feat：可选打开prompt显示，

src/chat/normal_chat/normal_chat.py

@@ -224,7 +224,7 @@ class NormalChat:
         mark_head = False
         first_bot_msg = None
         for msg in response_set:
-            if global_config.experimental.debug_show_chat_mode:
+            if global_config.debug.debug_show_chat_mode:
                 msg += "ⁿ"
             message_segment = Seg(type="text", data=msg)
             bot_message = MessageSending(
@@ -434,11 +434,12 @@ class NormalChat:
         # current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
         # 使用 self.stream_id
         # willing_log = f"[激活值:{await willing_manager.get_willing(self.stream_id):.2f}]" if is_willing else ""
-        logger.info(
-            f"[{mes_name}]"
-            f"{message.message_info.user_info.user_nickname}:"  # 使用 self.chat_stream
-            f"{message.processed_plain_text}[兴趣:{interested_rate:.2f}][回复概率:{reply_probability * 100:.1f}%]"
-        )
+        if reply_probability > 0.1:
+            logger.info(
+                f"[{mes_name}]"
+                f"{message.message_info.user_info.user_nickname}:"  # 使用 self.chat_stream
+                f"{message.processed_plain_text}[兴趣:{interested_rate:.2f}][回复概率:{reply_probability * 100:.1f}%]"
+            )
         do_reply = False
         response_set = None  # 初始化 response_set
         if random() < reply_probability:

src/chat/planner_actions/planner.py

@@ -119,10 +119,11 @@ class ActionPlanner:
             try:
                 llm_content, (reasoning_content, _) = await self.planner_llm.generate_response_async(prompt=prompt)
 
-                logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
-                logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
-                if reasoning_content:
-                    logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
+                if global_config.debug.show_prompt:
+                    logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
+                    logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
+                    if reasoning_content:
+                        logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
 
             except Exception as req_e:
                 logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")

src/chat/replyer/default_generator.py

@@ -217,7 +217,9 @@ class DefaultReplyer:
                         request_type=self.request_type,
                     )
 
-                    logger.info(f"{self.log_prefix}Prompt:\n{prompt}\n")
+                    if global_config.debug.show_prompt:
+                        logger.info(f"{self.log_prefix}Prompt:\n{prompt}\n")
+
                     content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt)
 
                     logger.info(f"最终回复: {content}")
@@ -560,7 +562,9 @@ class DefaultReplyer:
         for name, result, duration in task_results:
             results_dict[name] = result
             timing_logs.append(f"{name}: {duration:.4f}s")
-        logger.info(f"回复生成前信息获取时间: {'; '.join(timing_logs)}")
+            if duration > 8:
+                logger.warning(f"回复生成前信息获取耗时过长: {name} 耗时: {duration:.4f}s，请使用更快的模型")
+        logger.info(f"回复生成前信息获取耗时: {'; '.join(timing_logs)}")
 
         expression_habits_block = results_dict["build_expression_habits"]
         relation_info = results_dict["build_relation_info"]
@@ -850,7 +854,7 @@ class DefaultReplyer:
             type = msg_text[0]
             data = msg_text[1]
 
-            if global_config.experimental.debug_show_chat_mode and type == "text":
+            if global_config.debug.debug_show_chat_mode and type == "text":
                 data += "ᶠ"
 
             part_message_id = f"{thinking_id}_{i}"

src/config/config.py

@@ -35,6 +35,7 @@ from src.config.official_configs import (
     LPMMKnowledgeConfig,
     RelationshipConfig,
     ToolConfig,
+    DebugConfig,
 )
 
 install(extra_lines=3)
@@ -165,7 +166,7 @@ class Config(ConfigBase):
     maim_message: MaimMessageConfig
     lpmm_knowledge: LPMMKnowledgeConfig
     tool: ToolConfig
-
+    debug: DebugConfig
 
 def load_config(config_path: str) -> Config:
     """

src/config/official_configs.py

@@ -529,14 +529,21 @@ class TelemetryConfig(ConfigBase):
     enable: bool = True
     """是否启用遥测"""
 
+@dataclass
+class DebugConfig(ConfigBase):
+    """调试配置类"""
+    
+    debug_show_chat_mode: bool = False
+    """是否在回复后显示当前聊天模式"""
+
+    show_prompt: bool = False
+    """是否显示prompt"""
+
 
 @dataclass
 class ExperimentalConfig(ConfigBase):
     """实验功能配置类"""
 
-    debug_show_chat_mode: bool = False
-    """是否在回复后显示当前聊天模式"""
-
     enable_friend_chat: bool = False
     """是否启用好友聊天"""
 

src/mais4u/mais4u_chat/s4u_chat.py

@@ -77,8 +77,6 @@ class MessageSenderContainer:
                 msg_id = f"{current_time}_{random.randint(1000, 9999)}"
 
                 text_to_send = chunk
-                if global_config.experimental.debug_show_chat_mode:
-                    text_to_send += "ⁿ"
 
                 message_segment = Seg(type="text", data=text_to_send)
                 bot_message = MessageSending(

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "3.5.0"
+version = "3.6.0"
 
 #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
 #如果你想要修改配置文件，请在修改后将version的值进行变更
@@ -231,6 +231,11 @@ library_log_levels = { "aiohttp" = "WARNING"} # 设置特定库的日志级别
 # enable_thinking = <true|false> : 用于指定模型是否启用思考
 # thinking_budget = <int> : 用于指定模型思考最长长度
 
+[debug]
+show_prompt = false # 是否显示prompt
+debug_show_chat_mode = false # 是否在回复后显示当前聊天模式
+
+
 [model]
 model_max_output_length = 1000 # 模型单次返回的最大token数
 
@@ -366,7 +371,6 @@ key_file = "" # SSL密钥文件路径，仅在use_wss=true时有效
 enable = true
 
 [experimental] #实验性功能
-debug_show_chat_mode = false # 是否在回复后显示当前聊天模式
 enable_friend_chat = false # 是否启用好友聊天