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fix embeddings truncation

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overcuriousity 2025-08-04 20:03:49 +02:00
parent 3a5e8e88b2
commit 7c3cc7ec9a
6 changed files with 121 additions and 460 deletions
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73
.env.example
View File

@ -42,32 +42,35 @@ AI_EMBEDDINGS_MODEL=mistral-embed
# How many similar tools/concepts embeddings search returns as candidates # How many similar tools/concepts embeddings search returns as candidates
# 🔍 This is the FIRST filter - vector similarity matching # 🔍 This is the FIRST filter - vector similarity matching
# Lower = faster, less comprehensive | Higher = slower, more comprehensive # Lower = faster, less comprehensive | Higher = slower, more comprehensive
AI_EMBEDDING_CANDIDATES=40 AI_EMBEDDING_CANDIDATES=50
# Minimum similarity score threshold (0.0-1.0) # Minimum similarity score threshold (0.0-1.0)
# Lower = more results but less relevant | Higher = fewer but more relevant # Lower = more results but less relevant | Higher = fewer but more relevant
AI_SIMILARITY_THRESHOLD=0.3 AI_SIMILARITY_THRESHOLD=0.3
# === AI SELECTION FROM EMBEDDINGS ===
# When embeddings are enabled, how many top tools to send with full context
# 🎯 This is the SECOND filter - take best N from embeddings results
AI_EMBEDDING_SELECTION_LIMIT=30
AI_EMBEDDING_CONCEPTS_LIMIT=15
# === AI SELECTION STAGE === # === AI SELECTION STAGE ===
# Maximum tools the AI can select from embedding candidates # Maximum tools the AI can select from embedding candidates
# 🤖 This is the SECOND filter - AI intelligent selection # 🤖 This is the SECOND filter - AI intelligent selection
# Should be ≤ AI_EMBEDDING_CANDIDATES # Should be ≤ AI_EMBEDDING_CANDIDATES
AI_MAX_SELECTED_ITEMS=25 AI_MAX_SELECTED_ITEMS=25
# Maximum tools sent to AI for detailed analysis (micro-tasks) # === EMBEDDINGS EFFICIENCY THRESHOLDS ===
# 📋 This is the FINAL context size sent to AI models # Minimum tools required for embeddings to be considered useful
# Lower = less AI context, faster responses | Higher = more context, slower AI_EMBEDDINGS_MIN_TOOLS=8
AI_MAX_TOOLS_TO_ANALYZE=20
# Maximum concepts sent to AI for background knowledge selection # Maximum percentage of total tools that embeddings can return to be considered "filtering"
# 📚 Concepts are smaller than tools, so can be higher AI_EMBEDDINGS_MAX_REDUCTION_RATIO=0.75
AI_MAX_CONCEPTS_TO_ANALYZE=10
# === CONTEXT FLOW SUMMARY === # === CONTEXT FLOW SUMMARY ===
# 1. Vector Search: 111 total tools → AI_EMBEDDING_CANDIDATES (40) most similar # 1. Vector Search: 111 total tools → AI_EMBEDDING_CANDIDATES (40) most similar
# 2. AI Selection: 40 candidates → AI_MAX_SELECTED_ITEMS (25) best matches # 2. AI Selection: 40 candidates → AI_MAX_SELECTED_ITEMS (25) best matches
# 3. AI Analysis: 25 selected → AI_MAX_TOOLS_TO_ANALYZE (20) for micro-tasks # 3. Final Output: Recommendations based on analyzed subset
# 4. Final Output: Recommendations based on analyzed subset
# ============================================================================ # ============================================================================
# 4. AI PERFORMANCE & RATE LIMITING # 4. AI PERFORMANCE & RATE LIMITING
@ -107,12 +110,6 @@ AI_MAX_CONTEXT_TOKENS=3000
# Larger = more context per call | Smaller = faster responses # Larger = more context per call | Smaller = faster responses
AI_MAX_PROMPT_TOKENS=1200 AI_MAX_PROMPT_TOKENS=1200
# Timeout for individual micro-tasks (milliseconds)
AI_MICRO_TASK_TIMEOUT_MS=25000
# Maximum size of the processing queue
AI_QUEUE_MAX_SIZE=50
# ============================================================================ # ============================================================================
# 6. AUTHENTICATION & AUTHORIZATION (OPTIONAL) # 6. AUTHENTICATION & AUTHORIZATION (OPTIONAL)
# ============================================================================ # ============================================================================
@ -183,15 +180,6 @@ FORENSIC_AUDIT_RETENTION_HOURS=24
# Maximum audit entries per request # Maximum audit entries per request
FORENSIC_AUDIT_MAX_ENTRIES=50 FORENSIC_AUDIT_MAX_ENTRIES=50
# Enable detailed AI pipeline logging
AI_PIPELINE_DEBUG=false
# Enable performance metrics collection
AI_PERFORMANCE_METRICS=false
# Enable detailed micro-task debugging
AI_MICRO_TASK_DEBUG=false
# ============================================================================ # ============================================================================
# 10. QUALITY CONTROL & BIAS DETECTION (ADVANCED) # 10. QUALITY CONTROL & BIAS DETECTION (ADVANCED)
# ============================================================================ # ============================================================================
@ -207,37 +195,6 @@ CONFIDENCE_MINIMUM_THRESHOLD=40
CONFIDENCE_MEDIUM_THRESHOLD=60 CONFIDENCE_MEDIUM_THRESHOLD=60
CONFIDENCE_HIGH_THRESHOLD=80 CONFIDENCE_HIGH_THRESHOLD=80
# Bias detection settings
BIAS_DETECTION_ENABLED=false
BIAS_POPULARITY_THRESHOLD=0.7
BIAS_DIVERSITY_MINIMUM=0.6
BIAS_CELEBRITY_TOOLS=""
# Quality control thresholds
QUALITY_MIN_RESPONSE_LENGTH=50
QUALITY_MIN_SELECTION_COUNT=1
QUALITY_MAX_PROCESSING_TIME_MS=30000
# ============================================================================
# 11. USER INTERFACE DEFAULTS (OPTIONAL)
# ============================================================================
# Default UI behavior (users can override)
UI_SHOW_AUDIT_TRAIL_DEFAULT=false
UI_SHOW_CONFIDENCE_SCORES=true
UI_SHOW_BIAS_WARNINGS=true
UI_AUDIT_TRAIL_COLLAPSIBLE=true
# ============================================================================
# 12. CACHING & PERFORMANCE (OPTIONAL)
# ============================================================================
# Cache AI responses (milliseconds)
AI_RESPONSE_CACHE_TTL_MS=3600000
# Queue cleanup interval (milliseconds)
AI_QUEUE_CLEANUP_INTERVAL_MS=300000
# ============================================================================ # ============================================================================
# PERFORMANCE TUNING PRESETS # PERFORMANCE TUNING PRESETS
# ============================================================================ # ============================================================================
@ -245,21 +202,18 @@ AI_QUEUE_CLEANUP_INTERVAL_MS=300000
# 🚀 FOR FASTER RESPONSES (less comprehensive): # 🚀 FOR FASTER RESPONSES (less comprehensive):
# AI_EMBEDDING_CANDIDATES=20 # AI_EMBEDDING_CANDIDATES=20
# AI_MAX_SELECTED_ITEMS=15 # AI_MAX_SELECTED_ITEMS=15
# AI_MAX_TOOLS_TO_ANALYZE=10
# AI_MICRO_TASK_DELAY_MS=200 # AI_MICRO_TASK_DELAY_MS=200
# AI_MAX_CONTEXT_TOKENS=2000 # AI_MAX_CONTEXT_TOKENS=2000
# 🎯 FOR BETTER QUALITY (more comprehensive): # 🎯 FOR BETTER QUALITY (more comprehensive):
# AI_EMBEDDING_CANDIDATES=60 # AI_EMBEDDING_CANDIDATES=60
# AI_MAX_SELECTED_ITEMS=40 # AI_MAX_SELECTED_ITEMS=40
# AI_MAX_TOOLS_TO_ANALYZE=30
# AI_MICRO_TASK_DELAY_MS=800 # AI_MICRO_TASK_DELAY_MS=800
# AI_MAX_CONTEXT_TOKENS=4000 # AI_MAX_CONTEXT_TOKENS=4000
# 🔋 FOR LOW-POWER SYSTEMS (minimal resources): # 🔋 FOR LOW-POWER SYSTEMS (minimal resources):
# AI_EMBEDDING_CANDIDATES=15 # AI_EMBEDDING_CANDIDATES=15
# AI_MAX_SELECTED_ITEMS=10 # AI_MAX_SELECTED_ITEMS=10
# AI_MAX_TOOLS_TO_ANALYZE=8
# AI_RATE_LIMIT_MAX_REQUESTS=2 # AI_RATE_LIMIT_MAX_REQUESTS=2
# AI_MICRO_TASK_DELAY_MS=1000 # AI_MICRO_TASK_DELAY_MS=1000
@ -285,7 +239,6 @@ AI_QUEUE_CLEANUP_INTERVAL_MS=300000
# 🔍 WITH FULL MONITORING: # 🔍 WITH FULL MONITORING:
# - Enable FORENSIC_AUDIT_ENABLED=true # - Enable FORENSIC_AUDIT_ENABLED=true
# - Enable AI_PIPELINE_DEBUG=true
# - Configure audit retention and detail level # - Configure audit retention and detail level
# ============================================================================ # ============================================================================

126
src/config/forensic.config.ts
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@ -1,126 +0,0 @@
// src/config/forensic.config.ts
// Centralized configuration for forensic RAG enhancements
export const FORENSIC_CONFIG = {
audit: {
enabled: process.env.FORENSIC_AUDIT_ENABLED === 'true',
detailLevel: (process.env.FORENSIC_AUDIT_DETAIL_LEVEL as 'minimal' | 'standard' | 'verbose') || 'standard',
retentionHours: parseInt(process.env.FORENSIC_AUDIT_RETENTION_HOURS || '72', 10),
maxEntriesPerRequest: parseInt(process.env.FORENSIC_AUDIT_MAX_ENTRIES || '50', 10)
},
confidence: {
embeddingsWeight: parseFloat(process.env.CONFIDENCE_EMBEDDINGS_WEIGHT || '0.3'),
consensusWeight: parseFloat(process.env.CONFIDENCE_CONSENSUS_WEIGHT || '0.25'),
domainMatchWeight: parseFloat(process.env.CONFIDENCE_DOMAIN_MATCH_WEIGHT || '0.25'),
freshnessWeight: parseFloat(process.env.CONFIDENCE_FRESHNESS_WEIGHT || '0.2'),
minimumThreshold: parseInt(process.env.CONFIDENCE_MINIMUM_THRESHOLD || '40', 10),
highThreshold: parseInt(process.env.CONFIDENCE_HIGH_THRESHOLD || '80', 10),
mediumThreshold: parseInt(process.env.CONFIDENCE_MEDIUM_THRESHOLD || '60', 10)
},
bias: {
enabled: process.env.BIAS_DETECTION_ENABLED === 'true',
popularityThreshold: parseFloat(process.env.BIAS_POPULARITY_THRESHOLD || '0.7'),
diversityMinimum: parseFloat(process.env.BIAS_DIVERSITY_MINIMUM || '0.6'),
domainMismatchThreshold: parseFloat(process.env.BIAS_DOMAIN_MISMATCH_THRESHOLD || '0.3'),
warningThreshold: parseInt(process.env.BIAS_WARNING_THRESHOLD || '3', 10),
celebrityTools: (process.env.BIAS_CELEBRITY_TOOLS || 'Volatility 3,Wireshark,Autopsy,Maltego').split(',').map(t => t.trim())
},
// Quality thresholds for various metrics
quality: {
minResponseLength: parseInt(process.env.QUALITY_MIN_RESPONSE_LENGTH || '50', 10),
minSelectionCount: parseInt(process.env.QUALITY_MIN_SELECTION_COUNT || '1', 10),
maxProcessingTime: parseInt(process.env.QUALITY_MAX_PROCESSING_TIME_MS || '30000', 10)
},
// Display preferences
ui: {
showAuditTrailByDefault: process.env.UI_SHOW_AUDIT_TRAIL_DEFAULT === 'true',
showConfidenceScores: process.env.UI_SHOW_CONFIDENCE_SCORES !== 'false',
showBiasWarnings: process.env.UI_SHOW_BIAS_WARNINGS !== 'false',
auditTrailCollapsible: process.env.UI_AUDIT_TRAIL_COLLAPSIBLE !== 'false'
}
};
// Validation function to ensure configuration is valid
export function validateForensicConfig(): { valid: boolean; errors: string[] } {
const errors: string[] = [];
// Validate audit configuration
if (FORENSIC_CONFIG.audit.retentionHours < 1 || FORENSIC_CONFIG.audit.retentionHours > 168) {
errors.push('FORENSIC_AUDIT_RETENTION_HOURS must be between 1 and 168 (1 week)');
}
if (!['minimal', 'standard', 'verbose'].includes(FORENSIC_CONFIG.audit.detailLevel)) {
errors.push('FORENSIC_AUDIT_DETAIL_LEVEL must be one of: minimal, standard, verbose');
}
// Validate confidence weights sum to approximately 1.0
const weightSum = FORENSIC_CONFIG.confidence.embeddingsWeight +
FORENSIC_CONFIG.confidence.consensusWeight +
FORENSIC_CONFIG.confidence.domainMatchWeight +
FORENSIC_CONFIG.confidence.freshnessWeight;
if (Math.abs(weightSum - 1.0) > 0.05) {
errors.push(`Confidence weights must sum to 1.0 (currently ${weightSum.toFixed(3)})`);
}
// Validate threshold ranges
if (FORENSIC_CONFIG.confidence.minimumThreshold < 0 || FORENSIC_CONFIG.confidence.minimumThreshold > 100) {
errors.push('CONFIDENCE_MINIMUM_THRESHOLD must be between 0 and 100');
}
if (FORENSIC_CONFIG.confidence.highThreshold <= FORENSIC_CONFIG.confidence.mediumThreshold) {
errors.push('CONFIDENCE_HIGH_THRESHOLD must be greater than CONFIDENCE_MEDIUM_THRESHOLD');
}
// Validate bias thresholds
if (FORENSIC_CONFIG.bias.popularityThreshold < 0 || FORENSIC_CONFIG.bias.popularityThreshold > 1) {
errors.push('BIAS_POPULARITY_THRESHOLD must be between 0 and 1');
}
if (FORENSIC_CONFIG.bias.diversityMinimum < 0 || FORENSIC_CONFIG.bias.diversityMinimum > 1) {
errors.push('BIAS_DIVERSITY_MINIMUM must be between 0 and 1');
}
return {
valid: errors.length === 0,
errors
};
}
// Helper functions for configuration access
export function isAuditEnabled(): boolean {
return FORENSIC_CONFIG.audit.enabled;
}
export function getAuditDetailLevel(): 'minimal' | 'standard' | 'verbose' {
return FORENSIC_CONFIG.audit.detailLevel;
}
export function getConfidenceThresholds() {
return {
minimum: FORENSIC_CONFIG.confidence.minimumThreshold,
medium: FORENSIC_CONFIG.confidence.mediumThreshold,
high: FORENSIC_CONFIG.confidence.highThreshold
};
}
export function isBiasDetectionEnabled(): boolean {
return FORENSIC_CONFIG.bias.enabled;
}
// Initialize and validate configuration on module load
const configValidation = validateForensicConfig();
if (!configValidation.valid) {
console.warn('[FORENSIC CONFIG] Configuration validation failed:', configValidation.errors);
// In development, we might want to throw an error
if (process.env.NODE_ENV === 'development') {
throw new Error(`Forensic configuration invalid: ${configValidation.errors.join(', ')}`);
}
}
console.log('[FORENSIC CONFIG] Configuration loaded:', {
auditEnabled: FORENSIC_CONFIG.audit.enabled,
confidenceEnabled: true, // Always enabled
biasDetectionEnabled: FORENSIC_CONFIG.bias.enabled,
detailLevel: FORENSIC_CONFIG.audit.detailLevel
});

209
src/utils/aiPipeline.ts
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@ -78,17 +78,25 @@ class ImprovedMicroTaskAIPipeline {
private similarityThreshold: number; private similarityThreshold: number;
private microTaskDelay: number; private microTaskDelay: number;
// NEW: Embedding selection limits (top N from pre-filtered candidates)
private embeddingSelectionLimit: number;
private embeddingConceptsLimit: number;
// NEW: Embeddings efficiency thresholds
private embeddingsMinTools: number;
private embeddingsMaxReductionRatio: number;
private maxContextTokens: number; private maxContextTokens: number;
private maxPromptTokens: number; private maxPromptTokens: number;
// NEW: Audit Configuration // Audit Configuration
private auditConfig: { private auditConfig: {
enabled: boolean; enabled: boolean;
detailLevel: 'minimal' | 'standard' | 'verbose'; detailLevel: 'minimal' | 'standard' | 'verbose';
retentionHours: number; retentionHours: number;
}; };
// NEW: Temporary audit storage for pre-context operations // Temporary audit storage for pre-context operations
private tempAuditEntries: AuditEntry[] = []; private tempAuditEntries: AuditEntry[] = [];
constructor() { constructor() {
@ -98,20 +106,38 @@ class ImprovedMicroTaskAIPipeline {
model: this.getEnv('AI_ANALYZER_MODEL') model: this.getEnv('AI_ANALYZER_MODEL')
}; };
this.maxSelectedItems = parseInt(process.env.AI_MAX_SELECTED_ITEMS || '60', 10); // Core pipeline configuration
this.embeddingCandidates = parseInt(process.env.AI_EMBEDDING_CANDIDATES || '60', 10); this.maxSelectedItems = parseInt(process.env.AI_MAX_SELECTED_ITEMS || '25', 10);
this.similarityThreshold = 0.3; 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); 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);
// 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.maxContextTokens = parseInt(process.env.AI_MAX_CONTEXT_TOKENS || '4000', 10);
this.maxPromptTokens = parseInt(process.env.AI_MAX_PROMPT_TOKENS || '1500', 10); this.maxPromptTokens = parseInt(process.env.AI_MAX_PROMPT_TOKENS || '1500', 10);
// NEW: Initialize Audit Configuration // Audit configuration
this.auditConfig = { this.auditConfig = {
enabled: process.env.FORENSIC_AUDIT_ENABLED === 'true', enabled: process.env.FORENSIC_AUDIT_ENABLED === 'true',
detailLevel: (process.env.FORENSIC_AUDIT_DETAIL_LEVEL as any) || 'standard', detailLevel: (process.env.FORENSIC_AUDIT_DETAIL_LEVEL as any) || 'standard',
retentionHours: parseInt(process.env.FORENSIC_AUDIT_RETENTION_HOURS || '72', 10) 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`,
auditEnabled: this.auditConfig.enabled
});
} }
private getEnv(key: string): string { private getEnv(key: string): string {
@ -272,50 +298,49 @@ class ImprovedMicroTaskAIPipeline {
userQuery, userQuery,
this.embeddingCandidates, this.embeddingCandidates,
this.similarityThreshold this.similarityThreshold
) as SimilarityResult[]; // Type assertion for similarity property ) as SimilarityResult[];
console.log(`[IMPROVED PIPELINE] Embeddings found ${similarItems.length} similar items`); console.log(`[AI PIPELINE] Embeddings found ${similarItems.length} similar items`);
// FIXED: Create lookup maps for O(1) access while preserving original data // 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 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])); const conceptsMap = new Map<string, any>(toolsData.concepts.map((concept: any) => [concept.name, concept]));
// FIXED: Process in similarity order, preserving the ranking // Process in similarity order, preserving the ranking
const similarTools = similarItems const similarTools = similarItems
.filter((item): item is SimilarityResult => item.type === 'tool') .filter((item): item is SimilarityResult => item.type === 'tool')
.map(item => toolsMap.get(item.name)) .map(item => toolsMap.get(item.name))
.filter((tool): tool is any => tool !== undefined); // Proper type guard .filter((tool): tool is any => tool !== undefined);
const similarConcepts = similarItems const similarConcepts = similarItems
.filter((item): item is SimilarityResult => item.type === 'concept') .filter((item): item is SimilarityResult => item.type === 'concept')
.map(item => conceptsMap.get(item.name)) .map(item => conceptsMap.get(item.name))
.filter((concept): concept is any => concept !== undefined); // Proper type guard .filter((concept): concept is any => concept !== undefined);
console.log(`[IMPROVED PIPELINE] Similarity-ordered results: ${similarTools.length} tools, ${similarConcepts.length} concepts`); console.log(`[AI PIPELINE] Similarity-ordered results: ${similarTools.length} tools, ${similarConcepts.length} concepts`);
// Log the first few tools to verify ordering is preserved // FIXED: Better threshold logic - only use embeddings if we get meaningful filtering
if (similarTools.length > 0) { const totalAvailableTools = toolsData.tools.length;
console.log(`[IMPROVED PIPELINE] Top similar tools (in similarity order):`); const reductionRatio = similarTools.length / totalAvailableTools;
similarTools.slice(0, 5).forEach((tool, idx) => {
const originalSimilarItem = similarItems.find(item => item.name === tool.name);
console.log(` ${idx + 1}. ${tool.name} (similarity: ${originalSimilarItem?.similarity?.toFixed(4) || 'N/A'})`);
});
}
if (similarTools.length >= 15) { if (similarTools.length >= this.embeddingsMinTools && reductionRatio <= this.embeddingsMaxReductionRatio) {
candidateTools = similarTools; candidateTools = similarTools;
candidateConcepts = similarConcepts; candidateConcepts = similarConcepts;
selectionMethod = 'embeddings_candidates'; selectionMethod = 'embeddings_candidates';
console.log(`[IMPROVED PIPELINE] Using embeddings candidates in similarity order: ${candidateTools.length} tools`); console.log(`[AI PIPELINE] Using embeddings filtering: ${totalAvailableTools} → ${similarTools.length} tools (${(reductionRatio * 100).toFixed(1)}% reduction)`);
} else { } else {
console.log(`[IMPROVED PIPELINE] Embeddings insufficient (${similarTools.length} < 15), using full dataset`); if (similarTools.length < this.embeddingsMinTools) {
console.log(`[AI PIPELINE] Embeddings found too few tools (${similarTools.length} < ${this.embeddingsMinTools}), using full dataset`);
} else {
console.log(`[AI PIPELINE] Embeddings didn't filter enough (${(reductionRatio * 100).toFixed(1)}% > ${(this.embeddingsMaxReductionRatio * 100).toFixed(1)}%), using full dataset`);
}
candidateTools = toolsData.tools; candidateTools = toolsData.tools;
candidateConcepts = toolsData.concepts; candidateConcepts = toolsData.concepts;
selectionMethod = 'full_dataset'; selectionMethod = 'full_dataset';
} }
// NEW: Add Audit Entry for Embeddings Search with ordering verification // Enhanced audit entry with reduction statistics
if (this.auditConfig.enabled) { if (this.auditConfig.enabled) {
this.addAuditEntry(null, 'retrieval', 'embeddings-search', this.addAuditEntry(null, 'retrieval', 'embeddings-search',
{ query: userQuery, threshold: this.similarityThreshold, candidates: this.embeddingCandidates }, { query: userQuery, threshold: this.similarityThreshold, candidates: this.embeddingCandidates },
@ -323,21 +348,29 @@ class ImprovedMicroTaskAIPipeline {
candidatesFound: similarItems.length, candidatesFound: similarItems.length,
toolsInOrder: similarTools.slice(0, 3).map((t: any) => t.name), toolsInOrder: similarTools.slice(0, 3).map((t: any) => t.name),
conceptsInOrder: similarConcepts.slice(0, 3).map((c: any) => c.name), conceptsInOrder: similarConcepts.slice(0, 3).map((c: any) => c.name),
orderingPreserved: true reductionRatio: reductionRatio,
usingEmbeddings: selectionMethod === 'embeddings_candidates',
totalAvailable: totalAvailableTools,
filtered: similarTools.length
}, },
similarTools.length >= 15 ? 85 : 60, selectionMethod === 'embeddings_candidates' ? 85 : 60,
embeddingsStart, embeddingsStart,
{ selectionMethod, embeddingsEnabled: true, orderingFixed: true } {
selectionMethod,
embeddingsEnabled: true,
reductionAchieved: selectionMethod === 'embeddings_candidates',
tokenSavingsExpected: selectionMethod === 'embeddings_candidates'
}
); );
} }
} else { } else {
console.log(`[IMPROVED PIPELINE] Embeddings disabled, using full dataset`); console.log(`[AI PIPELINE] Embeddings disabled, using full dataset`);
candidateTools = toolsData.tools; candidateTools = toolsData.tools;
candidateConcepts = toolsData.concepts; candidateConcepts = toolsData.concepts;
selectionMethod = 'full_dataset'; selectionMethod = 'full_dataset';
} }
console.log(`[IMPROVED PIPELINE] AI will analyze ${candidateTools.length} candidate tools (ordering preserved: ${selectionMethod === 'embeddings_candidates'})`); console.log(`[AI PIPELINE] AI will analyze ${candidateTools.length} candidate tools (method: ${selectionMethod})`);
const finalSelection = await this.aiSelectionWithFullData(userQuery, candidateTools, candidateConcepts, mode, selectionMethod); const finalSelection = await this.aiSelectionWithFullData(userQuery, candidateTools, candidateConcepts, mode, selectionMethod);
return { return {
@ -387,15 +420,37 @@ class ImprovedMicroTaskAIPipeline {
related_software: concept.related_software || [] related_software: concept.related_software || []
})); }));
// Generate the German prompt with tool data // 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 ${toolsToSend.length} pre-filtered tools`);
} else {
// WITHOUT EMBEDDINGS: Send entire compressed database (original behavior)
toolsToSend = toolsWithFullData; // ALL tools from database
conceptsToSend = conceptsWithFullData; // ALL concepts from database
console.log(`[AI PIPELINE] Embeddings disabled: sending entire database (${toolsToSend.length} tools, ${conceptsToSend.length} concepts)`);
}
// Generate the German prompt with appropriately selected tool data
const basePrompt = getPrompt('toolSelection', mode, userQuery, selectionMethod, this.maxSelectedItems); const basePrompt = getPrompt('toolSelection', mode, userQuery, selectionMethod, this.maxSelectedItems);
const prompt = `${basePrompt} const prompt = `${basePrompt}
VERFÜGBARE TOOLS (mit vollständigen Daten): VERFÜGBARE TOOLS (mit vollständigen Daten):
${JSON.stringify(toolsWithFullData.slice(0, 30), null, 2)} ${JSON.stringify(toolsToSend, null, 2)}
VERFÜGBARE KONZEPTE (mit vollständigen Daten): VERFÜGBARE KONZEPTE (mit vollständigen Daten):
${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`; ${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}`);
try { try {
const response = await this.callAI(prompt, 2500); const response = await this.callAI(prompt, 2500);
@ -403,16 +458,15 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
const result = this.safeParseJSON(response, null); const result = this.safeParseJSON(response, null);
if (!result || !Array.isArray(result.selectedTools) || !Array.isArray(result.selectedConcepts)) { if (!result || !Array.isArray(result.selectedTools) || !Array.isArray(result.selectedConcepts)) {
console.error('[IMPROVED PIPELINE] AI selection returned invalid structure:', response.slice(0, 200)); console.error('[AI PIPELINE] AI selection returned invalid structure:', response.slice(0, 200));
// NEW: Add Audit Entry for Failed Selection
if (this.auditConfig.enabled) { if (this.auditConfig.enabled) {
this.addAuditEntry(null, 'selection', 'ai-tool-selection-failed', this.addAuditEntry(null, 'selection', 'ai-tool-selection-failed',
{ candidateCount: candidateTools.length, mode, prompt: prompt.slice(0, 200) }, { candidateCount: candidateTools.length, mode, prompt: prompt.slice(0, 200) },
{ error: 'Invalid JSON structure', response: response.slice(0, 200) }, { error: 'Invalid JSON structure', response: response.slice(0, 200) },
10, // Very low confidence 10,
selectionStart, selectionStart,
{ aiModel: this.config.model, selectionMethod } { aiModel: this.config.model, selectionMethod, tokensSent: estimatedTokens, toolsSent: toolsToSend.length }
); );
} }
@ -421,19 +475,15 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
const totalSelected = result.selectedTools.length + result.selectedConcepts.length; const totalSelected = result.selectedTools.length + result.selectedConcepts.length;
if (totalSelected === 0) { if (totalSelected === 0) {
console.error('[IMPROVED PIPELINE] AI selection returned no tools'); console.error('[AI PIPELINE] AI selection returned no tools');
throw new Error('AI selection returned empty selection'); throw new Error('AI selection returned empty selection');
} }
console.log(`[IMPROVED PIPELINE] AI selected: ${result.selectedTools.length} tools, ${result.selectedConcepts.length} concepts`); console.log(`[AI PIPELINE] AI selected: ${result.selectedTools.length} tools, ${result.selectedConcepts.length} concepts from ${toolsToSend.length} candidates`);
console.log(`[IMPROVED PIPELINE] AI reasoning: ${result.reasoning}`);
const selectedTools = candidateTools.filter(tool => result.selectedTools.includes(tool.name)); const selectedTools = candidateTools.filter(tool => result.selectedTools.includes(tool.name));
const selectedConcepts = candidateConcepts.filter(concept => result.selectedConcepts.includes(concept.name)); const selectedConcepts = candidateConcepts.filter(concept => result.selectedConcepts.includes(concept.name));
console.log(`[IMPROVED PIPELINE] Final selection: ${selectedTools.length} tools with bias prevention applied`);
// NEW: Add Audit Entry for Successful Selection
if (this.auditConfig.enabled) { if (this.auditConfig.enabled) {
const confidence = this.calculateSelectionConfidence(result, candidateTools.length); const confidence = this.calculateSelectionConfidence(result, candidateTools.length);
@ -443,11 +493,12 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
selectedToolCount: result.selectedTools.length, selectedToolCount: result.selectedTools.length,
selectedConceptCount: result.selectedConcepts.length, selectedConceptCount: result.selectedConcepts.length,
reasoning: result.reasoning?.slice(0, 200) + '...', reasoning: result.reasoning?.slice(0, 200) + '...',
finalToolNames: selectedTools.map(t => t.name) finalToolNames: selectedTools.map(t => t.name),
selectionEfficiency: `${toolsToSend.length} → ${result.selectedTools.length}`
}, },
confidence, confidence,
selectionStart, selectionStart,
{ aiModel: this.config.model, selectionMethod, promptTokens: this.estimateTokens(prompt) } { aiModel: this.config.model, selectionMethod, promptTokens: estimatedTokens, toolsSent: toolsToSend.length }
); );
} }
@ -457,69 +508,21 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
}; };
} catch (error) { } catch (error) {
console.error('[IMPROVED PIPELINE] AI selection failed:', error); console.error('[AI PIPELINE] AI selection failed:', error);
// NEW: Add Audit Entry for Selection Error
if (this.auditConfig.enabled) { if (this.auditConfig.enabled) {
this.addAuditEntry(null, 'selection', 'ai-tool-selection-error', this.addAuditEntry(null, 'selection', 'ai-tool-selection-error',
{ candidateCount: candidateTools.length, mode }, { candidateCount: candidateTools.length, mode },
{ error: error.message }, { error: error.message },
5, // Very low confidence 5,
selectionStart, selectionStart,
{ aiModel: this.config.model, selectionMethod } { aiModel: this.config.model, selectionMethod, tokensSent: estimatedTokens }
); );
} }
throw error;
console.log('[IMPROVED PIPELINE] Using emergency keyword-based selection');
return this.emergencyKeywordSelection(userQuery, candidateTools, candidateConcepts, mode);
} }
} }
private emergencyKeywordSelection(userQuery: string, candidateTools: any[], candidateConcepts: any[], mode: string) {
const emergencyStart = Date.now();
const queryLower = userQuery.toLowerCase();
const keywords = queryLower.split(/\s+/).filter(word => word.length > 3);
const scoredTools = candidateTools.map(tool => {
const toolText = (
tool.name + ' ' +
tool.description + ' ' +
(tool.tags || []).join(' ') + ' ' +
(tool.platforms || []).join(' ') + ' ' +
(tool.domains || []).join(' ')
).toLowerCase();
const score = keywords.reduce((acc, keyword) => {
return acc + (toolText.includes(keyword) ? 1 : 0);
}, 0);
return { tool, score };
}).filter(item => item.score > 0)
.sort((a, b) => b.score - a.score);
const maxTools = mode === 'workflow' ? 20 : 8;
const selectedTools = scoredTools.slice(0, maxTools).map(item => item.tool);
console.log(`[IMPROVED PIPELINE] Emergency selection: ${selectedTools.length} tools, keywords: ${keywords.slice(0, 5).join(', ')}`);
// NEW: Add Audit Entry for Emergency Selection
if (this.auditConfig.enabled) {
this.addAuditEntry(null, 'selection', 'emergency-keyword-selection',
{ keywords: keywords.slice(0, 10), candidateCount: candidateTools.length },
{ selectedCount: selectedTools.length, topScores: scoredTools.slice(0, 5).map(s => ({ name: s.tool.name, score: s.score })) },
40, // Moderate confidence for emergency selection
emergencyStart,
{ selectionMethod: 'emergency_keyword' }
);
}
return {
selectedTools,
selectedConcepts: candidateConcepts.slice(0, 3)
};
}
private async delay(ms: number): Promise<void> { private async delay(ms: number): Promise<void> {
return new Promise(resolve => setTimeout(resolve, ms)); return new Promise(resolve => setTimeout(resolve, ms));
} }
@ -826,7 +829,7 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
// NEW: Clear any previous temporary audit entries // NEW: Clear any previous temporary audit entries
this.tempAuditEntries = []; this.tempAuditEntries = [];
console.log(`[IMPROVED PIPELINE] Starting ${mode} query processing with context continuity and audit trail`); console.log(`[AI PIPELINE] Starting ${mode} query processing with context continuity and audit trail`);
try { try {
// Stage 1: Get intelligent candidates (embeddings + AI selection) // Stage 1: Get intelligent candidates (embeddings + AI selection)
@ -848,7 +851,7 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
// NEW: Merge any temporary audit entries from pre-context operations // NEW: Merge any temporary audit entries from pre-context operations
this.mergeTemporaryAuditEntries(context); this.mergeTemporaryAuditEntries(context);
console.log(`[IMPROVED PIPELINE] Starting micro-tasks with ${filteredData.tools.length} tools visible`); console.log(`[AI PIPELINE] Starting micro-tasks with ${filteredData.tools.length} tools visible`);
// NEW: Add initial audit entry // NEW: Add initial audit entry
this.addAuditEntry(context, 'initialization', 'pipeline-start', this.addAuditEntry(context, 'initialization', 'pipeline-start',
@ -925,9 +928,9 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
contextContinuityUsed: true contextContinuityUsed: true
}; };
console.log(`[IMPROVED PIPELINE] Completed: ${completedTasks} tasks, Failed: ${failedTasks} tasks`); console.log(`[AI PIPELINE] Completed: ${completedTasks} tasks, Failed: ${failedTasks} tasks`);
console.log(`[IMPROVED PIPELINE] Unique tools selected: ${context.seenToolNames.size}`); console.log(`[AI PIPELINE] Unique tools selected: ${context.seenToolNames.size}`);
console.log(`[IMPROVED PIPELINE] Audit trail entries: ${context.auditTrail.length}`); console.log(`[AI PIPELINE] Audit trail entries: ${context.auditTrail.length}`);
return { return {
recommendation: { recommendation: {
@ -939,7 +942,7 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
}; };
} catch (error) { } catch (error) {
console.error('[IMPROVED PIPELINE] Processing failed:', error); console.error('[AI PIPELINE] Processing failed:', error);
// NEW: Ensure temp audit entries are cleared even on error // NEW: Ensure temp audit entries are cleared even on error
this.tempAuditEntries = []; this.tempAuditEntries = [];

138
src/utils/dataService.ts
View File

@ -77,33 +77,8 @@ interface EnhancedCompressedToolsData {
domains: any[]; domains: any[];
phases: any[]; phases: any[];
'domain-agnostic-software': any[]; 'domain-agnostic-software': any[];
scenarios?: any[]; // Optional for AI processing scenarios?: any[];
skill_levels: any; skill_levels: any;
// Enhanced context for micro-tasks
domain_relationships: DomainRelationship[];
phase_dependencies: PhaseDependency[];
tool_compatibility_matrix: CompatibilityMatrix[];
}
interface DomainRelationship {
domain_id: string;
tool_count: number;
common_tags: string[];
skill_distribution: Record<string, number>;
}
interface PhaseDependency {
phase_id: string;
order: number;
depends_on: string | null;
enables: string | null;
is_parallel_capable: boolean;
typical_duration: string;
}
interface CompatibilityMatrix {
type: string;
groups: Record<string, string[]>;
} }
let cachedData: ToolsData | null = null; let cachedData: ToolsData | null = null;
@ -146,104 +121,6 @@ function generateDataVersion(data: any): string {
return Math.abs(hash).toString(36); return Math.abs(hash).toString(36);
} }
// Enhanced: Generate domain relationships for better AI understanding
function generateDomainRelationships(domains: any[], tools: any[]): DomainRelationship[] {
const relationships: DomainRelationship[] = [];
for (const domain of domains) {
const domainTools = tools.filter(tool =>
tool.domains && tool.domains.includes(domain.id)
);
const commonTags = domainTools
.flatMap(tool => tool.tags || [])
.reduce((acc: any, tag: string) => {
acc[tag] = (acc[tag] || 0) + 1;
return acc;
}, {});
const topTags = Object.entries(commonTags)
.sort(([,a], [,b]) => (b as number) - (a as number))
.slice(0, 5)
.map(([tag]) => tag);
relationships.push({
domain_id: domain.id,
tool_count: domainTools.length,
common_tags: topTags,
skill_distribution: domainTools.reduce((acc: any, tool: any) => {
acc[tool.skillLevel] = (acc[tool.skillLevel] || 0) + 1;
return acc;
}, {})
});
}
return relationships;
}
// Enhanced: Generate phase dependencies
function generatePhaseDependencies(phases: any[]): PhaseDependency[] {
const dependencies: PhaseDependency[] = [];
for (let i = 0; i < phases.length; i++) {
const phase = phases[i];
const nextPhase = phases[i + 1];
const prevPhase = phases[i - 1];
dependencies.push({
phase_id: phase.id,
order: i + 1,
depends_on: prevPhase?.id || null,
enables: nextPhase?.id || null,
is_parallel_capable: ['examination', 'analysis'].includes(phase.id), // Some phases can run in parallel
typical_duration: phase.id === 'data-collection' ? 'hours-days' :
phase.id === 'examination' ? 'hours-weeks' :
phase.id === 'analysis' ? 'days-weeks' :
'hours-days'
});
}
return dependencies;
}
// Enhanced: Generate tool compatibility matrix
function generateToolCompatibilityMatrix(tools: any[]): CompatibilityMatrix[] {
const matrix: CompatibilityMatrix[] = [];
// Group tools by common characteristics
const platformGroups = tools.reduce((acc: any, tool: any) => {
if (tool.platforms) {
tool.platforms.forEach((platform: string) => {
if (!acc[platform]) acc[platform] = [];
acc[platform].push(tool.name);
});
}
return acc;
}, {});
const phaseGroups = tools.reduce((acc: any, tool: any) => {
if (tool.phases) {
tool.phases.forEach((phase: string) => {
if (!acc[phase]) acc[phase] = [];
acc[phase].push(tool.name);
});
}
return acc;
}, {});
matrix.push({
type: 'platform_compatibility',
groups: platformGroups
});
matrix.push({
type: 'phase_synergy',
groups: phaseGroups
});
return matrix;
}
async function loadRawData(): Promise<ToolsData> { async function loadRawData(): Promise<ToolsData> {
if (!cachedData) { if (!cachedData) {
const yamlPath = path.join(process.cwd(), 'src/data/tools.yaml'); const yamlPath = path.join(process.cwd(), 'src/data/tools.yaml');
@ -337,27 +214,16 @@ export async function getCompressedToolsDataForAI(): Promise<EnhancedCompressedT
}; };
}); });
// Enhanced: Add rich context data
const domainRelationships = generateDomainRelationships(data.domains, compressedTools);
const phaseDependencies = generatePhaseDependencies(data.phases);
const toolCompatibilityMatrix = generateToolCompatibilityMatrix(compressedTools);
cachedCompressedData = { cachedCompressedData = {
tools: compressedTools, tools: compressedTools,
concepts: concepts, concepts: concepts,
domains: data.domains, domains: data.domains,
phases: data.phases, phases: data.phases,
'domain-agnostic-software': data['domain-agnostic-software'], 'domain-agnostic-software': data['domain-agnostic-software'],
scenarios: data.scenarios, // Include scenarios for context scenarios: data.scenarios,
skill_levels: data.skill_levels || {}, skill_levels: data.skill_levels || {},
// Enhanced context for micro-tasks
domain_relationships: domainRelationships,
phase_dependencies: phaseDependencies,
tool_compatibility_matrix: toolCompatibilityMatrix
}; };
console.log(`[DATA SERVICE] Generated enhanced compressed data: ${compressedTools.length} tools, ${concepts.length} concepts`);
console.log(`[DATA SERVICE] Added context: ${domainRelationships.length} domain relationships, ${phaseDependencies.length} phase dependencies`);
} }
return cachedCompressedData; return cachedCompressedData;

9
src/utils/rateLimitedQueue.ts
View File

@ -157,15 +157,6 @@ class RateLimitedQueue {
return status; return status;
} }
setDelay(ms: number): void {
if (!Number.isFinite(ms) || ms < 0) return;
this.delayMs = ms;
}
getDelay(): number {
return this.delayMs;
}
private async processQueue(): Promise<void> { private async processQueue(): Promise<void> {
if (this.isProcessing) { if (this.isProcessing) {
return; return;

26
src/utils/toolHelpers.ts
View File

@ -1,8 +1,3 @@
/**
* CONSOLIDATED Tool utility functions for consistent tool operations across the app
* Works in both server (Node.js) and client (browser) environments
*/
export interface Tool { export interface Tool {
name: string; name: string;
type?: 'software' | 'method' | 'concept'; type?: 'software' | 'method' | 'concept';
@ -18,10 +13,6 @@ export interface Tool {
related_concepts?: string[]; related_concepts?: string[];
} }
/**
* Creates a URL-safe slug from a tool name
* Used for URLs, IDs, and file names consistently across the app
*/
export function createToolSlug(toolName: string): string { export function createToolSlug(toolName: string): string {
if (!toolName || typeof toolName !== 'string') { if (!toolName || typeof toolName !== 'string') {
console.warn('[toolHelpers] Invalid toolName provided to createToolSlug:', toolName); console.warn('[toolHelpers] Invalid toolName provided to createToolSlug:', toolName);
@ -35,9 +26,6 @@ export function createToolSlug(toolName: string): string {
.replace(/^-|-$/g, ''); // Remove leading/trailing hyphens .replace(/^-|-$/g, ''); // Remove leading/trailing hyphens
} }
/**
* Finds a tool by name or slug from tools array
*/
export function findToolByIdentifier(tools: Tool[], identifier: string): Tool | undefined { export function findToolByIdentifier(tools: Tool[], identifier: string): Tool | undefined {
if (!identifier || !Array.isArray(tools)) return undefined; if (!identifier || !Array.isArray(tools)) return undefined;
@ -47,23 +35,9 @@ export function findToolByIdentifier(tools: Tool[], identifier: string): Tool |
); );
} }
/**
* Checks if tool has a valid project URL (hosted on CC24 server)
*/
export function isToolHosted(tool: Tool): boolean { export function isToolHosted(tool: Tool): boolean {
return tool.projectUrl !== undefined && return tool.projectUrl !== undefined &&
tool.projectUrl !== null && tool.projectUrl !== null &&
tool.projectUrl !== "" && tool.projectUrl !== "" &&
tool.projectUrl.trim() !== ""; tool.projectUrl.trim() !== "";
}
/**
* Determines tool category for styling/logic
*/
export function getToolCategory(tool: Tool): 'concept' | 'method' | 'hosted' | 'oss' | 'proprietary' {
if (tool.type === 'concept') return 'concept';
if (tool.type === 'method') return 'method';
if (isToolHosted(tool)) return 'hosted';
if (tool.license && tool.license !== 'Proprietary') return 'oss';
return 'proprietary';
} }