With **FastAPI**, using OpenAPI, you can define the names of these webhooks, the types of HTTP operations that your app can send (e.g. `POST`, `PUT`, etc.) and the request **bodies** that your app would send.

This can make it a lot easier for your users to **implement their APIs** to receive your **webhook** requests, they might even be able to autogenerate some of their own API code.

!!! info

## Documenting the callback

The actual callback code will depend heavily on your own API app.

And it will probably vary a lot from one app to the next.

It could be just one or two lines of code, like:

```Python
callback_url = "https://example.com/api/v1/invoices/events/"

As code duplication increments the chances of bugs, security issues, code desynchronization issues (when you update in one place but not in the others), etc.

And these models are all sharing a lot of the data and duplicating attribute names and types.

We could do better.

* `$`: ends there, doesn't have any more characters after `fixedquery`.

If you feel lost with all these **"regular expression"** ideas, don't worry. They are a hard topic for many people. You can still do a lot of stuff without needing regular expressions yet.

But whenever you need them and go and learn them, know that you can already use them directly in **FastAPI**.

### Pydantic v1 `regex` instead of `pattern`

So, to balance that out, imagine the following short story:

> You have to clean a big, dirty house.

*Yep, that's the whole story*.

---

There's no waiting 🕙 anywhere, just a lot of work to be done, on multiple places of the house.

        All that, on top of all the incredible help provided by a bunch of community members, the [FastAPI Experts](https://fastapi.tiangolo.com/fastapi-people/#experts), that give a lot of their time to come here and help others.

        That's a lot of work they are doing, but if more FastAPI users came to help others like them just a little bit more, it would be much less effort for them (and you and me 😅).

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

Let's imagine that loading the model can **take quite some time**, because it has to read a lot of **data from disk**. So you don't want to do it for every request.

So, if your application consumes a lot of memory (for example with machine learning models), and your server has a lot of CPU cores **but little memory**, then your container could end up trying to use more memory than what is available, and degrading performance a lot (or even crashing). 🚨

### Create a `Dockerfile`

    ```Python hl_lines="11"
    {!> ../../../docs_src/dependencies/tutorial001.py!}
    ```

But then we get a `dict` in the parameter `commons` of the *path operation function*.

And we know that editors can't provide a lot of support (like completion) for `dict`s, because they can't know their keys and value types.

We can do better...

## What makes a dependency

Up to now you have seen dependencies declared as functions.

Then we can test that it is used.

## Reading a `.env` file

If you have many settings that possibly change a lot, maybe in different environments, it might be useful to put them on a file and then read them from it as if they were environment variables.