FastAPI官方教程太棒了（上）

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/

posted @ 2022-06-06 09:07 测试开发刚哥阅读(1011) 评论(0) 编辑收藏举报

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