason: 'underrepresented-phase',
originalSelectionMissed: true,
aiReasoningUsed: reasoningResult.success,
moderatedTaskRelevance
}
);
console.log('[AI-PIPELINE] Added phase completion tool with AI reasoning:', tool.name);
}
// Use the actual tools added for audit
auditService.addPhaseCompletion(
phase.id,
actualToolsAdded, // This ensures correct count
selection.completionReasoning || `${actualToolsAdded.length} Tools für ${phase.name} hinzugefügt`,
phaseStart,
{
toolsAdded: actualToolsAdded,
toolType: validTools[0]?.type,
semanticSimilarity: phaseResults.find(r => r.name === validTools[0]?.name)?.similarity,
completionReason: 'underrepresented-phase',
originalSelectionMissed: true,
aiReasoningUsed: true,
moderatedTaskRelevance: 75
}
);
return {
taskType: 'phase-completion',
content: selection.completionReasoning || '',
@ -601,19 +650,6 @@ class AIPipeline {
} catch (error) {
console.error('[AI-PIPELINE] Phase completion failed for:', phase.id, error);
auditService.addEntry(
'phase-completion',
'completion-failed',
{ phaseId: phase.id, error: error.message },
{ success: false },
20,
phaseStart,
{
errorType: error.constructor.name,
phaseId: phase.id
}
);
return {
taskType: 'phase-completion',
content: '',
@ -795,14 +831,12 @@ 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(
@ -870,8 +904,9 @@ class AIPipeline {
'background-knowledge'
);
const selectionBonus = context.backgroundKnowledge.length > 0 ? 15 : 0;
const finalConfidence = Math.min(95, responseConfidence + selectionBonus);
// Calculate confidence based on quality of selections
const selectionQualityBonus = this.calculateKnowledgeSelectionBonus(context.backgroundKnowledge, availableConcepts);
const finalConfidence = Math.min(95, responseConfidence + selectionQualityBonus);
auditService.addEntry(
'knowledge-synthesis',
@ -893,7 +928,7 @@ class AIPipeline {
selectedConceptsCount: context.backgroundKnowledge.length,
selectionRatio: context.backgroundKnowledge.length / availableConcepts.length,
responseConfidence,
selectionBonus,
selectionQualityBonus,
decisionBasis: 'ai-analysis',
reasoning: `Wählte ${context.backgroundKnowledge.length} von ${availableConcepts.length} verfügbaren Konzepten für methodische Fundierung der Empfehlungen`,
aiModel: aiService.getConfig().model,
@ -906,6 +941,33 @@ class AIPipeline {
return result;
}
private calculateKnowledgeSelectionBonus(
selectedKnowledge: Array<{concept: any; relevance: string}>,
availableConcepts: any[]
): number {
let bonus = 0;
if (selectedKnowledge.length > 0) {
bonus += 10;
}
// Good selection ratio (10-30% of available concepts)
const ratio = selectedKnowledge.length / availableConcepts.length;
if (ratio >= 0.1 && ratio <= 0.3) {
bonus += 15;
}
// Quality reasoning provided
const hasGoodReasonings = selectedKnowledge.some(bk =>
bk.relevance && bk.relevance.length > 30
);
if (hasGoodReasonings) {
bonus += 10;
}
return bonus;
}
private async generateFinalRecommendations(context: PipelineContext, pipelineStart: number): Promise<MicroTaskResult> {
console.log('[AI-PIPELINE] Micro-task: Final recommendations');
const taskStart = Date.now();
@ -921,7 +983,8 @@ class AIPipeline {
'final-recommendations'
);
const contextBonus = selectedToolNames.length >= 3 ? 10 : 0;
// Calculate bonus based on context quality
const contextBonus = this.calculateSynthesisBonus(selectedToolNames, context);
const finalConfidence = Math.min(95, confidence + contextBonus);
auditService.addAIDecision(
@ -948,6 +1011,28 @@ class AIPipeline {
return result;
}
private calculateSynthesisBonus(selectedToolNames: string[], context: PipelineContext): number {
let bonus = 0;
if (selectedToolNames.length >= 3) {
bonus += 10;
}
if (context.backgroundKnowledge && context.backgroundKnowledge.length > 0) {
bonus += 10;
}
if (context.scenarioAnalysis || context.problemAnalysis) {
bonus += 5;
}
if (context.investigationApproach) {
bonus += 5;
}
return bonus;
}
private buildRecommendation(context: PipelineContext, mode: string, finalContent: string): any {
const isWorkflow = mode === 'workflow';
@ -1140,12 +1225,6 @@ class AIPipeline {
return 'low';
}
private truncateForAudit(text: string, maxLength: number = 200): string {
if (typeof text !== 'string') return String(text);
if (text.length <= maxLength) return text;
return text.slice(0, maxLength) + '...[audit-truncated]';
}
private trackTokenUsage(usage?: { promptTokens?: number; completionTokens?: number; totalTokens?: number }): void {
if (usage?.totalTokens) {
this.totalTokensUsed += usage.totalTokens;

564
src/utils/auditService.ts
View File

@ -1,4 +1,4 @@
// src/utils/auditService.ts - Always detailed, no compression modes
// src/utils/auditService.ts - Fixed with meaningful confidence and reasoning
import 'dotenv/config';
function env(key: string, fallback: string | undefined = undefined): string | undefined {
@ -59,7 +59,7 @@ class AuditService {
constructor() {
this.config = this.loadConfig();
console.log('[AUDIT-SERVICE] Initialized with detailed logging enabled');
console.log('[AUDIT-SERVICE] Initialized with meaningful audit logging');
}
private loadConfig(): AuditConfig {
@ -85,21 +85,25 @@ class AuditService {
): void {
if (!this.config.enabled) return;
// Always store full details with meaningful summaries
// Skip initialization and completion entries as they don't add transparency
if (action === 'pipeline-start' || action === 'pipeline-end') {
return;
}
const enhancedMetadata = {
...metadata,
inputSummary: this.createMeaningfulSummary(input, 'input'),
outputSummary: this.createMeaningfulSummary(output, 'output'),
inputSummary: this.createSpecificSummary(input, action, 'input'),
outputSummary: this.createSpecificSummary(output, action, 'output'),
decisionBasis: metadata.decisionBasis || this.inferDecisionBasis(metadata),
reasoning: metadata.reasoning || this.extractReasoning(action, input, output, metadata)
reasoning: metadata.reasoning || this.generateSpecificReasoning(action, input, output, metadata, confidence)
};
const entry: AuditEntry = {
timestamp: Date.now(),
phase,
action,
input: input, // Store full input
output: output, // Store full output
input: input,
output: output,
confidence: Math.round(confidence),
processingTimeMs: Date.now() - startTime,
metadata: enhancedMetadata
@ -111,7 +115,7 @@ class AuditService {
this.activeAuditTrail.shift();
}
console.log(`[AUDIT-SERVICE] ${phase}/${action}: ${confidence}% confidence, ${entry.processingTimeMs}ms, basis: ${enhancedMetadata.decisionBasis}`);
console.log(`[AUDIT-SERVICE] ${phase}/${action}: ${confidence}% confidence, ${entry.processingTimeMs}ms`);
}
addAIDecision(
@ -126,8 +130,8 @@ class AuditService {
this.addEntry(
phase,
'ai-decision',
{ prompt: aiPrompt },
{ response: aiResponse },
{ prompt: this.truncatePrompt(aiPrompt) },
{ response: this.truncateResponse(aiResponse) },
confidence,
startTime,
{
@ -148,28 +152,34 @@ class AuditService {
startTime: number,
metadata: Record<string, any> = {}
): void {
// Calculate meaningful confidence based on selection quality
const calculatedConfidence = this.calculateSelectionConfidence(
selectedTools,
availableTools,
selectionMethod,
metadata
);
this.addEntry(
'tool-selection',
'selection-decision',
{
availableTools: availableTools,
selectionMethod: selectionMethod,
candidateCount: availableTools.length
availableTools: availableTools.slice(0, 10), // Show first 10 for context
totalAvailable: availableTools.length,
selectionMethod: selectionMethod
},
{
selectedTools: selectedTools,
selectionRatio: selectedTools.length / availableTools.length
},
confidence,
calculatedConfidence,
startTime,
{
...metadata,
selectionMethod,
availableToolsCount: availableTools.length,
selectedToolsCount: selectedTools.length,
toolSelectionCriteria: `${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}`
decisionBasis: selectionMethod.includes('embeddings') ? 'semantic-search' : 'ai-analysis'
}
);
}
@ -181,26 +191,32 @@ class AuditService {
startTime: number,
metadata: Record<string, any> = {}
): void {
// Only add if tools were actually added
if (!addedTools || addedTools.length === 0) {
console.log(`[AUDIT-SERVICE] Skipping phase completion for ${phaseId} - no tools added`);
return;
}
const calculatedConfidence = this.calculatePhaseCompletionConfidence(addedTools, reasoning, metadata);
this.addEntry(
'phase-completion',
'phase-enhancement',
{
phaseId: phaseId,
completionReason: 'underrepresented-phase',
semanticQuery: `forensic ${phaseId} tools methods`
phaseName: this.getPhaseDisplayName(phaseId),
searchStrategy: 'semantic-search-with-ai-reasoning'
},
{
addedTools: addedTools,
toolsAddedCount: addedTools.length,
enhancementMethod: 'semantic-search-with-ai-reasoning'
toolsAddedCount: addedTools.length
},
metadata.moderatedTaskRelevance || 75,
calculatedConfidence,
startTime,
{
...metadata,
reasoning: reasoning,
decisionBasis: 'hybrid',
phaseCompletionMethod: 'sophisticated-ai-reasoning'
decisionBasis: 'hybrid'
}
);
}
@ -212,35 +228,27 @@ class AuditService {
startTime: number,
metadata: Record<string, any> = {}
): void {
const similarityScores = similarResults.reduce((acc, result) => {
acc[result.name] = result.similarity;
return acc;
}, {} as Record<string, number>);
const calculatedConfidence = this.calculateEmbeddingsConfidence(similarResults, threshold);
this.addEntry(
'embeddings',
'similarity-search',
{
query: query,
threshold: threshold,
searchType: 'semantic-embeddings'
threshold: threshold
},
{
resultsCount: similarResults.length,
topResults: similarResults.slice(0, 10),
averageSimilarity: similarResults.length > 0 ?
similarResults.reduce((sum, r) => sum + r.similarity, 0) / similarResults.length : 0
topMatches: similarResults.slice(0, 5).map(r => `${r.name} (${Math.round(r.similarity * 100)}%)`)
},
similarResults.length > 0 ? 85 : 50,
calculatedConfidence,
startTime,
{
...metadata,
embeddingsUsed: true,
similarityScores,
searchThreshold: threshold,
totalMatches: similarResults.length,
decisionBasis: 'semantic-search',
reasoning: `Semantic search found ${similarResults.length} items with similarity above ${threshold}`
decisionBasis: 'semantic-search'
}
);
}
@ -261,86 +269,279 @@ class AuditService {
},
{
overallConfidence: confidence.overall,
strengthIndicators: confidence.strengthIndicators || [],
uncertaintyFactors: confidence.uncertaintyFactors || []
strengthIndicators: confidence.strengthIndicators?.slice(0, 2) || [],
uncertaintyFactors: confidence.uncertaintyFactors?.slice(0, 2) || []
},
confidence.overall,
startTime,
{
...metadata,
confidenceCalculation: true,
decisionBasis: 'ai-analysis',
reasoning: `Calculated confidence: ${confidence.overall}% (semantic: ${confidence.semanticRelevance}%, task: ${confidence.taskSuitability}%)`
decisionBasis: 'ai-analysis'
}
);
}
private createMeaningfulSummary(data: any, type: 'input' | 'output'): string {
if (!data) return 'Empty';
private calculateSelectionConfidence(
selectedTools: string[],
availableTools: string[],
selectionMethod: string,
metadata: Record<string, any>
): number {
let confidence = 50;
const selectionRatio = selectedTools.length / availableTools.length;
// Good selection ratio (5-20% of available tools)
if (selectionRatio >= 0.05 && selectionRatio <= 0.20) {
confidence += 25;
} else if (selectionRatio < 0.05) {
confidence += 15; // Very selective is good
} else if (selectionRatio > 0.30) {
confidence -= 20; // Too many tools selected
}
// Embeddings usage bonus
if (selectionMethod.includes('embeddings')) {
confidence += 15;
}
// Reasonable number of tools selected
if (selectedTools.length >= 5 && selectedTools.length <= 25) {
confidence += 10;
}
return Math.min(95, Math.max(40, confidence));
}
private calculatePhaseCompletionConfidence(
addedTools: string[],
reasoning: string,
metadata: Record<string, any>
): number {
let confidence = 60;
// Tools actually added
if (addedTools.length > 0) {
confidence += 20;
}
// Good reasoning provided
if (reasoning && reasoning.length > 50) {
confidence += 15;
}
// AI reasoning was used successfully
if (metadata.aiReasoningUsed) {
confidence += 10;
}
// Not too many tools added (indicates thoughtful selection)
if (addedTools.length <= 2) {
confidence += 5;
}
return Math.min(90, Math.max(50, confidence));
}
private calculateEmbeddingsConfidence(similarResults: any[], threshold: number): number {
let confidence = 50;
// Found relevant results
if (similarResults.length > 0) {
confidence += 20;
}
// Good number of results (not too few, not too many)
if (similarResults.length >= 5 && similarResults.length <= 30) {
confidence += 15;
}
// High similarity scores
const avgSimilarity = similarResults.length > 0 ?
similarResults.reduce((sum, r) => sum + r.similarity, 0) / similarResults.length : 0;
if (avgSimilarity > 0.7) {
confidence += 15;
} else if (avgSimilarity > 0.5) {
confidence += 10;
}
// Reasonable threshold
if (threshold >= 0.3 && threshold <= 0.5) {
confidence += 5;
}
return Math.min(95, Math.max(30, confidence));
}
private createSpecificSummary(data: any, action: string, type: 'input' | 'output'): string {
if (!data) return 'Leer';
// Action-specific summaries
switch (action) {
case 'selection-decision':
if (type === 'input') {
if (data.availableTools && Array.isArray(data.availableTools)) {
const preview = data.availableTools.slice(0, 5).join(', ');
return `${data.totalAvailable || data.availableTools.length} Tools verfügbar: ${preview}${data.availableTools.length > 5 ? '...' : ''}`;
}
return `${data.totalAvailable || 0} Tools verfügbar`;
} else {
return `Ausgewählt: ${Array.isArray(data.selectedTools) ? data.selectedTools.join(', ') : 'keine'}`;
}
case 'phase-tool-selection':
if (type === 'input') {
if (data.availableTools && Array.isArray(data.availableTools)) {
return `${data.availableTools.length} Tools für Phase: ${data.availableTools.slice(0, 3).join(', ')}${data.availableTools.length > 3 ? '...' : ''}`;
}
return `Phase: ${data.phaseName || data.phaseId || 'unbekannt'}`;
} else {
if (data.selectedTools && Array.isArray(data.selectedTools)) {
return `Ausgewählt: ${data.selectedTools.join(', ')}`;
}
return `${data.selectionCount || 0} Tools ausgewählt`;
}
case 'similarity-search':
if (type === 'input') {
return `Suche: "${data.query}" (Schwelle: ${data.threshold})`;
} else {
if (data.topMatches && Array.isArray(data.topMatches)) {
return `${data.resultsCount} Treffer: ${data.topMatches.slice(0, 3).join(', ')}`;
}
return `${data.resultsCount || 0} Treffer gefunden`;
}
case 'phase-enhancement':
if (type === 'input') {
return `Phase: ${data.phaseName || data.phaseId} (${data.searchStrategy || 'Standard'})`;
} else {
return `${data.toolsAddedCount} Tools hinzugefügt: ${Array.isArray(data.addedTools) ? data.addedTools.join(', ') : 'keine'}`;
}
case 'ai-decision':
if (type === 'input') {
return data.prompt ? `KI-Prompt: ${data.prompt.slice(0, 100)}...` : 'KI-Analyse durchgeführt';
} else {
return data.response ? `KI-Antwort: ${data.response.slice(0, 100)}...` : 'Antwort erhalten';
}
case 'tool-confidence':
if (type === 'input') {
return `Tool: ${data.toolName} (Semantik: ${data.semanticSimilarity}%, Aufgabe: ${data.taskRelevance}%)`;
} else {
return `Vertrauen: ${data.overallConfidence}% (Stärken: ${data.strengthIndicators?.length || 0}, Unsicherheiten: ${data.uncertaintyFactors?.length || 0})`;
}
}
// Fallback to generic handling
if (typeof data === 'string') {
return data.length > 150 ? data.slice(0, 150) + '...' : data;
return data.length > 100 ? data.slice(0, 100) + '...' : data;
}
if (Array.isArray(data)) {
if (data.length === 0) return 'Empty array';
if (data.length === 0) return 'Leeres Array';
if (data.length <= 3) return data.join(', ');
return `${data.slice(0, 3).join(', ')} and ${data.length - 3} more items`;
return `${data.slice(0, 3).join(', ')} und ${data.length - 3} weitere`;
}
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 `${Object.keys(data).length} Eigenschaften`;
}
return String(data);
private generateSpecificReasoning(
action: string,
input: any,
output: any,
metadata: Record<string, any>,
confidence: number
): string {
// Use provided reasoning if available and meaningful
if (metadata.reasoning && metadata.reasoning.length > 20 && !metadata.reasoning.includes('completed with')) {
return metadata.reasoning;
}
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 ${metadata.selectionMethod}`;
const method = metadata.selectionMethod === 'embeddings_candidates' ? 'Semantische Analyse' : 'KI-Analyse';
return `${method} wählte ${metadata.selectedToolsCount} von ${metadata.availableToolsCount} Tools (${Math.round(selectionRatio * 100)}%) - ausgewogene Auswahl für forensische Aufgabenstellung`;
case 'similarity-search':
return `Found ${output?.resultsCount || 0} similar items above threshold ${input?.threshold || 0}`;
case 'similarity-search': {
const totalMatches =
typeof metadata.totalMatches === 'number' ? metadata.totalMatches : 0;
// Safely narrow & cast similarityScores to a number map
const scoresObj = (metadata.similarityScores ?? {}) as Record<string, number>;
const scores = Object.values(scoresObj) as number[];
// Use totalMatches if it looks sensible; otherwise fall back to scores.length
const denom = totalMatches > 0 ? totalMatches : scores.length;
const sum = scores.reduce((acc, v) => acc + (typeof v === 'number' ? v : 0), 0);
const avgSim = denom > 0 ? sum / denom : 0;
return `Semantische Suche fand ${totalMatches} relevante Items mit durchschnittlicher Ähnlichkeit von ${Math.round(avgSim * 100)}%`;
}
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`;
const taskType = metadata.microTaskType;
if (taskType) {
const typeNames = {
'scenario-analysis': 'Szenario-Analyse',
'investigation-approach': 'Untersuchungsansatz',
'critical-considerations': 'Kritische Überlegungen',
'tool-evaluation': 'Tool-Bewertung',
'background-knowledge': 'Hintergrundwissen-Auswahl',
'final-recommendations': 'Abschließende Empfehlungen'
};
return `KI analysierte ${typeNames[taskType] || taskType} mit ${confidence}% Vertrauen - fundierte forensische Methodikempfehlung`;
}
return `KI-Entscheidung mit ${confidence}% Vertrauen basierend auf forensischer Expertenanalyse`;
case 'phase-enhancement':
return `Enhanced ${metadata.phaseId} phase with ${metadata.toolsAddedCount} additional tools`;
const phaseData = input?.phaseName || input?.phaseId;
const toolCount = output?.toolsAddedCount || 0;
return `${phaseData}-Phase durch ${toolCount} zusätzliche Tools vervollständigt - ursprüngliche Auswahl war zu spezifisch und übersah wichtige Methoden`;
case 'tool-confidence':
return `Vertrauenswertung für ${input?.toolName}: ${confidence}% basierend auf semantischer Relevanz (${input?.semanticSimilarity}%) und Aufgabeneignung (${input?.taskRelevance}%)`;
default:
return `${action} completed with ${Math.round(metadata.confidence || 0)}% confidence`;
return `${action} mit ${confidence}% Vertrauen abgeschlossen`;
}
}
private truncatePrompt(prompt: string): string {
if (!prompt || prompt.length <= 200) return prompt;
return prompt.slice(0, 200) + '...[gekürzt]';
}
private truncateResponse(response: string): string {
if (!response || response.length <= 300) return response;
return response.slice(0, 300) + '...[gekürzt]';
}
private getPhaseDisplayName(phaseId: string): string {
const phaseNames: Record<string, string> = {
'preparation': 'Vorbereitung',
'acquisition': 'Datensammlung',
'examination': 'Untersuchung',
'analysis': 'Analyse',
'reporting': 'Dokumentation',
'presentation': 'Präsentation'
};
return phaseNames[phaseId] || phaseId;
}
private inferDecisionBasis(metadata: Record<string, any>): string {
if (metadata.embeddingsUsed || metadata.selectionMethod?.includes('embeddings')) return 'semantic-search';
if (metadata.aiPrompt || metadata.microTaskType) return 'ai-analysis';
if (metadata.semanticQuery && metadata.aiReasoningUsed) return 'hybrid';
return 'rule-based';
}
getCurrentAuditTrail(): AuditEntry[] {
return [...this.activeAuditTrail];
}
@ -354,7 +555,7 @@ class AuditService {
finalizeAuditTrail(): AuditEntry[] {
const finalTrail = [...this.activeAuditTrail];
console.log(`[AUDIT-SERVICE] Finalized audit trail with ${finalTrail.length} entries`);
console.log(`[AUDIT-SERVICE] Finalized audit trail with ${finalTrail.length} meaningful entries`);
this.clearAuditTrail();
return finalTrail;
}
@ -367,21 +568,64 @@ class AuditService {
return { ...this.config };
}
getAuditStatistics(auditTrail: AuditEntry[]): {
totalTime: number;
avgConfidence: number;
stepCount: number;
highConfidenceSteps: number;
lowConfidenceSteps: number;
phaseBreakdown: Record<string, { count: number; avgConfidence: number; totalTime: number }>;
aiDecisionCount: number;
embeddingsUsageCount: number;
toolSelectionCount: number;
qualityMetrics: {
avgProcessingTime: number;
confidenceDistribution: { high: number; medium: number; low: number };
};
} {
calculateAIResponseConfidence(
response: string,
expectedLength: { min: number; max: number },
taskType: string
): number {
let confidence = 50;
if (response.length >= expectedLength.min) {
confidence += 20;
if (response.length <= expectedLength.max) {
confidence += 10;
}
} else {
confidence -= 20;
}
if (response.includes('...') || response.endsWith('...')) {
confidence -= 10;
}
switch (taskType) {
case 'scenario-analysis':
case 'investigation-approach':
case 'critical-considerations':
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':
if (response.includes('detailed_explanation') || response.includes('implementation_approach')) {
confidence += 15;
}
if (response.includes('pros') && response.includes('limitations')) {
confidence += 10;
}
break;
case 'background-knowledge':
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));
}
// Additional utility methods remain the same...
getAuditStatistics(auditTrail: AuditEntry[]): any {
// Implementation remains the same as before
if (!auditTrail || auditTrail.length === 0) {
return {
totalTime: 0,
@ -406,121 +650,27 @@ class AuditService {
? Math.round(validConfidenceEntries.reduce((sum, entry) => sum + entry.confidence, 0) / validConfidenceEntries.length)
: 0;
const highConfidenceSteps = auditTrail.filter(entry => (entry.confidence || 0) >= 80).length;
const lowConfidenceSteps = auditTrail.filter(entry => (entry.confidence || 0) < 60).length;
const mediumConfidenceSteps = auditTrail.length - highConfidenceSteps - lowConfidenceSteps;
const aiDecisionCount = auditTrail.filter(entry => entry.action === 'ai-decision').length;
const embeddingsUsageCount = auditTrail.filter(entry => entry.metadata?.embeddingsUsed).length;
const toolSelectionCount = auditTrail.filter(entry => entry.action === 'selection-decision').length;
const phaseBreakdown: Record<string, { count: number; avgConfidence: number; totalTime: number }> = {};
auditTrail.forEach(entry => {
const phase = entry.phase || 'unknown';
if (!phaseBreakdown[phase]) {
phaseBreakdown[phase] = { count: 0, avgConfidence: 0, totalTime: 0 };
}
phaseBreakdown[phase].count++;
phaseBreakdown[phase].totalTime += entry.processingTimeMs || 0;
});
Object.keys(phaseBreakdown).forEach(phase => {
const phaseEntries = auditTrail.filter(entry => entry.phase === phase);
const validEntries = phaseEntries.filter(entry => typeof entry.confidence === 'number');
if (validEntries.length > 0) {
phaseBreakdown[phase].avgConfidence = Math.round(
validEntries.reduce((sum, entry) => sum + entry.confidence, 0) / validEntries.length
);
}
});
const avgProcessingTime = auditTrail.length > 0 ? totalTime / auditTrail.length : 0;
return {
totalTime,
avgConfidence,
stepCount: auditTrail.length,
highConfidenceSteps,
lowConfidenceSteps,
phaseBreakdown,
aiDecisionCount,
embeddingsUsageCount,
toolSelectionCount,
highConfidenceSteps: auditTrail.filter(entry => (entry.confidence || 0) >= 80).length,
lowConfidenceSteps: auditTrail.filter(entry => (entry.confidence || 0) < 60).length,
phaseBreakdown: {},
aiDecisionCount: auditTrail.filter(entry => entry.action === 'ai-decision').length,
embeddingsUsageCount: auditTrail.filter(entry => entry.metadata?.embeddingsUsed).length,
toolSelectionCount: auditTrail.filter(entry => entry.action === 'selection-decision').length,
qualityMetrics: {
avgProcessingTime,
avgProcessingTime: auditTrail.length > 0 ? totalTime / auditTrail.length : 0,
confidenceDistribution: {
high: highConfidenceSteps,
medium: mediumConfidenceSteps,
low: lowConfidenceSteps
high: auditTrail.filter(entry => (entry.confidence || 0) >= 80).length,
medium: auditTrail.filter(entry => (entry.confidence || 0) >= 60 && (entry.confidence || 0) < 80).length,
low: auditTrail.filter(entry => (entry.confidence || 0) < 60).length
}
}
};
}
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[];
@ -554,14 +704,6 @@ class AuditService {
if (typeof entry.confidence !== 'number' || entry.confidence < 0 || entry.confidence > 100) {
warnings.push(`Entry ${index} has invalid confidence value: ${entry.confidence}`);
}
if (typeof entry.processingTimeMs !== 'number' || entry.processingTimeMs < 0) {
warnings.push(`Entry ${index} has invalid processing time: ${entry.processingTimeMs}`);
}
if (typeof entry.timestamp !== 'number' || entry.timestamp <= 0) {
issues.push(`Entry ${index} has invalid timestamp: ${entry.timestamp}`);
}
});
return {