서로다른 버전의 **FastAPI**가 구체적이고 새로운 버전의 Starlette을 사용할 것입니다.

그러므로 **FastAPI**가 알맞은 Starlette 버전을 사용하도록 하십시오.

## Pydantic에 대해

Pydantic은 **FastAPI** 를 위한 검사를 포함하고 있습니다. 따라서, 새로운 버전의 Pydantic(`1.0.0`이상)은 항상 FastAPI와 호환됩니다.

작업을 하고 있는 `1.0.0` 이상의 모든 버전과 `2.0.0` 이하의 Pydantic 버전을 표시할 수 있습니다.

예를 들어 다음과 같습니다:

```txt
pydantic>=1.2.0,<2.0.0

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

///

### Using Pydantic's `exclude_unset` parameter

If you want to receive partial updates, it's very useful to use the parameter `exclude_unset` in Pydantic's model's `.model_dump()`.

Like `item.model_dump(exclude_unset=True)`.

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

from uuid import UUID

from fastapi.types import IncEx
from pydantic import BaseModel
from pydantic.color import Color
from pydantic.networks import AnyUrl, NameEmail
from pydantic.types import SecretBytes, SecretStr
from typing_extensions import Annotated, Doc

from ._compat import PYDANTIC_V2, UndefinedType, Url, _model_dump


# Taken from Pydantic v1 as is
def isoformat(o: Union[datetime.date, datetime.time]) -> str:

If you have a group of **query parameters** that are related, you can create a **Pydantic model** to declare them.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Query Parameters with a Pydantic Model

//// tab | Pydantic v2

In Pydantic version 2, you would use the attribute `model_config`, that takes a `dict` as described in <a href="https://docs.pydantic.dev/latest/api/config/" class="external-link" target="_blank">Pydantic's docs: Configuration</a>.

You can set `"json_schema_extra"` with a `dict` containing any additional data you would like to show up in the generated JSON Schema, including `examples`.

////

//// tab | Pydantic v1

## Import `Field`

First, you have to import it:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}


/// warning

Notice that `Field` is imported directly from `pydantic`, not from `fastapi` as are all the rest (`Query`, `Path`, `Body`, etc).

///

If you have a group of related **header parameters**, you can create a **Pydantic model** to declare them.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Header Parameters with a Pydantic Model

## Cookies with a Pydantic Model

Declare the **cookie** parameters that you need in a **Pydantic model**, and then declare the parameter as `Cookie`:

//// tab | Python 3.10+

```Python hl_lines="9-12  16"
{!> ../../docs_src/cookie_param_models/tutorial001_an_py310.py!}
```

////

//// tab | Python 3.9+

```Python hl_lines="9-12  16"
{!> ../../docs_src/cookie_param_models/tutorial001_an_py39.py!}
```

////

If you use <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a> as your editor, you can use the <a href="https://github.com/koxudaxi/pydantic-pycharm-plugin/" class="external-link" target="_blank">Pydantic PyCharm Plugin</a>.

It improves editor support for Pydantic models, with:

* auto-completion
* type checks
* refactoring
* searching
* inspections

///

## Use the model