Merge remote-tracking branch 'upstream/dev' into dev

src/plugins/chat_module/reasoning_chat/reasoning_chat.py

@@ -1,7 +1,7 @@
 import time
 from random import random
-import re
 
+from typing import List
 from ...memory_system.Hippocampus import HippocampusManager
 from ...moods.moods import MoodManager
 from ...config.config import global_config
@@ -18,6 +18,7 @@ from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
 from ...chat.chat_stream import chat_manager
 from ...person_info.relationship_manager import relationship_manager
 from ...chat.message_buffer import message_buffer
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
 
 # 定义日志配置
 chat_config = LogConfig(
@@ -57,7 +58,7 @@ class ReasoningChat:
 
         return thinking_id
 
-    async def _send_response_messages(self, message, chat, response_set, thinking_id):
+    async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
         """发送回复消息"""
         container = message_manager.get_container(chat.stream_id)
         thinking_message = None
@@ -76,6 +77,7 @@ class ReasoningChat:
         message_set = MessageSet(chat, thinking_id)
 
         mark_head = False
+        first_bot_msg = None
         for msg in response_set:
             message_segment = Seg(type="text", data=msg)
             bot_message = MessageSending(
@@ -95,9 +97,12 @@ class ReasoningChat:
             )
             if not mark_head:
                 mark_head = True
+                first_bot_msg = bot_message
             message_set.add_message(bot_message)
         message_manager.add_message(message_set)
 
+        return first_bot_msg
+
     async def _handle_emoji(self, message, chat, response):
         """处理表情包"""
         if random() < global_config.emoji_chance:
@@ -228,11 +233,22 @@ class ReasoningChat:
             timer2 = time.time()
             timing_results["创建思考消息"] = timer2 - timer1
 
+            logger.debug(f"创建捕捉器，thinking_id:{thinking_id}")
+
+            info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
+            info_catcher.catch_decide_to_response(message)
+
             # 生成回复
             timer1 = time.time()
-            response_set = await self.gpt.generate_response(message)
-            timer2 = time.time()
-            timing_results["生成回复"] = timer2 - timer1
+            try:
+                response_set = await self.gpt.generate_response(message, thinking_id)
+                timer2 = time.time()
+                timing_results["生成回复"] = timer2 - timer1
+
+                info_catcher.catch_after_generate_response(timing_results["生成回复"])
+            except Exception as e:
+                logger.error(f"回复生成出现错误：str{e}")
+                response_set = None
 
             if not response_set:
                 logger.info("为什么生成回复失败？")
@@ -240,10 +256,14 @@ class ReasoningChat:
 
             # 发送消息
             timer1 = time.time()
-            await self._send_response_messages(message, chat, response_set, thinking_id)
+            first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
             timer2 = time.time()
             timing_results["发送消息"] = timer2 - timer1
 
+            info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
+
+            info_catcher.done_catch()
+
             # 处理表情包
             timer1 = time.time()
             await self._handle_emoji(message, chat, response_set)
@@ -286,7 +306,7 @@ class ReasoningChat:
     def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
         """检查消息是否匹配过滤正则表达式"""
         for pattern in global_config.ban_msgs_regex:
-            if re.search(pattern, text):
+            if pattern.search(text):
                 logger.info(
                     f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
                 )

src/plugins/chat_module/reasoning_chat/reasoning_generator.py

@@ -2,13 +2,13 @@ import time
 from typing import List, Optional, Tuple, Union
 import random
 
-from ....common.database import db
 from ...models.utils_model import LLM_request
 from ...config.config import global_config
-from ...chat.message import MessageRecv, MessageThinking
+from ...chat.message import MessageThinking
 from .reasoning_prompt_builder import prompt_builder
 from ...chat.utils import process_llm_response
 from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
 
 # 定义日志配置
 llm_config = LogConfig(
@@ -38,7 +38,7 @@ class ResponseGenerator:
         self.current_model_type = "r1"  # 默认使用 R1
         self.current_model_name = "unknown model"
 
-    async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
+    async def generate_response(self, message: MessageThinking,thinking_id:str) -> Optional[Union[str, List[str]]]:
         """根据当前模型类型选择对应的生成函数"""
         # 从global_config中获取模型概率值并选择模型
         if random.random() < global_config.MODEL_R1_PROBABILITY:
@@ -52,7 +52,7 @@ class ResponseGenerator:
             f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
         )  # noqa: E501
 
-        model_response = await self._generate_response_with_model(message, current_model)
+        model_response = await self._generate_response_with_model(message, current_model,thinking_id)
 
         # print(f"raw_content: {model_response}")
 
@@ -65,8 +65,11 @@ class ResponseGenerator:
             logger.info(f"{self.current_model_type}思考，失败")
             return None
 
-    async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request):
+    async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request,thinking_id:str):
         sender_name = ""
+        
+        info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
+        
         if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
             sender_name = (
                 f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]"
@@ -91,45 +94,52 @@ class ResponseGenerator:
 
         try:
             content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
+            
+            info_catcher.catch_after_llm_generated(
+                prompt=prompt,
+                response=content,
+                reasoning_content=reasoning_content,
+                model_name=self.current_model_name)
+        
+        
         except Exception:
             logger.exception("生成回复时出错")
             return None
 
         # 保存到数据库
-        self._save_to_db(
-            message=message,
-            sender_name=sender_name,
-            prompt=prompt,
-            content=content,
-            reasoning_content=reasoning_content,
-            # reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
-        )
+        # self._save_to_db(
+        #     message=message,
+        #     sender_name=sender_name,
+        #     prompt=prompt,
+        #     content=content,
+        #     reasoning_content=reasoning_content,
+        #     # reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
+        # )
 
         return content
 
-    # def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
-    #                 content: str, content_check: str, reasoning_content: str, reasoning_content_check: str):
-    def _save_to_db(
-        self,
-        message: MessageRecv,
-        sender_name: str,
-        prompt: str,
-        content: str,
-        reasoning_content: str,
-    ):
-        """保存对话记录到数据库"""
-        db.reasoning_logs.insert_one(
-            {
-                "time": time.time(),
-                "chat_id": message.chat_stream.stream_id,
-                "user": sender_name,
-                "message": message.processed_plain_text,
-                "model": self.current_model_name,
-                "reasoning": reasoning_content,
-                "response": content,
-                "prompt": prompt,
-            }
-        )
+
+    # def _save_to_db(
+    #     self,
+    #     message: MessageRecv,
+    #     sender_name: str,
+    #     prompt: str,
+    #     content: str,
+    #     reasoning_content: str,
+    # ):
+    #     """保存对话记录到数据库"""
+    #     db.reasoning_logs.insert_one(
+    #         {
+    #             "time": time.time(),
+    #             "chat_id": message.chat_stream.stream_id,
+    #             "user": sender_name,
+    #             "message": message.processed_plain_text,
+    #             "model": self.current_model_name,
+    #             "reasoning": reasoning_content,
+    #             "response": content,
+    #             "prompt": prompt,
+    #         }
+    #     )
 
     async def _get_emotion_tags(self, content: str, processed_plain_text: str):
         """提取情感标签，结合立场和情绪"""

src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py

@@ -115,6 +115,18 @@ class PromptBuilder:
                         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 = ""