cleanup
This commit is contained in:
overcuriousity
parent
1d91dbf478
commit
6160620e24
@ -51,7 +51,7 @@ function cleanupExpiredRateLimits(): void {
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setInterval(cleanupExpiredRateLimits, 5 * 60 * 1000);
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function createEnhancementPrompt(input: string): string {
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return `Sie sind ein DFIR-Experte mit Spezialisierung auf forensische Methodik. Ein Nutzer beschreibt ein forensisches Szenario oder Problem. Analysieren Sie die Eingabe auf Vollständigkeit für eine wissenschaftlich fundierte forensische Untersuchung.
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return `Sie sind ein DFIR-Experte mit Spezialisierung auf forensische Methodik. Ein Nutzer beschreibt ein Szenario oder Problem. Analysieren Sie die Eingabe auf Vollständigkeit für eine wissenschaftlich fundierte Untersuchung.
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ANALYSIEREN SIE DIESE FORENSISCHEN KATEGORIEN:
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1. **Incident Context**: Was ist passiert? Welche Angriffsvektoren oder technischen Probleme liegen vor?
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@ -64,12 +64,12 @@ ANALYSIEREN SIE DIESE FORENSISCHEN KATEGORIEN:
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WENN die Beschreibung alle kritischen forensischen Aspekte abdeckt: Geben Sie eine leere Liste [] zurück.
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WENN wichtige forensische Details fehlen: Formulieren Sie 2-3 präzise Fragen, die die kritischsten Lücken für eine wissenschaftlich fundierte forensische Analyse schließen.
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WENN wichtige Details fehlen: Formulieren Sie 2-3 präzise Fragen, die die kritischsten Lücken für eine wissenschaftlich fundierte Analyse schließen.
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QUALITÄTSKRITERIEN FÜR FRAGEN:
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- Forensisch spezifisch, nicht allgemein (NICHT: "Mehr Details?")
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- Methodisch relevant (NICHT: "Wann passierte das?")
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- Priorisiert nach Auswirkung auf die forensische Untersuchungsqualität
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- Priorisiert nach Auswirkung auf die Untersuchungsqualität
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- Die Frage soll maximal 20 Wörter umfassen
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ANTWORTFORMAT (NUR JSON, KEIN ZUSÄTZLICHER TEXT):
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@ -116,7 +116,6 @@ export const POST: APIRoute = async ({ request }) => {
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const aiResponse = await enqueueApiCall(() =>
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aiService.callAI(systemPrompt, {
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maxTokens: 300,
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temperature: 0.7
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}), taskId);
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@ -107,7 +107,6 @@ class AIPipeline {
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const aiConfig = aiService.getConfig();
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const toolsDataHash = getDataVersion?.() || 'unknown';
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// Record the tools.yaml version being used
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auditService.addEntry(
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'initialization',
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'tools-data-loaded',
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@ -747,7 +746,7 @@ class AIPipeline {
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prompt,
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result.content,
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confidence,
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`Analysierte ${isWorkflow ? 'Szenario' : 'Problem'} basierend auf Nutzereingabe: "${context.userQuery.slice(0, 100)}..." - Identifizierte Kernaspekte und Herausforderungen für forensische Untersuchung`,
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`Analysierte ${isWorkflow ? 'Szenario' : 'Problem'} basierend auf Nutzereingabe: "${context.userQuery.slice(0, 100)}..." - Identifizierte Kernaspekte und Herausforderungen für Untersuchung`,
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taskStart,
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{
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toolsDataHash: toolsDataHash,
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@ -1184,21 +1183,18 @@ class AIPipeline {
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try {
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const response = await aiService.callMicroTaskAI(contextPrompt);
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// FIX: Ensure ALL AI calls generate audit entries
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const toolsDataHash = getDataVersion?.() || 'unknown';
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const aiConfig = aiService.getConfig();
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// Calculate response confidence for audit trail
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const responseConfidence = auditService.calculateAIResponseConfidence(
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response.content,
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this.getExpectedLengthForTaskType(taskType),
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taskType
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);
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// FIX: Always add AI decision audit entry for micro-tasks
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auditService.addAIDecision(
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this.getPhaseForTaskType(taskType),
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prompt, // Store original prompt without context
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prompt,
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response.content,
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responseConfidence,
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this.getReasoningForTaskType(taskType, response.content),
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@ -1224,7 +1220,6 @@ class AIPipeline {
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};
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} catch (error) {
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// FIX: Also audit failed AI calls for completeness
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auditService.addEntry(
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this.getPhaseForTaskType(taskType),
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'ai-decision-failed',
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@ -1237,7 +1232,7 @@ class AIPipeline {
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error: error.message,
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success: false
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},
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0, // Zero confidence for failed calls
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0,
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startTime,
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{
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toolsDataHash: getDataVersion?.() || 'unknown',
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@ -1299,7 +1294,7 @@ class AIPipeline {
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};
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const taskName = taskNames[taskType] || taskType;
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return `KI generierte ${taskName} (${responseLength} Zeichen) - forensisch fundierte Analyse mit methodischer Begründung`;
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return `KI generierte ${taskName} (${responseLength} Zeichen) - Analyse mit methodischer Begründung`;
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}
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private addToContextHistory(context: PipelineContext, newEntry: string): void {
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@ -26,7 +26,6 @@ export interface AuditEntry {
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completionTokens?: number;
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toolsDataHash?: string;
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embeddingsUsed?: boolean;
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//selectionMethod?: string;
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microTaskType?: string;
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confidenceFactors?: string[];
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reasoning?: string;
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@ -129,15 +128,15 @@ class AuditService {
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this.addEntry(
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phase,
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'ai-decision',
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{ prompt: this.createPromptSummary(aiPrompt) }, // Summary for display only
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{ response: aiResponse }, // STORE FULL RESPONSE - NO TRUNCATION
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{ prompt: this.createPromptSummary(aiPrompt) },
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{ response: aiResponse },
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confidence,
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startTime,
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{
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...metadata,
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reasoning,
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aiPrompt: aiPrompt, // Full prompt in metadata
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aiResponse: aiResponse, // Full response in metadata
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aiPrompt: aiPrompt,
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aiResponse: aiResponse,
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decisionBasis: 'ai-analysis'
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}
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);
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@ -146,7 +145,6 @@ class AuditService {
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addToolSelection(
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selectedTools: string[],
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availableTools: string[],
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//selectionMethod: string,
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confidence: number,
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startTime: number,
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metadata: Record<string, any> = {}
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@ -154,7 +152,6 @@ class AuditService {
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const calculatedConfidence = this.calculateSelectionConfidence(
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selectedTools,
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availableTools,
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//selectionMethod,
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metadata
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);
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@ -169,7 +166,6 @@ class AuditService {
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{
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availableTools: availableTools.slice(0, 10),
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totalAvailable: availableTools.length,
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//selectionMethod: selectionMethod
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},
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{
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selectedTools: selectedTools,
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@ -179,10 +175,8 @@ class AuditService {
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startTime,
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{
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...metadata,
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//selectionMethod,
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availableToolsCount: availableTools.length,
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selectedToolsCount: selectedTools.length,
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//decisionBasis: selectionMethod.includes('embeddings') ? 'semantic-search' : 'ai-analysis'
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decisionBasis
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}
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);
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@ -288,7 +282,6 @@ class AuditService {
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private calculateSelectionConfidence(
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selectedTools: string[],
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availableTools: string[],
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//selectionMethod: string,
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metadata: Record<string, any>
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): number {
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let confidence = 50;
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@ -303,10 +296,6 @@ class AuditService {
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confidence -= 20;
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}
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/*if (selectionMethod.includes('embeddings')) {
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confidence += 15;
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}*/
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if (selectedTools.length >= 5 && selectedTools.length <= 25) {
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confidence += 10;
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}
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@ -562,9 +551,9 @@ class AuditService {
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'background-knowledge': 'Hintergrundwissen-Auswahl',
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'final-recommendations': 'Abschließende Empfehlungen'
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};
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return `KI analysierte ${typeNames[taskType] || taskType} mit ${confidence}% Vertrauen - fundierte forensische Methodikempfehlung`;
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return `KI analysierte ${typeNames[taskType] || taskType} mit ${confidence}% Vertrauen - fundierte Empfehlung`;
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}
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return `KI-Entscheidung mit ${confidence}% Vertrauen basierend auf forensischer Expertenanalyse`;
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return `KI-Entscheidung mit ${confidence}% Vertrauen basierend auf agentischer Expertenanalyse`;
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case 'phase-enhancement':
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const phaseData = input?.phaseName || input?.phaseId;
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@ -593,7 +582,6 @@ class AuditService {
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private inferDecisionBasis(metadata: Record<string, any>): string {
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if (metadata.embeddingsUsed) return 'semantic-search';
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//if (metadata.embeddingsUsed || metadata.selectionMethod?.includes('embeddings')) return 'semantic-search';
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if (metadata.aiPrompt || metadata.microTaskType) return 'ai-analysis';
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if (metadata.semanticQuery && metadata.aiReasoningUsed) return 'hybrid';
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return 'rule-based';
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@ -31,7 +31,6 @@ interface EmbeddingsDatabase {
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}
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interface EmbeddingsConfig {
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//enabled: boolean;
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endpoint?: string;
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apiKey?: string;
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model?: string;
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@ -49,14 +48,12 @@ class EmbeddingsService {
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constructor() {
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this.config = this.loadConfig();
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console.log('[EMBEDDINGS-SERVICE] Initialized:', {
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//enabled: this.config.enabled,
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hasEndpoint: !!this.config.endpoint,
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hasModel: !!this.config.model
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});
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}
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private loadConfig(): EmbeddingsConfig {
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//const enabled = process.env.AI_EMBEDDINGS_ENABLED === 'true';
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const endpoint = process.env.AI_EMBEDDINGS_ENDPOINT;
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const apiKey = process.env.AI_EMBEDDINGS_API_KEY;
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const model = process.env.AI_EMBEDDINGS_MODEL;
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@ -64,7 +61,6 @@ class EmbeddingsService {
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const batchDelay = parseInt(process.env.AI_EMBEDDINGS_BATCH_DELAY_MS || '1000', 10);
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return {
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//enabled,
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endpoint,
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apiKey,
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model,
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@ -344,21 +340,8 @@ class EmbeddingsService {
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}
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}
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/*isEnabled(): boolean {
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return this.config.enabled;
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}*/
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/*getStats(): { enabled: boolean; initialized: boolean; count: number } {
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return {
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enabled: this.config.enabled,
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initialized: this.isInitialized,
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count: this.embeddings.length
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};
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}*/
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getStats(): {initialized: boolean; count: number } {
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return {
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//enabled: this.config.enabled,
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initialized: this.isInitialized,
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count: this.embeddings.length
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};
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@ -99,7 +99,6 @@ class ToolSelector {
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console.log('[TOOL-SELECTOR] Using embeddings for candidate selection');
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// FIX: Record the start time for audit trail
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const embeddingsSearchStart = Date.now();
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const similarItems = await embeddingsService.findSimilar(
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@ -110,13 +109,11 @@ class ToolSelector {
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console.log('[TOOL-SELECTOR] Embeddings found', similarItems.length, 'similar items');
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// FIX: Import and use auditService to record this embeddings search
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const { auditService } = await import('./auditService.js');
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const { getDataVersion } = await import('./dataService.js');
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const toolsDataHash = getDataVersion() || 'unknown';
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// FIX: Add audit entry for initial embeddings search that happens in BOTH modes
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auditService.addEmbeddingsSearch(
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userQuery,
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similarItems,
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@ -197,13 +194,11 @@ class ToolSelector {
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const softwareWithFullData = candidateSoftware.map(this.createToolData);
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const conceptsWithFullData = candidateConcepts.map(this.createConceptData);
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// Unified selection limits (method-agnostic)
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const maxTools = Math.min(this.config.embeddingSelectionLimit, this.config.noEmbeddingsToolLimit);
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const maxConcepts = Math.min(this.config.embeddingConceptsLimit, this.config.noEmbeddingsConceptLimit);
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const methodLimit = Math.ceil(maxTools * this.config.methodSelectionRatio);
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const softwareLimit = Math.floor(maxTools * this.config.softwareSelectionRatio);
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// Build tool list to send
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const toolsToSend: any[] = [
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...methodsWithFullData.slice(0, methodLimit),
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...softwareWithFullData.slice(0, softwareLimit),
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@ -211,7 +206,6 @@ class ToolSelector {
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const remainingCapacity = maxTools - toolsToSend.length;
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if (remainingCapacity > 0) {
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// Fill remainder from whichever bucket still has items
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const extraMethods = methodsWithFullData.slice(methodLimit, methodLimit + remainingCapacity);
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const extraSoftware = softwareWithFullData.slice(softwareLimit, softwareLimit + (remainingCapacity - extraMethods.length));
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toolsToSend.push(...extraMethods, ...extraSoftware);
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