v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution
- Frontend v4 accessible sur réseau local (192.168.1.40) - Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard) - Ollama GPU fonctionnel - Self-healing interactif - Dashboard confiance Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Dom
652
core/config.py
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652
core/config.py
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@@ -0,0 +1,652 @@
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"""
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Configuration centralisée pour RPA Vision V3
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Gestionnaire de configuration unifié qui élimine les incohérences entre composants.
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Utilise les variables d'environnement avec des valeurs par défaut sensées.
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En production, définir ENVIRONMENT=production pour forcer la configuration.
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"""
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import os
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import logging
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from pathlib import Path
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from dataclasses import dataclass, field
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from typing import Optional, List, Dict, Any, Callable
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import json
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from datetime import datetime
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logger = logging.getLogger(__name__)
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@dataclass
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class ValidationError:
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"""Erreur de validation de configuration"""
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field: str
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value: Any
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message: str
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severity: str = "error" # error, warning
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@dataclass
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class SystemConfig:
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"""Configuration système unifiée - Point central de toute configuration"""
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# Chemins unifiés
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base_path: Path = field(default_factory=lambda: Path.cwd())
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data_path: Path = field(default_factory=lambda: Path("data"))
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logs_path: Path = field(default_factory=lambda: Path("logs"))
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# Services
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api_host: str = "0.0.0.0"
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api_port: int = 8000
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dashboard_host: str = "0.0.0.0"
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dashboard_port: int = 5001
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worker_threads: int = 4
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# Base de données
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sessions_path: Path = field(default_factory=lambda: Path("data/sessions"))
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workflows_path: Path = field(default_factory=lambda: Path("data/workflows"))
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embeddings_path: Path = field(default_factory=lambda: Path("data/embeddings"))
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faiss_index_path: Path = field(default_factory=lambda: Path("data/faiss_index"))
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screenshots_path: Path = field(default_factory=lambda: Path("data/screenshots"))
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training_path: Path = field(default_factory=lambda: Path("data/training"))
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uploads_path: Path = field(default_factory=lambda: Path("data/training/uploads"))
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# Sécurité
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secret_key: str = "dev_secret_key_not_for_production"
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encryption_password: str = "dev_default_key_not_for_production"
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auth_enabled: bool = True
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allowed_origins: List[str] = field(default_factory=lambda: ["*"])
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# Monitoring
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health_check_interval: int = 30
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metrics_enabled: bool = True
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# Environment
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environment: str = "development"
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debug: bool = False
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# Models
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clip_model: str = "ViT-B-32"
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clip_pretrained: str = "openai"
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clip_device: str = "cpu"
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vlm_model: str = "qwen3-vl:8b"
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vlm_endpoint: str = "http://localhost:11434"
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owl_model: str = "google/owlv2-base-patch16-ensemble"
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owl_confidence_threshold: float = 0.1
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# FAISS
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faiss_dimensions: int = 512
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faiss_index_type: str = "Flat"
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faiss_metric: str = "cosine"
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faiss_nprobe: int = 8
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faiss_auto_optimize: bool = True
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faiss_migration_threshold: int = 10000
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# GPU
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gpu_idle_timeout_seconds: int = 300
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gpu_vram_threshold_mb: int = 1024
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gpu_max_load_retries: int = 3
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gpu_load_timeout_seconds: int = 30
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gpu_unload_timeout_seconds: int = 5
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def __post_init__(self):
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"""Normalise les chemins après initialisation"""
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# Convertir tous les chemins relatifs en absolus basés sur base_path
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for field_name in self.__dataclass_fields__:
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value = getattr(self, field_name)
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if isinstance(value, Path) and not value.is_absolute():
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if field_name != 'base_path':
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setattr(self, field_name, self.base_path / value)
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def ensure_directories(self):
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"""Crée tous les répertoires nécessaires avec les bonnes permissions"""
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directories = [
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self.data_path, self.logs_path, self.sessions_path,
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self.workflows_path, self.embeddings_path, self.faiss_index_path,
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self.screenshots_path, self.training_path, self.uploads_path
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]
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for directory in directories:
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try:
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directory.mkdir(parents=True, exist_ok=True)
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# Vérifier les permissions d'écriture
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test_file = directory / ".write_test"
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test_file.touch()
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test_file.unlink()
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logger.debug(f"Directory created/verified: {directory}")
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except Exception as e:
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logger.error(f"Failed to create/verify directory {directory}: {e}")
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raise
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class ConfigurationManager:
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"""Gestionnaire centralisé de configuration - Point unique de vérité"""
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def __init__(self):
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self._config: Optional[SystemConfig] = None
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self._config_watchers: List[Callable[[SystemConfig], None]] = []
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self._last_loaded: Optional[datetime] = None
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def load_config(self) -> SystemConfig:
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"""Charge la configuration depuis les variables d'environnement et fichiers"""
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try:
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# Charger depuis les variables d'environnement
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config = self._load_from_env()
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# Charger depuis le fichier de config si présent
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config_file = Path(".env")
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if config_file.exists():
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config = self._merge_from_file(config, config_file)
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# Valider la configuration
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validation_errors = self.validate_config(config)
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if any(error.severity == "error" for error in validation_errors):
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error_messages = [f"{error.field}: {error.message}"
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for error in validation_errors if error.severity == "error"]
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raise ValueError(f"Configuration validation failed: {'; '.join(error_messages)}")
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# Afficher les warnings
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warnings = [error for error in validation_errors if error.severity == "warning"]
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for warning in warnings:
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logger.warning(f"Configuration warning - {warning.field}: {warning.message}")
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# Créer les répertoires
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config.ensure_directories()
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self._config = config
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self._last_loaded = datetime.now()
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# Notifier les watchers
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for watcher in self._config_watchers:
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try:
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watcher(config)
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except Exception as e:
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logger.error(f"Configuration watcher failed: {e}")
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logger.info(f"Configuration loaded successfully (environment: {config.environment})")
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return config
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except Exception as e:
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logger.error(f"Failed to load configuration: {e}")
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raise
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def _load_from_env(self) -> SystemConfig:
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"""Charge la configuration depuis les variables d'environnement"""
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base_path = Path(os.getenv("BASE_PATH", Path.cwd()))
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return SystemConfig(
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# Chemins
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base_path=base_path,
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data_path=Path(os.getenv("DATA_PATH", "data")),
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logs_path=Path(os.getenv("LOGS_PATH", "logs")),
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sessions_path=Path(os.getenv("SESSIONS_PATH", "data/sessions")),
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workflows_path=Path(os.getenv("WORKFLOWS_PATH", "data/workflows")),
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embeddings_path=Path(os.getenv("EMBEDDINGS_PATH", "data/embeddings")),
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faiss_index_path=Path(os.getenv("FAISS_INDEX_PATH", "data/faiss_index")),
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screenshots_path=Path(os.getenv("SCREENSHOTS_PATH", "data/screenshots")),
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training_path=Path(os.getenv("TRAINING_PATH", "data/training")),
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uploads_path=Path(os.getenv("UPLOADS_PATH", "data/training/uploads")),
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# Services
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api_host=os.getenv("API_HOST", "0.0.0.0"),
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api_port=int(os.getenv("API_PORT", "8000")),
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dashboard_host=os.getenv("DASHBOARD_HOST", "0.0.0.0"),
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dashboard_port=int(os.getenv("DASHBOARD_PORT", "5001")),
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worker_threads=int(os.getenv("WORKER_THREADS", "4")),
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# Sécurité
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secret_key=os.getenv("SECRET_KEY", "dev_secret_key_not_for_production"),
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encryption_password=os.getenv("ENCRYPTION_PASSWORD", "dev_default_key_not_for_production"),
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auth_enabled=os.getenv("AUTH_ENABLED", "true").lower() == "true",
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allowed_origins=os.getenv("ALLOWED_ORIGINS", "*").split(","),
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# Monitoring
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health_check_interval=int(os.getenv("HEALTH_CHECK_INTERVAL", "30")),
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metrics_enabled=os.getenv("METRICS_ENABLED", "true").lower() == "true",
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# Environment
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environment=os.getenv("ENVIRONMENT", "development"),
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debug=os.getenv("DEBUG", "false").lower() == "true",
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# Models
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clip_model=os.getenv("CLIP_MODEL", "ViT-B-32"),
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clip_pretrained=os.getenv("CLIP_PRETRAINED", "openai"),
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clip_device=os.getenv("CLIP_DEVICE", "cpu"),
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vlm_model=os.getenv("VLM_MODEL", "qwen3-vl:8b"),
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vlm_endpoint=os.getenv("VLM_ENDPOINT", "http://localhost:11434"),
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owl_model=os.getenv("OWL_MODEL", "google/owlv2-base-patch16-ensemble"),
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owl_confidence_threshold=float(os.getenv("OWL_CONFIDENCE_THRESHOLD", "0.1")),
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# FAISS
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faiss_dimensions=int(os.getenv("FAISS_DIMENSIONS", "512")),
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faiss_index_type=os.getenv("FAISS_INDEX_TYPE", "Flat"),
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faiss_metric=os.getenv("FAISS_METRIC", "cosine"),
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faiss_nprobe=int(os.getenv("FAISS_NPROBE", "8")),
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faiss_auto_optimize=os.getenv("FAISS_AUTO_OPTIMIZE", "true").lower() == "true",
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faiss_migration_threshold=int(os.getenv("FAISS_MIGRATION_THRESHOLD", "10000")),
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# GPU
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gpu_idle_timeout_seconds=int(os.getenv("GPU_IDLE_TIMEOUT", "300")),
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gpu_vram_threshold_mb=int(os.getenv("GPU_VRAM_THRESHOLD_MB", "1024")),
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gpu_max_load_retries=int(os.getenv("GPU_MAX_LOAD_RETRIES", "3")),
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gpu_load_timeout_seconds=int(os.getenv("GPU_LOAD_TIMEOUT", "30")),
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gpu_unload_timeout_seconds=int(os.getenv("GPU_UNLOAD_TIMEOUT", "5"))
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)
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def _merge_from_file(self, config: SystemConfig, config_file: Path) -> SystemConfig:
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"""Merge configuration from file (future enhancement)"""
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# Pour l'instant, on utilise seulement les variables d'environnement
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# Cette méthode peut être étendue pour supporter les fichiers JSON/YAML
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return config
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def validate_config(self, config: SystemConfig) -> List[ValidationError]:
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"""Valide la cohérence de la configuration"""
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errors = []
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# Validation de l'environnement de production
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if config.environment == "production":
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if config.secret_key == "dev_secret_key_not_for_production":
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errors.append(ValidationError(
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"secret_key", config.secret_key,
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"SECRET_KEY must be set in production environment"
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))
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if config.encryption_password == "dev_default_key_not_for_production":
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errors.append(ValidationError(
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"encryption_password", config.encryption_password,
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"ENCRYPTION_PASSWORD must be set in production environment"
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))
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if config.debug:
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errors.append(ValidationError(
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"debug", config.debug,
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"DEBUG should be false in production environment",
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"warning"
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))
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# Validation des ports
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if config.api_port == config.dashboard_port:
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errors.append(ValidationError(
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"ports", f"api:{config.api_port}, dashboard:{config.dashboard_port}",
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"API and Dashboard ports must be different"
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))
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if not (1024 <= config.api_port <= 65535):
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errors.append(ValidationError(
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"api_port", config.api_port,
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"API port must be between 1024 and 65535"
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))
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if not (1024 <= config.dashboard_port <= 65535):
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errors.append(ValidationError(
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"dashboard_port", config.dashboard_port,
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"Dashboard port must be between 1024 and 65535"
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))
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# Validation des chemins
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try:
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config.base_path.resolve()
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except Exception as e:
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errors.append(ValidationError(
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"base_path", config.base_path,
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f"Invalid base path: {e}"
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))
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# Validation des modèles
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if config.faiss_dimensions <= 0:
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errors.append(ValidationError(
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"faiss_dimensions", config.faiss_dimensions,
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"FAISS dimensions must be positive"
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))
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if config.worker_threads <= 0:
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errors.append(ValidationError(
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"worker_threads", config.worker_threads,
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"Worker threads must be positive"
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))
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# Validation des timeouts
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if config.health_check_interval <= 0:
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errors.append(ValidationError(
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"health_check_interval", config.health_check_interval,
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"Health check interval must be positive"
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))
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return errors
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def apply_config(self, config: SystemConfig):
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"""Applique une nouvelle configuration"""
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# Valider d'abord
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validation_errors = self.validate_config(config)
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if any(error.severity == "error" for error in validation_errors):
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error_messages = [f"{error.field}: {error.message}"
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for error in validation_errors if error.severity == "error"]
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raise ValueError(f"Configuration validation failed: {'; '.join(error_messages)}")
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# Créer les répertoires
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config.ensure_directories()
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# Appliquer la configuration
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old_config = self._config
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self._config = config
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self._last_loaded = datetime.now()
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# Notifier les watchers du changement
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for watcher in self._config_watchers:
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try:
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watcher(config)
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except Exception as e:
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logger.error(f"Configuration watcher failed during apply: {e}")
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# En cas d'erreur, restaurer l'ancienne configuration
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self._config = old_config
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raise
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logger.info("Configuration applied successfully")
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def watch_config_changes(self, callback: Callable[[SystemConfig], None]):
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"""Enregistre un callback pour les changements de configuration"""
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self._config_watchers.append(callback)
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# Si on a déjà une configuration, appeler immédiatement le callback
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if self._config:
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try:
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callback(self._config)
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except Exception as e:
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logger.error(f"Configuration watcher failed during registration: {e}")
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def get_config(self) -> SystemConfig:
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"""Récupère la configuration actuelle"""
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if self._config is None:
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return self.load_config()
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return self._config
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def reload_config(self) -> SystemConfig:
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"""Recharge la configuration depuis les sources"""
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return self.load_config()
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# Instance globale du gestionnaire de configuration
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_config_manager: Optional[ConfigurationManager] = None
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def get_configuration_manager() -> ConfigurationManager:
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"""Récupère le gestionnaire de configuration global (singleton)"""
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global _config_manager
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if _config_manager is None:
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_config_manager = ConfigurationManager()
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return _config_manager
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def get_config() -> SystemConfig:
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"""Récupère la configuration système unifiée"""
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return get_configuration_manager().get_config()
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def reload_config() -> SystemConfig:
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"""Recharge la configuration système"""
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return get_configuration_manager().reload_config()
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# Backward compatibility - Keep old classes for gradual migration
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@dataclass
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class ServerConfig:
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"""Configuration du serveur - DEPRECATED: Use SystemConfig instead"""
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api_host: str = "0.0.0.0"
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api_port: int = 8000
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dashboard_host: str = "0.0.0.0"
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dashboard_port: int = 5001
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environment: str = "development"
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debug: bool = False
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@classmethod
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def from_env(cls) -> 'ServerConfig':
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logger.warning("ServerConfig is deprecated. Use SystemConfig via get_config() instead.")
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config = get_config()
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return cls(
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api_host=config.api_host,
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api_port=config.api_port,
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dashboard_host=config.dashboard_host,
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dashboard_port=config.dashboard_port,
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environment=config.environment,
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debug=config.debug
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)
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||||
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||||
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@dataclass
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class SecurityConfig:
|
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"""Configuration de sécurité - DEPRECATED: Use SystemConfig instead"""
|
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encryption_password: Optional[str] = None
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secret_key: Optional[str] = None
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allowed_origins: List[str] = field(default_factory=lambda: ["*"])
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@classmethod
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def from_env(cls, environment: str = "development") -> 'SecurityConfig':
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logger.warning("SecurityConfig is deprecated. Use SystemConfig via get_config() instead.")
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config = get_config()
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return cls(
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encryption_password=config.encryption_password,
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secret_key=config.secret_key,
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allowed_origins=config.allowed_origins
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)
|
||||
|
||||
|
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@dataclass
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class ModelConfig:
|
||||
"""Configuration des modèles ML - DEPRECATED: Use SystemConfig instead"""
|
||||
clip_model: str = "ViT-B-32"
|
||||
clip_pretrained: str = "openai"
|
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clip_device: str = "cpu"
|
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vlm_model: str = "qwen3-vl:8b"
|
||||
vlm_endpoint: str = "http://localhost:11434"
|
||||
owl_model: str = "google/owlv2-base-patch16-ensemble"
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||||
owl_confidence_threshold: float = 0.1
|
||||
|
||||
@classmethod
|
||||
def from_env(cls) -> 'ModelConfig':
|
||||
logger.warning("ModelConfig is deprecated. Use SystemConfig via get_config() instead.")
|
||||
config = get_config()
|
||||
return cls(
|
||||
clip_model=config.clip_model,
|
||||
clip_pretrained=config.clip_pretrained,
|
||||
clip_device=config.clip_device,
|
||||
vlm_model=config.vlm_model,
|
||||
vlm_endpoint=config.vlm_endpoint,
|
||||
owl_model=config.owl_model,
|
||||
owl_confidence_threshold=config.owl_confidence_threshold
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PathConfig:
|
||||
"""Configuration des chemins - DEPRECATED: Use SystemConfig instead"""
|
||||
data_path: Path = field(default_factory=lambda: Path("data"))
|
||||
models_path: Path = field(default_factory=lambda: Path("models"))
|
||||
logs_path: Path = field(default_factory=lambda: Path("logs"))
|
||||
uploads_path: Path = field(default_factory=lambda: Path("data/training/uploads"))
|
||||
sessions_path: Path = field(default_factory=lambda: Path("data/training/sessions"))
|
||||
|
||||
@classmethod
|
||||
def from_env(cls) -> 'PathConfig':
|
||||
logger.warning("PathConfig is deprecated. Use SystemConfig via get_config() instead.")
|
||||
config = get_config()
|
||||
return cls(
|
||||
data_path=config.data_path,
|
||||
models_path=Path("models"), # Not in SystemConfig yet
|
||||
logs_path=config.logs_path,
|
||||
uploads_path=config.uploads_path,
|
||||
sessions_path=config.sessions_path
|
||||
)
|
||||
|
||||
def ensure_directories(self):
|
||||
"""Crée tous les répertoires nécessaires"""
|
||||
config = get_config()
|
||||
config.ensure_directories()
|
||||
|
||||
|
||||
@dataclass
|
||||
class FAISSConfig:
|
||||
"""Configuration FAISS - DEPRECATED: Use SystemConfig instead"""
|
||||
dimensions: int = 512
|
||||
index_type: str = "Flat"
|
||||
metric: str = "cosine"
|
||||
nprobe: int = 8
|
||||
auto_optimize: bool = True
|
||||
migration_threshold: int = 10000
|
||||
|
||||
@classmethod
|
||||
def from_env(cls) -> 'FAISSConfig':
|
||||
logger.warning("FAISSConfig is deprecated. Use SystemConfig via get_config() instead.")
|
||||
config = get_config()
|
||||
return cls(
|
||||
dimensions=config.faiss_dimensions,
|
||||
index_type=config.faiss_index_type,
|
||||
metric=config.faiss_metric,
|
||||
nprobe=config.faiss_nprobe,
|
||||
auto_optimize=config.faiss_auto_optimize,
|
||||
migration_threshold=config.faiss_migration_threshold
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class GPUResourceConfig:
|
||||
"""Configuration for GPU resource management - DEPRECATED: Use SystemConfig instead"""
|
||||
ollama_endpoint: str = "http://localhost:11434"
|
||||
vlm_model: str = "qwen3-vl:8b"
|
||||
clip_model: str = "ViT-B-32"
|
||||
idle_timeout_seconds: int = 300
|
||||
vram_threshold_for_clip_gpu_mb: int = 1024
|
||||
max_load_retries: int = 3
|
||||
load_timeout_seconds: int = 30
|
||||
unload_timeout_seconds: int = 5
|
||||
|
||||
@classmethod
|
||||
def from_env(cls) -> 'GPUResourceConfig':
|
||||
logger.warning("GPUResourceConfig is deprecated. Use SystemConfig via get_config() instead.")
|
||||
config = get_config()
|
||||
return cls(
|
||||
ollama_endpoint=config.vlm_endpoint,
|
||||
vlm_model=config.vlm_model,
|
||||
clip_model=config.clip_model,
|
||||
idle_timeout_seconds=config.gpu_idle_timeout_seconds,
|
||||
vram_threshold_for_clip_gpu_mb=config.gpu_vram_threshold_mb,
|
||||
max_load_retries=config.gpu_max_load_retries,
|
||||
load_timeout_seconds=config.gpu_load_timeout_seconds,
|
||||
unload_timeout_seconds=config.gpu_unload_timeout_seconds
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class AppConfig:
|
||||
"""Configuration globale de l'application - DEPRECATED: Use SystemConfig instead"""
|
||||
server: ServerConfig
|
||||
security: SecurityConfig
|
||||
models: ModelConfig
|
||||
paths: PathConfig
|
||||
faiss: FAISSConfig
|
||||
gpu: GPUResourceConfig = field(default_factory=GPUResourceConfig)
|
||||
|
||||
@classmethod
|
||||
def from_env(cls) -> 'AppConfig':
|
||||
"""Charge la configuration depuis les variables d'environnement"""
|
||||
logger.warning("AppConfig is deprecated. Use SystemConfig via get_config() instead.")
|
||||
return cls(
|
||||
server=ServerConfig.from_env(),
|
||||
security=SecurityConfig.from_env(),
|
||||
models=ModelConfig.from_env(),
|
||||
paths=PathConfig.from_env(),
|
||||
faiss=FAISSConfig.from_env(),
|
||||
gpu=GPUResourceConfig.from_env()
|
||||
)
|
||||
|
||||
|
||||
# Exemple de fichier .env
|
||||
# Exemple de fichier .env
|
||||
ENV_TEMPLATE = """
|
||||
# RPA Vision V3 Configuration Unifiée
|
||||
# Copier ce fichier en .env et modifier les valeurs
|
||||
|
||||
# Environment
|
||||
ENVIRONMENT=development # development, staging, production
|
||||
DEBUG=false
|
||||
|
||||
# Base Path
|
||||
BASE_PATH=/opt/rpa_vision_v3 # Production path, use . for development
|
||||
|
||||
# Server
|
||||
API_HOST=0.0.0.0
|
||||
API_PORT=8000
|
||||
DASHBOARD_HOST=0.0.0.0
|
||||
DASHBOARD_PORT=5001
|
||||
WORKER_THREADS=4
|
||||
|
||||
# Security (REQUIRED in production)
|
||||
# SECRET_KEY=your_secure_secret_key_here
|
||||
# ENCRYPTION_PASSWORD=your_secure_password_here
|
||||
AUTH_ENABLED=true
|
||||
# ALLOWED_ORIGINS=https://yourdomain.com,https://api.yourdomain.com
|
||||
|
||||
# Data Paths (relative to BASE_PATH)
|
||||
DATA_PATH=data
|
||||
LOGS_PATH=logs
|
||||
SESSIONS_PATH=data/sessions
|
||||
WORKFLOWS_PATH=data/workflows
|
||||
EMBEDDINGS_PATH=data/embeddings
|
||||
FAISS_INDEX_PATH=data/faiss_index
|
||||
SCREENSHOTS_PATH=data/screenshots
|
||||
TRAINING_PATH=data/training
|
||||
UPLOADS_PATH=data/training/uploads
|
||||
|
||||
# Models
|
||||
CLIP_MODEL=ViT-B-32
|
||||
CLIP_PRETRAINED=openai
|
||||
CLIP_DEVICE=cpu
|
||||
VLM_MODEL=qwen3-vl:8b
|
||||
VLM_ENDPOINT=http://localhost:11434
|
||||
OWL_MODEL=google/owlv2-base-patch16-ensemble
|
||||
OWL_CONFIDENCE_THRESHOLD=0.1
|
||||
|
||||
# FAISS
|
||||
FAISS_DIMENSIONS=512
|
||||
FAISS_INDEX_TYPE=Flat
|
||||
FAISS_METRIC=cosine
|
||||
FAISS_NPROBE=8
|
||||
FAISS_AUTO_OPTIMIZE=true
|
||||
FAISS_MIGRATION_THRESHOLD=10000
|
||||
|
||||
# GPU
|
||||
GPU_IDLE_TIMEOUT=300
|
||||
GPU_VRAM_THRESHOLD_MB=1024
|
||||
GPU_MAX_LOAD_RETRIES=3
|
||||
GPU_LOAD_TIMEOUT=30
|
||||
GPU_UNLOAD_TIMEOUT=5
|
||||
|
||||
# Monitoring
|
||||
HEALTH_CHECK_INTERVAL=30
|
||||
METRICS_ENABLED=true
|
||||
"""
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Test de la configuration unifiée
|
||||
config_manager = get_configuration_manager()
|
||||
config = config_manager.load_config()
|
||||
|
||||
print("=== Configuration Système Unifiée ===")
|
||||
print(f"Environment: {config.environment}")
|
||||
print(f"Base Path: {config.base_path}")
|
||||
print(f"Data Path: {config.data_path}")
|
||||
print(f"API Port: {config.api_port}")
|
||||
print(f"Dashboard Port: {config.dashboard_port}")
|
||||
print(f"Sessions Path: {config.sessions_path}")
|
||||
print(f"CLIP Model: {config.clip_model}")
|
||||
print(f"Auth Enabled: {config.auth_enabled}")
|
||||
|
||||
# Test de validation
|
||||
validation_errors = config_manager.validate_config(config)
|
||||
if validation_errors:
|
||||
print("\n=== Validation Errors/Warnings ===")
|
||||
for error in validation_errors:
|
||||
print(f"[{error.severity.upper()}] {error.field}: {error.message}")
|
||||
else:
|
||||
print("\n✅ Configuration validation passed")
|
||||
|
||||
print(f"\n✅ Configuration Manager initialized successfully")
|
||||
Reference in New Issue
Block a user