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.

    * Parce que les structures de données de pydantic consistent seulement en une instance de classe que vous définissez; l'auto-complétion, le linting, mypy et votre intuition devrait être largement suffisante pour valider vos données.
* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Generic types with type parameters

There are some data structures that can contain other values, like `dict`, `list`, `set` and `tuple`. And the internal values can have their own type too.

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Types génériques avec des paramètres de types

Il existe certaines structures de données qui contiennent d'autres valeurs, comme `dict`, `list`, `set` et `tuple`. Et les valeurs internes peuvent elles aussi avoir leurs propres types.

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:

<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>

<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>

**FastAPI** provides a convenience tool to structure your application while keeping all the flexibility.

!!! info
    If you come from Flask, this would be the equivalent of Flask's Blueprints.

## An example file structure

Let's say you have a file structure like this:

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

* ✨ Update OpenAPI models, supporting recursive models and extensions. PR [#3628](https://github.com/tiangolo/fastapi/pull/3628) by [@tiangolo](https://github.com/tiangolo).
* ✨ Import and re-export data structures from Starlette, used by Request properties, on `fastapi.datastructures`. Initial PR [#1872](https://github.com/tiangolo/fastapi/pull/1872) by [@jamescurtin](https://github.com/jamescurtin).

### Docs