You also define in some way at which **moments** your app will send those requests or events.

And **your users** define in some way (for example in a web dashboard somewhere) the **URL** where your app should send those requests.

With **FastAPI** you get all of **Pydantic**'s features (as FastAPI is based on Pydantic for all the data handling):

* **No brainfuck**:
    * No new schema definition micro-language to learn.
    * If you know Python types you know how to use Pydantic.

# WebSockets

You can use <a href="https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API" class="external-link" target="_blank">WebSockets</a> with **FastAPI**.

## Install `WebSockets`

First you need to install `WebSockets`:

<div class="termy">

```console
$ pip install websockets

---> 100%
```

</div>

## WebSockets client

### In production

In most cases you would (and should) have a "termination proxy" handling HTTPS for you on top, this will depend on how you deploy your application, your provider might do this for you, or you might need to set it up yourself.

!!! tip

    Temporarily adopting this point of view (of the *external developer*) can help you feel like it's more obvious where to put the parameters, the Pydantic model for the body, for the response, etc. for that *external API*.

### Create a callback `APIRouter`

* <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
* `fastapi-cli` - to provide the `fastapi` command.

When you install `fastapi` it comes these standard dependencies.

## `fastapi-slim`

---

"_If you're looking to learn one **modern framework** for building REST APIs, check out **FastAPI** [...] It's fast, easy to use and easy to learn [...]_"

"_We've switched over to **FastAPI** for our **APIs** [...] I think you'll like it [...]_"

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

```

</div>

### Check the responses

Now, if you go to the URL with the port for Uvicorn: <a href="http://127.0.0.1:8000/app" class="external-link" target="_blank">http://127.0.0.1:8000/app</a>, you will see the normal response:

```JSON
{
    "message": "Hello World",
    "root_path": "/api/v1"
}
```

!!! tip

But if you want to directly use **Trio**, then you can use **Hypercorn** as it supports it. ✨

### Install Hypercorn with Trio

First you need to install Hypercorn with Trio support:

<div class="termy">

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Run with Trio