from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


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

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
    tags: list[str] = []


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

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
    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}

```Python hl_lines="22"
{!> ../../docs_src/body_multiple_params/tutorial002.py!}
```

////

In this case, **FastAPI** will notice that there is more than one body parameter in the function (there are two parameters that are Pydantic models).

So, it will then use the parameter names as keys (field names) in the body, and expect a body like:

```JSON
{
    "item": {
        "name": "Foo",
        "description": "The pretender",

```Python hl_lines="22"
{!> ../../docs_src/body_multiple_params/tutorial002.py!}
```

////

In diesem Fall wird **FastAPI** bemerken, dass es mehr als einen Body-Parameter in der Funktion gibt (zwei Parameter, die Pydantic-Modelle sind).

Es wird deshalb die Parameternamen als Schlüssel (Feldnamen) im Body verwenden, und erwartet einen Body wie folgt:

```JSON
{
    "item": {
        "name": "Foo",

/// info | "Informação"

Observe que o `TestClient` recebe dados que podem ser convertidos para JSON, não para modelos Pydantic.

Se você tiver um modelo Pydantic em seu teste e quiser enviar seus dados para o aplicativo durante o teste, poderá usar o `jsonable_encoder` descrito em [Codificador compatível com JSON](encoder.md){.internal-link target=_blank}.

///

from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel

app = FastAPI()


class CommonHeaders(BaseModel):
    model_config = {"extra": "forbid"}

    host: str
    save_data: bool
    if_modified_since: str | None = None
    traceparent: str | None = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: Annotated[CommonHeaders, Header()]):

from typing import Union

from fastapi import FastAPI, Header
from pydantic import BaseModel

app = FastAPI()


class CommonHeaders(BaseModel):
    model_config = {"extra": "forbid"}

    host: str
    save_data: bool
    if_modified_since: Union[str, None] = None
    traceparent: Union[str, None] = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: CommonHeaders = Header()):

Python 3.7+

FastAPI কিছু দানবেদের কাঁধে দাঁড়িয়ে আছে:

- <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a> ওয়েব অংশের জন্য.
- <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> ডেটা অংশগুলির জন্য.

## ইনস্টলেশন প্রক্রিয়া

<div class="termy">

```console
$ pip install fastapi

---> 100%
```

</div>

## Pydanticのモデル

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> はデータ検証を行うためのPythonライブラリです。

データの「形」を属性付きのクラスとして宣言します。

そして、それぞれの属性は型を持ちます。

さらに、いくつかの値を持つクラスのインスタンスを作成すると、その値を検証し、適切な型に変換して（もしそうであれば）全てのデータを持つオブジェクトを提供してくれます。

また、その結果のオブジェクトですべてのエディタのサポートを受けることができます。

Pydanticの公式ドキュメントから引用:

```Python