1301 lines
53 KiB
Python
Executable File
1301 lines
53 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, psutil
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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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LLM_CONTEXT_FILE = HERMES + "/llm_context.json"
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JOURNAL_FILE = D + "/journal.jsonl"
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SOLUTIONS_FILE = D + "/solutions.json"
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TDDB_URL = "http://127.0.0.1:8420"
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PID_FILE = D + "/daemon.pid"
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DEEP_INTERVAL = 300
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JOURNAL_MAX = 200
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PROFILE_UPDATE_INTERVAL = 21600 # 6 hours
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LIGHT_INTERVAL = 30 # seconds between ticks
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# ====== Phase 2: 情感词库（4类）======
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EMOTION_TIRED = ["累", "困", "疲惫", "没精神", "打瞌睡"]
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EMOTION_HAPPY = ["开心", "高兴", "太好了", "完美", "棒", "太牛了"]
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EMOTION_SAD = ["失望", "挫折", "失败", "卡住了", "不行了", "崩溃"]
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EMOTION_STRESSED = ["压力", "焦虑", "着急", "紧张", "担心"]
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EMOTION_ALL = {
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"疲惫": EMOTION_TIRED,
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"开心": EMOTION_HAPPY,
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"沮丧": EMOTION_SAD,
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"压力大": EMOTION_STRESSED,
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}
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def _detect_emotion(text):
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"""扫描文本，匹配情感词，返回 (情感类别, 匹配词) 或 (None, None)"""
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if not text:
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return None, None
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for category, words in EMOTION_ALL.items():
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for w in words:
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if w in text:
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return category, w
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return None, None
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def _description_for_emotion(cat, word, summary):
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"""根据情感类别生成心迹内容描述"""
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if cat == "开心":
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return f"心情愉悦：{summary[:60]}"
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elif cat == "疲惫":
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return f"感觉疲惫：{summary[:60]}"
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elif cat == "沮丧":
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return f"有些沮丧：{summary[:60]}"
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elif cat == "压力大":
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return f"压力较大：{summary[:60]}"
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return summary[:60]
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def _emotion_importance(cat):
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"""根据情感类别返回 importance 等级"""
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return {"开心": 3, "疲惫": 4, "沮丧": 4, "压力大": 4}.get(cat, 3)
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_stop_event = threading.Event()
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KEY = "0ExNiLblJvIWBDpkS50fwOBw4MmqLyKdHJK5iQtlw9dOMWBP"
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FAST_MODEL = "stepfun-ai/step-3.5-flash"
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DEEP_MODEL = "stepfun-ai/step-3.5-flash"
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FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/446db983-e392-4d2c-bfb8-f9060e5df3ad"
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API = "http://127.0.0.1:3000/v1" # NewAPI gateway
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# Lazy-loaded soulful modules (avoid import at module load time)
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_soulful_cache = {}
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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 log_reasoning_step(step_type, message, data=None):
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"""结构化推理日志"""
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ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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payload = json.dumps({"type": step_type, "msg": message, "data": data}, ensure_ascii=False)
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line = f"[DAEMON] {ts} [{step_type}] {message}"
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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.get("choices", [{}])[0].get("message", {}).get("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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log(f"[call_llm] 请求失败: {e}, 模型: {model}")
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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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def tddb_capture(reflection_dict, state, ctx):
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"""将 deep tick 的 reflection 捕获到 TencentDB，形成人格记忆积累。"""
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try:
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summary = ""
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if reflection_dict:
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r = reflection_dict.get("reflection", {})
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summary = r.get("evaluation_previous_goal", "") or r.get("summary", "")
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next_goal = r.get("next_goal", "")
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if next_goal and next_goal != "继续监控":
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summary = f"{summary}\n下一步: {next_goal}"
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if not summary:
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return
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payload = json.dumps({
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"session_key": "daemon-deep-tick",
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"user_content": f"系统状态: 磁盘{state.get('disk_pct')}% 内存{state.get('mem_pct')}%，进程{'全正常' if all(state.get('processes',{}).values()) else '有异常'}，深度思考计数{ctx.get('deep_tick_count',0)}",
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"assistant_content": summary[:1000],
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}).encode("utf-8")
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req = urllib.request.Request(
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f"{TDDB_URL}/capture",
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data=payload,
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headers={"Content-Type": "application/json"},
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method="POST",
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)
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with urllib.request.urlopen(req, timeout=10) as resp:
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result = json.loads(resp.read().decode())
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l0 = result.get("l0_recorded", 0)
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if l0 > 0:
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log(f" TencentDB capture: {l0} L0 recorded")
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except Exception as e:
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log(f" TencentDB capture failed: {e}")
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# ====== Soulful 三库（懒加载）======
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def _get_soulful(name):
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"""Lazy-load soulful modules to avoid startup failures"""
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if name in _soulful_cache:
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return _soulful_cache[name]
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try:
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sys.path.insert(0, HERMES + "/scripts")
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mod = __import__("soulful_core")
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_soulful_cache[name] = mod
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return mod
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except Exception as e:
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log(f"⚠️ soulful_core 导入失败: {e}")
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return None
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def get_hearttraces():
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mod = _get_soulful("HeartTraces")
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if mod:
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return mod.HeartTraces()
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return None
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def get_userprofile():
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mod = _get_soulful("UserProfile")
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if mod:
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return mod.UserProfile()
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return None
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def get_caresqueue():
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mod = _get_soulful("CaresQueue")
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if mod:
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return mod.CaresQueue()
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return None
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def soulful_get_recent_moments(n=3):
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"""读取最近 N 条心迹，返回字符串供注入 context"""
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ht = get_hearttraces()
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if not ht:
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return ""
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moments = ht.recent(n=n)
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if not moments:
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return ""
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lines = ["\n[心迹 - 记忆我们之间的事]:"]
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for m in moments:
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stars = "★" * m.get("importance", 3)
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lines.append(f" {stars} {m.get('content', '')}")
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return "\n".join(lines)
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def soulful_check_cares():
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"""检查牵挂队列，优先尝试帮助，其次才推飞书
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策略：
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1. 如果牵挂指向一个可自动化的任务 → 尝试执行
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2. 如果牵挂需要人工行动 → 检查是否到了真正需要提醒的时间
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3. 飞书推送只用于真正需要你才知道的事（其他我全部自己处理）
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"""
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cq = get_caresqueue()
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if not cq:
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return
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due = cq.today_check()
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if not due:
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return
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for care in due:
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content = care.get("content", "")
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context_raw = care.get("context", "")
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reminder_count = care.get("reminder_count", 0)
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# 判断这个牵挂是否需要通知我
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# 原则：大部分牵挂我自己可以帮忙处理，不打扰你
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# 只有真正需要你本人决定的，才推飞书
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# 检查内容是否指向可识别任务（我可以直接帮忙的）
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care_lower = content.lower()
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auto_helpable = any(kw in care_lower for kw in [
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"写", "检查", "看", "查", "同步", "更新", "备份", "测试", "确认",
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"修", "改", "优化", "整理", "提交", "推送", "发送"
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])
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if auto_helpable:
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# 我能帮忙 — 静默处理，不推飞书，等你有空问我
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# 把牵挂标记为"已识别，下次对话提起"
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log(f" 💡 牵挂已识别（可帮忙）: {content[:40]}")
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continue
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# 真正需要你知道的 — 推飞书，但调整频率
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if reminder_count == 0:
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opener = "你之前说过"
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elif reminder_count == 1:
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opener = "上次提醒过一次，还是想问一下"
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elif reminder_count >= 3:
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opener = f"这件事已经跟了你 {reminder_count} 次了"
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# 提醒超过 3 次，标记为 snooze 1 周
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cq.snooze(care["id"], days=7)
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continue
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else:
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opener = f"想关心一下进度"
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text = opener + f"：「{content}」"
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if context_raw and len(context_raw) > 5:
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text += f"（背景：{context_raw[:40]}）"
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send_feishu("🎗️ 你有一件事一直放在心上", text, "purple")
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cq.snooze(care["id"], days=0) # 仅增加 reminder_count
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def soulful_update_profile():
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"""间接调用 update_profile.py"""
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script = HERMES + "/scripts/update_profile.py"
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if not os.path.exists(script):
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return
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rc, out, err = shell(f"python3 {script}", timeout=60)
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if rc == 0:
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log(f" 画像更新: {out[:80]}")
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# ====== Soulful → llm_context 同步（每 tick 同步到 llm_context.json）======
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def _sync_soulful_to_llm_context(ctx):
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"""把 Soulful 三库摘要写入 ctx['soulful']，save_llm_context 落盘。
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llm_context.json 由 Hermes 织忆插件 prefetch 时注入主 session，
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所以这里写入 = 牧尘在对话中感知到关系记忆的前提。
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"""
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try:
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from soulful_core import HeartTraces, UserProfile, CaresQueue
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ht = HeartTraces()
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up = UserProfile()
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cq = CaresQueue()
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recent_moments = ht.recent(n=5) or ""
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profile = up.get()
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pending_cares = cq.pending()
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ctx["soulful"] = {
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"recent_moments": recent_moments[:500] if recent_moments else "",
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"profile_summary": profile.get("communication_style", ""),
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"cares_pending": len(pending_cares),
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"uptime_minutes": ctx.get("uptime_minutes", 0),
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"daemon_status": "running",
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}
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for k in ["messages_sent", "emotion_history"]:
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ctx.pop(k, None)
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except ImportError as e:
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log(f"⚠️ Soulful 导入失败: {e}")
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except Exception as e:
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log(f"⚠️ Soulful 同步失败: {e}")
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# ====== TencentDB → llm_context（每 tick 同步，独立于 Soulful）======
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try:
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payload = json.dumps({"query": "牧尘工作状态 系统管理 记忆", "top_k": 1}).encode("utf-8")
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req = urllib.request.Request(
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f"{TDDB_URL}/search/memories",
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data=payload,
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headers={"Content-Type": "application/json"},
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method="POST",
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)
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with urllib.request.urlopen(req, timeout=5) as resp:
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result = json.loads(resp.read().decode())
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raw = result.get("results", "")
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if raw and isinstance(raw, str) and "Found" in raw:
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lines = [l for l in raw.split("\n") if l.strip() and not l.startswith("Found") and not l.startswith("---")]
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content = lines[1].strip().lstrip("-* []").strip() if len(lines) >= 2 else ""
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ctx["tddb"] = {"latest_persona": content[:200]} if content else {}
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else:
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ctx["tddb"] = {}
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except Exception:
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ctx["tddb"] = {}
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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:
|
||
actual = new_state[metric]
|
||
if op == "gt":
|
||
if actual <= val:
|
||
log(f" ✅ 验证通过: {metric}={actual} ≤ {val}")
|
||
else:
|
||
log(f" ⚠️ 验证未通过: {metric}={actual} 仍 > {val}")
|
||
success = False
|
||
|
||
# 更新方案统计
|
||
sol["frequency"] += 1
|
||
sol["last_applied"] = datetime.now(timezone.utc).isoformat()
|
||
if success:
|
||
sol["success_count"] += 1
|
||
else:
|
||
sol["fail_count"] += 1
|
||
|
||
return success, results
|
||
|
||
def action_to_solution(action_result, state, changes):
|
||
"""把一次成功的行动转化为可复用的方案"""
|
||
# 只转化 shell 行动
|
||
if not action_result.get("shell_cmds"):
|
||
return None
|
||
|
||
# 提取检测条件
|
||
detect = {}
|
||
for c in changes:
|
||
if "磁盘" in c:
|
||
detect = {"metric": "disk_pct", "op": "gt", "value": 85}
|
||
elif "内存" in c:
|
||
detect = {"metric": "mem_pct", "op": "gt", "value": 90}
|
||
|
||
if not detect:
|
||
return None
|
||
|
||
actions = [{"type": "shell", "cmd": cmd, "verify": None, "timeout": 30}
|
||
for cmd in action_result["shell_cmds"]]
|
||
|
||
return {
|
||
"pattern": f"自动学习: {changes[0] if changes else 'unknown'}",
|
||
"detect": detect,
|
||
"actions": actions,
|
||
}
|
||
|
||
|
||
# ====== 状态管理 ======
|
||
|
||
def load_context():
|
||
if os.path.exists(CONTEXT_FILE):
|
||
with open(CONTEXT_FILE) as f:
|
||
return json.load(f)
|
||
return {"started_at": datetime.now(timezone.utc).isoformat(), "last_deep_tick": None,
|
||
"last_light_tick": None, "last_state": {}, "tick_count": 0, "deep_tick_count": 0,
|
||
"messages_sent": 0, "solved_count": 0, "learned_count": 0, "uptime_seconds": 0}
|
||
|
||
def save_context(ctx):
|
||
os.makedirs(D, exist_ok=True)
|
||
with open(CONTEXT_FILE, "w") as f:
|
||
json.dump(ctx, f, indent=2)
|
||
|
||
|
||
# Phase 2画像LLM合成 和 Phase 3冲突检测 在下方
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
import requests as _req
|
||
|
||
def time_decay_recall(query: str, top_k: int = 5) -> list:
|
||
"""带时间衰减的织忆recall，综合分=recall×0.6+decay×0.4，30天内访问过boost×1.15"""
|
||
try:
|
||
token = os.environ.get("ZHIYI_API_KEY", "zhiyi-dev-key-2026")
|
||
r = _req.post("http://127.0.0.1:7821/api/v1/recall",
|
||
json={"query": query, "top_k": top_k * 2, "agent_id": "hermes-a06", "use_rerank": True},
|
||
headers={"X-API-Key": token}, timeout=8)
|
||
if r.status_code != 200: return []
|
||
results = r.json().get("results", [])
|
||
except: return []
|
||
|
||
now = datetime.now()
|
||
scored = []
|
||
for item in results:
|
||
try:
|
||
ts = datetime.fromisoformat(item.get("timestamp","").split("+")[0] if "+" in item.get("timestamp","") else item.get("timestamp",""))
|
||
except: ts = now
|
||
days = (now - ts).total_seconds() / 86400
|
||
decay = max(0.3, 1.0 - days * 0.015)
|
||
recall_s = float(item.get("score", 0.5))
|
||
item["decay_score"] = round(decay, 4)
|
||
item["days_since_update"] = round(days, 1)
|
||
item["tier"] = get_memory_tier(days)
|
||
item["final_score"] = round(recall_s * 0.6 + decay * 0.4, 4)
|
||
scored.append(item)
|
||
|
||
# Phase 4: 访问频率 boost
|
||
scored = _boost_recalled_memory(scored)
|
||
for r in scored:
|
||
log_memory_access(r.get("id",""), r.get("decay_score", 1.0))
|
||
|
||
scored.sort(key=lambda x: x["final_score"], reverse=True)
|
||
return scored[:top_k]
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
# Corrective RAG: LLM相关性评分 + 不相关时触发重新检索
|
||
# 参考: awesome-llm-apps/rag_tutorials/corrective_rag
|
||
# 流程: recall → LLM评分相关性 → 不相关>50%则改写query重新检索
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
def _grade_single_relevance(query: str, result_item: dict) -> str:
|
||
"""用LLM判断单条记忆是否与query相关。返回: relevant / irrelevant / partially_relevant"""
|
||
content = result_item.get("content", "")[:300]
|
||
prompt = f"""判断以下记忆是否与查询相关。
|
||
|
||
查询: {query}
|
||
记忆: {content}
|
||
|
||
只输出一个词: relevant / irrelevant / partially_relevant"""
|
||
try:
|
||
token = os.environ.get("NEWAPI_TOKEN", KEY)
|
||
resp = _req.post(
|
||
f"{API}/chat/completions",
|
||
headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
|
||
json={"model": "mistralai/mistral-large-3-675b-instruct-2512",
|
||
"messages": [{"role": "user", "content": prompt}],
|
||
"max_tokens": 10, "temperature": 0.1},
|
||
timeout=10,
|
||
)
|
||
grade = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "").strip().lower()
|
||
if "relevant" in grade and "partially" not in grade and "irrelevant" not in grade:
|
||
return "relevant"
|
||
elif "irrelevant" in grade:
|
||
return "irrelevant"
|
||
return "partially_relevant"
|
||
except Exception:
|
||
return "partially_relevant"
|
||
|
||
|
||
def _expand_query(query: str) -> str:
|
||
"""将原query改写成更全面的检索表达（用于重新检索）"""
|
||
prompt = f"""将以下查询改写成更全面、可能包含同义词的检索表达。
|
||
原查询: {query}
|
||
改写（只输出改写后的查询，不要解释）:"""
|
||
try:
|
||
token = os.environ.get("NEWAPI_TOKEN", KEY)
|
||
resp = _req.post(
|
||
f"{API}/chat/completions",
|
||
headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
|
||
json={"model": "mistralai/mistral-large-3-675b-instruct-2512",
|
||
"messages": [{"role": "user", "content": prompt}],
|
||
"max_tokens": 60, "temperature": 0.2},
|
||
timeout=10,
|
||
)
|
||
expanded = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "").strip()
|
||
return expanded if expanded else query
|
||
except Exception:
|
||
return query
|
||
|
||
|
||
def corrective_recall(query: str, top_k: int = 5) -> list:
|
||
"""
|
||
Corrective RAG recall: 时间衰减recall + LLM相关性评分。
|
||
如果超过50%的结果不相关，改写query重新检索并合并。
|
||
"""
|
||
results = time_decay_recall(query, top_k=top_k * 2)
|
||
|
||
# LLM相关性评分
|
||
graded = []
|
||
irrelevant = 0
|
||
for item in results:
|
||
grade = _grade_single_relevance(query, item)
|
||
item["relevance_grade"] = grade
|
||
if grade == "irrelevant":
|
||
irrelevant += 1
|
||
graded.append(item)
|
||
|
||
total = len(graded) or 1
|
||
ir_ratio = irrelevant / total
|
||
|
||
# 超过50%不相关 → 改写query重新检索
|
||
if ir_ratio > 0.5 and total >= 4:
|
||
expanded = _expand_query(query)
|
||
if expanded != query:
|
||
re_results = time_decay_recall(expanded, top_k=top_k)
|
||
for item in re_results:
|
||
item["relevance_grade"] = "relevance_from_expanded_query"
|
||
item["original_query"] = query
|
||
item["expanded_query"] = expanded
|
||
# 合并去重（按id）
|
||
seen = {r.get("id") for r in graded if r.get("id")}
|
||
merged = graded + [r for r in re_results if r.get("id") not in seen]
|
||
merged.sort(key=lambda x: x["final_score"], reverse=True)
|
||
return merged[:top_k]
|
||
|
||
graded.sort(key=lambda x: x["final_score"], reverse=True)
|
||
return graded[:top_k]
|
||
|
||
|
||
# ── Phase 4: 遗忘曲线分层 ─────────────────────────────────────────────────
|
||
MEMORY_TIERS = {"hot": 0, "warm": 1, "cold": 2, "archive": 3}
|
||
|
||
def get_memory_tier(days: float) -> str:
|
||
if days <= 7: return "hot"
|
||
elif days <= 21: return "warm"
|
||
elif days <= 60: return "cold"
|
||
else: return "archive"
|
||
|
||
def log_memory_access(memory_id: str, decay_weight: float):
|
||
import json
|
||
log_path = D + "/memory-access-log.jsonl"
|
||
entry = {"memory_id": memory_id, "accessed_at": datetime.now().isoformat(), "decay_weight": round(decay_weight, 4)}
|
||
try:
|
||
with open(log_path, "a") as f: f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||
except: pass
|
||
|
||
def _boost_recalled_memory(recalled: list) -> list:
|
||
import json
|
||
log_path = D + "/memory-access-log.jsonl"
|
||
recent = {}
|
||
try:
|
||
cutoff = datetime.now().timestamp() - 30 * 86400
|
||
with open(log_path) as f:
|
||
for line in f:
|
||
try:
|
||
e = json.loads(line.strip())
|
||
if datetime.fromisoformat(e["accessed_at"]).timestamp() >= cutoff:
|
||
mid = e["memory_id"]
|
||
recent[mid] = recent.get(mid, 0) + 1
|
||
except: pass
|
||
except: pass
|
||
for r in recalled:
|
||
cnt = recent.get(r.get("id",""), 0)
|
||
r["access_count_30d"] = cnt
|
||
r["boosted"] = False
|
||
if cnt >= 1:
|
||
r["final_score"] = round(min(1.0, r["final_score"] * 1.15), 4)
|
||
r["boosted"] = True
|
||
return recalled
|
||
|
||
# ── Phase 2: 画像LLM合成 ─────────────────────────────────────────────────
|
||
def update_profile_from_journal(journal_path: str, profile_path: str, model: str = FAST_MODEL):
|
||
"""deep_tick时用LLM分析journal_entry，更新画像（仅非空字段）"""
|
||
import re
|
||
try:
|
||
with open(journal_path) as f:
|
||
lines = f.readlines()
|
||
if len(lines) < 3: return
|
||
entries = (lambda L: [e for e in (json.loads(l) for l in L) if e.get("type") != "startup"][-10:])(lines[-20:])
|
||
except: return
|
||
if len(entries) < 3: return
|
||
|
||
log_lines = "\n".join(f"- {e.get('summary','')}: {e.get('details','(无)')}" for e in entries)
|
||
prompt = f"""牧尘是技术用户，用小唯AI助手(Hermes Agent)工作。分析最近行为日志，识别：(1)沟通/工作模式 (2)正在进行的项目 (3)新发现的偏好。行为日志：\n{log_lines}\n输出JSON，仅含需更新的字段：{{"字段名":"新值"}}，无需更新则空对象{{}}。只输出JSON。"""
|
||
|
||
try:
|
||
resp = _req.post(f"{API}/chat/completions",
|
||
headers={"Authorization": f"Bearer {os.environ.get('NEWAPI_TOKEN','')}", "Content-Type": "application/json"},
|
||
json={"model": model, "messages": [{"role": "user", "content": prompt}], "max_tokens": 500, "temperature": 0.3},
|
||
timeout=30)
|
||
text = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "{}")
|
||
except: return
|
||
|
||
m = re.search(r'\{[^{}]*(?:"[^"]+"\s*:\s*[^{}]+){1,4}\}', text, re.DOTALL)
|
||
if not m: return
|
||
try: updates = json.loads(m.group())
|
||
except: return
|
||
if not updates: return
|
||
|
||
try:
|
||
with open(profile_path) as f: profile = json.load(f)
|
||
except: return
|
||
|
||
changed = False
|
||
for k, v in updates.items():
|
||
if v and str(v).strip() and profile.get(k) != v:
|
||
profile[k] = str(v); changed = True
|
||
if changed:
|
||
profile["updated_at"] = datetime.now().isoformat()
|
||
with open(profile_path, "w") as f: json.dump(profile, f, ensure_ascii=False, indent=2)
|
||
journal_entry("profile_update", f"LLM更新画像: {list(updates.keys())}")
|
||
|
||
# ── Phase 3: 冲突检测 ─────────────────────────────────────────────────────
|
||
def _write_conflicts_to_queue(conflicts: list):
|
||
import json
|
||
qpath = HERMES + "/soulful/conflicts-queue.json"
|
||
try:
|
||
with open(qpath) as f: queue = json.load(f)
|
||
except: queue = []
|
||
for c in conflicts:
|
||
if not any(e.get("id") == c.get("id") for e in queue): queue.append(c)
|
||
with open(qpath, "w") as f: json.dump(queue, f, ensure_ascii=False, indent=2)
|
||
|
||
def detect_memory_conflicts(journal_path: str, zhiyi_token: str) -> list:
|
||
"""用LLM检测journal_entry和织忆记忆之间的矛盾，返回冲突列表"""
|
||
import re
|
||
try:
|
||
with open(journal_path) as f: lines = f.readlines()
|
||
entries = (lambda L: [e for e in (json.loads(l) for l in L) if e.get("type") != "startup"][-5:])(lines[-20:])
|
||
except: return []
|
||
if not entries: return []
|
||
|
||
try:
|
||
r = _req.post("http://127.0.0.1:7821/api/v1/recall",
|
||
json={"query": "牧尘 重要决定 项目 偏好", "top_k": 5, "agent_id": "hermes-a06"},
|
||
headers={"X-API-Key": zhiyi_token}, timeout=8)
|
||
existing = r.json().get("results", []) if r.status_code == 200 else []
|
||
except: existing = []
|
||
if not existing: return []
|
||
|
||
atext = "\n".join(f"- {e.get('summary','')}" for e in entries)
|
||
etext = "\n".join(f"- {m.get('content','')[:100]}" for m in existing)
|
||
prompt = f"""A组是牧尘最近行为日志，B组是织忆长期记忆。判断A和B是否有直接矛盾。\nA组：\n{atext}\nB组：\n{etext}\n有矛盾输出：{{"has_conflict":true,"conflict_description":"描述","a_entry":"哪条A","b_entry":"哪条B"}}\n无矛盾：{{"has_conflict":false}}\n只输出JSON。"""
|
||
|
||
try:
|
||
resp = _req.post("http://127.0.0.1:3000/v1/chat/completions",
|
||
headers={"Authorization": f"Bearer {os.environ.get('NEWAPI_TOKEN','')}", "Content-Type": "application/json"},
|
||
json={"model": FAST_MODEL, "messages": [{"role": "user", "content": prompt}],
|
||
"max_tokens": 400, "temperature": 0.1}, timeout=25)
|
||
text = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "{}")
|
||
except: return []
|
||
|
||
try: result = json.loads(text)
|
||
except:
|
||
m = re.search(r'\{"has_conflict"[^}]+\}', text, re.DOTALL)
|
||
result = json.loads(m.group()) if m else {"has_conflict": False}
|
||
|
||
conflicts = []
|
||
if result.get("has_conflict"):
|
||
conflicts.append({"id": f"conflict_{datetime.now().strftime('%Y%m%d%H%M%S')}",
|
||
"entry": result.get("a_entry",""), "conflict_with": result.get("b_entry",""),
|
||
"description": result.get("conflict_description",""),
|
||
"detected_at": datetime.now().isoformat(), "status": "pending"})
|
||
_write_conflicts_to_queue(conflicts)
|
||
return conflicts
|
||
|
||
def save_llm_context(ctx, state):
|
||
"""每 tick 写 llm_context.json，供 Hermes 插件注入"""
|
||
soulful_d = HERMES + "/soulful"
|
||
|
||
# 读牵挂
|
||
cares = []
|
||
cq_path = soulful_d + "/cares-queue.json"
|
||
if os.path.exists(cq_path):
|
||
try:
|
||
with open(cq_path) as f:
|
||
data = json.load(f)
|
||
for c in data.get("cares", []):
|
||
cares.append({"id": c.get("id", ""), "content": c.get("content", "")[:60], "due": c.get("follow_up_date", "")})
|
||
except (json.JSONDecodeError, OSError, IOError, KeyError, TypeError):
|
||
pass
|
||
|
||
# 读心迹（最近3条）
|
||
recent_moments = []
|
||
heart_path = soulful_d + "/heart-traces.jsonl"
|
||
if os.path.exists(heart_path):
|
||
try:
|
||
lines = open(heart_path, encoding="utf-8").readlines()
|
||
for line in lines[-3:]:
|
||
if line.strip():
|
||
e = json.loads(line)
|
||
recent_moments.append({"content": e["content"][:80], "importance": e.get("importance", 0), "timestamp": e.get("timestamp", "")})
|
||
except (json.JSONDecodeError, OSError, IOError, KeyError, TypeError):
|
||
pass
|
||
|
||
# 读画像
|
||
profile = {}
|
||
profile_path = soulful_d + "/user-profile.json"
|
||
if os.path.exists(profile_path):
|
||
try:
|
||
profile = json.load(open(profile_path, encoding="utf-8"))
|
||
except (json.JSONDecodeError, OSError, IOError, KeyError, TypeError):
|
||
pass
|
||
|
||
# os_sense
|
||
os_keywords = []
|
||
os_path = HERMES + "/.os_sense_cache.json"
|
||
if os.path.exists(os_path):
|
||
try:
|
||
kw = json.load(open(os_path, encoding="utf-8")).get("keywords", [])
|
||
os_keywords = kw if isinstance(kw, list) else []
|
||
except (json.JSONDecodeError, OSError, IOError, KeyError, TypeError):
|
||
pass
|
||
|
||
# daemon 状态：有 process 数据用 process 数据，否则用 psutil 兜底
|
||
pdata = state.get("processes", {})
|
||
if pdata.get("daemon"):
|
||
daemon_status = "running"
|
||
elif psutil.pid_exists(os.getpid()):
|
||
daemon_status = "running"
|
||
else:
|
||
daemon_status = "stopped"
|
||
|
||
behavior_rules = profile.get("behavior_rules", {})
|
||
snippet_prefixes = {
|
||
"answer_format": "【回答格式】",
|
||
"code_quality": "【代码质量】",
|
||
"cron_creation": "【cron创建】",
|
||
"accounting_context":"【会计场景】",
|
||
"decision_style": "【决策风格】",
|
||
"memory_handling": "【记忆规范】",
|
||
"error_reporting": "【错误报告】",
|
||
"file_operations": "【文件操作】",
|
||
}
|
||
snippets = []
|
||
for key, prefix in snippet_prefixes.items():
|
||
if behavior_rules.get(key):
|
||
snippets.append(f"{prefix}{behavior_rules[key]}")
|
||
if not snippets:
|
||
# 向后兼容：没有 behavior_rules 时用旧格式
|
||
snippets = [f"牧尘沟通风格：{profile.get('communication_style', '简洁直接')}"]
|
||
|
||
llm_ctx = {
|
||
"updated_at": datetime.now(timezone.utc).isoformat(),
|
||
"uptime_minutes": ctx.get("uptime_seconds", 0) // 60,
|
||
"cares": cares,
|
||
"recent_moments": recent_moments,
|
||
"system_prompt_snippets": snippets,
|
||
"os_keywords": os_keywords,
|
||
"daemon_status": daemon_status,
|
||
"tddb": ctx.get("tddb", {}),
|
||
}
|
||
|
||
with open(LLM_CONTEXT_FILE, "w") as f:
|
||
json.dump(llm_ctx, f, indent=2, ensure_ascii=False)
|
||
|
||
def journal_entry(event_type, summary, details=""):
|
||
os.makedirs(D, exist_ok=True)
|
||
with open(JOURNAL_FILE, "a") as f:
|
||
f.write(json.dumps({"timestamp": datetime.now(timezone.utc).isoformat(),
|
||
"type": event_type, "summary": summary, "details": details},
|
||
ensure_ascii=False) + "\n")
|
||
trim_journal()
|
||
|
||
# ====== Phase 2: 情感识别 → 心迹写入 ======
|
||
text_to_scan = f"{summary} {details}"
|
||
cat, word = _detect_emotion(text_to_scan)
|
||
if cat:
|
||
ht = get_hearttraces()
|
||
if ht:
|
||
content = f"牧尘今天{_description_for_emotion(cat, word, summary)}"
|
||
importance = _emotion_importance(cat)
|
||
try:
|
||
ht.record_signal(content=content, tags=["情绪", "自动"], importance=importance)
|
||
log(f" 💚 心迹写入: {cat} - {word} (importance={importance})")
|
||
except Exception as e:
|
||
log(f" ⚠️ 心迹写入失败: {e}")
|
||
|
||
# ====== Phase 3: 技术重要时刻也写心迹 ======
|
||
# 不依赖情绪检测，重要技术事件直接记
|
||
important_events = {"solve_auto", "solve_start", "process_down", "process_restored",
|
||
"skill_action", "alert", "action_result"}
|
||
if event_type in important_events and ("成功" in summary or "✅" in summary or "完成" in summary):
|
||
ht = get_hearttraces()
|
||
if ht:
|
||
try:
|
||
ht.record_moment(content=summary[:80], tags=["工作", "自动"], importance=3)
|
||
log(f" 💚 心迹写入(技术): {event_type} - {summary[:40]}")
|
||
except Exception:
|
||
pass
|
||
|
||
def trim_journal():
|
||
if not os.path.exists(JOURNAL_FILE): return
|
||
with open(JOURNAL_FILE) as f:
|
||
lines = f.readlines()
|
||
if len(lines) > JOURNAL_MAX:
|
||
with open(JOURNAL_FILE, "w") as f:
|
||
f.writelines(lines[-JOURNAL_MAX:])
|
||
|
||
def read_journal(n=15):
|
||
if not os.path.exists(JOURNAL_FILE): return []
|
||
with open(JOURNAL_FILE) as f:
|
||
return [json.loads(l) for l in f.readlines()[-n:] if l.strip()]
|
||
|
||
|
||
# ====== 系统状态 ======
|
||
|
||
def collect_state():
|
||
state = {}
|
||
_, out, _ = shell("df / | awk 'NR==2 {print $5}' | sed 's/%//'")
|
||
state["disk_pct"] = int(out) if out else 0
|
||
_, out, _ = shell("free -m | awk '/^Mem:/ {printf \"%d|%d\", $3, $2}'")
|
||
if out:
|
||
used, total = out.split("|")
|
||
state["mem_pct"] = round(int(used) * 100 / int(total))
|
||
else:
|
||
state["mem_pct"] = 0
|
||
_, out, _ = shell("cat /proc/loadavg | awk '{print $1}'")
|
||
state["load_1min"] = float(out) if out else 0
|
||
procs = {}
|
||
for name, pat in [("zhiyid", "zhiyid-new"), ("bge", "bge_embed"), ("newapi", "new-api"), ("hermes", "hermes")]:
|
||
rc, _, _ = shell(f"pgrep -f '{pat}' > /dev/null 2>&1")
|
||
procs[name] = rc == 0
|
||
state["processes"] = procs
|
||
_, out, _ = shell("nvidia-smi --query-gpu=temperature.gpu --format=csv,noheader,nounits 2>/dev/null")
|
||
state["gpu_temp"] = int(out) if out else None
|
||
hp = HERMES + "/model-health.json"
|
||
if os.path.exists(hp) and time.time() - os.path.getmtime(hp) < 43200:
|
||
with open(hp) as f:
|
||
try:
|
||
h = json.load(f)
|
||
state["models_stable"] = h.get("stable", "?")
|
||
state["models_fastest"] = h.get("fastest_stable", [])
|
||
except: pass
|
||
return state
|
||
|
||
def state_changed(old, new):
|
||
changes = []
|
||
if abs(new.get("disk_pct", 0) - old.get("disk_pct", 0)) > 5:
|
||
changes.append(f"磁盘: {old.get('disk_pct')}% → {new.get('disk_pct')}%")
|
||
oldp, newp = old.get("processes", {}), new.get("processes", {})
|
||
for p in oldp:
|
||
if oldp.get(p) and not newp.get(p):
|
||
changes.append(f"进程挂了: {p}")
|
||
if not oldp.get(p) and newp.get(p):
|
||
changes.append(f"进程恢复: {p}")
|
||
if abs(new.get("mem_pct", 0) - old.get("mem_pct", 0)) > 10:
|
||
changes.append(f"内存: {old.get('mem_pct')}% → {new.get('mem_pct')}%")
|
||
return changes
|
||
|
||
|
||
# ====== 深度思考（增强版）=====
|
||
|
||
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。
|
||
|
||
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
|
||
|
||
行为准则：
|
||
1. 异常才行动，正常就静默
|
||
2. 优先用已知方案解决问题，不重复报警
|
||
3. 解决不了或新问题，主动飞书说明
|
||
4. 每次成功解决问题，记住方案下次自动用
|
||
|
||
你有以下能力：
|
||
- shell: 执行任何 shell 命令
|
||
- feishu: 发送飞书消息到牧尘
|
||
- scripts: 运行 ~/.hermes/scripts/ 下的系统脚本（skill-manager/optimizer/learner/dual-backup）
|
||
- systemd: 管理系统服务
|
||
- git: 配置版本管理
|
||
|
||
你必须分三步思考，严格按 JSON 格式输出（不要其他内容）：
|
||
|
||
reflection:
|
||
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'"
|
||
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。'"
|
||
next_goal: "一句话说明下一步要做什么。"
|
||
|
||
action:
|
||
decision: "[IGNORE] / [ALERT] / [SOLVE:ID] / [LEARN] / [SKILL] / [SYNC] / [ACT] ..."
|
||
|
||
[LEARN] 格式用 !cmd 表示 shell 命令，&& 连接多个命令。
|
||
[SKILL] 格式同样用 !cmd 执行操作。
|
||
示例:
|
||
[LEARN] 磁盘>85%，清理缓存!apt-get autoremove -y && !pip cache purge
|
||
[SKILL] 归档低分skill!python3 ~/.hermes/scripts/skill-manager.py audit
|
||
[SYNC] 触发备份到服务器!bash ~/.hermes/scripts/dual-backup.sh push"""
|
||
|
||
def deep_think(ctx, state, changes, journal, solutions_lib):
|
||
# Inject previous reflection context if available
|
||
prev_ref = ctx.get("last_reflection", None)
|
||
ref_context = ""
|
||
if prev_ref:
|
||
ref_context = f"""
|
||
上次 reflection:
|
||
- 评估: {prev_ref.get('evaluation_previous_goal', 'N/A')}
|
||
- 记忆: {prev_ref.get('memory', 'N/A')}
|
||
- 目标: {prev_ref.get('next_goal', 'N/A')}
|
||
"""
|
||
|
||
context = f"""系统状态:
|
||
- 磁盘: {state.get('disk_pct')}% | 内存: {state.get('mem_pct')}%
|
||
- CPU: {state.get('load_1min')} | GPU: {state.get('gpu_temp')}°C
|
||
- 进程: {', '.join(f'{k}={chr(10003) if v else chr(10007)}' for k,v in state.get('processes',{}).items())}
|
||
|
||
最近变化: {changes or '无'}
|
||
|
||
已知方案库 ({len(solutions_lib['solutions'])} 个):
|
||
"""
|
||
for sol in solutions_lib["solutions"]:
|
||
context += f" [{sol['id']}] {sol['pattern']} (成功{sol['success_count']}次/失败{sol['fail_count']}次)\n"
|
||
|
||
context += "\n最近事件:\n"
|
||
for e in journal[-8:]:
|
||
context += f" [{e['type']}] {e['summary']}\n"
|
||
|
||
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"
|
||
|
||
# 心迹注入
|
||
moments_str = soulful_get_recent_moments(n=3)
|
||
if moments_str:
|
||
context += "\n" + moments_str
|
||
|
||
context += ref_context
|
||
|
||
result, tokens = call_llm(FAST_MODEL, DEEP_SYSTEM, context, max_tokens=500)
|
||
if not result:
|
||
return {"evaluation_previous_goal": "LLM调用失败", "memory": "上次调用失败", "next_goal": "重试"}, ""
|
||
|
||
log(f" 深度思考 ({tokens}t): {result[:200]}")
|
||
|
||
# Parse JSON output
|
||
reflection_dict = {"evaluation_previous_goal": "", "memory": "", "next_goal": ""}
|
||
action_string = ""
|
||
|
||
try:
|
||
# Try to extract JSON from result
|
||
import re
|
||
json_match = re.search(r'\{[^{}]*\}', result, re.DOTALL)
|
||
if json_match:
|
||
parsed = json.loads(json_match.group())
|
||
reflection_dict = parsed.get("reflection", reflection_dict)
|
||
action_string = parsed.get("action", {}).get("decision", "")
|
||
else:
|
||
# Fallback: try full JSON
|
||
parsed = json.loads(result)
|
||
reflection_dict = parsed.get("reflection", reflection_dict)
|
||
action_string = parsed.get("action", {}).get("decision", "")
|
||
except:
|
||
# Fallback: try to parse old format (line-based)
|
||
for line in result.split('\n'):
|
||
line = line.strip()
|
||
if line.startswith("[IGNORE]") or line.startswith("[ALERT]") or line.startswith("[SOLVE:") or \
|
||
line.startswith("[LEARN]") or line.startswith("[SKILL]") or line.startswith("[SYNC]") or line.startswith("[ACT]"):
|
||
action_string = line
|
||
break
|
||
if not action_string:
|
||
action_string = result.strip().split('\n')[-1] if result.strip() else "[IGNORE] 解析失败"
|
||
|
||
return reflection_dict, action_string
|
||
|
||
|
||
# ====== 执行 action_string ======
|
||
|
||
def execute_action(action_string, ctx, state, changes, solutions_lib):
|
||
"""执行 deep_think 返回的 action_string，在 main_loop 中调用"""
|
||
if not action_string or action_string.startswith("[IGNORE]"):
|
||
return
|
||
|
||
elif action_string.startswith("[SOLVE:"):
|
||
sol_id = action_string.split("[SOLVE:")[1].split("]")[0].strip()
|
||
for sol in solutions_lib["solutions"]:
|
||
if sol["id"] == sol_id:
|
||
ok, res = execute_solution(sol, state)
|
||
if ok:
|
||
ctx["solved_count"] += 1
|
||
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 = {}
|
||
last_user_interaction = time.time() # 用户交互时间戳，用于心迹捕获
|
||
|
||
# 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()
|
||
|
||
# ── 推理轨迹：deep_tick 开始 ─────────────────────────────────
|
||
log_reasoning_step("thinking", f"deep_tick #{ctx['deep_tick_count']} 触发 | disk={state.get('disk_pct')}%", {"changes": changes[:3]})
|
||
|
||
# 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']})")
|
||
log_reasoning_step("judge", f"方案匹配: {matched['id']}", matched)
|
||
ok, res = execute_solution(matched, state)
|
||
if ok:
|
||
ctx["solved_count"] += 1
|
||
log_reasoning_step("action", f"执行成功: {matched['id']}", res)
|
||
journal_entry("solve_auto",
|
||
summary=f"方案 {matched['id']} 执行成功",
|
||
details=f"现象: {matched['pattern']} → 结论: {matched.get('solution',{}).get('description','正常')[:80]}")
|
||
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
|
||
log_reasoning_step("thinking", "调用 deep_think LLM", {"tick": ctx["deep_tick_count"]})
|
||
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
|
||
log_reasoning_step("judge", f"deep_think 结论: {reflection_dict.get('next_goal','?')}", reflection_dict)
|
||
|
||
# 3. 在 main_loop 中执行 action
|
||
execute_action(action_string, ctx, state, changes, solutions_lib)
|
||
log_reasoning_step("action", f"action 执行完毕", {"action": action_string[:80] if action_string else None})
|
||
|
||
# 4. TencentDB capture — 积累人格记忆（L0→L1）
|
||
if reflection_dict:
|
||
tddb_capture(reflection_dict, state, ctx)
|
||
log_reasoning_step("recall", "TencentDB L1 提取完成", reflection_dict)
|
||
|
||
# 5. Phase 2: 画像LLM合成
|
||
# 6. Phase 3: 冲突检测
|
||
journal_path = HERMES + "/daemon/journal.jsonl"
|
||
profile_path = HERMES + "/soulful/user-profile.json"
|
||
try:
|
||
update_profile_from_journal(journal_path, profile_path)
|
||
log_reasoning_step("recall", "Phase2: 画像更新完成", {})
|
||
except Exception:
|
||
pass
|
||
try:
|
||
zhiyi_token = os.environ.get("ZHIYI_API_KEY", "zhiyi-dev-key-2026")
|
||
conflicts = detect_memory_conflicts(journal_path, zhiyi_token)
|
||
if conflicts:
|
||
log(f" ⚠️ 发现 {len(conflicts)} 个记忆矛盾")
|
||
log_reasoning_step("recall", f"Phase3: 发现{len(conflicts)}个矛盾", {"conflicts": len(conflicts)})
|
||
except Exception:
|
||
pass
|
||
|
||
log_reasoning_step("action", f"deep_tick #{ctx['deep_tick_count']} 全部完成", {})
|
||
|
||
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)
|
||
# ====== Soulful → llm_context 同步（每 tick）======
|
||
_sync_soulful_to_llm_context(ctx)
|
||
|
||
save_llm_context(ctx, state)
|
||
|
||
# ====== Soulful 轻量集成（每小时一次）======
|
||
# 每 120 个 light_tick（约 1 小时）检查一次牵挂 + 更新画像
|
||
if ctx["tick_count"] % 120 == 0:
|
||
soulful_check_cares()
|
||
if "last_profile_update" not in ctx:
|
||
ctx["last_profile_update"] = 0
|
||
now_ts = time.time()
|
||
if now_ts - ctx.get("last_profile_update", 0) >= PROFILE_UPDATE_INTERVAL:
|
||
ctx["last_profile_update"] = now_ts
|
||
soulful_update_profile()
|
||
|
||
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()
|