=== "Python 3.8+ non-Annotated"

    !!! tip
        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="1"
    {!> ../../../docs_src/request_forms/tutorial001.py!}
    ```

## Define `Form` parameters

Create form parameters the same way you would for `Body` or `Query`:

=== "Python 3.9+"

    ```Python hl_lines="9"
    {!> ../../../docs_src/request_forms/tutorial001_an_py39.py!}
    ```

    return burgers
```

### More technical details

You might have noticed that `await` can only be used inside of functions defined with `async def`.

But at the same time, functions defined with `async def` have to be "awaited". So, functions with `async def` can only be called inside of functions defined with `async def` too.

So, about the egg and the chicken, how do you call the first `async` function?

from starlette.datastructures import UploadFile as StarletteUploadFile
from typing_extensions import Annotated, Doc


class UploadFile(StarletteUploadFile):
    """
    A file uploaded in a request.

    Define it as a *path operation function* (or dependency) parameter.

    If you are using a regular `def` function, you can use the `upload_file.file`
    attribute to access the raw standard Python file (blocking, not async), useful and

    * Como as estruturas de dados do Pydantic são apenas instâncias de classes que você define, a auto completação, _linting_, _mypy_ e a sua intuição devem funcionar corretamente com seus dados validados.
* Valida **estruturas complexas**:
    * Use modelos hierárquicos do Pydantic, `List` e `Dict` do `typing` do Python, etc.

Here we define the config `env_file` inside of your Pydantic `Settings` class, and set the value to the filename with the dotenv file we want to use.

### Creating the `Settings` only once with `lru_cache`

!!! tip
    To avoid confusion between the SQLAlchemy *models* and the Pydantic *models*, we will have the file `models.py` with the SQLAlchemy models, and the file `schemas.py` with the Pydantic models.

    These Pydantic models define more or less a "schema" (a valid data shape).

    So this will help us avoiding confusion while using both.

### Create initial Pydantic *models* / schemas

### Check the docs

Now, if you check the docs, they will show all the additional metadata:

<img src="/img/tutorial/metadata/image02.png">

### Order of tags

The order of each tag metadata dictionary also defines the order shown in the docs UI.

For example, even though `users` would go after `items` in alphabetical order, it is shown before them, because we added their metadata as the first dictionary in the list.

## OpenAPI URL

* 🐛 Fix FastAPI People GitHub Action: set HTTPX timeout for GraphQL query request. PR [#5222](https://github.com/tiangolo/fastapi/pull/5222) by [@iudeen](https://github.com/iudeen).
* 🐛 Make sure a parameter defined as required is kept required in OpenAPI even if defined as optional in another dependency. PR [#4319](https://github.com/tiangolo/fastapi/pull/4319) by [@cd17822](https://github.com/cd17822).

Mas ele foi criado antes da existência do _type hints_ do Python. Então, para definir todo o <abbr title="definição de como os dados devem ser formados">_schema_</abbr> você precisa utilizar específicas ferramentas e classes fornecidas pelo Marshmallow.

!!! check "**FastAPI** inspirado para"
    Usar código para definir "schemas" que forneçam, automaticamente, tipos de dados e validação.

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

## Memory