Initial commit: breakpilot-core - Shared Infrastructure
Docker Compose with 24+ services: - PostgreSQL (PostGIS), Valkey, MinIO, Qdrant - Vault (PKI/TLS), Nginx (Reverse Proxy) - Backend Core API, Consent Service, Billing Service - RAG Service, Embedding Service - Gitea, Woodpecker CI/CD - Night Scheduler, Health Aggregator - Jitsi (Web/XMPP/JVB/Jicofo), Mailpit Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Boenisch
353
docs-src/services/ki-daten-pipeline/architecture.md
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353
docs-src/services/ki-daten-pipeline/architecture.md
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# KI-Daten-Pipeline Architektur
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Diese Seite dokumentiert die technische Architektur der KI-Daten-Pipeline im Detail.
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## Systemuebersicht
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```mermaid
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graph TB
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subgraph Users["Benutzer"]
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U1[Entwickler]
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U2[Data Scientists]
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U3[Lehrer]
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end
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subgraph Frontend["Frontend (admin-v2)"]
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direction TB
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F1["OCR-Labeling<br/>/ai/ocr-labeling"]
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F2["RAG Pipeline<br/>/ai/rag-pipeline"]
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F3["Daten & RAG<br/>/ai/rag"]
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F4["Klausur-Korrektur<br/>/ai/klausur-korrektur"]
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end
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subgraph Backend["Backend Services"]
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direction TB
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B1["klausur-service<br/>Port 8086"]
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B2["embedding-service<br/>Port 8087"]
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end
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|
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subgraph Storage["Persistenz"]
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direction TB
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D1[(PostgreSQL<br/>Metadaten)]
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D2[(Qdrant<br/>Vektoren)]
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D3[(MinIO<br/>Bilder/PDFs)]
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end
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subgraph External["Externe APIs"]
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E1[OpenAI API]
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E2[Ollama]
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end
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U1 --> F1
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U2 --> F2
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U3 --> F4
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|
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F1 --> B1
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F2 --> B1
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F3 --> B1
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F4 --> B1
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|
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B1 --> D1
|
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B1 --> D2
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B1 --> D3
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B1 --> B2
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B2 --> E1
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B1 --> E2
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```
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## Komponenten-Details
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### OCR-Labeling Modul
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|
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```mermaid
|
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flowchart TB
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subgraph Upload["Upload-Prozess"]
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U1[Bilder hochladen] --> U2[MinIO speichern]
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U2 --> U3[Session erstellen]
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end
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|
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subgraph OCR["OCR-Verarbeitung"]
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O1[Bild laden] --> O2{Modell wählen}
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O2 -->|llama3.2-vision| O3a[Vision LLM]
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O2 -->|trocr| O3b[Transformer]
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O2 -->|paddleocr| O3c[PaddleOCR]
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O2 -->|donut| O3d[Document AI]
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O3a --> O4[OCR-Text]
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O3b --> O4
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O3c --> O4
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O3d --> O4
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end
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|
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subgraph Labeling["Labeling-Prozess"]
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L1[Queue laden] --> L2[Item anzeigen]
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L2 --> L3{Entscheidung}
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L3 -->|korrekt| L4[Bestaetigen]
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L3 -->|falsch| L5[Korrigieren]
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L3 -->|unklar| L6[Ueberspringen]
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L4 --> L7[PostgreSQL]
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L5 --> L7
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L6 --> L7
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||||
end
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subgraph Export["Export"]
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E1[Gelabelte Items] --> E2{Format}
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E2 -->|TrOCR| E3a[Transformer Format]
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E2 -->|Llama| E3b[Vision Format]
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E2 -->|Generic| E3c[JSON]
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end
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Upload --> OCR
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OCR --> Labeling
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Labeling --> Export
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```
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|
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### RAG Pipeline Modul
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|
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```mermaid
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flowchart TB
|
||||
subgraph Sources["Datenquellen"]
|
||||
S1[NiBiS PDFs]
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S2[Uploads]
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S3[Rechtskorpus]
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S4[Schulordnungen]
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end
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||||
|
||||
subgraph Processing["Verarbeitung"]
|
||||
direction TB
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P1[PDF Parser] --> P2[OCR falls noetig]
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P2 --> P3[Text Cleaning]
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P3 --> P4[Chunking<br/>1000 chars, 200 overlap]
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P4 --> P5[Metadata Extraction]
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end
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||||
|
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subgraph Embedding["Embedding"]
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E1[embedding-service] --> E2[OpenAI API]
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E2 --> E3[1536-dim Vektor]
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end
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subgraph Indexing["Indexierung"]
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I1{Collection waehlen}
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I1 -->|EH| I2a[bp_nibis_eh]
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I1 -->|Custom| I2b[bp_eh]
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I1 -->|Legal| I2c[bp_legal_corpus]
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I1 -->|Schul| I2d[bp_schulordnungen]
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I2a --> I3[Qdrant upsert]
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I2b --> I3
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I2c --> I3
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I2d --> I3
|
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end
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||||
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Sources --> Processing
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Processing --> Embedding
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Embedding --> Indexing
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```
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### Daten & RAG Modul
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```mermaid
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flowchart TB
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subgraph Query["Suchanfrage"]
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Q1[User Query] --> Q2[Query Embedding]
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Q2 --> Q3[1536-dim Vektor]
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end
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|
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subgraph Search["Qdrant Suche"]
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S1[Collection waehlen] --> S2[Vector Search]
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S2 --> S3[Top-k Results]
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S3 --> S4[Score Filtering]
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end
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subgraph Results["Ergebnisse"]
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R1[Chunks] --> R2[Metadata anreichern]
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R2 --> R3[Source URLs]
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R3 --> R4[Response]
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end
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Query --> Search
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Search --> Results
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```
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## Datenmodelle
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### OCR-Labeling
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```typescript
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interface OCRSession {
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id: string
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name: string
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source_type: 'klausur' | 'handwriting_sample' | 'scan'
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ocr_model: 'llama3.2-vision:11b' | 'trocr' | 'paddleocr' | 'donut'
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total_items: number
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labeled_items: number
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status: 'active' | 'completed' | 'archived'
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created_at: string
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}
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interface OCRItem {
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id: string
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session_id: string
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image_path: string
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ocr_text: string | null
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ocr_confidence: number | null
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||||
ground_truth: string | null
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||||
status: 'pending' | 'confirmed' | 'corrected' | 'skipped'
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||||
label_time_seconds: number | null
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}
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||||
```
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||||
|
||||
### RAG Pipeline
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||||
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||||
```typescript
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interface TrainingJob {
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id: string
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name: string
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status: 'queued' | 'preparing' | 'training' | 'validating' | 'completed' | 'failed' | 'paused'
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progress: number
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current_epoch: number
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total_epochs: number
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documents_processed: number
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total_documents: number
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config: {
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batch_size: number
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bundeslaender: string[]
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mixed_precision: boolean
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}
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}
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interface DataSource {
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id: string
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name: string
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collection: string
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document_count: number
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chunk_count: number
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status: 'active' | 'pending' | 'error'
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last_updated: string | null
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||||
}
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```
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### Legal Corpus
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```typescript
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interface RegulationStatus {
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code: string
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name: string
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fullName: string
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type: 'eu_regulation' | 'eu_directive' | 'de_law' | 'bsi_standard'
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||||
chunkCount: number
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||||
status: 'ready' | 'empty' | 'error'
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}
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|
||||
interface SearchResult {
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text: string
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regulation_code: string
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regulation_name: string
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article: string | null
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paragraph: string | null
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source_url: string
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score: number
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||||
}
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||||
```
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## Qdrant Collections
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### Konfiguration
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| Collection | Vektor-Dimension | Distanz-Metrik | Payload |
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|------------|-----------------|----------------|---------|
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| `bp_nibis_eh` | 1536 | COSINE | bundesland, fach, aufgabe |
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| `bp_eh` | 1536 | COSINE | user_id, klausur_id |
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| `bp_legal_corpus` | 1536 | COSINE | regulation, article, source_url |
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| `bp_schulordnungen` | 1536 | COSINE | bundesland, typ, datum |
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### Chunk-Strategie
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Originaldokument │
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│ Lorem ipsum dolor sit amet, consectetur adipiscing elit... │
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└─────────────────────────────────────────────────────────────┘
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│
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▼
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┌──────────────────────┐ ┌──────────────────────┐ ┌──────────────────────┐
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│ Chunk 1 │ │ Chunk 2 │ │ Chunk 3 │
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│ 0-1000 chars │ │ 800-1800 chars │ │ 1600-2600 chars │
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│ │ │ (200 overlap) │ │ (200 overlap) │
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└──────────────────────┘ └──────────────────────┘ └──────────────────────┘
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```
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## API-Authentifizierung
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Alle Endpunkte nutzen die zentrale Auth-Middleware:
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```mermaid
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sequenceDiagram
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participant C as Client
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participant A as API Gateway
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participant S as klausur-service
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participant D as Datenbank
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C->>A: Request + JWT Token
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A->>A: Token validieren
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A->>S: Forwarded Request
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S->>D: Daten abfragen
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D->>S: Response
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S->>C: JSON Response
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```
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## Monitoring & Metriken
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### Verfuegbare Metriken
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| Metrik | Beschreibung | Endpoint |
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|--------|--------------|----------|
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| `ocr_items_total` | Gesamtzahl OCR-Items | `/api/v1/ocr-label/stats` |
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| `ocr_accuracy_rate` | OCR-Genauigkeit | `/api/v1/ocr-label/stats` |
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| `rag_chunk_count` | Anzahl indexierter Chunks | `/api/legal-corpus/status` |
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| `rag_collection_status` | Collection-Status | `/api/legal-corpus/status` |
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### Logging
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```python
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# Strukturiertes Logging im klausur-service
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logger.info("OCR processing started", extra={
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"session_id": session_id,
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"item_count": item_count,
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"model": ocr_model
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})
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```
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## Fehlerbehandlung
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### Retry-Strategien
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|
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| Operation | Max Retries | Backoff |
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|-----------|-------------|---------|
|
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| OCR-Verarbeitung | 3 | Exponentiell (1s, 2s, 4s) |
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| Embedding-API | 5 | Exponentiell mit Jitter |
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| Qdrant-Upsert | 3 | Linear (1s) |
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|
||||
### Fallback-Verhalten
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```mermaid
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flowchart TD
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||||
A[Embedding Request] --> B{OpenAI verfuegbar?}
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B -->|Ja| C[OpenAI API]
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B -->|Nein| D{Lokales Modell?}
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D -->|Ja| E[Ollama Embedding]
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D -->|Nein| F[Error + Queue]
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```
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## Skalierung
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||||
|
||||
### Aktueller Stand
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||||
|
||||
- **Single Node**: Alle Services auf Mac Mini
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- **Qdrant**: Standalone, ~50k Chunks
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- **PostgreSQL**: Shared mit anderen Services
|
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|
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### Geplante Erweiterungen
|
||||
|
||||
1. **Qdrant Cluster**: Bei > 1M Chunks
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||||
2. **Worker Queue**: Redis-basiert fuer Batch-Jobs
|
||||
3. **GPU-Offloading**: OCR auf vast.ai GPU-Instanzen
|
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215
docs-src/services/ki-daten-pipeline/index.md
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215
docs-src/services/ki-daten-pipeline/index.md
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# KI-Daten-Pipeline
|
||||
|
||||
Die KI-Daten-Pipeline ist ein zusammenhaengendes System aus drei Modulen, das den Datenfluss von der Erfassung bis zur semantischen Suche abbildet.
|
||||
|
||||
## Uebersicht
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
subgraph OCR["OCR-Labeling"]
|
||||
A[Klausur-Scans] --> B[OCR Erkennung]
|
||||
B --> C[Ground Truth Labels]
|
||||
end
|
||||
|
||||
subgraph RAG["RAG Pipeline"]
|
||||
D[PDF Dokumente] --> E[Text-Extraktion]
|
||||
E --> F[Chunking]
|
||||
F --> G[Embedding]
|
||||
end
|
||||
|
||||
subgraph SEARCH["Daten & RAG"]
|
||||
H[Qdrant Collections]
|
||||
I[Semantische Suche]
|
||||
end
|
||||
|
||||
C -->|Export| D
|
||||
G -->|Indexierung| H
|
||||
H --> I
|
||||
I -->|Ergebnisse| J[Klausur-Korrektur]
|
||||
```
|
||||
|
||||
## Module
|
||||
|
||||
| Modul | Pfad | Funktion | Backend |
|
||||
|-------|------|----------|---------|
|
||||
| **OCR-Labeling** | `/ai/ocr-labeling` | Ground Truth fuer Handschrift-OCR | klausur-service:8086 |
|
||||
| **RAG Pipeline** | `/ai/rag-pipeline` | Dokument-Indexierung | klausur-service:8086 |
|
||||
| **Daten & RAG** | `/ai/rag` | Vektor-Suche & Collection-Mapping | klausur-service:8086 |
|
||||
|
||||
## Datenfluss
|
||||
|
||||
### 1. OCR-Labeling (Eingabe)
|
||||
|
||||
Das OCR-Labeling-Modul erfasst Ground Truth Daten fuer das Training von Handschrift-Erkennungsmodellen:
|
||||
|
||||
- **Upload**: Klausur-Scans (PDF/Bilder) werden hochgeladen
|
||||
- **OCR-Verarbeitung**: Mehrere OCR-Modelle erkennen den Text
|
||||
- `llama3.2-vision:11b` - Vision LLM (beste Qualitaet)
|
||||
- `trocr` - Microsoft Transformer (schnell)
|
||||
- `paddleocr` - PaddleOCR + LLM (4x schneller)
|
||||
- `donut` - Document Understanding (strukturiert)
|
||||
- **Labeling**: Manuelles Pruefen und Korrigieren der OCR-Ergebnisse
|
||||
- **Export**: Gelabelte Daten koennen exportiert werden fuer:
|
||||
- TrOCR Fine-Tuning
|
||||
- Llama Vision Fine-Tuning
|
||||
- Generic JSON
|
||||
|
||||
### 2. RAG Pipeline (Verarbeitung)
|
||||
|
||||
Die RAG Pipeline verarbeitet Dokumente und macht sie suchbar:
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A[Datenquellen] --> B[OCR/Text-Extraktion]
|
||||
B --> C[Chunking]
|
||||
C --> D[Embedding]
|
||||
D --> E[Qdrant Indexierung]
|
||||
|
||||
subgraph sources["Datenquellen"]
|
||||
S1[NiBiS PDFs]
|
||||
S2[Eigene EH]
|
||||
S3[Rechtskorpus]
|
||||
S4[Schulordnungen]
|
||||
end
|
||||
```
|
||||
|
||||
**Verarbeitungsschritte:**
|
||||
|
||||
1. **Dokumentenextraktion**: PDFs und Bilder werden per OCR in Text umgewandelt
|
||||
2. **Chunking**: Lange Texte werden in Abschnitte aufgeteilt
|
||||
- Chunk-Groesse: 1000 Zeichen
|
||||
- Ueberlappung: 200 Zeichen
|
||||
3. **Embedding**: Jeder Chunk wird in einen Vektor umgewandelt
|
||||
- Modell: `text-embedding-3-small`
|
||||
- Dimensionen: 1536
|
||||
4. **Indexierung**: Vektoren werden in Qdrant gespeichert
|
||||
|
||||
### 3. Daten & RAG (Ausgabe)
|
||||
|
||||
Das Daten & RAG Modul ermoeglicht die Verwaltung und Suche:
|
||||
|
||||
- **Collection-Uebersicht**: Status aller Qdrant Collections
|
||||
- **Semantische Suche**: Fragen werden in Vektoren umgewandelt und aehnliche Dokumente gefunden
|
||||
- **Regulierungs-Mapping**: Zeigt welche Regulierungen indexiert sind
|
||||
|
||||
## Qdrant Collections
|
||||
|
||||
| Collection | Inhalt | Status |
|
||||
|------------|--------|--------|
|
||||
| `bp_nibis_eh` | Offizielle NiBiS Erwartungshorizonte | Aktiv |
|
||||
| `bp_eh` | Benutzerdefinierte Erwartungshorizonte | Aktiv |
|
||||
| `bp_schulordnungen` | Schulordnungen aller Bundeslaender | In Arbeit |
|
||||
| `bp_legal_corpus` | Rechtskorpus (DSGVO, AI Act, BSI, etc.) | Aktiv |
|
||||
|
||||
## Technische Architektur
|
||||
|
||||
### Services
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
subgraph Frontend["Admin-v2 (Next.js)"]
|
||||
F1["/ai/ocr-labeling"]
|
||||
F2["/ai/rag-pipeline"]
|
||||
F3["/ai/rag"]
|
||||
end
|
||||
|
||||
subgraph Backend["klausur-service (Python)"]
|
||||
B1[OCR Endpoints]
|
||||
B2[Indexierungs-Jobs]
|
||||
B3[Such-API]
|
||||
end
|
||||
|
||||
subgraph Storage["Datenbanken"]
|
||||
D1[(PostgreSQL)]
|
||||
D2[(Qdrant)]
|
||||
D3[(MinIO)]
|
||||
end
|
||||
|
||||
F1 --> B1
|
||||
F2 --> B2
|
||||
F3 --> B3
|
||||
|
||||
B1 --> D1
|
||||
B1 --> D3
|
||||
B2 --> D2
|
||||
B3 --> D2
|
||||
```
|
||||
|
||||
### Backend-Endpunkte
|
||||
|
||||
#### OCR-Labeling (`/api/v1/ocr-label/`)
|
||||
|
||||
| Endpoint | Methode | Beschreibung |
|
||||
|----------|---------|--------------|
|
||||
| `/sessions` | GET/POST | Session-Verwaltung |
|
||||
| `/sessions/{id}/upload` | POST | Bilder hochladen |
|
||||
| `/queue` | GET | Labeling-Queue |
|
||||
| `/confirm` | POST | OCR bestaetigen |
|
||||
| `/correct` | POST | OCR korrigieren |
|
||||
| `/skip` | POST | Item ueberspringen |
|
||||
| `/stats` | GET | Statistiken |
|
||||
| `/export` | POST | Trainingsdaten exportieren |
|
||||
|
||||
#### RAG Pipeline (`/api/ai/rag-pipeline`)
|
||||
|
||||
| Action | Beschreibung |
|
||||
|--------|--------------|
|
||||
| `jobs` | Indexierungs-Jobs auflisten |
|
||||
| `dataset-stats` | Datensatz-Statistiken |
|
||||
| `create-job` | Neue Indexierung starten |
|
||||
| `pause` | Job pausieren |
|
||||
| `resume` | Job fortsetzen |
|
||||
| `cancel` | Job abbrechen |
|
||||
|
||||
#### Legal Corpus (`/api/legal-corpus/`)
|
||||
|
||||
| Endpoint | Beschreibung |
|
||||
|----------|--------------|
|
||||
| `/status` | Collection-Status |
|
||||
| `/search` | Semantische Suche |
|
||||
| `/ingest` | Dokumente indexieren |
|
||||
|
||||
## Integration mit Klausur-Korrektur
|
||||
|
||||
Die KI-Daten-Pipeline liefert Erwartungshorizont-Vorschlaege fuer die Klausur-Korrektur:
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant L as Lehrer
|
||||
participant K as Klausur-Korrektur
|
||||
participant R as RAG-Suche
|
||||
participant Q as Qdrant
|
||||
|
||||
L->>K: Schueler-Antwort pruefen
|
||||
K->>R: EH-Vorschlaege laden
|
||||
R->>Q: Semantische Suche
|
||||
Q->>R: Top-k Chunks
|
||||
R->>K: Relevante EH-Passagen
|
||||
K->>L: Bewertungsvorschlaege
|
||||
```
|
||||
|
||||
## Deployment
|
||||
|
||||
Die Module werden als Teil des admin-v2 Containers deployed:
|
||||
|
||||
```bash
|
||||
# 1. Sync
|
||||
rsync -avz --delete --exclude 'node_modules' --exclude '.next' --exclude '.git' \
|
||||
/Users/benjaminadmin/Projekte/breakpilot-pwa/admin-v2/ \
|
||||
macmini:/Users/benjaminadmin/Projekte/breakpilot-pwa/admin-v2/
|
||||
|
||||
# 2. Build & Deploy
|
||||
ssh macmini "/usr/local/bin/docker compose \
|
||||
-f /Users/benjaminadmin/Projekte/breakpilot-pwa/docker-compose.yml \
|
||||
build --no-cache admin-v2 && \
|
||||
/usr/local/bin/docker compose \
|
||||
-f /Users/benjaminadmin/Projekte/breakpilot-pwa/docker-compose.yml \
|
||||
up -d admin-v2"
|
||||
```
|
||||
|
||||
## Verwandte Dokumentation
|
||||
|
||||
- [OCR Labeling Spezifikation](../klausur-service/OCR-Labeling-Spec.md)
|
||||
- [RAG Admin Spezifikation](../klausur-service/RAG-Admin-Spec.md)
|
||||
- [NiBiS Ingestion Pipeline](../klausur-service/NiBiS-Ingestion-Pipeline.md)
|
||||
- [Multi-Agent Architektur](../../architecture/multi-agent.md)
|
||||
Reference in New Issue
Block a user