That way I could find the best ways to reduce code duplication as much as possible, to have completion everywhere, type and error checks, etc.

All in a way that provided the best development experience for all the developers.

## Requirements { #requirements }

     * @param notifyFlags
     *            the notifyFlags to set
     */
    public void setNotifyFlags(final int notifyFlags) {
        this.notifyFlags = notifyFlags;
    }

    /**
     * Set the completion filter specifying which changes to monitor
     *
     * @param completionFilter
     *            the completionFilter to set
     */
    public void setCompletionFilter(final int completionFilter) {

* /// warning: /// warning | Atenção
* you should: você deveria
* async context manager: gerenciador de contexto assíncrono
* autocomplete: autocompletar
* autocompletion: preenchimento automático
* auto-completion: preenchimento automático
* bug: bug
* context manager: gerenciador de contexto
* cross domain: cross domain (do not translate to "domínio cruzado")
* cross origin: cross origin (do not translate to "origem cruzada")

Then, you try with the old programmer's friend, editor autocompletion.

You type the first parameter of the function, `first_name`, then a dot (`.`) and then hit `Ctrl+Space` to trigger the completion.

But, sadly, you get nothing useful:

<img src="/img/python-types/image01.png">

### Add types { #add-types }

Let's modify a single line from the previous version.

 */
package org.codelibs.fess.llm;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

/**
 * Request object for LLM chat completion.
 *
 * @author FessProject
 */
public class LlmChatRequest {

    private List<LlmMessage> messages = new ArrayList<>();
    private String model;
    private Integer maxTokens;

        if (logger.isInfoEnabled()) {
            logger.info("Loaded LlmClient: {}", client.getClass().getSimpleName());
        }
        clientList.add(client);
    }

    /**
     * Performs a chat completion request using the configured LLM client.
     *
     * @param request the chat request
     * @return the chat response
     * @throws LlmException if LLM is not available or an error occurs
     */

                } finally {
                    endLatch.countDown();
                }
            });
        }

        // Start all threads
        startLatch.countDown();

        // Wait for completion
        assertTrue(endLatch.await(30, TimeUnit.SECONDS), "All threads should complete");
        executor.shutdown();

        // Then - All operations should succeed

{* ../../docs_src/security/tutorial002_an_py310.py hl[31] *}

Notice that we declare the type of `current_user` as the Pydantic model `User`.

This will help us inside of the function with all the completion and type checks.

/// tip

You might remember that request bodies are also declared with Pydantic models.

Here **FastAPI** won't get confused because you are using `Depends`.

///

/// check

* **Fewer bugs**: Reduce about 40% of human (developer) induced errors. *
* **Intuitive**: Great editor support. <dfn title="also known as auto-complete, autocompletion, IntelliSense">Completion</dfn> everywhere. Less time debugging.
* **Easy**: Designed to be easy to use and learn. Less time reading docs.
* **Short**: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.

你几乎不需要经常回来看文档。

在这里，你的编辑器可能会这样帮助你：

* 在 [Visual Studio Code](https://code.visualstudio.com/) 中:

![editor support](https://fastapi.tiangolo.com/img/vscode-completion.png)

* 在 [PyCharm](https://www.jetbrains.com/pycharm/) 中:

![editor support](https://fastapi.tiangolo.com/img/pycharm-completion.png)

你将能进行代码补全，这是在之前你可能曾认为不可能的事。例如，在来自请求 JSON 体（可能是嵌套的）中的键 `price`。

不会再输错键名，来回翻看文档，或者来回滚动寻找你最后使用的 `username` 或者 `user_name`。

### 简洁 { #short }