# OpenAPI `models`

OpenAPI Pydantic models used to generate and validate the generated OpenAPI.

# Extra Models { #extra-models }

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

/// danger

# Query Parameter Models { #query-parameter-models }

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`. 🤓

///

# Cookie Parameter Models { #cookie-parameter-models }

If you have a group of **cookies** 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`. 🤓

///

/// tip

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

///

## Pydantic Models for Forms { #pydantic-models-for-forms }

You just need to declare a **Pydantic model** with the fields you want to receive as **form fields**, and then declare the parameter as `Form`:

{* ../../docs_src/request_form_models/tutorial001_an_py310.py hl[9:11,15] *}

**FastAPI** will **extract** the data for **each field** from the **form data** in the request and give you the Pydantic model you defined.

# Body - 巢狀模型 { #body-nested-models }

使用 **FastAPI**，你可以定義、驗證、文件化，並使用任意深度的巢狀模型（感謝 Pydantic）。

## 列表欄位 { #list-fields }

你可以將屬性定義為某個子型別。例如，Python 的 `list`：

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

這會讓 `tags` 成為一個列表，儘管尚未宣告列表元素的型別。

## 具有型別參數的列表欄位 { #list-fields-with-type-parameter }

不過，Python 有一種專門的方式來宣告具有內部型別（「型別參數」）的列表：

### 宣告帶有型別參數的 `list` { #declare-a-list-with-a-type-parameter }

# Header 参数模型 { #header-parameter-models }

如果您有一组相关的 **header 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**能够**重用模型**，并且可以一次性声明所有参数的验证和元数据。😎

/// note | 注意

自 FastAPI 版本 `0.115.0` 起支持此功能。🤓

///

## 使用 Pydantic 模型的 Header 参数 { #header-parameters-with-a-pydantic-model }

在 **Pydantic 模型**中声明所需的 **header 参数**，然后将参数声明为 `Header` :

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

# クッキーパラメータモデル { #cookie-parameter-models }

もし関連する**複数のクッキー**から成るグループがあるなら、それらを宣言するために、**Pydanticモデル**を作成できます。🍪

こうすることで、**複数の場所**で**そのPydanticモデルを再利用**でき、バリデーションやメタデータを、すべてのパラメータに対して一度に宣言できます。😎

/// note | 備考

この機能は、FastAPIのバージョン `0.115.0` からサポートされています。🤓

///

/// tip | 豆知識

これと同じテクニックは `Query` 、 `Cookie` 、 `Header` にも適用できます。 😎

///

## Pydanticモデルを使用したクッキー { #cookies-with-a-pydantic-model }

# 본문 - 중첩 모델 { #body-nested-models }

**FastAPI**를 사용하면 (Pydantic 덕분에) 임의로 깊게 중첩된 모델을 정의, 검증, 문서화하고 사용할 수 있습니다.

## 리스트 필드 { #list-fields }

어트리뷰트를 서브타입으로 정의할 수 있습니다. 예를 들어 파이썬 `list`는:

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

이는 `tags`를 리스트로 만들지만, 리스트 요소의 타입을 선언하지는 않습니다.

## 타입 매개변수가 있는 리스트 필드 { #list-fields-with-type-parameter }

하지만 파이썬에는 내부 타입, 즉 "타입 매개변수"를 사용해 리스트를 선언하는 특정한 방법이 있습니다: