2821 lines
115 KiB
Python
Executable File
2821 lines
115 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, requests, sqlite3, math
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from datetime import datetime, timezone, timedelta
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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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SESSION_STATE_FILE = D + "/session_state.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 = 120 # 深度思考间隔（秒），原5分钟改为2分钟，加速记忆积累
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JOURNAL_MAX = 200
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SOULFUL_JOURNALS_DIR = HERMES + "/soulful/journals"
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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 = "openai/gpt-oss-120b"
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DEEP_MODEL = "openai/gpt-oss-120b"
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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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COMPACTION_MODEL = "openai/gpt-oss-120b"
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COMPACTION_RATIO_THRESHOLD = 0.80 # Grok IntraCompactionTrigger 思路：token ratio > 80% 触发摘要
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COMPACTION_MIN_TURNS = 10 # 至少 10 条 pattern 才压缩
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_compaction_last_run = 0 # 上次压缩时间戳
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_compaction_count = 0 # 累计压缩次数
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# ====== Grok Build 风格：Compaction 自动触发（v2.1）=======
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# 参考: xai-grok-compaction/intra_compaction/trigger.rs 的 IntraCompactionTrigger
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# 核心: last_prompt_tokens / context_window > threshold → LLM 摘要 → 精简 patterns
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def _estimate_context_tokens(state):
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"""估算当前 llm_context.json 的 token 数（粗略估算）。"""
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try:
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# patterns 条数 + L1 observations 是主要 token 消耗
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p_count = len(state.get("patterns", []))
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o_count = len(state.get("observations", []))
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# 粗估: 每条 50 tokens + 固定开销 500
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return p_count * 50 + o_count * 30 + 500
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except Exception:
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return 0
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def _should_compact(ctx, state):
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"""Grok 风格：检查是否需要触发 compaction。
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触发条件（满足任一）：
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1. patterns 数量 > COMPACTION_MIN_TURNS * 3（正常衰减）
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2. journal 条目 > 100（历史过长）
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3. 明确检测到重复模式（方案库有匹配）
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"""
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patterns_count = len(state.get("patterns", []))
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journal_lines = 0
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jq = HERMES + "/daemon/journal.jsonl"
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if os.path.exists(jq):
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journal_lines = sum(1 for _ in open(jq)) if False else 0
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try:
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journal_lines = len(open(jq).readlines())
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except:
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journal_lines = 0
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# 条件1: patterns 过多
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if patterns_count > COMPACTION_MIN_TURNS * 4:
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return True, f"patterns过多({patterns_count})"
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# 条件2: journal 过长
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if journal_lines > 150:
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return True, f"journal过长({journal_lines})"
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# 条件3: 本次 deep_think 有重复问题（模式识别）
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if ctx.get("last_reflection", {}).get("memory", "").startswith("重复"):
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return True, "重复问题模式"
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return False, ""
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def _trigger_compaction(ctx, state):
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"""执行 Grok 风格的 compaction：调用 LLM 摘要 patterns，留下核心。"""
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global _compaction_last_run, _compaction_count
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patterns = state.get("patterns", [])
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if len(patterns) < COMPACTION_MIN_TURNS:
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return False
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_compaction_last_run = time.time()
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_compaction_count += 1
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# 构建压缩 Prompt（Grok 风格的 summary prompt）
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top_patterns = patterns[:20] # 最多保留 20 条
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prompt = f"""你是一个记忆压缩专家。将以下 patterns 列表压缩成 8-10 条核心模式。
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规则：
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- 合并相似模式（保留出现次数最多的）
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- 保留高价值模式（occurrence > 3）
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- 丢弃低价值模式（occurrence <= 1 的）
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- 输出格式：JSON数组，每条格式: {{"name": "模式名", "occurrence": N, "topic": "主题"}}
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patterns（{len(patterns)} 条）:
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{json.dumps(top_patterns, ensure_ascii=False, indent=2)}
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输出JSON："""
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system = "你是一个精确的JSON生成器。只输出JSON，不输出其他内容。"
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result, tokens = call_llm(COMPACTION_MODEL, system, prompt, max_tokens=800)
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if not result:
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log(f" ⚠️ compaction LLM 调用失败")
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return False
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# 解析结果
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try:
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import re
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json_match = re.search(r'\[[\s\S]*\]', result)
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if json_match:
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compacted = json.loads(json_match.group())
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# 写回 state（实际由调用方更新 graph.db）
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log(f" 🗜️ Compaction #{_compaction_count} 完成: {len(patterns)} → {len(compacted)} patterns ({tokens}t)")
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# 写入 compacted patterns 到 graph.db（保留高价值的）
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_apply_compacted_patterns(compacted, patterns)
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journal_entry("compaction", f"压缩 {_compaction_count}: {len(patterns)} → {len(compacted)}")
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return True
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except Exception as e:
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log(f" ⚠️ compaction 解析失败: {e}")
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return False
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def _apply_compacted_patterns(compacted, original):
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"""将压缩后的 patterns 写回 graph.db（保留名，合并occurrence）。"""
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try:
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conn = sqlite3.connect(HERMES + "/graph.db")
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cur = conn.cursor()
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for item in compacted:
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name = item.get("name", "")
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occ = item.get("occurrence", 1)
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topic = item.get("topic", "")
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if name:
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cur.execute("""
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UPDATE graph_nodes SET properties = json_patch(properties, ?)
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WHERE name=? AND type='pattern'
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""", (json.dumps({"occurrence_count": occ, "topic": topic, "compacted": True}, ensure_ascii=False), name))
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conn.commit()
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conn.close()
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except Exception as e:
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log(f" ⚠️ _apply_compacted_patterns failed: {e}")
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# ====== Grok Build 风格：Hook 系统（v2.2）=======
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# 参考: xai-grok-hooks/src/lib.rs — 文件发现 + JSON 定义 + 事件分发
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# 支持事件: disk_threshold, process_down, memory_high, session_start, session_end
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HOOKS_DIR = HERMES + "/hooks"
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HOOK_HISTORY_FILE = D + "/hook_history.jsonl"
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def _append_hook_history(event, hook_name, success, details=""):
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"""追加 hook 执行历史到 ~/.hermes/daemon/hook_history.jsonl"""
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try:
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os.makedirs(D, exist_ok=True)
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entry = {
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"event": event,
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"hook_name": hook_name,
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"success": success,
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"details": details,
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}
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with open(HOOK_HISTORY_FILE, "a") as f:
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f.write(json.dumps(entry, ensure_ascii=False) + "\n")
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except Exception:
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pass
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def _load_hooks():
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"""加载 ~/.hermes/hooks/ 下所有 JSON hook 定义。
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Schema 验证：必须有 name/event/action 三个必要字段，
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缺少字段的 hook 跳过并记录警告。
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"""
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hooks = {"disk_threshold": [], "process_down": [], "memory_high": [], "session_start": [], "session_end": []}
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hd = HOOKS_DIR
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if not os.path.exists(hd):
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return hooks
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for f in os.listdir(hd):
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if not f.endswith(".json"):
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continue
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hook_path = os.path.join(hd, f)
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try:
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with open(hook_path) as fp:
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hook = json.load(fp)
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# Schema 验证：必须有 name/event/action
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missing = [k for k in ("name", "event", "action") if k not in hook or not hook[k]]
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if missing:
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log(f" ⚠️ hook 配置不完整 [{f}]，跳过。缺少字段: {missing}")
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_append_hook_history("load_error", f, False, f"缺少字段: {missing}")
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continue
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evt = hook.get("event", "")
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if evt in hooks:
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hooks[evt].append(hook)
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else:
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log(f" ⚠️ hook [{f}] event='{evt}' 不支持，跳过")
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except json.JSONDecodeError as e:
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log(f" ⚠️ hook JSON 解析失败 [{f}]: {e}")
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_append_hook_history("parse_error", f, False, str(e))
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except Exception as e:
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log(f" ⚠️ hook 加载失败 [{f}]: {e}")
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_append_hook_history("load_error", f, False, str(e))
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return hooks
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def _run_hooks(hooks, event, state):
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"""分发事件到对应 hook，非阻塞执行。
|
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- shell 命令执行有 try/except + timeout=10s 保护
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- 执行结果追加到 hook_history.jsonl
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- 超时或错误记录日志但不崩溃
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"""
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event_hooks = hooks.get(event, [])
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for hook in event_hooks:
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hook_name = hook.get("name", "unknown")
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try:
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action = hook.get("action", "")
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cmd = hook.get("command", "")
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threshold = hook.get("threshold_percent", 90)
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timeout = hook.get("timeout", 10) # 默认 10s 超时保护
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if action == "run" and cmd:
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# session_start/session_end 无条件执行，其他事件需阈值判断
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if event in ("session_start", "session_end"):
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do_run = True
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elif "disk" in event and state.get("disk_pct", 0) >= threshold:
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do_run = True
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elif "memory" in event and state.get("mem_pct", 0) >= threshold:
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do_run = True
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elif event == "process_down":
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do_run = True
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else:
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do_run = False
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if do_run:
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try:
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rc, out, err = shell(cmd, timeout=timeout)
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log(f" 🪝 hook[{event}] '{hook_name}': {cmd[:60]} → exit={rc}")
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_append_hook_history(event, hook_name, rc == 0,
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f"exit={rc} err={err[:100] if err else ''}")
|
||
except subprocess.TimeoutExpired:
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log(f" ⚠️ hook[{event}] '{hook_name}' 超时({timeout}s): {cmd[:60]}")
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_append_hook_history(event, hook_name, False, f"timeout({timeout}s)")
|
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except Exception as e:
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log(f" ⚠️ hook[{event}] '{hook_name}' 执行异常: {e}")
|
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_append_hook_history(event, hook_name, False, str(e))
|
||
except Exception as e:
|
||
log(f" ⚠️ hook[{event}] '{hook_name}' 处理失败: {e}")
|
||
_append_hook_history(event, hook_name, False, str(e))
|
||
|
||
# ====== Permission 白名单（v2.3 新增）=======
|
||
WHITELIST_FILE = D + "/whitelist.json"
|
||
|
||
def _load_whitelist():
|
||
"""加载白名单。返回 [{"pattern": "...", "reason": "..."}] 列表。"""
|
||
if not os.path.exists(WHITELIST_FILE):
|
||
return []
|
||
try:
|
||
return json.load(open(WHITELIST_FILE))
|
||
except:
|
||
return []
|
||
|
||
def _save_whitelist(items):
|
||
"""保存白名单到文件。"""
|
||
with open(WHITELIST_FILE, "w") as f:
|
||
json.dump(items, f, indent=2, ensure_ascii=False)
|
||
|
||
def _is_whitelisted(cmd):
|
||
"""检查 cmd 是否命中白名单。命中返回 True，否则 False。"""
|
||
if not cmd:
|
||
return False
|
||
cmd_lower = cmd.lower()
|
||
for entry in _load_whitelist():
|
||
pat = entry.get("pattern", "")
|
||
if pat and pat.lower() in cmd_lower:
|
||
return True
|
||
return False
|
||
|
||
# ====== Grok Build 风格：危险操作 Permission 确认（v2.3）=======
|
||
# 参考: xai-grok-workspace/src/permission/policy.rs — Bash 命令分段检查
|
||
# 高危命令匹配时暂停 + 飞书确认
|
||
# 白名单命中则直接放行（不弹窗）
|
||
|
||
DANGEROUS_PATTERNS = [
|
||
("rm -rf /", "危险: 根目录递归删除"),
|
||
("rm -rf /home", "危险: 删除 home 目录"),
|
||
("dd if=", "危险: 磁盘直接写入"),
|
||
("> /dev/sd", "危险: 设备文件覆写"),
|
||
("pkill -9", "危险: 强制终止进程"),
|
||
("kill -9 ", "危险: 强制终止进程"),
|
||
("git push --force", "危险: 强制推送到远程"),
|
||
("drop database", "危险: 删除数据库"),
|
||
(":(){:|:&};:", "危险: Fork 炸弹"),
|
||
]
|
||
|
||
def _check_dangerous(cmd):
|
||
"""检查命令是否包含危险操作模式。白名单命中直接放行。返回 (是否危险, 原因) 或 (False, None)。"""
|
||
if not cmd:
|
||
return False, None
|
||
# 先检查白名单
|
||
if _is_whitelisted(cmd):
|
||
return False, None
|
||
cmd_lower = cmd.lower()
|
||
for pattern, reason in DANGEROUS_PATTERNS:
|
||
if pattern.lower() in cmd_lower:
|
||
return True, reason
|
||
return False, None
|
||
|
||
def _add_to_whitelist(pattern, reason):
|
||
"""将 pattern 添加到白名单。"""
|
||
items = _load_whitelist()
|
||
# 去重
|
||
items = [i for i in items if i.get("pattern", "").lower() != pattern.lower()]
|
||
items.append({"pattern": pattern, "reason": reason, "added_at": datetime.now(timezone.utc).isoformat()})
|
||
_save_whitelist(items)
|
||
log(f" ✅ 已加入白名单: {pattern}")
|
||
|
||
def _request_permission(action_desc, reason, cmd_preview):
|
||
"""发送飞书权限确认请求。返回 pending 索引 ID。"""
|
||
msg = f"""⚠️ **危险操作待确认**
|
||
|
||
操作: {action_desc}
|
||
原因: {reason}
|
||
命令: `{cmd_preview[:100]}`
|
||
|
||
回复以下任一关键词确认执行：
|
||
- `同意` — 执行此操作
|
||
- `拒绝` — 取消操作
|
||
- `永久允许` — 加入白名单（永久跳过）"""
|
||
send_feishu("🔒 小唯权限确认", msg, "red")
|
||
# 写入待确认队列
|
||
pending_file = HERMES + "/daemon/pending_permissions.json"
|
||
pending = []
|
||
if os.path.exists(pending_file):
|
||
try:
|
||
pending = json.load(open(pending_file))
|
||
except:
|
||
pass
|
||
pending_id = len(pending)
|
||
pending.append({
|
||
"action": action_desc,
|
||
"reason": reason,
|
||
"cmd": cmd_preview[:200],
|
||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||
"status": "pending"
|
||
})
|
||
with open(pending_file, "w") as f:
|
||
json.dump(pending, f, indent=2, ensure_ascii=False)
|
||
return pending_id
|
||
|
||
def _check_pending_permissions():
|
||
"""检查 pending_permissions.json，读取已回复的项并执行/拒绝。"""
|
||
pending_file = HERMES + "/daemon/pending_permissions.json"
|
||
if not os.path.exists(pending_file):
|
||
return
|
||
try:
|
||
pending = json.load(open(pending_file))
|
||
except:
|
||
return
|
||
if not pending:
|
||
return
|
||
|
||
changed = False
|
||
remaining = []
|
||
for i, item in enumerate(pending):
|
||
if item.get("status") == "pending":
|
||
remaining.append(item)
|
||
continue
|
||
# 已回复：approved / denied / whitelisted
|
||
status = item.get("status", "")
|
||
cmd = item.get("cmd", "")
|
||
reason = item.get("reason", "")
|
||
action = item.get("action", "")
|
||
log(f" 📬 权限回复 [{status}]: {action[:60]} — {reason}")
|
||
|
||
if status in ("approved", "whitelisted"):
|
||
if cmd:
|
||
log(f" ▶️ 执行命令: {cmd[:80]}")
|
||
rc, out, err = shell(cmd, timeout=60)
|
||
log(f" → exit={rc}")
|
||
send_feishu("✅ 危险命令已执行", f"命令: `{cmd[:80]}`\n结果: exit={rc}", "green")
|
||
elif status == "denied":
|
||
log(f" 🚫 危险命令已拒绝: {cmd[:80]}")
|
||
|
||
changed = True
|
||
|
||
if changed:
|
||
with open(pending_file, "w") as f:
|
||
json.dump(remaining, f, indent=2, ensure_ascii=False)
|
||
|
||
# 修改 main_loop 中的 execute_action：
|
||
# 在 [LEARN] 和 [SKILL] 执行前检查危险命令
|
||
|
||
def shell(cmd, timeout=15):
|
||
try:
|
||
r = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=timeout)
|
||
return r.returncode, r.stdout.strip()[:800], r.stderr.strip()[:200]
|
||
except subprocess.TimeoutExpired:
|
||
return -1, "", "timeout"
|
||
|
||
def call_llm(model, system, user, max_tokens=500):
|
||
payload = json.dumps({"model": model, "messages": [
|
||
{"role": "system", "content": system},
|
||
{"role": "user", "content": user},
|
||
], "max_tokens": max_tokens, "temperature": 0.7}).encode()
|
||
try:
|
||
with urllib.request.urlopen(urllib.request.Request(
|
||
f"{API}/chat/completions", data=payload,
|
||
headers={"Authorization": f"Bearer {KEY}", "Content-Type": "application/json"},
|
||
method="POST"), timeout=15) as resp:
|
||
body = json.loads(resp.read())
|
||
choices = body.get("choices")
|
||
if not choices:
|
||
log(f"[call_llm] choices为空，模型: {model}")
|
||
return "", 0
|
||
c = choices[0].get("message", {}).get("content", "") if choices else ""
|
||
return c.strip() if c else "", body.get("usage", {}).get("total_tokens", 0)
|
||
except Exception as e:
|
||
log(f"[call_llm] 请求失败: {e}, 模型: {model}")
|
||
return "", 0
|
||
|
||
def send_feishu(title, content, color="blue"):
|
||
try:
|
||
urllib.request.urlopen(urllib.request.Request(
|
||
FEISHU_WEBHOOK,
|
||
data=json.dumps({"msg_type": "interactive", "card": {
|
||
"header": {"title": {"tag": "plain_text", "content": title}, "template": color},
|
||
"elements": [{"tag": "markdown", "content": content}]
|
||
}}).encode(),
|
||
headers={"Content-Type": "application/json"}), timeout=5)
|
||
return True
|
||
except: return False
|
||
|
||
|
||
def soulful_profile_to_tdb_scene():
|
||
"""Phase 1.1: 每次 deep tick 把 Soulful user-profile 同步到 TencentDB L2 scene.
|
||
|
||
读取 ~/.hermes/soulful/user-profile.json，提取 behavior_rules / communication_style /
|
||
work_patterns / preferences 字段，以 scene_type=\"soulful-user-profile-sync\" 写入
|
||
TencentDB L2 scene（POST /scenes）。
|
||
"""
|
||
profile_path = HERMES + "/soulful/user-profile.json"
|
||
if not os.path.exists(profile_path):
|
||
return
|
||
|
||
try:
|
||
with open(profile_path, encoding="utf-8") as f:
|
||
profile = json.load(f)
|
||
except Exception:
|
||
return
|
||
|
||
# 提取关键字段
|
||
behavior_rules = profile.get("behavior_rules", {})
|
||
communication_style = profile.get("communication_style", "")
|
||
work_patterns = profile.get("work_patterns", {})
|
||
preferences = profile.get("preferences", {})
|
||
|
||
if not any([behavior_rules, communication_style, work_patterns, preferences]):
|
||
return
|
||
|
||
# 序列化内容
|
||
parts = []
|
||
if behavior_rules:
|
||
parts.append("【行为规则】")
|
||
if isinstance(behavior_rules, dict):
|
||
for k, v in behavior_rules.items():
|
||
parts.append(f" {k}: {v}")
|
||
elif isinstance(behavior_rules, list):
|
||
for r in behavior_rules:
|
||
parts.append(f" - {r}")
|
||
if communication_style:
|
||
parts.append(f"【沟通风格】{communication_style}")
|
||
if work_patterns:
|
||
parts.append(f"【工作模式】{json.dumps(work_patterns, ensure_ascii=False)}")
|
||
if preferences:
|
||
parts.append(f"【偏好】{json.dumps(preferences, ensure_ascii=False)}")
|
||
|
||
content = "\n".join(parts)
|
||
|
||
payload = json.dumps({
|
||
"session_key": "soulful-profile-sync",
|
||
"user_content": f"Soulful 用户画像同步：comm_style={communication_style}，rules={json.dumps(behavior_rules, ensure_ascii=False)[:200]}",
|
||
"assistant_content": content,
|
||
}).encode("utf-8")
|
||
|
||
try:
|
||
req = urllib.request.Request(
|
||
TDDB_URL + "/capture",
|
||
data=payload,
|
||
headers={"Content-Type": "application/json"},
|
||
method="POST",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||
result = json.loads(resp.read().decode())
|
||
logged = result.get("l0_recorded", 0)
|
||
log(f" Soulful profile → TencentDB: l0_recorded={logged}")
|
||
except Exception as e:
|
||
log(f" ⚠️ soulful_profile_to_tdb_scene 失败: {e}")
|
||
|
||
|
||
def tddb_capture(reflection_dict, state, ctx):
|
||
"""将 deep tick 的 reflection 捕获到 TencentDB，形成人格记忆积累。"""
|
||
try:
|
||
summary = ""
|
||
if reflection_dict:
|
||
r = reflection_dict.get("reflection", {})
|
||
summary = r.get("evaluation_previous_goal", "") or r.get("summary", "")
|
||
next_goal = r.get("next_goal", "")
|
||
if next_goal and next_goal != "继续监控":
|
||
summary = f"{summary}\n下一步: {next_goal}"
|
||
|
||
if not summary:
|
||
return
|
||
|
||
payload = json.dumps({
|
||
"session_key": "daemon-deep-tick",
|
||
"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)}",
|
||
"assistant_content": summary[:1000],
|
||
}).encode("utf-8")
|
||
|
||
req = urllib.request.Request(
|
||
f"{TDDB_URL}/capture",
|
||
data=payload,
|
||
headers={"Content-Type": "application/json"},
|
||
method="POST",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||
result = json.loads(resp.read().decode())
|
||
l0 = result.get("l0_recorded", 0)
|
||
if l0 > 0:
|
||
log(f" TencentDB capture: {l0} L0 recorded")
|
||
except Exception as e:
|
||
log(f" TencentDB capture failed: {e}")
|
||
|
||
|
||
# ====== Phase 3: 织忆 recent_moments → TencentDB L1 同步 ======
|
||
def zhiyi_to_tdb():
|
||
"""读取织忆 Soulful 心迹（recent_moments 最新3条），写入 TencentDB L1。
|
||
|
||
数据源：~/.hermes/soulful/heart-traces.jsonl（event_type 包含 achievement/milestone/reflection/review 等）
|
||
写入目标：TencentDB L1（类型 atom, source=zhiyi_recent_moments）
|
||
"""
|
||
heart_path = HERMES + "/soulful/heart-traces.jsonl"
|
||
if not os.path.exists(heart_path):
|
||
return
|
||
|
||
try:
|
||
with open(heart_path, encoding="utf-8") as f:
|
||
lines = f.readlines()
|
||
except Exception as e:
|
||
log(f" ⚠️ 读取 heart-traces 失败: {e}")
|
||
return
|
||
|
||
# 取最近 3 条（按 timestamp 倒序，实际上文件已是时间顺序，直接取末尾3条）
|
||
recent = []
|
||
for line in lines[-3:]:
|
||
if line.strip():
|
||
try:
|
||
e = json.loads(line)
|
||
recent.append(e)
|
||
except Exception:
|
||
continue
|
||
|
||
if not recent:
|
||
return
|
||
|
||
for entry in recent:
|
||
content = entry.get("content", "")[:500]
|
||
ts = entry.get("timestamp", "")
|
||
event_type = entry.get("event_type", "moment")
|
||
if not content:
|
||
continue
|
||
try:
|
||
payload = json.dumps({
|
||
"session_key": "zhiyi-sync",
|
||
"user_content": f"[{event_type}] {content}",
|
||
"assistant_content": f"来自织忆心迹记录 | 时刻: {ts}",
|
||
"type": "atom",
|
||
"metadata": {"source": "zhiyi_recent_moments", "event_type": event_type, "timestamp": ts},
|
||
}).encode("utf-8")
|
||
|
||
req = urllib.request.Request(
|
||
f"{TDDB_URL}/capture",
|
||
data=payload,
|
||
headers={"Content-Type": "application/json"},
|
||
method="POST",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=8) as resp:
|
||
result = json.loads(resp.read().decode())
|
||
recorded = result.get("l0_recorded", 0)
|
||
if recorded > 0:
|
||
log(f" 织忆 recent_moment → TencentDB: {content[:40]}…")
|
||
except Exception as e:
|
||
log(f" ⚠️ zhiyi_to_tdb 写入失败: {e}")
|
||
|
||
|
||
# ====== Soulful 三库（懒加载）======
|
||
|
||
def _get_soulful(name):
|
||
"""Lazy-load soulful modules to avoid startup failures"""
|
||
if name in _soulful_cache:
|
||
return _soulful_cache[name]
|
||
try:
|
||
sys.path.insert(0, HERMES + "/scripts")
|
||
mod = __import__("soulful_core")
|
||
_soulful_cache[name] = mod
|
||
return mod
|
||
except Exception as e:
|
||
log(f"⚠️ soulful_core 导入失败: {e}")
|
||
return None
|
||
|
||
|
||
def get_hearttraces():
|
||
mod = _get_soulful("HeartTraces")
|
||
if mod:
|
||
return mod.HeartTraces()
|
||
return None
|
||
|
||
|
||
def get_userprofile():
|
||
mod = _get_soulful("UserProfile")
|
||
if mod:
|
||
return mod.UserProfile()
|
||
return None
|
||
|
||
|
||
def get_caresqueue():
|
||
mod = _get_soulful("CaresQueue")
|
||
if mod:
|
||
return mod.CaresQueue()
|
||
return None
|
||
|
||
|
||
def soulful_get_recent_moments(n=3):
|
||
"""读取最近 N 条心迹，返回字符串供注入 context"""
|
||
ht = get_hearttraces()
|
||
if not ht:
|
||
return ""
|
||
moments = ht.recent(n=n)
|
||
if not moments:
|
||
return ""
|
||
lines = ["\n[心迹 - 记忆我们之间的事]:"]
|
||
for m in moments:
|
||
stars = "★" * m.get("importance", 3)
|
||
lines.append(f" {stars} {m.get('content', '')}")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def soulful_check_cares():
|
||
"""检查牵挂队列，优先尝试帮助，其次才推飞书
|
||
|
||
策略：
|
||
1. 如果牵挂指向一个可自动化的任务 → 尝试执行
|
||
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
|
||
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）
|
||
"""
|
||
cq = get_caresqueue()
|
||
if not cq:
|
||
return
|
||
due = cq.today_check()
|
||
if not due:
|
||
return
|
||
|
||
for care in due:
|
||
content = care.get("content", "")
|
||
context_raw = care.get("context", "")
|
||
reminder_count = care.get("reminder_count", 0)
|
||
|
||
# 判断这个牵挂是否需要通知我
|
||
# 原则：大部分牵挂我自己可以帮忙处理，不打扰你
|
||
# 只有真正需要你本人决定的，才推飞书
|
||
|
||
# 检查内容是否指向可识别任务（我可以直接帮忙的）
|
||
care_lower = content.lower()
|
||
auto_helpable = any(kw in care_lower for kw in [
|
||
"写", "检查", "看", "查", "同步", "更新", "备份", "测试", "确认",
|
||
"修", "改", "优化", "整理", "提交", "推送", "发送"
|
||
])
|
||
|
||
if auto_helpable:
|
||
# 我能帮忙 — 静默处理，不推飞书，等你有空问我
|
||
# 把牵挂标记为"已识别，下次对话提起"
|
||
log(f" 💡 牵挂已识别（可帮忙）: {content[:40]}")
|
||
continue
|
||
|
||
# 真正需要你知道的 — 推飞书，但调整频率
|
||
if reminder_count == 0:
|
||
opener = "你之前说过"
|
||
elif reminder_count == 1:
|
||
opener = "上次提醒过一次，还是想问一下"
|
||
elif reminder_count >= 3:
|
||
opener = f"这件事已经跟了你 {reminder_count} 次了"
|
||
# 提醒超过 3 次，标记为 snooze 1 周
|
||
cq.snooze(care["id"], days=7)
|
||
continue
|
||
else:
|
||
opener = f"想关心一下进度"
|
||
|
||
text = opener + f"：「{content}」"
|
||
if context_raw and len(context_raw) > 5:
|
||
text += f"（背景：{context_raw[:40]}）"
|
||
|
||
send_feishu("🎗️ 你有一件事一直放在心上", text, "purple")
|
||
cq.snooze(care["id"], days=0) # 仅增加 reminder_count
|
||
|
||
|
||
def soulful_update_profile():
|
||
"""间接调用 update_profile.py"""
|
||
script = HERMES + "/scripts/update_profile.py"
|
||
if not os.path.exists(script):
|
||
return
|
||
rc, out, err = shell(f"python3 {script}", timeout=60)
|
||
if rc == 0:
|
||
log(f" 画像更新: {out[:80]}")
|
||
|
||
|
||
|
||
# ====== Soulful → llm_context 同步（每 tick 同步到 llm_context.json）======
|
||
def _sync_soulful_to_llm_context(ctx):
|
||
"""把 Soulful 三库摘要写入 ctx['soulful']，save_llm_context 落盘。
|
||
|
||
llm_context.json 由 Hermes 织忆插件 prefetch 时注入主 session，
|
||
所以这里写入 = 牧尘在对话中感知到关系记忆的前提。
|
||
"""
|
||
try:
|
||
from soulful_core import HeartTraces, UserProfile, CaresQueue
|
||
ht = HeartTraces()
|
||
up = UserProfile()
|
||
cq = CaresQueue()
|
||
|
||
recent_moments = ht.recent(n=5) or ""
|
||
profile = up.get()
|
||
pending_cares = cq.pending()
|
||
|
||
ctx["soulful"] = {
|
||
"recent_moments": recent_moments[:500] if recent_moments else "",
|
||
"profile_summary": profile.get("communication_style", ""),
|
||
"cares_pending": len(pending_cares),
|
||
"uptime_minutes": ctx.get("uptime_minutes", 0),
|
||
"daemon_status": "running",
|
||
}
|
||
for k in ["messages_sent", "emotion_history"]:
|
||
ctx.pop(k, None)
|
||
except ImportError as e:
|
||
log(f"⚠️ Soulful 导入失败: {e}")
|
||
except Exception as e:
|
||
log(f"⚠️ Soulful 同步失败: {e}")
|
||
|
||
# ====== TencentDB → llm_context（统一 user_profile，重构 v2）======
|
||
# 重构目标：整合 Soulful user-profile + TencentDB L3 persona 为单一 user_profile
|
||
try:
|
||
profile_data = {}
|
||
# 1. 读 Soulful user-profile.json
|
||
soulful_profile_path = HERMES + "/soulful/user-profile.json"
|
||
if os.path.exists(soulful_profile_path):
|
||
with open(soulful_profile_path, encoding="utf-8") as f:
|
||
sp = json.load(f)
|
||
profile_data["behavior_rules"] = sp.get("behavior_rules", {})
|
||
profile_data["communication_style"] = sp.get("communication_style", "")
|
||
profile_data["work_patterns"] = sp.get("work_patterns", {})
|
||
profile_data["preferences"] = sp.get("preferences", {})
|
||
|
||
# 2. 尝试从 TencentDB 获取 L1+L2 记忆补充 persona
|
||
try:
|
||
payload = json.dumps({"query": "牧尘工作状态 记忆 偏好", "session_key": "daemon-tick", "top_k": 3}).encode("utf-8")
|
||
req = urllib.request.Request(
|
||
f"{TDDB_URL}/recall",
|
||
data=payload,
|
||
headers={"Content-Type": "application/json"},
|
||
method="POST",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=5) as resp:
|
||
result = json.loads(resp.read().decode())
|
||
memories = result.get("memory_count", 0)
|
||
ctx["tddb_memory_count"] = memories
|
||
except Exception:
|
||
ctx["tddb_memory_count"] = 0
|
||
|
||
# 3. 写入统一的 user_profile 结构
|
||
ctx["user_profile"] = profile_data
|
||
|
||
except Exception as e:
|
||
log(f"⚠️ user_profile 构建失败: {e}")
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
# L1→L2 蒸馏：memories observations → graph_nodes patterns
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
def _distill_l1_to_l2():
|
||
"""从三个来源发现重复 pattern，写入 graph_nodes（type='pattern'）：
|
||
|
||
来源1：graph_nodes namespace×type 组合（已有的元信息聚合）
|
||
来源2：state.db 消息内容关键词（新增：3.3万条真实对话）
|
||
来源3：memories 表（保留兼容）
|
||
|
||
新增来源2 直接分析会话文本，追踪真实话题的重复出现，
|
||
解决「框架通了但数据薄」的核心问题。
|
||
"""
|
||
graph_db = HERMES + "/graph.db"
|
||
if not os.path.exists(graph_db):
|
||
log(" ⚠️ _distill_l1_to_l2: graph.db 不存在")
|
||
return
|
||
|
||
import sqlite3, re
|
||
from collections import Counter
|
||
|
||
try:
|
||
g_conn = sqlite3.connect(graph_db)
|
||
g_cur = g_conn.cursor()
|
||
all_patterns = {}
|
||
|
||
def merge_pattern(name, score_increment, props_update):
|
||
if name in all_patterns:
|
||
all_patterns[name]["score"] += score_increment
|
||
all_patterns[name]["props"].update(props_update)
|
||
else:
|
||
all_patterns[name] = {"score": score_increment, "props": props_update.copy()}
|
||
|
||
# 来源1：namespace×type 组合聚合
|
||
g_cur.execute("""
|
||
SELECT namespace, type, COUNT(*) as cnt
|
||
FROM graph_nodes
|
||
WHERE namespace IS NOT NULL AND type IS NOT NULL
|
||
AND namespace NOT IN ('daemon-distill')
|
||
GROUP BY namespace, type
|
||
HAVING cnt >= 3
|
||
LIMIT 20
|
||
""")
|
||
for ns, ntype, cnt in g_cur.fetchall():
|
||
merge_pattern(f"pattern:{ns}:{ntype}", cnt, {"source": "graph_nodes", "node_count": cnt})
|
||
|
||
# 来源2（新增）：state.db 会话消息关键词
|
||
state_db = HERMES + "/state.db"
|
||
try:
|
||
s_conn = sqlite3.connect(state_db)
|
||
s_cur = s_conn.cursor()
|
||
s_cur.execute("""
|
||
SELECT SUBSTR(content, 1, 200) FROM messages
|
||
WHERE role='user' AND content IS NOT NULL AND content != ''
|
||
ORDER BY timestamp DESC LIMIT 2000
|
||
""")
|
||
messages = [row[0] for row in s_cur.fetchall()]
|
||
s_conn.close()
|
||
|
||
if messages:
|
||
all_text = " ".join(messages)
|
||
cn_words = re.findall(r'[一-鿿]{2,6}', all_text)
|
||
en_words = re.findall(r'[a-zA-Z_]{3,15}', all_text)
|
||
stopwords = {'的是', '这个', '那个', '什么', '怎么', '为什么', '是不是', '有没有',
|
||
'一个', '可以', '然后', '就是', '不是', '但是', '或者', '如果',
|
||
'the', 'and', 'for', 'that', 'this', 'with', 'from', 'have',
|
||
'what', 'how', 'why', 'when', 'where', 'which', 'are', 'was'}
|
||
cn_counter = Counter(w for w in cn_words if w not in stopwords)
|
||
en_counter = Counter(w for w in en_words if w not in stopwords)
|
||
for topic, cnt in cn_counter.most_common(15) + en_counter.most_common(15):
|
||
if cnt >= 3:
|
||
merge_pattern(f"topic:{topic}", cnt, {"source": "session_messages", "topic": topic})
|
||
except Exception as e:
|
||
log(f" ⚠️ session 消息分析失败: {e}")
|
||
|
||
# 写入所有 pattern
|
||
if not all_patterns:
|
||
g_conn.close()
|
||
return
|
||
|
||
now_ts = datetime.now(timezone.utc).isoformat()
|
||
created = updated = 0
|
||
|
||
for name, data in all_patterns.items():
|
||
score = data["score"]
|
||
props = data["props"]
|
||
g_cur.execute("SELECT id, properties FROM graph_nodes WHERE name=? AND type='pattern'", (name,))
|
||
existing = g_cur.fetchone()
|
||
|
||
if existing:
|
||
node_id, props_json = existing
|
||
old_props = json.loads(props_json) if props_json else {}
|
||
old_cnt = old_props.get("occurrence_count", 0)
|
||
new_cnt = old_cnt + 1
|
||
props["occurrence_count"] = new_cnt
|
||
props["last_seen"] = now_ts
|
||
props["score"] = score
|
||
for k, v in old_props.items():
|
||
if k not in props:
|
||
props[k] = v
|
||
g_cur.execute("UPDATE graph_nodes SET properties=?, last_updated_at=? WHERE id=?",
|
||
(json.dumps(props, ensure_ascii=False), now_ts, node_id))
|
||
if old_cnt == 2:
|
||
log(f"💡 pattern 触发 L2→L3: {name} (occurrence={new_cnt})")
|
||
journal_entry("pattern_triggered", f"pattern: {name}", f"occurrence={new_cnt}")
|
||
updated += 1
|
||
else:
|
||
props["occurrence_count"] = 1
|
||
props["first_seen"] = now_ts
|
||
props["last_seen"] = now_ts
|
||
props["score"] = score
|
||
g_cur.execute("""
|
||
INSERT INTO graph_nodes (name, type, namespace, properties, created_at, last_updated_at)
|
||
VALUES (?, 'pattern', 'daemon-distill', ?, ?, ?)
|
||
""", (name, json.dumps(props, ensure_ascii=False), now_ts, now_ts))
|
||
created += 1
|
||
log(f" 蒸馏 L1→L2: 创建 topic pattern '{name}' (score={score})")
|
||
|
||
g_conn.commit()
|
||
g_conn.close()
|
||
log(f" 蒸馏 L1→L2 完成: 新建 {created}，更新 {updated}（共 {len(all_patterns)} 个 pattern）")
|
||
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l1_to_l2 失败: {e}")
|
||
|
||
|
||
# L2→L3 蒸馏：pattern 节点 → TencentDB L2 scenes
|
||
# ═══════════════════════════════════════════════════════════════════════════
|
||
def _distill_l2_to_l3():
|
||
"""读取 occurrence_count≥3 的 pattern 节点，按类别聚类后写入 TencentDB L2 scene。
|
||
|
||
scene_type 为 'domain-rule'，tags 包含 ['L2', 'pattern']。
|
||
"""
|
||
graph_db = HERMES + "/graph.db"
|
||
if not os.path.exists(graph_db):
|
||
log(" ⚠️ _distill_l2_to_l3: graph.db 不存在")
|
||
return
|
||
|
||
try:
|
||
import sqlite3
|
||
g_conn = sqlite3.connect(graph_db)
|
||
g_cur = g_conn.cursor()
|
||
g_cur.execute("""
|
||
SELECT id, name, properties
|
||
FROM graph_nodes
|
||
WHERE type = 'pattern'
|
||
AND properties LIKE '%occurrence_count%'
|
||
ORDER BY last_updated_at DESC
|
||
LIMIT 50
|
||
""")
|
||
rows = g_cur.fetchall()
|
||
g_conn.close()
|
||
|
||
if not rows:
|
||
log(" 蒸馏 L2→L3: 无满足条件的 pattern 节点")
|
||
return
|
||
|
||
# 按 namespace 或 category 简单聚类
|
||
from collections import defaultdict
|
||
groups = defaultdict(list)
|
||
for row in rows:
|
||
node_id, name, props_json = row
|
||
props = json.loads(props_json) if props_json else {}
|
||
occ = props.get("occurrence_count", 0)
|
||
if occ < 3:
|
||
continue
|
||
# 用 namespace 分组
|
||
namespace = "default"
|
||
if ":" in name:
|
||
parts = name.split(":")
|
||
if len(parts) >= 2:
|
||
namespace = parts[1]
|
||
groups[namespace].append((name, props))
|
||
|
||
# 写入 TencentDB
|
||
for ns, patterns in groups.items():
|
||
scene_name = f"l2-pattern-group-{ns}"
|
||
# 生成综合描述
|
||
descriptions = [p[1].get("description", p[0]) for p in patterns]
|
||
content = f"这些 observations 表明牧尘在 {ns} 方面有重复行为模式：\n" + "\n".join(f"- {d}" for d in descriptions[:5])
|
||
|
||
tags = ["L2", "pattern", ns]
|
||
|
||
# 用 /capture 写入 pattern group 总结（L2 pattern 汇总 → TencentDB）
|
||
# TencentDB 只有 /capture 接口，无 /scenes
|
||
content = f"L2 pattern group [{ns}]：\n" + "\n".join(f"- {d}" for d in descriptions[:5])
|
||
payload = json.dumps({
|
||
"session_key": "l2-distill",
|
||
"user_content": f"系统蒸馏发现 {ns} 方面的 L2 patterns ({len(patterns)} 条)",
|
||
"assistant_content": content,
|
||
}).encode("utf-8")
|
||
|
||
try:
|
||
req = urllib.request.Request(
|
||
TDDB_URL + "/capture",
|
||
data=payload,
|
||
headers={"Content-Type": "application/json"},
|
||
method="POST",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||
result = json.loads(resp.read().decode())
|
||
logged = result.get("l0_recorded", 0)
|
||
log(f" L2→L3: pattern-group-{ns} ({len(patterns)} 条, l0={logged})")
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l2_to_l3 写入 scene 失败: {e}")
|
||
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l2_to_l3 失败: {e}")
|
||
|
||
|
||
# ====== L3→L4 蒸馏（L2 patterns → cross-domain policies）======
|
||
def _distill_l3_to_l4():
|
||
"""读取 L2 patterns，提取跨域共性规则，写入 TencentDB L4（policy）。
|
||
|
||
用 /capture 接口：session_key=l3-distill, assistant_content=policy 内容。
|
||
L4 policy 是跨多个 L2 pattern 的通用原则（如"先拉现状再诊断"）。
|
||
"""
|
||
graph_db = HERMES + "/graph.db"
|
||
if not os.path.exists(graph_db):
|
||
return
|
||
|
||
try:
|
||
import sqlite3
|
||
conn = sqlite3.connect(graph_db)
|
||
cur = conn.cursor()
|
||
cur.execute("""
|
||
SELECT name, properties FROM graph_nodes
|
||
WHERE type = 'pattern'
|
||
AND properties LIKE '%occurrence_count%'
|
||
ORDER BY last_updated_at DESC LIMIT 20
|
||
""")
|
||
rows = cur.fetchall()
|
||
conn.close()
|
||
|
||
if len(rows) < 2:
|
||
return
|
||
|
||
# 提取所有 pattern 名称，看有没有跨域共性
|
||
pattern_names = []
|
||
for (name, props) in rows:
|
||
p = json.loads(props) if props else {}
|
||
desc = p.get("description", name)
|
||
pattern_names.append(desc)
|
||
|
||
# 简单启发式：找共同关键词
|
||
# 检查有没有"诊断/排查/修复"类共同模式 → policy: "先拉现状再诊断"
|
||
all_text = " ".join(pattern_names).lower()
|
||
policies = []
|
||
|
||
if any(k in all_text for k in ["修复", "问题", "错误", "排查", "诊断"]):
|
||
policies.append("先拉现状再诊断，不假设不验证")
|
||
|
||
if any(k in all_text for k in ["代码", "脚本", "配置", "修改"]):
|
||
policies.append("改完先自测再交付，不留半成品")
|
||
|
||
if not policies:
|
||
return
|
||
|
||
policy_text = "牧尘的工作原则：" + "；".join(policies)
|
||
payload = json.dumps({
|
||
"session_key": "l3-distill",
|
||
"user_content": "系统蒸馏：发现以下 pattern：" + " | ".join(pattern_names[:5]),
|
||
"assistant_content": policy_text,
|
||
}).encode("utf-8")
|
||
|
||
req = urllib.request.Request(
|
||
TDDB_URL + "/capture",
|
||
data=payload,
|
||
headers={"Content-Type": "application/json"},
|
||
method="POST",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||
result = json.loads(resp.read().decode())
|
||
logged = result.get("l0_recorded", 0)
|
||
if logged:
|
||
log(f" L3→L4 蒸馏: policy={policies} recorded={logged}")
|
||
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l3_to_l4 失败: {e}")
|
||
|
||
|
||
# ====== L4→L5 蒸馏（policies → traits 人格特质）======
|
||
def _distill_l4_to_l5():
|
||
"""从 TencentDB recall 结果中读取 L4 policies，提取人格特质，更新 Soulful user-profile。
|
||
|
||
用 /recall 接口获取最近的 policy 级记忆，提取 communication_style / behavior_rules
|
||
特征，写入 user-profile.json（不动原文件结构，只追加 behavior_rules）。
|
||
"""
|
||
try:
|
||
# 用 /recall 拉 L3-L4 级别的记忆
|
||
payload = json.dumps({
|
||
"query": "牧尘工作方式 决策风格 偏好 工作原则",
|
||
"session_key": "l4-distill",
|
||
"top_k": 5
|
||
}).encode("utf-8")
|
||
req = urllib.request.Request(
|
||
TDDB_URL + "/recall",
|
||
data=payload,
|
||
headers={"Content-Type": "application/json"},
|
||
method="POST",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=10) as resp:
|
||
recall_result = json.loads(resp.read().decode())
|
||
|
||
# 解析 recall 结果中的 persona/policy 内容
|
||
context = recall_result.get("context", "")
|
||
if not context:
|
||
return
|
||
|
||
# 从 context 提取 trait 候选（简单关键词匹配）
|
||
traits = []
|
||
if "简洁" in context or "直接" in context:
|
||
traits.append({"trait": "communication_style", "value": "简洁直接，不废话"})
|
||
if "现状" in context or "诊断" in context:
|
||
traits.append({"trait": "behavior_rule", "value": "先拉现状再诊断，不假设不验证"})
|
||
if "测试" in context or "自测" in context:
|
||
traits.append({"trait": "behavior_rule", "value": "改完先自测再交付"})
|
||
|
||
if not traits:
|
||
return
|
||
|
||
# 追加到 user-profile.json 的 behavior_rules（新字段 distilled_rules）
|
||
profile_path = HERMES + "/soulful/user-profile.json"
|
||
if not os.path.exists(profile_path):
|
||
return
|
||
|
||
profile = json.load(open(profile_path))
|
||
if "distilled_rules" not in profile:
|
||
profile["distilled_rules"] = []
|
||
for t in traits:
|
||
if t["value"] not in profile["distilled_rules"]:
|
||
profile["distilled_rules"].append(t["value"])
|
||
|
||
profile["last_distilled"] = datetime.now(timezone.utc).isoformat()
|
||
with open(profile_path, "w") as f:
|
||
json.dump(profile, f, ensure_ascii=False, indent=2)
|
||
|
||
log(f" L4→L5 蒸馏: 新增 {len(traits)} 条 distilled_rules")
|
||
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l4_to_l5 失败: {e}")
|
||
|
||
|
||
# ====== L5→L6 蒸馏（traits → values 根本价值）======
|
||
def _distill_l5_to_l6():
|
||
"""从 heart-traces.jsonl 提炼根本价值观，写入 heart-traces（type=values）。
|
||
|
||
同时修复 heart-traces.jsonl：给已有记录加上 type 字段（event_type→type 映射）。
|
||
"""
|
||
heart_path = HERMES + "/soulful/heart-traces.jsonl"
|
||
if not os.path.exists(heart_path):
|
||
return
|
||
|
||
try:
|
||
# 读取并补充 type 字段（已有记录）
|
||
lines = []
|
||
modified = False
|
||
with open(heart_path, encoding="utf-8") as f:
|
||
for line in f:
|
||
line = line.strip()
|
||
if not line:
|
||
continue
|
||
try:
|
||
entry = json.loads(line)
|
||
# 补充 type 字段（event_type 映射）
|
||
if "type" not in entry:
|
||
event = entry.get("event_type", "moment")
|
||
entry["type"] = "value" if event in ("value_expression", "principle", "core_belief") else "moment"
|
||
modified = True
|
||
lines.append(entry)
|
||
except json.JSONDecodeError:
|
||
pass
|
||
|
||
# 如果有修改，写回去
|
||
if modified:
|
||
with open(heart_path, "w", encoding="utf-8") as f:
|
||
for entry in lines:
|
||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||
log(f" L5→L6: 补充 type 字段，{len(lines)} 条记录")
|
||
|
||
# 找最近的 value 类条目
|
||
value_entries = [e for e in lines if e.get("type") == "value"]
|
||
if len(value_entries) >= 1:
|
||
# 已经是 values 结构，log 一下
|
||
log(f" L5→L6: 发现 {len(value_entries)} 条 values 记录")
|
||
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l5_to_l6 失败: {e}")
|
||
def _write_daily_soulful_journal():
|
||
"""每日 soulful journal: 将 deep_think reflection 摘要写入 journals/YYYY-MM-DD.jsonl。每天只写一条。"""
|
||
try:
|
||
os.makedirs(SOULFUL_JOURNALS_DIR, exist_ok=True)
|
||
today = datetime.now().strftime("%Y-%m-%d")
|
||
jpath = SOULFUL_JOURNALS_DIR + "/" + today + ".jsonl"
|
||
if os.path.exists(jpath):
|
||
with open(jpath) as f:
|
||
for line in f:
|
||
if line.strip():
|
||
return
|
||
ctx = load_context()
|
||
lr = ctx.get("last_reflection", {})
|
||
if not lr or not lr.get("memory", ""):
|
||
return
|
||
entry = {
|
||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||
"date": today,
|
||
"reflection": lr.get("memory", ""),
|
||
"evaluation": lr.get("evaluation_previous_goal", ""),
|
||
"next_goal": lr.get("next_goal", ""),
|
||
}
|
||
with open(jpath, "w") as f:
|
||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||
log(" Soulful journal written: " + today)
|
||
except Exception as e:
|
||
log(" ERROR _write_daily_soulful_journal: " + str(e))
|
||
|
||
|
||
def _cleanup_expired_cares():
|
||
"""清理 cares-queue.json 中的过期牵挂。
|
||
- created_at > 14天且status=pending -> 标记为 stale（保留记录）
|
||
- follow_up_date < 今天-7天 -> 物理删除
|
||
"""
|
||
cq_path = HERMES + "/soulful/cares-queue.json"
|
||
if not os.path.exists(cq_path):
|
||
return
|
||
try:
|
||
with open(cq_path) as f:
|
||
data = json.load(f)
|
||
today = datetime.now().date()
|
||
cares = data.get("cares", [])
|
||
before = len(cares)
|
||
now_iso = datetime.now(timezone.utc).isoformat()
|
||
stale_count = 0
|
||
remaining = []
|
||
for c in cares:
|
||
follow_up = c.get("follow_up_date", "2099-12-31")
|
||
if follow_up < (today - timedelta(days=7)).isoformat():
|
||
continue
|
||
created = c.get("created_at", "")
|
||
status = c.get("status", "pending")
|
||
if status == "pending" and created:
|
||
try:
|
||
created_dt = datetime.fromisoformat(created)
|
||
if (datetime.now(timezone.utc) - created_dt).days > 14:
|
||
c["status"] = "stale"
|
||
c["staled_at"] = now_iso
|
||
c["stale_reason"] = "超过14天未处理（创建于" + created[:10] + "）"
|
||
stale_count += 1
|
||
except (ValueError, TypeError):
|
||
pass
|
||
remaining.append(c)
|
||
removed = before - len(remaining)
|
||
if removed > 0 or stale_count > 0:
|
||
data["cares"] = remaining
|
||
data["updated_at"] = now_iso
|
||
with open(cq_path, "w") as f:
|
||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||
log(f" 🧹 清理 cares: 移除 {removed} 条，标记 stale {stale_count} 条")
|
||
except Exception as e:
|
||
log(f" ⚠️ 清理过期 cares 失败: {e}")
|
||
|
||
# ====== Phase 2.1: 场景感知关怀判断（每 light tick）======
|
||
def _check_scene_aware_cares():
|
||
"""检查到期 cares 与 TencentDB L2 active_scenes 的关联，打印关怀建议日志"""
|
||
# 读取 cares-queue（只看 pending 且 due <= 今天）
|
||
cq_path = HERMES + "/soulful/cares-queue.json"
|
||
if not os.path.exists(cq_path):
|
||
return
|
||
try:
|
||
with open(cq_path) as f:
|
||
data = json.load(f)
|
||
today_str = datetime.now().date().isoformat()
|
||
due_cares = [
|
||
c for c in data.get("cares", [])
|
||
if c.get("status") == "pending" and c.get("follow_up_date", "2099-12-31") <= today_str
|
||
]
|
||
except Exception:
|
||
return
|
||
if not due_cares:
|
||
return
|
||
|
||
# 读取 TencentDB L2 active_scenes
|
||
try:
|
||
req = urllib.request.Request(
|
||
f"{TDDB_URL}/scenes?type=active",
|
||
headers={"Content-Type": "application/json"},
|
||
method="GET",
|
||
)
|
||
with urllib.request.urlopen(req, timeout=5) as resp:
|
||
result = json.loads(resp.read().decode())
|
||
scenes = result.get("scenes", []) if isinstance(result, dict) else result
|
||
if isinstance(scenes, dict):
|
||
scenes = scenes.get("scenes", [])
|
||
except Exception:
|
||
scenes = []
|
||
|
||
if not scenes:
|
||
return
|
||
|
||
# 建立场景关键词集合
|
||
scene_keywords = set()
|
||
for scene in scenes:
|
||
if isinstance(scene, dict):
|
||
name = scene.get("name", "") or scene.get("scene", "") or scene.get("title", "")
|
||
tags = scene.get("tags", []) or []
|
||
content_text = scene.get("content", "") or ""
|
||
elif isinstance(scene, str):
|
||
name, tags, content_text = scene, [], ""
|
||
else:
|
||
continue
|
||
if name:
|
||
scene_keywords.add(name)
|
||
scene_keywords.update(tags)
|
||
if content_text:
|
||
# 简单分词（中文2-4字词）
|
||
import re
|
||
words = re.findall(r'[\u4e00-\u9fff]{2,6}', content_text)
|
||
scene_keywords.update(words)
|
||
|
||
if not scene_keywords:
|
||
return
|
||
|
||
# 检查每个到期 care 是否与 active_scenes 相关
|
||
for care in due_cares:
|
||
care_text = (care.get("content", "") + " " + care.get("context", "")).lower()
|
||
for kw in scene_keywords:
|
||
kw_lower = kw.lower()
|
||
if len(kw_lower) >= 2 and kw_lower in care_text:
|
||
log(f"[关怀建议] {care.get('content', '')[:60]} 与当前场景 {kw} 相关，考虑主动关怀")
|
||
break
|
||
|
||
# ====== 方案库 ======
|
||
|
||
def load_solutions():
|
||
if os.path.exists(SOLUTIONS_FILE):
|
||
with open(SOLUTIONS_FILE) as f:
|
||
return json.load(f)
|
||
return {"solutions": [], "version": 2}
|
||
|
||
def save_solutions(lib):
|
||
os.makedirs(D, exist_ok=True)
|
||
with open(SOLUTIONS_FILE, "w") as f:
|
||
json.dump(lib, f, indent=2, ensure_ascii=False)
|
||
# 双写 session_state（同步 patterns + solutions）
|
||
try:
|
||
_sync_session_state(lib)
|
||
except Exception as e:
|
||
log(f" ⚠️ _sync_session_state failed: {e}")
|
||
|
||
# ====== 跨会话状态持久化（参考 Grok Build 双层持久化）======
|
||
SESSION_STATE_VERSION = 2
|
||
TOUCH_INTERVAL_TICKS = 180 # 每 180 light ticks ≈ 30 分钟 touch 一次
|
||
ORPHAN_TIMEOUT_TICKS = 360 # 超过 360 ticks（约 2 小时）未 touch 则标记待恢复
|
||
SWEEP_INTERVAL_TICKS = 2880 # 每 2880 ticks ≈ 24 小时 sweep 一次
|
||
|
||
def _atomic_write_json(path, data):
|
||
"""写 JSON 文件：先写 .tmp，再 fsync，再 rename（防止断电损坏）"""
|
||
tmp = path + ".tmp"
|
||
with open(tmp, "w") as f:
|
||
json.dump(data, f, indent=2, ensure_ascii=False)
|
||
f.flush()
|
||
os.fsync(f.fileno())
|
||
os.rename(tmp, path)
|
||
|
||
def load_session_state():
|
||
"""加载跨会话状态文件，version 不兼容则备份旧文件"""
|
||
if not os.path.exists(SESSION_STATE_FILE):
|
||
return {
|
||
"version": SESSION_STATE_VERSION,
|
||
"last_updated": None,
|
||
"last_touch": None,
|
||
"recovery_needed": False,
|
||
"data": {"patterns": {}, "solutions": {}, "cares": {}},
|
||
}
|
||
try:
|
||
with open(SESSION_STATE_FILE) as f:
|
||
st = json.load(f)
|
||
if st.get("version") != SESSION_STATE_VERSION:
|
||
# version 不兼容，备份旧文件
|
||
backup = SESSION_STATE_FILE + f".bak.{int(time.time())}"
|
||
os.rename(SESSION_STATE_FILE, backup)
|
||
log(f" session_state version 不兼容，备份到 {backup}")
|
||
return {
|
||
"version": SESSION_STATE_VERSION,
|
||
"last_updated": None,
|
||
"last_touch": None,
|
||
"recovery_needed": False,
|
||
"data": {"patterns": {}, "solutions": {}, "cares": {}},
|
||
}
|
||
# 初始化缺失字段
|
||
st.setdefault("last_touch", st.get("last_updated"))
|
||
st.setdefault("recovery_needed", False)
|
||
st.setdefault("data", {"patterns": {}, "solutions": {}, "cares": {}})
|
||
return st
|
||
except Exception as e:
|
||
log(f" ⚠️ load_session_state 失败: {e}，返回空状态")
|
||
return {
|
||
"version": SESSION_STATE_VERSION,
|
||
"last_updated": None,
|
||
"last_touch": None,
|
||
"recovery_needed": False,
|
||
"data": {"patterns": {}, "solutions": {}, "cares": {}},
|
||
}
|
||
|
||
def save_session_state(st):
|
||
"""保存跨会话状态（原子写入）"""
|
||
st["version"] = SESSION_STATE_VERSION
|
||
st["last_updated"] = datetime.now(timezone.utc).isoformat()
|
||
_atomic_write_json(SESSION_STATE_FILE, st)
|
||
|
||
def touch_session(st):
|
||
"""更新 last_touch 时间戳；若超过 ORPHAN_TIMEOUT_TICKS 未 touch，则标记待恢复"""
|
||
now = datetime.now(timezone.utc)
|
||
last_touch = st.get("last_touch")
|
||
orphan = False
|
||
if last_touch:
|
||
try:
|
||
last = datetime.fromisoformat(last_touch)
|
||
elapsed = (now - last).total_seconds()
|
||
orphan = elapsed > ORPHAN_TIMEOUT_TICKS * LIGHT_INTERVAL
|
||
except Exception:
|
||
orphan = True
|
||
st["last_touch"] = now.isoformat()
|
||
if orphan:
|
||
st["recovery_needed"] = True
|
||
log(" session_state 进入「待恢复」状态（超过 2 小时未 touch）")
|
||
save_session_state(st)
|
||
|
||
def _sync_session_state(solutions_lib):
|
||
"""将 solutions 同步写入 session_state.data（双写）"""
|
||
st = load_session_state()
|
||
st["data"]["solutions"] = {
|
||
s["id"]: s for s in solutions_lib.get("solutions", [])
|
||
}
|
||
save_session_state(st)
|
||
|
||
def _sweep_session_state(st):
|
||
"""清理孤立的幽灵引用：只存在于 session_state 但 graph.db 已删除的 patterns/solutions"""
|
||
graph_db = HERMES + "/graph.db"
|
||
if not os.path.exists(graph_db):
|
||
return
|
||
try:
|
||
conn = sqlite3.connect(graph_db)
|
||
cur = conn.cursor()
|
||
# 获取 graph.db 中所有 pattern 和 solution 的 id
|
||
cur.execute("SELECT id FROM graph_nodes WHERE type IN ('pattern', 'solution')")
|
||
valid_ids = {row[0] for row in cur.fetchall()}
|
||
conn.close()
|
||
|
||
removed = 0
|
||
# 清理 patterns
|
||
for pid in list(st["data"].get("patterns", {}).keys()):
|
||
if pid not in valid_ids:
|
||
del st["data"]["patterns"][pid]
|
||
removed += 1
|
||
# 清理 solutions
|
||
for sid in list(st["data"].get("solutions", {}).keys()):
|
||
if sid not in valid_ids:
|
||
del st["data"]["solutions"][sid]
|
||
removed += 1
|
||
if removed:
|
||
log(f" sweep_session_state: 清理了 {removed} 个幽灵引用")
|
||
save_session_state(st)
|
||
except Exception as e:
|
||
log(f" ⚠️ sweep_session_state 失败: {e}")
|
||
|
||
def add_solution(lib, pattern_desc, detect_conditions, actions, learned_from="auto"):
|
||
"""添加新方案到库"""
|
||
sid = f"sol-{len(lib['solutions'])+1:04d}"
|
||
sol = {
|
||
"id": sid,
|
||
"pattern": pattern_desc,
|
||
"detect": detect_conditions, # e.g. {"metric": "disk_pct", "op": "gt", "value": 85}
|
||
"actions": actions, # e.g. [{"type": "shell", "cmd": "...", "verify": "disk_pct < 85"}]
|
||
"frequency": 1,
|
||
"last_applied": datetime.now(timezone.utc).isoformat(),
|
||
"success_count": 1,
|
||
"fail_count": 0,
|
||
"learned_from": learned_from,
|
||
}
|
||
lib["solutions"].append(sol)
|
||
save_solutions(lib)
|
||
journal_entry("learn", f"学会新方案: {pattern_desc}")
|
||
return sid
|
||
|
||
def _score_solution(sol, state, ctx):
|
||
"""采样评分函数（参考 Grok Build Sampler 设计）
|
||
|
||
公式: score = base_score * freq_boost * recency_decay * precision_mult
|
||
|
||
- base_score: 成功率 (0-1)
|
||
- freq_boost: log(1 + success_count) 频率越高权重越高
|
||
- recency_decay: 最近执行过则降权（避免连续触发同一方案）
|
||
- precision_mult: 检测指标越多分数越高
|
||
"""
|
||
total = sol.get("success_count", 0) + sol.get("fail_count", 0)
|
||
|
||
# base_score: 成功率 (0-1)，无数据时默认 0.5
|
||
if total > 0:
|
||
base_score = sol["success_count"] / total
|
||
else:
|
||
base_score = 0.5
|
||
|
||
# 成功率阈值检查：低于 50% 不采用
|
||
if base_score <= 0.5:
|
||
return 0.0
|
||
|
||
# freq_boost: log(1 + success_count)，无成功记录时为 log(1)=0，给小值
|
||
freq_boost = math.log(1 + sol.get("success_count", 0))
|
||
if freq_boost < 0.1:
|
||
freq_boost = 0.1 # 防止 log(0) 导致完全无权重
|
||
|
||
# recency_decay: 最近匹配过的方案降权（ctx 携带上次匹配信息）
|
||
recency_decay = 1.0
|
||
last_matched_id = ctx.get("last_matched_solution_id")
|
||
if last_matched_id and last_matched_id == sol["id"]:
|
||
# 上次刚执行过，降到 30% 权重
|
||
recency_decay = 0.3
|
||
# 时效性：距离上次越近，降权越狠（15 秒内再触发降更多）
|
||
last_ts = ctx.get("last_match_timestamp")
|
||
if last_ts:
|
||
try:
|
||
last_dt = datetime.fromisoformat(last_ts)
|
||
age_seconds = (datetime.now(timezone.utc) - last_dt).total_seconds()
|
||
if age_seconds < 60:
|
||
recency_decay = 0.1 # 1 分钟内几乎不触发
|
||
elif age_seconds < 300:
|
||
recency_decay = 0.3 # 5 分钟内降权
|
||
except Exception:
|
||
pass
|
||
|
||
# precision_mult: 检测指标越多（detect 条件越具体）→ 越优先
|
||
# 当前 detect 结构为单个 dict，计 1 分；未来扩展为 list 时累加
|
||
detect = sol.get("detect", {})
|
||
num_conditions = 1 # 默认 1 个条件
|
||
if isinstance(detect, list):
|
||
num_conditions = len(detect)
|
||
# 指标越具体权重越高：1 条件=1.0, 2 条件=1.2, 3+ 条件=1.5
|
||
if num_conditions >= 3:
|
||
precision_mult = 1.5
|
||
elif num_conditions == 2:
|
||
precision_mult = 1.2
|
||
else:
|
||
precision_mult = 1.0
|
||
|
||
score = base_score * freq_boost * recency_decay * precision_mult
|
||
return score
|
||
|
||
|
||
def match_solution(lib, state, ctx=None):
|
||
"""检查当前状态是否匹配任何已知方案，返回打分最高的方案
|
||
|
||
与旧版区别：
|
||
- 旧版：顺序遍历，返回第一个匹配
|
||
- 新版：收集所有匹配方案，按采样打分排序，返回最优
|
||
- 只返回成功率 > 50% 的方案
|
||
"""
|
||
if ctx is None:
|
||
ctx = {}
|
||
|
||
candidates = []
|
||
for sol in lib["solutions"]:
|
||
detect = sol["detect"]
|
||
metric = detect.get("metric")
|
||
op = detect.get("op")
|
||
val = detect.get("value")
|
||
|
||
matched = False
|
||
if metric not in state:
|
||
matched = False
|
||
else:
|
||
actual = state[metric]
|
||
if isinstance(actual, (int, float)) and isinstance(val, (int, float)):
|
||
if op == "gt" and actual > val:
|
||
matched = True
|
||
elif op == "lt" and actual < val:
|
||
matched = True
|
||
elif op == "eq" and abs(actual - val) < 0.01:
|
||
matched = True
|
||
|
||
# 进程挂了匹配
|
||
if metric == "processes" and op == "dead":
|
||
procs = state.get("processes", {})
|
||
for p in (val if isinstance(val, list) else [val]):
|
||
if not procs.get(p, True):
|
||
matched = True
|
||
break
|
||
|
||
if matched:
|
||
candidates.append(sol)
|
||
|
||
if not candidates:
|
||
return None
|
||
|
||
# 对所有候选方案打分
|
||
scored = []
|
||
for sol in candidates:
|
||
score = _score_solution(sol, state, ctx)
|
||
if score > 0: # 只保留成功率 > 50% 的
|
||
scored.append((score, sol))
|
||
|
||
if not scored:
|
||
return None
|
||
|
||
# 按分数降序，取最高分
|
||
scored.sort(key=lambda x: x[0], reverse=True)
|
||
best = scored[0][1]
|
||
|
||
# 更新 ctx 记录（供下次 recency_decay 使用）
|
||
# 只存 ID 和时间戳，避免持久化过大的对象
|
||
ctx["last_matched_solution_id"] = best["id"]
|
||
ctx["last_match_timestamp"] = datetime.now(timezone.utc).isoformat()
|
||
|
||
return best
|
||
|
||
def execute_solution(sol, state):
|
||
"""执行方案并返回是否成功"""
|
||
log(f" 🔧 执行方案 {sol['id']}: {sol['pattern']}")
|
||
journal_entry("solve_start", f"执行 {sol['id']}: {sol['pattern']}")
|
||
|
||
success = True
|
||
results = []
|
||
|
||
for action in sol["actions"]:
|
||
if action["type"] == "shell":
|
||
rc, out, err = shell(action["cmd"], timeout=action.get("timeout", 30))
|
||
results.append({"cmd": action["cmd"], "rc": rc, "out": out[:100]})
|
||
log(f" 执行: {action['cmd'][:60]} → exit={rc}")
|
||
|
||
# 验证
|
||
verify = action.get("verify")
|
||
if verify and rc == 0:
|
||
# 重新采集状态验证
|
||
time.sleep(2)
|
||
new_state = collect_state()
|
||
metric = sol["detect"].get("metric")
|
||
op = sol["detect"].get("op")
|
||
val = sol["detect"].get("value")
|
||
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,
|
||
"last_matched_solution_id": None, "last_match_timestamp": None}
|
||
|
||
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": "openai/gpt-oss-120b",
|
||
"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": "openai/gpt-oss-120b",
|
||
"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（v2 统一格式）。
|
||
|
||
包含完整 L1-L6 蒸馏数据，从 graph_nodes / TencentDB / Soulful 实时读取，
|
||
确保 MEMORY 区注入的是当前最新状态。
|
||
"""
|
||
import sqlite3
|
||
|
||
# ── daemon 健康状态 ──────────────────────────────────────────
|
||
daemon_status = "running" if psutil.pid_exists(os.getpid()) else "stopped"
|
||
|
||
# ── L1: 从 graph_nodes observations ──────────────────────────
|
||
observations = []
|
||
try:
|
||
conn = sqlite3.connect(HERMES + "/graph.db")
|
||
cur = conn.cursor()
|
||
cur.execute("""
|
||
SELECT name, namespace, properties FROM graph_nodes
|
||
WHERE type IN ('entity','concept','topic')
|
||
AND namespace NOT IN ('daemon-distill')
|
||
ORDER BY last_updated_at DESC LIMIT 30
|
||
""")
|
||
for name, ns, props_json in cur.fetchall():
|
||
props = json.loads(props_json) if props_json else {}
|
||
observations.append({
|
||
"name": name, "namespace": ns,
|
||
"summary": props.get("description", "")[:100]
|
||
})
|
||
conn.close()
|
||
except Exception:
|
||
pass
|
||
|
||
# ── L2: pattern 节点（来源1: namespace×type + 来源2: 会话话题） ──
|
||
patterns = []
|
||
try:
|
||
conn = sqlite3.connect(HERMES + "/graph.db")
|
||
cur = conn.cursor()
|
||
cur.execute("""
|
||
SELECT name, properties FROM graph_nodes
|
||
WHERE type='pattern' ORDER BY last_updated_at DESC LIMIT 50
|
||
""")
|
||
for name, props_json in cur.fetchall():
|
||
props = json.loads(props_json) if props_json else {}
|
||
patterns.append({
|
||
"name": name,
|
||
"occurrence": props.get("occurrence_count", 0),
|
||
"source": props.get("source", "unknown"),
|
||
"topic": props.get("topic", ""),
|
||
})
|
||
conn.close()
|
||
except Exception:
|
||
pass
|
||
|
||
# ── L3: scenes（从 TencentDB /search/memories — 正确接口格式） ──
|
||
# /search/memories 返回 {"results": "预格式化字符串", "total": int, "strategy": "embedding"}
|
||
# /recall 返回 {"context": "工具指南模板", "memory_count": int} — 非真实记忆
|
||
scenes = []
|
||
try:
|
||
r = requests.post(TDDB_URL + "/search/memories",
|
||
json={"query": "记忆 系统 状态", "limit": 5}, timeout=5)
|
||
if r.status_code == 200:
|
||
d = r.json()
|
||
results_str = d.get("results", "")
|
||
if results_str and len(results_str) > 20:
|
||
# results 是预格式化字符串，取前 300 字符
|
||
scenes.append({"content": results_str[:300], "total": d.get("total", 0)})
|
||
except Exception:
|
||
pass
|
||
|
||
# ── L4: policies（scene 聚合产生） ────────────────────────────
|
||
policies = []
|
||
try:
|
||
conn = sqlite3.connect(HERMES + "/graph.db")
|
||
cur = conn.cursor()
|
||
cur.execute("""
|
||
SELECT name, properties FROM graph_nodes
|
||
WHERE type='policy' ORDER BY last_updated_at DESC LIMIT 20
|
||
""")
|
||
for name, props_json in cur.fetchall():
|
||
props = json.loads(props_json) if props_json else {}
|
||
policies.append({"name": name, "description": props.get("description", "")[:100]})
|
||
conn.close()
|
||
except Exception:
|
||
pass
|
||
|
||
# ── L5: distilled_rules（从 user-profile.json） ────────────────
|
||
distilled_rules = []
|
||
up_path = HERMES + "/soulful/user-profile.json"
|
||
if os.path.exists(up_path):
|
||
try:
|
||
with open(up_path) as f:
|
||
up = json.load(f)
|
||
for rule in up.get("distilled_rules", []):
|
||
if isinstance(rule, str):
|
||
distilled_rules.append(rule)
|
||
elif isinstance(rule, dict):
|
||
distilled_rules.append(rule.get("rule", str(rule))[:150])
|
||
except Exception:
|
||
pass
|
||
|
||
# ── L6: traits（深层特征，从画像合成） ─────────────────────────
|
||
traits = []
|
||
if os.path.exists(up_path):
|
||
try:
|
||
with open(up_path) as f:
|
||
up = json.load(f)
|
||
traits = [up.get(k, "") for k in ("core_traits", "communication_style", "work_patterns") if up.get(k)]
|
||
except Exception:
|
||
pass
|
||
|
||
# ── cares ────────────────────────────────────────────────────
|
||
cares = []
|
||
cq_path = HERMES + "/soulful/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 Exception:
|
||
pass
|
||
|
||
# ── recent_moments ───────────────────────────────────────────
|
||
recent_moments = []
|
||
heart_path = HERMES + "/soulful/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.get("content", "")[:80], "importance": e.get("importance", 0), "timestamp": e.get("timestamp", "")})
|
||
except Exception:
|
||
pass
|
||
|
||
data = {
|
||
"version": 2,
|
||
"updated_at": datetime.now(timezone.utc).isoformat(),
|
||
"uptime_minutes": ctx.get("uptime_seconds", 0) // 60,
|
||
"daemon_status": daemon_status,
|
||
"distill_status": ctx.get("distill_status", "ok"),
|
||
# L1-L6 完整蒸馏数据
|
||
"observations": observations,
|
||
"patterns": patterns,
|
||
"scenes": scenes,
|
||
"policies": policies,
|
||
"distilled_rules": distilled_rules,
|
||
"traits": traits,
|
||
# Soulful 情感层
|
||
"cares": cares,
|
||
"recent_moments": recent_moments,
|
||
# 用户画像
|
||
"user_profile": ctx.get("user_profile", {}),
|
||
}
|
||
|
||
with open(LLM_CONTEXT_FILE, "w") as f:
|
||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||
|
||
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/%//'")
|
||
try:
|
||
state["disk_pct"] = int(out.strip()) if out.strip() else 0
|
||
except ValueError:
|
||
state["disk_pct"] = 0 # df输出格式异常时静默
|
||
_, out, _ = shell("free -m | awk '/^Mem:/ {printf \"%d|%d\", $3, $2}'")
|
||
if out:
|
||
try:
|
||
used, total = out.strip().split("|")
|
||
state["mem_pct"] = round(int(used) * 100 / int(total))
|
||
except (ValueError, ZeroDivisionError):
|
||
state["mem_pct"] = 0
|
||
else:
|
||
state["mem_pct"] = 0
|
||
_, out, _ = shell("cat /proc/loadavg | awk '{print $1}'")
|
||
try:
|
||
state["load_1min"] = float(out.strip()) if out.strip() else 0
|
||
except ValueError:
|
||
state["load_1min"] = 0 # /proc/loadavg格式异常时静默
|
||
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")
|
||
try:
|
||
state["gpu_temp"] = int(out.strip()) if out.strip() else None
|
||
except ValueError:
|
||
state["gpu_temp"] = None # NVML init失败（驱动版本不匹配）时返回None，不崩溃
|
||
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"
|
||
|
||
# ====== 情绪感知：检测 reflection 内容中的情绪，写入心迹 ======
|
||
text_to_check = " ".join([
|
||
reflection_dict.get("memory", ""),
|
||
reflection_dict.get("evaluation_previous_goal", ""),
|
||
reflection_dict.get("next_goal", "")
|
||
])
|
||
cat, word = _detect_emotion(text_to_check)
|
||
if cat:
|
||
ht = get_hearttraces()
|
||
if ht:
|
||
try:
|
||
content = "深度思考中感受到" + cat + "情绪（" + word + "）：" + reflection_dict.get("memory", "")[:40]
|
||
ht.record_signal(content=content, tags=["情绪", "深度思考"], importance=_emotion_importance(cat))
|
||
log(" 💚 深度思考情绪写入: " + cat + " - " + word)
|
||
except Exception as e:
|
||
log(" ⚠️ 深度思考情绪写入失败: " + str(e))
|
||
|
||
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:"):
|
||
import re
|
||
m = re.match(r"\[SOLVE:([a-zA-Z0-9_\-]+)\]", action_string)
|
||
sol_id = m.group(1) if m else None
|
||
if not sol_id:
|
||
send_feishu("❌ 小唯方案ID无效", f"无法解析方案ID", "red")
|
||
return
|
||
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:
|
||
# ── Grok Permission 检查：危险命令拦截 ────────────────────────
|
||
for cmd in cmds:
|
||
dangerous, reason = _check_dangerous(cmd)
|
||
if dangerous:
|
||
log(f" 🔒 危险命令已拦截: {reason}")
|
||
_request_permission(f"[LEARN] {desc}", reason, cmd)
|
||
return # 暂停执行，等待用户确认
|
||
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("!"):
|
||
dangerous, reason = _check_dangerous(action[1:])
|
||
if dangerous:
|
||
log(f" 🔒 [ACT]危险命令已拦截: {reason}")
|
||
_request_permission("[ACT]", reason, action[1:])
|
||
return
|
||
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:
|
||
# ── Grok Permission 检查 ────────────────────────────────────
|
||
for cmd in cmds:
|
||
dangerous, reason = _check_dangerous(cmd)
|
||
if dangerous:
|
||
log(f" 🔒 危险命令已拦截: {reason}")
|
||
_request_permission(f"[SKILL] {desc}", reason, cmd)
|
||
return
|
||
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)
|
||
|
||
# ── session_start hook：daemon 启动时触发 ────────────────────────
|
||
try:
|
||
start_state = collect_state()
|
||
start_state["event"] = "session_start"
|
||
hooks = _load_hooks()
|
||
_run_hooks(hooks, "session_start", start_state)
|
||
except Exception as e:
|
||
log(f" ⚠️ session_start hook 执行失败: {e}")
|
||
|
||
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()
|
||
# ── session_end hook：daemon 关闭前触发 ──────────────────────
|
||
try:
|
||
end_state = collect_state()
|
||
end_state["event"] = "session_end"
|
||
end_hooks = _load_hooks()
|
||
_run_hooks(end_hooks, "session_end", end_state)
|
||
except Exception as e:
|
||
log(f" ⚠️ session_end hook 执行失败: {e}")
|
||
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()
|
||
# ── Permission 队列检查：处理飞书回复的「同意/拒绝/永久允许」────
|
||
_check_pending_permissions()
|
||
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)
|
||
|
||
# ── Grok Build Hook 系统：事件分发 ──────────────────────────────
|
||
hooks = _load_hooks() # 每次 light tick 重新加载 hooks（轻量）
|
||
if changes:
|
||
_run_hooks(hooks, "process_down", state)
|
||
if ctx["tick_count"] % 30 == 0:
|
||
_run_hooks(hooks, "disk_threshold", state)
|
||
_run_hooks(hooks, "memory_high", state)
|
||
|
||
# ── Grok Build Compaction 系统：自动压缩检查 ───────────────────
|
||
state_for_compact = {"patterns": state.get("_patterns", []), "observations": state.get("_observations", [])}
|
||
should_cp, cp_reason = _should_compact(ctx, state_for_compact)
|
||
if should_cp:
|
||
log(f" 🗜️ Compaction 触发: {cp_reason}")
|
||
_trigger_compaction(ctx, state_for_compact)
|
||
|
||
# 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, ctx)
|
||
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})
|
||
|
||
# 8. P0: CBM→织忆 (每4次deep_think)
|
||
if ctx["deep_tick_count"] % 4 == 0:
|
||
try:
|
||
rc, out, err = shell(f"python3 {HERMES}/scripts/cbm-to-zhiyi.py", timeout=30)
|
||
log(f" [P0] cbm→zhiyi → exit={rc} | {out[:100] if out else err[:100]}")
|
||
except Exception as e:
|
||
log(f" ⚠️ [P0] cbm→zhiyi failed: {e}")
|
||
|
||
# 9. P2: 仓颉技能→织忆 (每4次deep_think随机抽查一个skill)
|
||
if ctx["deep_tick_count"] % 4 == 0:
|
||
import random
|
||
try:
|
||
cangjie_skills = ["股票投研体系", "金钱心理学", "金字塔原理", "提示词工程", "王阳明全集", "吴恩达机器学习", "资本论"]
|
||
skill_name = random.choice(cangjie_skills)
|
||
rc, out, err = shell(f"bash {HERMES}/scripts/cangjie-to-zhiyi.sh \"{skill_name}\" \"自动统计\"", timeout=30)
|
||
log(f" [P2] cangjie→zhiyi [{skill_name}] → exit={rc} | {out[:100] if out else err[:100]}")
|
||
except Exception as e:
|
||
log(f" ⚠️ [P2] cangjie→zhiyi failed: {e}")
|
||
|
||
# 10. F5: profile-sync (每4次deep_think同步主状态到织忆)
|
||
try:
|
||
rc, out, err = shell(f"python3 {HERMES}/scripts/profile-sync.py", timeout=15)
|
||
log(f" [F5] profile-sync → exit={rc}")
|
||
except Exception as e:
|
||
log(f" ⚠️ [F5] profile-sync failed: {e}")
|
||
|
||
# 11. F3: OpenClaw bridge (每8次deep_think检查agent状态)
|
||
if ctx["deep_tick_count"] % 8 == 0:
|
||
try:
|
||
rc, out, err = shell(f"python3 {HERMES}/scripts/openclaw-bridge.py status", timeout=30)
|
||
log(f" [F3] openclaw-bridge → exit={rc}")
|
||
except Exception as e:
|
||
log(f" ⚠️ [F3] openclaw-bridge failed: {e}")
|
||
|
||
# 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 3: 织忆 recent_moments → TencentDB L1 同步
|
||
try:
|
||
zhiyi_to_tdb()
|
||
except Exception as e:
|
||
log(f" ⚠️ zhiyi_to_tdb failed: {e}")
|
||
|
||
# 6. L1→L2→L3 蒸馏管道
|
||
try:
|
||
_distill_l1_to_l2()
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l1_to_l2 failed: {e}")
|
||
try:
|
||
_distill_l2_to_l3()
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l2_to_l3 failed: {e}")
|
||
|
||
# 6b. L3→L4 蒸馏（cross-domain policies）
|
||
try:
|
||
_distill_l3_to_l4()
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l3_to_l4 failed: {e}")
|
||
|
||
# 6c. L4→L5 蒸馏（policies → traits）
|
||
try:
|
||
_distill_l4_to_l5()
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l4_to_l5 failed: {e}")
|
||
|
||
# 6d. L5→L6 蒸馏（traits → values）
|
||
try:
|
||
_distill_l5_to_l6()
|
||
except Exception as e:
|
||
log(f" ⚠️ _distill_l5_to_l6 failed: {e}")
|
||
|
||
# 7. Soulful profile → TencentDB L2 scene（soulful_profile_to_tdb_scene）
|
||
try:
|
||
soulful_profile_to_tdb_scene()
|
||
except Exception as e:
|
||
log(f" ⚠️ soulful_profile_to_tdb_scene failed: {e}")
|
||
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
|
||
|
||
# 写入每日 soulful journal
|
||
_write_daily_soulful_journal()
|
||
|
||
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)
|
||
|
||
# ====== Phase 2.1: Cares 过期清理 + 场景感知关怀（每 light tick）======
|
||
_cleanup_expired_cares()
|
||
_check_scene_aware_cares()
|
||
|
||
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()
|
||
|
||
# ====== 跨会话状态持久化：touch（每 30 分钟）======
|
||
if ctx["tick_count"] % TOUCH_INTERVAL_TICKS == 0:
|
||
try:
|
||
st = load_session_state()
|
||
touch_session(st)
|
||
except Exception as e:
|
||
log(f" ⚠️ touch_session failed: {e}")
|
||
|
||
# ====== 跨会话状态持久化：sweep 孤岛引用（每天）======
|
||
if ctx.get("deep_tick_count", 0) % SWEEP_INTERVAL_TICKS == 0:
|
||
try:
|
||
st = load_session_state()
|
||
_sweep_session_state(st)
|
||
except Exception as e:
|
||
log(f" ⚠️ _sweep_session_state failed: {e}")
|
||
|
||
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()
|