import org.opensearch.index.query.AbstractQueryBuilder;
import org.opensearch.index.query.QueryBuilder;
import org.opensearch.index.query.QueryShardContext;

/**
 * A query builder for a stored LTR (Learning to Rank) query.
 * This builder constructs a query that uses a pre-trained LTR model
 * to re-rank search results based on a given set of features.
 */

///

/// tip | 豆知識

"AlexNet"、"ResNet"そして"LeNet"は機械学習<abbr title="Technically, Deep Learning model architectures">モデル</abbr>の名前です。

///

### *パスパラメータ*の宣言

次に、作成したenumクラスである`ModelName`を使用した型アノテーションをもつ*パスパラメータ*を作成します:

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

### ドキュメントの確認

  html_url: https://github.com/zhanymkanov/fastapi-best-practices
  stars: 14644
  owner_login: zhanymkanov
  owner_html_url: https://github.com/zhanymkanov
- name: machine-learning-zoomcamp
  html_url: https://github.com/DataTalksClub/machine-learning-zoomcamp
  stars: 12320
  owner_login: DataTalksClub
  owner_html_url: https://github.com/DataTalksClub
- name: fastapi_mcp
  html_url: https://github.com/tadata-org/fastapi_mcp

### Memoria por Proceso { #memory-per-process }

Ahora, cuando el programa carga cosas en memoria, por ejemplo, un modelo de Machine Learning en una variable, o el contenido de un archivo grande en una variable, todo eso **consume un poco de la memoria (RAM)** del servidor.

클라이언트에 반환하기 전에 해당 값(이 경우 문자열)으로 변환됩니다:

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

클라이언트는 아래의 JSON 응답을 얻습니다:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## 경로를 포함하는 경로 매개변수

경로를 포함하는 *경로 작동* `/files/{file_path}`이 있다고 해봅시다.

그런데 이 경우 `file_path` 자체가 `home/johndoe/myfile.txt`와 같은 경로를 포함해야 합니다.

* **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.

    "article"
);
suggester.indexer().indexFromDocument(reader, 4, 50);
```

### Search Analytics

```java
// Track user queries for analytics
QueryLog userQuery = new QueryLog("machine learning tutorials", "user456");
suggester.indexer().indexFromQueryLog(userQuery);

// Get trending searches
PopularWordsResponse trending = suggester.popularWords()
    .setSize(10)
    .execute()
    .getResponse();

{* ../../docs_src/path_params/tutorial005_py39.py hl[18,21,23] *}
Вы отправите клиенту такой JSON-ответ:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Path-параметры, содержащие пути { #path-parameters-containing-paths }

Предположим, что есть *операция пути* с путем `/files/{file_path}`.

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

İstemci tarafında şuna benzer bir JSON yanıtı ile karşılaşırsınız:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Yol İçeren Yol Parametreleri

Farz edelim ki elinizde `/files/{file_path}` isminde bir *yol operasyonu* var.

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.