fix：修复神秘魔法士

src/plugins/heartFC_chat/heartFC_chat.py

@@ -654,8 +654,6 @@ class HeartFChatting:
                 logger.warning(f"{log_prefix}[Replier-{thinking_id}] LLM生成了一个空回复集。")
                 return None
 
-            # --- 准备并返回结果 --- #
-            # logger.info(f"{log_prefix}[Replier-{thinking_id}] 成功生成了回复集: {' '.join(response_set)[:50]}...")
             return response_set
 
         except Exception as e:

src/plugins/heartFC_chat/heartFC_generator.py

@@ -47,10 +47,6 @@ class HeartFCGenerator:
     ) -> Optional[List[str]]:
         """根据当前模型类型选择对应的生成函数"""
 
-        logger.info(
-            f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
-        )
-
         arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
 
         with Timer() as t_generate_response:
@@ -80,27 +76,25 @@ class HeartFCGenerator:
         model: LLMRequest,
         thinking_id: str,
     ) -> str:
-        sender_name = ""
-
         info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
 
-        sender_name = f"<{message.chat_stream.user_info.platform}:{message.chat_stream.user_info.user_id}:{message.chat_stream.user_info.user_nickname}:{message.chat_stream.user_info.user_cardname}>"
-
         with Timer() as t_build_prompt:
             prompt = await prompt_builder.build_prompt(
                 build_mode="focus",
                 reason=reason,
                 current_mind_info=current_mind_info,
                 structured_info=structured_info,
-                message_txt=message.processed_plain_text,
-                sender_name=sender_name,
+                message_txt="",
+                sender_name="",
                 chat_stream=message.chat_stream,
             )
-        logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
+        # logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
 
         try:
             content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
 
+            logger.info(f"\nprompt:{prompt}\n生成回复{content}\n")
+
             info_catcher.catch_after_llm_generated(
                 prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
             )

src/plugins/heartFC_chat/heartflow_prompt_builder.py

@@ -25,13 +25,13 @@ def init_prompt():
 {structured_info}
 {chat_target}
 {chat_talking_prompt}
-现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
+现在你想要在群里发言或者回复。\n
 你的网名叫{bot_name}，{prompt_personality} {prompt_identity}。
 你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
 你刚刚脑子里在想：
 {current_mind_info}
 {reason}
-回复尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。请一次只回复一个话题，不要同时回复多个人。{prompt_ger}
+回复尽量简短一些。请注意把握聊天内容，不要回复的太有条理，可以有个性。请一次只回复一个话题，不要同时回复多个人，不用指出你回复的是谁。{prompt_ger}
 请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话 ，注意只输出回复内容。
 {moderation_prompt}。注意：不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""",
         "heart_flow_prompt",
@@ -95,12 +95,12 @@ class PromptBuilder:
 
         elif build_mode == "focus":
             return await self._build_prompt_focus(
-                reason, current_mind_info, structured_info, chat_stream, message_txt, sender_name
+                reason, current_mind_info, structured_info, chat_stream,
             )
         return None
 
     async def _build_prompt_focus(
-        self, reason, current_mind_info, structured_info, chat_stream, message_txt: str, sender_name: str = "某人"
+        self, reason, current_mind_info, structured_info, chat_stream
     ) -> tuple[str, str]:
         individuality = Individuality.get_instance()
         prompt_personality = individuality.get_prompt(type="personality", x_person=2, level=1)
@@ -128,26 +128,6 @@ class PromptBuilder:
             read_mark=0.0,
         )
 
-        # 关键词检测与反应
-        keywords_reaction_prompt = ""
-        for rule in global_config.keywords_reaction_rules:
-            if rule.get("enable", False):
-                if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
-                    logger.info(
-                        f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
-                    )
-                    keywords_reaction_prompt += rule.get("reaction", "") + "，"
-                else:
-                    for pattern in rule.get("regex", []):
-                        result = pattern.search(message_txt)
-                        if result:
-                            reaction = rule.get("reaction", "")
-                            for name, content in result.groupdict().items():
-                                reaction = reaction.replace(f"[{name}]", content)
-                            logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
-                            keywords_reaction_prompt += reaction + "，"
-                            break
-
         # 中文高手(新加的好玩功能)
         prompt_ger = ""
         if random.random() < 0.04:
@@ -164,8 +144,6 @@ class PromptBuilder:
             if chat_in_group
             else await global_prompt_manager.get_prompt_async("chat_target_private1"),
             chat_talking_prompt=chat_talking_prompt,
-            sender_name=sender_name,
-            message_txt=message_txt,
             bot_name=global_config.BOT_NICKNAME,
             prompt_personality=prompt_personality,
             prompt_identity=prompt_identity,
@@ -174,7 +152,6 @@ class PromptBuilder:
             else await global_prompt_manager.get_prompt_async("chat_target_private2"),
             current_mind_info=current_mind_info,
             reason=reason,
-            keywords_reaction_prompt=keywords_reaction_prompt,
             prompt_ger=prompt_ger,
             moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
         )