db.add(db_user)
    db.commit()
    db.refresh(db_user)
    return db_user


def get_items(db: Session, skip: int = 0, limit: int = 100):
    return db.query(models.Item).offset(skip).limit(limit).all()


def create_user_item(db: Session, item: schemas.ItemCreate, user_id: int):
    db_item = models.Item(**item.dict(), owner_id=user_id)
    db.add(db_item)
    db.commit()
    db.refresh(db_item)

    ```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)):

                """
            ),
        ],
        refreshUrl: Annotated[
            Optional[str],
            Doc(
                """
                The URL to refresh the token and obtain a new one.
                """
            ),
        ] = None,
        scheme_name: Annotated[
            Optional[str],
            Doc(
                """

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"

### 创建数据

现在创建实用程序函数来创建数据。

它的步骤是：

* 使用您的数据创建一个 SQLAlchemy 模型*实例。*
* 使用`add`来将该实例对象添加到您的数据库。
* 使用`commit`来对数据库的事务提交（以便保存它们）。
* 使用`refresh`来刷新您的数据库实例（以便它包含来自数据库的任何新数据，例如生成的 ID）。

```Python hl_lines="18-24  31-36"
{!../../../docs_src/sql_databases/sql_app/crud.py!}
```

!!! tip
    SQLAlchemy 模型`User`包含一个`hashed_password`，它应该是一个包含散列的安全密码。

    ```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.

🏧 😐 🌐❔ 👆 💭 👆 💪 🔗 🤙 🔠 🔁 (🎲 💗 🕰) 🎏 📨 ↩️ ⚙️ "💾" 💲, 👆 💪 ⚒ 🔢 `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。

  - author: Raf Rasenberg
    author_link: https://rafrasenberg.com/about/
    link: https://rafrasenberg.com/fastapi-lambda/
    title: 'FastAPI lambda container: serverless simplified'
  - author: Teresa N. Fontanella De Santis
    author_link: https://dev.to/
    link: https://dev.to/teresafds/authorization-on-fastapi-with-casbin-41og
    title: Authorization on FastAPI with Casbin
  - author: New Relic