app = FastAPI(
    servers=[
        {"url": "/", "description": "Default, relative server"},
        {
            "url": "http://staging.localhost.tiangolo.com:8000",
            "description": "Staging but actually localhost still",
        },
        {"url": "https://prod.example.com"},
    ]
)


@app.get("/foo")
def foo():
    return {"message": "Hello World"}


client = TestClient(app)

```

////

The last `CommonQueryParams`, in:

```Python
... Depends(CommonQueryParams)
```

...is what **FastAPI** will actually use to know what is the dependency.

It is from this one that FastAPI will extract the declared parameters and that is what FastAPI will actually call.

---

In this case, the first `CommonQueryParams`, in:

//// tab | Python 3.9+

```Python

The webhooks that you define will end up in the **OpenAPI** schema and the automatic **docs UI**.

/// info

The `app.webhooks` object is actually just an `APIRouter`, the same type you would use when structuring your app with multiple files.

///

## Return Type and Data Filtering { #return-type-and-data-filtering }

Let's continue from the previous example. We wanted to **annotate the function with one type**, but we wanted to be able to return from the function something that actually includes **more data**.

We want FastAPI to keep **filtering** the data using the response model. So that even though the function returns more data, the response will only include the fields declared in the response model.

from fastapi.testclient import TestClient

from docs_src.path_operation_advanced_configuration.tutorial006_py39 import app

client = TestClient(app)


def test_post():
    response = client.post("/items/", content=b"this is actually not validated")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "size": 30,
        "content": {
            "name": "Maaaagic",
            "price": 42,

* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).
    * This way you can update only the values actually set by the user, instead of overriding values already stored with default values in your model.
* Create a copy of the stored model, updating its attributes with the received partial updates (using the `update` parameter).

Import `SQLModel` and create a database model:

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

The `Hero` class is very similar to a Pydantic model (in fact, underneath, it actually *is a Pydantic model*).

There are a few differences:

```

Using `Optional[str]` instead of just `str` will let the editor help you detect errors where you could be assuming that a value is always a `str`, when it could actually be `None` too.

`Optional[Something]` is actually a shortcut for `Union[Something, None]`, they are equivalent.

This also means that in Python 3.10, you can use `Something | None`:

//// tab | Python 3.10+

```Python hl_lines="1"

Now that we have all the security flow, let's make the application actually secure, using <abbr title="JSON Web Tokens">JWT</abbr> tokens and secure password hashing.

This code is something you can actually use in your application, save the password hashes in your database, etc.

We are going to start from where we left in the previous chapter and increment it.

/// note | Technical Details

You could also use `from starlette.staticfiles import StaticFiles`.

**FastAPI** provides the same `starlette.staticfiles` as `fastapi.staticfiles` just as a convenience for you, the developer. But it actually comes directly from Starlette.

///

### What is "Mounting" { #what-is-mounting }

"Mounting" means adding a complete "independent" application in a specific path, that then takes care of handling all the sub-paths.