# Body - Fields

The same way you can declare additional validation and metadata in *path operation function* parameters with `Query`, `Path` and `Body`, you can declare validation and metadata inside of Pydantic models using Pydantic's `Field`.

## Import `Field`

First, you have to import it:

=== "Python 3.10+"

    ```Python hl_lines="4"
    {!> ../../../docs_src/body_fields/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

```JSON
{"item_id":"foo"}
```

## Path parameters with types

You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

!!! check
    This will give you editor support inside of your function, with error checks, completion, etc.

!!! tip
    If you have strict type checks in your editor, mypy, etc, you can declare the function return type as `Any`.

    That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the `response_model`.

### `response_model` Priority

    ] = True,
) -> Any:
    """
    Declare a FastAPI Security dependency.

    The only difference with a regular dependency is that it can declare OAuth2
    scopes that will be integrated with OpenAPI and the automatic UI docs (by default
    at `/docs`).

    It takes a single "dependable" callable (like a function).

    Don't call it directly, FastAPI will call it for you.

    Read more about it in the

The dependency injection system requires pre-registration of the dependencies and the dependencies are solved based on the declared types. So, it's not possible to declare more than one "component" that provides a certain type.

# Background Tasks - `BackgroundTasks`

You can declare a parameter in a *path operation function* or dependency function with the type `BackgroundTasks`, and then you can use it to schedule the execution of background tasks after the response is sent.

You can import it directly from `fastapi`:

```python
from fastapi import BackgroundTasks
```

# `Request` class

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip

    ```Python hl_lines="31"
    {!> ../../../docs_src/security/tutorial002.py!}
    ```

Notice that we declare the type of `current_user` as the Pydantic model `User`.

This will help us inside of the function with all the completion and type checks.

!!! tip
    You might remember that request bodies are also declared with Pydantic models.

    Here **FastAPI** won't get confused because you are using `Depends`.

!!! check

You can also declare singular values to be received as part of the body.

```Python hl_lines="2"
{!../../../docs_src/python_types/tutorial004.py!}
```

## Declaring types

You just saw the main place to declare type hints. As function parameters.

This is also the main place you would use them with **FastAPI**.

### Simple types

You can declare all the standard Python types, not only `str`.

You can use, for example:

* `int`
* `float`
* `bool`
* `bytes`