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- Introduced the `exercise_enrichment_admin` API for batch exercise enrichment, allowing superadmins to filter candidates, preview, and apply skills. - Updated the access layer documentation to include the new endpoint and its exempt status. - Enhanced the frontend with a new admin page for exercise enrichment and updated navigation to include this feature. - Incremented version to 0.8.179 and updated changelog to reflect these additions and improvements.
67 lines
2.8 KiB
Markdown
67 lines
2.8 KiB
Markdown
# Superadmin: Übungs-Anreicherung per KI
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Stand: 2026-05-23 · App 0.8.178
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## Zweck
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Plattform-weites Werkzeug für Superadmins, um Übungen (typisch `draft`, ohne Skills) **batchweise** per KI mit Fähigkeiten anzureichern und kontrolliert auf `in_review` zu setzen.
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Verbessert indirekt die Planungs-KI (`POST /api/planning/exercise-suggest`), die gegen Skill-Profile rankt — unvollständige `exercise_skills` führen dort zu Volltext-dominiertem Ranking.
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## UI
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- Route: `/admin/exercise-enrichment` (nur Superadmin)
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- Admin-Menü: „Übungs-Anreicherung“
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## API
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Prefix: `/api/admin/exercise-enrichment`
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| Methode | Pfad | Beschreibung |
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|---------|------|--------------|
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| GET | `/candidates` | Paginierte Kandidaten (Filter: status, visibility, focus_area, without_skills, with_ai_suggested_skills, include_club, search) |
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| POST | `/preview` | Dry-Run — `{ exercise_ids[], modes: { skills, summary }, merge_mode }` |
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| POST | `/apply` | `{ items: [{ exercise_id, merged_skills }], merge_mode, set_status }` |
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Auth: `require_auth` + `is_superadmin` — **kein** `TenantContext` (EXEMPT, siehe ACCESS_LAYER_ENDPOINT_AUDIT.md).
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## KI
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Wiederverwendet `run_exercise_form_ai_suggestion` → Prompts `exercise_skill_suggestions` (MVP Pflicht), optional `exercise_summary`. Skill-Katalog via `build_contextual_skills_catalog_block` / `ai_skill_retrieval_profiles`.
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## Merge-Modi (Skills)
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- `additive` (Default): manuelle Skills bleiben; KI ergänzt neue; bestehende `ai_suggested`-Links werden aktualisiert
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- `replace_ai_only`: nur `ai_suggested=true` entfernen, dann KI-Set anwenden
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- `replace_all`: alle Skills ersetzen (explizit)
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## Defaults
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- Kandidaten: **Status** primär (Default `draft`); Sichtbarkeit Default **`private`**, wählbar bis „Alle“
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- Skill-Merge Default: **`replace_all`** (alle Skills KI-neu, `ai_suggested=true` — unterscheidbar von manuell)
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- Nach Apply: `set_status=in_review` (nie automatisch `approved`)
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- Batch: keine Gesamtgrenze (bis 10.000 IDs); **Analyze** + explizite Nutzerbestätigung; HTTP-Chunks à 25/100
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## Inhalte (modular)
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| Modus | Prompt | Apply-Felder |
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|-------|--------|--------------|
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| Skills | `exercise_skill_suggestions` | `exercise_skills` inkl. Intensität, required/target_level, `ai_suggested` |
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| Summary | `exercise_summary` | `summary`, `summary_ai_generated=true` |
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| Anleitung | `exercise_instruction_rewrite` | `goal`, `execution`, `preparation`, `trainer_notes` |
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## API (ergänzt)
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| Methode | Pfad | Beschreibung |
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|---------|------|--------------|
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| GET | `/candidate-ids` | Alle IDs zum Filter (Select-all) |
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| POST | `/analyze` | `{ exercise_ids[], modes }` → Kosten-Schätzung vor Start |
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## Keine Migration
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Bestehende Spalte `exercise_skills.ai_suggested` reicht; kein Enrichment-Log in MVP.
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## Tests
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`backend/tests/test_exercise_enrichment_admin.py` — 403, Merge-Logik, Status draft→in_review.
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