fix: Restore all files lost during destructive rebase
A previous `git pull --rebase origin main` dropped 177 local commits,
losing 3400+ files across admin-v2, backend, studio-v2, website,
klausur-service, and many other services. The partial restore attempt
(660295e2) only recovered some files.
This commit restores all missing files from pre-rebase ref 98933f5e
while preserving post-rebase additions (night-scheduler, night-mode UI,
NightModeWidget dashboard integration).
Restored features include:
- AI Module Sidebar (FAB), OCR Labeling, OCR Compare
- GPU Dashboard, RAG Pipeline, Magic Help
- Klausur-Korrektur (8 files), Abitur-Archiv (5+ files)
- Companion, Zeugnisse-Crawler, Screen Flow
- Full backend, studio-v2, website, klausur-service
- All compliance SDKs, agent-core, voice-service
- CI/CD configs, documentation, scripts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
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ai-content-generator/README.md
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ai-content-generator/README.md
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# AI Content Generator Service
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Automatische Generierung von H5P-Lerninhalten mit Claude AI und YouTube-Integration.
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## Übersicht
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Der AI Content Generator analysiert hochgeladene Lernmaterialien und generiert automatisch alle 8 H5P-Content-Typen:
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1. **Quiz** - Multiple Choice Fragen
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2. **Interactive Video** - YouTube-Videos mit Interaktionen
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3. **Course Presentation** - Präsentationsfolien
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4. **Flashcards** - Lernkarten
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5. **Timeline** - Chronologische Darstellungen
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6. **Drag and Drop** - Zuordnungsaufgaben
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7. **Fill in the Blanks** - Lückentexte
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8. **Memory Game** - Memory-Paare
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## Funktionen
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### Material-Analyse
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- **PDF**: Textextraktion aus mehrseitigen PDFs
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- **Images**: OCR-Texterkennung (Tesseract)
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- **DOCX**: Word-Dokument Analyse
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- **Text**: Plain-Text Dateien
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### YouTube-Integration
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- Automatische Video-Suche (optional)
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- Transkript-Analyse (deutsch/englisch)
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- KI-generierte Interaktionen mit Zeitstempeln
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### Claude AI Integration
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- Altersgerechte Content-Generierung
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- Basiert auf hochgeladenen Materialien
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- JSON-strukturierte Ausgaben
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## Installation
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### Lokale Entwicklung
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```bash
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cd ai-content-generator
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# Virtual Environment erstellen
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python3 -m venv venv
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source venv/bin/activate
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# Dependencies installieren
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pip install -r requirements.txt
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# Tesseract OCR installieren (für Image-Analyse)
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# macOS:
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brew install tesseract tesseract-lang
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# Ubuntu/Debian:
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sudo apt-get install tesseract-ocr tesseract-ocr-deu
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```
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### Umgebungsvariablen
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Kopiere `.env.example` zu `.env`:
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```bash
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cp .env.example .env
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```
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Setze deinen Anthropic API Key:
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```env
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ANTHROPIC_API_KEY=your-api-key-here
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```
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### Docker
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```bash
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# Content Services starten (inkl. AI Generator)
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docker-compose -f docker-compose.content.yml up -d
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# Nur AI Generator neu bauen
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docker-compose -f docker-compose.content.yml up -d --build ai-content-generator
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```
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## API Endpoints
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### Health Check
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```bash
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GET /health
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```
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### Content Generierung starten
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```bash
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POST /api/generate-content
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Content-Type: multipart/form-data
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# Form Data:
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- topic: string (z.B. "Das Auge")
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- description: string (optional)
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- target_grade: string (z.B. "5-6", "7-8")
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- materials: File[] (PDFs, Images, DOCX)
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```
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**Beispiel:**
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```bash
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curl -X POST http://localhost:8004/api/generate-content \
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-F "topic=Das Auge" \
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-F "description=Biologie Thema für Klasse 7" \
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-F "target_grade=7-8" \
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-F "materials=@auge_skizze.pdf" \
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-F "materials=@auge_text.docx"
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```
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**Response:**
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```json
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{
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"job_id": "550e8400-e29b-41d4-a716-446655440000",
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"status": "pending",
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"message": "Content generation started"
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}
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```
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### Generation Status prüfen
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```bash
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GET /api/generation-status/{job_id}
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```
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**Response:**
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```json
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{
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"job_id": "550e8400-e29b-41d4-a716-446655440000",
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"status": "processing",
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"progress": 50,
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"current_step": "Generating Quiz questions..."
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}
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```
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### Generierte Inhalte abrufen
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```bash
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GET /api/generated-content/{job_id}
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```
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**Response:**
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```json
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{
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"topic": "Das Auge",
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"target_grade": "7-8",
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"generated_at": "2025-01-15T10:30:00Z",
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"content_types": {
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"quiz": {
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"type": "quiz",
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"title": "Quiz: Das Auge",
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"questions": [...]
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},
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"interactive_video": {
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"type": "interactive-video",
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"videoUrl": "https://youtube.com/watch?v=...",
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"interactions": [...]
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},
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"flashcards": {...},
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"timeline": {...},
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"drag_drop": {...},
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"fill_blanks": {...},
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"memory": {...},
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"course_presentation": {...}
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}
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}
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```
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### YouTube Video-Suche
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```bash
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POST /api/youtube-search
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Content-Type: application/json
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{
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"query": "Das Auge Biologie",
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"max_results": 5
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}
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```
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## Content-Typen Struktur
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### Quiz
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```json
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{
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"type": "quiz",
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"title": "Quiz: Das Auge",
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"questions": [
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{
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"question": "Was ist die Funktion der Pupille?",
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"options": ["A", "B", "C", "D"],
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"correct_answer": 0,
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"explanation": "..."
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}
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]
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}
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```
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### Interactive Video
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```json
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{
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"type": "interactive-video",
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"videoUrl": "https://youtube.com/watch?v=xyz",
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"interactions": [
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{
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"time": "01:30",
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"seconds": 90,
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"type": "question",
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"title": "Verständnisfrage",
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"content": "Was wurde gerade erklärt?"
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}
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]
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}
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```
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### Flashcards
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```json
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{
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"type": "flashcards",
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"cards": [
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{
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"id": 1,
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"front": "Begriff",
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"back": "Definition"
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}
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]
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}
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```
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### Timeline
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```json
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{
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"type": "timeline",
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"events": [
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{
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"id": 1,
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"year": "1800",
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"title": "Ereignis",
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"description": "..."
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}
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]
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}
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```
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### Drag and Drop
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```json
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{
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"type": "drag-drop",
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"title": "Zuordnung",
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"zones": [
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{"id": 1, "name": "Kategorie 1"}
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],
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"draggables": [
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{"id": 1, "text": "Element", "correctZoneId": 1}
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]
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}
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```
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### Fill in the Blanks
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```json
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{
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"type": "fill-blanks",
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"title": "Lückentext",
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"text": "Das Auge hat eine *Linse* und eine *Netzhaut*.",
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"hints": "Tipps..."
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}
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```
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### Memory Game
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```json
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{
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"type": "memory",
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"pairs": [
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{
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"id": 1,
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"card1": "Begriff 1",
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"card2": "Zugehöriger Begriff"
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}
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]
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}
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```
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### Course Presentation
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```json
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{
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"type": "course-presentation",
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"slides": [
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{
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"id": 1,
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"title": "Folie 1",
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"content": "...",
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"backgroundColor": "#ffffff"
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}
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]
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}
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```
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## Technologie-Stack
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- **FastAPI**: Web Framework
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- **Anthropic Claude**: AI Content Generation
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- **youtube-transcript-api**: YouTube Transkript-Analyse
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- **PyPDF2**: PDF-Verarbeitung
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- **Pillow + Tesseract**: OCR für Bilder
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- **python-docx**: Word-Dokument Verarbeitung
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## Entwicklung
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### Tests ausführen
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```bash
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# Unit Tests (TODO)
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pytest tests/
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# Integration Tests (TODO)
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pytest tests/integration/
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```
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### Service lokal starten
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```bash
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source venv/bin/activate
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uvicorn app.main:app --reload --port 8004
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```
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API-Dokumentation: http://localhost:8004/docs
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## Limitierungen
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- **YouTube API**: Ohne API Key wird Fallback verwendet
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- **OCR**: Erfordert Tesseract Installation
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- **Anthropic API**: Kostenpflichtig (Claude API Key erforderlich)
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- **Job Store**: In-Memory (TODO: Redis Backend)
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## TODO
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- [ ] Redis Backend für Job Store
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- [ ] Celery für Background Tasks
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- [ ] Rate Limiting
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- [ ] Unit & Integration Tests
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- [ ] API Authentication
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- [ ] Webhook Notifications
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- [ ] Batch Processing
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- [ ] Content Quality Validation
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## Support
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Bei Fragen oder Problemen:
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- GitHub Issues
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- Dokumentation: `/docs/ai-content-generator/`
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