Settings and Environment Variables

In many cases your application could need some external settings or configurations, for example secret keys, database credentials, credentials for email services, etc.

Most of these settings are variable (can change), like database URLs. And many could be sensitive, like secrets.

For this reason it's common to provide them in environment variables that are read by the application.

An environment variable (also known as an env var) is a value that lives outside of the Python code, in the operating system, and can be read by your application and other programs.

You can create an environment variable for a command when you run it. You will see the platform-specific commands below.

These environment variables can only handle text strings, as they are external to Python and have to be compatible with other programs and the rest of the system (and even with different operating systems, such as Linux, Windows, and macOS).

That means that any value read in Python from an environment variable will be a str, and any conversion to a different type or any validation has to be done in code.

Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with Pydantic: Settings management.

Add the pydantic-settings package to your project:

$ uv add pydantic-settings
---> 100%

It also comes included when you install the all extras with:

$ uv add "fastapi[all]"
---> 100%

Import BaseSettings from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with Field().

from fastapi import FastAPI
from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50


settings = Settings()
app = FastAPI()


@app.get("/info")
async def info():
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,
    }

Then, when you create an instance of that Settings class (in this case, in the settings object), Pydantic will read the environment variables in a case-insensitive way, so, an upper-case variable APP_NAME will still be read for the attribute app_name.

Next it will convert and validate the data. So, when you use that settings object, you will have data of the types you declared (e.g. items_per_user will be an int).

Then you can use the new settings object in your application:

from fastapi import FastAPI
from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50


settings = Settings()
app = FastAPI()


@app.get("/info")
async def info():
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,
    }

Next, you would run the server passing the configurations as environment variables, for example you could set an ADMIN_EMAIL and APP_NAME with:

$ ADMIN_EMAIL="deadpool@example.com" APP_NAME="ChimichangApp" uv run fastapi run main.py

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

$ $Env:ADMIN_EMAIL = "deadpool@example.com"
$ $Env:APP_NAME = "ChimichangApp"
$ uv run fastapi run main.py

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

And then the admin_email setting would be set to "deadpool@example.com".

The app_name would be "ChimichangApp".

And the items_per_user would keep its default value of 50.

You could put those settings in another module file as you saw in Bigger Applications - Multiple Files.

For example, you could have a file config.py with:

from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50


settings = Settings()

And then use it in a file main.py:

from fastapi import FastAPI

from .config import settings

app = FastAPI()


@app.get("/info")
async def info():
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,
    }

In some occasions it might be useful to provide the settings from a dependency, instead of having a global object with settings that is used everywhere.

This could be especially useful during testing, as it's very easy to override a dependency with your own custom settings.

Coming from the previous example, your config.py file could look like:

from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50

Notice that now we don't create a default instance settings = Settings().

Now we create a dependency that returns a new config.Settings().

from functools import lru_cache
from typing import Annotated

from fastapi import Depends, FastAPI

from .config import Settings

app = FastAPI()


@lru_cache
def get_settings():
    return Settings()


@app.get("/info")
async def info(settings: Annotated[Settings, Depends(get_settings)]):
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,
    }

And then we can require it from the path operation function as a dependency and use it anywhere we need it.

from functools import lru_cache
from typing import Annotated

from fastapi import Depends, FastAPI

from .config import Settings

app = FastAPI()


@lru_cache
def get_settings():
    return Settings()


@app.get("/info")
async def info(settings: Annotated[Settings, Depends(get_settings)]):
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,
    }

Then it would be very easy to provide a different settings object during testing by creating a dependency override for get_settings:

from fastapi.testclient import TestClient

from .config import Settings
from .main import app, get_settings

client = TestClient(app)


def get_settings_override():
    return Settings(admin_email="testing_admin@example.com")


app.dependency_overrides[get_settings] = get_settings_override


def test_app():
    response = client.get("/info")
    data = response.json()
    assert data == {
        "app_name": "Awesome API",
        "admin_email": "testing_admin@example.com",
        "items_per_user": 50,
    }

In the dependency override we set a new value for the admin_email when creating the new Settings object, and then we return that new object.

Then we can test that it is used.

If you have many settings that possibly change a lot, maybe in different environments, it might be useful to put them on a file and then read them from it as if they were environment variables.

This practice is common enough that it has a name, these environment variables are commonly placed in a file .env, and the file is called a "dotenv".

Pydantic has support for reading from these types of files using an external library. You can read more at Pydantic Settings: Dotenv (.env) support.

You could have a .env file with:

ADMIN_EMAIL="deadpool@example.com"
APP_NAME="ChimichangApp"

And then update your config.py with:

from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50

    model_config = SettingsConfigDict(env_file=".env")

Here we define the config env_file inside of your Pydantic Settings class, and set the value to the filename with the dotenv file we want to use.

Reading a file from disk is normally a costly (slow) operation, so you probably want to do it only once and then reuse the same settings object, instead of reading it for each request.

But every time we do:

Settings()

a new Settings object would be created, and at creation it would read the .env file again.

If the dependency function was just like:

def get_settings():
    return Settings()

we would create that object for each request, and we would be reading the .env file for each request. ⚠️

But as we are using the @lru_cache decorator on top, the Settings object will be created only once, the first time it's called. ✔️

from functools import lru_cache
from typing import Annotated

from fastapi import Depends, FastAPI

from . import config

app = FastAPI()


@lru_cache
def get_settings():
    return config.Settings()


@app.get("/info")
async def info(settings: Annotated[config.Settings, Depends(get_settings)]):
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,
    }

Then for any subsequent call of get_settings() in the dependencies for the next requests, instead of executing the internal code of get_settings() and creating a new Settings object, it will return the same object that was returned on the first call, again and again.

@lru_cache modifies the function it decorates to return the same value that was returned the first time, instead of computing it again, executing the code of the function every time.

So, the function below it will be executed once for each combination of arguments. And then the values returned by each of those combinations of arguments will be used again and again whenever the function is called with exactly the same combination of arguments.

For example, if you have a function:

@lru_cache
def say_hi(name: str, salutation: str = "Ms."):
    return f"Hello {salutation} {name}"

your program could execute like this:

sequenceDiagram

participant code as Code
participant function as say_hi()
participant execute as Execute function

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Camila")
        function ->> execute: execute function code
        execute ->> code: return the result
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")
        function ->> code: return stored result
    end

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Rick")
        function ->> execute: execute function code
        execute ->> code: return the result
    end

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Rick", salutation="Mr.")
        function ->> execute: execute function code
        execute ->> code: return the result
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Rick")
        function ->> code: return stored result
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")
        function ->> code: return stored result
    end

In the case of our dependency get_settings(), the function doesn't even take any arguments, so it always returns the same value.

That way, it behaves almost as if it was just a global variable. But as it uses a dependency function, then we can override it easily for testing.

@lru_cache is part of functools which is part of Python's standard library, you can read more about it in the Python docs for @lru_cache.

You can use Pydantic Settings to handle the settings or configurations for your application, with all the power of Pydantic models.

• By using a dependency you can simplify testing.
• You can use .env files with it.
• Using @lru_cache lets you avoid reading the dotenv file again and again for each request, while allowing you to override it during testing.