查询参数模型¶
如果你有一组相关的**查询参数**，你可以创建一个**Pydantic模型**来声明它们。
这将允许你在**多个地方**重用该模型，并且还可以一次性为所有参数声明验证和元数据。😎
Note
自 FastAPI 版本 0.115.0 起支持此功能。🤓
使用 Pydantic 模型的查询参数¶
在**Pydantic模型**中声明你需要的**查询参数**，然后将参数声明为 Query：
from typing import Annotated, Literal
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
app = FastAPI()
class FilterParams(BaseModel):
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
return filter_query
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Annotated, Literal
app = FastAPI()
class FilterParams(BaseModel):
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
return filter_query
from typing import List
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Annotated, Literal
app = FastAPI()
class FilterParams(BaseModel):
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: List[str] = []
@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
return filter_query
Tip
如果可能，建议使用 Annotated 版本。
from typing import Literal
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
app = FastAPI()
class FilterParams(BaseModel):
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):
return filter_query
Tip
如果可能，建议使用 Annotated 版本。
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Literal
app = FastAPI()
class FilterParams(BaseModel):
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):
return filter_query
Tip
如果可能，建议使用 Annotated 版本。
from typing import Literal
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
app = FastAPI()
class FilterParams(BaseModel):
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):
return filter_query
FastAPI 将从请求中的**查询参数**中提取每个字段的数据，并为你提供定义的 Pydantic 模型。
查看文档¶
你可以在 /docs 的文档 UI 中看到查询参数：
禁止额外的查询参数¶
在某些特殊用例（可能不是很常见）中，你可能希望**限制**你想要接收的查询参数。
你可以使用 Pydantic 的模型配置来 forbid 任何 extra 字段：
from typing import Annotated, Literal
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
app = FastAPI()
class FilterParams(BaseModel):
model_config = {"extra": "forbid"}
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
return filter_query
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Annotated, Literal
app = FastAPI()
class FilterParams(BaseModel):
model_config = {"extra": "forbid"}
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
return filter_query
from typing import List
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Annotated, Literal
app = FastAPI()
class FilterParams(BaseModel):
model_config = {"extra": "forbid"}
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: List[str] = []
@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
return filter_query
Tip
如果可能，建议使用 Annotated 版本。
from typing import Literal
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
app = FastAPI()
class FilterParams(BaseModel):
model_config = {"extra": "forbid"}
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):
return filter_query
Tip
如果可能，建议使用 Annotated 版本。
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Literal
app = FastAPI()
class FilterParams(BaseModel):
model_config = {"extra": "forbid"}
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: list[str] = []
@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):
return filter_query
Tip
如果可能，建议使用 Annotated 版本。
from typing import List
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Literal
app = FastAPI()
class FilterParams(BaseModel):
model_config = {"extra": "forbid"}
limit: int = Field(100, gt=0, le=100)
offset: int = Field(0, ge=0)
order_by: Literal["created_at", "updated_at"] = "created_at"
tags: List[str] = []
@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):
return filter_query
如果客户端尝试在**查询参数**中发送一些**额外**的数据，他们将收到一个**错误**响应。
例如，如果客户端尝试发送一个值为 plumbus 的 tool 查询参数，如：
https://example.com/items/?limit=10&tool=plumbus
他们将收到一个**错误**响应，告诉他们查询参数 tool 是不允许的：
{
"detail": [
{
"type": "extra_forbidden",
"loc": ["query", "tool"],
"msg": "Extra inputs are not permitted",
"input": "plumbus"
}
]
}
总结¶
你可以在 FastAPI 中使用 Pydantic 模型 来声明 查询参数。😎
Tip
剧透警告：你还可以使用 Pydantic 模型来声明 cookies 和 headers，但你将在教程后面部分了解到这些内容。🤫