return {"fresh_value": fresh_value}
    ```

## Recap

Apart from all the fancy words used here, the **Dependency Injection** system is quite simple.

Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

!!! tip
    All this might not seem as useful with these simple examples.

# Dependencies in path operation decorators

In some cases you don't really need the return value of a dependency inside your *path operation function*.

Or the dependency doesn't return a value.

But you still need it to be executed/solved.

For those cases, instead of declaring a *path operation function* parameter with `Depends`, you can add a `list` of `dependencies` to the *path operation decorator*.

!!! tip
    To declare File bodies, you need to use `File`, because otherwise the parameters would be interpreted as query parameters or body (JSON) parameters.

The files will be uploaded as "form data".

    * Como as estruturas de dados do Pydantic são apenas instâncias de classes que você define, a auto completação, _linting_, _mypy_ e a sua intuição devem funcionar corretamente com seus dados validados.
* Valida **estruturas complexas**:
    * Use modelos hierárquicos do Pydantic, `List` e `Dict` do `typing` do Python, etc.

const div = document.querySelector('.github-topic-projects')

async function getDataBatch(page) {
    const response = await fetch(`https://api.github.com/search/repositories?q=topic:fastapi&per_page=100&page=${page}`, { headers: { Accept: 'application/vnd.github.mercy-preview+json' } })
    const data = await response.json()
    return data
}

async function getData() {
    let page = 1
    let data = []
    let dataBatch = await getDataBatch(page)

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
    hashed_password = hashed_password,
)
```

!!! warning
    The supporting additional functions are just to demo a possible flow of the data, but they of course are not providing any real security.

## Reduce duplication

Reducing code duplication is one of the core ideas in **FastAPI**.

sequenceDiagram

participant code as Code
participant function as say_hi()
participant execute as Funktion ausgeführt

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Camila")
        function ->> execute: führe Code der Funktion aus
        execute ->> code: gib das Resultat zurück
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")

# Query Parameters

When you declare other function parameters that are not part of the path parameters, they are automatically interpreted as "query" parameters.

```Python hl_lines="9"
{!../../../docs_src/query_params/tutorial001.py!}
```

The query is the set of key-value pairs that go after the `?` in a URL, separated by `&` characters.

For example, in the URL:

```
http://127.0.0.1:8000/items/?skip=0&limit=10
```

            ws.onmessage = function(event) {
                var messages = document.getElementById('messages')
                var message = document.createElement('li')
                var content = document.createTextNode(event.data)
                message.appendChild(content)
                messages.appendChild(message)
            };
            function sendMessage(event) {

```

### Advantages of `Annotated`

**Using `Annotated` is recommended** instead of the default value in function parameters, it is **better** for multiple reasons. 🤓

The **default** value of the **function parameter** is the **actual default** value, that's more intuitive with Python in general. 😌