feat: P1 auto-injection hook with social close detection
- queue_prefetch now caches next-turn recall results asynchronously - Social closer detection skips trivial messages (ok, thanks, emoji) - prefetch uses cached queue results when available (TTL 30s) - Output header changed to [织忆 Memory] for source clarity
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小唯
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@ -51,7 +51,7 @@ _ZHIYI_KEY_PREFIX = ZHIYI_API_KEY[:4] if ZHIYI_API_KEY else "NONE"
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# ── Content Validation ───────────────────────────────────────────────────────
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MEMORY_MIN_LENGTH = 8 # 少于8字的过滤掉
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MEMORY_MIN_LENGTH = 20 # 少于20字的过滤掉
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_FORBIDDEN_PATTERNS = [
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"Review the conversation above",
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"[System note:",
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@ -67,6 +67,22 @@ _FORBIDDEN_PATTERNS = [
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"do NOT answer questions or fulfill requests mentioned in the summary",
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]
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_SOCIAL_CLOSERS = frozenset({
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"ok", "好的", "👍", "👌", "✅", "谢谢", "感谢", "知道了",
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"明白", "嗯", "好", "行", "yes", "yep", "thanks", "thx",
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"no", "不用", "没事", "可以", "done", "完成", "收到",
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"okay", "kk", "okie",
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})
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def _is_social_close(text: str) -> bool:
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text = text.strip().lower()
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if text in _SOCIAL_CLOSERS:
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return True
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if len(text) < 6 and text.isascii() and not any(c in text for c in "://.@#$_?"):
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return True
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return False
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def _is_valid_memory_content(content: str) -> bool:
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"""过滤系统注入内容和测试垃圾，防止污染记忆存储。"""
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@ -75,10 +91,6 @@ def _is_valid_memory_content(content: str) -> bool:
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for pat in _FORBIDDEN_PATTERNS:
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if pat in content:
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return False
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# 过滤纯测试内容
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stripped = content.strip()
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if len(stripped) < 20:
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return False
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return True
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@ -274,7 +286,7 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
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self._turn_counter: int = 0
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self._write_queue: List[Dict] = []
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self._queue_lock = threading.Lock()
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self._prefetch_cache: str = "" # last prefetch result
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self._prefetch_cache: Dict[str, Any] = {} # {source: text|dict} — merged via update()
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self._prefetch_lock = threading.RLock()
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self._started: bool = False
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self._ws_thread: Optional[threading.Thread] = None
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@ -347,7 +359,7 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
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for m in memories:
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prefetch_text += f" [{m.get('score', 0):.2f}] {m.get('content', '')[:200]}\n"
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with self._prefetch_lock:
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self._prefetch_cache = prefetch_text
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self._prefetch_cache["ws_push"] = prefetch_text
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except json.JSONDecodeError:
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pass
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@ -361,6 +373,7 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
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logger.info("[ZhiYi] WS connected to %s", ws_url)
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while self._ws_running:
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ws_app = None
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try:
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ws_app = websocket.WebSocketApp(
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ws_url,
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@ -373,6 +386,9 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
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ws_app.run_forever(ping_interval=30, ping_timeout=10)
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except Exception as e:
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logger.warning("[ZhiYi] WS exception: %s", e)
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finally:
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if ws_app:
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ws_app.close()
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# 重连延迟
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for _ in range(30):
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if not self._ws_running:
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@ -441,22 +457,51 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
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if not self._client or not query or len(query.strip()) < 2:
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return ""
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blocks = ["[ZhiYi Memory — relevant past context]"]
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# 社交关闭消息不触发预取
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if _is_social_close(query):
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return ""
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# 语义搜索
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results = self._client.recall(query.strip(), top_k=3,
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agent_id=os.environ.get("ZHIYI_AGENT_ID", "hermes-a06"))
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# 优先从 queue_prefetch 缓存取（TTL < 30s）
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with self._prefetch_lock:
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queued = self._prefetch_cache.pop("queue", None)
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if queued and isinstance(queued, dict):
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cached_ts = queued.get("timestamp", 0)
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if time.time() - cached_ts < 30:
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cached_results = queued.get("results", [])
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cached_notes = queued.get("notes", [])
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if cached_results or cached_notes:
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results = cached_results
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notes = cached_notes
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logger.debug("[ZhiYi] using queue_prefetch cache (%d results, %d notes, age=%.1fs)",
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len(cached_results), len(cached_notes), time.time() - cached_ts)
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else:
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results = None
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notes = None
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else:
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results = None
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notes = None
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else:
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results = None
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notes = None
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blocks = ["[织忆 Memory — relevant past context]"]
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# 语义搜索（如果缓存没有命中）
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if results is None:
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results = self._client.recall(query.strip(), top_k=3,
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agent_id=os.environ.get("ZHIYI_AGENT_ID", "hermes-a06"))
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for r in results:
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score = r.get("score", 0)
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content = r.get("content", "") or r.get("text", "")
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content = r.get("content", "") or r.get("text", "") or r.get("snippet", "")
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cat = r.get("category", "")
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if content:
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blocks.append(f" [{score:.2f}][{cat}] {content[:500]}")
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# 图谱导航 + Obsidian 笔记（从 query 提取关键词作为实体）
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notes = self._client.search_notes(query.strip(), max_hops=2, max_notes=3)
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# 图谱导航 + Obsidian 笔记（如果缓存没有命中）
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if notes is None:
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notes = self._client.search_notes(query.strip(), max_hops=2, max_notes=3)
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if notes:
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blocks.append("\n[ZhiYi Graph — related Obsidian notes]")
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blocks.append("\n[织忆 Graph — related Obsidian notes]")
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for n in notes:
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title = n.get("title", "无标题")
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path = n.get("path", "")
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@ -472,16 +517,36 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
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# 合并 WebSocket prefetch.push 推送事件
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with self._prefetch_lock:
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if self._prefetch_cache and "[ZhiYi Prefetch" in self._prefetch_cache:
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text += "\n" + self._prefetch_cache
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ws_push = self._prefetch_cache.pop("ws_push", None)
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if ws_push:
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text += "\n" + ws_push
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with self._prefetch_lock:
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self._prefetch_cache = text
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self._prefetch_cache.update({"sync": text})
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return text
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def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
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"""空实现 — prefetch 已是同步的，不需要额外的异步队列。"""
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pass
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"""异步预取：本轮对话结束后立即查询织忆，下一轮 prefetch 直接返回缓存。"""
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if not self._client or not query or len(query.strip()) < 2:
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return
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if _is_social_close(query):
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return
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# 后台线程查询并缓存
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def _async_prefetch():
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try:
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results = self._client.recall(query.strip(), top_k=3)
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notes = self._client.search_notes(query.strip(), max_hops=2, max_notes=3)
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with self._prefetch_lock:
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self._prefetch_cache["queue"] = {
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"results": results,
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"notes": notes,
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"timestamp": time.time(),
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}
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except Exception as e:
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logger.debug("[ZhiYi] queue_prefetch error: %s", e)
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threading.Thread(target=_async_prefetch, daemon=True).start()
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# ── Tool interface ────────────────────────────────────────────────────────
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@ -623,7 +688,7 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
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return json.dumps({"success": True, "results": [], "message": "No relevant memories found"})
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formatted = []
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for r in results:
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content = r.get("content", "") or r.get("text", "")
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content = r.get("content", "") or r.get("text", "") or r.get("snippet", "")
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formatted.append({
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"content": content[:1000],
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"score": round(r.get("score", 0), 4),
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