本文件用來測試用於翻譯文件的 <abbr title="Large Language Model - 大型語言模型">LLM</abbr> 是否理解 `scripts/translate.py` 中的 `general_prompt`，以及 `docs/{language code}/llm-prompt.md` 中的語言特定提示。語言特定提示會附加在 `general_prompt` 後面。

此處新增的測試會提供給所有語言特定提示的設計者參考。

使用方式：

* 準備語言特定提示 - `docs/{language code}/llm-prompt.md`。
* 針對本文件做一次全新的翻譯為你想要的目標語言（例如使用 `translate.py` 的 `translate-page` 指令）。這會在 `docs/{language code}/docs/_llm-test.md` 產生翻譯檔。
* 檢查翻譯是否正確。

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        final LlmChatRequest result = request.addSystemMessage("System prompt");

        assertSame(request, result);
        assertEquals(1, request.getMessages().size());
        assertEquals(LlmMessage.ROLE_SYSTEM, request.getMessages().get(0).getRole());
        assertEquals("System prompt", request.getMessages().get(0).getContent());
    }

    @Test
    public void test_addUserMessage() {

Si no lo recibe, devuelve un error HTTP 401 "Unauthorized".

Y devuelve un header `WWW-Authenticate` con un valor de `Basic`, y un parámetro `realm` opcional.

Eso le dice al navegador que muestre el prompt integrado para un nombre de usuario y contraseña.

Luego, cuando escribes ese nombre de usuario y contraseña, el navegador los envía automáticamente en el header.

## Simple HTTP Basic Auth { #simple-http-basic-auth }

  override fun getPasswordAuthentication(): PasswordAuthentication? {
    calls.add(
      "host=$requestingHost port=$requestingPort site=${requestingSite.hostName} " +
        "url=$requestingURL type=$requestorType prompt=$requestingPrompt " +
        "protocol=$requestingProtocol scheme=$requestingScheme",
    )
    return authentication
  }

  companion object {
    /** base64("username:password")  */

{* ../../docs_src/stream_json_lines/tutorial001_py310.py ln[33:36] hl[34] *}

## Événements envoyés par le serveur (SSE) { #server-sent-events-sse }

## Context

The Gradle core platform provides many services to the Gradle platforms and builds logic. One such group of services allows logic to interact with the build user, to provide diagnostics, progress information, prompt for questions, and so on. Currently, these services are part of the core platform runtime architecture module.

A downside of this structure is that it is difficult to do focused work on the Gradle UI.

## Decision

       * we're copying into a `contents` array whose type allows it to contain nulls. Still, it's
       * worth noting that we promise not to put nulls into the array in the first `size` elements.
       * We uphold that promise here because our callers promise that `elements` will not contain
       * nulls in its first `n` elements.
       */
      System.arraycopy(elements, 0, contents, size, n);
      size += n;

    requirements {
        requiresOs(Os.LINUX)
    }

    params {
        text(
            "wrapperVersion",
            "should-be-overridden",
            display = ParameterDisplay.PROMPT,
            allowEmpty = false,
            description =
                "The version of Gradle to update to. " +
                    "Can be a specific version or one of 'latest', 'release-candidate', 'release-milestone'",

 * such as Ollama, OpenAI, and Google Gemini.
 *
 * In addition to low-level chat operations, this interface defines
 * high-level RAG workflow methods that allow each provider to optimize
 * prompt construction, parameter tuning, and response parsing.
 */
public interface LlmClient {

    /**
     * Performs a chat completion request.
     *