cleanup

src/components/AIQueryInterface.astro

@@ -702,12 +702,10 @@ class AIQueryInterface {
       toolsByPhase[phase] = [];
     });
 
-    // DEBUG: Log recommendation structure
     console.log('[AI Results] Recommendation structure:', recommendation);
     console.log('[AI Results] Recommended tools:', recommendation.recommended_tools);
 
     recommendation.recommended_tools?.forEach(recTool => {
-      // DEBUG: Log each tool's confidence data
       console.log('[AI Results] Tool confidence data:', recTool.name, recTool.confidence);
       
       if (toolsByPhase[recTool.phase]) {
@@ -716,7 +714,7 @@ class AIQueryInterface {
           toolsByPhase[recTool.phase].push({
             ...fullTool,
             recommendation: recTool,
-            confidence: recTool.confidence, // Ensure confidence is passed
+            confidence: recTool.confidence, 
             justification: recTool.justification,
             priority: recTool.priority,
             recommendationStrength: recTool.recommendationStrength
@@ -836,13 +834,11 @@ class AIQueryInterface {
       return '';
     }
     
-    // Calculate summary statistics
     const totalTime = auditTrail.reduce((sum, entry) => sum + entry.processingTimeMs, 0);
     const avgConfidence = auditTrail.reduce((sum, entry) => sum + entry.confidence, 0) / auditTrail.length;
     const lowConfidenceSteps = auditTrail.filter(entry => entry.confidence < 60).length;
     const highConfidenceSteps = auditTrail.filter(entry => entry.confidence >= 80).length;
     
-    // Group entries by phase for better organization
     const groupedEntries = auditTrail.reduce((groups, entry) => {
       if (!groups[entry.phase]) groups[entry.phase] = [];
       groups[entry.phase].push(entry);
@@ -1048,7 +1044,6 @@ class AIQueryInterface {
       second: '2-digit' 
     });
     
-    // Reuse existing grid and text utilities
     return `
       <div class="border-l-2 pl-3 py-2 mb-2" style="border-left-color: ${confidenceColor};">
         <div class="flex justify-between items-center mb-1">

src/config/prompts.ts

@@ -2,7 +2,6 @@
 
 export const AI_PROMPTS = {
   
-  // Main tool selection prompt
   toolSelection: (mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number) => {
     const modeInstruction = mode === 'workflow' 
       ? 'Der Benutzer möchte einen UMFASSENDEN WORKFLOW mit mehreren Tools/Methoden über verschiedene Phasen. Wählen Sie 15-25 Tools aus, die den vollständigen Untersuchungslebenszyklus abdecken.'
@@ -51,7 +50,6 @@ Antworten Sie NUR mit diesem JSON-Format:
 }`;
   },
 
-  // Scenario analysis prompt
   scenarioAnalysis: (isWorkflow: boolean, userQuery: string) => {
     const analysisType = isWorkflow ? 'forensische Szenario' : 'technische Problem';
     const considerations = isWorkflow ? 
@@ -74,7 +72,6 @@ ${considerations}
 WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen, Aufzählungen oder Markdown-Formatierung. Maximum 150 Wörter.`;
   },
 
-  // Investigation approach prompt
   investigationApproach: (isWorkflow: boolean, userQuery: string) => {
     const approachType = isWorkflow ? 'Untersuchungsansatz' : 'Lösungsansatz';
     const considerations = isWorkflow ?
@@ -96,7 +93,6 @@ ${considerations}
 WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen oder Markdown. Maximum 150 Wörter.`;
   },
 
-  // Critical considerations prompt
   criticalConsiderations: (isWorkflow: boolean, userQuery: string) => {
     const considerationType = isWorkflow ? 'kritische forensische Überlegungen' : 'wichtige methodische Voraussetzungen';
     const aspects = isWorkflow ?
@@ -179,7 +175,6 @@ WICHTIG:
 - "pros" soll die Stärken für diese spezifische Aufgabe hervorheben`;
   },
 
-  // Background knowledge selection prompt
   backgroundKnowledgeSelection: (userQuery: string, mode: string, selectedToolNames: string[], availableConcepts: any[]) => {
     return `Wählen Sie relevante forensische Konzepte für das Verständnis der empfohlenen Methodik.
 
@@ -200,7 +195,6 @@ Antworten Sie AUSSCHLIESSLICH mit diesem JSON-Format:
 ]`;
   },
 
-  // Final recommendations prompt
   finalRecommendations: (isWorkflow: boolean, userQuery: string, selectedToolNames: string[]) => {
     const prompt = isWorkflow ? 
       `Erstellen Sie eine Workflow-Empfehlung basierend auf DFIR-Prinzipien.
@@ -225,7 +219,6 @@ WICHTIG: Antworten Sie NUR in fließendem deutschen Text ohne Listen oder Markdo
   }
 } as const;
 
-// Type-safe prompt function with proper overloads
 export function getPrompt(key: 'toolSelection', mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number): string;
 export function getPrompt(key: 'scenarioAnalysis', isWorkflow: boolean, userQuery: string): string;
 export function getPrompt(key: 'investigationApproach', isWorkflow: boolean, userQuery: string): string;
@@ -238,7 +231,6 @@ export function getPrompt(promptKey: keyof typeof AI_PROMPTS, ...args: any[]): s
   try {
     const promptFunction = AI_PROMPTS[promptKey];
     if (typeof promptFunction === 'function') {
-      // Use type assertion since we've validated the function exists
       return (promptFunction as (...args: any[]) => string)(...args);
     } else {
       console.error(`[PROMPTS] Invalid prompt key: ${promptKey}`);

src/pages/index.astro

@@ -265,20 +265,15 @@ const phases = data.phases;
       return;
     }
     
-    // AI Button click handler
     if (aiQueryBtn) {
       aiQueryBtn.addEventListener('click', () => {
-        // Visual feedback
         aiQueryBtn.classList.add('activated');
         setTimeout(() => aiQueryBtn.classList.remove('activated'), 400);
         
-        // Switch to AI view
         switchToView('ai');
         
-        // Trigger existing view change system
         window.dispatchEvent(new CustomEvent('viewChanged', { detail: 'ai' }));
         
-        // Scroll to AI interface
         if (window.scrollToElementById) {
           window.scrollToElementById('ai-interface');
         } else {
@@ -294,14 +289,12 @@ const phases = data.phases;
     const filtersSection = document.getElementById('filters-section');
     const noResults = document.getElementById('no-results');
     
-    // Hide all views first
     if (toolsGrid) toolsGrid.style.display = 'none';
     if (matrixContainer) matrixContainer.style.display = 'none';
     if (aiInterface) aiInterface.style.display = 'none';
     if (filtersSection) filtersSection.style.display = 'none';
     if (noResults) noResults.style.display = 'none';
     
-    // Show selected view
     switch (view) {
       case 'grid':
         if (toolsGrid) toolsGrid.style.display = 'block';

src/utils/aiPipeline.ts

@@ -63,17 +63,15 @@ interface AnalysisContext {
   
   auditTrail: AuditEntry[];
   
-  // Store actual similarity data from embeddings
   embeddingsSimilarities: Map<string, number>;
 }
 
 interface ConfidenceMetrics {
-  overall: number;                    // 0-100: Combined confidence score
+  overall: number;                    
-  semanticRelevance: number;          // How well tool description matches query (from embeddings)
+  semanticRelevance: number;         
-  taskSuitability: number;           // AI-determined fitness for this specific task  
+  taskSuitability: number;          
-  methodologicalConsistency: number; // How well different analysis steps agree
+  uncertaintyFactors: string[];   
-  uncertaintyFactors: string[];      // Specific reasons why this might not work
+  strengthIndicators: string[]; 
-  strengthIndicators: string[];      // Specific reasons why this is a good choice
 }
 
 class ImprovedMicroTaskAIPipeline {
@@ -102,10 +100,10 @@ class ImprovedMicroTaskAIPipeline {
   };
 
   private confidenceConfig: {
-    semanticWeight: number;        // Weight for embeddings similarity
+    semanticWeight: number;       
-    suitabilityWeight: number;     // Weight for AI task fit evaluation
+    suitabilityWeight: number;  
-    consistencyWeight: number;     // Weight for cross-validation agreement
+    consistencyWeight: number;    
-    reliabilityWeight: number;     // Weight for tool quality indicators
+    reliabilityWeight: number;    
     minimumThreshold: number;
     mediumThreshold: number;
     highThreshold: number;
@@ -143,10 +141,9 @@ class ImprovedMicroTaskAIPipeline {
       retentionHours: parseInt(process.env.FORENSIC_AUDIT_RETENTION_HOURS || '72', 10)
     };
     
-    // Updated confidence weights - more focused on AI evaluation
     this.confidenceConfig = {
-      semanticWeight: parseFloat(process.env.CONFIDENCE_SEMANTIC_WEIGHT || '0.3'),     // Embeddings similarity
+      semanticWeight: parseFloat(process.env.CONFIDENCE_SEMANTIC_WEIGHT || '0.3'),   
-      suitabilityWeight: parseFloat(process.env.CONFIDENCE_SUITABILITY_WEIGHT || '0.7'), // AI task fit evaluation  
+      suitabilityWeight: parseFloat(process.env.CONFIDENCE_SUITABILITY_WEIGHT || '0.7'), 
       consistencyWeight: 0,    
       reliabilityWeight: 0,
       minimumThreshold: parseInt(process.env.CONFIDENCE_MINIMUM_THRESHOLD || '40', 10),
@@ -235,7 +232,7 @@ class ImprovedMicroTaskAIPipeline {
     const selectionRatio = result.selectedTools.length / candidateCount;
     const hasReasoning = result.reasoning && result.reasoning.length > 50;
     
-    let confidence = 60; // Base confidence
+    let confidence = 60; 
     
     if (selectionRatio > 0.05 && selectionRatio < 0.3) confidence += 20;
     else if (selectionRatio <= 0.05) confidence -= 10; 
@@ -386,7 +383,6 @@ class ImprovedMicroTaskAIPipeline {
     let candidateConcepts: any[] = [];
     let selectionMethod = 'unknown';
     
-    // Initialize embeddings similarities storage
     context.embeddingsSimilarities = new Map<string, number>();
     
     if (process.env.AI_EMBEDDINGS_ENABLED === 'true') {
@@ -409,7 +405,6 @@ class ImprovedMicroTaskAIPipeline {
       
       console.log(`[AI PIPELINE] Embeddings found ${similarItems.length} similar items`);
       
-      // Store actual similarity scores for confidence calculation
       similarItems.forEach(item => {
         context.embeddingsSimilarities.set(item.name, item.similarity);
       });
@@ -707,18 +702,14 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     limitations: string[] = []
   ): ConfidenceMetrics {
     
-    // 1. Semantic Relevance: Real embeddings similarity score
     const rawSemanticRelevance = context.embeddingsSimilarities.has(tool.name) ? 
       context.embeddingsSimilarities.get(tool.name)! * 100 : 50;
     
-    // 2. Task Suitability: Enhanced with phase-awareness for workflow mode
     let enhancedTaskSuitability = taskRelevance;
     
     if (context.mode === 'workflow') {
-      // In workflow mode, boost score if tool is well-matched to its assigned phase
       const toolSelection = context.selectedTools?.find(st => st.tool.name === tool.name);
       if (toolSelection && tool.phases && tool.phases.includes(toolSelection.phase)) {
-        // Boost for phase alignment (but cap at 100)
         const phaseBonus = Math.min(15, 100 - taskRelevance);
         enhancedTaskSuitability = Math.min(100, taskRelevance + phaseBonus);
         
@@ -726,7 +717,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       }
     }
     
-    // Simple weighted combination - no artificial scaling
     const overall = (
       rawSemanticRelevance * this.confidenceConfig.semanticWeight +
       enhancedTaskSuitability * this.confidenceConfig.suitabilityWeight
@@ -747,7 +737,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       overall: Math.round(overall),
       semanticRelevance: Math.round(rawSemanticRelevance),
       taskSuitability: Math.round(enhancedTaskSuitability), 
-      methodologicalConsistency: 0,
       uncertaintyFactors,
       strengthIndicators
     };
@@ -756,18 +745,15 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   private identifySpecificUncertaintyFactors(tool: any, context: AnalysisContext, limitations: string[], confidence: number): string[] {
     const factors: string[] = [];
     
-    // Add AI-identified limitations first (most specific)
     if (limitations && limitations.length > 0) {
-      factors.push(...limitations.slice(0, 2)); // Limit to top 2 to leave room for others
+      factors.push(...limitations.slice(0, 2));
     }
     
-    // Low semantic similarity
     const similarity = context.embeddingsSimilarities.get(tool.name) || 0.5;
     if (similarity < 0.7) {
       factors.push('Geringe semantische Ähnlichkeit zur Anfrage - Tool-Beschreibung passt möglicherweise nicht optimal');
     }
     
-    // Skill level vs scenario complexity mismatch
     if (tool.skillLevel === 'expert' && /schnell|rapid|triage|urgent|sofort/i.test(context.userQuery)) {
       factors.push('Experten-Tool für zeitkritisches Szenario - Setup und Einarbeitung könnten zu lange dauern');
     }
@@ -776,35 +762,29 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       factors.push('Einsteiger-Tool für komplexe Analyse - könnte funktionale Limitierungen haben');
     }
     
-    // Access and deployment concerns
     if (tool.type === 'software' && !isToolHosted(tool) && tool.accessType === 'download') {
       factors.push('Installation und Setup erforderlich');
     }
     
-    // License restrictions
     if (tool.license === 'Proprietary') {
       factors.push('Kommerzielle Software - Lizenzkosten und rechtliche Beschränkungen zu beachten');
     }
     
-    // Low overall confidence warning
     if (confidence < 60) {
       factors.push('Moderate Gesamtbewertung - alternative Ansätze sollten ebenfalls betrachtet werden');
     }
     
-    return factors.slice(0, 4); // Limit to 4 most relevant factors
+    return factors.slice(0, 4);
   }
 
-  // NEW: Identify specific strength indicators
   private identifySpecificStrengthIndicators(tool: any, context: AnalysisContext, confidence: number): string[] {
     const indicators: string[] = [];
     
-    // High semantic similarity
     const similarity = context.embeddingsSimilarities.get(tool.name) || 0.5;
     if (similarity >= 0.7) {
       indicators.push('Sehr gute semantische Übereinstimmung mit Ihrer Anfrage');
     }
     
-    // Quality indicators
     if (tool.knowledgebase === true) {
       indicators.push('Umfassende Dokumentation und Wissensbasis verfügbar');
     }
@@ -813,17 +793,15 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       indicators.push('Sofort verfügbar über gehostete Lösung - kein Setup erforderlich');
     }
     
-    // Skill level match
     if (tool.skillLevel === 'intermediate' || tool.skillLevel === 'advanced') {
       indicators.push('Ausgewogenes Verhältnis zwischen Funktionalität und Benutzerfreundlichkeit');
     }
     
-    // Method alignment
     if (tool.type === 'method' && /methodik|vorgehen|prozess|ansatz/i.test(context.userQuery)) {
       indicators.push('Methodischer Ansatz passt zu Ihrer prozeduralen Anfrage');
     }
     
-    return indicators.slice(0, 4); // Limit to 4 most important indicators
+    return indicators.slice(0, 4); 
   }
 
   private async analyzeScenario(context: AnalysisContext): Promise<MicroTaskResult> {
@@ -902,11 +880,9 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         validSelections.forEach((sel: any) => {
           const tool = phaseTools.find((t: any) => t.name === sel.toolName);
           if (tool) {
-            // Ensure taskRelevance is a number
             const taskRelevance = typeof sel.taskRelevance === 'number' ? 
               sel.taskRelevance : parseInt(String(sel.taskRelevance)) || 70;
             
-            // Derive priority automatically from score
             const priority = this.derivePriorityFromScore(taskRelevance);
             
             this.addToolToSelection(context, tool, phase.id, priority, sel.justification, taskRelevance, sel.limitations);
@@ -967,7 +943,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
           hasExplanation: !!evaluation.detailed_explanation,
           hasImplementationApproach: !!evaluation.implementation_approach,
           prosCount: evaluation.pros?.length || 0,
-          limitationsCount: evaluation.limitations?.length || 0, // ← Updated field name
+          limitationsCount: evaluation.limitations?.length || 0,
           hasLimitations: Array.isArray(evaluation.limitations) && evaluation.limitations.length > 0
         },
         70,
@@ -1101,7 +1077,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       const context: AnalysisContext = {
         userQuery,
         mode,
-        filteredData: {}, // Will be populated by getIntelligentCandidates
+        filteredData: {},
         contextHistory: [],
         maxContextLength: this.maxContextTokens,
         currentContextLength: 0,
@@ -1124,9 +1100,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         startTime,
         { auditEnabled: this.auditConfig.enabled, confidenceScoringEnabled: true }
       );
-
+   
-      // MICRO-TASK SEQUENCE WITH ENHANCED CONFIDENCE TRACKING
-      
       const analysisResult = await this.analyzeScenario(context);
       if (analysisResult.success) completeTasks++; else failedTasks++;
       await this.delay(this.microTaskDelay);
@@ -1234,7 +1208,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
             components: {
               semantic: confidence.semanticRelevance,
               suitability: confidence.taskSuitability,
-              consistency: confidence.methodologicalConsistency
             }
           },
           confidence.overall,
@@ -1286,7 +1259,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
           detailed_explanation: st.tool.evaluation?.detailed_explanation || '',
           implementation_approach: st.tool.evaluation?.implementation_approach || '',
           pros: st.tool.evaluation?.pros || [],
-          cons: st.tool.evaluation?.limitations || [], // ← FIXED: Use limitations as cons for display
+          cons: st.tool.evaluation?.limitations || [],
           alternatives: st.tool.evaluation?.alternatives || '',
           confidence: confidence,
           recommendationStrength: confidence.overall >= this.confidenceConfig.highThreshold ? 'strong' : 

src/utils/embeddings.ts

@@ -31,7 +31,7 @@ interface SimilarityResult extends EmbeddingData {
 class EmbeddingsService {
   private embeddings: EmbeddingData[] = [];
   private isInitialized = false;
-  private initializationPromise: Promise<void> | null = null; // ADD THIS LINE
+  private initializationPromise: Promise<void> | null = null;
   private readonly embeddingsPath = path.join(process.cwd(), 'data', 'embeddings.json');
   private readonly batchSize: number;
   private readonly batchDelay: number;
@@ -43,24 +43,19 @@ class EmbeddingsService {
     this.batchDelay = parseInt(process.env.AI_EMBEDDINGS_BATCH_DELAY_MS || '1000', 10);
   }
 
-  // REPLACE the existing initialize method with this:
   async initialize(): Promise<void> {
-    // If initialization is already in progress, wait for it
     if (this.initializationPromise) {
       return this.initializationPromise;
     }
 
-    // If already initialized, return immediately
     if (this.isInitialized) {
       return Promise.resolve();
     }
 
-    // Start initialization and store the promise
     this.initializationPromise = this.performInitialization();
     return this.initializationPromise;
   }
 
-  // ADD THIS NEW METHOD:
   private async performInitialization(): Promise<void> {
     if (!this.enabled) {
       console.log('[EMBEDDINGS] Embeddings disabled, skipping initialization');
@@ -70,13 +65,11 @@ class EmbeddingsService {
     try {
       console.log('[EMBEDDINGS] Initializing embeddings system...');
       
-      // Create data directory if it doesn't exist
       await fs.mkdir(path.dirname(this.embeddingsPath), { recursive: true });
       
       const toolsData = await getCompressedToolsDataForAI();
       const currentDataHash = this.hashData(toolsData);
       
-      // Try to load existing embeddings
       const existingEmbeddings = await this.loadEmbeddings();
       
       if (existingEmbeddings && existingEmbeddings.version === currentDataHash) {
@@ -336,12 +329,10 @@ class EmbeddingsService {
 
 
 
-// Global instance
 const embeddingsService = new EmbeddingsService();
 
 export { embeddingsService, type EmbeddingData, type SimilarityResult };
 
-// Auto-initialize on import in server environment
 if (typeof window === 'undefined' && process.env.NODE_ENV !== 'test') {
   embeddingsService.initialize().catch(error => {
     console.error('[EMBEDDINGS] Auto-initialization failed:', error);

src/utils/rateLimitedQueue.ts

@@ -96,7 +96,6 @@ class RateLimitedQueue {
 
       this.tasks.push(queuedTask);
 
-      // Kick the processor soon.
       setTimeout(() => {
         this.processQueue();
       }, 100);
@@ -170,7 +169,7 @@ class RateLimitedQueue {
           .filter((t) => t.status === "queued")
           .sort((a, b) => a.addedAt - b.addedAt)[0];
 
-        if (!nextTask) break; // No more work
+        if (!nextTask) break;
 
         nextTask.status = "processing";
         nextTask.startedAt = Date.now();