Beschreiben Sie Ihr forensisches Szenario und erhalten Sie maßgeschneiderte Workflow-Empfehlungen
@@ -169,16 +169,16 @@ const domainAgnosticSoftware = data['domain-agnostic-software'] || [];
- ${this.renderHeader('Empfohlener DFIR-Workflow', originalQuery)}
+ ${this.renderHeader('Untersuchungsansatz', originalQuery)}
${this.renderContextualAnalysis(recommendation, 'workflow')}
${this.renderBackgroundKnowledge(recommendation.background_knowledge)}
${this.renderWorkflowPhases(toolsByPhase, phaseOrder, phaseNames)}
@@ -721,7 +721,7 @@ class AIQueryInterface {
displayToolResults(recommendation, originalQuery) {
const html = `
- ${this.renderHeader('Passende Empfehlungen', originalQuery)}
+ ${this.renderHeader('Handlungsempfehlung', originalQuery)}
${this.renderContextualAnalysis(recommendation, 'tool')}
${this.renderBackgroundKnowledge(recommendation.background_knowledge)}
${this.renderToolRecommendations(recommendation.recommended_tools)}
diff --git a/src/styles/global.css b/src/styles/global.css
index ca7ceee..79d1dce 100644
--- a/src/styles/global.css
+++ b/src/styles/global.css
@@ -2015,6 +2015,7 @@ input[type="checkbox"] {
gap: 1rem;
max-width: 1200px;
margin: 0 auto;
+ margin-top: 1rem;
}
.phase-header {
diff --git a/src/utils/aiPipeline.ts b/src/utils/aiPipeline.ts
index 34dd1ee..5b14b4d 100644
--- a/src/utils/aiPipeline.ts
+++ b/src/utils/aiPipeline.ts
@@ -357,6 +357,17 @@ class ImprovedMicroTaskAIPipeline {
let candidateConcepts: any[] = [];
let selectionMethod = 'unknown';
+ // WAIT for embeddings initialization if embeddings are enabled
+ if (process.env.AI_EMBEDDINGS_ENABLED === 'true') {
+ try {
+ console.log('[AI PIPELINE] Waiting for embeddings initialization...');
+ await embeddingsService.waitForInitialization();
+ console.log('[AI PIPELINE] Embeddings ready, proceeding with similarity search');
+ } catch (error) {
+ console.error('[AI PIPELINE] Embeddings initialization failed, falling back to full dataset:', error);
+ }
+ }
+
if (embeddingsService.isEnabled()) {
const embeddingsStart = Date.now();
const similarItems = await embeddingsService.findSimilar(
@@ -425,7 +436,7 @@ class ImprovedMicroTaskAIPipeline {
);
}
} else {
- console.log(`[AI PIPELINE] Embeddings disabled, using full dataset`);
+ console.log(`[AI PIPELINE] Embeddings disabled or not ready, using full dataset`);
candidateTools = toolsData.tools;
candidateConcepts = toolsData.concepts;
selectionMethod = 'full_dataset';
diff --git a/src/utils/embeddings.ts b/src/utils/embeddings.ts
index b7bbbaa..d70d0ca 100644
--- a/src/utils/embeddings.ts
+++ b/src/utils/embeddings.ts
@@ -31,6 +31,7 @@ interface SimilarityResult extends EmbeddingData {
class EmbeddingsService {
private embeddings: EmbeddingData[] = [];
private isInitialized = false;
+ private initializationPromise: Promise | null = null; // ADD THIS LINE
private readonly embeddingsPath = path.join(process.cwd(), 'data', 'embeddings.json');
private readonly batchSize: number;
private readonly batchDelay: number;
@@ -42,7 +43,25 @@ class EmbeddingsService {
this.batchDelay = parseInt(process.env.AI_EMBEDDINGS_BATCH_DELAY_MS || '1000', 10);
}
+ // REPLACE the existing initialize method with this:
async initialize(): Promise {
+ // If initialization is already in progress, wait for it
+ if (this.initializationPromise) {
+ return this.initializationPromise;
+ }
+
+ // If already initialized, return immediately
+ if (this.isInitialized) {
+ return Promise.resolve();
+ }
+
+ // Start initialization and store the promise
+ this.initializationPromise = this.performInitialization();
+ return this.initializationPromise;
+ }
+
+ // ADD THIS NEW METHOD:
+ private async performInitialization(): Promise {
if (!this.enabled) {
console.log('[EMBEDDINGS] Embeddings disabled, skipping initialization');
return;
@@ -74,9 +93,29 @@ class EmbeddingsService {
} catch (error) {
console.error('[EMBEDDINGS] Failed to initialize:', error);
this.isInitialized = false;
+ throw error;
+ } finally {
+ this.initializationPromise = null;
}
}
+ async waitForInitialization(): Promise {
+ if (!this.enabled) {
+ return Promise.resolve();
+ }
+
+ if (this.isInitialized) {
+ return Promise.resolve();
+ }
+
+ if (this.initializationPromise) {
+ await this.initializationPromise;
+ return;
+ }
+
+ return this.initialize();
+ }
+
private hashData(data: any): string {
return Buffer.from(JSON.stringify(data)).toString('base64').slice(0, 32);
}
@@ -127,7 +166,6 @@ class EmbeddingsService {
'Content-Type': 'application/json'
};
- // API key is optional for Ollama but required for Mistral/OpenAI
if (apiKey) {
headers['Authorization'] = `Bearer ${apiKey}`;
}
@@ -148,12 +186,10 @@ class EmbeddingsService {
const data = await response.json();
- // Detect Ollama format
if (Array.isArray(data.embeddings)) {
return data.embeddings;
}
- // Detect OpenAI/Mistral format
if (Array.isArray(data.data)) {
return data.data.map((item: any) => item.embedding);
}
@@ -170,7 +206,6 @@ class EmbeddingsService {
const contents = allItems.map(item => this.createContentString(item));
this.embeddings = [];
- // Process in batches to respect rate limits
for (let i = 0; i < contents.length; i += this.batchSize) {
const batch = contents.slice(i, i + this.batchSize);
const batchItems = allItems.slice(i, i + this.batchSize);
@@ -198,7 +233,6 @@ class EmbeddingsService {
});
});
- // Rate limiting delay between batches
if (i + this.batchSize < contents.length) {
await new Promise(resolve => setTimeout(resolve, this.batchDelay));
}
@@ -213,7 +247,6 @@ class EmbeddingsService {
}
public async embedText(text: string): Promise {
- // Re‑use the private batch helper to avoid auth duplication
const [embedding] = await this.generateEmbeddingsBatch([text.toLowerCase()]);
return embedding;
}
@@ -239,25 +272,21 @@ class EmbeddingsService {
}
try {
- // Generate embedding for query
const queryEmbeddings = await this.generateEmbeddingsBatch([query.toLowerCase()]);
const queryEmbedding = queryEmbeddings[0];
console.log(`[EMBEDDINGS] Computing similarities for ${this.embeddings.length} items`);
- // Calculate similarities - properly typed
const similarities: SimilarityResult[] = this.embeddings.map(item => ({
...item,
similarity: this.cosineSimilarity(queryEmbedding, item.embedding)
}));
- // Filter by threshold and sort by similarity (descending - highest first)
const results = similarities
.filter(item => item.similarity >= threshold)
- .sort((a, b) => b.similarity - a.similarity) // CRITICAL: Ensure descending order
+ .sort((a, b) => b.similarity - a.similarity)
.slice(0, maxResults);
- // ENHANCED: Verify ordering is correct
const orderingValid = results.every((item, index) => {
if (index === 0) return true;
return item.similarity <= results[index - 1].similarity;
@@ -270,15 +299,13 @@ class EmbeddingsService {
});
}
- // ENHANCED: Log top results for debugging
console.log(`[EMBEDDINGS] Found ${results.length} similar items (threshold: ${threshold})`);
if (results.length > 0) {
- console.log('[EMBEDDINGS] Top 5 similarity matches:');
- results.slice(0, 5).forEach((item, idx) => {
+ console.log('[EMBEDDINGS] Top 10 similarity matches:');
+ results.slice(0, 10).forEach((item, idx) => {
console.log(` ${idx + 1}. ${item.name} (${item.type}) = ${item.similarity.toFixed(4)}`);
});
- // Verify first result is indeed the highest
const topSimilarity = results[0].similarity;
const hasHigherSimilarity = results.some(item => item.similarity > topSimilarity);
if (hasHigherSimilarity) {