```

FastAPI will use this return type to:

* **Validate** the returned data.
    * If the data is invalid (e.g. you are missing a field), it means that *your* app code is broken, not returning what it should, and it will return a server error instead of returning incorrect data. This way you and your clients can be certain that they will receive the data and the data shape expected.
* Add a **JSON Schema** for the response, in the OpenAPI *path operation*.

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

One of the main features needed by API systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would...

## Recap

With **FastAPI**, by using short, intuitive and standard Python type declarations, you get:

* Editor support: error checks, autocompletion, etc.
* Data "<abbr title="converting the string that comes from an HTTP request into Python data">parsing</abbr>"
* Data validation
* API annotation and automatic documentation

And you only have to declare them once.

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

In that case, it would mean the JSON attributes, and data types they have, etc.

#### OpenAPI and JSON Schema

* To pass a JSON body, pass a Python object (e.g. a `dict`) to the parameter `json`.
* If you need to send *Form Data* instead of JSON, use the `data` parameter instead.
* To pass *headers*, use a `dict` in the `headers` parameter.
* For *cookies*, a `dict` in the `cookies` parameter.

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` nghĩa là:

    Truyền các khóa và giá trị của dict `second_user_data` trực tiếp như các tham số kiểu key-value, tương đương với: `User(id=4, name="Mary", joined="2018-11-30")`

## Other models

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

`UploadFile` ✔️ 📄 `async` 👩‍🔬. 👫 🌐 🤙 🔗 📁 👩‍🔬 🔘 (⚙️ 🔗 `SpooledTemporaryFile`).

* `write(data)`: ✍ `data` (`str` ⚖️ `bytes`) 📁.
* `read(size)`: ✍ `size` (`int`) 🔢/🦹 📁.
* `seek(offset)`: 🚶 🔢 🧘 `offset` (`int`) 📁.
    * 🤶 Ⓜ., `await myfile.seek(0)` 🔜 🚶 ▶️ 📁.