remove：移除tool_use模型，修复Jargon提取问题，修改统计为tool统计

src/chat/replyer/group_generator.py

@@ -577,29 +577,6 @@ class DefaultReplyer:
         duration = end_time - start_time
         return name, result, duration
 
-    async def _build_disabled_jargon_explanation(self) -> str:
-        """当关闭黑话解释时使用的占位协程，避免额外的LLM调用"""
-        return ""
-
-    async def _build_unknown_words_jargon(self, unknown_words: Optional[List[str]], chat_id: str) -> str:
-        """针对 Planner 提供的未知词语列表执行黑话检索"""
-        if not unknown_words:
-            return ""
-        # 清洗未知词语列表，只保留非空字符串
-        concepts: List[str] = []
-        for item in unknown_words:
-            if isinstance(item, str):
-                s = item.strip()
-                if s:
-                    concepts.append(s)
-        if not concepts:
-            return ""
-        try:
-            return await retrieve_concepts_with_jargon(concepts, chat_id)
-        except Exception as e:
-            logger.error(f"未知词语黑话检索失败: {e}")
-            return ""
-
     async def _build_jargon_explanation(
         self,
         chat_id: str,
@@ -609,19 +586,14 @@ class DefaultReplyer:
     ) -> str:
         """
         统一的黑话解释构建函数：
-        - 根据 enable_jargon_explanation / jargon_mode 决定具体策略
+        - 根据 enable_jargon_explanation 决定是否启用
         """
+        del unknown_words
         enable_jargon_explanation = getattr(global_config.expression, "enable_jargon_explanation", True)
         if not enable_jargon_explanation:
             return ""
 
-        jargon_mode = getattr(global_config.expression, "jargon_mode", "context")
-
-        # planner 模式：仅使用 Planner 的 unknown_words
-        if jargon_mode == "planner":
-            return await self._build_unknown_words_jargon(unknown_words, chat_id)
-
-        # 默认 / context 模式：使用上下文自动匹配黑话
+        # 使用上下文自动匹配黑话
         try:
             return await explain_jargon_in_context(chat_id, messages_short, chat_talking_prompt_short) or ""
         except Exception as e:
@@ -1209,7 +1181,7 @@ class DefaultReplyer:
             prompt = await prompt_manager.render_prompt(template_prompt)
             generation_result = await llm_api.generate(
                 llm_api.LLMServiceRequest(
-                    task_name="tool_use",
+                    task_name="utils",
                     request_type="replyer.lpmm_knowledge",
                     prompt=prompt,
                     tool_options=[search_knowledge_tool.get_tool_definition()],

src/chat/replyer/maisaka_generator.py

@@ -20,8 +20,8 @@ from src.services.llm_service import LLMServiceClient
 from src.maisaka.message_adapter import (
     get_message_kind,
     get_message_role,
+    get_message_source,
     get_message_text,
-    is_perception_message,
     parse_speaker_content,
 )
 
@@ -121,6 +121,9 @@ class MaisakaReplyGenerator:
             role = get_message_role(message)
             timestamp = self._format_message_time(message)
 
+            if get_message_source(message) == "user_reference":
+                continue
+
             if role == "user":
                 guided_reply = self._extract_guided_bot_reply(message)
                 if guided_reply:
@@ -148,7 +151,6 @@ class MaisakaReplyGenerator:
         chat_history: List[SessionMessage],
         reply_reason: str,
         expression_habits: str = "",
-        jargon_explanation: str = "",
     ) -> str:
         """构建 Maisaka replyer 提示词。"""
         current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -167,8 +169,6 @@ class MaisakaReplyGenerator:
         extra_sections: List[str] = []
         if expression_habits.strip():
             extra_sections.append(expression_habits.strip())
-        if jargon_explanation.strip():
-            extra_sections.append(jargon_explanation.strip())
 
         user_sections = [
             f"当前时间：{current_time}",
@@ -198,7 +198,6 @@ class MaisakaReplyGenerator:
         log_reply: bool = True,
         chat_history: Optional[List[SessionMessage]] = None,
         expression_habits: str = "",
-        jargon_explanation: str = "",
         selected_expression_ids: Optional[List[int]] = None,
     ) -> Tuple[bool, ReplyGenerationResult]:
         """结合上下文生成 Maisaka 的最终可见回复。"""
@@ -223,20 +222,20 @@ class MaisakaReplyGenerator:
             f"Maisaka replyer start: stream_id={stream_id} reply_reason={reply_reason!r} "
             f"history_size={len(chat_history)} target_message_id="
             f"{reply_message.message_id if reply_message else None} "
-            f"expression_count={len(result.selected_expression_ids)} "
-            f"jargon_enabled={bool(jargon_explanation.strip())}"
+            f"expression_count={len(result.selected_expression_ids)}"
         )
 
         filtered_history = [
             message
             for message in chat_history
-            if get_message_role(message) != "system" and get_message_kind(message) != "perception"
+            if get_message_role(message) != "system"
+            and get_message_kind(message) != "perception"
+            and get_message_source(message) != "user_reference"
         ]
         prompt = self._build_prompt(
             chat_history=filtered_history,
             reply_reason=reply_reason or "",
             expression_habits=expression_habits,
-            jargon_explanation=jargon_explanation,
         )
         result.completion.request_prompt = prompt