name: "Generate FastAPI People"
description: "Generate the data for the FastAPI People page"
author: "Sebastián Ramírez <******@****.***>"
inputs:
  token:
    description: 'User token, to read the GitHub API. Can be passed in using {{ secrets.FASTAPI_PEOPLE }}'
    required: true
runs:
  using: 'docker'

Successfully installed fastapi pydantic uvicorn
```

</div>

/// info

还有其他文件格式和工具来定义和安装依赖项。

 我将在下面的部分中向你展示一个使用 Poetry 的示例。 👇

///

### 创建 **FastAPI** 代码

* 创建`app`目录并进入。
* 创建一个空文件`__init__.py`。
* 创建一个 `main.py` 文件：



```Python
from typing import Union

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():

```

////

/// info

FastAPI unterstützt (und empfiehlt die Verwendung von) `Annotated` seit Version 0.95.0.

Wenn Sie eine ältere Version haben, werden Sie Fehler angezeigt bekommen, wenn Sie versuchen, `Annotated` zu verwenden.

```Python hl_lines="2-3  23"
{!../../docs_src/wsgi/tutorial001.py!}
```

## Es ansehen

Jetzt wird jede Anfrage unter dem Pfad `/v1/` von der Flask-Anwendung verarbeitet.

Und der Rest wird von **FastAPI** gehandhabt.

Wenn Sie das mit Uvicorn ausführen und auf <a href="http://localhost:8000/v1/" class="external-link" target="_blank">http://localhost:8000/v1/</a> gehen, sehen Sie die Response von Flask:

```txt

```Python hl_lines="2-3  23"
{!../../docs_src/wsgi/tutorial001.py!}
```

## Conferindo

Agora todas as requisições sob o caminho `/v1/` serão manipuladas pela aplicação utilizando Flask.

E o resto será manipulado pelo **FastAPI**.

Se você rodar a aplicação e ir até <a href="http://localhost:8000/v1/" class="external-link" target="_blank">http://localhost:8000/v1/</a>, você verá o retorno do Flask:

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

from fastapi.testclient import TestClient

from docs_src.query_params_str_validations.tutorial012_an import app

client = TestClient(app)


def test_default_query_values():
    url = "/items/"
    response = client.get(url)
    assert response.status_code == 200, response.text
    assert response.json() == {"q": ["foo", "bar"]}


def test_multi_query_values():
    url = "/items/?q=baz&q=foobar"
    response = client.get(url)

import pytest
from fastapi.testclient import TestClient

from ...utils import needs_py310


@pytest.fixture(name="client")
def get_client():
    from docs_src.query_params_str_validations.tutorial014_an_py310 import app

    client = TestClient(app)
    return client


@needs_py310
def test_hidden_query(client: TestClient):
    response = client.get("/items?hidden_query=somevalue")

---

## Установка FastAPI

Первый шаг — установить FastAPI.

Для руководства вы, возможно, захотите установить его со всеми дополнительными зависимостями и функциями:

<div class="termy">

```console
$ pip install "fastapi[all]"

---> 100%
```

</div>

```

/// warning | "警告"

当你直接返回一个像上面例子中的 `Response` 对象时，它会直接返回。

FastAPI 不会用模型等对该响应进行序列化。

确保其中有你想要的数据，且返回的值为合法的 JSON（如果你使用 `JSONResponse` 的话）。

///

/// note | "技术细节"

你也可以使用 `from starlette.responses import JSONResponse`。　

出于方便，**FastAPI** 为开发者提供同 `starlette.responses` 一样的 `fastapi.responses`。但是大多数可用的响应都是直接来自 Starlette。`status` 也是一样。

///

## OpenAPI 和 API 文档

import pytest
from dirty_equals import HasRepr, IsDict, IsOneOf
from fastapi import Depends, FastAPI
from fastapi.exceptions import ResponseValidationError
from fastapi.testclient import TestClient

from .utils import needs_pydanticv2


@pytest.fixture(name="client")
def get_client():
    from pydantic import BaseModel, ValidationInfo, field_validator

    app = FastAPI()

    class ModelB(BaseModel):
        username: str