When implementing the callback yourself, you could use something like <a href="https://www.python-httpx.org" class="external-link" target="_blank">HTTPX</a> or <a href="https://requests.readthedocs.io/" class="external-link" target="_blank">Requests</a>.

## Write the callback documentation code

This code won't be executed in your app, we only need it to *document* how that *external API* should look like.

It's relatively tightly coupled with relational databases (like MySQL or PostgreSQL), so, having a NoSQL database (like Couchbase, MongoDB, Cassandra, etc) as the main store engine is not very easy.

    For example, you could use it to read and verify passwords generated by another system (like Django) but hash any new passwords with a different algorithm like Bcrypt.

    And be compatible with all of them at the same time.

Create a utility function to hash a password coming from the user.

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself....

    "paths": {
            // More stuff here
    }
}
```

In this example, the "Proxy" could be something like **Traefik**. And the server would be something like **Uvicorn**, running your FastAPI application.

### Providing the `root_path`

To achieve this, you can use the command line option `--root-path` like:

<div class="termy">

```console
$ uvicorn main:app --root-path /api/v1

In many cases your application could need some external settings or configurations, for example secret keys, database credentials, credentials for email services, etc.

Most of these settings are variable (can change), like database URLs. And many could be sensitive, like secrets.

For this reason it's common to provide them in environment variables that are read by the application.

## Environment Variables

!!! tip

#### Unwrapping a `dict` and extra keywords

And then adding the extra keyword argument `hashed_password=hashed_password`, like in:

```Python
UserInDB(**user_in.dict(), hashed_password=hashed_password)
```

...ends up being like:

```Python
UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],

    Notice that `response_model` is a parameter of the "decorator" method (`get`, `post`, etc). Not of your *path operation function*, like all the parameters and body.

`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.

```Python hl_lines="2  14"
{!../../../docs_src/custom_response/tutorial007.py!}
```

#### Using `StreamingResponse` with file-like objects

If you have a file-like object (e.g. the object returned by `open()`), you can create a generator function to iterate over that file-like object.

That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.

But that file doesn't exist, our dependencies are in a file at `app/dependencies.py`.

Remember how our app/file structure looks like:

<img src="/img/tutorial/bigger-applications/package.svg">

---

The two dots `..`, like in:

```Python
from ..dependencies import get_token_header
```

mean: