feat: replay visuel VLM-first, worker séparé, package Léa, AZERTY, sécurité HTTPS
Pipeline replay visuel : - VLM-first : l'agent appelle Ollama directement pour trouver les éléments - Template matching en fallback (seuil strict 0.90) - Stop immédiat si élément non trouvé (pas de clic blind) - Replay depuis session brute (/replay-session) sans attendre le VLM - Vérification post-action (screenshot hash avant/après) - Gestion des popups (Enter/Escape/Tab+Enter) Worker VLM séparé : - run_worker.py : process distinct du serveur HTTP - Communication par fichiers (_worker_queue.txt + _replay_active.lock) - Le serveur HTTP ne fait plus jamais de VLM → toujours réactif - Service systemd rpa-worker.service Capture clavier : - raw_keys (vk + press/release) pour replay exact indépendant du layout - Fix AZERTY : ToUnicodeEx + AltGr detection - Enter capturé comme \n, Tab comme \t - Filtrage modificateurs seuls (Ctrl/Alt/Shift parasites) - Fusion text_input consécutifs, dédup key_combo Sécurité & Internet : - HTTPS Let's Encrypt (lea.labs + vwb.labs.laurinebazin.design) - Token API fixe dans .env.local - HTTP Basic Auth sur VWB - Security headers (HSTS, CSP, nosniff) - CORS domaines publics, plus de wildcard Infrastructure : - DPI awareness (SetProcessDpiAwareness) Python + Rust - Métadonnées système (dpi_scale, window_bounds, monitors, os_theme) - Template matching multi-scale [0.5, 2.0] - Résolution dynamique (plus de hardcode 1920x1080) - VLM prefill fix (47x speedup, 3.5s au lieu de 180s) Modules : - core/auth/ : credential vault (Fernet AES), TOTP (RFC 6238), auth handler - core/federation/ : LearningPack export/import anonymisé, FAISS global - deploy/ : package Léa (config.txt, Lea.bat, install.bat, LISEZMOI.txt) UX : - Filtrage OS (VWB + Chat montrent que les workflows de l'OS courant) - Bibliothèque persistante (cache local + SQLite) - Clustering hybride (titre fenêtre + DBSCAN) - EdgeConstraints + PostConditions peuplés - GraphBuilder compound actions (toutes les frappes) Agent Rust : - Token Bearer auth (network.rs) - sysinfo.rs (DPI, résolution, window bounds via Win32 API) - config.txt lu automatiquement - Support Chrome/Brave/Firefox (pas que Edge) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Dom
@@ -55,6 +55,8 @@ def list_learned_workflows():
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Query params:
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machine_id: Filtrer par machine (optionnel)
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os: Filtrer par OS — 'windows' ou 'linux' (optionnel).
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Filtre sur la portion OS du machine_id (ex: DESKTOP-58D5CAC_windows).
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Response:
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{
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@@ -76,6 +78,7 @@ def list_learned_workflows():
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}
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"""
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machine_id = request.args.get('machine_id')
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os_filter = request.args.get('os') # 'windows' ou 'linux'
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from services.learned_workflow_bridge import list_learned_workflows_from_disk
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@@ -132,6 +135,14 @@ def list_learned_workflows():
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if machine_id:
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merged = [w for w in merged if w.get("machine_id") == machine_id]
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# Filtrer par OS si demandé (cherche 'windows' ou 'linux' dans le machine_id)
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if os_filter:
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os_filter_lower = os_filter.lower()
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merged = [
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w for w in merged
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if os_filter_lower in (w.get("machine_id") or "").lower()
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]
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# Enrichir : vérifier si déjà importé dans le VWB
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for wf in merged:
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existing = Workflow.query.filter(
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@@ -58,9 +58,15 @@ db.init_app(app)
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migrate = Migrate(app, db)
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cache = Cache(app)
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_ALLOWED_ORIGINS = [
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"http://localhost:3002",
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"http://localhost:5002",
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"https://vwb.labs.laurinebazin.design",
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"https://lea.labs.laurinebazin.design",
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]
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socketio = SocketIO(
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app,
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cors_allowed_origins="*",
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cors_allowed_origins=_ALLOWED_ORIGINS,
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async_mode='threading',
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logger=True,
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engineio_logger=True
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@@ -204,6 +210,16 @@ def set_security_headers(response):
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response.headers['X-Content-Type-Options'] = 'nosniff'
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response.headers['X-Frame-Options'] = 'SAMEORIGIN'
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response.headers['X-XSS-Protection'] = '1; mode=block'
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response.headers['Referrer-Policy'] = 'strict-origin-when-cross-origin'
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response.headers['Content-Security-Policy'] = (
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"default-src 'self'; "
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"script-src 'self' 'unsafe-inline' 'unsafe-eval'; "
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"style-src 'self' 'unsafe-inline'; "
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"img-src 'self' data: blob:; "
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"connect-src 'self' ws: wss: http://localhost:* https://vwb.labs.laurinebazin.design https://lea.labs.laurinebazin.design; "
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"font-src 'self' data:; "
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"frame-ancestors 'self'"
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)
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return response
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@@ -67,6 +67,11 @@ import os
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import json
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import requests
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import re
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try:
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from vlm_provider import vlm_hub
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except ImportError:
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from visual_workflow_builder.backend.vlm_provider import vlm_hub
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try:
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import cv2
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import numpy as np
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@@ -624,21 +629,33 @@ def find_anchor_with_vlm(
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) -> Optional[Dict[str, Any]]:
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"""
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Utilise un VLM (Vision Language Model) pour trouver l'ancre sur l'écran.
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Le VLM comprend le contexte visuel et peut distinguer:
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- Une icône dans le dock vs le même logo dans la fenêtre principale
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- Un bouton actif vs sa copie dans une miniature
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Args:
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screenshot_base64: Capture d'écran complète en base64
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anchor_image_base64: Image de l'ancre à trouver en base64
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anchor_description: Description textuelle de l'élément (optionnel)
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screen_width: Largeur de l'écran
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screen_height: Hauteur de l'écran
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Returns:
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Dict avec coordonnées {x, y, confidence} ou None si non trouvé
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En priorité via Gemini Cloud (vlm_hub), sinon via Ollama local.
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"""
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# 1. Essayer via le VLM Hub (Gemini Cloud)
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if vlm_hub.use_cloud and vlm_hub.google_api_key:
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print(f"🧠 [VLM Hub] Tentative via Gemini Cloud pour: '{anchor_description}'...")
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coords = vlm_hub.detect_ui_element(
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screenshot=screenshot_base64,
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anchor_image=anchor_image_base64,
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description=anchor_description or "l'élément UI spécifié"
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)
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if coords and coords.get('found'):
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# Convertir bbox normalisée [ymin, xmin, ymax, xmax] (0-1000) en pixels [x, y]
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bbox = coords.get('bbox')
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if bbox and len(bbox) == 4:
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# Gemini retourne souvent [ymin, xmin, ymax, xmax] sur une échelle 0-1000
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y1, x1, y2, x2 = bbox
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x = int((x1 + x2) / 2 * screen_width / 1000)
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y = int((y1 + y2) / 2 * screen_height / 1000)
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confidence = float(coords.get('confidence', 0.9))
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print(f"✅ [VLM Hub] Gemini a trouvé l'élément à ({x}, {y}) avec confiance {confidence:.0%}")
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return {
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"found": True, "x": x, "y": y, "center_x": x, "center_y": y,
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"confidence": confidence, "method": "gemini_cloud"
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}
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# 2. Fallback sur Ollama Local
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if not check_ollama_available():
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print("⚠️ [VLM] Ollama/qwen2.5vl non disponible, fallback sur pyautogui")
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return None
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@@ -976,7 +993,9 @@ def find_anchor_multiscale(anchor_image_base64: str, scales: List[float] = None,
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Dict avec les coordonnées trouvées ou None
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"""
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if scales is None:
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scales = [0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
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# Plage étendue 0.5x-2.0x pour couvrir les écarts de résolution importants
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# (ex: apprentissage 2560x1600 → replay 1280x720 = ratio ~0.5x)
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scales = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.5, 1.75, 2.0]
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if not CV2_AVAILABLE:
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return None
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Binary file not shown.
@@ -0,0 +1,20 @@
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{
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"anchor_id": "anchor_01a7b6e80168_1769095224",
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"bounding_box": {
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"x": 52.666666666666664,
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"y": 23.866658528645832,
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"width": 100,
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"height": 24
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},
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"original_size": {
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"width": 120,
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"height": 44
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},
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"thumbnail_size": {
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"width": 120,
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"height": 44
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},
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"created_at": "2026-01-22T16:20:24.448773",
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"original_file_size": 4172,
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"thumbnail_file_size": 1342
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}
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{
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"anchor_id": "anchor_07961dc0aa07_1769032466",
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"bounding_box": {
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"x": 788.4444173177084,
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},
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},
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"thumbnail_size": {
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"width": 153,
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},
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"original_file_size": 15929,
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}
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{
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}
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{
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}
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{
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{
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{
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BIN
visual_workflow_builder/backend/data/learning/target_memory.db
Normal file
BIN
visual_workflow_builder/backend/data/learning/target_memory.db
Normal file
Binary file not shown.
1575
visual_workflow_builder/backend/data/templates/templates.json
Normal file
1575
visual_workflow_builder/backend/data/templates/templates.json
Normal file
File diff suppressed because it is too large
Load Diff
0
visual_workflow_builder/backend/data/vwb.db
Normal file
0
visual_workflow_builder/backend/data/vwb.db
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
@@ -0,0 +1,70 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"parameters": {
|
||||
"target": "input",
|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
"stepType": "type",
|
||||
"parameters": {
|
||||
"target": "input",
|
||||
"text": "${username}"
|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
@@ -0,0 +1,27 @@
|
||||
{
|
||||
"id": "wf_820b2a9e6270",
|
||||
"name": "Test Corrections VWB",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
{
|
||||
"id": "wf_90ecda5de0ed",
|
||||
"name": "Test Workflow",
|
||||
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|
||||
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|
||||
"created_at": "2025-12-13T10:59:07.152509",
|
||||
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|
||||
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|
||||
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|
||||
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"text": "${username}"
|
||||
},
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||||
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||||
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{
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||||
"name": "username",
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||||
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|
||||
"value": "default_user",
|
||||
"description": null
|
||||
},
|
||||
{
|
||||
"name": "count",
|
||||
"type": "number",
|
||||
"value": 5,
|
||||
"description": null
|
||||
}
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||||
],
|
||||
"settings": {
|
||||
"timeout": 300000,
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||||
"retry_on_failure": true,
|
||||
"max_retries": 3,
|
||||
"enable_self_healing": true,
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||||
"enable_analytics": true
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||||
},
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||||
"tags": [],
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||||
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||||
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||||
"_serialization": {
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||||
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||||
"serialized_at": "2025-12-03T20:26:25.640548",
|
||||
"format": "json"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
{
|
||||
"id": "wf_b86cac3286b3",
|
||||
"name": "test",
|
||||
"description": "test",
|
||||
"version": "1.0.0",
|
||||
"created_at": "2026-01-08T12:50:25.266435",
|
||||
"updated_at": "2026-01-08T12:50:25.266469",
|
||||
"created_by": "test",
|
||||
"nodes": [],
|
||||
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|
||||
"variables": [],
|
||||
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|
||||
"timeout": 300000,
|
||||
"retry_on_failure": true,
|
||||
"max_retries": 3,
|
||||
"enable_self_healing": true,
|
||||
"enable_analytics": true
|
||||
},
|
||||
"tags": [],
|
||||
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||||
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||||
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||||
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||||
"serialized_at": "2026-01-08T12:50:25.266526",
|
||||
"format": "json"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,65 @@
|
||||
{
|
||||
"id": "wf_bb224c0d2ada",
|
||||
"name": "Variables Workflow",
|
||||
"description": "",
|
||||
"version": "1.0.0",
|
||||
"created_at": "2025-12-03T20:47:50.401853",
|
||||
"updated_at": "2025-12-03T20:47:50.401939",
|
||||
"created_by": "system",
|
||||
"nodes": [
|
||||
{
|
||||
"id": "type_username",
|
||||
"type": "type",
|
||||
"position": {
|
||||
"x": 100,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"target": "input",
|
||||
"text": "${username}"
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"selected": false,
|
||||
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||||
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||||
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||||
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||||
}
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||||
],
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||||
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||||
"variables": [
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||||
{
|
||||
"name": "username",
|
||||
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|
||||
"value": "default_user",
|
||||
"description": null
|
||||
},
|
||||
{
|
||||
"name": "count",
|
||||
"type": "number",
|
||||
"value": 5,
|
||||
"description": null
|
||||
}
|
||||
],
|
||||
"settings": {
|
||||
"timeout": 300000,
|
||||
"retry_on_failure": true,
|
||||
"max_retries": 3,
|
||||
"enable_self_healing": true,
|
||||
"enable_analytics": true
|
||||
},
|
||||
"tags": [],
|
||||
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|
||||
"is_template": false,
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||||
"_serialization": {
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||||
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||||
"serialized_at": "2025-12-03T20:47:50.401975",
|
||||
"format": "json"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,443 @@
|
||||
{
|
||||
"id": "wf_d6c3d79a9f3b",
|
||||
"name": "Long Workflow",
|
||||
"description": "",
|
||||
"version": "1.0.0",
|
||||
"created_at": "2025-12-03T20:26:26.141033",
|
||||
"updated_at": "2026-01-22T09:29:19.076505",
|
||||
"created_by": "system",
|
||||
"nodes": [
|
||||
{
|
||||
"id": "wait_0",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 100,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
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|
||||
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|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 1",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_1",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 150,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
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|
||||
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|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 2",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_2",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 200,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
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|
||||
"status": null,
|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 3",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_3",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 250,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
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|
||||
"status": null,
|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 4",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_4",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 300,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
"highlighted": false,
|
||||
"status": null,
|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 5",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_5",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 350,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
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||||
"status": null,
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||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 6",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_6",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 400,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
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||||
"self_healing": null,
|
||||
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|
||||
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|
||||
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|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 7",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_7",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 450,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
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|
||||
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|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 8",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_8",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 500,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"self_healing": null,
|
||||
"selected": false,
|
||||
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|
||||
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|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 9",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "wait_9",
|
||||
"type": "wait",
|
||||
"position": {
|
||||
"x": 550,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 80
|
||||
},
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"input_ports": [],
|
||||
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|
||||
"self_healing": null,
|
||||
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|
||||
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|
||||
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|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null,
|
||||
"data": {
|
||||
"label": "Étape 10",
|
||||
"stepType": "wait",
|
||||
"parameters": {
|
||||
"duration": 200
|
||||
},
|
||||
"isVWBCatalogAction": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"id": "edge_1",
|
||||
"source": "wait_0",
|
||||
"target": "wait_1",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "edge_2",
|
||||
"source": "wait_1",
|
||||
"target": "wait_2",
|
||||
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|
||||
"target_port": "in",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "edge_3",
|
||||
"source": "wait_2",
|
||||
"target": "wait_3",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
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|
||||
"selected": false,
|
||||
"animated": false
|
||||
},
|
||||
{
|
||||
"id": "edge_4",
|
||||
"source": "wait_3",
|
||||
"target": "wait_4",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
"style": null,
|
||||
"selected": false,
|
||||
"animated": false
|
||||
},
|
||||
{
|
||||
"id": "edge_5",
|
||||
"source": "wait_4",
|
||||
"target": "wait_5",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
"style": null,
|
||||
"selected": false,
|
||||
"animated": false
|
||||
},
|
||||
{
|
||||
"id": "edge_6",
|
||||
"source": "wait_5",
|
||||
"target": "wait_6",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
"style": null,
|
||||
"selected": false,
|
||||
"animated": false
|
||||
},
|
||||
{
|
||||
"id": "edge_7",
|
||||
"source": "wait_6",
|
||||
"target": "wait_7",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
"style": null,
|
||||
"selected": false,
|
||||
"animated": false
|
||||
},
|
||||
{
|
||||
"id": "edge_8",
|
||||
"source": "wait_7",
|
||||
"target": "wait_8",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
"style": null,
|
||||
"selected": false,
|
||||
"animated": false
|
||||
},
|
||||
{
|
||||
"id": "edge_9",
|
||||
"source": "wait_8",
|
||||
"target": "wait_9",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
"style": null,
|
||||
"selected": false,
|
||||
"animated": false
|
||||
}
|
||||
],
|
||||
"variables": [],
|
||||
"settings": {
|
||||
"timeout": 300000,
|
||||
"retry_on_failure": true,
|
||||
"max_retries": 3,
|
||||
"enable_self_healing": true,
|
||||
"enable_analytics": true
|
||||
},
|
||||
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@@ -0,0 +1,93 @@
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@@ -0,0 +1,484 @@
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@@ -0,0 +1,93 @@
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|
||||
"nodes": [
|
||||
{
|
||||
"id": "node-1",
|
||||
"type": "click",
|
||||
"position": {
|
||||
"x": 100,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 100
|
||||
},
|
||||
"parameters": {
|
||||
"target": "button"
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"selected": false,
|
||||
"highlighted": false,
|
||||
"status": null,
|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null
|
||||
},
|
||||
{
|
||||
"id": "node-2",
|
||||
"type": "type",
|
||||
"position": {
|
||||
"x": 300,
|
||||
"y": 100
|
||||
},
|
||||
"size": {
|
||||
"width": 200,
|
||||
"height": 100
|
||||
},
|
||||
"parameters": {
|
||||
"text": "Hello World"
|
||||
},
|
||||
"input_ports": [],
|
||||
"output_ports": [],
|
||||
"selected": false,
|
||||
"highlighted": false,
|
||||
"status": null,
|
||||
"label": null,
|
||||
"description": null,
|
||||
"color": null
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"id": "edge-1",
|
||||
"source": "node-1",
|
||||
"target": "node-2",
|
||||
"source_port": "out",
|
||||
"target_port": "in",
|
||||
"condition": null,
|
||||
"style": null,
|
||||
"selected": false,
|
||||
"animated": false
|
||||
}
|
||||
],
|
||||
"variables": [
|
||||
{
|
||||
"name": "username",
|
||||
"type": "string",
|
||||
"value": "testuser",
|
||||
"description": "Nom d utilisateur"
|
||||
}
|
||||
],
|
||||
"settings": {
|
||||
"timeout": 300000,
|
||||
"retry_on_failure": true,
|
||||
"max_retries": 3,
|
||||
"enable_self_healing": true,
|
||||
"enable_analytics": true
|
||||
},
|
||||
"tags": [],
|
||||
"category": null,
|
||||
"is_template": false,
|
||||
"_serialization": {
|
||||
"version": "1.0.0",
|
||||
"serialized_at": "2025-12-12T09:30:28.846516",
|
||||
"format": "json"
|
||||
}
|
||||
}
|
||||
BIN
visual_workflow_builder/backend/instance/vwb_v3.db
Normal file
BIN
visual_workflow_builder/backend/instance/vwb_v3.db
Normal file
Binary file not shown.
BIN
visual_workflow_builder/backend/instance/workflows.db
Normal file
BIN
visual_workflow_builder/backend/instance/workflows.db
Normal file
Binary file not shown.
@@ -0,0 +1,95 @@
|
||||
{
|
||||
"workflow_id": "wf_372eabf626f8_1773752892",
|
||||
"workflow_name": "test_windows",
|
||||
"description": "",
|
||||
"tags": [],
|
||||
"steps": [
|
||||
{
|
||||
"order": 0,
|
||||
"action_type": "click_anchor",
|
||||
"label": "click_anchor",
|
||||
"parameters": {
|
||||
"visual_anchor": {
|
||||
"anchor_id": "anchor_e54628b75227_1773829452",
|
||||
"bounding_box": {
|
||||
"x": 560,
|
||||
"y": 1536,
|
||||
"width": 230,
|
||||
"height": 59
|
||||
}
|
||||
}
|
||||
},
|
||||
"has_anchor": true
|
||||
},
|
||||
{
|
||||
"order": 0,
|
||||
"action_type": "type_text",
|
||||
"label": "type_text",
|
||||
"parameters": {
|
||||
"clear_before": true,
|
||||
"text": "bloc note"
|
||||
},
|
||||
"has_anchor": false
|
||||
},
|
||||
{
|
||||
"order": 0,
|
||||
"action_type": "click_anchor",
|
||||
"label": "click_anchor",
|
||||
"parameters": {
|
||||
"visual_anchor": {
|
||||
"anchor_id": "anchor_dd32b09238ca_1773833606",
|
||||
"bounding_box": {
|
||||
"x": 670,
|
||||
"y": 310,
|
||||
"width": 51,
|
||||
"height": 51
|
||||
}
|
||||
}
|
||||
},
|
||||
"has_anchor": true
|
||||
},
|
||||
{
|
||||
"order": 0,
|
||||
"action_type": "wait_for_anchor",
|
||||
"label": "wait_for_anchor",
|
||||
"parameters": {
|
||||
"visual_anchor": {
|
||||
"anchor_id": "anchor_2d51b88900d7_1773833805",
|
||||
"bounding_box": {
|
||||
"x": 287,
|
||||
"y": 290,
|
||||
"width": 70,
|
||||
"height": 92
|
||||
}
|
||||
}
|
||||
},
|
||||
"has_anchor": true
|
||||
},
|
||||
{
|
||||
"order": 0,
|
||||
"action_type": "type_text",
|
||||
"label": "type_text",
|
||||
"parameters": {
|
||||
"text": "hello"
|
||||
},
|
||||
"has_anchor": false
|
||||
}
|
||||
],
|
||||
"exported_at": "2026-03-18T11:38:45.271209",
|
||||
"metadata": {
|
||||
"step_count": 5,
|
||||
"action_types": [
|
||||
"wait_for_anchor",
|
||||
"type_text",
|
||||
"click_anchor"
|
||||
],
|
||||
"has_anchors": true,
|
||||
"warnings": [
|
||||
"Étape 1 (click_anchor): pas de label personnalisé",
|
||||
"Étape 2 (type_text): pas de label personnalisé",
|
||||
"Étape 3 (click_anchor): pas de label personnalisé",
|
||||
"Étape 4 (wait_for_anchor): pas de label personnalisé",
|
||||
"Étape 5 (type_text): pas de label personnalisé"
|
||||
]
|
||||
}
|
||||
}
|
||||
165
visual_workflow_builder/backend/vlm_provider.py
Normal file
165
visual_workflow_builder/backend/vlm_provider.py
Normal file
@@ -0,0 +1,165 @@
|
||||
import os
|
||||
import io
|
||||
import json
|
||||
import base64
|
||||
from typing import Optional, Dict, Any, List
|
||||
from PIL import Image
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Charger les variables d'environnement
|
||||
env_paths = [
|
||||
os.path.join(os.getcwd(), ".env.local"),
|
||||
os.path.join(os.getcwd(), "rpa_vision_v3/.env.local"),
|
||||
os.path.join(os.path.dirname(__file__), "../../../.env.local")
|
||||
]
|
||||
for path in env_paths:
|
||||
if os.path.exists(path):
|
||||
load_dotenv(path, override=True)
|
||||
break
|
||||
|
||||
class VLMProvider:
|
||||
"""Hub de Vision Sémantique Multi-Fournisseurs (OpenAI, Gemini, Anthropic, Ollama)"""
|
||||
|
||||
def __init__(self):
|
||||
# Clés API
|
||||
self.openai_key = os.getenv("OPENAI_API_KEY")
|
||||
self.gemini_key = os.getenv("GOOGLE_API_KEY")
|
||||
self.anthropic_key = os.getenv("ANTHROPIC_API_KEY")
|
||||
self.deepseek_key = os.getenv("DEEPSEEK_API_KEY")
|
||||
|
||||
# Configuration Ollama Local
|
||||
self.ollama_url = os.getenv("OLLAMA_URL", "http://localhost:11434")
|
||||
self.local_model = os.getenv("VLM_MODEL", "qwen3-vl:8b")
|
||||
|
||||
# Priorité par défaut
|
||||
self.preferred_cloud = "openai" # gpt-4o est la référence UI
|
||||
print(f"🔧 [VLM Hub] Initialisé. OpenAI: {bool(self.openai_key)}, Gemini: {bool(self.gemini_key)}, Anthropic: {bool(self.anthropic_key)}")
|
||||
|
||||
def _to_base64(self, image_input) -> str:
|
||||
"""Convertit n'importe quel input image en base64 pur"""
|
||||
if isinstance(image_input, Image.Image):
|
||||
buffer = io.BytesIO()
|
||||
image_input.save(buffer, format="PNG")
|
||||
return base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||||
elif isinstance(image_input, str):
|
||||
if image_input.startswith("data:image"):
|
||||
return image_input.split(",", 1)[1]
|
||||
elif os.path.exists(image_input):
|
||||
with open(image_input, "rb") as f:
|
||||
return base64.b64encode(f.read()).decode("utf-8")
|
||||
return image_input # Base64 brut supposé
|
||||
return base64.b64encode(image_input).decode("utf-8")
|
||||
|
||||
def detect_ui_element(self, screenshot, anchor_image=None, description: str = "") -> Optional[Dict[str, Any]]:
|
||||
"""Tente de localiser l'élément en essayant les fournisseurs par ordre de qualité"""
|
||||
|
||||
# 1. Tenter OpenAI (Référence Vision UI)
|
||||
if self.openai_key:
|
||||
res = self._call_openai(screenshot, anchor_image, description)
|
||||
if res and res.get('found'): return res
|
||||
|
||||
# 2. Tenter Gemini (Excellent backup Vision)
|
||||
if self.gemini_key:
|
||||
res = self._call_gemini(screenshot, anchor_image, description)
|
||||
if res and res.get('found'): return res
|
||||
|
||||
# 3. Tenter Anthropic (Précision logique)
|
||||
if self.anthropic_key:
|
||||
res = self._call_anthropic(screenshot, anchor_image, description)
|
||||
if res and res.get('found'): return res
|
||||
|
||||
# 4. Fallback Local (Ollama) - Crucial pour le DGX Spark
|
||||
return self._call_ollama_local(screenshot, anchor_image, description)
|
||||
|
||||
def _call_openai(self, screenshot, anchor_image, description):
|
||||
try:
|
||||
from openai import OpenAI
|
||||
client = OpenAI(api_key=self.openai_key)
|
||||
prompt = f"Expert UI: Localise précisément '{description}'. Retourne JSON: {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax] (0-1000), 'confidence': float}}"
|
||||
|
||||
content = [{"type": "text", "text": prompt}]
|
||||
content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{self._to_base64(screenshot)}"}})
|
||||
if anchor_image:
|
||||
content.append({"type": "text", "text": "Ancre de référence:"})
|
||||
content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{self._to_base64(anchor_image)}"}})
|
||||
|
||||
response = client.chat.completions.create(
|
||||
model="gpt-4o",
|
||||
messages=[{"role": "user", "content": content}],
|
||||
response_format={"type": "json_object"},
|
||||
temperature=0
|
||||
)
|
||||
return json.loads(response.choices[0].message.content)
|
||||
except Exception as e:
|
||||
print(f"⚠️ [Hub] OpenAI Error: {e}")
|
||||
return None
|
||||
|
||||
def _call_gemini(self, screenshot, anchor_image, description):
|
||||
try:
|
||||
from google import genai
|
||||
client = genai.Client(api_key=self.gemini_key)
|
||||
prompt = f"Expert UI: Localise précisément '{description}'. Retourne JSON: {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax] (0-1000), 'confidence': float}}"
|
||||
|
||||
contents = [prompt, Image.open(io.BytesIO(base64.b64decode(self._to_base64(screenshot))))]
|
||||
if anchor_image:
|
||||
contents.append(Image.open(io.BytesIO(base64.b64decode(self._to_base64(anchor_image)))))
|
||||
|
||||
response = client.models.generate_content(
|
||||
model="gemini-1.5-flash",
|
||||
contents=contents,
|
||||
config={"response_mime_type": "application/json"}
|
||||
)
|
||||
return json.loads(response.text)
|
||||
except Exception as e:
|
||||
print(f"⚠️ [Hub] Gemini Error: {e}")
|
||||
return None
|
||||
|
||||
def _call_anthropic(self, screenshot, anchor_image, description):
|
||||
try:
|
||||
import anthropic
|
||||
client = anthropic.Anthropic(api_key=self.anthropic_key)
|
||||
# Claude 3.5 Sonnet supporte la vision mais pas le format JSON strict en sortie nativement via config
|
||||
# On utilise un prompt renforcé
|
||||
prompt = f"Localise '{description}'. Réponds UNIQUEMENT en JSON : {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax], 'confidence': float}}"
|
||||
|
||||
content = [{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": self._to_base64(screenshot)}},
|
||||
{"type": "text", "text": prompt}]
|
||||
|
||||
response = client.messages.create(
|
||||
model="claude-3-5-sonnet-20241022",
|
||||
max_tokens=1000,
|
||||
messages=[{"role": "user", "content": content}]
|
||||
)
|
||||
text = response.content[0].text
|
||||
return json.loads(text[text.find('{'):text.rfind('}')+1])
|
||||
except Exception as e:
|
||||
print(f"⚠️ [Hub] Anthropic Error: {e}")
|
||||
return None
|
||||
|
||||
def _call_ollama_local(self, screenshot, anchor_image, description):
|
||||
"""Appel à Ollama local (Mode DGX Spark / Offline)"""
|
||||
try:
|
||||
import requests
|
||||
print(f"🏠 [Hub] Fallback Local Ollama ({self.local_model})...")
|
||||
prompt = f"Localise l'élément '{description}'. Retourne JSON: {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax] (0-1000)}}"
|
||||
|
||||
payload = {
|
||||
"model": self.local_model,
|
||||
"prompt": prompt,
|
||||
"images": [self._to_base64(screenshot)],
|
||||
"stream": False,
|
||||
"format": "json"
|
||||
}
|
||||
if anchor_image:
|
||||
payload["images"].append(self._to_base64(anchor_image))
|
||||
|
||||
response = requests.post(f"{self.ollama_url}/api/generate", json=payload, timeout=60)
|
||||
if response.status_code == 200:
|
||||
return json.loads(response.json().get('response', '{}'))
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"❌ [Hub] Local Ollama Error: {e}")
|
||||
return {"found": False, "error": str(e)}
|
||||
|
||||
# Instance unique
|
||||
vlm_hub = VLMProvider()
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user