chore: Spring AI 重构
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abel533
@@ -32,58 +32,85 @@ Agent --[spawn A]--[spawn B]--[other work]----
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## 工作原理
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1. BackgroundManager 用线程安全的通知队列追踪任务。
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1. BackgroundManager 用线程安全的并发容器追踪任务。Java 使用 `ConcurrentHashMap` 和 `CopyOnWriteArrayList` 代替 Python 的手动加锁。
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```python
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class BackgroundManager:
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def __init__(self):
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self.tasks = {}
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self._notification_queue = []
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self._lock = threading.Lock()
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```java
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public class BackgroundManager {
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private static final int TIMEOUT_SECONDS = 300;
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private final Map<String, TaskInfo> tasks = new ConcurrentHashMap<>();
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private final List<Notification> notificationQueue = new CopyOnWriteArrayList<>();
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private final ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
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record TaskInfo(String status, String result, String command) {}
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public record Notification(String taskId, String status, String command, String result) {}
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}
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```
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2. `run()` 启动守护线程, 立即返回。
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2. `backgroundRun()` 提交虚拟线程 (Java 21), 立即返回。相比 Python 的 `daemon=True` 线程，虚拟线程更轻量、由 JVM 调度。
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```python
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def run(self, command: str) -> str:
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task_id = str(uuid.uuid4())[:8]
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self.tasks[task_id] = {"status": "running", "command": command}
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thread = threading.Thread(
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target=self._execute, args=(task_id, command), daemon=True)
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thread.start()
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return f"Background task {task_id} started"
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```java
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@Tool(description = "Run a command in a background thread. Returns task_id immediately without waiting.")
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public String backgroundRun(
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@ToolParam(description = "The shell command to run in background") String command) {
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String taskId = UUID.randomUUID().toString().substring(0, 8);
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tasks.put(taskId, new TaskInfo("running", null, command));
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executor.submit(() -> execute(taskId, command));
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return "Background task " + taskId + " started: "
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+ command.substring(0, Math.min(80, command.length()));
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}
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```
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3. 子进程完成后, 结果进入通知队列。
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3. 子进程完成后, 结果进入通知队列。使用 `ProcessBuilder` 执行命令，支持超时控制。
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```python
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def _execute(self, task_id, command):
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try:
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r = subprocess.run(command, shell=True, cwd=WORKDIR,
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capture_output=True, text=True, timeout=300)
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output = (r.stdout + r.stderr).strip()[:50000]
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except subprocess.TimeoutExpired:
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output = "Error: Timeout (300s)"
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with self._lock:
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self._notification_queue.append({
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"task_id": task_id, "result": output[:500]})
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```java
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private void execute(String taskId, String command) {
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String status, output;
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try {
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ProcessBuilder pb = new ProcessBuilder("sh", "-c", command);
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pb.redirectErrorStream(true);
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Process process = pb.start();
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try (BufferedReader reader = new BufferedReader(
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new InputStreamReader(process.getInputStream()))) {
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output = reader.lines().collect(Collectors.joining("\n"));
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}
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boolean finished = process.waitFor(TIMEOUT_SECONDS, TimeUnit.SECONDS);
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if (!finished) { process.destroyForcibly(); status = "timeout"; }
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else { status = "completed"; }
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} catch (Exception e) { output = "Error: " + e.getMessage(); status = "error"; }
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tasks.put(taskId, new TaskInfo(status, output, command));
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notificationQueue.add(new Notification(taskId, status, command, output));
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}
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```
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4. 每次 LLM 调用前排空通知队列。
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4. 每次用户输入时排空通知队列, 注入系统提示。Spring AI 的 `ChatClient` 管理内部工具循环, 因此改为在每次用户输入时 drain 通知并构建系统提示, 核心概念不变: fire and forget。
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```python
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def agent_loop(messages: list):
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while True:
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notifs = BG.drain_notifications()
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if notifs:
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notif_text = "\n".join(
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f"[bg:{n['task_id']}] {n['result']}" for n in notifs)
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messages.append({"role": "user",
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"content": f"<background-results>\n{notif_text}\n"
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f"</background-results>"})
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messages.append({"role": "assistant",
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"content": "Noted background results."})
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response = client.messages.create(...)
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```java
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AgentRunner.interactive("s08", userMessage -> {
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// Drain 后台任务通知（对应 Python 中循环前的 drain_notifications）
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var notifs = bgManager.drainNotifications();
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String bgContext = "";
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if (!notifs.isEmpty()) {
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String notifText = notifs.stream()
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.map(n -> "[bg:" + n.taskId() + "] " + n.status() + ": " + n.result())
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.collect(Collectors.joining("\n"));
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bgContext = "\n\n<background-results>\n" + notifText + "\n</background-results>";
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}
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String system = "You are a coding agent. Use backgroundRun for long-running commands."
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+ bgContext;
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ChatClient chatClient = ChatClient.builder(chatModel)
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.defaultSystem(system)
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.defaultTools(new BashTool(), new ReadFileTool(),
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new WriteFileTool(), new EditFileTool(), bgManager)
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.build();
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return chatClient.prompt().user(userMessage).call().content();
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});
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```
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循环保持单线程。只有子进程 I/O 被并行化。
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@@ -92,16 +119,16 @@ def agent_loop(messages: list):
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| 组件 | 之前 (s07) | 之后 (s08) |
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|----------------|------------------|------------------------------------|
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| Tools | 8 | 6 (基础 + background_run + check) |
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| 执行方式 | 仅阻塞 | 阻塞 + 后台线程 |
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| 通知机制 | 无 | 每轮排空的队列 |
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| 并发 | 无 | 守护线程 |
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| Tools | 8 | 6 (基础 + backgroundRun + check) |
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| 执行方式 | 仅阻塞 | 阻塞 + 虚拟线程 (Java 21) |
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| 通知机制 | 无 | 每轮排空的 ConcurrentLinkedQueue |
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| 并发 | 无 | 虚拟线程 (更轻量, JVM 调度) |
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## 试一试
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```sh
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cd learn-claude-code
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python agents/s08_background_tasks.py
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mvn exec:java -Dexec.mainClass=io.mybatis.learn.s08.S08BackgroundTasks
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```
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试试这些 prompt (英文 prompt 对 LLM 效果更好, 也可以用中文):
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