* Have shared logic (the same code logic again and again).
* Share database connections.
* Enforce security, authentication, role requirements, etc.
* And many other things...

All these, while minimizing code repetition.

## First Steps

Let's see a very simple example. It will be so simple that it is not very useful, for now.

But this way we can focus on how the **Dependency Injection** system works.

}

async function getData() {
    let page = 1
    let data = []
    let dataBatch = await getDataBatch(page)
    data = data.concat(dataBatch.items)
    const totalCount = dataBatch.total_count
    while (data.length < totalCount) {
        page += 1
        dataBatch = await getDataBatch(page)
        data = data.concat(dataBatch.items)
    }
    return data
}

function setupTermynal() {

    tmp_path = tmp_path_factory.mktemp("data")
    cwd = os.getcwd()
    os.chdir(tmp_path)
    test_db = Path("./test.db")
    if test_db.is_file():  # pragma: nocover
        test_db.unlink()
    # Import while creating the client to create the DB after starting the test session
    from docs_src.sql_databases.sql_app_py310.tests import test_sql_app

    # Ensure import side effects are re-executed
    importlib.reload(test_sql_app)

# Full Stack FastAPI Template

Templates, while typically come with a specific setup, are designed to be flexible and customizable. This allows you to modify and adapt them to your project's requirements, making them an excellent starting point. 🏁

You can use this template to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you.

@app.get("/")
async def get():
    return HTMLResponse(html)


@app.websocket("/ws/{client_id}")
async def websocket_endpoint(websocket: WebSocket, client_id: int):
    await manager.connect(websocket)
    try:
        while True:
            data = await websocket.receive_text()
            await manager.send_personal_message(f"You wrote: {data}", websocket)
            await manager.broadcast(f"Client #{client_id} says: {data}")

    But **FastAPI** will know that it has to solve `query_extractor` first, to pass the results of that to `query_or_cookie_extractor` while calling it.

```mermaid
graph TB

query_extractor(["query_extractor"])
query_or_cookie_extractor(["query_or_cookie_extractor"])

read_query["/items/"]

    The app above is a minimal and simple example to demonstrate how to handle and broadcast messages to several WebSocket connections.

    But keep in mind that, as everything is handled in memory, in a single list, it will only work while the process is running, and will only work with a single process.

The whole **FastAPI** framework is based to satisfy that. Autocompletion works everywhere.

You will rarely need to come back to the docs.

Here's how your editor might help you:

    And the `dict` you receive as `weights` will actually have `int` keys and `float` values.

## Recap

With **FastAPI** you have the maximum flexibility provided by Pydantic models, while keeping your code simple, short and elegant.

But with all the benefits:

* Editor support (completion everywhere!)
* Data conversion (a.k.a. parsing / serialization)
* Data validation
* Schema documentation

If you declare the type of your *path operation function* parameter as `bytes`, **FastAPI** will read the file for you and you will receive the contents as `bytes`.

Keep in mind that this means that the whole contents will be stored in memory. This will work well for small files.

But there are several cases in which you might benefit from using `UploadFile`.

## File Parameters with `UploadFile`