improve xiaoniu group awareness and solver suppression

plugins/ai_auto_response/main.py

@@ -163,6 +163,8 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             "timestamp": message.get("timestamp"),
         }
         self._append_group_message(room_id, normalized_message)
+        recent_messages = self.group_messages.get(room_id) or self.memory_store.get_recent_messages(room_id)
+        conversation_hints = self._build_conversation_hints(recent_messages, sender, content)
 
         memory_hints = self.memory_store.build_memory_hints(room_id, sender)
         self._sync_member_memory(room_id, sender, sender_name, memory_hints.get("member_context", {}))
@@ -198,8 +200,15 @@ class AIAutoResponsePlugin(MessagePluginInterface):
         )
 
         allow_proactive = bool(self.mode_config.get("allow_proactive_reply", True))
+        acceptance_state = self.flow_manager.get_acceptance_state(room_id)
         reply_mode = self.response_planner.choose_reply_mode(trigger.__dict__, flow_state.state)
-        should_reply = self.response_planner.should_reply(trigger.__dict__, flow_state.state, allow_proactive)
+        should_reply = self.response_planner.should_reply(
+            trigger.__dict__,
+            flow_state.state,
+            allow_proactive,
+            acceptance_state,
+            conversation_hints,
+        )
         if not should_reply:
             self._log_event(
                 "skip",
@@ -209,6 +218,8 @@ class AIAutoResponsePlugin(MessagePluginInterface):
                 trigger_type=trigger.trigger_type,
                 reply_mode=reply_mode,
                 flow_state=flow_state.state,
+                acceptance_state=acceptance_state,
+                solver=self._yn(conversation_hints.get("has_recent_human_solver")),
             )
             return False, "skip"
         if not self._pass_cooldown(room_id, trigger.__dict__):
@@ -222,7 +233,6 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             )
             return False, "cooldown"
 
-        recent_messages = self.group_messages.get(room_id) or self.memory_store.get_recent_messages(room_id)
         vector_memories = []
         if self.vector_memory.should_search(reply_mode, trigger.trigger_type, memory_hints.get("returning_member_state", "")):
             vector_memories = self.vector_memory.search(content, room_id, sender)
@@ -232,6 +242,7 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             sender=sender,
             group_mode=group_profile.get("mode", ""),
             knowledge_domain=group_profile.get("knowledge_domain", ""),
+            acceptance_state=acceptance_state,
             reply_mode=reply_mode,
             recent_message_count=len(recent_messages),
             vector_hit_count=len(vector_memories),
@@ -372,6 +383,54 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             f"12. 回答时优先服从当前群画像里的知识域和回答风格，不要跨领域乱发挥。\n"
         )
 
+    @staticmethod
+    def _build_conversation_hints(recent_messages: List[Dict], current_sender: str, current_content: str) -> Dict[str, Any]:
+        previous_messages = list(recent_messages[:-1]) if recent_messages else []
+        recent_window = previous_messages[-4:]
+        solver_count = 0
+        solver_senders = set()
+        current_tokens = AIAutoResponsePlugin._extract_overlap_tokens(current_content)
+        for item in recent_window:
+            sender = str(item.get("sender", "") or "")
+            if not sender or sender == current_sender:
+                continue
+            content = str(item.get("content") or item.get("message") or "").strip().lower()
+            if AIAutoResponsePlugin._looks_like_answer(content) and AIAutoResponsePlugin._has_topic_overlap(current_tokens, content):
+                solver_count += 1
+                solver_senders.add(sender)
+        return {
+            "has_recent_human_solver": solver_count >= 2 and len(solver_senders) >= 1,
+            "solver_count": solver_count,
+        }
+
+    @staticmethod
+    def _looks_like_answer(content: str) -> bool:
+        if not content:
+            return False
+        answer_keywords = [
+            "先", "然后", "重启", "配置", "日志", "接口", "看一下", "试试", "排查",
+            "报错", "原因", "因为", "改成", "装", "部署", "重现", "检查", "确认",
+        ]
+        if len(content) >= 18:
+            return True
+        return any(keyword in content for keyword in answer_keywords)
+
+    @staticmethod
+    def _extract_overlap_tokens(content: str) -> set[str]:
+        text = str(content or "").lower()
+        tokens = set(re.findall(r"[a-z0-9_\\-]{3,}", text))
+        for keyword in ["报错", "日志", "配置", "接口", "插件", "部署", "docker", "python", "openclaw", "机器人", "qdrant", "ollama"]:
+            if keyword in text:
+                tokens.add(keyword)
+        return tokens
+
+    @staticmethod
+    def _has_topic_overlap(current_tokens: set[str], previous_content: str) -> bool:
+        if not current_tokens:
+            return False
+        previous_tokens = AIAutoResponsePlugin._extract_overlap_tokens(previous_content)
+        return bool(current_tokens & previous_tokens)
+
     @staticmethod
     def _sanitize_response(response: str) -> str:
         if not response:
@@ -527,13 +586,16 @@ class AIAutoResponsePlugin(MessagePluginInterface):
                 f"[XIAONIU] SKIP room={room} user={sender} "
                 f"reason={data.get('reason', '')} "
                 f"trigger={data.get('trigger_type', 'none')} "
-                f"mode={data.get('reply_mode', '')}"
+                f"mode={data.get('reply_mode', '')} "
+                f"acc={data.get('acceptance_state', '-') or '-'} "
+                f"solver={data.get('solver', '-') or '-'}"
             ).strip()
 
         if event == "context":
             return (
                 f"[XIAONIU] CTX room={room} user={sender} "
                 f"mode={data.get('reply_mode', '')} "
+                f"acc={data.get('acceptance_state', '-') or '-'} "
                 f"recent={data.get('recent_message_count', 0)} "
                 f"vector={data.get('vector_hit_count', 0)}"
             ).strip()