from typing import Union

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None


class User(BaseModel):
    username: str
    full_name: Union[str, None] = None


@app.put("/items/{item_id}")
async def update_item(
    *,
    item_id: int,

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float = 10.5


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The Bar fighters", "price": 62, "tax": 20.2},
    "baz": {
        "name": "Baz",
        "description": "There goes my baz",

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: float = 10.5


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The Bar fighters", "price": 62, "tax": 20.2},
    "baz": {
        "name": "Baz",

pkg log/slog, func ErrorContext(context.Context, string, ...interface{}) #61200
pkg log/slog, func Error(string, ...interface{}) #56345
pkg log/slog, func Float64(string, float64) Attr #56345
pkg log/slog, func Float64Value(float64) Value #56345
pkg log/slog, func Group(string, ...interface{}) Attr #59204
pkg log/slog, func GroupValue(...Attr) Value #56345

from fastapi.testclient import TestClient
from inline_snapshot import snapshot


@dataclass
class Item:
    id: uuid.UUID
    name: str
    price: float
    tags: list[str] = field(default_factory=list)
    description: Union[str, None] = None
    tax: Union[float, None] = None


app = FastAPI()


@app.get("/item", response_model=Item)
async def read_item():
    return {
        "id": uuid.uuid4(),

# Tipos de dados extras { #extra-data-types }

Até agora, você tem usado tipos de dados comuns, tais como:

* `int`
* `float`
* `str`
* `bool`

Mas você também pode usar tipos de dados mais complexos.

E você ainda terá os mesmos recursos que viu até agora:

* Ótimo suporte do editor.
* Conversão de dados das requisições recebidas.
* Conversão de dados para os dados da resposta.
* Validação de dados.

from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: Union[str, None] = None
    description: Union[str, None] = None
    price: Union[float, None] = None
    tax: float = 10.5
    tags: list[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},

			// return false anyways for both situations.
			return false
		default:
			if lm.lock(id, source, isWriteLock) {
				return true
			}
			time.Sleep(time.Duration(r.Float64() * float64(lockRetryInterval)))
		}
	}
}

// Unlock unlocks the write lock.
//
// It is a run-time error if lm is not locked on entry to Unlock.
func (lm *LRWMutex) Unlock() {
	isWriteLock := true

# 额外数据类型

到目前为止，您一直在使用常见的数据类型，如:

* `int`
* `float`
* `str`
* `bool`

但是您也可以使用更复杂的数据类型。

您仍然会拥有现在已经看到的相同的特性:

* 很棒的编辑器支持。
* 传入请求的数据转换。
* 响应数据转换。
* 数据验证。
* 自动补全和文档。

## 其他数据类型

下面是一些你可以使用的其他数据类型:

* `UUID`:
    * 一种标准的 "通用唯一标识符" ，在许多数据库和系统中用作ID。
    * 在请求和响应中将以 `str` 表示。
* `datetime.datetime`:
    * 一个 Python `datetime.datetime`.

from fastapi import FastAPI
from pydantic import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


app = FastAPI()


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):