Creating Config Files Using Pydantic

In FastAPI, using configuration files is a common practice to manage application settings, database credentials, and other environment-specific variables. Pydantic, a powerful data validation library, can be used to create and validate configuration files in a structured and type-safe manner.

Creating a Configuration Model

Import Pydantic:

Python

from pydantic import BaseModel

Define the Configuration Model:

Python

class Settings(BaseModel):

debug: bool = True

database_url: str = "postgresql://user:password@host:port/database"

secret_key: str

This Settings model defines three configuration options: debug, database_url, and secret_key. The debug option is set to True by default, while the database_url and secret_key options are left as required fields.

Loading the Configuration

Create a Function to Load the Configuration:

from dotenv import load_dotenv
from pathlib import Path

def get_settings():
load_dotenv()
return Settings()

This function loads environment variables from a .env file using the dotenv library and creates a Settings instance using the loaded values.

Using the Configuration in FastAPI

Create a Dependency:

from fastapi import Depends

def get_settings(settings: Settings = Depends(get_settings)):
return settings

Inject the Configuration into Endpoints:

from fastapi import APIRouter, Depends

router = APIRouter()

@router.get(“/”)
def read_root(settings: Settings = Depends(get_settings)):
return {“debug”: settings.debug, “database_url”: settings.database_url}

Additional Considerations

• Environment Variables: You can set configuration values as environment variables and load them using the dotenv library. This allows you to manage different configurations for different environments (e.g., development, testing, production).
• Secret Management: For sensitive information like API keys or database passwords, consider using a secret management service or storing them in environment variables that are not checked into your source code.
• Validation: Pydantic provides built-in validation to ensure that configuration values adhere to the specified types and constraints.
• Configuration Files: While using environment variables is a common approach, you can also store configuration values in dedicated configuration files (e.g., settings.py) and load them using Python’s built-in configuration modules.