Body - Nested Models | qiufeng's blog

python

发布日期: 2020-04-14

文章字数: 1.1k

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Body - Nested Models

可以将属性定义为子类型，例如 python 的 list

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


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

tags 表示实体列表，尽管没有显式声明每个实体的类型

List fields with subtype

可以通过 python 的 typing 标准库来声明具有子类型的列表，例如

from typing import List

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


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

Set types

使用 set 类型可以声明一组不重复的实体

from typing import Set

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = set()


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

通过 set，即使接收到一组重复数据，也将会被自动转化为一组没有重复的实体

这些也会被自动加入注解

Nested Models

Pydantic 模型的每一个属性都有其类型，并且这些类型可以是另一个 Pydantic 模型，因此可以声明嵌套的 JSON 对象，并且这些对象可以有特定的属性名称，类型和验证

所有这些都可以被任意嵌套

from typing import Set

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Image(BaseModel):
    url: str
    name: str


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = []
    image: Image = None


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

在上面的例子中，Image 模型作为 Item 模型的一个属性

FastAPI 期望的 request body 类似于

{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2,
    "tags": ["rock", "metal", "bar"],
    "image": {
        "url": "http://example.com/baz.jpg",
        "name": "The Foo live"
    }
}

仅仅使用上述声明以及 FastAPI，将可以获得

IDE 支持
数据自动转换
数据验证
自动文档

Special types and validation

除了类似于 str，int，float 普通的单一类型，还可以使用继承于 str 的更加复杂的类型

例如，在 Image 模型里面，我们可以用 HttpUrl 类型来代替普通的 str，例如

from typing import Set

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = []
    image: Image = None


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

url 属性将会自动检查是否是合法的 URL，并且在自动文档中标注

Attributes with lists of submodels

Ptdantic 模型同样可以作为 list，set 等的子类型，例如

from typing import List, Set

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = []
    images: List[Image] = None


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

上面的例子将会期待 body

{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2,
    "tags": [
        "rock",
        "metal",
        "bar"
    ],
    "images": [
        {
            "url": "http://example.com/baz.jpg",
            "name": "The Foo live"
        },
        {
            "url": "http://example.com/dave.jpg",
            "name": "The Baz"
        }
    ]
}

Deeply nested models

可以定义任意深度嵌套的模型

from typing import List, Set

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = []
    images: List[Image] = None


class Offer(BaseModel):
    name: str
    description: str = None
    price: float
    items: List[Item]


@app.post("/offers/")
async def create_offer(*, offer: Offer):
    return offer

Bodies of pure lists

如果希望 JSON body 的顶层值是一个 JSON array，可以如下声明

from typing import List

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


@app.post("/images/multiple/")
async def create_multiple_images(*, images: List[Image]):
    return images

Bodies of arbitrary `dict`s

同样可以使用具有某些类型的键和某些类型的值的 dict 来声明 request body

可以被用在如果某些键和值名字不确定的情况，例如

from typing import Dict

from fastapi import FastAPI

app = FastAPI()


@app.post("/index-weights/")
async def create_index_weights(weights: Dict[int, float]):
    return weights