"""
Configuration settings for the BERDL Datalake MCP Server.
This service enables AI assistants to interact with Delta Lake tables stored in MinIO through Spark,
implementing the Model Context Protocol (MCP) for natural language data operations.
"""
import logging
import os
from functools import lru_cache
from pydantic import BaseModel, Field
APP_VERSION = "0.1.0"
SERVICE_ROOT_PATH = "/apis/mcp"
class Settings(BaseModel):
"""
Application settings for the BERDL Datalake MCP Server.
"""
app_name: str = "BERDL Datalake MCP Server"
app_description: str = (
"FastAPI service for AI assistants to interact with Delta Lake tables via Spark"
)
api_version: str = APP_VERSION
service_root_path: str = os.getenv("SERVICE_ROOT_PATH", SERVICE_ROOT_PATH)
log_level: str = Field(
default=os.getenv("LOG_LEVEL", "INFO"),
description="Logging level for the application",
)
# HTTP request timeout - should be shorter than proxy/gateway timeout
# to ensure the server returns a clean 408 before proxy returns 504
request_timeout_seconds: float = Field(
default=float(os.getenv("REQUEST_TIMEOUT_SECONDS", "55")),
description=(
"Maximum time in seconds for HTTP requests before returning 408 timeout. "
"Set this lower than your proxy/gateway timeout (e.g., 55s if proxy is 60s)."
),
)
@lru_cache()
def get_settings() -> Settings:
"""
Get the application settings.
Uses lru_cache to avoid loading the settings for every request.
"""
return Settings()
def configure_logging():
"""Configure logging for the application."""
settings = get_settings()
log_level = getattr(logging, settings.log_level.upper(), logging.INFO)
logging.basicConfig(
level=log_level,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
if settings.log_level.upper() not in logging.getLevelNamesMapping():
logging.warning(
"Unrecognized log level '%s'. Falling back to 'INFO'.",
settings.log_level,
)
