Now you can import and use the client code, it could look like this, notice that you get autocompletion for the methods:

<img src="/img/tutorial/generate-clients/image02.png">

You will also get autocompletion for the payload to send:

<img src="/img/tutorial/generate-clients/image03.png">

!!! tip
    Notice the autocompletion for `name` and `price`, that was defined in the FastAPI application, in the `Item` model.

다음과 같이 표시됩니다.

<img src="/img/tutorial/debugging/image01.png">

---

Pycharm을 사용하는 경우 다음을 수행할 수 있습니다

* "Run" 메뉴를 엽니다
* "Debug..." 옵션을 선택합니다.
* 그러면 상황에 맞는 메뉴가 나타납니다.
* 디버그할 파일을 선택합니다(이 경우 `main.py`).

그런 다음 **FastAPI** 코드로 서버를 시작하고 중단점 등에서 중지합니다.

다음과 같이 표시됩니다.

* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

<img src="/img/tutorial/behind-a-proxy/image01.png">

But if we access the docs UI at the "official" URL using the proxy with port `9999`, at `/api/v1/docs`, it works correctly! 🎉

You can check it at <a href="http://127.0.0.1:9999/api/v1/docs" class="external-link" target="_blank">http://127.0.0.1:9999/api/v1/docs</a>:

<img src="/img/tutorial/behind-a-proxy/image02.png">

Right as we wanted it. ✔️

В цей же момент, ви спробуєте викликати автозаповнення з допомогою `Ctrl+Space` і побачите:

<img src="/img/python-types/image02.png">

Разом з цим, ви можете прокручувати, переглядати опції, допоки ви не знайдете одну, що звучить схоже:

<img src="/img/python-types/image03.png">

## Більше мотивації

Перевірте цю функцію, вона вже має анотацію типу:

```Python hl_lines="1"

```Python hl_lines="3-16  19-31"
{!../../../docs_src/metadata/tutorial001.py!}
```

!!! tip
    👆 💪 ✍ ✍ `description` 🏑 & ⚫️ 🔜 ✍ 🔢.

⏮️ 👉 📳, 🏧 🛠️ 🩺 🔜 👀 💖:

<img src="/img/tutorial/metadata/image01.png">

## 🗃 🔖

👆 💪 🚮 🌖 🗃 🎏 🔖 ⚙️ 👪 👆 ➡ 🛠️ ⏮️ 🔢 `openapi_tags`.

⚫️ ✊ 📇 ⚗ 1️⃣ 📖 🔠 🔖.

🔠 📖 💪 🔌:

* `name` (**✔**): `str` ⏮️ 🎏 📛 👆 ⚙️ `tags` 🔢 👆 *➡ 🛠️* &amp; `APIRouter`Ⓜ.

## 在文档中查看

当你查看自动化文档时，你可以检查输入模型和输出模型是否都具有自己的 JSON Schema：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

并且两种模型都将在交互式 API 文档中使用：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## 响应模型编码参数

你的响应模型可以具有默认值，例如：

```Python hl_lines="11  13-14"
{!../../../docs_src/response_model/tutorial004.py!}
```

## ドキュメントを見る

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます。

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

そして、両方のモデルは、対話型のAPIドキュメントに使用されます:

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

```Python hl_lines="11 13 14"
{!../../../docs_src/response_model/tutorial004.py!}

    "bar"
  ]
}
```

!!! tip
    📣 🔢 🔢 ⏮️ 🆎 `list`, 💖 🖼 🔛, 👆 💪 🎯 ⚙️ `Query`, ⏪ ⚫️ 🔜 🔬 📨 💪.

🎓 🛠️ 🩺 🔜 ℹ ➡️, ✔ 💗 💲:

<img src="/img/tutorial/query-params-str-validations/image02.png">

### 🔢 🔢 📇 / 💗 💲 ⏮️ 🔢

&amp; 👆 💪 🔬 🔢 `list` 💲 🚥 👌 🚚:

=== "🐍 3️⃣.6️⃣ &amp; 🔛"

    ```Python hl_lines="9"
    {!> ../../../docs_src/query_params_str_validations/tutorial012.py!}

When you see the automatic docs, you can check that the input model and output model will both have their own JSON Schema:

<img src="/img/tutorial/response-model/image01.png">

And both models will be used for the interactive API documentation:

<img src="/img/tutorial/response-model/image02.png">

## Other Return Type Annotations