The important thing to keep in mind is that the `sub` key should have a unique identifier across the entire application, and it should be a string.

## Check it

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "enum",
                    "loc": ["path", "model_name"],
                    "msg": "Input should be 'alexnet', 'resnet' or 'lenet'",
                    "input": "foo",
                    "ctx": {"expected": "'alexnet', 'resnet' or 'lenet'"},
                }
            ]
        }
    ) | IsDict(

```

!!! tip
    If you were connecting to a different database (e.g. PostgreSQL), you would need to change the `DATABASE_URL`.

## Create the tables

In this case, we are creating the tables in the same Python file, but in production, you would probably want to create them with Alembic, integrated with migrations, etc.

Here, this section would run directly, right before starting your **FastAPI** application.

* Create an `engine`.

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["path", "item_id"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "foo",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

    raise HTTPException(status_code=204)


@app.get("/http-no-body-statuscode-with-detail-exception")
async def no_body_status_code_with_detail_exception():
    raise HTTPException(status_code=204, detail="I should just disappear!")


@app.get("/starlette-items/{item_id}")
async def read_starlette_item(item_id: str):
    if item_id not in items:
        raise StarletteHTTPException(status_code=404, detail="Item not found")

They are generally all related to the same application.

The analogy in the relational database world would be a "database" (a specific database, not the database server).

The analogy in **MongoDB** would be a "collection".

In the code, a `Bucket` represents the main entrypoint of communication with the database.

This utility function will:

Here's an example of how an HTTPS API could look like, step by step, paying attention mainly to the ideas important for developers.

### Domain Name

It would probably all start by you **acquiring** some **domain name**. Then, you would configure it in a DNS server (possibly your same cloud provider).

    Because we are using a relative URL, if your API was located at `https://example.com/`, then it would refer to `https://example.com/token`. But if your API was located at `https://example.com/api/v1/`, then it would refer to `https://example.com/api/v1/token`.

        {
            "detail": [
                {
                    "type": "string_pattern_mismatch",
                    "loc": ["body", "grant_type"],
                    "msg": "String should match pattern 'password'",
                    "input": "incorrect",
                    "ctx": {"pattern": "password"},
                }
            ]
        }
    ) | IsDict(

<font color="#4E9A06">INFO</font>:     Application startup complete.
<font color="#4E9A06">INFO</font>:     Uvicorn running on <b>http://0.0.0.0:8000</b> (Press CTRL+C to quit)
```

</div>

That would work for most of the cases. 😎

You could use that command for example to start your **FastAPI** app in a container, in a server, etc.

## ASGI Servers

Let's go a little deeper into the details.