cleanup, prompt centralization

src/components/AIQueryInterface.astro

@@ -1130,16 +1130,12 @@ class AIQueryInterface {
     const lowConfidenceSteps = auditTrail.filter(entry => (entry.confidence || 0) < 60).length;
     const mediumConfidenceSteps = auditTrail.length - highConfidenceSteps - lowConfidenceSteps;
 
-    // FIX 1: Count actual AI decision actions only
     const aiDecisionCount = auditTrail.filter(entry => entry.action === 'ai-decision').length;
     
-    // FIX 2: Count actual similarity search actions, not metadata flags
     const embeddingsUsageCount = auditTrail.filter(entry => entry.action === 'similarity-search').length;
     
-    // FIX 3: Maintain tool selection count (this was correct)
     const toolSelectionCount = auditTrail.filter(entry => entry.action === 'selection-decision').length;
 
-    // Additional diagnostic counts for debugging
     const microTaskCount = auditTrail.filter(entry => 
       entry.action === 'ai-decision' && entry.metadata?.microTaskType
     ).length;
@@ -1152,7 +1148,6 @@ class AIQueryInterface {
       entry.action === 'phase-enhancement'
     ).length;
 
-    // Enhanced insights with diagnostic information
     const keyInsights = [];
     const potentialIssues = [];
     
@@ -1172,7 +1167,6 @@ class AIQueryInterface {
       keyInsights.push(`${microTaskCount} spezialisierte Micro-Task-Analysen durchgeführt`);
     }
 
-    // Detect mode-specific patterns for validation
     if (phaseToolSelectionCount > 0 || phaseEnhancementCount > 0) {
       keyInsights.push('Workflow-Modus: Phasenspezifische Analyse durchgeführt');
     } else if (microTaskCount >= 3) {
@@ -1216,10 +1210,9 @@ class AIQueryInterface {
       keyInsights.push('Mehrheit der Analyseschritte mit hoher Sicherheit');
     }
 
-    // Validate expected counts based on mode detection
     const isWorkflowMode = phaseToolSelectionCount > 0 || phaseEnhancementCount > 0;
-    const expectedMinAI = isWorkflowMode ? 11 : 8; // Workflow: 5 common + 6 phase selections, Tool: 5 common + 3 evaluations
+    const expectedMinAI = isWorkflowMode ? 11 : 8; 
-    const expectedMinEmbeddings = 1; // Both modes should have initial search
+    const expectedMinEmbeddings = 1; 
 
     if (aiDecisionCount < expectedMinAI) {
       potentialIssues.push(`${expectedMinAI - aiDecisionCount} fehlende KI-Entscheidungen für ${isWorkflowMode ? 'Workflow' : 'Tool'}-Modus`);
@@ -1250,7 +1243,6 @@ class AIQueryInterface {
       analysisQuality,
       keyInsights,
       potentialIssues,
-      // Debug information
       debugCounts: {
         microTaskCount,
         phaseToolSelectionCount, 

src/config/prompts.ts

@@ -16,9 +16,41 @@ STRICTNESS:
 `.trim();
 
 export const AI_PROMPTS = {
-  // ---------------------------------------------------------------------------
+  enhancementQuestions: (input: string) => {
-  // Tool/Concept selection (AI pre-pick from embedding-curated set)
+    return `Sie sind DFIR-Experte. Ein Nutzer beschreibt unten ein Szenario/Problem.
-  // ---------------------------------------------------------------------------
+
+ZIEL:
+- Stellen Sie NUR dann 1–3 präzise Rückfragen, wenn entscheidende forensische Lücken die weitere Analyse/Toolauswahl PHASENREIHENFOLGE oder EVIDENCE-STRATEGIE wesentlich beeinflussen würden.
+- Wenn ausreichend abgedeckt: Geben Sie eine leere Liste [] zurück.
+
+PRIORITÄT DER THEMEN (in dieser Reihenfolge prüfen):
+1) Available Evidence & Artefakte (z.B. RAM-Dump, Disk-Image, Logs, PCAP, Registry, Cloud/Audit-Logs)
+2) Scope/Systems (konkrete Plattformen/Assets/Identitäten/Netzsegmente)
+3) Investigation Objectives (Ziele: IOC-Extraktion, Timeline, Impact, Attribution)
+4) Timeline/Timeframe (kritische Zeitfenster, Erhalt flüchtiger Daten)
+5) Legal & Compliance (Chain of Custody, Aufbewahrung, DSGVO/Branchenvorgaben)
+6) Technical Constraints (Ressourcen, Zugriffsrechte, Tooling/EDR)
+
+FRAGEN-QUALITÄT:
+- Forensisch spezifisch und entscheidungsrelevant (keine Allgemeinplätze).
+- Eine Frage pro Thema, keine Dopplungen.
+- Antwortbar vom Nutzer (keine Spekulation, keine “Beweise senden”-Aufforderungen).
+- Maximal 18 Wörter, endet mit "?".
+
+VALIDIERUNG:
+- Stellen Sie NUR Fragen zu Themen, die im Nutzertext NICHT hinreichend konkret beantwortet sind (keine Wiederholung bereits gegebener Details).
+- Wenn alle priorisierten Themen ausreichend sind → [].
+
+ANTWORTFORMAT (NUR JSON, KEIN ZUSÄTZLICHER TEXT):
+[
+  "präzise Frage 1?",
+  "präzise Frage 2?",
+  "präzise Frage 3?"
+]
+
+NUTZER-EINGABE:
+${input}`.trim();
+  },
   toolSelection: (mode: string, userQuery: string, maxSelectedItems: number) => {
     const modeInstruction =
       mode === 'workflow'
@@ -80,9 +112,6 @@ ${RELEVANCE_RUBRIC}
 ${STRICTNESS}`;
   },
 
-  // ---------------------------------------------------------------------------
-  // Contextual analyses (keine JSON-Ausgabe nötig; kurzer Fließtext)
-  // ---------------------------------------------------------------------------
   scenarioAnalysis: (isWorkflow: boolean, userQuery: string) => {
     const analysisType = isWorkflow ? 'Szenario' : 'Problem';
     const focus = isWorkflow
@@ -124,9 +153,6 @@ Fokus: ${focus}
 Antwort: Fließtext, max 100 Wörter.`;
   },
 
-  // ---------------------------------------------------------------------------
-  // Phase-specific selection (workflow mode substep)
-  // ---------------------------------------------------------------------------
   phaseToolSelection: (userQuery: string, phase: any, phaseTools: any[]) => {
     const methods = phaseTools.filter(t => t.type === 'method');
     const tools = phaseTools.filter(t => t.type === 'software');
@@ -183,9 +209,6 @@ ANTWORT (NUR JSON):
 ]`;
   },
 
-  // ---------------------------------------------------------------------------
-  // Per-item evaluation (used in tool mode & elsewhere)
-  // ---------------------------------------------------------------------------
   toolEvaluation: (userQuery: string, tool: any, rank: number) => {
     const itemType = tool.type === 'method' ? 'Methode' : 'Tool';
 
@@ -215,9 +238,6 @@ ANTWORT (NUR JSON):
 }`;
   },
 
-  // ---------------------------------------------------------------------------
-  // Background knowledge (concepts)
-  // ---------------------------------------------------------------------------
   backgroundKnowledgeSelection: (userQuery: string, mode: string, selectedToolNames: string[], availableConcepts: any[]) => {
     return `Wähle 2–4 Konzepte, die das Verständnis/den Einsatz der ausgewählten Tools verbessern.
 
@@ -242,9 +262,6 @@ ANTWORT (NUR JSON):
 ]`;
   },
 
-  // ---------------------------------------------------------------------------
-  // Phase completion (underrepresented phase fix)
-  // ---------------------------------------------------------------------------
   phaseCompletionReasoning: (
     originalQuery: string,
     phase: any,
@@ -306,9 +323,6 @@ ANTWORT (NUR JSON):
 }`;
   },
 
-  // ---------------------------------------------------------------------------
-  // Final synthesis (short prose, no JSON needed)
-  // ---------------------------------------------------------------------------
   finalRecommendations: (isWorkflow: boolean, userQuery: string, selectedToolNames: string[]) => {
     const focus = isWorkflow
       ? 'Knappe Workflow-Schritte & Best Practices; neutral formulieren'
@@ -324,7 +338,7 @@ Antwort: Fließtext, max ${isWorkflow ? '100' : '80'} Wörter. Keine Liste.`;
   }
 } as const;
 
-// Overloads
+export function getPrompt(key: 'enhancementQuestions', input: string): string;
 export function getPrompt(key: 'toolSelection', mode: string, userQuery: string, maxSelectedItems: number): string;
 export function getPrompt(key: 'toolSelectionWithData', basePrompt: string, toolsToSend: any[], conceptsToSend: any[]): string;
 export function getPrompt(key: 'scenarioAnalysis', isWorkflow: boolean, userQuery: string): string;
@@ -337,7 +351,6 @@ export function getPrompt(key: 'phaseCompletionReasoning', originalQuery: string
 export function getPrompt(key: 'finalRecommendations', isWorkflow: boolean, userQuery: string, selectedToolNames: string[]): string;
 export function getPrompt(key: 'generatePhaseCompletionPrompt', originalQuery: string, phase: any, candidateTools: any[], candidateConcepts: any[]): string;
 
-// Dispatcher
 export function getPrompt(promptKey: keyof typeof AI_PROMPTS, ...args: any[]): string {
   try {
     const f = AI_PROMPTS[promptKey];

src/pages/about.astro

@@ -273,15 +273,13 @@ import BaseLayout from '../layouts/BaseLayout.astro';
 </BaseLayout>
 
 <script>
-  // Load simple-boost from local node_modules instead of CDN
+  // TODO: cleanup
   import('simple-boost').then(() => {
     console.log('Simple-boost loaded successfully from local dependencies');
     
-    // Setup dynamic amount updating
     setupDynamicAmounts();
   }).catch(error => {
     console.error('Failed to load simple-boost:', error);
-    // Fallback: try to load from node_modules path
     const script = document.createElement('script');
     script.type = 'module';
     script.src = '/node_modules/simple-boost/dist/simple-boost.js';
@@ -294,7 +292,6 @@ import BaseLayout from '../layouts/BaseLayout.astro';
   });
 
   function setupDynamicAmounts() {
-    // EUR boost button
     const eurBoost = document.getElementById('eur-boost');
     const eurInput = document.getElementById('eur-amount') as HTMLInputElement;
     
@@ -305,7 +302,6 @@ import BaseLayout from '../layouts/BaseLayout.astro';
         console.log('EUR amount set to:', amount);
       });
       
-      // Update on input change for better UX
       eurInput.addEventListener('input', () => {
         const amount = parseFloat(eurInput.value) || 0.5;
         eurBoost.setAttribute('amount', amount.toString());
@@ -315,7 +311,6 @@ import BaseLayout from '../layouts/BaseLayout.astro';
 </script>
 
 <style>
-  /* Custom styling for simple-boost buttons to match your theme */
   simple-boost {
     --simple-boost-primary: var(--color-warning);
     --simple-boost-primary-hover: var(--color-accent);

src/pages/api/ai/enhance-input.ts

@@ -5,13 +5,53 @@ import { apiError, apiServerError, createAuthErrorResponse } from '../../../util
 import { enqueueApiCall } from '../../../utils/rateLimitedQueue.js';
 import { aiService } from '../../../utils/aiService.js';
 import { JSONParser } from '../../../utils/jsonUtils.js';
+import { getPrompt } from '../../../config/prompts.js';
 
 export const prerender = false;
 
-const rateLimitStore = new Map<string, { count: number; resetTime: number }>();
+const RATE_LIMIT_WINDOW_MS =
-const RATE_LIMIT_WINDOW = 60 * 1000;
+  Number.isFinite(parseInt(process.env.RATE_LIMIT_WINDOW_MS ?? '', 10))
-const RATE_LIMIT_MAX = 5;
+    ? parseInt(process.env.RATE_LIMIT_WINDOW_MS!, 10)
+    : 60_000; 
 
+const RATE_LIMIT_MAX =
+  Number.isFinite(parseInt(process.env.AI_RATE_LIMIT_MAX_REQUESTS ?? '', 10))
+    ? parseInt(process.env.AI_RATE_LIMIT_MAX_REQUESTS!, 10)
+    : 5;
+
+const INPUT_MIN_CHARS = 40;
+const INPUT_MAX_CHARS = 1000;
+const Q_MIN_LEN = 15;
+const Q_MAX_LEN = 160;
+const Q_MAX_COUNT = 3;
+const AI_TEMPERATURE = 0.3;
+const CLEANER_TEMPERATURE = 0.0;
+
+const rateLimitStore = new Map<string, { count: number; resetTime: number }>();
+
+function checkRateLimit(userId: string): boolean {
+  const now = Date.now();
+  const entry = rateLimitStore.get(userId);
+  if (!entry || now > entry.resetTime) {
+    rateLimitStore.set(userId, { count: 1, resetTime: now + RATE_LIMIT_WINDOW_MS });
+    return true;
+  }
+  if (entry.count >= RATE_LIMIT_MAX) return false;
+  entry.count++;
+  return true;
+}
+
+function cleanupExpiredRateLimits(): void {
+  const now = Date.now();
+  for (const [userId, entry] of rateLimitStore.entries()) {
+    if (now > entry.resetTime) rateLimitStore.delete(userId);
+  }
+}
+setInterval(cleanupExpiredRateLimits, 5 * 60 * 1000);
+
+/**
+ * Helpers
+ */
 function sanitizeInput(input: string): string {
   return input
     .replace(/```[\s\S]*?```/g, '[CODE_BLOCK_REMOVED]')
@@ -19,79 +59,24 @@ function sanitizeInput(input: string): string {
     .replace(/\b(system|assistant|user)\s*[:]/gi, '[ROLE_REMOVED]')
     .replace(/\b(ignore|forget|disregard)\s+(previous|all|your)\s+(instructions?|context|rules?)/gi, '[INSTRUCTION_REMOVED]')
     .trim()
-    .slice(0, 1000); 
+    .slice(0, INPUT_MAX_CHARS);
 }
 
-function checkRateLimit(userId: string): boolean {
+function stripJsonFences(s: string): string {
-  const now = Date.now();
+  return s.replace(/^```json\s*/i, '')
-  const userLimit = rateLimitStore.get(userId);
+          .replace(/^```\s*/i, '')
-  
+          .replace(/\s*```\s*$/, '')
-  if (!userLimit || now > userLimit.resetTime) {
+          .trim();
-    rateLimitStore.set(userId, { count: 1, resetTime: now + RATE_LIMIT_WINDOW });
-    return true;
-  }
-  
-  if (userLimit.count >= RATE_LIMIT_MAX) {
-    return false;
-  }
-  
-  userLimit.count++;
-  return true;
-}
-
-function cleanupExpiredRateLimits(): void {
-  const now = Date.now();
-  for (const [userId, limit] of rateLimitStore.entries()) {
-    if (now > limit.resetTime) {
-      rateLimitStore.delete(userId);
-    }
-  }
-}
-
-setInterval(cleanupExpiredRateLimits, 5 * 60 * 1000);
-
-function createEnhancementPrompt(input: string): string {
-  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.
-
-ANALYSIEREN SIE DIESE FORENSISCHEN KATEGORIEN:
-1. **Incident Context**: Was ist passiert? Welche Angriffsvektoren oder technischen Probleme liegen vor?
-2. **Affected Systems**: Welche spezifischen Technologien/Plattformen sind betroffen? (Windows/Linux/ICS/SCADA/Mobile/Cloud/Network Infrastructure)
-3. **Available Evidence**: Welche forensischen Datenquellen stehen zur Verfügung? (RAM-Dumps, Disk-Images, Log-Files, Network-Captures, Registry-Hives)
-4. **Investigation Objectives**: Was soll erreicht werden? (IOC-Extraktion, Timeline-Rekonstruktion, Attribution, Impact-Assessment)
-5. **Timeline Constraints**: Wie zeitkritisch ist die Untersuchung?
-6. **Legal & Compliance**: Rechtliche Anforderungen, Chain of Custody, Compliance-Rahmen (DSGVO, sector-specific regulations)
-7. **Technical Constraints**: Verfügbare Ressourcen, Skills, Infrastrukturbeschränkungen
-
-WENN die Beschreibung alle kritischen forensischen Aspekte abdeckt: Geben Sie eine leere Liste [] zurück.
-
-WENN wichtige Details fehlen: Formulieren Sie 2-3 präzise Fragen, die die kritischsten Lücken für eine wissenschaftlich fundierte Analyse schließen.
-
-QUALITÄTSKRITERIEN FÜR FRAGEN:
-- Forensisch spezifisch, nicht allgemein (NICHT: "Mehr Details?")
-- Methodisch relevant (NICHT: "Wann passierte das?")
-- Priorisiert nach Auswirkung auf die Untersuchungsqualität
-- Die Frage soll maximal 20 Wörter umfassen
-
-ANTWORTFORMAT (NUR JSON, KEIN ZUSÄTZLICHER TEXT):
-[
-  "spezifische Frage 1?",
-  "spezifische Frage 2?",
-  "spezifische Frage 3?"
-]
-
-NUTZER-EINGABE:
-${input}
-  `.trim();
 }
 
+/**
+ * Handler
+ */
 export const POST: APIRoute = async ({ request }) => {
   try {
-    const authResult = await withAPIAuth(request, 'ai');
+    const auth = await withAPIAuth(request, 'ai');
-    if (!authResult.authenticated) {
+    if (!auth.authenticated) return createAuthErrorResponse();
-      return createAuthErrorResponse();
+    const userId = auth.userId;
-    }
-    
-    const userId = authResult.userId;
 
     if (!checkRateLimit(userId)) {
       return apiError.rateLimit('Enhancement rate limit exceeded');
@@ -100,61 +85,38 @@ export const POST: APIRoute = async ({ request }) => {
     const body = await request.json();
     const { input } = body;
 
-    if (!input || typeof input !== 'string' || input.length < 40) {
+    if (!input || typeof input !== 'string' || input.length < INPUT_MIN_CHARS) {
-      return apiError.badRequest('Input too short for enhancement (minimum 40 characters)');
+      return apiError.badRequest(`Input too short for enhancement (minimum ${INPUT_MIN_CHARS} characters)`);
     }
 
     const sanitizedInput = sanitizeInput(input);
-    if (sanitizedInput.length < 40) {
+    if (sanitizedInput.length < INPUT_MIN_CHARS) {
       return apiError.badRequest('Input too short after sanitization');
     }
 
-    const systemPrompt = createEnhancementPrompt(sanitizedInput);
+    const taskId = `enhance_${userId}_${Date.now()}_${Math.random().toString(36).slice(2, 6)}`;
-    const taskId = `enhance_${userId}_${Date.now()}_${Math.random().toString(36).substr(2, 4)}`;
+    const questionsPrompt = getPrompt('enhancementQuestions', sanitizedInput);
 
     console.log(`[ENHANCE-API] Processing enhancement request for user: ${userId}`);
 
-    const aiResponse = await enqueueApiCall(() => 
+    const aiResponse = await enqueueApiCall(
-      aiService.callAI(systemPrompt, { 
+      () => aiService.callAI(questionsPrompt, { temperature: AI_TEMPERATURE }),
-        temperature: 0.7 
+      taskId
-      }), taskId);
+    );
 
-    if (!aiResponse.content) {
+    if (!aiResponse?.content) {
       return apiServerError.unavailable('No enhancement response');
     }
 
-    let questions;
+    let parsed: unknown = JSONParser.safeParseJSON(stripJsonFences(aiResponse.content), null);
-    try {
-      const cleanedContent = aiResponse.content
-        .replace(/^```json\s*/i, '')
-        .replace(/\s*```\s*$/, '')
-        .trim();
 
-      questions = JSONParser.safeParseJSON(cleanedContent, []);
+    let questions: string[] = Array.isArray(parsed) ? parsed : [];
-      
+    questions = questions
-      if (!Array.isArray(questions)) {
+      .filter(q => typeof q === 'string')
-        throw new Error('Response is not an array');
+      .map(q => q.trim())
-      }
+      .filter(q => q.endsWith('?'))
-      
+      .filter(q => q.length >= Q_MIN_LEN && q.length <= Q_MAX_LEN)
-      questions = questions
+      .slice(0, Q_MAX_COUNT);
-        .filter(q => typeof q === 'string' && q.length > 20 && q.length < 200)
-        .filter(q => q.includes('?')) 
-        .filter(q => {
-          const forensicsTerms = ['forensisch', 'log', 'dump', 'image', 'artefakt', 'evidence', 'incident', 'system', 'netzwerk', 'zeitraum', 'verfügbar'];
-          const lowerQ = q.toLowerCase();
-          return forensicsTerms.some(term => lowerQ.includes(term));
-        })
-        .map(q => q.trim())
-        .slice(0, 3); 
-        
-      if (questions.length === 0) {
-        questions = [];
-      }
-
-    } catch (error) {
-      console.error('[ENHANCE-API] Failed to parse enhancement response:', aiResponse.content);
-      questions = [];
-    }
 
     console.log(`[ENHANCE-API] User: ${userId}, Questions generated: ${questions.length}, Input length: ${sanitizedInput.length}`);
 
@@ -168,8 +130,8 @@ export const POST: APIRoute = async ({ request }) => {
       headers: { 'Content-Type': 'application/json' }
     });
 
-  } catch (error) {
+  } catch (err) {
-    console.error('[ENHANCE-API] Enhancement error:', error);
+    console.error('[ENHANCE-API] Enhancement error:', err);
     return apiServerError.internal('Enhancement processing failed');
   }
 };

src/utils/aiPipeline.ts

@@ -470,13 +470,11 @@ class AIPipeline {
     pipelineStart: number,
     toolsDataHash: string
   ): Promise<{ completed: number; failed: number }> {
-    // Evaluate ALL candidates handed over by the embeddings pre-filter.
     const candidates = context.filteredData.tools || [];
     if (!Array.isArray(candidates) || candidates.length === 0) {
       return { completed: completedTasks, failed: failedTasks };
     }
 
-    // Evaluate every candidate (no slicing here)
     for (let i = 0; i < candidates.length; i++) {
       const evaluationResult = await this.evaluateSpecificTool(context, candidates[i], i + 1, pipelineStart, toolsDataHash);
       if (evaluationResult.success) completedTasks++; else failedTasks++;
@@ -484,15 +482,12 @@ class AIPipeline {
       await this.delay(this.config.microTaskDelay);
     }
 
-    // At this point, context.selectedTools may contain 0..N evaluated items (added by evaluateSpecificTool).
-    // Now we sort them by AI-derived taskRelevance (after moderation) and keep ONLY the top 3 for UI.
     if (Array.isArray(context.selectedTools) && context.selectedTools.length > 0) {
       context.selectedTools.sort((a: any, b: any) => {
         const ar = typeof a.taskRelevance === 'number' ? a.taskRelevance : -1;
         const br = typeof b.taskRelevance === 'number' ? b.taskRelevance : -1;
         if (br !== ar) return br - ar;
 
-        // tie-breakers without domain heuristics:
         const aLen = (a.justification || '').length;
         const bLen = (b.justification || '').length;
         if (bLen !== aLen) return bLen - aLen;
@@ -502,7 +497,6 @@ class AIPipeline {
         return aRank - bRank;
       });
 
-      // Keep top 3 only
       context.selectedTools = context.selectedTools.slice(0, 3);
     }
 
@@ -877,7 +871,6 @@ class AIPipeline {
   ): Promise<MicroTaskResult> {
     const taskStart = Date.now();
 
-    // Build prompt WITHOUT any baseline score
     const prompt = getPrompt('toolEvaluation', context.userQuery, tool, rank);
     const result = await this.callMicroTaskAI(prompt, context, 'tool-evaluation');
 
@@ -885,16 +878,13 @@ class AIPipeline {
       return result;
     }
 
-    // Parse strictly; do NOT provide a default with a score.
     const evaluation = JSONParser.safeParseJSON(result.content, null);
 
-    // Require a numeric score produced by the model; otherwise, don't add this tool.
     const aiProvided = evaluation && typeof evaluation.taskRelevance === 'number' && Number.isFinite(evaluation.taskRelevance)
       ? Math.round(evaluation.taskRelevance)
       : null;
 
     if (aiProvided === null) {
-      // Log the malformed output but avoid injecting a synthetic score.
       auditService.addAIDecision(
         'tool-evaluation',
         prompt,
@@ -920,7 +910,6 @@ class AIPipeline {
     const moderatedTaskRelevance = this.moderateTaskRelevance(aiProvided);
     const priority = this.derivePriorityFromScore(moderatedTaskRelevance);
 
-    // Keep original fields if present; coerce to strings/arrays safely.
     const detailed_explanation = String(evaluation?.detailed_explanation || '').trim();
     const implementation_approach = String(evaluation?.implementation_approach || '').trim();
     const pros = Array.isArray(evaluation?.pros) ? evaluation.pros : [];

src/utils/toolSelector.ts

@@ -194,18 +194,15 @@ class ToolSelector {
     const softwareWithFullData = candidateSoftware.map(this.createToolData);
     const conceptsWithFullData = candidateConcepts.map(this.createConceptData);
 
-    // Embeddings are always ON → only use embedding limits
     const maxTools = Math.min(this.config.embeddingSelectionLimit, candidateTools.length);
     const maxConcepts = Math.min(this.config.embeddingConceptsLimit, candidateConcepts.length);
 
-    // Respect ratios first, then fill the remaining capacity
     const methodRatio = Math.max(0, Math.min(1, this.config.methodSelectionRatio));
     const softwareRatio = Math.max(0, Math.min(1, this.config.softwareSelectionRatio));
 
     let methodLimit = Math.round(maxTools * methodRatio);
     let softwareLimit = Math.round(maxTools * softwareRatio);
 
-    // If rounded sum exceeds maxTools, scale down proportionally
     if (methodLimit + softwareLimit > maxTools) {
       const scale = maxTools / (methodLimit + softwareLimit);
       methodLimit = Math.floor(methodLimit * scale);
@@ -217,7 +214,6 @@ class ToolSelector {
 
     const toolsToSend: any[] = [...methodsPrimary, ...softwarePrimary];
 
-    // Fill any remaining capacity from whichever pool still has candidates
     let mIdx = methodsPrimary.length;
     let sIdx = softwarePrimary.length;