310 lines
9.2 KiB
Python
310 lines
9.2 KiB
Python
"""
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multica_proxy.py — multica CLI 调用代理
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封装 multica CLI 调用，自动带缓存和限流保护。
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各 Agent 心跳脚本中用 multica_proxy 替代直接 subprocess.run(["multica",...])
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依赖：rate_limiter.py（CacheManager, RequestScheduler, CoordinatedPoller）
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作者：陆怀瑾（COO）
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日期：2026-06-23
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"""
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import os
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import sys
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import json
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import subprocess
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import hashlib
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from typing import Any, Dict, Optional
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# 确保能找到 rate_limiter
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_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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if _SCRIPT_DIR not in sys.path:
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sys.path.insert(0, _SCRIPT_DIR)
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from rate_limiter import CacheManager, RequestScheduler, CoordinatedPoller, Priority
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# ============================================================================
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# 全局单例
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# ============================================================================
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_cache = CacheManager()
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_scheduler: Optional[RequestScheduler] = None
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_poller: Optional[CoordinatedPoller] = None
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def _get_scheduler() -> RequestScheduler:
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"""获取或创建调度器单例"""
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global _scheduler
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if _scheduler is None:
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_scheduler = RequestScheduler(rate=40/60, capacity=40, enable_cache=True)
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_scheduler.start()
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return _scheduler
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def _get_poller() -> CoordinatedPoller:
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"""获取或创建统一轮询器单例"""
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global _poller
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if _poller is None:
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_poller = CoordinatedPoller(_get_scheduler(), poll_interval=15*60)
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return _poller
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# ============================================================================
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# 缓存查询辅助
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# ============================================================================
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def _make_cache_key(cmd: list) -> str:
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"""为 CLI 命令生成缓存键"""
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return hashlib.md5(json.dumps(cmd, sort_keys=True).encode()).hexdigest()
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def _cache_category(cmd: list) -> str:
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"""根据命令推断缓存类别"""
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cmd_str = " ".join(str(x) for x in cmd)
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if "workboard" in cmd_str:
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return "workboard"
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if "config" in cmd_str or "agent" in cmd_str:
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return "config"
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if "wiki" in cmd_str or "knowledge" in cmd_str:
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return "knowledge"
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if "user" in cmd_str or "member" in cmd_str:
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return "user"
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return "workboard" # 默认 5 分钟
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# ============================================================================
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# 核心代理函数
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# ============================================================================
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# OpenClaw 工作区 ID（全局常量）
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# 用于所有 multica CLI 调用，确保隔离会话也能正确查询
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_WORKSPACE_ID = "54344e11-6bb2-4d95-a5e5-c8b075a07cea"
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def _inject_workspace_id(cmd: list) -> list:
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"""自动注入 workspace-id 到 multica CLI 命令"""
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if len(cmd) >= 2 and cmd[0] == "multica" and "--workspace-id" not in cmd:
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# 插入在命令和子命令之后、标志之前
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insert_idx = 1
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while insert_idx < len(cmd) and not cmd[insert_idx].startswith("--"):
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insert_idx += 1
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new_cmd = cmd[:insert_idx] + ["--workspace-id", _WORKSPACE_ID] + cmd[insert_idx:]
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return new_cmd
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return cmd
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def run_multica(cmd: list, use_cache: bool = True, timeout: int = 30) -> Dict[str, Any]:
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"""
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执行 multica CLI 命令（带缓存和限流）
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参数：
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cmd: 命令列表，如 ["multica", "issue", "list", "--output", "json"]
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use_cache: 是否使用缓存
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timeout: 超时时间（秒）
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返回：
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{"success": bool, "data": Any, "from_cache": bool, "error": str|None}
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"""
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# 自动注入 workspace-id，确保隔离会话正确查询
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cmd = _inject_workspace_id(cmd)
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category = _cache_category(cmd)
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# 1. 尝试从缓存获取
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if use_cache:
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cached = _cache.get(category, cmd)
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if cached is not None:
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return {"success": True, "data": cached, "from_cache": True, "error": None}
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# 2. 执行 CLI 命令
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try:
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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timeout=timeout
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)
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if result.returncode != 0:
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error_msg = result.stderr.strip() or f"Exit code {result.returncode}"
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return {"success": False, "data": None, "from_cache": False, "error": error_msg}
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# 尝试解析 JSON
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try:
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data = json.loads(result.stdout)
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except json.JSONDecodeError:
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data = result.stdout.strip()
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# 3. 写入缓存
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if use_cache:
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_cache.set(category, cmd, data)
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return {"success": True, "data": data, "from_cache": False, "error": None}
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except subprocess.TimeoutExpired:
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return {"success": False, "data": None, "from_cache": False, "error": f"Command timed out after {timeout}s"}
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except Exception as e:
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return {"success": False, "data": None, "from_cache": False, "error": str(e)}
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def run_openclaw_workboard(cmd: list, use_cache: bool = True, timeout: int = 30) -> Dict[str, Any]:
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"""
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执行 openclaw workboard CLI 命令（带缓存）
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参数同 run_multica
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"""
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return run_multica(cmd, use_cache=use_cache, timeout=timeout)
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# ============================================================================
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# 便捷函数：心跳脚本中直接替换
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# ============================================================================
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def multica_issue_list_my_todo(assignee_id: str) -> Dict[str, Any]:
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"""
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获取分配给我的待办 Issue 列表
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替代: multica issue list --assignee-id <id> --status todo --output json
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"""
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return run_multica([
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"multica", "issue", "list",
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"--assignee-id", assignee_id,
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"--status", "todo",
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"--output", "json"
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])
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def multica_issue_list_in_progress() -> Dict[str, Any]:
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"""
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获取所有进行中的 Issue 列表（超时检测用）
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替代: multica issue list --status in_progress --output json
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"""
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return run_multica([
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"multica", "issue", "list",
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"--status", "in_progress",
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"--output", "json"
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])
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def multica_issue_get(issue_id: str) -> Dict[str, Any]:
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"""
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获取单个 Issue 详情
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替代: multica issue get <id> --output json
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"""
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return run_multica([
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"multica", "issue", "get",
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issue_id,
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"--output", "json"
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])
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def openclaw_workboard_list() -> Dict[str, Any]:
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"""
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获取 WorkBoard 卡片列表
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替代: openclaw workboard list --json
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"""
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return run_multica([
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"openclaw", "workboard", "list", "--json"
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])
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def openclaw_workboard_read(card_id: str) -> Dict[str, Any]:
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"""
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获取单个 WorkBoard 卡片
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替代: openclaw workboard read <id> --json
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"""
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return run_multica([
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"openclaw", "workboard", "read", card_id, "--json"
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])
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# ============================================================================
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# 缓存管理
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# ============================================================================
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def get_cache_stats() -> Dict[str, Any]:
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"""获取缓存统计"""
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return _cache.get_stats()
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def clear_cache(category: Optional[str] = None) -> int:
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"""
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清理缓存
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参数：
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category: 指定类别清理，None 表示全部清理
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返回：清理条目数
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"""
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if category:
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return _cache.clear_expired()
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else:
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count = len(_cache._cache)
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_cache.clear()
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return count
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# ============================================================================
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# 统一轮询器（仅 COO 使用）
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# ============================================================================
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def start_coordinated_poller() -> CoordinatedPoller:
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"""
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启动 COO 统一轮询器
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仅 COO Agent 调用此函数
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"""
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poller = _get_poller()
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if not poller._running:
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poller.start()
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return poller
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def subscribe_to_poller(callback) -> None:
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"""
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订阅 COO 统一轮询结果
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其他 Agent 调用此函数，不再各自调 multica CLI
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"""
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_get_poller().subscribe(callback)
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def get_poller_status() -> Dict[str, Any]:
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"""获取轮询器状态"""
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poller = _get_poller()
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return {
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"running": poller._running,
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"poll_interval": poller.poll_interval,
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"subscriber_count": len(poller._subscribers)
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}
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# ============================================================================
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# 健康检查
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# ============================================================================
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def health_check() -> Dict[str, Any]:
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"""检查 multica_proxy 健康状态"""
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scheduler = _get_scheduler()
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return {
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"status": "ok",
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"cache": get_cache_stats(),
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"scheduler": scheduler.get_status(),
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"poller": get_poller_status()
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}
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# ============================================================================
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# 测试
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# ============================================================================
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if __name__ == "__main__":
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print("=== multica_proxy 健康检查 ===")
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print(json.dumps(health_check(), indent=2, ensure_ascii=False))
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print("\n=== 测试缓存 ===")
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# 第一次调用（无缓存）
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result1 = run_multica(["echo", "test1"], use_cache=True)
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print(f"第1次: from_cache={result1['from_cache']}")
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# 第二次调用（应命中缓存）
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result2 = run_multica(["echo", "test1"], use_cache=True)
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print(f"第2次: from_cache={result2['from_cache']}")
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print("\n测试完成")
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