71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
from typing import List
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from pydantic import RedisDsn
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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"""
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Configuración básica de la aplicación
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"""
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# Configuración básica de la aplicación
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APP_NAME: str = "File Manager API"
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DEBUG: bool = False
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HOST: str = "0.0.0.0"
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PORT: int = 8000
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# Configuración de CORS para React frontend
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ALLOWED_ORIGINS: List[str] = [
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"http://localhost:3000", # React dev server
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"http://localhost:5173",
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"http://frontend:3000", # Docker container name
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]
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REDIS_OM_URL: RedisDsn
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# Azure Blob Storage configuración
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AZURE_STORAGE_CONNECTION_STRING: str
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AZURE_STORAGE_ACCOUNT_NAME: str = ""
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AZURE_CONTAINER_NAME: str = "files"
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# Qdrant Vector DB configuración
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QDRANT_URL: str
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QDRANT_API_KEY: str
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VECTOR_DB_TYPE: str = "qdrant" # Para futuro: soportar otros tipos
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# Azure OpenAI configuración
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AZURE_OPENAI_ENDPOINT: str
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AZURE_OPENAI_API_KEY: str
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AZURE_OPENAI_API_VERSION: str = "2024-02-01"
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AZURE_OPENAI_EMBEDDING_MODEL: str = "text-embedding-3-large"
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AZURE_OPENAI_EMBEDDING_DEPLOYMENT: str = "text-embedding-3-large"
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# Rate limiting para embeddings (ajustar según tier de Azure OpenAI)
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# S0 tier: batch_size=16, delay=1.0 es seguro
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# Tier superior: batch_size=100, delay=0.1
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EMBEDDING_BATCH_SIZE: int = 16
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EMBEDDING_DELAY_BETWEEN_BATCHES: float = 1.0
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EMBEDDING_MAX_RETRIES: int = 5
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# Google Cloud / Vertex AI configuración
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GOOGLE_APPLICATION_CREDENTIALS: str
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GOOGLE_CLOUD_PROJECT: str
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GOOGLE_CLOUD_LOCATION: str = "us-central1"
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GEMINI_MODEL: str = "gemini-2.0-flash"
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# LandingAI configuración
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LANDINGAI_API_KEY: str
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LANDINGAI_ENVIRONMENT: str = "production" # "production" o "eu"
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# Schemas storage
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SCHEMAS_DIR: str = "./data/schemas"
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class Config:
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env_file = ".env"
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case_sensitive = True
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# Instancia global de configuración
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settings = Settings.model_validate({})
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