"""
Centralized configuration loader for the application.
Loads graph_config.yaml once and provides easy access to prompts with variable injection.
"""
from pathlib import Path
import yaml
# Import auto-generated models from config_models.py
# To regenerate: just generate-models
from src.config.config_models import Model as GraphConfig
class _ConfigLoader:
"""Internal singleton configuration loader."""
CONFIG_FILENAME = "graph_config.yaml"
_instance: "_ConfigLoader | None" = None
_config: GraphConfig | None = None
def __new__(cls):
if cls._instance is None:
cls._instance = super(_ConfigLoader, cls).__new__(cls)
return cls._instance
def __init__(self):
if self._config is None:
self._load_config()
def _load_config(self):
"""Load the graph configuration from YAML file and validate with Pydantic."""
# Get the root directory (ml-mcp)
root_dir = Path(__file__).parent.parent.parent
config_path = root_dir / self.CONFIG_FILENAME
try:
with open(config_path, "r", encoding="utf-8") as f:
yaml_data = yaml.safe_load(f)
# Parse and validate with Pydantic
self._config = GraphConfig(**yaml_data)
except FileNotFoundError:
raise FileNotFoundError(f"Configuration file not found at {config_path}")
except yaml.YAMLError as e:
raise ValueError(f"Invalid YAML in configuration file: {e}")
def get(self) -> GraphConfig:
"""Get the loaded configuration."""
if self._config is None:
self._load_config()
return self._config # type: ignore
def reload(self):
"""Reload the configuration from disk."""
self._load_config()
# Convenience function for easy access with full autocomplete
def get_config() -> GraphConfig:
"""Get the singleton configuration instance with full IDE autocomplete support."""
return _ConfigLoader().get()
