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Python / FastAPI Basics Interview Questions

How do you manage environment variables and settings in FastAPI with Pydantic Settings?

Use pydantic-settings to define a typed settings class that reads from environment variables and .env files. Inject settings via a dependency so they can be overridden in tests.

# pip install pydantic-settings
from pydantic_settings import BaseSettings, SettingsConfigDict
from functools import lru_cache
from fastapi import FastAPI, Depends
from typing import Annotated

class Settings(BaseSettings):
    app_name:    str = "My API"
    debug:       bool = False
    db_url:      str = "sqlite:///./test.db"
    secret_key:  str
    jwt_expire_minutes: int = 30

    model_config = SettingsConfigDict(
        env_file=".env",          # read from .env file
        env_file_encoding="utf-8",
        case_sensitive=False,     # DB_URL and db_url both work
    )

@lru_cache  # singleton — reads .env once, cached for app lifetime
def get_settings() -> Settings:
    return Settings()

SettingsDep = Annotated[Settings, Depends(get_settings)]

app = FastAPI()

@app.get("/info")
def app_info(settings: SettingsDep):
    return {"app_name": settings.app_name, "debug": settings.debug}


# .env file (never commit to git)
SECRET_KEY=my-super-secret-key-here
DB_URL=postgresql+asyncpg://user:pass@localhost/mydb
DEBUG=false


# tests — override settings easily
from app.main import app, get_settings
from app.config import Settings

def override_settings():
    return Settings(secret_key="test-key", db_url="sqlite:///./test.db")

app.dependency_overrides[get_settings] = override_settings

Why decorate get_settings() with @lru_cache?
What is the advantage of injecting Settings via Depends() rather than importing the settings object directly?

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