from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


class User(BaseModel):
    username: str
    full_name: str | None = None


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

from fastapi import FastAPI
from pydantic.v1 import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None

    class Config:
        schema_extra = {
            "examples": [
                {
                    "name": "Foo",
                    "description": "A very nice Item",
                    "price": 35.4,

from typing import Optional

from fastapi import Depends, FastAPI, Security
from fastapi.security.open_id_connect_url import OpenIdConnect
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()

oid = OpenIdConnect(openIdConnectUrl="/openid", auto_error=False)


class User(BaseModel):
    username: str


def get_current_user(oauth_header: Optional[str] = Security(oid)):
    if oauth_header is None:

E depois nós validamos esse dado com o modelo Pydantic (capturando a exceção `ValidationError`), e se nós obtemos um erro ao ler o token JWT ou validando os dados com o Pydantic, nós levantamos a exceção `HTTPException` que criamos anteriormente.

Para isso, nós atualizamos o modelo Pydantic `TokenData` com a nova propriedade `scopes`.

    mod.engine.dispose()


def test_crud_app(client: TestClient):
    # TODO: this warns that SQLModel.from_orm is deprecated in Pydantic v1, refactor
    # this if using obj.model_validate becomes independent of Pydantic v2
    with warnings.catch_warnings(record=True):
        warnings.simplefilter("always")
        # No heroes before creating
        response = client.get("heroes/")

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 = None
    price: float
    tax: float | None = None
    tags: set[str] = set()
    images: list[Image] | None = None


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

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

    model_config = {
        "json_schema_extra": {
            "examples": [
                {
                    "name": "Foo",
                    "description": "A very nice Item",

`SQLModel`을 가져오고 데이터베이스 모델을 생성합니다:

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

`Hero` 클래스는 Pydantic 모델과 매우 유사합니다 (실제로 내부적으로 *Pydantic 모델이기도 합니다*).

몇 가지 차이점이 있습니다:

* `table=True`는 SQLModel에 이 모델이 *테이블 모델*이며, 단순한 데이터 모델이 아니라 SQL 데이터베이스의 **테이블**을 나타낸다는 것을 알려줍니다. (다른 일반적인 Pydantic 클래스처럼) 단순한 *데이터 모델*이 아닙니다.

from dataclasses import field  # (1)

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


@dataclass
class Item:
    name: str
    description: str | None = None


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)

from dataclasses import field  # (1)
from typing import Union

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


@dataclass
class Item:
    name: str
    description: Union[str, None] = None


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)