108 lines
4.5 KiB
Markdown
108 lines
4.5 KiB
Markdown
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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**Dokumenten-Check** is a document pre-verification system for Frigosped (freight forwarding). It checks transport documents and general terms & conditions (AGB) against configurable question catalogs, with AI-powered document translation and evaluation. Built on **Oracle APEX** + **Oracle Database 23c**.
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## Database Setup
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```bash
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# Connect to Oracle DB and run scripts in order:
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sqlplus user/pass@db @"Scripts/Datenmodell DocumentCheck.sql"
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sqlplus user/pass@db @"Scripts/Fragenkatalog-AGB anlegen.sql"
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```
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No build/compilation step — APEX applications deploy directly to Oracle instances.
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## Architecture
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### Data Model (10 tables in `Scripts/Datenmodell DocumentCheck.sql`)
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**Configuration hierarchy:**
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- `dc_document_types` → `dc_question_catalogs` → `dc_question_categories` → `dc_questions`
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**Execution hierarchy:**
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- `dc_projects` → `dc_project_documents` → `dc_results`
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**Supporting:** `dc_users`, `dc_user_roles`, `dc_reference_documents`
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### Key Patterns
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- **Audit triggers on all tables**: Every table has BEFORE INSERT/UPDATE triggers that set `created_at`, `created_by`, `updated_at`, `updated_by`, `row_version`. User identity comes from `APEX_APPLICATION.G_USER`.
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- **Oracle Identity columns**: All PKs use `GENERATED BY DEFAULT AS IDENTITY`.
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- **Config-driven evaluation**: Each question defines `evaluation_type`, `threshold`, `result_on_deviation` (ABWEICHUNG/HINWEIS), plus example answers for 0%/100% cases.
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- **Async processing**: Projects progress through `PENDING → IN_PROGRESS → COMPLETED` with a 0–100% `processing_progress` field and email notification on completion.
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- **Filestorage**: `dc_project_documents` stores the`original_file` (BLOB) with MIME type and filename metadata. The original text `original_text` (CLOB) after after OCR and conversion to Markdown and the `translated_file` after Translation to German (CLOB)
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### Result Types
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- `OK`, `UNKLAR` (unclear), `NOK` (not OK)
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- Boolean flags: `is_deviation`, `is_warning`
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### User Roles
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- `ADMIN` — configuration management
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- `AUDITOR` — document verification
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## Key Files
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| File | Purpose |
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|------|---------|
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| `Scripts/Datenmodell DocumentCheck.sql` | Full DB schema: 10 tables + 8 triggers |
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| `Scripts/Fragenkatalog-AGB anlegen.sql` | Sample AGB question catalog (6 categories, 25 questions) |
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| `Scripts/ORDS REST Services.sql` | All 11 ORDS REST endpoints (idempotent, run after schema) |
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| `Doku/Projektbeschreibung.md` | German requirements specification |
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| `Doku/Dokumenten-Check.md` | Data model + UI dialog specifications |
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## Backend Service (Quarkus)
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Located in `dc-backend/`. Java 21, Quarkus 3.15.1, LangChain4J 0.36.2.
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```bash
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# Dev-Modus (hot reload)
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cd dc-backend
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mvn quarkus:dev
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# Produktion bauen
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mvn package -DskipTests
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java -jar target/quarkus-app/quarkus-run.jar
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```
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**Trigger-Endpunkt:**
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```bash
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POST http://localhost:8090/check/{projectId}
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# → 202 Accepted, Verarbeitung läuft asynchron
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```
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### Backend-Architektur
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```
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CheckResource POST /check/{projectId} → 202, Fire & Forget
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CheckOrchestrationService Haupt-Ablauf (Phase A: OCR/Übersetzung, Phase B: Auswertung)
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DocumentProcessingService PDF→KI-OCR, DOCX→POI→Markdown, ODT→ODF-Toolkit→Markdown
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EvaluationService translate() + evaluate() via LangChain4J ChatLanguageModel
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OrdsClient (REST Client) MicroProfile REST Client für alle /api/dc/-Endpunkte
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AiModelProducer (CDI) @Named("main") gpt-oss:20b + @Named("ocr") quen3.5:9b
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```
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### KI-Modelle
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| Qualifier | Modell | Zweck |
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|-----------|--------|-------|
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| `@Named("main")` | `gpt-oss:20b` | Übersetzung + Fragenauswertung |
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| `@Named("ocr")` | `quen3.5:9b` | PDF-Seiten als Bild → Markdown-Text |
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Beide sprechen `https://ollama.aquantico.de` an (Ollama-API `/api/chat`) mit `X-API-KEY`-Header.
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Konfiguration in `dc-backend/src/main/resources/application.properties`.
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### Verarbeitungsablauf
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1. ORDS: Projekt laden + `status → IN_PROGRESS`
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2. ORDS: Dokumente + Fragen laden
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3. ORDS: Alte Ergebnisse löschen (Re-Processing-fähig)
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4. **Phase A** (pro Dokument): Download → OCR/Konvertierung → Übersetzung → ORDS `PUT /texts`
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5. **Phase B** (pro Dokument × Frage): Auswertung → Abweichungs-Berechnung → ORDS `POST /results`
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6. ORDS: `status → COMPLETED`
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Abweichung/Hinweis wird in Java berechnet (nicht durch KI): `score < threshold` → prüfe `result_handling` (ABWEICHUNG/HINWEIS).
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