Dom/rpa_vision_v3
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858e6007f924b4d4f59b17c63aa494e97f05baee
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3 Commits

Author SHA1 Message Date
Dom
858e6007f9 feat(vwb-v3): Architecture Thin Client fonctionnelle 
API = Source de vérité unique (SQLite + Flask)
- Backend: API v3 avec session, workflow, capture, execute
- Frontend: Vanilla TypeScript, pas de state local
- Contrats stricts pour les actions RPA
- Drag & drop pour réorganiser les étapes
- Insertion d'étapes entre deux existantes
- Bibliothèque de captures (sessionStorage)
- Exécution avec coordonnées statiques (pyautogui)

Fonctionne mais fragile (coordonnées fixes, pas de détection visuelle)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2026-01-23 12:07:13 +01:00
Dom
38a1a5ddd8 feat(coaching): Implement complete COACHING mode infrastructure 
Add comprehensive COACHING mode system with:

Backend:
- core/coaching module with session persistence and metrics
- CoachingSessionPersistence for pause/resume sessions
- CoachingMetricsCollector with learning progress tracking
- REST API blueprint for coaching sessions management
- Execution integration with COACHING mode support

Frontend:
- CoachingPanel component with keyboard shortcuts
- Decision buttons (accept/reject/correct/manual/skip)
- Real-time stats display and correction editor
- CorrectionPacksDashboard for pack visualization
- WebSocket hooks for real-time COACHING events

Metrics & Monitoring:
- WorkflowLearningMetrics with confidence scoring
- GlobalCoachingMetrics for system-wide analytics
- AUTO mode readiness detection (85% acceptance threshold)
- Learning progress levels (OBSERVATION → COACHING → AUTO)

Tests:
- E2E tests for complete OBSERVATION → AUTO journey
- Session persistence and recovery tests
- Metrics threshold validation tests

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2026-01-19 08:40:54 +01:00
Dom
d8756883c5 feat(corrections): Add Correction Packs system for cross-workflow learning 
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>
 2026-01-18 18:48:35 +01:00