FastAPI 基礎學習(十) Pydantic復雜模型

作者：麥克煎蛋   出處：https://www.cnblogs.com/mazhiyong/ 轉載請保留這段聲明，謝謝！

 

一、Pydantic模型的附加信息

與前面講過的Query、Path、Body類似，我們也可以為Pydantic模型添加附加信息，基於模塊Field。

1、導入Field模塊

from pydantic import BaseModel, Field

2、聲明模型屬性

class Item(BaseModel):
    name: str
    description: str = Field(None, title="The description of the item", max_length=300)
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: float = None

Field模塊的參數與Query、Path、Body等相同。

 

完整示例：

from fastapi import Body, FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str = Field(None, title="The description of the item", max_length=300)
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: float = None


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

二、Pydantic嵌套模型

任意嵌套深度的Pydantic模型，我們都可以進行定義、校驗、自動文檔化和靈活使用。

1、模型的屬性可以是數據集合類型

比如list，dict,tuple,set等等。

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

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

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

 

我們也可以直接定義一個字典類型的body，通常指定了鍵和值的數據類型。

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

2、嵌套模型

每一個Pydantic模型的屬性都有一個類型，這個類型也可以是另一個Pydantic模型。

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

 

FastAPI期望的Request Body內容格式如下：

{
    "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"
    }
}

 

我們也可以把Pydantic模型作為list、set等集合類型的元素類型。

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


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

這里FastAPI期望的Request Body內容格式如下：

{
    "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"
        }
    ]
}

3、深層嵌套模型

我們可以定義任意深度的嵌套模型，如下示例：

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