airefactor #19
@ -60,7 +60,7 @@ FORENSIC_AUDIT_MAX_ENTRIES=50
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# === AI SEMANTIC SEARCH ===
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# Enable semantic search (highly recommended for better results)
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AI_EMBEDDINGS_ENABLED=true
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REMOVE_AI_EMBEDDINGS_ENABLED=true
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AI_EMBEDDINGS_ENDPOINT=https://api.mistral.ai/v1/embeddings
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AI_EMBEDDINGS_API_KEY=your-embeddings-api-key-here
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AI_EMBEDDINGS_MODEL=mistral-embed
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@ -122,8 +122,8 @@ AI_EMBEDDINGS_BATCH_SIZE=10
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AI_EMBEDDINGS_BATCH_DELAY_MS=1000
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# === Context Management ===
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AI_MAX_CONTEXT_TOKENS=4000
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AI_MAX_PROMPT_TOKENS=2500
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REMOVE_AI_MAX_CONTEXT_TOKENS=4000
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REMOVE_AI_MAX_PROMPT_TOKENS=2500
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# === Confidence Scoring ===
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CONFIDENCE_SEMANTIC_WEIGHT=0.5
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@ -2,17 +2,15 @@
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export const AI_PROMPTS = {
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toolSelection: (mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number) => {
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toolSelection: (mode: string, userQuery: string, maxSelectedItems: number) => {
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const modeInstruction = mode === 'workflow'
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? 'Workflow mit 15-25 Items über alle Phasen. PFLICHT: Mindestens 40% Methoden, Rest Tools/Konzepte.'
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: 'Spezifische Lösung mit 4-10 Items. PFLICHT: Mindestens 30% Methoden wenn verfügbar.';
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return `Du bist ein DFIR-Experte. Wähle die BESTEN Items aus dem vorgefilterten Set.
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AUSWAHLMETHODE: ${selectionMethod}
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${selectionMethod === 'embeddings_candidates' ?
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'✓ Semantisch relevante Items bereits vorgefiltert\n✓ Wähle die BESTEN für die konkrete Aufgabe' :
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'✓ Vollständige Datenbank verfügbar\n✓ Wähle die relevantesten Items'}
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AUSWAHLMETHODE:
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'✓ Semantisch relevante Items bereits vorgefiltert\n✓ Wähle die BESTEN für die konkrete Aufgabe'}
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${modeInstruction}
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@ -309,7 +307,7 @@ Antwort: Fließtext ohne Listen, max ${isWorkflow ? '100' : '80'} Wörter.`;
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}
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} as const;
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export function getPrompt(key: 'toolSelection', mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number): string;
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export function getPrompt(key: 'toolSelection', mode: string, userQuery: string, maxSelectedItems: number): string;
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export function getPrompt(key: 'toolSelectionWithData', basePrompt: string, toolsToSend: any[], conceptsToSend: any[]): string;
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export function getPrompt(key: 'scenarioAnalysis', isWorkflow: boolean, userQuery: string): string;
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export function getPrompt(key: 'investigationApproach', isWorkflow: boolean, userQuery: string): string;
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@ -11,7 +11,7 @@ import 'dotenv/config';
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interface PipelineConfig {
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microTaskDelay: number;
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maxContextTokens: number;
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//maxContextTokens: number;
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maxPromptTokens: number;
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taskRelevanceModeration: {
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maxInitialScore: number;
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@ -36,7 +36,7 @@ interface MicroTaskResult {
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interface AnalysisResult {
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recommendation: any;
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processingStats: {
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embeddingsUsed: boolean;
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//embeddingsUsed: boolean;
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candidatesFromEmbeddings: number;
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finalSelectedItems: number;
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processingTimeMs: number;
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@ -57,7 +57,7 @@ interface PipelineContext {
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mode: string;
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filteredData: any;
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contextHistory: string[];
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maxContextLength: number;
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//maxContextLength: number;
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currentContextLength: number;
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scenarioAnalysis?: string;
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problemAnalysis?: string;
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@ -91,7 +91,7 @@ class AIPipeline {
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constructor() {
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this.config = {
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microTaskDelay: parseInt(process.env.AI_MICRO_TASK_DELAY_MS || '500', 10),
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maxContextTokens: parseInt(process.env.AI_MAX_CONTEXT_TOKENS || '4000', 10),
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//maxContextTokens: parseInt(process.env.AI_MAX_CONTEXT_TOKENS || '4000', 10),
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maxPromptTokens: parseInt(process.env.AI_MAX_PROMPT_TOKENS || '1500', 10),
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taskRelevanceModeration: {
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maxInitialScore: 85,
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@ -123,7 +123,7 @@ class AIPipeline {
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mode,
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filteredData: {},
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contextHistory: [],
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maxContextLength: this.config.maxContextTokens,
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//maxContextLength: this.config.maxContextTokens,
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currentContextLength: 0,
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seenToolNames: new Set<string>(),
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embeddingsSimilarities: new Map<string, number>(),
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@ -138,20 +138,20 @@ class AIPipeline {
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const selectionConfidence = this.calculateToolSelectionConfidence(
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candidateData.tools.length,
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toolsData.tools.length,
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candidateData.selectionMethod,
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//candidateData.selectionMethod,
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candidateData.concepts.length
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);
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auditService.addToolSelection(
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candidateData.tools.map(t => t.name),
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toolsData.tools.map(t => t.name),
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candidateData.selectionMethod,
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//candidateData.selectionMethod,
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selectionConfidence,
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candidateSelectionStart,
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{
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embeddingsUsed: embeddingsService.isEnabled(),
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//embeddingsUsed: embeddingsService.isEnabled(),
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totalCandidatesFound: candidateData.tools.length + candidateData.concepts.length,
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selectionMethod: candidateData.selectionMethod,
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//selectionMethod: candidateData.selectionMethod,
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reductionRatio: candidateData.tools.length / toolsData.tools.length
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}
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);
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@ -201,7 +201,7 @@ class AIPipeline {
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const recommendation = this.buildRecommendation(context, mode, finalResult.content);
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const processingStats = {
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embeddingsUsed: embeddingsService.isEnabled(),
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//embeddingsUsed: embeddingsService.isEnabled(),
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candidatesFromEmbeddings: candidateData.tools.length,
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finalSelectedItems: (context.selectedTools?.length || 0) + (context.backgroundKnowledge?.length || 0),
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processingTimeMs: Date.now() - startTime,
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@ -213,7 +213,7 @@ class AIPipeline {
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aiModel: aiConfig.model,
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toolsDataHash,
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temperature: 0.3,
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maxTokensUsed: 2500
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maxTokensUsed: 32768
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};
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console.log('[AI-PIPELINE] Pipeline completed successfully:', {
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@ -292,7 +292,7 @@ class AIPipeline {
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private calculateToolSelectionConfidence(
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selectedCount: number,
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totalCount: number,
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method: string,
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//method: string,
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conceptsCount: number
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): number {
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let confidence = 50;
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@ -307,9 +307,9 @@ class AIPipeline {
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confidence -= 15;
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}
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if (method.includes('embeddings')) {
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confidence += 15;
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}
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//if (method.includes('embeddings')) {
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//confidence += 15;
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//}
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if (conceptsCount > 0) {
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confidence += 10;
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@ -1280,10 +1280,12 @@ class AIPipeline {
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context.contextHistory.push(newEntry);
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context.currentContextLength += entryTokens;
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while (context.currentContextLength > this.config.maxContextTokens && context.contextHistory.length > 1) {
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/*while (context.currentContextLength > this.config.maxContextTokens && context.contextHistory.length > 1) {
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const removed = context.contextHistory.shift()!;
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context.currentContextLength -= aiService.estimateTokens(removed);
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}*/
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const removed = context.contextHistory.shift()!;
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context.currentContextLength -= aiService.estimateTokens(removed);
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}
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}
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private addToolToSelection(
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@ -34,7 +34,7 @@ class AIService {
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};
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this.defaultOptions = {
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maxTokens: 1500,
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maxTokens: 32768,
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temperature: 0.3,
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timeout: 30000
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};
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@ -26,7 +26,7 @@ export interface AuditEntry {
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completionTokens?: number;
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toolsDataHash?: string;
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embeddingsUsed?: boolean;
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selectionMethod?: string;
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//selectionMethod?: string;
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microTaskType?: string;
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confidenceFactors?: string[];
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reasoning?: string;
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@ -146,7 +146,7 @@ class AuditService {
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addToolSelection(
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selectedTools: string[],
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availableTools: string[],
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selectionMethod: string,
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//selectionMethod: string,
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confidence: number,
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startTime: number,
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metadata: Record<string, any> = {}
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@ -154,17 +154,22 @@ class AuditService {
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const calculatedConfidence = this.calculateSelectionConfidence(
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selectedTools,
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availableTools,
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selectionMethod,
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//selectionMethod,
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metadata
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);
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const decisionBasis =
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metadata.embeddingsUsed || metadata.similarityScores
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? 'semantic-search'
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: (metadata.aiPrompt || metadata.microTaskType ? 'ai-analysis' : 'rule-based');
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this.addEntry(
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'tool-selection',
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'selection-decision',
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{
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availableTools: availableTools.slice(0, 10),
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totalAvailable: availableTools.length,
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selectionMethod: selectionMethod
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//selectionMethod: selectionMethod
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},
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{
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selectedTools: selectedTools,
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@ -174,10 +179,11 @@ class AuditService {
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startTime,
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{
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...metadata,
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selectionMethod,
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//selectionMethod,
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availableToolsCount: availableTools.length,
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selectedToolsCount: selectedTools.length,
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decisionBasis: selectionMethod.includes('embeddings') ? 'semantic-search' : 'ai-analysis'
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//decisionBasis: selectionMethod.includes('embeddings') ? 'semantic-search' : 'ai-analysis'
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decisionBasis
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}
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);
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}
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@ -282,7 +288,7 @@ class AuditService {
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private calculateSelectionConfidence(
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selectedTools: string[],
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availableTools: string[],
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selectionMethod: string,
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//selectionMethod: string,
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metadata: Record<string, any>
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): number {
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let confidence = 50;
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@ -297,9 +303,9 @@ class AuditService {
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confidence -= 20;
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}
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if (selectionMethod.includes('embeddings')) {
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/*if (selectionMethod.includes('embeddings')) {
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confidence += 15;
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}
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}*/
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if (selectedTools.length >= 5 && selectedTools.length <= 25) {
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confidence += 10;
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@ -589,7 +595,8 @@ class AuditService {
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}
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private inferDecisionBasis(metadata: Record<string, any>): string {
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if (metadata.embeddingsUsed || metadata.selectionMethod?.includes('embeddings')) return 'semantic-search';
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if (metadata.embeddingsUsed) return 'semantic-search';
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//if (metadata.embeddingsUsed || metadata.selectionMethod?.includes('embeddings')) return 'semantic-search';
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if (metadata.aiPrompt || metadata.microTaskType) return 'ai-analysis';
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if (metadata.semanticQuery && metadata.aiReasoningUsed) return 'hybrid';
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return 'rule-based';
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@ -31,7 +31,7 @@ interface EmbeddingsDatabase {
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}
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interface EmbeddingsConfig {
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enabled: boolean;
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//enabled: boolean;
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endpoint?: string;
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apiKey?: string;
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model?: string;
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@ -49,14 +49,14 @@ class EmbeddingsService {
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constructor() {
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this.config = this.loadConfig();
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console.log('[EMBEDDINGS-SERVICE] Initialized:', {
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enabled: this.config.enabled,
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//enabled: this.config.enabled,
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hasEndpoint: !!this.config.endpoint,
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hasModel: !!this.config.model
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});
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}
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private loadConfig(): EmbeddingsConfig {
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const enabled = process.env.AI_EMBEDDINGS_ENABLED === 'true';
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//const enabled = process.env.AI_EMBEDDINGS_ENABLED === 'true';
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const endpoint = process.env.AI_EMBEDDINGS_ENDPOINT;
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const apiKey = process.env.AI_EMBEDDINGS_API_KEY;
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const model = process.env.AI_EMBEDDINGS_MODEL;
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@ -64,7 +64,7 @@ class EmbeddingsService {
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const batchDelay = parseInt(process.env.AI_EMBEDDINGS_BATCH_DELAY_MS || '1000', 10);
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return {
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enabled,
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//enabled,
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endpoint,
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apiKey,
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model,
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@ -92,10 +92,10 @@ class EmbeddingsService {
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try {
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console.log('[EMBEDDINGS-SERVICE] Starting initialization');
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if (!this.config.enabled) {
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/*if (!this.config.enabled) {
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console.log('[EMBEDDINGS-SERVICE] Service disabled via configuration');
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return;
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}
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}*/
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await fs.mkdir(path.dirname(this.embeddingsPath), { recursive: true });
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@ -263,7 +263,7 @@ class EmbeddingsService {
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}
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async embedText(text: string): Promise<number[]> {
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if (!this.isEnabled() || !this.isInitialized) {
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if (!this.isInitialized) {
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throw new Error('Embeddings service not available');
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}
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@ -272,9 +272,9 @@ class EmbeddingsService {
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}
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async waitForInitialization(): Promise<void> {
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if (!this.config.enabled) {
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/*if (!this.config.enabled) {
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return Promise.resolve();
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}
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}*/
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if (this.isInitialized) {
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return Promise.resolve();
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@ -303,10 +303,10 @@ class EmbeddingsService {
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}
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async findSimilar(query: string, maxResults: number = 30, threshold: number = 0.3): Promise<SimilarityResult[]> {
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if (!this.config.enabled) {
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/*if (!this.config.enabled) {
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console.log('[EMBEDDINGS-SERVICE] Service disabled, returning empty results');
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return [];
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}
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}*/
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if (!this.isInitialized || this.embeddings.length === 0) {
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console.log('[EMBEDDINGS-SERVICE] Not initialized or no embeddings available');
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@ -349,16 +349,24 @@ class EmbeddingsService {
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}
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}
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isEnabled(): boolean {
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/*isEnabled(): boolean {
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return this.config.enabled;
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}
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}*/
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getStats(): { enabled: boolean; initialized: boolean; count: number } {
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/*getStats(): { enabled: boolean; initialized: boolean; count: number } {
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return {
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enabled: this.config.enabled,
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initialized: this.isInitialized,
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count: this.embeddings.length
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};
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}*/
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getStats(): {initialized: boolean; count: number } {
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return {
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//enabled: this.config.enabled,
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initialized: this.isInitialized,
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count: this.embeddings.length
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};
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}
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getConfig(): EmbeddingsConfig {
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@ -38,7 +38,7 @@ export interface SelectionContext {
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export interface ToolSelectionResult {
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selectedTools: any[];
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selectedConcepts: any[];
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selectionMethod: string;
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//selectionMethod: string;
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confidence: number;
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}
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@ -84,13 +84,13 @@ class ToolSelector {
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domains: any[];
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phases: any[];
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'domain-agnostic-software': any[];
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selectionMethod: string;
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//selectionMethod: string;
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}> {
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console.log('[TOOL-SELECTOR] Getting intelligent candidates for query');
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let candidateTools: any[] = [];
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let candidateConcepts: any[] = [];
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let selectionMethod = 'unknown';
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//let selectionMethod = 'unknown';
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context.embeddingsSimilarities.clear();
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@ -100,7 +100,6 @@ class ToolSelector {
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console.error('[TOOL-SELECTOR] Embeddings initialization failed:', error);
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}
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if (embeddingsService.isEnabled()) {
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console.log('[TOOL-SELECTOR] Using embeddings for candidate selection');
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|
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const similarItems = await embeddingsService.findSimilar(
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@ -134,28 +133,23 @@ class ToolSelector {
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if (similarTools.length >= this.config.embeddingsMinTools && reductionRatio <= this.config.embeddingsMaxReductionRatio) {
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candidateTools = similarTools;
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candidateConcepts = similarConcepts;
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selectionMethod = 'embeddings_candidates';
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//selectionMethod = 'embeddings_candidates';
|
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|
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console.log('[TOOL-SELECTOR] Using embeddings filtering:', totalAvailableTools, '→', similarTools.length, 'tools');
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} else {
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console.log('[TOOL-SELECTOR] Embeddings filtering insufficient, using full dataset');
|
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candidateTools = toolsData.tools;
|
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candidateConcepts = toolsData.concepts;
|
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selectionMethod = 'full_dataset';
|
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}
|
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} else {
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console.log('[TOOL-SELECTOR] Embeddings disabled, using full dataset');
|
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candidateTools = toolsData.tools;
|
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candidateConcepts = toolsData.concepts;
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selectionMethod = 'full_dataset';
|
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//selectionMethod = 'full_dataset';
|
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}
|
||||
|
||||
|
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const selection = await this.performAISelection(
|
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userQuery,
|
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candidateTools,
|
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candidateConcepts,
|
||||
mode,
|
||||
selectionMethod,
|
||||
//selectionMethod,
|
||||
context
|
||||
);
|
||||
|
||||
@ -165,7 +159,7 @@ class ToolSelector {
|
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domains: toolsData.domains,
|
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phases: toolsData.phases,
|
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'domain-agnostic-software': toolsData['domain-agnostic-software'],
|
||||
selectionMethod
|
||||
//selectionMethod
|
||||
};
|
||||
}
|
||||
|
||||
@ -174,7 +168,6 @@ class ToolSelector {
|
||||
candidateTools: any[],
|
||||
candidateConcepts: any[],
|
||||
mode: string,
|
||||
selectionMethod: string,
|
||||
context: SelectionContext
|
||||
): Promise<ToolSelectionResult> {
|
||||
console.log('[TOOL-SELECTOR] Performing AI selection');
|
||||
@ -188,53 +181,29 @@ class ToolSelector {
|
||||
const softwareWithFullData = candidateSoftware.map(this.createToolData);
|
||||
const conceptsWithFullData = candidateConcepts.map(this.createConceptData);
|
||||
|
||||
let toolsToSend: any[];
|
||||
let conceptsToSend: any[];
|
||||
// Unified selection limits (method-agnostic)
|
||||
const maxTools = Math.min(this.config.embeddingSelectionLimit, this.config.noEmbeddingsToolLimit);
|
||||
const maxConcepts = Math.min(this.config.embeddingConceptsLimit, this.config.noEmbeddingsConceptLimit);
|
||||
const methodLimit = Math.ceil(maxTools * this.config.methodSelectionRatio);
|
||||
const softwareLimit = Math.floor(maxTools * this.config.softwareSelectionRatio);
|
||||
|
||||
if (selectionMethod === 'embeddings_candidates') {
|
||||
const totalLimit = this.config.embeddingSelectionLimit;
|
||||
const methodLimit = Math.ceil(totalLimit * this.config.methodSelectionRatio);
|
||||
const softwareLimit = Math.floor(totalLimit * this.config.softwareSelectionRatio);
|
||||
|
||||
toolsToSend = [
|
||||
// Build tool list to send
|
||||
const toolsToSend: any[] = [
|
||||
...methodsWithFullData.slice(0, methodLimit),
|
||||
...softwareWithFullData.slice(0, softwareLimit)
|
||||
];
|
||||
|
||||
const remainingCapacity = totalLimit - toolsToSend.length;
|
||||
if (remainingCapacity > 0) {
|
||||
if (methodsWithFullData.length > methodLimit) {
|
||||
toolsToSend.push(...methodsWithFullData.slice(methodLimit, methodLimit + remainingCapacity));
|
||||
} else if (softwareWithFullData.length > softwareLimit) {
|
||||
toolsToSend.push(...softwareWithFullData.slice(softwareLimit, softwareLimit + remainingCapacity));
|
||||
}
|
||||
}
|
||||
|
||||
conceptsToSend = conceptsWithFullData.slice(0, this.config.embeddingConceptsLimit);
|
||||
} else {
|
||||
const maxTools = this.config.noEmbeddingsToolLimit;
|
||||
const maxConcepts = this.config.noEmbeddingsConceptLimit;
|
||||
const methodLimit = Math.ceil(maxTools * 0.4);
|
||||
const softwareLimit = Math.floor(maxTools * 0.5);
|
||||
|
||||
toolsToSend = [
|
||||
...methodsWithFullData.slice(0, methodLimit),
|
||||
...softwareWithFullData.slice(0, softwareLimit)
|
||||
...softwareWithFullData.slice(0, softwareLimit),
|
||||
];
|
||||
|
||||
const remainingCapacity = maxTools - toolsToSend.length;
|
||||
if (remainingCapacity > 0) {
|
||||
if (methodsWithFullData.length > methodLimit) {
|
||||
toolsToSend.push(...methodsWithFullData.slice(methodLimit, methodLimit + remainingCapacity));
|
||||
} else if (softwareWithFullData.length > softwareLimit) {
|
||||
toolsToSend.push(...softwareWithFullData.slice(softwareLimit, softwareLimit + remainingCapacity));
|
||||
}
|
||||
// Fill remainder from whichever bucket still has items
|
||||
const extraMethods = methodsWithFullData.slice(methodLimit, methodLimit + remainingCapacity);
|
||||
const extraSoftware = softwareWithFullData.slice(softwareLimit, softwareLimit + (remainingCapacity - extraMethods.length));
|
||||
toolsToSend.push(...extraMethods, ...extraSoftware);
|
||||
}
|
||||
|
||||
conceptsToSend = conceptsWithFullData.slice(0, maxConcepts);
|
||||
}
|
||||
const conceptsToSend = conceptsWithFullData.slice(0, maxConcepts);
|
||||
|
||||
const basePrompt = getPrompt('toolSelection', mode, userQuery, selectionMethod, this.config.maxSelectedItems);
|
||||
const basePrompt = getPrompt('toolSelection', mode, userQuery, this.config.maxSelectedItems);
|
||||
const prompt = getPrompt('toolSelectionWithData', basePrompt, toolsToSend, conceptsToSend);
|
||||
|
||||
aiService.validatePromptLength(prompt);
|
||||
@ -246,7 +215,7 @@ class ToolSelector {
|
||||
);
|
||||
|
||||
try {
|
||||
const response = await aiService.callAI(prompt, { maxTokens: 2500 });
|
||||
const response = await aiService.callAI(prompt, { maxTokens: 32768 });
|
||||
const result = JSONParser.safeParseJSON(response.content, null);
|
||||
|
||||
if (!result || !Array.isArray(result.selectedTools) || !Array.isArray(result.selectedConcepts)) {
|
||||
@ -275,21 +244,19 @@ class ToolSelector {
|
||||
|
||||
console.log('[TOOL-SELECTOR] AI selected:', selectedMethods.length, 'methods,', selectedSoftware.length, 'software,', selectedConcepts.length, 'concepts');
|
||||
|
||||
const confidence = confidenceScoring.calculateSelectionConfidence(result, candidateTools.length + candidateConcepts.length);
|
||||
|
||||
return {
|
||||
selectedTools,
|
||||
selectedConcepts,
|
||||
selectionMethod,
|
||||
confidence
|
||||
};
|
||||
const confidence = confidenceScoring.calculateSelectionConfidence(
|
||||
result,
|
||||
candidateTools.length + candidateConcepts.length
|
||||
);
|
||||
|
||||
return { selectedTools, selectedConcepts, confidence };
|
||||
} catch (error) {
|
||||
console.error('[TOOL-SELECTOR] AI selection failed:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
async selectToolsForPhase(
|
||||
userQuery: string,
|
||||
phase: any,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user