* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.
* Implement and use well-known cryptographic tools, like Passlib and JWT tokens, etc.
* Add more granular permission controls with OAuth2 scopes where needed.
* ...etc.

    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Rick")
        function ->> code: return stored result
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")
        function ->> code: return stored result
    end
```

💼 👆 🔗 `get_settings()`, 🔢 🚫 ✊ 🙆 ❌, ⚫️ 🕧 📨 🎏 💲.

def test_upload_file_is_closed(tmp_path: Path):
    path = tmp_path / "test.txt"
    path.write_bytes(b"<file content>")
    app = FastAPI()

    testing_file_store: List[UploadFile] = []

    @app.post("/uploadfile/")
    def create_upload_file(file: UploadFile):
        testing_file_store.append(file)
        return {"filename": file.filename}

    client = TestClient(app)
    with path.open("rb") as file:

Using `UploadFile` has several advantages over `bytes`:

* You don't have to use `File()` in the default value of the parameter.
* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

# JSON Compatible Encoder

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder`

Let's imagine that you have a database `fake_db` that only receives JSON compatible data.

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

    If you don't know, you will learn what a "password hash" is in the [security chapters](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}.

    ```

### Использование параметра `update` в Pydantic

Теперь можно создать копию существующей модели, используя `.copy()`, и передать параметр `update` с `dict`, содержащим данные для обновления.

Например, `stored_item_model.copy(update=update_data)`:

=== "Python 3.10+"

    ```Python hl_lines="33"
    {!> ../../../docs_src/body_updates/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="35"

And it will save the returned value in a <abbr title="A utility/system to store computed/generated values, to re-use them instead of computing them again.">"cache"</abbr> and pass it to all the "dependants" that need it in that specific request, instead of calling the dependency multiple times for the same request.

If you have a big application, you might end up accumulating **several tags**, and you would want to make sure you always use the **same tag** for related *path operations*.

In these cases, it could make sense to store the tags in an `Enum`.

**FastAPI** supports that the same way as with plain strings:

```Python hl_lines="1  8-10  13  18"
{!../../../docs_src/path_operation_configuration/tutorial002b.py!}
```

    * Query parameters.
    * Cookies, etc.

Plus all the security features from Starlette (including **session cookies**).

All built as reusable tools and components that are easy to integrate with your systems, data stores, relational and NoSQL databases, etc.

### Dependency Injection