We can do better...

## What makes a dependency { #what-makes-a-dependency }

Up to now you have seen dependencies declared as functions.

But that's not the only way to declare dependencies (although it would probably be the more common).

The key factor is that a dependency should be a "callable".

///

### Import `Depends` { #import-depends }

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[3] *}

### Declare the dependency, in the "dependant" { #declare-the-dependency-in-the-dependant }

The same way you use `Body`, `Query`, etc. with your *path operation function* parameters, use `Depends` with a new parameter:

But if you return a `Response` directly (or any subclass, like `JSONResponse`), the data won't be automatically converted (even if you declare a `response_model`), and the documentation won't be automatically generated (for example, including the specific "media type", in the HTTP header `Content-Type` as part of the generated OpenAPI).

These functions can be declared with `async def` or normal `def`.

### `startup` event { #startup-event }

To add a function that should be run before the application starts, declare it with the event `"startup"`:

{* ../../docs_src/events/tutorial001.py hl[8] *}

## Dependencies errors and return values { #dependencies-errors-and-return-values }

You can use the same dependency *functions* you use normally.

### Dependency requirements { #dependency-requirements }

They can declare request requirements (like headers) or other sub-dependencies:

{* ../../docs_src/dependencies/tutorial006_an_py39.py hl[8,13] *}

### Raise exceptions { #raise-exceptions }

{* ../../docs_src/security/tutorial002_an_py310.py hl[31] *}

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`.

///

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[13] *}

Let's focus on the parameters declared:

* Even though this function is a dependency ("dependable") itself, it also declares another dependency (it "depends" on something else).
    * It depends on the `query_extractor`, and assigns the value returned by it to the parameter `q`.
* It also declares an optional `last_query` cookie, as a `str`.

   *
   * @return this method does not return; it always throws
   * @throws IOException when the given throwable is an IOException
   * @throws X1 when the given throwable is of the declared type X1
   * @throws X2 when the given throwable is of the declared type X2
   */
  public <X1 extends Exception, X2 extends Exception> RuntimeException rethrow(
      Throwable e, Class<X1> declaredType1, Class<X2> declaredType2) throws IOException, X1, X2 {

///

## Adicione informações extras

Você pode declarar informação extra em `Field`, `Query`, `Body`, etc. E isso será incluído no JSON Schema gerado.

Você irá aprender mais sobre adicionar informações extras posteriormente nessa documentação, quando estiver aprendendo a declarar exemplos.

## Recapitulando

Você pode usar `Field` do Pydantic para declarar validações extras e metadados para atributos do modelo.

# `Response` class

You can declare a parameter in a *path operation function* or dependency to be of type `Response` and then you can set data for the response like headers or cookies.

You can also use it directly to create an instance of it and return it from your *path operations*.

You can import it directly from `fastapi`:

```python
from fastapi import Response
```