For example, in an HTTP Bearer token scheme, the client will send a header
    like:

    ```
    Authorization: Bearer deadbeef12346
    ```

    In this case:

    * `scheme` will have the value `"Bearer"`
    * `credentials` will have the value `"deadbeef12346"`
    """

    scheme: Annotated[
        str,
        Doc(
            """

    ```

FastAPI will use this return type to:

* **Validate** the returned data.
    * If the data is invalid (e.g. you are missing a field), it means that *your* app code is broken, not returning what it should, and it will return a server error instead of returning incorrect data. This way you and your clients can be certain that they will receive the data and the data shape expected.

You will have inline errors for the data that you send:

<img src="/img/tutorial/generate-clients/image04.png">

The response object will also have autocompletion:

<img src="/img/tutorial/generate-clients/image05.png">

## FastAPI App with Tags

In many cases your FastAPI app will be bigger, and you will probably use tags to separate different groups of *path operations*.

            """
            'Whitelist' validation step. The parameter field will be the single one
            allowed by the alias or set of aliases defined.
            """
        ),
    ] = None,
    serialization_alias: Annotated[
        Union[str, None],
        Doc(
            """
            'Blacklist' validation step. The vanilla parameter field will be the

```JSON
{
    "message": "Hello World",
    "root_path": "/api/v1"
}
```

So, it won't expect to be accessed at `http://127.0.0.1:8000/api/v1/app`.

You will see the JSON response as:

```JSON
{"message": "Hello World"}
```

### Interactive API docs

Now go to <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server
    * **Starlette**: (uses Uvicorn) a web microframework

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures

) -> Union[DefaultPlaceholder, DefaultType]:
    """
    Pass items or `DefaultPlaceholder`s by descending priority.

    The first one to _not_ be a `DefaultPlaceholder` will be returned.

    Otherwise, the first item (a `DefaultPlaceholder`) will be returned.
    """
    items = (first_item,) + extra_items
    for item in items:
        if not isinstance(item, DefaultPlaceholder):
            return item

    <div class="termy">

    ```console
    $ pip install "uvicorn[standard]"

    ---> 100%
    ```

    </div>

    !!! tip
        By adding the `standard`, Uvicorn will install and use some recommended extra dependencies.

        That including `uvloop`, the high-performance drop-in replacement for `asyncio`, that provides the big concurrency performance boost.