fulldata
This commit is contained in:
overcuriousity
parent
2cb25d1dd6
commit
70fb012d63
@ -1594,6 +1594,7 @@ class AIQueryInterface {
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const exportData = {
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metadata: {
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timestamp: new Date().toISOString(),
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version: '2.0',
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toolsDataHash: toolsDataHash,
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aiModel: aiModel,
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aiParameters: aiParameters,
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@ -1607,8 +1608,7 @@ class AIQueryInterface {
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auditTrail: undefined
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},
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auditTrail: this.currentRecommendation.auditTrail || [],
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rawContext: rawContext,
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checksum: this.calculateDataChecksum(this.currentRecommendation)
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rawContext: rawContext
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};
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const blob = new Blob([JSON.stringify(exportData, null, 2)], {
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@ -1622,42 +1622,16 @@ class AIQueryInterface {
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a.click();
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URL.revokeObjectURL(url);
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console.log('[AI Interface] Analysis downloaded with enhanced structure:', {
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version: '1.1',
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console.log('[AI Interface] Analysis downloaded with verified hash structure:', {
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version: '2.0',
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aiModel,
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toolsDataHash: toolsDataHash.slice(0, 8) + '...',
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toolsDataHash: toolsDataHash.slice(0, 12) + '...',
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tokensUsed: aiParameters.totalTokensUsed,
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auditEntries: exportData.auditTrail.length,
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checksum: exportData.checksum.slice(0, 8) + '...'
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hashVerifiable: toolsDataHash !== 'unknown'
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});
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}
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calculateDataChecksum(data) {
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if (!data) return 'empty';
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try {
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const keyData = {
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recommendedToolsCount: data.recommended_tools?.length || 0,
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backgroundKnowledgeCount: data.background_knowledge?.length || 0,
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hasScenarioAnalysis: !!(data.scenario_analysis || data.problem_analysis),
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hasApproach: !!data.investigation_approach,
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processingTimeMs: data.processingStats?.processingTimeMs || 0
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};
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const dataString = JSON.stringify(keyData);
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let hash = 0;
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for (let i = 0; i < dataString.length; i++) {
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const char = dataString.charCodeAt(i);
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hash = ((hash << 5) - hash) + char;
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hash = hash & hash;
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}
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return Math.abs(hash).toString(36);
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} catch (error) {
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console.error('[AI Interface] Checksum calculation failed:', error);
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return 'error';
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}
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}
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validateUploadStructure(data) {
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try {
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const isValid = !!(
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@ -4,15 +4,12 @@ import { aiService } from './aiService.js';
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import { toolSelector, type SelectionContext } from './toolSelector.js';
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import { confidenceScoring, type AnalysisContext } from './confidenceScoring.js';
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import { embeddingsService } from './embeddings.js';
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import { auditService, type AuditEntry } from './auditService.js';
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import { auditService } from './auditService.js';
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import { JSONParser } from './jsonUtils.js';
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import { getPrompt } from '../config/prompts.js';
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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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maxPromptTokens: number;
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taskRelevanceModeration: {
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maxInitialScore: number;
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maxWithPhaseBonus: number;
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@ -36,7 +33,6 @@ 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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candidatesFromEmbeddings: number;
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finalSelectedItems: number;
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processingTimeMs: number;
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@ -57,7 +53,6 @@ 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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currentContextLength: number;
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scenarioAnalysis?: string;
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problemAnalysis?: string;
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@ -91,16 +86,12 @@ 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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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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maxWithPhaseBonus: 95,
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moderationThreshold: 80
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}
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};
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console.log('[AI-PIPELINE] Initialized with dynamic phase handling');
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}
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async processQuery(userQuery: string, mode: string): Promise<AnalysisResult> {
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@ -109,8 +100,6 @@ class AIPipeline {
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let failedTasks = 0;
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this.totalTokensUsed = 0;
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console.log('[AI-PIPELINE] Starting', mode, 'analysis pipeline');
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auditService.clearAuditTrail();
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try {
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@ -118,90 +107,102 @@ class AIPipeline {
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const aiConfig = aiService.getConfig();
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const toolsDataHash = getDataVersion?.() || 'unknown';
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// Record the tools.yaml version being used
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auditService.addEntry(
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'initialization',
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'tools-data-loaded',
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{
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toolsFile: 'tools.yaml',
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hashAlgorithm: 'SHA256'
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},
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{
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toolsDataHash: toolsDataHash,
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toolsCount: toolsData.tools.length,
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conceptsCount: toolsData.concepts.length,
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domainsCount: toolsData.domains.length,
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phasesCount: toolsData.phases.length
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},
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100,
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Date.now(),
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{
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toolsDataHash: toolsDataHash,
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verification: `Users can verify with: sha256sum src/data/tools.yaml`,
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dataVersion: toolsDataHash.slice(0, 12),
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reasoning: `Geladen: ${toolsData.tools.length} Tools, ${toolsData.concepts.length} Konzepte aus tools.yaml (Hash: ${toolsDataHash.slice(0, 12)}...)`
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}
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);
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const context: PipelineContext = {
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userQuery,
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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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currentContextLength: 0,
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seenToolNames: new Set<string>(),
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embeddingsSimilarities: new Map<string, number>(),
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phaseMetadata: this.initializePhaseMetadata(toolsData.phases)
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};
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console.log('[AI-PIPELINE] Phase 1: Tool candidate selection');
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const candidateSelectionStart = Date.now();
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const candidateData = await toolSelector.getIntelligentCandidates(userQuery, toolsData, mode, context);
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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.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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selectionConfidence,
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candidateSelectionStart,
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{
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//embeddingsUsed: embeddingsService.isEnabled(),
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toolsDataHash: toolsDataHash,
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totalCandidatesFound: candidateData.tools.length + candidateData.concepts.length,
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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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context.filteredData = candidateData;
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console.log('[AI-PIPELINE] Phase 2: Contextual analysis');
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const analysisResult = await this.analyzeScenario(context, startTime);
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const analysisResult = await this.analyzeScenario(context, startTime, toolsDataHash);
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if (analysisResult.success) completedTasks++; else failedTasks++;
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this.trackTokenUsage(analysisResult.aiUsage);
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await this.delay(this.config.microTaskDelay);
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const approachResult = await this.generateApproach(context, startTime);
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const approachResult = await this.generateApproach(context, startTime, toolsDataHash);
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if (approachResult.success) completedTasks++; else failedTasks++;
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this.trackTokenUsage(approachResult.aiUsage);
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await this.delay(this.config.microTaskDelay);
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const considerationsResult = await this.generateCriticalConsiderations(context, startTime);
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const considerationsResult = await this.generateCriticalConsiderations(context, startTime, toolsDataHash);
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if (considerationsResult.success) completedTasks++; else failedTasks++;
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this.trackTokenUsage(considerationsResult.aiUsage);
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await this.delay(this.config.microTaskDelay);
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console.log('[AI-PIPELINE] Phase 3: Tool-specific analysis');
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if (mode === 'workflow') {
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const workflowResults = await this.processWorkflowMode(context, toolsData, completedTasks, failedTasks, startTime);
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const workflowResults = await this.processWorkflowMode(context, toolsData, completedTasks, failedTasks, startTime, toolsDataHash);
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completedTasks = workflowResults.completed;
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failedTasks = workflowResults.failed;
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} else {
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const toolResults = await this.processToolMode(context, completedTasks, failedTasks, startTime);
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const toolResults = await this.processToolMode(context, completedTasks, failedTasks, startTime, toolsDataHash);
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completedTasks = toolResults.completed;
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failedTasks = toolResults.failed;
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}
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console.log('[AI-PIPELINE] Phase 4: Knowledge synthesis');
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const knowledgeResult = await this.selectBackgroundKnowledge(context, startTime);
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const knowledgeResult = await this.selectBackgroundKnowledge(context, startTime, toolsDataHash);
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if (knowledgeResult.success) completedTasks++; else failedTasks++;
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this.trackTokenUsage(knowledgeResult.aiUsage);
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await this.delay(this.config.microTaskDelay);
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const finalResult = await this.generateFinalRecommendations(context, startTime);
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const finalResult = await this.generateFinalRecommendations(context, startTime, toolsDataHash);
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if (finalResult.success) completedTasks++; else failedTasks++;
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this.trackTokenUsage(finalResult.aiUsage);
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const recommendation = this.buildRecommendation(context, mode, finalResult.content);
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const recommendation = this.buildRecommendation(context, mode, finalResult.content, toolsDataHash);
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const processingStats = {
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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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@ -216,16 +217,6 @@ class AIPipeline {
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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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mode,
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processingTimeMs: processingStats.processingTimeMs,
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completedTasks,
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failedTasks,
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finalItems: processingStats.finalSelectedItems,
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totalTokensUsed: this.totalTokensUsed,
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auditEntries: processingStats.auditEntriesGenerated
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});
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const finalAuditTrail = auditService.finalizeAuditTrail();
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return {
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@ -249,7 +240,6 @@ class AIPipeline {
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phaseComplexity: Map<string, number>;
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} {
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if (!phases || !Array.isArray(phases)) {
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console.warn('[AI-PIPELINE] No phases data available, using fallback');
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return {
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phases: [],
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criticalPhaseIds: [],
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@ -268,20 +258,12 @@ class AIPipeline {
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const phaseComplexity = new Map<string, number>();
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phases.forEach(phase => {
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let complexity = 1;
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if (phase.typical_tools?.length > 5) complexity += 1;
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if (phase.key_activities?.length > 3) complexity += 1;
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if (phase.description?.length > 100) complexity += 1;
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phaseComplexity.set(phase.id, complexity);
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});
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console.log('[AI-PIPELINE] Initialized phase metadata:', {
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totalPhases: phases.length,
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criticalPhases: criticalPhaseIds.length,
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avgComplexity: Array.from(phaseComplexity.values()).reduce((sum, c) => sum + c, 0) / phases.length
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});
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return {
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phases,
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criticalPhaseIds,
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@ -292,11 +274,9 @@ 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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conceptsCount: number
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): number {
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let confidence = 50;
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const selectionRatio = selectedCount / totalCount;
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if (selectionRatio >= 0.05 && selectionRatio <= 0.20) {
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@ -307,17 +287,8 @@ 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 (conceptsCount > 0) {
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confidence += 10;
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}
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if (selectedCount >= 8 && selectedCount <= 25) {
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confidence += 10;
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}
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if (conceptsCount > 0) confidence += 10;
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if (selectedCount >= 8 && selectedCount <= 25) confidence += 10;
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return Math.min(95, Math.max(40, confidence));
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}
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@ -327,7 +298,8 @@ class AIPipeline {
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toolsData: any,
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completedTasks: number,
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failedTasks: number,
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pipelineStart: number
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pipelineStart: number,
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toolsDataHash: string
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): Promise<{ completed: number; failed: number }> {
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const phases = toolsData.phases || [];
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@ -337,10 +309,7 @@ class AIPipeline {
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tool && tool.phases && Array.isArray(tool.phases) && tool.phases.includes(phase.id)
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);
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if (phaseTools.length === 0) {
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console.log(`[AI-PIPELINE] No tools available for phase: ${phase.id}`);
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continue;
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}
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if (phaseTools.length === 0) continue;
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const selections = await toolSelector.selectToolsForPhase(context.userQuery, phase, phaseTools, context);
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@ -370,6 +339,7 @@ class AIPipeline {
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phaseConfidence,
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phaseStart,
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{
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toolsDataHash: toolsDataHash,
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phaseId: phase.id,
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availableToolsCount: phaseTools.length,
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selectedToolsCount: selections.length,
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@ -383,7 +353,6 @@ class AIPipeline {
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if (tool) {
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const moderatedTaskRelevance = this.moderateTaskRelevance(sel.taskRelevance);
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const priority = this.derivePriorityFromScore(moderatedTaskRelevance);
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const dynamicLimitations = this.generateDynamicLimitations(tool, phase, sel);
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this.addToolToSelection(context, tool, phase.id, priority, sel.justification, moderatedTaskRelevance, dynamicLimitations);
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@ -405,6 +374,7 @@ class AIPipeline {
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moderatedTaskRelevance || 70,
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phaseStart,
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{
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toolsDataHash: toolsDataHash,
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toolType: tool.type,
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priority,
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moderationApplied: sel.taskRelevance !== moderatedTaskRelevance,
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@ -418,7 +388,7 @@ class AIPipeline {
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await this.delay(this.config.microTaskDelay);
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}
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const completionResult = await this.completeUnderrepresentedPhases(context, toolsData, pipelineStart);
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const completionResult = await this.completeUnderrepresentedPhases(context, toolsData, pipelineStart, toolsDataHash);
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completedTasks += completionResult.completed;
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failedTasks += completionResult.failed;
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@ -437,7 +407,6 @@ class AIPipeline {
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}
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): number {
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let confidence = 60;
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const isCritical = phaseMetadata?.criticalPhaseIds.includes(phaseId) || false;
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const phaseComplexity = phaseMetadata?.phaseComplexity.get(phaseId) || 1;
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@ -454,13 +423,8 @@ class AIPipeline {
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confidence += 10;
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}
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if (isCritical && selectedCount >= 2) {
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confidence += 10;
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}
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if (phaseComplexity > 2 && selectedCount >= phaseComplexity) {
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confidence += 5;
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}
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if (isCritical && selectedCount >= 2) confidence += 10;
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if (phaseComplexity > 2 && selectedCount >= phaseComplexity) confidence += 5;
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const avgRelevance = selections.length > 0 ?
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selections.reduce((sum, s) => sum + (s.taskRelevance || 70), 0) / selections.length : 0;
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@ -504,12 +468,13 @@ class AIPipeline {
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context: PipelineContext,
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completedTasks: number,
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failedTasks: number,
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pipelineStart: number
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pipelineStart: number,
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toolsDataHash: string
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): Promise<{ completed: number; failed: number }> {
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const topTools = context.filteredData.tools.slice(0, 3);
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for (let i = 0; i < topTools.length; i++) {
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const evaluationResult = await this.evaluateSpecificTool(context, topTools[i], i + 1, pipelineStart);
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const evaluationResult = await this.evaluateSpecificTool(context, topTools[i], i + 1, pipelineStart, toolsDataHash);
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if (evaluationResult.success) completedTasks++; else failedTasks++;
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this.trackTokenUsage(evaluationResult.aiUsage);
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await this.delay(this.config.microTaskDelay);
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@ -521,7 +486,8 @@ class AIPipeline {
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private async completeUnderrepresentedPhases(
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context: PipelineContext,
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toolsData: any,
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pipelineStart: number
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pipelineStart: number,
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toolsDataHash: string
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): Promise<{ completed: number; failed: number }> {
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const phases = toolsData.phases || [];
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const selectedPhases = new Map<string, number>();
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@ -538,15 +504,10 @@ class AIPipeline {
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return count <= 1;
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});
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if (underrepresentedPhases.length === 0) {
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console.log('[AI-PIPELINE] All phases adequately represented');
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return { completed: 0, failed: 0 };
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}
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console.log('[AI-PIPELINE] Completing underrepresented phases:', underrepresentedPhases.map((p: any) => p.id).join(', '));
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if (underrepresentedPhases.length === 0) return { completed: 0, failed: 0 };
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for (const phase of underrepresentedPhases) {
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const result = await this.completePhaseWithSemanticSearchAndAI(context, phase, toolsData, pipelineStart);
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const result = await this.completePhaseWithSemanticSearchAndAI(context, phase, toolsData, pipelineStart, toolsDataHash);
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if (result.success) completedTasks++; else failedTasks++;
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await this.delay(this.config.microTaskDelay);
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}
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@ -558,13 +519,12 @@ class AIPipeline {
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context: PipelineContext,
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phase: any,
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toolsData: any,
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pipelineStart: number
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pipelineStart: number,
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toolsDataHash: string
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): Promise<MicroTaskResult> {
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const phaseStart = Date.now();
|
||||
const phaseQuery = `forensic ${phase.name.toLowerCase()} tools methods`;
|
||||
|
||||
console.log('[AI-PIPELINE] Phase completion for:', phase.id);
|
||||
|
||||
try {
|
||||
const phaseResults = await embeddingsService.findSimilar(phaseQuery, 20, 0.2);
|
||||
|
||||
@ -574,6 +534,7 @@ class AIPipeline {
|
||||
0.2,
|
||||
phaseStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
phaseId: phase.id,
|
||||
phaseName: phase.name,
|
||||
completionPurpose: 'underrepresented-phase-enhancement'
|
||||
@ -581,7 +542,6 @@ class AIPipeline {
|
||||
);
|
||||
|
||||
if (phaseResults.length === 0) {
|
||||
console.log('[AI-PIPELINE] No semantic results for phase:', phase.id);
|
||||
return {
|
||||
taskType: 'phase-completion',
|
||||
content: '',
|
||||
@ -613,7 +573,6 @@ class AIPipeline {
|
||||
.slice(0, 2);
|
||||
|
||||
if (phaseTools.length === 0) {
|
||||
console.log('[AI-PIPELINE] No suitable tools for phase completion:', phase.id);
|
||||
return {
|
||||
taskType: 'phase-completion',
|
||||
content: '',
|
||||
@ -623,10 +582,9 @@ class AIPipeline {
|
||||
}
|
||||
|
||||
const selectionPrompt = getPrompt('generatePhaseCompletionPrompt', context.userQuery, phase, phaseTools, phaseConcepts);
|
||||
const selectionResult = await this.callMicroTaskAI(selectionPrompt, context, 800, 'phase-completion-selection');
|
||||
const selectionResult = await this.callMicroTaskAI(selectionPrompt, context, 'phase-completion-selection');
|
||||
|
||||
if (!selectionResult.success) {
|
||||
console.error('[AI-PIPELINE] Phase completion selection failed for:', phase.id);
|
||||
return {
|
||||
taskType: 'phase-completion',
|
||||
content: '',
|
||||
@ -648,7 +606,6 @@ class AIPipeline {
|
||||
.slice(0, 2);
|
||||
|
||||
if (validTools.length === 0) {
|
||||
console.log('[AI-PIPELINE] No valid tools selected for phase completion:', phase.id);
|
||||
return {
|
||||
taskType: 'phase-completion',
|
||||
content: selection.completionReasoning || '',
|
||||
@ -660,8 +617,6 @@ class AIPipeline {
|
||||
const actualToolsAdded = validTools.map(tool => tool.name);
|
||||
|
||||
for (const tool of validTools) {
|
||||
console.log('[AI-PIPELINE] Generating AI reasoning for phase completion tool:', tool.name);
|
||||
|
||||
const reasoningPrompt = getPrompt(
|
||||
'phaseCompletionReasoning',
|
||||
context.userQuery,
|
||||
@ -671,7 +626,7 @@ class AIPipeline {
|
||||
selection.completionReasoning || 'Nachergänzung zur Vervollständigung der Phasenabdeckung'
|
||||
);
|
||||
|
||||
const reasoningResult = await this.callMicroTaskAI(reasoningPrompt, context, 400, 'phase-completion-reasoning');
|
||||
const reasoningResult = await this.callMicroTaskAI(reasoningPrompt, context, 'phase-completion-reasoning');
|
||||
|
||||
let detailedJustification: string;
|
||||
let moderatedTaskRelevance = 75;
|
||||
@ -695,8 +650,6 @@ class AIPipeline {
|
||||
moderatedTaskRelevance,
|
||||
dynamicLimitations
|
||||
);
|
||||
|
||||
console.log('[AI-PIPELINE] Added phase completion tool with AI reasoning:', tool.name);
|
||||
}
|
||||
|
||||
auditService.addPhaseCompletion(
|
||||
@ -705,6 +658,7 @@ class AIPipeline {
|
||||
selection.completionReasoning || `${actualToolsAdded.length} Tools für ${phase.name} hinzugefügt`,
|
||||
phaseStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
toolsAdded: actualToolsAdded,
|
||||
toolType: validTools[0]?.type,
|
||||
semanticSimilarity: phaseResults.find(r => r.name === validTools[0]?.name)?.similarity,
|
||||
@ -723,8 +677,6 @@ class AIPipeline {
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
console.error('[AI-PIPELINE] Phase completion failed for:', phase.id, error);
|
||||
|
||||
return {
|
||||
taskType: 'phase-completion',
|
||||
content: '',
|
||||
@ -756,9 +708,7 @@ class AIPipeline {
|
||||
}
|
||||
|
||||
private moderateTaskRelevance(taskRelevance: number): number {
|
||||
if (typeof taskRelevance !== 'number') {
|
||||
return 70;
|
||||
}
|
||||
if (typeof taskRelevance !== 'number') return 70;
|
||||
|
||||
let moderated = Math.min(taskRelevance, this.config.taskRelevanceModeration.maxInitialScore);
|
||||
|
||||
@ -770,13 +720,12 @@ class AIPipeline {
|
||||
return Math.round(Math.min(moderated, this.config.taskRelevanceModeration.maxInitialScore));
|
||||
}
|
||||
|
||||
private async analyzeScenario(context: PipelineContext, pipelineStart: number): Promise<MicroTaskResult> {
|
||||
console.log('[AI-PIPELINE] Micro-task: Scenario analysis');
|
||||
private async analyzeScenario(context: PipelineContext, pipelineStart: number, toolsDataHash: string): Promise<MicroTaskResult> {
|
||||
const taskStart = Date.now();
|
||||
const isWorkflow = context.mode === 'workflow';
|
||||
const prompt = getPrompt('scenarioAnalysis', isWorkflow, context.userQuery);
|
||||
|
||||
const result = await this.callMicroTaskAI(prompt, context, 400, 'scenario-analysis');
|
||||
const result = await this.callMicroTaskAI(prompt, context, 'scenario-analysis');
|
||||
|
||||
if (result.success) {
|
||||
if (isWorkflow) {
|
||||
@ -801,6 +750,7 @@ class AIPipeline {
|
||||
`Analysierte ${isWorkflow ? 'Szenario' : 'Problem'} basierend auf Nutzereingabe: "${context.userQuery.slice(0, 100)}..." - Identifizierte Kernaspekte und Herausforderungen für forensische Untersuchung`,
|
||||
taskStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
microTaskType: 'scenario-analysis',
|
||||
analysisType: isWorkflow ? 'scenario' : 'problem',
|
||||
contentLength: result.content.length,
|
||||
@ -814,13 +764,12 @@ class AIPipeline {
|
||||
return result;
|
||||
}
|
||||
|
||||
private async generateApproach(context: PipelineContext, pipelineStart: number): Promise<MicroTaskResult> {
|
||||
console.log('[AI-PIPELINE] Micro-task: Investigation approach');
|
||||
private async generateApproach(context: PipelineContext, pipelineStart: number, toolsDataHash: string): Promise<MicroTaskResult> {
|
||||
const taskStart = Date.now();
|
||||
const isWorkflow = context.mode === 'workflow';
|
||||
const prompt = getPrompt('investigationApproach', isWorkflow, context.userQuery);
|
||||
|
||||
const result = await this.callMicroTaskAI(prompt, context, 400, 'investigation-approach');
|
||||
const result = await this.callMicroTaskAI(prompt, context, 'investigation-approach');
|
||||
|
||||
if (result.success) {
|
||||
context.investigationApproach = result.content;
|
||||
@ -840,6 +789,7 @@ class AIPipeline {
|
||||
`Entwickelte ${isWorkflow ? 'Untersuchungs' : 'Lösungs'}ansatz unter Berücksichtigung der Szenario-Analyse - Strukturierte Herangehensweise für forensische Methodik`,
|
||||
taskStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
microTaskType: 'investigation-approach',
|
||||
approachType: isWorkflow ? 'investigation' : 'solution',
|
||||
contentLength: result.content.length,
|
||||
@ -854,13 +804,12 @@ class AIPipeline {
|
||||
return result;
|
||||
}
|
||||
|
||||
private async generateCriticalConsiderations(context: PipelineContext, pipelineStart: number): Promise<MicroTaskResult> {
|
||||
console.log('[AI-PIPELINE] Micro-task: Critical considerations');
|
||||
private async generateCriticalConsiderations(context: PipelineContext, pipelineStart: number, toolsDataHash: string): Promise<MicroTaskResult> {
|
||||
const taskStart = Date.now();
|
||||
const isWorkflow = context.mode === 'workflow';
|
||||
const prompt = getPrompt('criticalConsiderations', isWorkflow, context.userQuery);
|
||||
|
||||
const result = await this.callMicroTaskAI(prompt, context, 350, 'critical-considerations');
|
||||
const result = await this.callMicroTaskAI(prompt, context, 'critical-considerations');
|
||||
|
||||
if (result.success) {
|
||||
context.criticalConsiderations = result.content;
|
||||
@ -880,6 +829,7 @@ class AIPipeline {
|
||||
'Identifizierte kritische Überlegungen für forensische Untersuchung - Berücksichtigung von Beweissicherung, Chain of Custody und methodischen Herausforderungen',
|
||||
taskStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
microTaskType: 'critical-considerations',
|
||||
contentLength: result.content.length,
|
||||
decisionBasis: 'ai-analysis',
|
||||
@ -896,9 +846,9 @@ class AIPipeline {
|
||||
context: PipelineContext,
|
||||
tool: any,
|
||||
rank: number,
|
||||
pipelineStart: number
|
||||
pipelineStart: number,
|
||||
toolsDataHash: string
|
||||
): Promise<MicroTaskResult> {
|
||||
console.log('[AI-PIPELINE] Micro-task: Tool evaluation for:', tool.name);
|
||||
const taskStart = Date.now();
|
||||
const existingSelection = context.selectedTools?.find((st: any) => st.tool && st.tool.name === tool.name);
|
||||
const originalTaskRelevance = existingSelection?.taskRelevance || 70;
|
||||
@ -906,7 +856,7 @@ class AIPipeline {
|
||||
const priority = this.derivePriorityFromScore(moderatedTaskRelevance);
|
||||
|
||||
const prompt = getPrompt('toolEvaluation', context.userQuery, tool, rank, moderatedTaskRelevance);
|
||||
const result = await this.callMicroTaskAI(prompt, context, 1000, 'tool-evaluation');
|
||||
const result = await this.callMicroTaskAI(prompt, context, 'tool-evaluation');
|
||||
|
||||
if (result.success) {
|
||||
const evaluation = JSONParser.safeParseJSON(result.content, {
|
||||
@ -942,6 +892,7 @@ class AIPipeline {
|
||||
`Bewertete Tool "${tool.name}" (Rang ${rank}) - Analysierte Eignung für spezifische Aufgabenstellung mit Fokus auf praktische Anwendbarkeit und methodische Integration`,
|
||||
taskStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
microTaskType: 'tool-evaluation',
|
||||
toolName: tool.name,
|
||||
toolType: tool.type,
|
||||
@ -964,8 +915,7 @@ class AIPipeline {
|
||||
return result;
|
||||
}
|
||||
|
||||
private async selectBackgroundKnowledge(context: PipelineContext, pipelineStart: number): Promise<MicroTaskResult> {
|
||||
console.log('[AI-PIPELINE] Micro-task: Background knowledge selection');
|
||||
private async selectBackgroundKnowledge(context: PipelineContext, pipelineStart: number, toolsDataHash: string): Promise<MicroTaskResult> {
|
||||
const taskStart = Date.now();
|
||||
const availableConcepts = context.filteredData.concepts;
|
||||
|
||||
@ -980,7 +930,7 @@ class AIPipeline {
|
||||
|
||||
const selectedToolNames = context.selectedTools?.map((st: any) => st.tool && st.tool.name).filter(Boolean) || [];
|
||||
const prompt = getPrompt('backgroundKnowledgeSelection', context.userQuery, context.mode, selectedToolNames, availableConcepts);
|
||||
const result = await this.callMicroTaskAI(prompt, context, 700, 'background-knowledge');
|
||||
const result = await this.callMicroTaskAI(prompt, context, 'background-knowledge');
|
||||
|
||||
if (result.success) {
|
||||
const selections = JSONParser.safeParseJSON(result.content, []);
|
||||
@ -1017,6 +967,7 @@ class AIPipeline {
|
||||
finalConfidence,
|
||||
taskStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
microTaskType: 'background-knowledge',
|
||||
availableConceptsCount: availableConcepts.length,
|
||||
selectedConceptsCount: context.backgroundKnowledge.length,
|
||||
@ -1041,32 +992,25 @@ class AIPipeline {
|
||||
): number {
|
||||
let bonus = 0;
|
||||
|
||||
if (selectedKnowledge.length > 0) {
|
||||
bonus += 10;
|
||||
}
|
||||
if (selectedKnowledge.length > 0) bonus += 10;
|
||||
|
||||
const ratio = selectedKnowledge.length / availableConcepts.length;
|
||||
if (ratio >= 0.1 && ratio <= 0.3) {
|
||||
bonus += 15;
|
||||
}
|
||||
if (ratio >= 0.1 && ratio <= 0.3) bonus += 15;
|
||||
|
||||
const hasGoodReasonings = selectedKnowledge.some(bk =>
|
||||
bk.relevance && bk.relevance.length > 30
|
||||
);
|
||||
if (hasGoodReasonings) {
|
||||
bonus += 10;
|
||||
}
|
||||
if (hasGoodReasonings) bonus += 10;
|
||||
|
||||
return bonus;
|
||||
}
|
||||
|
||||
private async generateFinalRecommendations(context: PipelineContext, pipelineStart: number): Promise<MicroTaskResult> {
|
||||
console.log('[AI-PIPELINE] Micro-task: Final recommendations');
|
||||
private async generateFinalRecommendations(context: PipelineContext, pipelineStart: number, toolsDataHash: string): Promise<MicroTaskResult> {
|
||||
const taskStart = Date.now();
|
||||
const selectedToolNames = context.selectedTools?.map((st: any) => st.tool && st.tool.name).filter(Boolean) || [];
|
||||
const prompt = getPrompt('finalRecommendations', context.mode === 'workflow', context.userQuery, selectedToolNames);
|
||||
|
||||
const result = await this.callMicroTaskAI(prompt, context, 350, 'final-recommendations');
|
||||
const result = await this.callMicroTaskAI(prompt, context, 'final-recommendations');
|
||||
|
||||
if (result.success) {
|
||||
const confidence = auditService.calculateAIResponseConfidence(
|
||||
@ -1086,6 +1030,7 @@ class AIPipeline {
|
||||
`Generierte abschließende ${context.mode}-Empfehlungen basierend auf ausgewählten ${selectedToolNames.length} Tools - Synthese aller Analyseschritte zu kohärenter Handlungsempfehlung`,
|
||||
taskStart,
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
microTaskType: 'final-recommendations',
|
||||
mode: context.mode,
|
||||
selectedToolsCount: selectedToolNames.length,
|
||||
@ -1105,30 +1050,17 @@ class AIPipeline {
|
||||
private calculateSynthesisBonus(selectedToolNames: string[], context: PipelineContext): number {
|
||||
let bonus = 0;
|
||||
|
||||
if (selectedToolNames.length >= 3) {
|
||||
bonus += 10;
|
||||
}
|
||||
|
||||
if (context.backgroundKnowledge && context.backgroundKnowledge.length > 0) {
|
||||
bonus += 10;
|
||||
}
|
||||
|
||||
if (context.scenarioAnalysis || context.problemAnalysis) {
|
||||
bonus += 5;
|
||||
}
|
||||
|
||||
if (context.investigationApproach) {
|
||||
bonus += 5;
|
||||
}
|
||||
if (selectedToolNames.length >= 3) bonus += 10;
|
||||
if (context.backgroundKnowledge && context.backgroundKnowledge.length > 0) bonus += 10;
|
||||
if (context.scenarioAnalysis || context.problemAnalysis) bonus += 5;
|
||||
if (context.investigationApproach) bonus += 5;
|
||||
|
||||
return bonus;
|
||||
}
|
||||
|
||||
private buildRecommendation(context: PipelineContext, mode: string, finalContent: string): any {
|
||||
private buildRecommendation(context: PipelineContext, mode: string, finalContent: string, toolsDataHash: string): any {
|
||||
const isWorkflow = mode === 'workflow';
|
||||
|
||||
console.log('[AI-PIPELINE] Building recommendation for', mode, 'mode with', context.selectedTools?.length || 0, 'tools');
|
||||
|
||||
const base = {
|
||||
[isWorkflow ? 'scenario_analysis' : 'problem_analysis']:
|
||||
isWorkflow ? context.scenarioAnalysis : context.problemAnalysis,
|
||||
@ -1161,6 +1093,7 @@ class AIPipeline {
|
||||
confidence,
|
||||
Date.now(),
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
phase: st.phase,
|
||||
priority: st.priority,
|
||||
toolType: st.tool.type,
|
||||
@ -1205,6 +1138,7 @@ class AIPipeline {
|
||||
confidence,
|
||||
Date.now(),
|
||||
{
|
||||
toolsDataHash: toolsDataHash,
|
||||
rank: st.tool.evaluation?.rank || 1,
|
||||
toolType: st.tool.type,
|
||||
moderatedTaskRelevance: st.taskRelevance
|
||||
@ -1237,7 +1171,6 @@ class AIPipeline {
|
||||
private async callMicroTaskAI(
|
||||
prompt: string,
|
||||
context: PipelineContext,
|
||||
maxTokens: number = 500,
|
||||
taskType: string = 'micro-task'
|
||||
): Promise<MicroTaskResult> {
|
||||
const startTime = Date.now();
|
||||
@ -1245,15 +1178,11 @@ class AIPipeline {
|
||||
let contextPrompt = prompt;
|
||||
if (context.contextHistory.length > 0) {
|
||||
const contextSection = `BISHERIGE ANALYSE:\n${context.contextHistory.join('\n\n')}\n\nAKTUELLE AUFGABE:\n`;
|
||||
const combinedPrompt = contextSection + prompt;
|
||||
|
||||
if (aiService.estimateTokens(combinedPrompt) <= this.config.maxPromptTokens) {
|
||||
contextPrompt = combinedPrompt;
|
||||
}
|
||||
contextPrompt = contextSection + prompt;
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await aiService.callMicroTaskAI(contextPrompt, maxTokens);
|
||||
const response = await aiService.callMicroTaskAI(contextPrompt);
|
||||
|
||||
return {
|
||||
taskType,
|
||||
@ -1280,13 +1209,11 @@ class AIPipeline {
|
||||
context.contextHistory.push(newEntry);
|
||||
context.currentContextLength += entryTokens;
|
||||
|
||||
/*while (context.currentContextLength > this.config.maxContextTokens && context.contextHistory.length > 1) {
|
||||
const removed = context.contextHistory.shift()!;
|
||||
context.currentContextLength -= aiService.estimateTokens(removed);
|
||||
}*/
|
||||
if (context.contextHistory.length > 1) {
|
||||
const removed = context.contextHistory.shift()!;
|
||||
context.currentContextLength -= aiService.estimateTokens(removed);
|
||||
}
|
||||
}
|
||||
|
||||
private addToolToSelection(
|
||||
context: PipelineContext,
|
||||
|
||||
@ -8,7 +8,6 @@ export interface AIServiceConfig {
|
||||
}
|
||||
|
||||
export interface AICallOptions {
|
||||
maxTokens?: number;
|
||||
temperature?: number;
|
||||
timeout?: number;
|
||||
}
|
||||
@ -34,9 +33,8 @@ class AIService {
|
||||
};
|
||||
|
||||
this.defaultOptions = {
|
||||
maxTokens: 32768,
|
||||
temperature: 0.3,
|
||||
timeout: 30000
|
||||
timeout: 60000
|
||||
};
|
||||
|
||||
console.log('[AI-SERVICE] Initialized with model:', this.config.model);
|
||||
@ -55,7 +53,6 @@ class AIService {
|
||||
|
||||
console.log('[AI-SERVICE] Making API call:', {
|
||||
promptLength: prompt.length,
|
||||
maxTokens: mergedOptions.maxTokens,
|
||||
temperature: mergedOptions.temperature
|
||||
});
|
||||
|
||||
@ -70,7 +67,6 @@ class AIService {
|
||||
const requestBody = {
|
||||
model: this.config.model,
|
||||
messages: [{ role: 'user', content: prompt }],
|
||||
max_tokens: mergedOptions.maxTokens,
|
||||
temperature: mergedOptions.temperature
|
||||
};
|
||||
|
||||
@ -122,11 +118,10 @@ class AIService {
|
||||
}
|
||||
}
|
||||
|
||||
async callMicroTaskAI(prompt: string, maxTokens: number = 500): Promise<AIResponse> {
|
||||
async callMicroTaskAI(prompt: string): Promise<AIResponse> {
|
||||
return this.callAI(prompt, {
|
||||
maxTokens,
|
||||
temperature: 0.3,
|
||||
timeout: 15000
|
||||
timeout: 30000
|
||||
});
|
||||
}
|
||||
|
||||
@ -134,14 +129,6 @@ class AIService {
|
||||
return Math.ceil(text.length / 4);
|
||||
}
|
||||
|
||||
validatePromptLength(prompt: string, maxTokens: number = 35000): void {
|
||||
const estimatedTokens = this.estimateTokens(prompt);
|
||||
if (estimatedTokens > maxTokens) {
|
||||
console.warn('[AI-SERVICE] WARNING: Prompt may exceed model limits:', estimatedTokens);
|
||||
throw new Error(`Prompt too long: ${estimatedTokens} tokens (max: ${maxTokens})`);
|
||||
}
|
||||
}
|
||||
|
||||
getConfig(): AIServiceConfig {
|
||||
return { ...this.config };
|
||||
}
|
||||
|
||||
@ -85,7 +85,7 @@ let cachedData: ToolsData | null = null;
|
||||
let cachedRandomizedData: ToolsData | null = null;
|
||||
let cachedCompressedData: EnhancedCompressedToolsData | null = null;
|
||||
let lastRandomizationDate: string | null = null;
|
||||
let dataVersion: string | null = null;
|
||||
let cachedToolsHash: string | null = null;
|
||||
|
||||
function seededRandom(seed: number): () => number {
|
||||
let x = Math.sin(seed) * 10000;
|
||||
@ -110,17 +110,6 @@ function shuffleArray<T>(array: T[], randomFn: () => number): T[] {
|
||||
return shuffled;
|
||||
}
|
||||
|
||||
function generateDataVersion(data: any): string {
|
||||
const str = JSON.stringify(data, Object.keys(data).sort());
|
||||
let hash = 0;
|
||||
for (let i = 0; i < str.length; i++) {
|
||||
const char = str.charCodeAt(i);
|
||||
hash = ((hash << 5) - hash) + char;
|
||||
hash = hash & hash;
|
||||
}
|
||||
return Math.abs(hash).toString(36);
|
||||
}
|
||||
|
||||
async function loadRawData(): Promise<ToolsData> {
|
||||
if (!cachedData) {
|
||||
const yamlPath = path.join(process.cwd(), 'src/data/tools.yaml');
|
||||
@ -142,8 +131,9 @@ async function loadRawData(): Promise<ToolsData> {
|
||||
};
|
||||
}
|
||||
|
||||
dataVersion = generateDataVersion(cachedData);
|
||||
console.log(`[DATA SERVICE] Loaded enhanced data version: ${dataVersion}`);
|
||||
const { getToolsFileHash } = await import('./hashUtils.js');
|
||||
cachedToolsHash = await getToolsFileHash();
|
||||
console.log(`[DATA SERVICE] Loaded data with hash: ${cachedToolsHash.slice(0, 12)}...`);
|
||||
|
||||
} catch (error) {
|
||||
if (error instanceof z.ZodError) {
|
||||
@ -234,7 +224,7 @@ export async function getCompressedToolsDataForAI(): Promise<EnhancedCompressedT
|
||||
}
|
||||
|
||||
export function getDataVersion(): string | null {
|
||||
return dataVersion;
|
||||
return cachedToolsHash;
|
||||
}
|
||||
|
||||
export function clearCache(): void {
|
||||
@ -242,7 +232,7 @@ export function clearCache(): void {
|
||||
cachedRandomizedData = null;
|
||||
cachedCompressedData = null;
|
||||
lastRandomizationDate = null;
|
||||
dataVersion = null;
|
||||
cachedToolsHash = null;
|
||||
|
||||
console.log('[DATA SERVICE] Enhanced cache cleared');
|
||||
}
|
||||
@ -100,7 +100,8 @@ class EmbeddingsService {
|
||||
await fs.mkdir(path.dirname(this.embeddingsPath), { recursive: true });
|
||||
|
||||
const toolsData = await getCompressedToolsDataForAI();
|
||||
const currentDataHash = await this.hashToolsFile();
|
||||
const { getToolsFileHash } = await import('./hashUtils.js');
|
||||
const currentDataHash = await getToolsFileHash();
|
||||
|
||||
const existing = await this.loadEmbeddings();
|
||||
|
||||
@ -129,12 +130,6 @@ class EmbeddingsService {
|
||||
}
|
||||
}
|
||||
|
||||
private async hashToolsFile(): Promise<string> {
|
||||
const file = path.join(process.cwd(), 'src', 'data', 'tools.yaml');
|
||||
const raw = await fs.readFile(file, 'utf8');
|
||||
return crypto.createHash('sha256').update(raw).digest('hex');
|
||||
}
|
||||
|
||||
private async loadEmbeddings(): Promise<EmbeddingsDatabase | null> {
|
||||
try {
|
||||
const data = await fs.readFile(this.embeddingsPath, 'utf8');
|
||||
|
||||
20
src/utils/hashUtils.ts
Normal file
20
src/utils/hashUtils.ts
Normal file
@ -0,0 +1,20 @@
|
||||
// src/utils/hashUtils.ts
|
||||
import { promises as fs } from 'fs';
|
||||
import path from 'path';
|
||||
import crypto from 'crypto';
|
||||
|
||||
export async function getToolsFileHash(): Promise<string> {
|
||||
const file = path.join(process.cwd(), 'src', 'data', 'tools.yaml');
|
||||
const raw = await fs.readFile(file, 'utf8');
|
||||
return crypto.createHash('sha256').update(raw).digest('hex');
|
||||
}
|
||||
|
||||
export function getToolsFileHashSync(): string | null {
|
||||
try {
|
||||
const file = path.join(process.cwd(), 'src', 'data', 'tools.yaml');
|
||||
const raw = require('fs').readFileSync(file, 'utf8');
|
||||
return crypto.createHash('sha256').update(raw).digest('hex');
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
@ -38,7 +38,6 @@ export interface SelectionContext {
|
||||
export interface ToolSelectionResult {
|
||||
selectedTools: any[];
|
||||
selectedConcepts: any[];
|
||||
//selectionMethod: string;
|
||||
confidence: number;
|
||||
}
|
||||
|
||||
@ -84,13 +83,11 @@ class ToolSelector {
|
||||
domains: any[];
|
||||
phases: any[];
|
||||
'domain-agnostic-software': any[];
|
||||
//selectionMethod: string;
|
||||
}> {
|
||||
console.log('[TOOL-SELECTOR] Getting intelligent candidates for query');
|
||||
|
||||
let candidateTools: any[] = [];
|
||||
let candidateConcepts: any[] = [];
|
||||
//let selectionMethod = 'unknown';
|
||||
|
||||
context.embeddingsSimilarities.clear();
|
||||
|
||||
@ -133,23 +130,19 @@ class ToolSelector {
|
||||
if (similarTools.length >= this.config.embeddingsMinTools && reductionRatio <= this.config.embeddingsMaxReductionRatio) {
|
||||
candidateTools = similarTools;
|
||||
candidateConcepts = similarConcepts;
|
||||
//selectionMethod = 'embeddings_candidates';
|
||||
|
||||
console.log('[TOOL-SELECTOR] Using embeddings filtering:', totalAvailableTools, '→', similarTools.length, 'tools');
|
||||
} else {
|
||||
console.log('[TOOL-SELECTOR] Embeddings filtering insufficient, using full dataset');
|
||||
candidateTools = toolsData.tools;
|
||||
candidateConcepts = toolsData.concepts;
|
||||
//selectionMethod = 'full_dataset';
|
||||
}
|
||||
|
||||
|
||||
const selection = await this.performAISelection(
|
||||
userQuery,
|
||||
candidateTools,
|
||||
candidateConcepts,
|
||||
mode,
|
||||
//selectionMethod,
|
||||
context
|
||||
);
|
||||
|
||||
@ -158,8 +151,7 @@ class ToolSelector {
|
||||
concepts: selection.selectedConcepts,
|
||||
domains: toolsData.domains,
|
||||
phases: toolsData.phases,
|
||||
'domain-agnostic-software': toolsData['domain-agnostic-software'],
|
||||
//selectionMethod
|
||||
'domain-agnostic-software': toolsData['domain-agnostic-software']
|
||||
};
|
||||
}
|
||||
|
||||
@ -206,8 +198,6 @@ class ToolSelector {
|
||||
const basePrompt = getPrompt('toolSelection', mode, userQuery, this.config.maxSelectedItems);
|
||||
const prompt = getPrompt('toolSelectionWithData', basePrompt, toolsToSend, conceptsToSend);
|
||||
|
||||
aiService.validatePromptLength(prompt);
|
||||
|
||||
console.log('[TOOL-SELECTOR] Sending to AI:',
|
||||
toolsToSend.filter((t: any) => t.type === 'method').length, 'methods,',
|
||||
toolsToSend.filter((t: any) => t.type === 'software').length, 'software,',
|
||||
@ -215,7 +205,7 @@ class ToolSelector {
|
||||
);
|
||||
|
||||
try {
|
||||
const response = await aiService.callAI(prompt, { maxTokens: 32768 });
|
||||
const response = await aiService.callAI(prompt);
|
||||
const result = JSONParser.safeParseJSON(response.content, null);
|
||||
|
||||
if (!result || !Array.isArray(result.selectedTools) || !Array.isArray(result.selectedConcepts)) {
|
||||
@ -256,7 +246,6 @@ class ToolSelector {
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
async selectToolsForPhase(
|
||||
userQuery: string,
|
||||
phase: any,
|
||||
@ -278,7 +267,7 @@ class ToolSelector {
|
||||
const prompt = getPrompt('phaseToolSelection', userQuery, phase, availableTools);
|
||||
|
||||
try {
|
||||
const response = await aiService.callMicroTaskAI(prompt, 1000);
|
||||
const response = await aiService.callMicroTaskAI(prompt);
|
||||
const selections = JSONParser.safeParseJSON(response.content, []);
|
||||
|
||||
if (Array.isArray(selections)) {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user