Using Pydantic to define and read in yaml configuration files. Provides built in config validation!

config.py

from yaml_base_model import YamlBaseModel
from pydantic import BaseModel

from typing import List

from enum import Enum 

class Category(Enum): 
    GREETING: str = "Greeting"
    CODE: str = "Code"

        
class Response(BaseModel): 
    """Dummy key, value response"""
    key: str
    value: str
    category: Category = Category.GREETING


class Config(YamlBaseModel): 
    """Dummy config file structure to read in"""
    data: List[Response]
    
   
if __name__ == "__main__": 
    # Sucessfully loaded config
    config = Config.from_yaml("./config.yaml")
    
    assert isinstance(config, Config)
    assert isinstance(config.data[0], Response)
    
    from pydantic import ValidationError
    # Fail to load config
    file = "./incorrect_config.yaml"
    try: 
        incorrect_config = Config.from_yaml(file)
    except ValidationError: 
        print(f"Unable to load {file}. Incountered a validation error")
    

config.yaml

data: 
  - key: Hello
    value: World
  - key: Python
    value: Pydantic
    category: Code
    

incorrect_config.yaml

data: 
  - key: Hello
    value: World
  - Fail to load because this is just a string

incorrect_config_2.yaml

data: 
  - key: Hello
    value: World
    category: Unsupported category. i.e. Free validation!

yaml_base_model.py

from pydantic import BaseModel
import yaml

from typing import Union
from pathlib import Path


class YamlBaseModel(BaseModel): 
    """Pydantic.BaseModel with yaml read class method. 
    
    Allows for documentation of yaml config structure and easy read in but with the pydantic runtime validation.
    
    """
    @classmethod 
    def from_yaml(cls, file: Union[str, Path]) -> "YamlBaseModel": 
        file = Path(file)
        
        with open(file, "r") as f: 
            data = yaml.safe_load(f)
            
        return cls.parse_obj(data)