## Simple usage

If you look at it, *path operation functions* are declared to be used whenever a *path* and *operation* matches, and then **FastAPI** takes care of calling the function with the correct parameters, extracting the data from the request.

Actually, all (or most) of the web frameworks work in this same way.

You never call those functions directly. They are called by your framework (in this case, **FastAPI**).

                </mxCell>

    response = client.post("/cookie-and-object/")
    assert response.status_code == 200, response.text
    assert response.json() == {"message": "Come to the dark side, we have cookies"}

from fastapi import Header, HTTPException
from typing_extensions import Annotated


async def get_token_header(x_token: Annotated[str, Header()]):
    if x_token != "fake-super-secret-token":
        raise HTTPException(status_code=400, detail="X-Token header invalid")


async def get_query_token(token: str):
    if token != "jessica":

* Traiter des données :
    * Par exemple, si vous recevez un fichier qui doit passer par un traitement lent, vous pouvez retourner une réponse «Accepted» (HTTP 202) puis faire le traitement en arrière-plan.


## Utiliser `BackgroundTasks`

Pour commencer, importez `BackgroundTasks` et définissez un paramètre dans votre *fonction de chemin* avec `BackgroundTasks` comme type déclaré.

# JSON Compatible Encoder

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder`

Let's imagine that you have a database `fake_db` that only receives JSON compatible data.

)
def read_slow_users(skip: int = 0, limit: int = 100):
    global sleep_time
    sleep_time = max(0, sleep_time - 1)
    time.sleep(sleep_time)  # Fake long processing request
    users = crud.get_users(skip=skip, limit=limit)

# JSON 兼容编码器

在某些情况下，您可能需要将数据类型（如Pydantic模型）转换为与JSON兼容的数据类型（如`dict`、`list`等）。

比如，如果您需要将其存储在数据库中。

对于这种要求， **FastAPI**提供了`jsonable_encoder()`函数。

## 使用`jsonable_encoder`

让我们假设你有一个数据库名为`fake_db`，它只能接收与JSON兼容的数据。

例如，它不接收`datetime`这类的对象，因为这些对象与JSON不兼容。

因此，`datetime`对象必须将转换为包含<a href="https://en.wikipedia.org/wiki/ISO_8601" class="external-link" target="_blank">ISO格式化</a>的`str`类型对象。

同样，这个数据库也不会接收Pydantic模型（带有属性的对象），而只接收`dict`。

    ```

!!! tip
    All this might seem contrived. And it might not be very clear how is it useful yet.

    These examples are intentionally simple, but show how it all works.

    In the chapters about security, there are utility functions that are implemented in this same way.

# Sub-dependencies

You can create dependencies that have **sub-dependencies**.

They can be as **deep** as you need them to be.

**FastAPI** will take care of solving them.

## First dependency "dependable"

You could create a first dependency ("dependable") like:

=== "Python 3.10+"

    ```Python hl_lines="8-9"
    {!> ../../../docs_src/dependencies/tutorial005_an_py310.py!}
    ```

=== "Python 3.9+"