name: Issue Manager

on:
  schedule:
    - cron: "10 3 * * *"
  issue_comment:
    types:
      - created
  issues:
    types:
      - labeled
  pull_request_target:
    types:
      - labeled
  workflow_dispatch:

jobs:
  issue-manager:
    if: github.repository_owner == 'tiangolo'
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context
        env:

manager = ConnectionManager()


@app.get("/")
async def get():
    return HTMLResponse(html)


@app.websocket("/ws/{client_id}")
async def websocket_endpoint(websocket: WebSocket, client_id: int):
    await manager.connect(websocket)
    try:
        while True:
            data = await websocket.receive_text()

manager = ConnectionManager()


@app.get("/")
async def get():
    return HTMLResponse(html)


@app.websocket("/ws/{client_id}")
async def websocket_endpoint(websocket: WebSocket, client_id: int):
    await manager.connect(websocket)
    try:
        while True:
            data = await websocket.receive_text()

Sie erfordern in der Regel komplexere Konfigurationen und einen Nachrichten-/Job-Queue-Manager wie RabbitMQ oder Redis, ermöglichen Ihnen jedoch die Ausführung von Hintergrundtasks in mehreren Prozessen und insbesondere auf mehreren Servern.

```Python
with open("file.txt") as file:
    file.read()
```

In recent versions of Python, there's also an **async context manager**. You would use it with `async with`:

```Python
async with lifespan(app):
    await do_stuff()
```

But Gunicorn supports working as a **process manager** and allowing users to tell it which specific **worker process class** to use. Then Gunicorn would start one or more **worker processes** using that class.

And **Uvicorn** has a **Gunicorn-compatible worker class**.

@asynccontextmanager
async def contextmanager_in_threadpool(
    cm: ContextManager[_T],
) -> AsyncGenerator[_T, None]:
    # blocking __exit__ from running waiting on a free thread
    # can create race conditions/deadlocks if the context manager itself
    # has its own internal pool (e.g. a database connection pool)
    # to avoid this we let __exit__ run without a capacity limit
    # since we're creating a new limiter for each call, any non-zero limit

```txt
fastapi>=0.45.0,<0.46.0
```

that would mean that you would use the versions `0.45.0` or above, but less than `0.46.0`, for example, a version `0.45.2` would still be accepted.

If you use any other tool to manage your installations, like Poetry, Pipenv, or others, they all have a way that you can use to define specific versions for your packages.

## Available versions

# 自定义请求与 APIRoute 类

有时，我们要覆盖 `Request` 与 `APIRoute` 类使用的逻辑。

尤其是中间件里的逻辑。

例如，在应用处理请求体前，预先读取或操控请求体。

!!! danger "危险"

    本章内容**较难**。

    **FastAPI** 新手可跳过本章。

## 用例

常见用例如下：

* 把 <a href="https://msgpack.org/index.html" class="external-link" target="_blank">`msgpack`</a> 等非 JSON 请求体转换为 JSON
* 解压 gzip 压缩的请求体
* 自动记录所有请求体的日志

## 处理自定义请求体编码

下面学习如何使用自定义 `Request` 子类压缩 gizp 请求。

```Python hl_lines="17-18"
{!../../../docs_src/response_model/tutorial002.py!}
```

现在，每当浏览器使用一个密码创建用户时，API 都会在响应中返回相同的密码。

在这个案例中，这可能不算是问题，因为用户自己正在发送密码。

但是，如果我们在其他的*路径操作*中使用相同的模型，则可能会将用户的密码发送给每个客户端。

!!! danger
    永远不要存储用户的明文密码，也不要在响应中发送密码。

## 添加输出模型

相反，我们可以创建一个有明文密码的输入模型和一个没有明文密码的输出模型：

=== "Python 3.10+"

    ```Python hl_lines="9  11  16"
    {!> ../../../docs_src/response_model/tutorial003_py310.py!}