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Python 第三流行的 Web 框架

在 2020 年的 Python 开发者调查结果中，有这样一段话：“FastAPI 在此次调查迭代中首次被引为选项，表现为 Python 第三流行的 Web 框架。”

FastAPI 创立于 2018 年 12 月，不到 2 年就成为仅次于 Flask 和 Django 的第三流行的 Web 框架。而又经过了一年发展来到 2022 年，虽然 2021 年 Python 开发者调查结果还没有出来，但是从 GitHub 的 star 来看，Flask 58.7k，Django 63.6k，FastAPI 44.2k，这个差距缩得越来越小。

FastAPI 特性

这里就不做机器翻译了，大家看下原文：

我说下我选择 FastAPI 的理由：首先就是 HttpRunner 集成了 FastAPI，有大佬背书，相信这个框架足以优秀。其次是注解，用多了 SpringBoot 以后，越来越喜欢注解，层次清晰。对于前后端分离项目来说，Flask 虽然非常精简却又自带了 Jinja 模板引擎，Django 虽然是百宝箱却又显得太重，而 FastAPI 介于两者之间，就是一个纯粹的后端应用。并且 FastAPI 是基于 Starlette 框架的，集成了实用功能比如类型检查、OpenAPI（Swagger）等等，这跟我基于 pytest 框架做 tep 测试工具的理念很相似。

安装

对 Python 版本要求是 3.6+。

先安装 FastAPI：

pip install fastapi

再安装 ASGI 服务器，比如 Uvicorn：

pip install "uvicorn[standard]"

也可以同时安装 fastapi 和 uvicorn：

pip install "fastapi[all]"

运行

写个main.py文件：

from typing import Optional

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Optional[str] = None):
    return {"item_id": item_id, "q": q}

在命令行输入启动应用：

uvicorn main:app --reload

main是 Python 模块名。

app是app = FastAPI()。

--reload在代码变化时自动重启服务器。

打开浏览器访问：

http://127.0.0.1:8000/items/5?q=somequery

就能看到 JSON 响应：

{"item_id": 5, "q": "somequery"}

访问：

http://127.0.0.1:8000/docs

就能看到 Swagger 接口文档：

pydantic

pydantic 是一个数据验证的库，FastAPI 使用它来做模型校验。比如：

from typing import Optional

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Optional[bool] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Optional[str] = None):
    return {"item_id": item_id, "q": q}


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

Item是个入参模型，它的 name 必须 str 类型，price 必须 float 类型，is_offer 是可选的，可以为 bool 类型或不传。

PUT http://127.0.0.1:8000/items/6
{
    "name": "dongfanger",
    "price": 2.3,
    "is_offer":  true
}


{
    "item_name": "dongfanger",
    "item_id": 6
}

路径参数

把路径参数传递给函数：

from fastapi import FastAPI

app = FastAPI()


@app.get("/items/{item_id}")
async def read_item(item_id):
    return {"item_id": item_id}

也可以指定 Python 类型：

from fastapi import FastAPI

app = FastAPI()


@app.get("/items/{item_id}")
async def read_item(item_id: int):
    return {"item_id": item_id}

效果是访问 http://127.0.0.1:8000/items/foo 会返回{"item_id":"foo"}。

指定了 Python 类型后，FastAPI 会强制检查，比如传 str 会报错：

http://127.0.0.1:8000/items/foo

{
    "detail": [
        {
            "loc": [
                "path",
                "item_id"
            ],
            "msg": "value is not a valid integer",
            "type": "type_error.integer"
        }
    ]
}

传 float 也会报错：

http://127.0.0.1:8000/items/4.2

匹配先后顺序

代码定义的先后顺序会决定匹配结果，比如正常来说，下面的/users/me会返回{"user_id": "the current user"}：

from fastapi import FastAPI

app = FastAPI()


@app.get("/users/me")
async def read_user_me():
    return {"user_id": "the current user"}


@app.get("/users/{user_id}")
async def read_user(user_id: str):
    return {"user_id": user_id}

假如这 2 个 path 定义顺序反过来，那么/users/me就会匹配到/users/{user_id}而返回{"user_id": me}了。

枚举路径

借助于 Enun 类，可以实现枚举路径：

from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name == ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":
        return {"model_name": model_name, "message": "LeCNN all the images"}

    return {"model_name": model_name, "message": "Have some residuals"}

效果：

path 匹配

FastAPI 提供了一个 path 类型，可以用来对文件路径进行格式匹配：

from fastapi import FastAPI

app = FastAPI()


@app.get("/files/{file_path:path}")
async def read_file(file_path: str):
    return {"file_path": file_path}

查询参数

查询参数是跟在路径参数后面，用?分隔用&连接的参数，比如http://127.0.0.1:8000/items/?skip=0&limit=10。

实现：

from fastapi import FastAPI

app = FastAPI()

fake_items_db = [{"item_name": "Foo"}, {"item_name": "Bar"}, {"item_name": "Baz"}]


@app.get("/items/")
async def read_item(skip: int = 0, limit: int = 10):
    return fake_items_db[skip : skip + limit]

参数是可选的并且设置了默认值：limit: int = 10

参数是可选的，无默认值：limit: Optional[int] = None

注意：是否可选是由 None 来决定的，而 Optional 只是为编译器提供支持，跟 FastAPI 没有关系。

参数是必填的：limit: int

请求体

FastAPI 的请求体借助于 pydantic 来实现：

from typing import Optional

from fastapi import FastAPI
from pydantic import BaseModel


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


app = FastAPI()


@app.post("/items/")
async def create_item(item: Item):
    return item

继承于 BaseModel 来自定义 Model，FastAPI 会自动转换为 JSON。

Pydantic PyCharm Plugin 插件提高编码体验：

auto-completion

type checks

refactoring

searching

inspections

路径参数+查询参数+请求体

总结一下，在函数参数中，url path 中定义的叫做路径参数，没有定义的叫做查询参数，类型是 pydantic model 的叫做请求体，FastAPI 会根据这套规则来自动识别：

from typing import Optional

from fastapi import FastAPI
from pydantic import BaseModel


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


app = FastAPI()


@app.put("/items/{item_id}")
async def create_item(item_id: int, item: Item, q: Optional[str] = None):
    result = {"item_id": item_id, **item.dict()}
    if q:
        result.update({"q": q})
    return result

查询参数字符串校验

FastAPI 提供了 Query 来支持对入参的字符串校验，比如最小长度和最大长度：

from typing import Optional

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Optional[str] = Query(None, min_length=3, max_length=50, regex="^fixedquery$")
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})
    return results

甚至其中也能包含正则表达式：regex="^fixedquery$"。

用 Query 时指定默认值：

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: str = Query("fixedquery", min_length=3)):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})
    return results

用 Query 时必填：

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: str = Query(..., min_length=3)):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})
    return results

查询参数传 list

from typing import List, Optional

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: Optional[List[str]] = Query(None)):
    query_items = {"q": q}
    return query_items

指定默认值：

from typing import List

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: List[str] = Query(["foo", "bar"])):
    query_items = {"q": q}
    return query_items

url 就像这样：http://localhost:8000/items/?q=foo&q=bar

指定别名，比如http://127.0.0.1:8000/items/?item-query=foobaritems中的item-query不是 Python 变量命名，那么可以设置别名：

from typing import Optional

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: Optional[str] = Query(None, alias="item-query")):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})
    return results

路径参数数字校验

查询参数用Query做字符串（String）校验，路径参数用Path做数字（Numeric）校验：

from fastapi import FastAPI, Path

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    *,
    item_id: int = Path(..., title="The ID of the item to get", gt=0, le=1000),
    q: str,
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})
    return results

路径参数永远都是必填的，因为它是 url 一部分。...表示必填，就算设置为 None 也没有用，仍然是必填。

ge表示大于等于，greater equal。

le表示小于等于，less equal。

gt表示大于，greater than。

lt表示小于，less than。

请求体-多参数

一、如果请求体嵌套了多个 JSON：

{
    "item": {
        "name": "Foo",
        "description": "The pretender",
        "price": 42.0,
        "tax": 3.2
    },
    "user": {
        "username": "dave",
        "full_name": "Dave Grohl"
    }
}

那么就需要在 FastAPI 中定义多参数：

from typing import Optional

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


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


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

这里定义了 2 个 Model：Item 和 User。

二、而如果多个参数中有个参数只是单个值，比如这里的importance：

{
    "item": {
        "name": "Foo",
        "description": "The pretender",
        "price": 42.0,
        "tax": 3.2
    },
    "user": {
        "username": "dave",
        "full_name": "Dave Grohl"
    },
    "importance": 5
}

那么定义成变量并赋值= Body()即可：

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

三、在只有一个 Item 的时候，FastAPI 默认会接收这样的 body：

{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}

假如想把 item 也放到 JSON 中：

{
    "item": {
        "name": "Foo",
        "description": "The pretender",
        "price": 42.0,
        "tax": 3.2
    }
}

那么可以使用Body(embed=True))：

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


@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

请求体-字段

Pydantic 提供了Field来给 body 中的字段添加额外校验：

from typing import Union

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

app = FastAPI()


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

跟 FastAPI 提供的Query、Path、Body作用类似。

请求体-嵌套模型

传 List：

from typing import List, Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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

传 Set，自动去重：

from typing import Set, Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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

传 Model：

from typing import Set, Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


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


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

入参会像这样：

{
    "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"
    }
}

对于 url，pydantic 提供了 HttpUrl 来做校验：
class Image(BaseModel):
    url: HttpUrl
    name: str

传 Model 的 List：

from typing import List, Set, Union

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


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


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


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

入参像这样：

{
    "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"
        }
    ]
}

添加示例请求

通过Config和schema_extra添加示例请求：

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: Union[float, None] = None

    class Config:
        schema_extra = {
            "example": {
                "name": "Foo",
                "description": "A very nice Item",
                "price": 35.4,
                "tax": 3.2,
            }
        }


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

在使用以下任一时，都可以添加 example：

Path()
Query()
Header()
Cookie()
Body()
Form()
File()

比如：

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str = Field(example="Foo")
    description: Union[str, None] = Field(default=None, example="A very nice Item")
    price: float = Field(example=35.4)
    tax: Union[float, None] = Field(default=None, example=3.2)


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

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


@app.put("/items/{item_id}")
async def update_item(
    item_id: int,
    item: Item = Body(
        example={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    ),
):
    results = {"item_id": item_id, "item": item}
    return results

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


@app.put("/items/{item_id}")
async def update_item(
    *,
    item_id: int,
    item: Item = Body(
        examples={
            "normal": {
                "summary": "A normal example",
                "description": "A **normal** item works correctly.",
                "value": {
                    "name": "Foo",
                    "description": "A very nice Item",
                    "price": 35.4,
                    "tax": 3.2,
                },
            },
            "converted": {
                "summary": "An example with converted data",
                "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                "value": {
                    "name": "Bar",
                    "price": "35.4",
                },
            },
            "invalid": {
                "summary": "Invalid data is rejected with an error",
                "value": {
                    "name": "Baz",
                    "price": "thirty five point four",
                },
            },
        },
    ),
):
    results = {"item_id": item_id, "item": item}
    return results

额外数据类型

FastAPI 除了支持常见的数据类型：

int
float
str
bool

还支持额外的数据类型：

UUID
datetime.datetime
datetime.date
datetime.time
datetime.timedelta
frozenset
bytes
Decimal

示例：

from datetime import datetime, time, timedelta
from typing import Union
from uuid import UUID

from fastapi import Body, FastAPI

app = FastAPI()


@app.put("/items/{item_id}")
async def read_items(
    item_id: UUID,
    start_datetime: Union[datetime, None] = Body(default=None),
    end_datetime: Union[datetime, None] = Body(default=None),
    repeat_at: Union[time, None] = Body(default=None),
    process_after: Union[timedelta, None] = Body(default=None),
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "repeat_at": repeat_at,
        "process_after": process_after,
        "start_process": start_process,
        "duration": duration,
    }

from typing import Union

from fastapi import Cookie, FastAPI

app = FastAPI()


@app.get("/items/")
async def read_items(ads_id: Union[str, None] = Cookie(default=None)):
    return {"ads_id": ads_id}

跟Query和 Path用法类似。

from typing import Union

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(user_agent: Union[str, None] = Header(default=None)):
    return {"User-Agent": user_agent}

多重 header 用 List，比如：

from typing import Union

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(user_agent: Union[str, None] = Header(default=None)):
    return {"User-Agent": user_agent}

X-Token: foo
X-Token: bar

{
    "X-Token values": [
        "bar",
        "foo"
    ]
}

响应模型

通过response_model定义返回模型：

from typing import List, Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


@app.post("/items/", response_model=Item)
async def create_item(item: Item):
    return item

response_model 的作用是对函数返回值进行过滤，比如：

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr

app = FastAPI()


class UserIn(BaseModel):
    username: str
    password: str
    email: EmailStr
    full_name: Union[str, None] = None


class UserOut(BaseModel):
    username: str
    email: EmailStr
    full_name: Union[str, None] = None


@app.post("/user/", response_model=UserOut)
async def create_user(user: UserIn):
    return user

函数返回值是 UserIn 模型的对象 user，而 response_model 的值为 UserOut（UserOut 相比于 UserIn 来说，没有 password），那么 FastAPI 的响应，就是用 UserOut 对 UserIn 进行了过滤，返回的是没有 password 的 UserOut。

响应模型可以返回默认值：

from typing import List, 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
    tags: List[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
    "baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}


@app.get("/items/{item_id}", response_model=Item, response_model_exclude_unset=True)
async def read_item(item_id: str):
    return items[item_id]

response_model_exclude_unset=True不返回未显式设置的字段，response_model_exclude_defaults不返回带默认值的字段，response_model_exclude_none不返回 None 的字段。

附加模型

在上面的示例中，UserIn 是入参，UserOut 是出参，不包含 password，但是在实际情况中，还需要第三个模型 UserInDB，在存入数据库时，把 password 进行加密。

代码实现如下：

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr

app = FastAPI()


class UserIn(BaseModel):
    username: str
    password: str
    email: EmailStr
    full_name: Union[str, None] = None


class UserOut(BaseModel):
    username: str
    email: EmailStr
    full_name: Union[str, None] = None


class UserInDB(BaseModel):
    username: str
    hashed_password: str
    email: EmailStr
    full_name: Union[str, None] = None


def fake_password_hasher(raw_password: str):
    return "supersecret" + raw_password


def fake_save_user(user_in: UserIn):
    hashed_password = fake_password_hasher(user_in.password)
    user_in_db = UserInDB(**user_in.dict(), hashed_password=hashed_password)
    print("User saved! ..not really")
    return user_in_db


@app.post("/user/", response_model=UserOut)
async def create_user(user_in: UserIn):
    user_saved = fake_save_user(user_in)
    return user_saved

重点是user_in_db = UserInDB(**user_in.dict(), hashed_password=hashed_password)里面的**user_in.dict()。

user_in 是 UserIn 类的 Pydantic 模型，它有个dict()方法能返回字典。**是拆包，把字典拆成 key value 的形式，上面这行代码等价于：

UserInDB(
    username="john",
    password="secret",
    email="john.doe@example.com",
    full_name=None,
    hashed_password=hashed_password
)

也相当于：

UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
    hashed_password = hashed_password,
)

FastAPI 的一大设计原则是尽量减少重复代码，所以对于 UserIn、UserOut、UserInDB 可以把里面的相同字段抽象为一个 UserBase，其他 Model 继承即可：

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr

app = FastAPI()


class UserBase(BaseModel):
    username: str
    email: EmailStr
    full_name: Union[str, None] = None


class UserIn(UserBase):
    password: str


class UserOut(UserBase):
    pass


class UserInDB(UserBase):
    hashed_password: str


def fake_password_hasher(raw_password: str):
    return "supersecret" + raw_password


def fake_save_user(user_in: UserIn):
    hashed_password = fake_password_hasher(user_in.password)
    user_in_db = UserInDB(**user_in.dict(), hashed_password=hashed_password)
    print("User saved! ..not really")
    return user_in_db


@app.post("/user/", response_model=UserOut)
async def create_user(user_in: UserIn):
    user_saved = fake_save_user(user_in)
    return user_saved

response_model除了定义一个 Model 以外，也能定义多个附加模型。

比如 Union：

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class BaseItem(BaseModel):
    description: str
    type: str


class CarItem(BaseItem):
    type = "car"


class PlaneItem(BaseItem):
    type = "plane"
    size: int


items = {
    "item1": {"description": "All my friends drive a low rider", "type": "car"},
    "item2": {
        "description": "Music is my aeroplane, it's my aeroplane",
        "type": "plane",
        "size": 5,
    },
}


@app.get("/items/{item_id}", response_model=Union[PlaneItem, CarItem])
async def read_item(item_id: str):
    return items[item_id]

比如 List：

from typing import List

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str


items = [
    {"name": "Foo", "description": "There comes my hero"},
    {"name": "Red", "description": "It's my aeroplane"},
]


@app.get("/items/", response_model=List[Item])
async def read_items():
    return items

比如 Dict：

from typing import Dict

from fastapi import FastAPI

app = FastAPI()


@app.get("/keyword-weights/", response_model=Dict[str, float])
async def read_keyword_weights():
    return {"foo": 2.3, "bar": 3.4}

参考资料：

https://fastapi.tiangolo.com/

Python 第三流行的 Web 框架#

FastAPI 特性#

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