* cross origin: cross origin (do not translate to "origem cruzada")
* Cross-Origin Resource Sharing: Cross-Origin Resource Sharing (do not translate to "Compartilhamento de Recursos de Origem Cruzada")
* Deep Learning: Deep Learning (do not translate to "Aprendizado Profundo")
* dependable: dependable
* dependencies: dependências
* deprecated: descontinuado
* docs: documentação
* FastAPI app: aplicação FastAPI

* production (meaning production software or environment): продакшн (do not change the ending, for example, translate `in production` as `в продакшн` (not `в продакшене`))
* completion (meaning code auto-completion): автозавершение
* editor (meaning component of IDE): редактор кода
* adopt (meaning start to use): использовать (or `начать использовать`)
* headers (meaning HTTP-headers): HTTP-заголовки

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

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

## Caso de uso { #use-case }

Vamos começar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.

Vamos imaginar que você tem alguns **modelos de machine learning** que deseja usar para lidar com as requisições. 🤖

* 100% test coverage: cobertura de tests del 100%
* back and forth: de un lado a otro
* I/O (as in "input and output"): I/O (do not translate to "E/S")
* Machine Learning: Machine Learning (do not translate to "Aprendizaje Automático")
* Deep Learning: Deep Learning (do not translate to "Aprendizaje Profundo")
* callback hell: callback hell (do not translate to "infierno de callbacks")
* tip: Consejo (do not translate to "tip")

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":
        return {"model_name": model_name, "message": "LeCNN all the images"}

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

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

... müsaitliğime ve diğer faktörlere bağlı olarak daha sonra gelebilir. 😅 🎉

## Machine Learning modelleri, spaCy ve FastAPI

GitHub: <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a>

### Machine Learning modelleri, spaCy ve FastAPI - Features

* **spaCy** NER model entegrasyonu.

import org.codelibs.fess.util.ComponentUtil;
import org.opensearch.search.rescore.QueryRescorerBuilder;
import org.opensearch.search.rescore.RescorerBuilder;

/**
 * Learning to Rank query rescorer implementation.
 */
public class LtrQueryRescorer implements QueryRescorer {

    /**
     * Default constructor.
     */
    public LtrQueryRescorer() {
        // Default constructor