Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

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.

 * handling text normalization, content extraction, and similar document hash encoding/decoding.
 * It also manages document processing requests and integrates with the crawler system.
 *
 */
public class DocumentHelper {
    private static final Logger logger = LogManager.getLogger(DocumentHelper.class);

    /** Prefix used for encoded similar document hashes */
    protected static final String SIMILAR_DOC_HASH_PREFIX = "$";

Then we can use `secrets.compare_digest()` to ensure that `credentials.username` is `"stanleyjobson"`, and that `credentials.password` is `"swordfish"`.

{* ../../docs_src/security/tutorial007_an_py39.py hl[1,12:24] *}

This would be similar to:

```Python
if not (credentials.username == "stanleyjobson") or not (credentials.password == "swordfish"):
    # Return some error
    ...
```

    validations:
      required: true
  - type: textarea
    id: current-behavior
    attributes:
      label: Current Behavior (optional)
      description: Is there a way to achieve the same or similar outcome today?
    validations:
      required: false
  - type: textarea
    id: context
    attributes:
      label: Context
      description: |

It achieves something somewhat similar to what can be done with Flask-apispec.

It has an integrated dependency injection system, inspired by Angular 2. It requires pre-registering the "injectables" (like all the other dependency injection systems I know), so, it adds to the verbosity and code repetition.

As the parameters are described with TypeScript types (similar to Python type hints), editor support is quite good.

* `swagger_css_url`: la URL donde el HTML para tu documentación de Swagger UI puede obtener el archivo **CSS**. Esta es la URL personalizada del CDN.

Y de manera similar para ReDoc...

{* ../../docs_src/custom_docs_ui/tutorial001_py39.py hl[2:6,11:19,22:24,27:33] *}

/// tip | Consejo

La *path operation* para `swagger_ui_redirect` es una herramienta cuando utilizas OAuth2.

            This will be used to extract the data and for the generated OpenAPI.
            It is particularly useful when you can't use the name you want because it
            is a Python reserved keyword or similar.
            """
        ),
    ] = None,
    alias_priority: Annotated[
        Union[int, None],
        Doc(
            """
            Priority of the alias. This affects whether an alias generator is used.

Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar requests. 🤖

Los mismos modelos son compartidos entre requests, por lo que no es un modelo por request, o uno por usuario o algo similar.

Imaginemos que cargar el modelo puede **tomar bastante tiempo**, porque tiene que leer muchos **datos del disco**. Entonces no quieres hacerlo para cada request.

#### Usando `StreamingResponse` con objetos similares a archivos { #using-streamingresponse-with-file-like-objects }

Si tienes un <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">objeto similar a un archivo</a> (por ejemplo, el objeto devuelto por `open()`), puedes crear una función generadora para iterar sobre ese objeto similar a un archivo.

# Dependencias Globales { #global-dependencies }

Para algunos tipos de aplicaciones, podrías querer agregar dependencias a toda la aplicación.

Similar a como puedes [agregar `dependencies` a los *path operation decorators*](dependencies-in-path-operation-decorators.md){.internal-link target=_blank}, puedes agregarlos a la aplicación de `FastAPI`.

En ese caso, se aplicarán a todas las *path operations* en la aplicación: