feat(ai): localize AI features
This commit is contained in:
Sepehr Ramezani
@@ -28,7 +28,7 @@ export class AutoLabelCreationService {
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* @param userId - User ID (for authorization)
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* @returns Suggested labels or null if not enough notes/no patterns found
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*/
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async suggestLabels(notebookId: string, userId: string): Promise<AutoLabelSuggestion | null> {
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async suggestLabels(notebookId: string, userId: string, language: string = 'en'): Promise<AutoLabelSuggestion | null> {
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// 1. Get notebook with existing labels
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const notebook = await prisma.notebook.findFirst({
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where: {
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@@ -84,7 +84,7 @@ export class AutoLabelCreationService {
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}
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// 2. Use AI to detect recurring themes
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const suggestions = await this.detectRecurringThemes(notes, notebook)
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const suggestions = await this.detectRecurringThemes(notes, notebook, language)
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return suggestions
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}
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@@ -94,13 +94,14 @@ export class AutoLabelCreationService {
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*/
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private async detectRecurringThemes(
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notes: any[],
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notebook: any
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notebook: any,
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language: string
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): Promise<AutoLabelSuggestion | null> {
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const existingLabelNames = new Set<string>(
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notebook.labels.map((l: any) => l.name.toLowerCase())
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)
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const prompt = this.buildPrompt(notes, existingLabelNames)
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const prompt = this.buildPrompt(notes, existingLabelNames, language)
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try {
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const config = await getSystemConfig()
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@@ -128,9 +129,9 @@ export class AutoLabelCreationService {
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}
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/**
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* Build prompt for AI (always in French - interface language)
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* Build prompt for AI (localized)
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*/
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private buildPrompt(notes: any[], existingLabelNames: Set<string>): string {
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private buildPrompt(notes: any[], existingLabelNames: Set<string>, language: string = 'en'): string {
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const notesSummary = notes
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.map((note, index) => {
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const title = note.title || 'Sans titre'
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@@ -141,7 +142,8 @@ export class AutoLabelCreationService {
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const existingLabels = Array.from(existingLabelNames).join(', ')
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return `
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const instructions: Record<string, string> = {
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fr: `
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Tu es un assistant qui détecte les thèmes récurrents dans des notes pour suggérer de nouvelles étiquettes.
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CARNET ANALYSÉ :
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@@ -182,7 +184,178 @@ Exemples de bonnes étiquettes :
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- "marie", "jean", "équipe" (personnes)
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Ta réponse (JSON seulement) :
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`.trim(),
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en: `
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You are an assistant that detects recurring themes in notes to suggest new labels.
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ANALYZED NOTEBOOK:
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${notes.length} notes
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EXISTING LABELS (do not suggest these):
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${existingLabels || 'None'}
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NOTEBOOK NOTES:
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${notesSummary}
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TASK:
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Analyze the notes and detect recurring themes (keywords, subjects, places, people).
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A theme must appear in at least 5 different notes to be suggested.
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RESPONSE FORMAT (JSON):
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{
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"labels": [
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{
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"nom": "label_name",
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"note_indices": [0, 5, 12, 23, 45],
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"confiance": 0.85
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}
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]
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}
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RULES:
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- Label name must be short (max 1-2 words)
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- A theme must appear in 5+ notes to be suggested
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- Confidence must be > 0.60
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- Do not suggest labels that already exist
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- Prioritize places, people, clear categories
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- Maximum 5 suggestions
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Examples of good labels:
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- "tokyo", "kyoto", "osaka" (places)
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- "hotels", "restaurants", "flights" (categories)
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- "mary", "john", "team" (people)
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Your response (JSON only):
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`.trim(),
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fa: `
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شما یک دستیار هستید که تمهای تکرارشونده در یادداشتها را برای پیشنهاد برچسبهای جدید شناسایی میکنید.
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دفترچه تحلیل شده:
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${notes.length} یادداشت
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برچسبهای موجود (اینها را پیشنهاد ندهید):
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${existingLabels || 'هیچ'}
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یادداشتهای دفترچه:
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${notesSummary}
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وظیفه:
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یادداشتها را تحلیل کنید و تمهای تکرارشونده (کلمات کلیدی، موضوعات، مکانها، افراد) را شناسایی کنید.
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یک تم باید حداقل در ۵ یادداشت مختلف ظاهر شود تا پیشنهاد داده شود.
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فرمت پاسخ (JSON):
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{
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"labels": [
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{
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"nom": "نام_برچسب",
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"note_indices": [0, 5, 12, 23, 45],
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"confiance": 0.85
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}
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]
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}
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قوانین:
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- نام برچسب باید کوتاه باشد (حداکثر ۱-۲ کلمه)
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- یک تم باید در ۵+ یادداشت ظاهر شود تا پیشنهاد داده شود
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- اطمینان باید > 0.60 باشد
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- برچسبهایی که قبلاً وجود دارند را پیشنهاد ندهید
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- اولویت با مکانها، افراد، دستهبندیهای واضح است
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- حداکثر ۵ پیشنهاد
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مثالهای برچسب خوب:
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- "توکیو"، "کیوتو"، "اوزاکا" (مکانها)
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- "هتلها"، "رستورانها"، "پروازها" (دستهبندیها)
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- "مریم"، "علی"، "تیم" (افراد)
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پاسخ شما (فقط JSON):
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`.trim(),
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es: `
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Eres un asistente que detecta temas recurrentes en notas para sugerir nuevas etiquetas.
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CUADERNO ANALIZADO:
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${notes.length} notas
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ETIQUETAS EXISTENTES (no sugerir estas):
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${existingLabels || 'Ninguna'}
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NOTAS DEL CUADERNO:
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${notesSummary}
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TAREA:
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Analiza las notas y detecta temas recurrentes (palabras clave, temas, lugares, personas).
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Un tema debe aparecer en al menos 5 notas diferentes para ser sugerido.
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FORMATO DE RESPUESTA (JSON):
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{
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"labels": [
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{
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"nom": "nombre_etiqueta",
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"note_indices": [0, 5, 12, 23, 45],
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"confiance": 0.85
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}
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]
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}
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REGLAS:
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- El nombre de la etiqueta debe ser corto (máx 1-2 palabras)
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- Un tema debe aparecer en 5+ notas para ser sugerido
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- La confianza debe ser > 0.60
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- No sugieras etiquetas que ya existen
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- Prioriza lugares, personas, categorías claras
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- Máximo 5 sugerencias
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Ejemplos de buenas etiquetas:
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- "tokio", "kyoto", "osaka" (lugares)
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- "hoteles", "restaurantes", "vuelos" (categorías)
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- "maría", "juan", "equipo" (personas)
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Tu respuesta (solo JSON):
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`.trim(),
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de: `
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Du bist ein Assistent, der wiederkehrende Themen in Notizen erkennt, um neue Labels vorzuschlagen.
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ANALYSIERTES NOTIZBUCH:
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${notes.length} Notizen
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VORHANDENE LABELS (schlage diese nicht vor):
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${existingLabels || 'Keine'}
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NOTIZBUCH-NOTIZEN:
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${notesSummary}
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AUFGABE:
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Analysiere die Notizen und erkenne wiederkehrende Themen (Schlüsselwörter, Themen, Orte, Personen).
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Ein Thema muss in mindestens 5 verschiedenen Notizen erscheinen, um vorgeschlagen zu werden.
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ANTWORTFORMAT (JSON):
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{
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"labels": [
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{
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"nom": "label_name",
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"note_indices": [0, 5, 12, 23, 45],
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"confiance": 0.85
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}
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]
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}
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REGELN:
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- Der Labelname muss kurz sein (max 1-2 Wörter)
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- Ein Thema muss in 5+ Notizen erscheinen, um vorgeschlagen zu werden
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- Konfidenz muss > 0.60 sein
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- Schlage keine Labels vor, die bereits existieren
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- Priorisiere Orte, Personen, klare Kategorien
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- Maximal 5 Vorschläge
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Beispiele für gute Labels:
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- "tokio", "kyoto", "osaka" (Orte)
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- "hotels", "restaurants", "flüge" (Kategorien)
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- "maria", "johannes", "team" (Personen)
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Deine Antwort (nur JSON):
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`.trim()
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}
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return instructions[language] || instructions['en'] || instructions['fr']
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}
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/**
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