Implement a complete system for capitalizing user corrections across multiple
workflows and sessions. This enables automatic application of learned fixes
when similar failures occur in different contexts.
New components:
- core/corrections/models.py: CorrectionKey, Correction, CorrectionPack models
- core/corrections/correction_repository.py: JSON storage with atomic writes
- core/corrections/aggregator.py: Aggregation by hash and quality filtering
- core/corrections/correction_pack_service.py: CRUD, export/import, versioning
- backend/api/correction_packs.py: REST API with 15 endpoints
Features:
- MD5-based key hashing for correction deduplication
- Export/import in JSON and YAML formats
- Version history with rollback support
- Cross-workflow pattern detection
- Integration with SelfHealingEngine for automatic application
- 29 unit tests (all passing)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Création service learning_integration.py (pont VWB <-> LearningManager)
- Enregistrement automatique des workflows à la création
- Enregistrement des résultats d'exécution (succès/échec + confiance)
- Endpoints API: /workflows/<id>/feedback et /workflows/<id>/learning
- Boutons feedback (pouce vert/rouge) dans VWBExecutorExtension
- Fix: VariableAutocomplete inputRef pour setSelectionRange
- Amélioration: Chips cliquables pour insérer les variables
Le système apprend maintenant des exécutions et feedbacks utilisateur.
États: OBSERVATION -> COACHING -> AUTO_CANDIDATE -> AUTO_CONFIRMED
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
