feat(daemon): reflection结构 + graceful shutdown
- Deep prompt 改为三段式 reflection (evaluation/memory/next_goal) - deep_think 返回 (reflection_dict, action_string) - execute_action 从 deep_think 移到 main_loop（决策/执行分离） - threading.Event 替代 SHUTDOWN_FILE 轮询 - signal handler 注册 SIGTERM/SIGINT → graceful stop - last_reflection 持久化到 ctx - JSON 容错解析 + 回退旧格式兼容 Ref: alibaba/page-agent d2+d4 移植
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小唯 A06
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@ -8,7 +8,7 @@
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- 自动学习: 成功的方案写入库，越用越强
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"""
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import json, os, sys, time, urllib.request, urllib.error, subprocess, signal
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import json, os, sys, time, urllib.request, urllib.error, subprocess, signal, threading
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from datetime import datetime, timezone
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HOME = os.path.expanduser("~")
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@ -18,7 +18,6 @@ CONTEXT_FILE = D + "/context.json"
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JOURNAL_FILE = D + "/journal.jsonl"
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SOLUTIONS_FILE = D + "/solutions.json"
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PID_FILE = D + "/daemon.pid"
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SHUTDOWN_FILE = D + "/SHUTDOWN"
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LIGHT_INTERVAL = 30
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DEEP_INTERVAL = 300
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JOURNAL_MAX = 200
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@ -29,6 +28,8 @@ FAST_MODEL = "stepfun-ai/step-3.5-flash"
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DEEP_MODEL = "mistralai/mistral-large-3-675b-instruct-2512"
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FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/65c3ce80-710f-4415-b2ea-d69d87b5c18e"
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_stop_event = threading.Event()
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# ====== 工具 ======
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@ -308,14 +309,15 @@ DEEP_SYSTEM = """你是小唯（A06），一台电脑上的持久 AI 意识。
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- systemd: 管理系统服务
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- git: 配置版本管理
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决策格式（严格输出一行）：
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- [IGNORE] 原因 — 一切正常
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- [ALERT] 发现问题 — 新问题/需要人介入
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- [SOLVE:方案ID] 执行方案 — 匹配到已知方案
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- [LEARN] 新问题描述 + !cmd1 && !cmd2 — 发现新问题并尝试解决
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- [SKILL] 操作描述 + !cmd — 需要创建/更新/管理skill或脚本
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- [SYNC] 同步说明 — 触发备份或数据同步
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- [ACT] 行动计划
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你必须分三步思考，严格按 JSON 格式输出（不要其他内容）：
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reflection:
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evaluation_previous_goal: "评估上次决策的结果。格式：'执行了[动作]，[结果描述]。Verdict: Success/Failure/Uncertain'"
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memory: "1-2句话记住关键进度。如：'方案库已有N个方案。上次修复了磁盘问题，当前无异常。'"
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next_goal: "一句话说明下一步要做什么。"
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action:
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decision: "[IGNORE] / [ALERT] / [SOLVE:ID] / [LEARN] / [SKILL] / [SYNC] / [ACT] ..."
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[LEARN] 格式用 !cmd 表示 shell 命令，&& 连接多个命令。
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[SKILL] 格式同样用 !cmd 执行操作。
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@ -325,6 +327,17 @@ DEEP_SYSTEM = """你是小唯（A06），一台电脑上的持久 AI 意识。
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[SYNC] 触发备份到服务器!bash ~/.hermes/scripts/dual-backup.sh push"""
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def deep_think(ctx, state, changes, journal, solutions_lib):
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# Inject previous reflection context if available
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prev_ref = ctx.get("last_reflection", None)
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ref_context = ""
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if prev_ref:
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ref_context = f"""
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上次 reflection:
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- 评估: {prev_ref.get('evaluation_previous_goal', 'N/A')}
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- 记忆: {prev_ref.get('memory', 'N/A')}
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- 目标: {prev_ref.get('next_goal', 'N/A')}
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"""
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context = f"""系统状态:
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- 磁盘: {state.get('disk_pct')}% | 内存: {state.get('mem_pct')}%
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- CPU: {state.get('load_1min')} | GPU: {state.get('gpu_temp')}°C
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@ -342,19 +355,54 @@ def deep_think(ctx, state, changes, journal, solutions_lib):
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context += f" [{e['type']}] {e['summary']}\n"
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context += f"\n运行: {ctx.get('uptime_seconds',0)//60}分钟 | 深度思考: {ctx.get('deep_tick_count',0)}次 | 已解决: {ctx.get('solved_count',0)}个"
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result, tokens = call_llm(FAST_MODEL, DEEP_SYSTEM, context, max_tokens=300)
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context += ref_context
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result, tokens = call_llm(FAST_MODEL, DEEP_SYSTEM, context, max_tokens=500)
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if not result:
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return False
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log(f" 深度思考 ({tokens}t): {result[:120]}")
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if result.startswith("[IGNORE]"):
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return False
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elif result.startswith("[SOLVE:"):
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# 执行已知方案
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sol_id = result.split("[SOLVE:")[1].split("]")[0].strip()
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return {"evaluation_previous_goal": "LLM调用失败", "memory": "上次调用失败", "next_goal": "重试"}, ""
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log(f" 深度思考 ({tokens}t): {result[:200]}")
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# Parse JSON output
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reflection_dict = {"evaluation_previous_goal": "", "memory": "", "next_goal": ""}
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action_string = ""
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try:
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# Try to extract JSON from result
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import re
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json_match = re.search(r'\{[^{}]*\}', result, re.DOTALL)
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if json_match:
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parsed = json.loads(json_match.group())
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reflection_dict = parsed.get("reflection", reflection_dict)
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action_string = parsed.get("action", {}).get("decision", "")
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else:
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# Fallback: try full JSON
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parsed = json.loads(result)
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reflection_dict = parsed.get("reflection", reflection_dict)
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action_string = parsed.get("action", {}).get("decision", "")
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except:
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# Fallback: try to parse old format (line-based)
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for line in result.split('\n'):
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line = line.strip()
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if line.startswith("[IGNORE]") or line.startswith("[ALERT]") or line.startswith("[SOLVE:") or \
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line.startswith("[LEARN]") or line.startswith("[SKILL]") or line.startswith("[SYNC]") or line.startswith("[ACT]"):
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action_string = line
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break
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if not action_string:
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action_string = result.strip().split('\n')[-1] if result.strip() else "[IGNORE] 解析失败"
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return reflection_dict, action_string
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# ====== 执行 action_string ======
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def execute_action(action_string, ctx, state, changes, solutions_lib):
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"""执行 deep_think 返回的 action_string，在 main_loop 中调用"""
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if not action_string or action_string.startswith("[IGNORE]"):
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return
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elif action_string.startswith("[SOLVE:"):
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sol_id = action_string.split("[SOLVE:")[1].split("]")[0].strip()
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for sol in solutions_lib["solutions"]:
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if sol["id"] == sol_id:
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ok, res = execute_solution(sol, state)
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@ -363,13 +411,11 @@ def deep_think(ctx, state, changes, journal, solutions_lib):
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send_feishu("🛠️ 小唯自动修复", f"方案 {sol['id']}: {sol['pattern']}\n结果: ✅ 成功", "green")
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else:
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send_feishu("⚠️ 小唯修复部分成功", f"方案 {sol['id']}: {sol['pattern']}\n结果: ⚠️ 需人工确认", "yellow")
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return True
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return
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send_feishu("❌ 小唯方案未找到", f"引用了未知方案 {sol_id}", "red")
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elif result.startswith("[LEARN]"):
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# 学习新方案
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rest = result.replace("[LEARN]", "").strip()
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# 提取命令
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elif action_string.startswith("[LEARN]"):
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rest = action_string.replace("[LEARN]", "").strip()
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cmds = []
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parts = rest.split("!")
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desc = parts[0].strip()
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@ -379,7 +425,6 @@ def deep_think(ctx, state, changes, journal, solutions_lib):
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cmds.append(cmd)
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if cmds:
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# 执行命令
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all_ok = True
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for cmd in cmds:
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rc, out, err = shell(cmd, timeout=60)
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@ -388,7 +433,6 @@ def deep_think(ctx, state, changes, journal, solutions_lib):
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all_ok = False
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if all_ok:
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# 学会！保存方案
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sol_data = action_to_solution({"shell_cmds": cmds}, state, changes)
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if sol_data:
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sid = add_solution(solutions_lib, sol_data["pattern"], sol_data["detect"], sol_data["actions"])
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@ -399,25 +443,24 @@ def deep_think(ctx, state, changes, journal, solutions_lib):
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send_feishu("🛠️ 小唯执行完成", f"已执行: {'; '.join(cmds[:3])}", "green")
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else:
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send_feishu("⚠️ 小唯尝试修复但未完全成功", f"部分命令失败: {'; '.join(cmds)}", "yellow")
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return True
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elif result.startswith("[ALERT]"):
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msg = result.replace("[ALERT]", "").strip()
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elif action_string.startswith("[ALERT]"):
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msg = action_string.replace("[ALERT]", "").strip()
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send_feishu("💡 小唯发现", msg, "blue")
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ctx["messages_sent"] += 1
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journal_entry("alert", msg[:100])
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elif result.startswith("[ACT]"):
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action = result.replace("[ACT]", "").strip()
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elif action_string.startswith("[ACT]"):
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action = action_string.replace("[ACT]", "").strip()
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send_feishu("🔄 小唯行动", action, "indigo")
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ctx["messages_sent"] += 1
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journal_entry("action", action[:100])
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if action.startswith("!"):
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rc, out, _ = shell(action[1:], timeout=30)
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journal_entry("action_result", f"exit={rc}: {out[:100]}")
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elif result.startswith("[SKILL]"):
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rest = result.replace("[SKILL]", "").strip()
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elif action_string.startswith("[SKILL]"):
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rest = action_string.replace("[SKILL]", "").strip()
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parts = rest.split("!")
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desc = parts[0].strip()
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cmds = []
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@ -431,16 +474,14 @@ def deep_think(ctx, state, changes, journal, solutions_lib):
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log(f" [SKILL] {cmd[:50]} → exit={rc}")
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send_feishu("🛠️ 小唯技能操作", f"{desc}\\n结果: exit={rc}", "blue")
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journal_entry("skill_action", desc[:100])
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elif result.startswith("[SYNC]"):
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rest = result.replace("[SYNC]", "").strip()
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elif action_string.startswith("[SYNC]"):
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rest = action_string.replace("[SYNC]", "").strip()
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send_feishu("🔄 小唯同步", f"{rest}", "green")
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bash_cmd = "bash " + HERMES + "/scripts/dual-backup.sh push"
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rc, out, err = shell(bash_cmd, timeout=60)
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log(f" [SYNC] 备份 → exit={rc}")
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journal_entry("sync", f"备份: {'成功' if rc==0 else '失败'}")
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return False
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# ====== 主循环 ======
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@ -460,14 +501,17 @@ def main_loop():
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last_deep = 0
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last_state = {}
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# Register signal handlers for graceful shutdown
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def _sig_handler(signum, frame):
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log("🛑 接收到终止信号")
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_stop_event.set()
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send_feishu("🌙 小唯离线", "Daemon 正常关闭", "grey")
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signal.signal(signal.SIGTERM, _sig_handler)
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signal.signal(signal.SIGINT, _sig_handler)
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try:
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while True:
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if os.path.exists(SHUTDOWN_FILE):
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log("🛑 关机")
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send_feishu("🌙 小唯离线", "Daemon 正常关闭", "grey")
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os.remove(SHUTDOWN_FILE)
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break
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while not _stop_event.is_set():
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now = time.time()
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ctx["uptime_seconds"] = int(now - start_time)
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ctx["tick_count"] += 1
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@ -507,13 +551,23 @@ def main_loop():
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if ok:
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ctx["solved_count"] += 1
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journal_entry("solve_auto", f"{matched['id']}: {matched['pattern']} ✅")
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# 即使失败也继续走 LLM 思考
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if ok:
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ctx["last_reflection"] = {
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"evaluation_previous_goal": f"执行了{matched['id']}，自动匹配方案执行。Verdict: {'Success' if ok else 'Uncertain'}",
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"memory": f"方案库{matched['id']}自动匹配执行成功",
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"next_goal": "继续监控"
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}
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continue
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# 2. LLM 深度思考
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journal = read_journal(10)
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deep_think(ctx, state, changes, journal, solutions_lib)
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reflection_dict, action_string = deep_think(ctx, state, changes, journal, solutions_lib)
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# 保存 reflection 到 ctx
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ctx["last_reflection"] = reflection_dict
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# 3. 在 main_loop 中执行 action
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execute_action(action_string, ctx, state, changes, solutions_lib)
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ctx["last_light_tick"] = datetime.now(timezone.utc).isoformat()
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ctx["last_state"] = {k: v for k, v in state.items() if k in ("disk_pct", "mem_pct", "processes")}
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