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

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

If something seems interesting and useful to your project, go ahead and check it, but otherwise, you might probably just skip them.

!!! tip

In this example we'll create a temporary database only for the tests.

## File structure

We create a new file at `sql_app/tests/test_sql_app.py`.

So the new file structure looks like:

``` hl_lines="9-11"
.
└── sql_app
    ├── __init__.py
    ├── crud.py
    ├── database.py
    ├── main.py
    ├── models.py

In a real application, you probably would have your tests in a different file.

And your **FastAPI** application might also be composed of several files/modules, etc.

### **FastAPI** app file

Let's say you have a file structure as described in [Bigger Applications](bigger-applications.md){.internal-link target=_blank}:

```
.
├── app
│   ├── __init__.py
│   └── main.py
```

In the file `main.py` you have your **FastAPI** app:

!!! tip
    Notice how each model's attribute with a type, default value and `Field` has the same structure as a *path operation function's* parameter, with `Field` instead of `Path`, `Query` and `Body`.

## Add extra information

    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Optional[str] = None):
    return {"item_id": item_id, "q": q}
```

* Vous devriez maintenant avoir une structure de répertoire telle que :

```
.
├── app
│   └── main.py
└── Dockerfile
```

## Construire l'image Docker

<font color="#3465A4">INFO    </font> Resolved absolute path <font color="#75507B">/home/user/code/awesomeapp/</font><font color="#AD7FA8">main.py</font>
<font color="#3465A4">INFO    </font> Searching for package file structure from directories with <font color="#3465A4">__init__.py</font> files
<font color="#3465A4">INFO    </font> Importing from <font color="#75507B">/home/user/code/</font><font color="#AD7FA8">awesomeapp</font>

## Dependencies for groups of *path operations*

    ```Python hl_lines="3"
    {!> ../../../docs_src/cookie_params/tutorial001.py!}
    ```

## Declare `Cookie` parameters

Then declare the cookie parameters using the same structure as with `Path` and `Query`.

The first value is the default value, you can pass all the extra validation or annotation parameters:

=== "Python 3.10+"

    ```Python hl_lines="9"