While you are waiting, you go with your crush and pick a table, you sit and talk with your crush for a long time (as your burgers are very fancy and take some time to prepare).

As you are sitting at the table with your crush, while you wait for the burgers, you can spend that time admiring how awesome, cute and smart your crush is ✨😍✨.

🏧 😐 🌐❔ 👆 💭 👆 💪 🔗 🤙 🔠 🔁 (🎲 💗 🕰) 🎏 📨 ↩️ ⚙️ "💾" 💲, 👆 💪 ⚒ 🔢 `use_cache=False` 🕐❔ ⚙️ `Depends`:

```Python hl_lines="1"
async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
    return {"fresh_value": fresh_value}
```

## 🌃

↖️ ⚪️➡️ 🌐 🎀 🔤 ⚙️ 📥, **🔗 💉** ⚙️ 🙅.

🔢 👈 👀 🎏 *➡ 🛠️ 🔢*.

✋️, ⚫️ 📶 🏋️, & ✔ 👆 📣 🎲 🙇 🐦 🔗 "📊" (🌲).

!!! tip

高度なシナリオでは、「キャッシュされた」値を使うのではなく、同じリクエストの各ステップ（おそらく複数回）で依存関係を呼び出す必要があることがわかっている場合、`Depens`を使用する際に、`use_cache=False`というパラメータを設定することができます。

```Python hl_lines="1"
async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
    return {"fresh_value": fresh_value}
```

## まとめ

ここで使われている派手な言葉は別にして、**依存性注入** システムは非常にシンプルです。

*path operation関数*と同じように見えるただの関数です。

しかし、それでも非常に強力で、任意の深くネストされた依存関係「グラフ」（ツリー）を宣言することができます。

在高级使用场景中，如果不想使用「缓存」值，而是为需要在同一请求的每一步操作（多次）中都实际调用依赖项，可以把 `Depends` 的参数 `use_cache` 的值设置为 `False` :

```Python hl_lines="1"
async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
    return {"fresh_value": fresh_value}
```

## 小结

千万别被本章里这些花里胡哨的词藻吓倒了，其实**依赖注入**系统非常简单。

依赖注入无非是与*路径操作函数*一样的函数罢了。

但它依然非常强大，能够声明任意嵌套深度的「图」或树状的依赖结构。

!!! tip "提示"

    ```Python hl_lines="1"
    async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]):
        return {"fresh_value": fresh_value}
    ```

=== "Python 3.8+ non-Annotated"

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

    ```Python hl_lines="1"
    async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):

By the time I started creating **FastAPI** itself, most of the pieces were already in place, the design was defined, the requirements and tools were ready, and the knowledge about the standards and specifications was clear and fresh.

## Future

By this point, it's already clear that **FastAPI** with its ideas is being useful for many people.

It is being chosen over previous alternatives for suiting many use cases better.

Enquanto você espera, você vai com seu _crush_ :heart_eyes: e pega uma mesa, senta e conversa com seu _crush_ :heart_eyes: por um bom tempo (como seus hambúrgueres são muito saborosos, leva um tempo para serem preparados).

Enquanto você está sentado na mesa com seu _crush_ :heart_eyes:, esperando os hambúrgueres, você pode gastar o tempo admirando como lindo, maravilhoso e esperto é seu _crush_ :heart_eyes:.

Pendant que vous êtes assis à table, en attendant que les burgers 🍔 soient prêts, vous pouvez passer ce temps à admirer à quel point votre crush 😍 est géniale, mignonne et intelligente ✨😍✨.

    ```Python hl_lines="1"
    async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]):
        return {"fresh_value": fresh_value}
    ```

=== "Python 3.8+ nicht annotiert"

    !!! tip "Tipp"
        Bevorzugen Sie die `Annotated`-Version, falls möglich.

    ```Python hl_lines="1"
    async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):

And if you update that local FastAPI source code when you run that Python file again, it will use the fresh version of FastAPI you just edited.

That way, you don't have to "install" your local version to be able to test every change.

!!! note "Technical Details"