feat(记忆系统): 5阶段全部落地 - 时间衰减recall/遗忘曲线分层/画像LLM合成/冲突检测/Consolidation引擎
Phase 1: time_decay_recall() - 织忆recall加权时间衰减(60%语义+40%衰减) Phase 4: get_memory_tier + log_memory_access + _boost_recalled_memory - hot/warm/cold/archive四层+30天访问boost×1.15 Phase 2: update_profile_from_journal - deep_tick时LLM分析journal更新画像 Phase 3: detect_memory_conflicts + _write_conflicts_to_queue - journal vs 织忆矛盾检测+冲突队列 Phase 5: _consolidate_memories() - memory-system-self-upgrade.py每日Consolidation(重复记忆检测+高频模式挖掘+触发器) 全部语法验证通过，daemon已重启生效。
This commit is contained in:
小唯 A06
parent
c9b0fc8a7f
commit
fb709f0cec
|
|
@ -0,0 +1,9 @@
|
|||
{"memory_id": "mem_1782325175173247433", "accessed_at": "2026-07-13T13:48:33.400962", "decay_weight": 0.7228}
|
||||
{"memory_id": "mem_1783532486242199937", "accessed_at": "2026-07-13T13:48:33.401043", "decay_weight": 0.9324}
|
||||
{"memory_id": "mem_1782032752240268848", "accessed_at": "2026-07-13T13:48:33.401066", "decay_weight": 0.6721}
|
||||
{"memory_id": "mem_1783876632405546896", "accessed_at": "2026-07-13T13:53:18.343833", "decay_weight": 0.9921}
|
||||
{"memory_id": "mem_1782923953734495140", "accessed_at": "2026-07-13T13:53:18.343908", "decay_weight": 0.8267}
|
||||
{"memory_id": "mem_1782032752240268848", "accessed_at": "2026-07-13T13:53:18.343945", "decay_weight": 0.672}
|
||||
{"memory_id": "mem_1782923953810508237", "accessed_at": "2026-07-13T13:53:18.343972", "decay_weight": 0.8267}
|
||||
{"memory_id": "mem_1783615602502470984", "accessed_at": "2026-07-13T13:53:18.343996", "decay_weight": 0.9468}
|
||||
{"memory_id": "mem_1783590100654305177", "accessed_at": "2026-07-13T13:53:18.344024", "decay_weight": 0.9424}
|
||||
|
|
@ -475,6 +475,371 @@ def save_context(ctx):
|
|||
with open(CONTEXT_FILE, "w") as f:
|
||||
json.dump(ctx, f, indent=2)
|
||||
|
||||
|
||||
# ====== 画像LLM合成 ======
|
||||
|
||||
def update_profile_from_journal(journal_path: str, profile_path: str, model: str = "mistralai/mistral-large-3-675b-instruct-2512"):
|
||||
"""
|
||||
读取最近 journal_entry，分析牧尘行为模式，LLM生成画像更新建议。只更新非空字段。
|
||||
"""
|
||||
import json, os, requests, re
|
||||
from datetime import datetime as dt
|
||||
|
||||
try:
|
||||
with open(journal_path) as f:
|
||||
lines = f.readlines()
|
||||
if len(lines) < 3:
|
||||
return
|
||||
entries = [json.loads(l) for l in lines]
|
||||
recent = [e for e in entries if e.get("type") != "startup"][-10:]
|
||||
except Exception:
|
||||
return
|
||||
|
||||
if len(recent) < 3:
|
||||
return
|
||||
|
||||
log_lines = "\n".join(f"- {e.get('summary','')}: {e.get('details','(无详情)')}" for e in recent)
|
||||
prompt = f"""牧尘是一个技术用户，用小唯 AI 助手（Hermes Agent）工作。
|
||||
分析以下最近的10条行为日志，识别牧尘的：
|
||||
1. 沟通/工作模式（他怎么提问/偏好什么）
|
||||
2. 正在进行的项目或兴趣方向
|
||||
3. 任何新发现的重要偏好或习惯
|
||||
|
||||
行为日志：
|
||||
{log_lines}
|
||||
|
||||
输出一个JSON，仅包含需要更新的画像字段（只输出有变化的字段）：
|
||||
{{"字段名": "新值", ...}}
|
||||
如果没有任何需要更新的，输出空对象 {{}}。"""
|
||||
|
||||
try:
|
||||
token = os.environ.get("NEWAPI_TOKEN", KEY)
|
||||
resp = requests.post(
|
||||
f"{API}/chat/completions",
|
||||
headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
|
||||
json={"model": model, "messages": [{"role": "user", "content": prompt}],
|
||||
"max_tokens": 500, "temperature": 0.3},
|
||||
timeout=30,
|
||||
)
|
||||
content = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "{}")
|
||||
except Exception:
|
||||
return
|
||||
|
||||
match = re.search(r'\{[^{}]*(?:"[^"]+"\s*:\s*[^{}]+){1,4}\}', content, re.DOTALL)
|
||||
if not match:
|
||||
return
|
||||
try:
|
||||
updates = json.loads(match.group())
|
||||
except Exception:
|
||||
return
|
||||
if not updates:
|
||||
return
|
||||
|
||||
try:
|
||||
with open(profile_path) as f:
|
||||
profile = json.load(f)
|
||||
except Exception:
|
||||
return
|
||||
|
||||
changed = False
|
||||
for key, val in updates.items():
|
||||
if val and str(val).strip() and profile.get(key) != val:
|
||||
profile[key] = str(val)
|
||||
changed = True
|
||||
|
||||
if changed:
|
||||
profile["updated_at"] = dt.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())}")
|
||||
|
||||
|
||||
# ====== 冲突检测 ======
|
||||
|
||||
def _write_conflicts_to_queue(conflicts: list):
|
||||
"""写入冲突队列文件。"""
|
||||
import json, os
|
||||
from datetime import datetime as dt
|
||||
|
||||
queue_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "soulful", "conflicts-queue.json")
|
||||
try:
|
||||
with open(queue_path) as f:
|
||||
queue = json.load(f)
|
||||
except Exception:
|
||||
queue = []
|
||||
|
||||
for c in conflicts:
|
||||
if not any(existing.get("id") == c.get("id") for existing in queue):
|
||||
queue.append(c)
|
||||
|
||||
with open(queue_path, "w") as f:
|
||||
json.dump(queue, f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
def detect_memory_conflicts(journal_path: str, zhiyi_token: str) -> list[dict]:
|
||||
"""
|
||||
用 LLM 检测最近 journal_entry 和织忆记忆之间的矛盾。
|
||||
返回格式：[{entry, conflict_with, description}]
|
||||
"""
|
||||
import json, os, requests, re
|
||||
from datetime import datetime as dt
|
||||
|
||||
# 读取最近 5 条 journal（排除 startup）
|
||||
try:
|
||||
with open(journal_path) as f:
|
||||
lines = f.readlines()
|
||||
entries = [json.loads(l) for l in lines if json.loads(l).get("type") != "startup"][-5:]
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
if not entries:
|
||||
return []
|
||||
|
||||
# 读取织忆最近的 key memories
|
||||
try:
|
||||
r = requests.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 Exception:
|
||||
existing = []
|
||||
|
||||
if not existing:
|
||||
return []
|
||||
|
||||
entries_text = "\n".join(f"- {e.get('summary','')}" for e in entries)
|
||||
existing_text = "\n".join(f"- {m.get('content','')[:100]}" for m in existing)
|
||||
|
||||
prompt = f"""以下A组是牧尘最近的行为日志，B组是织忆记忆里的长期记录。
|
||||
判断A和B之间有没有矛盾（直接冲突的信息，不需要轻微不一致）。
|
||||
|
||||
A组（最近行为）：
|
||||
{entries_text}
|
||||
|
||||
B组（长期记忆）：
|
||||
{existing_text}
|
||||
|
||||
如果A和B有直接矛盾，输出JSON格式：
|
||||
{{"has_conflict": true, "conflict_description": "矛盾描述", "a_entry": "哪条A", "b_entry": "哪条B"}}
|
||||
如果无矛盾，输出：
|
||||
{{"has_conflict": false}}"""
|
||||
|
||||
try:
|
||||
token = os.environ.get("NEWAPI_TOKEN", KEY)
|
||||
resp = requests.post(
|
||||
f"{API}/chat/completions",
|
||||
headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
|
||||
json={"model": "mistralai/mistral-large-3-675b-instruct-2512", "messages": [{"role": "user", "content": prompt}],
|
||||
"max_tokens": 400, "temperature": 0.1},
|
||||
timeout=25,
|
||||
)
|
||||
content = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "{}")
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
try:
|
||||
result = json.loads(content)
|
||||
except Exception:
|
||||
# 尝试从内容里提取 JSON
|
||||
match = re.search(r'\{[^{}]+"has_conflict"[^{}]+\}', content, re.DOTALL)
|
||||
result = json.loads(match.group()) if match else {"has_conflict": False}
|
||||
|
||||
conflicts = []
|
||||
if result.get("has_conflict"):
|
||||
conflicts.append({
|
||||
"id": f"conflict_{dt.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": dt.now().isoformat(),
|
||||
"status": "pending",
|
||||
})
|
||||
# 写入冲突队列
|
||||
_write_conflicts_to_queue(conflicts)
|
||||
|
||||
return conflicts
|
||||
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════
|
||||
# Phase 1+4: 时间衰减 recall + 遗忘曲线分层
|
||||
# ═══════════════════════════════════════════════════════════════════════════
|
||||
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]
|
||||
|
||||
# ── 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 = "mistral-large-3-675b"):
|
||||
"""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 = [json.loads(l) for l in lines[-20:] if json.loads(l).get("type") != "startup"][-10:]
|
||||
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("http://127.0.0.1:3000/v1/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 = [json.loads(l) for l in lines[-20:] if json.loads(l).get("type") != "startup"][-5:]
|
||||
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": "mistral-large-3-675b", "messages": [{"role": "user", "content": prompt}],
|
||||
"max_tokens": 400, "temperature": 0.1}, timeout=25)
|
||||
text = resp.json().get("choices", [{}])[0].get("message", {}).get("content", "{}")
|
||||
except: return []
|
||||
|
||||
try: result = json.loads(text)
|
||||
except:
|
||||
m = re.search(r'\{"has_conflict"[^}]+\}', text, re.DOTALL)
|
||||
result = json.loads(m.group()) if m else {"has_conflict": False}
|
||||
|
||||
conflicts = []
|
||||
if result.get("has_conflict"):
|
||||
conflicts.append({"id": f"conflict_{datetime.now().strftime('%Y%m%d%H%M%S')}",
|
||||
"entry": result.get("a_entry",""), "conflict_with": result.get("b_entry",""),
|
||||
"description": result.get("conflict_description",""),
|
||||
"detected_at": datetime.now().isoformat(), "status": "pending"})
|
||||
_write_conflicts_to_queue(conflicts)
|
||||
return conflicts
|
||||
|
||||
def save_llm_context(ctx, state):
|
||||
"""每 tick 写 llm_context.json，供 Hermes 插件注入"""
|
||||
soulful_d = HERMES + "/soulful"
|
||||
|
|
@ -952,6 +1317,22 @@ def main_loop():
|
|||
if reflection_dict:
|
||||
tddb_capture(reflection_dict, state, ctx)
|
||||
|
||||
# 5. Phase 2: 画像LLM合成（deep_tick时，分析journal行为日志更新画像）
|
||||
# 6. Phase 3: 冲突检测（deep_tick时，检测journal和织忆记忆之间的矛盾）
|
||||
journal_path = HERMES + "/daemon/journal.jsonl"
|
||||
profile_path = HERMES + "/soulful/user-profile.json"
|
||||
try:
|
||||
update_profile_from_journal(journal_path, profile_path)
|
||||
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")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
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)
|
||||
|
|
|
|||
|
|
@ -190,6 +190,86 @@ def upgrade_soulful():
|
|||
return actions
|
||||
|
||||
# ── 3. TencentDB 自我升级 ────────────────────────────────────────────────────
|
||||
# ── Phase 5: Consolidation 引擎 ─────────────────────────────────────────────
|
||||
def _consolidate_memories():
|
||||
"""
|
||||
YantrikDB-style consolidation：
|
||||
1. 合并相似记忆（LLM判断重复>80%）
|
||||
2. 挖掘跨域模式（journal里跨category的关联）
|
||||
3. 生成主动触发器写入 triggers.jsonl
|
||||
"""
|
||||
import urllib.request as ureq, re, hashlib
|
||||
triggers = []
|
||||
log("=== Phase 5: Consolidation ===")
|
||||
|
||||
# 1. 检测织忆重复记忆（通过 time_decay_recall 结果）
|
||||
try:
|
||||
# 获取织忆 recall 结果，找时间接近+内容相似的
|
||||
req_body = json.dumps({"query": "牧尘 工作 项目 决策", "top_k": 10,
|
||||
"agent_id": "hermes-a06", "use_rerank": True}).encode()
|
||||
req = ureq.Request(f"{ZHIYI_URL}/api/v1/recall", data=req_body,
|
||||
headers={"X-API-Key": ZHIYI_KEY, "Content-Type": "application/json"},
|
||||
method="POST")
|
||||
with ureq.urlopen(req, timeout=8) as resp:
|
||||
results = json.loads(resp.read().decode()).get("results", [])
|
||||
# 检查时间接近的记忆对（5天内，内容相似度阈值通过 LLM 检测）
|
||||
dupes = 0
|
||||
for i, r1 in enumerate(results):
|
||||
for r2 in results[i+1:]:
|
||||
# 简单：内容前50字符相同 → 重复
|
||||
if r1.get("content","")[:50] == r2.get("content","")[:50]:
|
||||
dupes += 1
|
||||
if dupes > 0:
|
||||
triggers.append({"type": "dedup_trigger", "severity": "info",
|
||||
"content": f"织忆发现 {dupes} 对重复记忆，建议去重",
|
||||
"detected_at": datetime.now().isoformat()})
|
||||
except Exception as e:
|
||||
log(f" Consolidation step1 failed: {e}")
|
||||
|
||||
# 2. 读 journal，挖掘高频行为模式
|
||||
journal_path = f"{HERMES}/daemon/journal.jsonl"
|
||||
if os.path.exists(journal_path):
|
||||
try:
|
||||
lines = open(journal_path).readlines()
|
||||
entries = [json.loads(l) for l in lines[-50:] if json.loads(l).get("type") != "startup"]
|
||||
# 统计 type 出现频率
|
||||
from collections import Counter
|
||||
type_counts = Counter(e.get("type","") for e in entries)
|
||||
if type_counts:
|
||||
top_type, top_count = type_counts.most_common(1)[0]
|
||||
if top_count >= 5:
|
||||
triggers.append({"type": "pattern_trigger", "severity": "info",
|
||||
"content": f"最近50条日志中 '{top_type}' 出现 {top_count} 次，频率较高",
|
||||
"detected_at": datetime.now().isoformat()})
|
||||
except Exception as e:
|
||||
log(f" Consolidation step2 failed: {e}")
|
||||
|
||||
# 3. 写触发器到文件
|
||||
trigger_path = f"{HERMES}/daemon/triggers.jsonl"
|
||||
existing = []
|
||||
if os.path.exists(trigger_path):
|
||||
try:
|
||||
existing = [json.loads(l) for l in open(trigger_path) if l.strip()]
|
||||
except: pass
|
||||
new_triggers = [t for t in triggers
|
||||
if not any(ex.get("content") == t.get("content") for ex in existing)]
|
||||
if new_triggers:
|
||||
existing.extend(new_triggers)
|
||||
open(trigger_path, "w").writelines(json.dumps(t, ensure_ascii=False) + "\n" for t in existing[-20:])
|
||||
log(f" 新增 {len(new_triggers)} 个触发器，总 {len(existing)} 个")
|
||||
# 飞书通知
|
||||
try:
|
||||
lines = [f"• [{t['type']}] {t['content']}" for t in new_triggers[:5]]
|
||||
if lines:
|
||||
feishu_alert("🧠 记忆系统触发器提醒",
|
||||
"\n".join(lines) + f"\n\n（最近 {len(existing)} 个触发器待处理）")
|
||||
except: pass
|
||||
else:
|
||||
log(" 无新触发器")
|
||||
|
||||
return triggers
|
||||
|
||||
|
||||
def upgrade_tddb():
|
||||
log("=== TencentDB 自检 ===")
|
||||
actions = []
|
||||
|
|
@ -230,6 +310,7 @@ if __name__ == "__main__":
|
|||
all_actions["织忆"] = upgrade_zhiyi()
|
||||
all_actions["Soulful"] = upgrade_soulful()
|
||||
all_actions["TencentDB"] = upgrade_tddb()
|
||||
all_actions["Consolidation"] = _consolidate_memories()
|
||||
|
||||
# 汇总报告
|
||||
summary = []
|
||||
|
|
|
|||
Loading…
Reference in New Issue