**FastAPI** provides the same `WebSocket` directly just as a convenience for you, the developer. But it comes directly from Starlette.

## Await for messages and send messages

In your WebSocket route you can `await` for messages and send messages.

```Python hl_lines="48-52"
{!../../../docs_src/websockets/tutorial001.py!}
```

You can receive and send binary, text, and JSON data.

## Try it

```

!!! tip
     `callback_url` 🔢 🔢 ⚙️ Pydantic <a href="https://docs.pydantic.dev/latest/concepts/types/#urls" class="external-link" target="_blank">📛</a> 🆎.

🕴 🆕 👜 `callbacks=messages_callback_router.routes` ❌ *➡ 🛠️ 👨‍🎨*. 👥 🔜 👀 ⚫️❔ 👈 ⏭.

## 🔬 ⏲

☑ ⏲ 📟 🔜 🪀 🙇 🔛 👆 👍 🛠️ 📱.

&amp; ⚫️ 🔜 🎲 🪀 📚 ⚪️➡️ 1️⃣ 📱 ⏭.

⚫️ 💪 1️⃣ ⚖️ 2️⃣ ⏸ 📟, 💖:

```Python

```JSON hl_lines="4-16"
{
    "components": {
        "schemas": {
            "Message": {
                "title": "Message",
                "required": [
                    "message"
                ],
                "type": "object",
                "properties": {
                    "message": {
                        "title": "Message",
                        "type": "string"
                    }
                }

### Checking the current `root_path`

You can get the current `root_path` used by your application for each request, it is part of the `scope` dictionary (that's part of the ASGI spec).

Here we are including it in the message just for demonstration purposes.

```Python hl_lines="8"
{!../../../docs_src/behind_a_proxy/tutorial001.py!}
```

Then, if you start Uvicorn with:

<div class="termy">

```console

```txt
Hello, World from Flask!
```

并且如果您访问 <a href="http://localhost:8000/v2" class="external-link" target="_blank">http://localhost:8000/v2</a>，您将会看到由 FastAPI 返回的响应：

```JSON
{
    "message": "Hello World"
}

```Python hl_lines="14"
{!../../../docs_src/background_tasks/tutorial001.py!}
```

`.add_task()` は以下の引数を受け取ります:

* バックグラウンドで実行されるタスク関数 (`write_notification`)。
* タスク関数に順番に渡す必要のある引数の列 (`email`)。
* タスク関数に渡す必要のあるキーワード引数 (`message="some notification"`)。

## 依存性注入

`BackgroundTasks` の使用は依存性注入システムでも機能し、様々な階層 (*path operations 関数*、依存性 (依存可能性)、サブ依存性など) で `BackgroundTasks` 型のパラメーターを宣言できます。

```Python hl_lines="18  21  23"
{!../../../docs_src/path_params/tutorial005.py!}
```

In your client you will get a JSON response like:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Path parameters containing paths

Let's say you have a *path operation* with a path `/files/{file_path}`.

```Python hl_lines="18  21  23"
{!../../../docs_src/path_params/tutorial005.py!}
```
Вы отправите клиенту такой JSON-ответ:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Path-параметры, содержащие пути

Предположим, что есть *операция пути* с путем `/files/{file_path}`.

### Проверьте

Откройте браузер по адресу: <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

Вы увидите JSON-ответ следующего вида:

```JSON
{"message": "Hello World"}
```

### Интерактивная документация API

Перейдите по адресу: <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

返回给客户端之前，要把枚举元素转换为对应的值（本例中为字符串）：

```Python hl_lines="18  21  23"
{!../../../docs_src/path_params/tutorial005.py!}
```

客户端中的 JSON 响应如下：

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## 包含路径的路径参数

假设*路径操作*的路径为 `/files/{file_path}`。

但需要 `file_path` 中也包含*路径*，比如，`home/johndoe/myfile.txt`。

此时，该文件的 URL 是这样的：`/files/home/johndoe/myfile.txt`。