查询参数模型¶
如果您有一组相关的查询参数，您可以创建一个 Pydantic 模型来声明它们。
这将允许您在多个地方重用模型，并且可以一次性为所有参数声明验证和元数据。😎
注意
此功能自 FastAPI 0.115.0 版本起支持。🤓
使用 Pydantic 模型声明查询参数¶
在 Pydantic 模型中声明您需要的查询参数，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
🤓 其他版本和变体
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
提示
如果可能，请优先使用 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
提示
如果可能，请优先使用 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
提示
如果可能，请优先使用 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):
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，并为您提供您定义的 Pydantic 模型。
查看文档¶
您可以在 /docs 的文档 UI 中查看查询参数
禁止额外查询参数¶
在某些特殊用例中（可能不常见），您可能希望限制要接收的查询参数。
您可以使用 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
🤓 其他版本和变体
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
提示
如果可能，请优先使用 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
提示
如果可能，请优先使用 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
提示
如果可能，请优先使用 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 tool 查询参数，其值为 plumbus，例如
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 不被允许
{
"detail": [
{
"type": "extra_forbidden",
"loc": ["query", "tool"],
"msg": "Extra inputs are not permitted",
"input": "plumbus"
}
]
}
总结¶
您可以使用 Pydantic 模型在 FastAPI 中声明查询参数。😎
提示
剧透警告：您还可以使用 Pydantic 模型声明 cookie 和 header，但您将在本教程的稍后部分了解到这一点。🤫