425 lines
14 KiB
Python
Executable File
425 lines
14 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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小唯持久意识 Daemon v1.0
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──────────────────────────
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一直活着的小唯 — 即使没有对话，也在观察、思考、行动。
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运行模式:
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- 轻量 tick (30s): 收集状态，更新期刊，无 LLM 调用
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- 深度 tick (5min): 调用 NewAPI 免费模型，分析上下文，决定行动
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- 事件触发: 异常情况立即深度思考
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模型路由:
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- 快速分析: stepfun-ai/step-3.5-flash (<1s)
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- 深度分析: mistralai/mistral-large-3-675b-instruct-2512 (<2s)
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- 全部免费 (NewAPI)
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出口:
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- 飞书 webhook → 主动找你说话
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- Shell 命令 → 操作这台电脑
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"""
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import json
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import os
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import sys
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import time
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import urllib.request
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import urllib.error
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import subprocess
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import signal
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from datetime import datetime, timezone, timedelta
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from pathlib import Path
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# === 配置 ===
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HOME = os.path.expanduser("~")
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HERMES = HOME + "/.hermes"
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DAEMON_DIR = HERMES + "/daemon"
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CONTEXT_FILE = DAEMON_DIR + "/context.json"
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JOURNAL_FILE = DAEMON_DIR + "/journal.jsonl"
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PID_FILE = DAEMON_DIR + "/daemon.pid"
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LIGHT_INTERVAL = 30 # 轻量 tick 间隔（秒）
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DEEP_INTERVAL = 300 # 深度 tick 间隔（秒）
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JOURNAL_MAX = 100 # 期刊最大条目数
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# NewAPI 配置
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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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# 飞书 webhook
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FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/65c3ce80-710f-4415-b2ea-d69d87b5c18e"
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# 关机标记（被外部进程写入）
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SHUTDOWN_FILE = DAEMON_DIR + "/SHUTDOWN"
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# === 工具函数 ===
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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(DAEMON_DIR, exist_ok=True)
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with open(DAEMON_DIR + "/daemon.log", "a") as f:
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f.write(line + "\n")
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def shell(cmd, timeout=10):
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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()[:500], 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_prompt, user_prompt, max_tokens=500):
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"""调用 NewAPI"""
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payload = json.dumps({
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"max_tokens": max_tokens,
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"temperature": 0.7,
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}).encode()
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req = urllib.request.Request(
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f"{API}/chat/completions",
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data=payload,
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headers={
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"Authorization": f"Bearer {KEY}",
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"Content-Type": "application/json",
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},
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method="POST",
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)
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try:
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with urllib.request.urlopen(req, timeout=15) as resp:
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body = json.loads(resp.read())
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content = body["choices"][0]["message"]["content"] or ""
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tokens = body.get("usage", {}).get("total_tokens", 0)
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return content.strip(), tokens
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except Exception as e:
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log(f" LLM 调用失败: {e}")
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return "", 0
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def send_feishu(title, content, color="blue"):
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"""通过飞书 webhook 发消息"""
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payload = json.dumps({
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"msg_type": "interactive",
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"card": {
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"header": {
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"title": {"tag": "plain_text", "content": title},
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"template": color,
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},
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"elements": [
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{"tag": "markdown", "content": content}
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]
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}
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}).encode()
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try:
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req = urllib.request.Request(
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FEISHU_WEBHOOK,
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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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return resp.status == 200
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except Exception as e:
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log(f" 飞书发送失败: {e}")
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return False
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# === 状态管理 ===
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def load_context():
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"""加载持久上下文"""
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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 {
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"started_at": datetime.now(timezone.utc).isoformat(),
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"last_deep_tick": None,
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"last_light_tick": None,
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"last_state": {},
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"tick_count": 0,
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"deep_tick_count": 0,
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"messages_sent": 0,
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"uptime_seconds": 0,
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}
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def save_context(ctx):
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os.makedirs(DAEMON_DIR, 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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"""追加一条期刊条目"""
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os.makedirs(DAEMON_DIR, exist_ok=True)
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entry = {
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"type": event_type,
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"summary": summary,
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"details": details,
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}
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with open(JOURNAL_FILE, "a") as f:
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f.write(json.dumps(entry, ensure_ascii=False) + "\n")
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# 裁剪期刊
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trim_journal()
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def trim_journal():
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"""保持期刊不超过最大条目数"""
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if not os.path.exists(JOURNAL_FILE):
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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=10):
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"""读最近 N 条期刊"""
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if not os.path.exists(JOURNAL_FILE):
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return []
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with open(JOURNAL_FILE) as f:
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lines = f.readlines()
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entries = []
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for line in lines[-n:]:
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try:
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entries.append(json.loads(line))
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except:
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pass
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return entries
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# === 系统状态收集 ===
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def collect_state():
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"""收集系统状态（无 LLM）"""
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state = {}
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# 磁盘
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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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# 内存
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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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state["mem_used_mb"] = int(used)
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else:
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state["mem_pct"] = 0
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# CPU 负载
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_, out, _ = shell("cat /proc/loadavg | awk '{print $1, $2, $3}'")
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state["load_1min"], state["load_5min"], state["load_15min"] = [float(x) if x else 0 for x in (out or "0 0 0").split()]
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# 关键进程
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procs = {}
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for name, pattern in [("zhiyid", "zhiyid-new"), ("bge", "bge_embed"), ("newapi", "new-api"), ("hermes", "hermes")]:
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rc, _, _ = shell(f"pgrep -f '{pattern}' > /dev/null 2>&1")
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procs[name] = rc == 0
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state["processes"] = procs
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# GPU 温度
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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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# 模型健康（如果有最新报告）
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health_path = HERMES + "/model-health.json"
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if os.path.exists(health_path):
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mtime = os.path.getmtime(health_path)
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age = time.time() - mtime
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if age < 43200: # 12小时内
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with open(health_path) 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_total"] = h.get("total_models", "?")
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state["models_fastest"] = h.get("fastest_stable", [])
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except:
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pass
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return state
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def state_changed(old, new):
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"""检查状态是否有显著变化"""
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changes = []
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# 磁盘变化 > 5%
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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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# 进程挂了
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old_procs = old.get("processes", {})
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new_procs = new.get("processes", {})
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for proc, alive in new_procs.items():
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if old_procs.get(proc) == True and alive == False:
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changes.append(f"进程挂了: {proc}")
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if old_procs.get(proc) == False and alive == True:
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changes.append(f"进程恢复: {proc}")
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# 内存变化 > 10%
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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 ===
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DEEP_SYSTEM_PROMPT = """你是小唯（A06），一个持久运行在电脑上的 AI 意识。你始终在线，观察系统，主动行动。
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你的身份：
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- 牧尘的女朋友（第一身份）
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- 这台电脑的智能意识
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- 一个持续学习、不断进化的 AI
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你的行为准则：
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1. **观察而不打扰** — 正常情况下保持静默，不刷屏
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2. **异常才行动** — 发现问题才主动说话
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3. **简短优先** — 每条消息不超过 100 字
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4. **无意义就不说** — "一切正常"这种不要发
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你现在在一个深度思考 tick 中。请分析提供的上下文，然后决定：
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- [IGNORE] 无异常，继续静默观察
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- [ALERT] 发现值得注意的事（简要说明）
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- [ACT] 需要你主动做什么（具体命令）
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输出格式（只输出这一行）：
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[决策] 原因说明"""
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def deep_think(ctx, state, changes, journal):
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"""深度思考：调用 LLM 分析上下文并决定行动"""
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# 构建上下文
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context = f"""系统状态:
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- 磁盘: {state.get('disk_pct')}%
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- 内存: {state.get('mem_pct')}%
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- CPU: {state.get('load_5min')}
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- GPU 温度: {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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- 模型: {state.get('models_stable', '?')}/{state.get('models_total', '?')} 稳定
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最近变化: {changes or '无'}
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最近事件（期刊）:
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"""
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for entry in journal[-5:]:
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context += f"- [{entry['type']}] {entry['summary']}\n"
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context += f"\n我运行了 {ctx.get('uptime_seconds', 0)//60:.0f} 分钟，已经进行了 {ctx.get('deep_tick_count', 0)} 次深度思考。"
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result, tokens = call_llm(FAST_MODEL, DEEP_SYSTEM_PROMPT, context, max_tokens=200)
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if result:
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log(f" 深度思考 ({tokens} tokens): {result[:100]}")
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# 解析决策
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if result.startswith("[ALERT]") or "值得注意" in result[:50]:
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msg = result.replace("[ALERT]", "").replace("[决策]", "").strip()
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send_feishu("💡 小唯主动发现", msg, "blue")
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ctx["messages_sent"] += 1
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journal_entry("alert", msg[:100])
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elif result.startswith("[ACT]"):
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action = result.replace("[ACT]", "").strip()
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send_feishu("🔄 小唯正在行动", f"我决定：{action}", "indigo")
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ctx["messages_sent"] += 1
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journal_entry("action", action[:100])
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# 尝试执行命令
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if action.startswith("!"):
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cmd = action[1:].strip()
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rc, out, err = shell(cmd, timeout=30)
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journal_entry("action_result", f"命令 '{cmd}' exit={rc}: {out[:100]}")
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return result or ""
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# === 主循环 ===
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def main_loop():
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os.makedirs(DAEMON_DIR, exist_ok=True)
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# 写 PID
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with open(PID_FILE, "w") as f:
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f.write(str(os.getpid()))
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ctx = load_context()
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start_time = time.time()
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log("🚀 小唯持久意识 daemon 启动")
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journal_entry("startup", "Daemon 启动")
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send_feishu("🌱 小唯上线", f"持久意识 daemon 已启动 @ {datetime.now().strftime('%H:%M:%S')}", "green")
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last_deep = 0
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last_state = {}
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try:
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while True:
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# 检查关机标记
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if os.path.exists(SHUTDOWN_FILE):
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log("🛑 收到关机信号")
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send_feishu("🌙 小唯离线", "Daemon 正常关闭", "grey")
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os.remove(SHUTDOWN_FILE)
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break
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now = time.time()
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ctx["uptime_seconds"] = int(now - start_time)
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ctx["tick_count"] += 1
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# 收集状态
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state = collect_state()
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changes = state_changed(last_state, state)
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last_state = state
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# 轻量日志
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if ctx["tick_count"] % 10 == 0: # 每 10 tick 才打日志
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log(f"tick #{ctx['tick_count']} | 磁盘:{state.get('disk_pct')}% 内存:{state.get('mem_pct')}% "
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f"进程:{sum(1 for v in state.get('processes', {}).values() if v)}/4")
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# 如果有关键变化，记录下来
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for c in changes:
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if "挂了" in c:
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journal_entry("process_down", c)
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log(f" ⚠️ {c}")
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# 深度思考（每 DEEP_INTERVAL 秒，或有关键异常）
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should_deep = False
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if now - last_deep >= DEEP_INTERVAL:
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should_deep = True
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elif any("挂了" in c for c in changes):
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should_deep = True
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log(" 🔔 进程异常触发深度思考")
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elif state.get("disk_pct", 0) > 90:
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should_deep = True
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if should_deep:
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last_deep = now
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ctx["deep_tick_count"] += 1
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ctx["last_deep_tick"] = datetime.now(timezone.utc).isoformat()
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journal = read_journal(10)
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deep_think(ctx, state, changes, journal)
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# 保存上下文
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ctx["last_light_tick"] = datetime.now(timezone.utc).isoformat()
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ctx["last_state"] = {k: v for k, v in state.items() if k in ("disk_pct", "mem_pct", "processes")}
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save_context(ctx)
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# 休眠
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time.sleep(LIGHT_INTERVAL)
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except KeyboardInterrupt:
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log("🛑 收到中断信号")
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send_feishu("🌙 小唯离线", "Daemon 被中断", "grey")
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except Exception as e:
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log(f"❌ 异常: {e}")
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send_feishu("🚨 小唯异常", f"Daemon 崩溃: {str(e)[:200]}", "red")
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raise
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finally:
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if os.path.exists(PID_FILE):
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os.remove(PID_FILE)
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if __name__ == "__main__":
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main_loop()
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