return [pet1, pet2]


client = TestClient(app)


def test_filter_top_level_model():
    response = client.post(
        "/users", json={"email": "******@****.***", "password": "secret"}
    )
    assert response.json() == {"email": "******@****.***"}


def test_filter_second_level_model():
    response = client.get("/pets/1")
    assert response.json() == {

Es kann vom Frontend-Team verwendet werden (das auch Sie selbst sein können).

Es kann von Anwendungen und Systemen Dritter verwendet werden.

Und es kann auch von Ihnen selbst verwendet werden, um dieselbe Anwendung zu debuggen, zu prüfen und zu testen.

## Der `password`-Flow

from . import models, schemas


def get_user(user_id: int):
    return models.User.filter(models.User.id == user_id).first()


def get_user_by_email(email: str):
    return models.User.filter(models.User.email == email).first()


def get_users(skip: int = 0, limit: int = 100):
    return list(models.User.select().offset(skip).limit(limit))


def create_user(user: schemas.UserCreate):

{!../../../docs_src/response_cookies/tutorial002.py!}
```

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

Standardmäßig konvertiert **FastAPI** diesen Rückgabewert automatisch nach JSON, mithilfe des `jsonable_encoder`, der in [JSON-kompatibler Encoder](../tutorial/encoder.md){.internal-link target=_blank} erläutert wird.

Dann würde es hinter den Kulissen diese JSON-kompatiblen Daten (z. B. ein `dict`) in eine `JSONResponse` einfügen, die zum Senden der Response an den Client verwendet würde.

Sie können jedoch direkt eine `JSONResponse` von Ihren *Pfadoperationen* zurückgeben.

...although in this example we are only creating and reading.

### Read data

Import `Session` from `sqlalchemy.orm`, this will allow you to declare the type of the `db` parameters and have better type checks and completion in your functions.

Import `models` (the SQLAlchemy models) and `schemas` (the Pydantic *models* / schemas).

Create utility functions to:

* Read a single user by ID and by email.

                * **FastAPI** adds a reference here to the global JSON Schemas in another place in your OpenAPI instead of including it directly. This way, other applications and clients can use those JSON Schemas directly, provide better code generation tools, etc.

The generated responses in the OpenAPI for this *path operation* will be:

```JSON hl_lines="3-12"
{
    "responses": {
        "404": {

</div>

With this feature from **Pydantic v2**, your API documentation is more **precise**, and if you have autogenerated clients and SDKs, they will be more precise too, with a better **developer experience** and consistency. 🎉

## Do not Separate Schemas

Now, there are some cases where you might want to have the **same schema for input and output**.

### Test edildi

* 100% <abbr title="Kodun ne kadarının test edildiği">test coverage</abbr>.
* 100% <abbr title="Python type annotations, with this your editor and external tools can give you better support">typeları belirtilmiş</abbr> codebase.
* FastAPI ile yapılan bir çok proje insanlar tarafından kullanılıyor.

## Starlette özellikleri

Python will complain if you put a value with a "default" before a value that doesn't have a "default".

But you can re-order them, and have the value without a default (the query parameter `q`) first.

It doesn't matter for **FastAPI**. It will detect the parameters by their names, types and default declarations (`Query`, `Path`, etc), it doesn't care about the order.

So, you can declare your function as:

=== "Python 3.8 non-Annotated"