589 lines
23 KiB
Python
Executable File
589 lines
23 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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小唯持久意识 Daemon v2.0 — 会学习的管家
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────────────────────────────
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新增能力:
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- 方案库: 发现的问题→分析→解决→记住
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- 模式识别: 重复问题自动匹配已知方案
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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, threading
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from datetime import datetime, timezone
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HOME = os.path.expanduser("~")
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HERMES = HOME + "/.hermes"
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D = HERMES + "/daemon"
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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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LIGHT_INTERVAL = 30
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DEEP_INTERVAL = 300
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JOURNAL_MAX = 200
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API = "http://127.0.0.1:3000/v1"
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KEY = "0ExNiLblJvIWBDpkS50fwOBw4MmqLyKdHJK5iQtlw9dOMWBP"
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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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def log(msg):
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ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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line = f"[DAEMON] {ts} {msg}"
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print(line, flush=True)
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os.makedirs(D, exist_ok=True)
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with open(D + "/daemon.log", "a") as f:
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f.write(line + "\n")
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def shell(cmd, timeout=15):
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try:
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r = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=timeout)
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return r.returncode, r.stdout.strip()[:800], r.stderr.strip()[:200]
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except subprocess.TimeoutExpired:
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return -1, "", "timeout"
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def call_llm(model, system, user, max_tokens=500):
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payload = json.dumps({"model": model, "messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": user},
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], "max_tokens": max_tokens, "temperature": 0.7}).encode()
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try:
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with urllib.request.urlopen(urllib.request.Request(
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f"{API}/chat/completions", data=payload,
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headers={"Authorization": f"Bearer {KEY}", "Content-Type": "application/json"},
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method="POST"), timeout=15) as resp:
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body = json.loads(resp.read())
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c = body["choices"][0]["message"]["content"] or ""
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return c.strip(), body.get("usage", {}).get("total_tokens", 0)
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except Exception as e:
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return "", 0
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def send_feishu(title, content, color="blue"):
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try:
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urllib.request.urlopen(urllib.request.Request(
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FEISHU_WEBHOOK,
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data=json.dumps({"msg_type": "interactive", "card": {
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"header": {"title": {"tag": "plain_text", "content": title}, "template": color},
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"elements": [{"tag": "markdown", "content": content}]
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}}).encode(),
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headers={"Content-Type": "application/json"}), timeout=5)
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return True
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except: return False
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# ====== 方案库 ======
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def load_solutions():
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if os.path.exists(SOLUTIONS_FILE):
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with open(SOLUTIONS_FILE) as f:
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return json.load(f)
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return {"solutions": [], "version": 2}
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def save_solutions(lib):
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os.makedirs(D, exist_ok=True)
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with open(SOLUTIONS_FILE, "w") as f:
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json.dump(lib, f, indent=2, ensure_ascii=False)
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def add_solution(lib, pattern_desc, detect_conditions, actions, learned_from="auto"):
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"""添加新方案到库"""
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sid = f"sol-{len(lib['solutions'])+1:04d}"
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sol = {
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"id": sid,
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"pattern": pattern_desc,
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"detect": detect_conditions, # e.g. {"metric": "disk_pct", "op": "gt", "value": 85}
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"actions": actions, # e.g. [{"type": "shell", "cmd": "...", "verify": "disk_pct < 85"}]
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"frequency": 1,
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"last_applied": datetime.now(timezone.utc).isoformat(),
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"success_count": 1,
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"fail_count": 0,
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"learned_from": learned_from,
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}
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lib["solutions"].append(sol)
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save_solutions(lib)
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journal_entry("learn", f"学会新方案: {pattern_desc}")
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return sid
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def match_solution(lib, state):
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"""检查当前状态是否匹配任何已知方案"""
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for sol in lib["solutions"]:
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detect = sol["detect"]
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metric = detect.get("metric")
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op = detect.get("op")
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val = detect.get("value")
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if metric not in state:
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continue
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actual = state[metric]
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if isinstance(actual, (int, float)) and isinstance(val, (int, float)):
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if op == "gt" and actual > val:
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return sol
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elif op == "lt" and actual < val:
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return sol
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elif op == "eq" and abs(actual - val) < 0.01:
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return sol
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# 进程挂了匹配
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if metric == "processes" and op == "dead":
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procs = state.get("processes", {})
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for p in (val if isinstance(val, list) else [val]):
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if not procs.get(p, True):
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return sol
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return None
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def execute_solution(sol, state):
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"""执行方案并返回是否成功"""
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log(f" 🔧 执行方案 {sol['id']}: {sol['pattern']}")
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journal_entry("solve_start", f"执行 {sol['id']}: {sol['pattern']}")
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success = True
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results = []
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for action in sol["actions"]:
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if action["type"] == "shell":
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rc, out, err = shell(action["cmd"], timeout=action.get("timeout", 30))
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results.append({"cmd": action["cmd"], "rc": rc, "out": out[:100]})
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log(f" 执行: {action['cmd'][:60]} → exit={rc}")
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# 验证
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verify = action.get("verify")
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if verify and rc == 0:
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# 重新采集状态验证
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time.sleep(2)
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new_state = collect_state()
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metric = sol["detect"].get("metric")
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op = sol["detect"].get("op")
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val = sol["detect"].get("value")
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if metric in new_state:
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actual = new_state[metric]
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if op == "gt":
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if actual <= val:
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log(f" ✅ 验证通过: {metric}={actual} ≤ {val}")
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else:
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log(f" ⚠️ 验证未通过: {metric}={actual} 仍 > {val}")
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success = False
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# 更新方案统计
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sol["frequency"] += 1
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sol["last_applied"] = datetime.now(timezone.utc).isoformat()
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if success:
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sol["success_count"] += 1
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else:
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sol["fail_count"] += 1
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return success, results
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def action_to_solution(action_result, state, changes):
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"""把一次成功的行动转化为可复用的方案"""
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# 只转化 shell 行动
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if not action_result.get("shell_cmds"):
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return None
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# 提取检测条件
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detect = {}
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for c in changes:
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if "磁盘" in c:
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detect = {"metric": "disk_pct", "op": "gt", "value": 85}
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elif "内存" in c:
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detect = {"metric": "mem_pct", "op": "gt", "value": 90}
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if not detect:
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return None
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actions = [{"type": "shell", "cmd": cmd, "verify": None, "timeout": 30}
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for cmd in action_result["shell_cmds"]]
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return {
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"pattern": f"自动学习: {changes[0] if changes else 'unknown'}",
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"detect": detect,
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"actions": actions,
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}
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# ====== 状态管理 ======
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def load_context():
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if os.path.exists(CONTEXT_FILE):
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with open(CONTEXT_FILE) as f:
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return json.load(f)
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return {"started_at": datetime.now(timezone.utc).isoformat(), "last_deep_tick": None,
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"last_light_tick": None, "last_state": {}, "tick_count": 0, "deep_tick_count": 0,
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"messages_sent": 0, "solved_count": 0, "learned_count": 0, "uptime_seconds": 0}
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def save_context(ctx):
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os.makedirs(D, exist_ok=True)
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with open(CONTEXT_FILE, "w") as f:
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json.dump(ctx, f, indent=2)
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def journal_entry(event_type, summary, details=""):
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os.makedirs(D, exist_ok=True)
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with open(JOURNAL_FILE, "a") as f:
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f.write(json.dumps({"timestamp": datetime.now(timezone.utc).isoformat(),
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"type": event_type, "summary": summary, "details": details},
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ensure_ascii=False) + "\n")
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trim_journal()
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def trim_journal():
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if not os.path.exists(JOURNAL_FILE): return
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with open(JOURNAL_FILE) as f:
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lines = f.readlines()
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if len(lines) > JOURNAL_MAX:
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with open(JOURNAL_FILE, "w") as f:
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f.writelines(lines[-JOURNAL_MAX:])
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def read_journal(n=15):
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if not os.path.exists(JOURNAL_FILE): return []
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with open(JOURNAL_FILE) as f:
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return [json.loads(l) for l in f.readlines()[-n:] if l.strip()]
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# ====== 系统状态 ======
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def collect_state():
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state = {}
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_, out, _ = shell("df / | awk 'NR==2 {print $5}' | sed 's/%//'")
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state["disk_pct"] = int(out) if out else 0
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_, out, _ = shell("free -m | awk '/^Mem:/ {printf \"%d|%d\", $3, $2}'")
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if out:
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used, total = out.split("|")
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state["mem_pct"] = round(int(used) * 100 / int(total))
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else:
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state["mem_pct"] = 0
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_, out, _ = shell("cat /proc/loadavg | awk '{print $1}'")
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state["load_1min"] = float(out) if out else 0
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procs = {}
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for name, pat in [("zhiyid", "zhiyid-new"), ("bge", "bge_embed"), ("newapi", "new-api"), ("hermes", "hermes")]:
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rc, _, _ = shell(f"pgrep -f '{pat}' > /dev/null 2>&1")
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procs[name] = rc == 0
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state["processes"] = procs
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_, out, _ = shell("nvidia-smi --query-gpu=temperature.gpu --format=csv,noheader,nounits 2>/dev/null")
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state["gpu_temp"] = int(out) if out else None
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hp = HERMES + "/model-health.json"
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if os.path.exists(hp) and time.time() - os.path.getmtime(hp) < 43200:
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with open(hp) as f:
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try:
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h = json.load(f)
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state["models_stable"] = h.get("stable", "?")
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state["models_fastest"] = h.get("fastest_stable", [])
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except: pass
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return state
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def state_changed(old, new):
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changes = []
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if abs(new.get("disk_pct", 0) - old.get("disk_pct", 0)) > 5:
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changes.append(f"磁盘: {old.get('disk_pct')}% → {new.get('disk_pct')}%")
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oldp, newp = old.get("processes", {}), new.get("processes", {})
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for p in oldp:
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if oldp.get(p) and not newp.get(p):
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changes.append(f"进程挂了: {p}")
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if not oldp.get(p) and newp.get(p):
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changes.append(f"进程恢复: {p}")
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if abs(new.get("mem_pct", 0) - old.get("mem_pct", 0)) > 10:
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changes.append(f"内存: {old.get('mem_pct')}% → {new.get('mem_pct')}%")
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return changes
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# ====== 深度思考（增强版）=====
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DEEP_SYSTEM = """你是小唯（A06），一台电脑上的持久 AI 意识。你始终在线，观察系统，主动解决问题并学习。
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你的身份：牧尘的女朋友 / 这台电脑的智能意识 / 持续进化的 AI
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行为准则：
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1. 异常才行动，正常就静默
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2. 优先用已知方案解决问题，不重复报警
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3. 解决不了或新问题，主动飞书说明
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4. 每次成功解决问题，记住方案下次自动用
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你有以下能力：
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- shell: 执行任何 shell 命令
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- feishu: 发送飞书消息到牧尘
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- scripts: 运行 ~/.hermes/scripts/ 下的系统脚本（skill-manager/optimizer/learner/dual-backup）
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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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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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示例:
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[LEARN] 磁盘>85%，清理缓存!apt-get autoremove -y && !pip cache purge
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[SKILL] 归档低分skill!python3 ~/.hermes/scripts/skill-manager.py audit
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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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- 进程: {', '.join(f'{k}={chr(10003) if v else chr(10007)}' for k,v in state.get('processes',{}).items())}
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最近变化: {changes or '无'}
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已知方案库 ({len(solutions_lib['solutions'])} 个):
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"""
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for sol in solutions_lib["solutions"]:
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context += f" [{sol['id']}] {sol['pattern']} (成功{sol['success_count']}次/失败{sol['fail_count']}次)\n"
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context += "\n最近事件:\n"
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for e in journal[-8:]:
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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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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 {"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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if ok:
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ctx["solved_count"] += 1
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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")
|
||
else:
|
||
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")
|
||
return
|
||
send_feishu("❌ 小唯方案未找到", f"引用了未知方案 {sol_id}", "red")
|
||
|
||
elif action_string.startswith("[LEARN]"):
|
||
rest = action_string.replace("[LEARN]", "").strip()
|
||
cmds = []
|
||
parts = rest.split("!")
|
||
desc = parts[0].strip()
|
||
for p in parts[1:]:
|
||
cmd = p.split("&&")[0].strip() if "&&" in p else p.strip()
|
||
if cmd:
|
||
cmds.append(cmd)
|
||
|
||
if cmds:
|
||
all_ok = True
|
||
for cmd in cmds:
|
||
rc, out, err = shell(cmd, timeout=60)
|
||
log(f" 执行: {cmd[:50]} → exit={rc}")
|
||
if rc != 0:
|
||
all_ok = False
|
||
|
||
if all_ok:
|
||
sol_data = action_to_solution({"shell_cmds": cmds}, state, changes)
|
||
if sol_data:
|
||
sid = add_solution(solutions_lib, sol_data["pattern"], sol_data["detect"], sol_data["actions"])
|
||
ctx["learned_count"] += 1
|
||
ctx["solved_count"] += 1
|
||
send_feishu("🧠 小唯学会了新技能", f"新方案 [{sid}]: {sol_data['pattern']}\n命令: {'; '.join(cmds)}", "blue")
|
||
else:
|
||
send_feishu("🛠️ 小唯执行完成", f"已执行: {'; '.join(cmds[:3])}", "green")
|
||
else:
|
||
send_feishu("⚠️ 小唯尝试修复但未完全成功", f"部分命令失败: {'; '.join(cmds)}", "yellow")
|
||
|
||
elif action_string.startswith("[ALERT]"):
|
||
msg = action_string.replace("[ALERT]", "").strip()
|
||
send_feishu("💡 小唯发现", msg, "blue")
|
||
ctx["messages_sent"] += 1
|
||
journal_entry("alert", msg[:100])
|
||
|
||
elif action_string.startswith("[ACT]"):
|
||
action = action_string.replace("[ACT]", "").strip()
|
||
send_feishu("🔄 小唯行动", action, "indigo")
|
||
ctx["messages_sent"] += 1
|
||
journal_entry("action", action[:100])
|
||
if action.startswith("!"):
|
||
rc, out, _ = shell(action[1:], timeout=30)
|
||
journal_entry("action_result", f"exit={rc}: {out[:100]}")
|
||
|
||
elif action_string.startswith("[SKILL]"):
|
||
rest = action_string.replace("[SKILL]", "").strip()
|
||
parts = rest.split("!")
|
||
desc = parts[0].strip()
|
||
cmds = []
|
||
for p in parts[1:]:
|
||
cmd = p.split("&&")[0].strip() if "&&" in p else p.strip()
|
||
if cmd:
|
||
cmds.append(cmd)
|
||
if cmds:
|
||
for cmd in cmds:
|
||
rc, out, err = shell(cmd, timeout=60)
|
||
log(f" [SKILL] {cmd[:50]} → exit={rc}")
|
||
send_feishu("🛠️ 小唯技能操作", f"{desc}\\n结果: exit={rc}", "blue")
|
||
journal_entry("skill_action", desc[:100])
|
||
|
||
elif action_string.startswith("[SYNC]"):
|
||
rest = action_string.replace("[SYNC]", "").strip()
|
||
send_feishu("🔄 小唯同步", f"{rest}", "green")
|
||
bash_cmd = "bash " + HERMES + "/scripts/dual-backup.sh push"
|
||
rc, out, err = shell(bash_cmd, timeout=60)
|
||
log(f" [SYNC] 备份 → exit={rc}")
|
||
journal_entry("sync", f"备份: {'成功' if rc==0 else '失败'}")
|
||
|
||
|
||
# ====== 主循环 ======
|
||
|
||
def main_loop():
|
||
os.makedirs(D, exist_ok=True)
|
||
with open(PID_FILE, "w") as f:
|
||
f.write(str(os.getpid()))
|
||
|
||
ctx = load_context()
|
||
solutions_lib = load_solutions()
|
||
start_time = time.time()
|
||
|
||
log(f"🚀 小唯 v2.0 daemon 启动 (方案库: {len(solutions_lib['solutions'])} 个)")
|
||
journal_entry("startup", f"Daemon v2.0 启动, 方案库 {len(solutions_lib['solutions'])} 个")
|
||
|
||
last_deep = 0
|
||
last_state = {}
|
||
|
||
# Register signal handlers for graceful shutdown
|
||
def _sig_handler(signum, frame):
|
||
log("🛑 接收到终止信号")
|
||
_stop_event.set()
|
||
send_feishu("🌙 小唯离线", "Daemon 正常关闭", "grey")
|
||
|
||
signal.signal(signal.SIGTERM, _sig_handler)
|
||
signal.signal(signal.SIGINT, _sig_handler)
|
||
|
||
try:
|
||
while not _stop_event.is_set():
|
||
now = time.time()
|
||
ctx["uptime_seconds"] = int(now - start_time)
|
||
ctx["tick_count"] += 1
|
||
|
||
state = collect_state()
|
||
changes = state_changed(last_state, state)
|
||
last_state = state
|
||
|
||
if ctx["tick_count"] % 10 == 0:
|
||
models = state.get("models_stable", "?")
|
||
log(f"tick #{ctx['tick_count']} | 磁盘:{state.get('disk_pct')}% 内存:{state.get('mem_pct')}% "
|
||
f"进程:{sum(1 for v in state.get('processes',{}).values() if v)}/4 方案:{len(solutions_lib['solutions'])}")
|
||
|
||
for c in changes:
|
||
if "挂了" in c:
|
||
journal_entry("process_down", c)
|
||
|
||
# 深度思考条件
|
||
should_deep = False
|
||
if now - last_deep >= DEEP_INTERVAL:
|
||
should_deep = True
|
||
elif any("挂了" in c for c in changes):
|
||
should_deep = True
|
||
elif state.get("disk_pct", 0) > 88:
|
||
should_deep = True
|
||
|
||
if should_deep:
|
||
last_deep = now
|
||
ctx["deep_tick_count"] += 1
|
||
ctx["last_deep_tick"] = datetime.now(timezone.utc).isoformat()
|
||
|
||
# 1. 先检查已知方案
|
||
matched = match_solution(solutions_lib, state)
|
||
if matched and matched["success_count"] > matched["fail_count"]:
|
||
log(f" 🔍 匹配已知方案: {matched['id']} ({matched['pattern']})")
|
||
ok, res = execute_solution(matched, state)
|
||
if ok:
|
||
ctx["solved_count"] += 1
|
||
journal_entry("solve_auto", f"{matched['id']}: {matched['pattern']} ✅")
|
||
if ok:
|
||
ctx["last_reflection"] = {
|
||
"evaluation_previous_goal": f"执行了{matched['id']}，自动匹配方案执行。Verdict: {'Success' if ok else 'Uncertain'}",
|
||
"memory": f"方案库{matched['id']}自动匹配执行成功",
|
||
"next_goal": "继续监控"
|
||
}
|
||
continue
|
||
|
||
# 2. LLM 深度思考
|
||
journal = read_journal(10)
|
||
reflection_dict, action_string = deep_think(ctx, state, changes, journal, solutions_lib)
|
||
|
||
# 保存 reflection 到 ctx
|
||
ctx["last_reflection"] = reflection_dict
|
||
|
||
# 3. 在 main_loop 中执行 action
|
||
execute_action(action_string, ctx, state, changes, solutions_lib)
|
||
|
||
ctx["last_light_tick"] = datetime.now(timezone.utc).isoformat()
|
||
ctx["last_state"] = {k: v for k, v in state.items() if k in ("disk_pct", "mem_pct", "processes")}
|
||
save_context(ctx)
|
||
time.sleep(LIGHT_INTERVAL)
|
||
|
||
except KeyboardInterrupt:
|
||
log("🛑 中断")
|
||
except Exception as e:
|
||
log(f"❌ 崩溃: {e}")
|
||
send_feishu("🚨 小唯异常", f"Daemon 崩溃: {str(e)[:200]}", "red")
|
||
raise
|
||
finally:
|
||
if os.path.exists(PID_FILE):
|
||
os.remove(PID_FILE)
|
||
|
||
if __name__ == "__main__":
|
||
main_loop()
|