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.

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference.

So you can come back and see exactly what you need.

## Run the code

All the code blocks can be copied and used directly (they are actually tested Python files).

    That when called, return instances of classes of the same name.

    So, you import `Query`, which is a function. And when you call it, it returns an instance of a class also named `Query`.

    These functions are there (instead of just using the classes directly) so that your editor doesn't mark errors about their types.

Und sie senden eine Anfrage mit dem Benutzernamen `johndoe` und dem Passwort `love123`.

Dann würde der Python-Code in Ihrer Anwendung etwa so aussehen:

```Python
if "johndoe" == "stanleyjobson" and "love123" == "swordfish":
    ...
```

    But in FastAPI, using normal functions (`def`) more than one thread could interact with the database for the same request, so we need to make SQLite know that it should allow that with `connect_args={"check_same_thread": False}`.

    Also, we will make sure each request gets its own database connection session in a dependency, so there's no need for that default mechanism.

### Create a `SessionLocal` class

But a variable like `user-agent` is invalid in Python.

So, by default, `Header` will convert the parameter names characters from underscore (`_`) to hyphen (`-`) to extract and document the headers.

Also, HTTP headers are case-insensitive, so, you can declare them with standard Python style (also known as "snake_case").

Then, when you create an instance of that `Settings` class (in this case, in the `settings` object), Pydantic will read the environment variables in a case-insensitive way, so, an upper-case variable `APP_NAME` will still be read for the attribute `app_name`.

Next it will convert and validate the data. So, when you use that `settings` object, you will have data of the types you declared (e.g. `items_per_user` will be an `int`).

### Use the `settings`

For a **web API**, it normally involves putting it in a **remote machine**, with a **server program** that provides good performance, stability, etc, so that your **users** can **access** the application efficiently and without interruptions or problems.

{!../../../docs_src/handling_errors/tutorial003.py!}
```

Here, if you request `/unicorns/yolo`, the *path operation* will `raise` a `UnicornException`.

But it will be handled by the `unicorn_exception_handler`.

So, you will receive a clean error, with an HTTP status code of `418` and a JSON content of:

```JSON
{"message": "Oops! yolo did something. There goes a rainbow..."}
```

!!! note "Technical Details"

{!../../../docs_src/response_cookies/tutorial001.py!}
```

!!! tip
    Keep in mind that if you return a response directly instead of using the `Response` parameter, FastAPI will return it directly.

    So, you will have to make sure your data is of the correct type. E.g. it is compatible with JSON, if you are returning a `JSONResponse`.

    And also that you are not sending any data that should have been filtered by a `response_model`.