!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

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

# Response Model - Return Type

You can declare the type used for the response by annotating the *path operation function* **return type**.

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

=== "Python 3.10+"

    ```Python hl_lines="16  21"
    {!> ../../../docs_src/response_model/tutorial001_01_py310.py!}
    ```

# `Request` class

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip

You can use the same type declarations with `str`, `float`, `bool` and many other complex data types.

Several of these are explored in the next chapters of the tutorial.

## Order matters

When creating *path operations*, you can find situations where you have a fixed path.

#### Install `openapi-ts`

You can install `openapi-ts` in your frontend code with:

<div class="termy">

```console
$ npm install @hey-api/openapi-ts --save-dev

---> 100%
```

</div>

#### Generate Client Code

To generate the client code you can use the command line application `openapi-ts` that would now be installed.

# Background Tasks - `BackgroundTasks`

You can declare a parameter in a *path operation function* or dependency function with the type `BackgroundTasks`, and then you can use it to schedule the execution of background tasks after the response is sent.

You can import it directly from `fastapi`:

```python
from fastapi import BackgroundTasks
```

## Testing locally with Traefik

You can easily run the experiment locally with a stripped path prefix using <a href="https://docs.traefik.io/" class="external-link" target="_blank">Traefik</a>.

<a href="https://github.com/containous/traefik/releases" class="external-link" target="_blank">Download Traefik</a>, it's a single binary, you can extract the compressed file and run it directly from the terminal.

class SecurityScopes:
    """
    This is a special class that you can define in a parameter in a dependency to
    obtain the OAuth2 scopes required by all the dependencies in the same chain.

    This way, multiple dependencies can have different scopes, even when used in the
    same *path operation*. And with this, you can access all the scopes required in
    all those dependencies in a single place.

!!! info
    Note that the `TestClient` receives data that can be converted to JSON, not Pydantic models.

    If you have a Pydantic model in your test and you want to send its data to the application during testing, you can use the `jsonable_encoder` described in [JSON Compatible Encoder](encoder.md){.internal-link target=_blank}.

## Run it

## Install the Server Program

When you install FastAPI, it comes with a production server, Uvicorn, and you can start it with the `fastapi run` command.

But you can also install an ASGI server manually:

=== "Uvicorn"