# Streaming

## Stream JSON Lines

To stream JSON Lines, declare the return type and use `yield` to return the data.

```python
@app.get("/items/stream")
async def stream_items() -> AsyncIterable[Item]:
    for item in items:
        yield item
```

## Server-Sent Events (SSE)

To stream Server-Sent Events, use `response_class=EventSourceResponse` and `yield` items from the endpoint.

With `Form` you can declare the same configurations as with `Body` (and `Query`, `Path`, `Cookie`), including validation, examples, an alias (e.g. `user-name` instead of `username`), etc.

/// info

`Form` is a class that inherits directly from `Body`.

///

/// tip

The files and form fields will be uploaded as form data and you will receive the files and form fields.

And you can declare some of the files as `bytes` and some as `UploadFile`.

/// warning

You can declare multiple `File` and `Form` parameters in a *path operation*, but you can't also declare `Body` fields that you expect to receive as JSON, as the request will have the body encoded using `multipart/form-data` instead of `application/json`.

///

/// 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".

# Cookie Parameter Models { #cookie-parameter-models }

If you have a group of **cookies** that are related, you can create a **Pydantic model** to declare them. 🍪

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

/// tip

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <dfn title="Technically, Deep Learning model architectures">models</dfn>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

{* ../../docs_src/path_params/tutorial005_py310.py hl[16] *}

# Declare Request Example Data { #declare-request-example-data }

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

{* ../../docs_src/python_types/tutorial004_py310.py hl[2] *}

## Declaring types { #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 { #simple-types }

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

You can use, for example:

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

## Additional Responses { #additional-responses }

You probably have seen how to declare the `response_model` and `status_code` for a *path operation*.

That defines the metadata about the main response of a *path operation*.

You can also declare additional responses with their models, status codes, etc.

## Importe `Header` { #import-header }

Primeiro importe `Header`:

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

## Declare parâmetros de `Header` { #declare-header-parameters }

Então declare os paramêtros de cabeçalho usando a mesma estrutura que em `Path`, `Query` e `Cookie`.

Você pode definir o valor padrão, assim como todas as validações extras ou parâmetros de anotação: