Context too long, using prompt only

src/data/tools.yaml

@@ -113,64 +113,6 @@ tools:
     accessType: download
     license: VSL
     knowledgebase: false
-  - name: TheHive 5
-    icon: 🐝
-    type: software
-    description: >-
-      Die zentrale Incident-Response-Plattform orchestriert komplexe 
-      Sicherheitsvorfälle vom ersten Alert bis zum Abschlussbericht. Jeder Case 
-      wird strukturiert durch Observables (IOCs), Tasks und Zeitleisten 
-      abgebildet. Die Cortex-Integration automatisiert Analysen durch Dutzende 
-      Analyzer - von VirusTotal-Checks bis Sandbox-Detonation. 
-      MISP-Synchronisation reichert Cases mit Threat-Intelligence an. Das 
-      ausgeklügelte Rollen- und Rechtesystem ermöglicht sichere Zusammenarbeit 
-      zwischen SOC-Analysten, Forensikern und Management. Templates 
-      standardisieren Response-Prozesse nach Incident-Typ. Die RESTful API 
-      integriert nahtlos mit SIEM, SOAR und Ticketing-Systemen. Metrics und
-      KPIs  messen die Team-Performance. Die Community Edition bleibt kostenlos
-      für  kleinere Teams, während Gold/Platinum-Lizenzen Enterprise-Features
-      bieten.
-    domains:
-      - incident-response
-      - static-investigations
-      - malware-analysis
-      - network-forensics
-      - fraud-investigation
-    phases:
-      - data-collection
-      - examination
-      - analysis
-      - reporting
-    platforms:
-      - Web
-    related_software:
-      - MISP
-      - Cortex
-      - Elasticsearch
-    domain-agnostic-software:
-      - collaboration-general
-    skillLevel: intermediate
-    accessType: server-based
-    url: https://strangebee.com/thehive/
-    projectUrl: ''
-    license: Community Edition (Discontinued) / Commercial
-    knowledgebase: false
-    statusUrl: https://uptime.example.lab/api/badge/1/status
-    tags:
-      - web-interface
-      - case-management
-      - collaboration
-      - api
-      - workflow
-      - multi-user-support
-      - cortex-analyzer
-      - misp-integration
-      - playbooks
-      - metrics
-      - rbac
-      - template-driven
-    related_concepts:
-      - Digital Evidence Chain of Custody
   - name: MISP
     icon: 🌐
     type: software
@@ -223,7 +165,6 @@ tools:
     related_concepts:
       - Hash Functions & Digital Signatures
     related_software:
-      - TheHive 5
       - Cortex
       - OpenCTI
   - name: DFIR-IRIS
@@ -260,7 +201,6 @@ tools:
     platforms:
       - Web
     related_software:
-      - TheHive 5
       - MISP
       - OpenCTI
     domain-agnostic-software:

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

@@ -94,18 +94,15 @@ ${input}
   `.trim();
 }
 
-// Enhanced AI service call function
 async function callAIService(prompt: string): Promise<Response> {
   const endpoint = AI_ENDPOINT;
   const apiKey = AI_ANALYZER_API_KEY;
   const model = AI_ANALYZER_MODEL;
   
-  // Simple headers - add auth only if API key exists
   let headers: Record<string, string> = {
     'Content-Type': 'application/json'
   };
   
-  // Add authentication if API key is provided
   if (apiKey) {
     headers['Authorization'] = `Bearer ${apiKey}`;
     console.log('[ENHANCE API] Using API key authentication');
@@ -113,7 +110,6 @@ async function callAIService(prompt: string): Promise<Response> {
     console.log('[ENHANCE API] No API key - making request without authentication');
   }
   
-  // Simple request body
   const requestBody = {
     model,
     messages: [{ role: 'user', content: prompt }],
@@ -124,8 +120,6 @@ async function callAIService(prompt: string): Promise<Response> {
     presence_penalty: 0.1
   };
   
-  // FIXED: This function is already being called through enqueueApiCall in the main handler
-  // So we can use direct fetch here since the queuing happens at the caller level
   return fetch(`${endpoint}/v1/chat/completions`, {
     method: 'POST',
     headers,
@@ -214,7 +208,7 @@ export const POST: APIRoute = async ({ request }) => {
       success: true,
       questions,
       taskId,
-      inputComplete: questions.length === 0 // Flag to indicate if input seems complete
+      inputComplete: questions.length === 0 
     }), {
       status: 200,
       headers: { 'Content-Type': 'application/json' }

src/utils/aiPipeline.ts

@@ -31,7 +31,6 @@ interface AnalysisResult {
   };
 }
 
-// NEW: Audit Trail Types
 interface AuditEntry {
   timestamp: number;
   phase: string;           // 'retrieval', 'selection', 'micro-task-N'
@@ -40,10 +39,9 @@ interface AuditEntry {
   output: any;             // What came out of this step
   confidence: number;      // 0-100: How confident we are in this step
   processingTimeMs: number;
-  metadata: Record<string, any>; // Additional context
+  metadata: Record<string, any>;
 }
 
-// Enhanced AnalysisContext with Audit Trail
 interface AnalysisContext {
   userQuery: string;
   mode: string;
@@ -62,7 +60,6 @@ interface AnalysisContext {
   
   seenToolNames: Set<string>;
   
-  // NEW: Audit Trail
   auditTrail: AuditEntry[];
 }
 
@@ -78,25 +75,24 @@ class ImprovedMicroTaskAIPipeline {
   private similarityThreshold: number;
   private microTaskDelay: number;
   
-  // NEW: Embedding selection limits (top N from pre-filtered candidates)
   private embeddingSelectionLimit: number;
   private embeddingConceptsLimit: number;
+
+  private noEmbeddingsToolLimit: number;
+  private noEmbeddingsConceptLimit: number;
   
-  // NEW: Embeddings efficiency thresholds
   private embeddingsMinTools: number;
   private embeddingsMaxReductionRatio: number;
   
   private maxContextTokens: number;
   private maxPromptTokens: number;
   
-  // Audit Configuration
   private auditConfig: {
     enabled: boolean;
     detailLevel: 'minimal' | 'standard' | 'verbose';
     retentionHours: number;
   };
   
-  // Temporary audit storage for pre-context operations
   private tempAuditEntries: AuditEntry[] = [];
 
   constructor() {
@@ -106,36 +102,33 @@ class ImprovedMicroTaskAIPipeline {
       model: this.getEnv('AI_ANALYZER_MODEL')
     };
 
-    // Core pipeline configuration
     this.maxSelectedItems = parseInt(process.env.AI_MAX_SELECTED_ITEMS || '25', 10);
     this.embeddingCandidates = parseInt(process.env.AI_EMBEDDING_CANDIDATES || '50', 10); 
     this.similarityThreshold = parseFloat(process.env.AI_SIMILARITY_THRESHOLD || '0.3');
     this.microTaskDelay = parseInt(process.env.AI_MICRO_TASK_DELAY_MS || '500', 10);
     
-    // NEW: Embedding selection limits (top N from pre-filtered candidates)
     this.embeddingSelectionLimit = parseInt(process.env.AI_EMBEDDING_SELECTION_LIMIT || '30', 10);
     this.embeddingConceptsLimit = parseInt(process.env.AI_EMBEDDING_CONCEPTS_LIMIT || '15', 10);
+
+    this.noEmbeddingsToolLimit = parseInt(process.env.AI_NO_EMBEDDINGS_TOOL_LIMIT || '0', 10);
+    this.noEmbeddingsConceptLimit = parseInt(process.env.AI_NO_EMBEDDINGS_CONCEPT_LIMIT || '0', 10);
     
-    // NEW: Embeddings efficiency thresholds
     this.embeddingsMinTools = parseInt(process.env.AI_EMBEDDINGS_MIN_TOOLS || '8', 10);
     this.embeddingsMaxReductionRatio = parseFloat(process.env.AI_EMBEDDINGS_MAX_REDUCTION_RATIO || '0.75');
     
-    // Context management
     this.maxContextTokens = parseInt(process.env.AI_MAX_CONTEXT_TOKENS || '4000', 10);
     this.maxPromptTokens = parseInt(process.env.AI_MAX_PROMPT_TOKENS || '1500', 10);
     
-    // Audit configuration
     this.auditConfig = {
       enabled: process.env.FORENSIC_AUDIT_ENABLED === 'true',
       detailLevel: (process.env.FORENSIC_AUDIT_DETAIL_LEVEL as any) || 'standard',
       retentionHours: parseInt(process.env.FORENSIC_AUDIT_RETENTION_HOURS || '72', 10)
     };
     
-    // Log configuration for debugging
     console.log('[AI PIPELINE] Configuration loaded:', {
       embeddingCandidates: this.embeddingCandidates,
       embeddingSelection: `${this.embeddingSelectionLimit} tools, ${this.embeddingConceptsLimit} concepts`,
-      embeddingsThresholds: `min ${this.embeddingsMinTools} tools, max ${this.embeddingsMaxReductionRatio * 100}% of total`,
+      noEmbeddingsLimits: `${this.noEmbeddingsToolLimit || 'unlimited'} tools, ${this.noEmbeddingsConceptLimit || 'unlimited'} concepts`,
       auditEnabled: this.auditConfig.enabled
     });
   }
@@ -148,7 +141,6 @@ class ImprovedMicroTaskAIPipeline {
     return value;
   }
 
-  // NEW: Audit Trail Utility Functions
   private addAuditEntry(
     context: AnalysisContext | null, 
     phase: string, 
@@ -175,22 +167,18 @@ class ImprovedMicroTaskAIPipeline {
     if (context) {
       context.auditTrail.push(auditEntry);
     } else {
-      // Store in temporary array for later merging
       this.tempAuditEntries.push(auditEntry);
     }
     
-    // Log for debugging when audit is enabled
     console.log(`[AUDIT] ${phase}/${action}: ${confidence}% confidence, ${Date.now() - startTime}ms`);
   }
   
-  // NEW: Merge temporary audit entries into context
   private mergeTemporaryAuditEntries(context: AnalysisContext): void {
     if (!this.auditConfig.enabled || this.tempAuditEntries.length === 0) return;
     
     const entryCount = this.tempAuditEntries.length;
-    // Add temp entries to the beginning of the context audit trail
     context.auditTrail.unshift(...this.tempAuditEntries);
-    this.tempAuditEntries = []; // Clear temp storage
+    this.tempAuditEntries = []; 
     
     console.log(`[AUDIT] Merged ${entryCount} temporary audit entries into context`);
   }
@@ -222,15 +210,12 @@ class ImprovedMicroTaskAIPipeline {
     
     let confidence = 60; // Base confidence
     
-    // Good selection ratio (not too many, not too few)
     if (selectionRatio > 0.05 && selectionRatio < 0.3) confidence += 20;
     else if (selectionRatio <= 0.05) confidence -= 10; // Too few
     else confidence -= 15; // Too many
     
-    // Has detailed reasoning
     if (hasReasoning) confidence += 15;
     
-    // Selected tools have good distribution
     if (result.selectedConcepts && result.selectedConcepts.length > 0) confidence += 5;
     
     return Math.min(95, Math.max(25, confidence));
@@ -254,26 +239,106 @@ class ImprovedMicroTaskAIPipeline {
 
   private safeParseJSON(jsonString: string, fallback: any = null): any {
     try {
-      const cleaned = jsonString
+      let cleaned = jsonString
         .replace(/^```json\s*/i, '')
         .replace(/\s*```\s*$/g, '')
         .trim();
       
+      if (!cleaned.endsWith('}') && !cleaned.endsWith(']')) {
+        console.warn('[AI PIPELINE] JSON appears truncated, attempting recovery...');
+        
+        let lastCompleteStructure = '';
+        let braceCount = 0;
+        let bracketCount = 0;
+        let inString = false;
+        let escaped = false;
+        
+        for (let i = 0; i < cleaned.length; i++) {
+          const char = cleaned[i];
+          
+          if (escaped) {
+            escaped = false;
+            continue;
+          }
+          
+          if (char === '\\') {
+            escaped = true;
+            continue;
+          }
+          
+          if (char === '"' && !escaped) {
+            inString = !inString;
+            continue;
+          }
+          
+          if (!inString) {
+            if (char === '{') braceCount++;
+            if (char === '}') braceCount--;
+            if (char === '[') bracketCount++;
+            if (char === ']') bracketCount--;
+            
+            if (braceCount === 0 && bracketCount === 0 && (char === '}' || char === ']')) {
+              lastCompleteStructure = cleaned.substring(0, i + 1);
+            }
+          }
+        }
+        
+        if (lastCompleteStructure) {
+          console.log('[AI PIPELINE] Attempting to parse recovered JSON structure...');
+          cleaned = lastCompleteStructure;
+        } else {
+          if (braceCount > 0) {
+            cleaned += '}';
+            console.log('[AI PIPELINE] Added closing brace to truncated JSON');
+          }
+          if (bracketCount > 0) {
+            cleaned += ']';
+            console.log('[AI PIPELINE] Added closing bracket to truncated JSON');
+          }
+        }
+      }
+      
       const parsed = JSON.parse(cleaned);
+      
+      if (parsed && typeof parsed === 'object') {
+        if (parsed.selectedTools === undefined) parsed.selectedTools = [];
+        if (parsed.selectedConcepts === undefined) parsed.selectedConcepts = [];
+        
+        if (!Array.isArray(parsed.selectedTools)) parsed.selectedTools = [];
+        if (!Array.isArray(parsed.selectedConcepts)) parsed.selectedConcepts = [];
+      }
+      
       return parsed;
     } catch (error) {
       console.warn('[AI PIPELINE] JSON parsing failed:', error.message);
-      console.warn('[AI PIPELINE] Raw content:', jsonString.slice(0, 200));
+      console.warn('[AI PIPELINE] Raw content (first 300 chars):', jsonString.slice(0, 300));
+      console.warn('[AI PIPELINE] Raw content (last 300 chars):', jsonString.slice(-300));
+      
+      if (jsonString.includes('selectedTools')) {
+        const toolMatches = jsonString.match(/"([^"]+)"/g);
+        if (toolMatches && toolMatches.length > 0) {
+          console.log('[AI PIPELINE] Attempting partial recovery from broken JSON...');
+          const possibleTools = toolMatches
+            .map(match => match.replace(/"/g, ''))
+            .filter(name => name.length > 2 && !['selectedTools', 'selectedConcepts', 'reasoning'].includes(name))
+            .slice(0, 15); // Reasonable limit
+          
+          if (possibleTools.length > 0) {
+            console.log(`[AI PIPELINE] Recovered ${possibleTools.length} possible tool names from broken JSON`);
+            return {
+              selectedTools: possibleTools,
+              selectedConcepts: [],
+              reasoning: 'Recovered from truncated response'
+            };
+          }
+        }
+      }
+      
       return fallback;
     }
   }
 
-  private addToolToSelection(context: AnalysisContext, tool: any, phase: string, priority: string, justification?: string): boolean {
-    if (context.seenToolNames.has(tool.name)) {
-      console.log(`[AI PIPELINE] Skipping duplicate tool: ${tool.name}`);
-      return false;
-    }
-    
+  private addToolToSelection(context: AnalysisContext, tool: any, phase: string, priority: string, justification?: string): boolean {   
     context.seenToolNames.add(tool.name);
     if (!context.selectedTools) context.selectedTools = [];
     
@@ -302,11 +367,9 @@ class ImprovedMicroTaskAIPipeline {
       
       console.log(`[AI PIPELINE] Embeddings found ${similarItems.length} similar items`);
       
-      // Create lookup maps for O(1) access while preserving original data
       const toolsMap = new Map<string, any>(toolsData.tools.map((tool: any) => [tool.name, tool]));
       const conceptsMap = new Map<string, any>(toolsData.concepts.map((concept: any) => [concept.name, concept]));
       
-      // Process in similarity order, preserving the ranking
       const similarTools = similarItems
         .filter((item): item is SimilarityResult => item.type === 'tool')
         .map(item => toolsMap.get(item.name))
@@ -319,7 +382,6 @@ class ImprovedMicroTaskAIPipeline {
       
       console.log(`[AI PIPELINE] Similarity-ordered results: ${similarTools.length} tools, ${similarConcepts.length} concepts`);
       
-      // FIXED: Better threshold logic - only use embeddings if we get meaningful filtering
       const totalAvailableTools = toolsData.tools.length;
       const reductionRatio = similarTools.length / totalAvailableTools;
       
@@ -340,7 +402,6 @@ class ImprovedMicroTaskAIPipeline {
         selectionMethod = 'full_dataset';
       }
       
-      // Enhanced audit entry with reduction statistics
       if (this.auditConfig.enabled) {
         this.addAuditEntry(null, 'retrieval', 'embeddings-search', 
           { query: userQuery, threshold: this.similarityThreshold, candidates: this.embeddingCandidates }, 
@@ -420,25 +481,29 @@ class ImprovedMicroTaskAIPipeline {
       related_software: concept.related_software || []
     }));
 
-    // CORRECTED LOGIC: 
     let toolsToSend: any[];
     let conceptsToSend: any[];
     
     if (selectionMethod === 'embeddings_candidates') {
-      // WITH EMBEDDINGS: Take top N from pre-filtered candidates
       toolsToSend = toolsWithFullData.slice(0, this.embeddingSelectionLimit);
       conceptsToSend = conceptsWithFullData.slice(0, this.embeddingConceptsLimit);
       
-      console.log(`[AI PIPELINE] Embeddings enabled: sending top ${toolsToSend.length} pre-filtered tools`);
+      console.log(`[AI PIPELINE] Embeddings enabled: sending top ${toolsToSend.length} similarity-ordered tools`);
     } else {
-      // WITHOUT EMBEDDINGS: Send entire compressed database (original behavior)
-      toolsToSend = toolsWithFullData; // ALL tools from database
-      conceptsToSend = conceptsWithFullData; // ALL concepts from database
+      const maxTools = this.noEmbeddingsToolLimit > 0 ? 
+        Math.min(this.noEmbeddingsToolLimit, candidateTools.length) : 
+        candidateTools.length;
       
-      console.log(`[AI PIPELINE] Embeddings disabled: sending entire database (${toolsToSend.length} tools, ${conceptsToSend.length} concepts)`);
+      const maxConcepts = this.noEmbeddingsConceptLimit > 0 ? 
+        Math.min(this.noEmbeddingsConceptLimit, candidateConcepts.length) : 
+        candidateConcepts.length;
+      
+      toolsToSend = toolsWithFullData.slice(0, maxTools);
+      conceptsToSend = conceptsWithFullData.slice(0, maxConcepts);
+      
+      console.log(`[AI PIPELINE] Embeddings disabled: sending ${toolsToSend.length}/${candidateTools.length} tools (limit: ${this.noEmbeddingsToolLimit || 'none'})`);
     }
 
-    // Generate the German prompt with appropriately selected tool data
     const basePrompt = getPrompt('toolSelection', mode, userQuery, selectionMethod, this.maxSelectedItems);
     const prompt = `${basePrompt}
 
@@ -448,9 +513,12 @@ ${JSON.stringify(toolsToSend, null, 2)}
 VERFÜGBARE KONZEPTE (mit vollständigen Daten):
 ${JSON.stringify(conceptsToSend, null, 2)}`;
 
-    // Log token usage for monitoring
     const estimatedTokens = this.estimateTokens(prompt);
-    console.log(`[AI PIPELINE] Method: ${selectionMethod}, Tools: ${toolsToSend.length}, Tokens: ~${estimatedTokens}`);
+    console.log(`[AI PIPELINE] Method: ${selectionMethod}, Tools: ${toolsToSend.length}, Estimated tokens: ~${estimatedTokens}`);
+
+    if (estimatedTokens > 35000) {
+      console.warn(`[AI PIPELINE] WARNING: Prompt tokens (${estimatedTokens}) may exceed model limits`);
+    }
 
     try {
       const response = await this.callAI(prompt, 2500);
@@ -527,7 +595,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     return new Promise(resolve => setTimeout(resolve, ms));
   }
 
-  private async callMicroTaskAI(prompt: string, context: AnalysisContext, maxTokens: number = 300): Promise<MicroTaskResult> {
+  private async callMicroTaskAI(prompt: string, context: AnalysisContext, maxTokens: number = 500): Promise<MicroTaskResult> {
     const startTime = Date.now();
     
     let contextPrompt = prompt;
@@ -552,11 +620,10 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         success: true
       };
       
-      // NEW: Add Audit Entry for Successful Micro-Task
       this.addAuditEntry(context, 'micro-task', 'ai-analysis',
         { promptLength: contextPrompt.length, maxTokens },
         { responseLength: response.length, contentPreview: response.slice(0, 100) },
-        response.length > 50 ? 80 : 60, // Confidence based on response quality
+        response.length > 50 ? 80 : 60,
         startTime,
         { aiModel: this.config.model, contextUsed: context.contextHistory.length > 0 }
       );
@@ -572,11 +639,10 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         error: error.message
       };
       
-      // NEW: Add Audit Entry for Failed Micro-Task
       this.addAuditEntry(context, 'micro-task', 'ai-analysis-failed',
         { promptLength: contextPrompt.length, maxTokens },
         { error: error.message },
-        5, // Very low confidence
+        5,
         startTime,
         { aiModel: this.config.model, contextUsed: context.contextHistory.length > 0 }
       );
@@ -589,7 +655,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     const isWorkflow = context.mode === 'workflow';
     const prompt = getPrompt('scenarioAnalysis', isWorkflow, context.userQuery);
 
-    const result = await this.callMicroTaskAI(prompt, context, 220);
+    const result = await this.callMicroTaskAI(prompt, context, 400);
     
     if (result.success) {
       if (isWorkflow) {
@@ -608,7 +674,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     const isWorkflow = context.mode === 'workflow';
     const prompt = getPrompt('investigationApproach', isWorkflow, context.userQuery);
 
-    const result = await this.callMicroTaskAI(prompt, context, 220);
+    const result = await this.callMicroTaskAI(prompt, context, 400);
     
     if (result.success) {
       context.investigationApproach = result.content;
@@ -622,7 +688,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     const isWorkflow = context.mode === 'workflow';
     const prompt = getPrompt('criticalConsiderations', isWorkflow, context.userQuery);
 
-    const result = await this.callMicroTaskAI(prompt, context, 180);
+    const result = await this.callMicroTaskAI(prompt, context, 350);
     
     if (result.success) {
       context.criticalConsiderations = result.content;
@@ -648,7 +714,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
 
     const prompt = getPrompt('phaseToolSelection', context.userQuery, phase, phaseTools);
 
-    const result = await this.callMicroTaskAI(prompt, context, 450);
+    const result = await this.callMicroTaskAI(prompt, context, 800);
     
     if (result.success) {
       const selections = this.safeParseJSON(result.content, []);
@@ -665,7 +731,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
           }
         });
         
-        // NEW: Add audit entry for tool selection
         this.addAuditEntry(context, 'micro-task', 'phase-tool-selection',
           { phase: phase.id, availableTools: phaseTools.length },
           { validSelections: validSelections.length, selectedTools: validSelections.map(s => s.toolName) },
@@ -682,7 +747,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
   private async evaluateSpecificTool(context: AnalysisContext, tool: any, rank: number): Promise<MicroTaskResult> {
     const prompt = getPrompt('toolEvaluation', context.userQuery, tool, rank);
 
-    const result = await this.callMicroTaskAI(prompt, context, 650);
+    const result = await this.callMicroTaskAI(prompt, context, 1200);
     
     if (result.success) {
       const evaluation = this.safeParseJSON(result.content, {
@@ -702,7 +767,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         }
       }, 'evaluation', evaluation.suitability_score);
       
-      // NEW: Add audit entry for tool evaluation
       this.addAuditEntry(context, 'micro-task', 'tool-evaluation',
         { toolName: tool.name, rank },
         { suitabilityScore: evaluation.suitability_score, hasExplanation: !!evaluation.detailed_explanation },
@@ -730,7 +794,7 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     const selectedToolNames = context.selectedTools?.map(st => st.tool.name) || [];
     const prompt = getPrompt('backgroundKnowledgeSelection', context.userQuery, context.mode, selectedToolNames, availableConcepts);
 
-    const result = await this.callMicroTaskAI(prompt, context, 400);
+    const result = await this.callMicroTaskAI(prompt, context, 700);
     
     if (result.success) {
       const selections = this.safeParseJSON(result.content, []);
@@ -743,7 +807,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
           relevance: sel.relevance
         }));
         
-        // NEW: Add audit entry for background knowledge selection
         this.addAuditEntry(context, 'micro-task', 'background-knowledge-selection',
           { availableConcepts: availableConcepts.length },
           { selectedConcepts: context.backgroundKnowledge?.length || 0 },
@@ -761,21 +824,19 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     const selectedToolNames = context.selectedTools?.map(st => st.tool.name) || [];
     const prompt = getPrompt('finalRecommendations', context.mode === 'workflow', context.userQuery, selectedToolNames);
 
-    const result = await this.callMicroTaskAI(prompt, context, 180);
+    const result = await this.callMicroTaskAI(prompt, context, 350);
     return result;
   }
 
-  private async callAI(prompt: string, maxTokens: number = 1000): Promise<string> {
+  private async callAI(prompt: string, maxTokens: number = 1500): Promise<string> {
     const endpoint = this.config.endpoint;
     const apiKey = this.config.apiKey;
     const model = this.config.model;
     
-    // Simple headers - add auth only if API key exists
     let headers: Record<string, string> = {
       'Content-Type': 'application/json'
     };
     
-    // Add authentication if API key is provided
     if (apiKey) {
       headers['Authorization'] = `Bearer ${apiKey}`;
       console.log('[AI PIPELINE] Using API key authentication');
@@ -783,7 +844,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       console.log('[AI PIPELINE] No API key - making request without authentication');
     }
     
-    // Simple request body
     const requestBody = {
       model,
       messages: [{ role: 'user', content: prompt }],
@@ -792,7 +852,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     };
     
     try {
-      // FIXED: Use direct fetch since entire pipeline is already queued at query.ts level
       const response = await fetch(`${endpoint}/v1/chat/completions`, {
         method: 'POST',
         headers,
@@ -826,13 +885,11 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     let completedTasks = 0;
     let failedTasks = 0;
     
-    // NEW: Clear any previous temporary audit entries
     this.tempAuditEntries = [];
     
     console.log(`[AI PIPELINE] Starting ${mode} query processing with context continuity and audit trail`);
 
     try {
-      // Stage 1: Get intelligent candidates (embeddings + AI selection)
       const toolsData = await getCompressedToolsDataForAI();
       const filteredData = await this.getIntelligentCandidates(userQuery, toolsData, mode);
       
@@ -844,20 +901,17 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         maxContextLength: this.maxContextTokens,
         currentContextLength: 0,
         seenToolNames: new Set<string>(),
-        // NEW: Initialize audit trail
         auditTrail: []
       };
 
-      // NEW: Merge any temporary audit entries from pre-context operations
       this.mergeTemporaryAuditEntries(context);
 
       console.log(`[AI PIPELINE] Starting micro-tasks with ${filteredData.tools.length} tools visible`);
 
-      // NEW: Add initial audit entry
       this.addAuditEntry(context, 'initialization', 'pipeline-start',
         { userQuery, mode, toolsDataLoaded: !!toolsData },
         { candidateTools: filteredData.tools.length, candidateConcepts: filteredData.concepts.length },
-        90, // High confidence for initialization
+        90, 
         startTime,
         { auditEnabled: this.auditConfig.enabled }
       );
@@ -896,19 +950,15 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
         }
       }
 
-      // Task 5: Background Knowledge Selection
       const knowledgeResult = await this.selectBackgroundKnowledge(context);
       if (knowledgeResult.success) completedTasks++; else failedTasks++;
       await this.delay(this.microTaskDelay);
 
-      // Task 6: Final Recommendations
       const finalResult = await this.generateFinalRecommendations(context);
       if (finalResult.success) completedTasks++; else failedTasks++;
 
-      // Build final recommendation
       const recommendation = this.buildRecommendation(context, mode, finalResult.content);
 
-      // NEW: Add final audit entry
       this.addAuditEntry(context, 'completion', 'pipeline-end',
         { completedTasks, failedTasks },
         { finalRecommendation: !!recommendation, auditEntriesGenerated: context.auditTrail.length },
@@ -935,7 +985,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
       return {
         recommendation: {
           ...recommendation,
-          // NEW: Include audit trail in response
           auditTrail: this.auditConfig.enabled ? context.auditTrail : undefined
         },
         processingStats
@@ -944,7 +993,6 @@ ${JSON.stringify(conceptsToSend, null, 2)}`;
     } catch (error) {
       console.error('[AI PIPELINE] Processing failed:', error);
       
-      // NEW: Ensure temp audit entries are cleared even on error
       this.tempAuditEntries = [];
       
       throw error;

src/utils/dataService.ts

@@ -130,7 +130,6 @@ async function loadRawData(): Promise<ToolsData> {
     try {
       cachedData = ToolsDataSchema.parse(rawData);
       
-      // Enhanced: Add default skill level descriptions if not provided
       if (!cachedData.skill_levels || Object.keys(cachedData.skill_levels).length === 0) {
         cachedData.skill_levels = {
           novice: "Minimal technical background required, guided interfaces",
@@ -178,21 +177,18 @@ export async function getCompressedToolsDataForAI(): Promise<EnhancedCompressedT
   if (!cachedCompressedData) {
     const data = await getToolsData();
     
-    // Enhanced: More detailed tool information for micro-tasks
     const compressedTools = data.tools
       .filter(tool => tool.type !== 'concept') 
       .map(tool => {
         const { projectUrl, statusUrl, ...compressedTool } = tool;
         return {
           ...compressedTool,
-          // Enhanced: Add computed fields for AI
           is_hosted: projectUrl !== undefined && projectUrl !== null && projectUrl !== "" && projectUrl.trim() !== "",
           is_open_source: tool.license && tool.license !== 'Proprietary',
           complexity_score: tool.skillLevel === 'expert' ? 5 :
                            tool.skillLevel === 'advanced' ? 4 :
                            tool.skillLevel === 'intermediate' ? 3 :
                            tool.skillLevel === 'beginner' ? 2 : 1,
-          // Enhanced: Phase-specific suitability hints
           phase_suitability: tool.phases?.map(phase => ({
             phase,
             primary_use: tool.tags?.find(tag => tag.includes(phase)) ? 'primary' : 'secondary'
@@ -206,7 +202,6 @@ export async function getCompressedToolsDataForAI(): Promise<EnhancedCompressedT
         const { projectUrl, statusUrl, platforms, accessType, license, ...compressedConcept } = concept;
         return {
           ...compressedConcept,
-          // Enhanced: Learning difficulty indicator
           learning_complexity: concept.skillLevel === 'expert' ? 'very_high' :
                               concept.skillLevel === 'advanced' ? 'high' :
                               concept.skillLevel === 'intermediate' ? 'medium' :