audit trail detail
This commit is contained in:
overcuriousity
parent
5c3c308225
commit
e63ec367a5
@ -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;
|
||||
}
|
||||
|
||||
@ -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;
|
||||
|
||||
@ -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 {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user