Merge pull request #706 from UnCLAS-Prommer/dev

彻底大修_execute_request炸程序的问题，同时修了一些typo

src/heart_flow/heartflow.py

@@ -18,7 +18,7 @@ heartflow_config = LogConfig(
 logger = get_module_logger("heartflow", config=heartflow_config)
 
 
-class CuttentState:
+class CurrentState:
     def __init__(self):
         self.willing = 0
         self.current_state_info = ""
@@ -34,7 +34,7 @@ class Heartflow:
     def __init__(self):
         self.current_mind = "你什么也没想"
         self.past_mind = []
-        self.current_state: CuttentState = CuttentState()
+        self.current_state: CurrentState = CurrentState()
         self.llm_model = LLM_request(
             model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
         )
@@ -102,7 +102,11 @@ class Heartflow:
         current_thinking_info = self.current_mind
         mood_info = self.current_state.mood
         related_memory_info = "memory"
+        try:
             sub_flows_info = await self.get_all_subheartflows_minds()
+        except Exception as e:
+            logger.error(f"获取子心流的想法失败: {e}")
+            return
 
         schedule_info = bot_schedule.get_current_num_task(num=4, time_info=True)
 
@@ -111,26 +115,29 @@ class Heartflow:
         prompt += f"{personality_info}\n"
         prompt += f"你想起来{related_memory_info}。"
         prompt += f"刚刚你的主要想法是{current_thinking_info}。"
-        prompt += f"你还有一些小想法，因为你在参加不同的群聊天，是你正在做的事情：{sub_flows_info}\n"
+        prompt += f"你还有一些小想法，因为你在参加不同的群聊天，这是你正在做的事情：{sub_flows_info}\n"
         prompt += f"你现在{mood_info}。"
         prompt += "现在你接下去继续思考，产生新的想法，但是要基于原有的主要想法，不要分点输出，"
         prompt += "输出连贯的内心独白，不要太长，但是记得结合上述的消息，关注新内容:"
 
-        reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
+        try:
+            response, reasoning_content = await self.llm_model.generate_response_async(prompt)
+        except Exception as e:
+            logger.error(f"内心独白获取失败: {e}")
+            return
+        self.update_current_mind(response)
 
-        self.update_current_mind(reponse)
+        self.current_mind = response
-
-        self.current_mind = reponse
         logger.info(f"麦麦的总体脑内状态：{self.current_mind}")
         # logger.info("麦麦想了想，当前活动:")
         # await bot_schedule.move_doing(self.current_mind)
 
         for _, subheartflow in self._subheartflows.items():
-            subheartflow.main_heartflow_info = reponse
+            subheartflow.main_heartflow_info = response
 
-    def update_current_mind(self, reponse):
+    def update_current_mind(self, response):
         self.past_mind.append(self.current_mind)
-        self.current_mind = reponse
+        self.current_mind = response
 
     async def get_all_subheartflows_minds(self):
         sub_minds = ""
@@ -167,9 +174,9 @@ class Heartflow:
         prompt += """现在请你总结这些聊天内容，注意关注聊天内容对原有的想法的影响，输出连贯的内心独白
         不要太长，但是记得结合上述的消息，要记得你的人设，关注新内容:"""
 
-        reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
+        response, reasoning_content = await self.llm_model.generate_response_async(prompt)
 
-        return reponse
+        return response
 
     def create_subheartflow(self, subheartflow_id):
         """

src/heart_flow/observation.py

@@ -142,7 +142,11 @@ class ChattingObservation(Observation):
         prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，
         以及聊天中的一些重要信息，注意识别你自己的发言，记得不要分点，不要太长，精简的概括成一段文本\n"""
         prompt += "总结概括："
+        try:
             self.observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
+        except Exception as e:
+            print(f"获取总结失败: {e}")
+            self.observe_info = ""
         print(f"prompt：{prompt}")
         print(f"self.observe_info：{self.observe_info}")
 

src/heart_flow/sub_heartflow.py

@@ -22,7 +22,7 @@ subheartflow_config = LogConfig(
 logger = get_module_logger("subheartflow", config=subheartflow_config)
 
 
-class CuttentState:
+class CurrentState:
     def __init__(self):
         self.willing = 0
         self.current_state_info = ""
@@ -40,7 +40,7 @@ class SubHeartflow:
 
         self.current_mind = ""
         self.past_mind = []
-        self.current_state: CuttentState = CuttentState()
+        self.current_state: CurrentState = CurrentState()
         self.llm_model = LLM_request(
             model=global_config.llm_sub_heartflow, temperature=0.5, max_tokens=600, request_type="sub_heart_flow"
         )
@@ -143,11 +143,11 @@ class SubHeartflow:
     #     prompt += f"你现在{mood_info}\n"
     #     prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
     #     prompt += "但是记得结合上述的消息，要记得维持住你的人设，关注聊天和新内容，不要思考太多:"
-    #     reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
+    #     response, reasoning_content = await self.llm_model.generate_response_async(prompt)
 
-    #     self.update_current_mind(reponse)
+    #     self.update_current_mind(response)
 
-    #     self.current_mind = reponse
+    #     self.current_mind = response
     #     logger.debug(f"prompt:\n{prompt}\n")
     #     logger.info(f"麦麦的脑内状态：{self.current_mind}")
 
@@ -217,9 +217,15 @@ class SubHeartflow:
         prompt += f"你注意到有人刚刚说：{message_txt}\n"
         prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
         prompt += "记得结合上述的消息，要记得维持住你的人设，注意自己的名字，关注有人刚刚说的内容，不要思考太多:"
-        reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
 
-        self.update_current_mind(reponse)
+        try:
+            response, reasoning_content = await self.llm_model.generate_response_async(prompt)
+        except Exception as e:
+            logger.error(f"回复前内心独白获取失败: {e}")
+            response = ""
+        self.update_current_mind(response)
+
+        self.current_mind = response
 
         logger.debug(f"prompt:\n{prompt}\n")
         logger.info(f"麦麦的思考前脑内状态：{self.current_mind}")
@@ -264,12 +270,14 @@ class SubHeartflow:
         prompt += f"你现在{mood_info}"
         prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
         prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
+        try:
+            response, reasoning_content = await self.llm_model.generate_response_async(prompt)
+        except Exception as e:
+            logger.error(f"回复后内心独白获取失败: {e}")
+            response = ""
+        self.update_current_mind(response)
 
-        reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
+        self.current_mind = response
-
-        self.update_current_mind(reponse)
-
-        self.current_mind = reponse
         logger.info(f"麦麦回复后的脑内状态：{self.current_mind}")
 
         self.last_reply_time = time.time()
@@ -302,10 +310,13 @@ class SubHeartflow:
         prompt += f"你现在{mood_info}。"
         prompt += "现在请你思考，你想不想发言或者回复，请你输出一个数字，1-10，1表示非常不想，10表示非常想。"
         prompt += "请你用<>包裹你的回复意愿，输出<1>表示不想回复，输出<10>表示非常想回复。请你考虑，你完全可以不回复"
-
+        try:
             response, reasoning_content = await self.llm_model.generate_response_async(prompt)
             # 解析willing值
             willing_match = re.search(r"<(\d+)>", response)
+        except Exception as e:
+            logger.error(f"意愿判断获取失败: {e}")
+            willing_match = None
         if willing_match:
             self.current_state.willing = int(willing_match.group(1))
         else:
@@ -313,9 +324,9 @@ class SubHeartflow:
 
         return self.current_state.willing
 
-    def update_current_mind(self, reponse):
+    def update_current_mind(self, response):
         self.past_mind.append(self.current_mind)
-        self.current_mind = reponse
+        self.current_mind = response
 
     async def get_prompt_info(self, message: str, threshold: float):
         start_time = time.time()

src/plugins/PFC/pfc.py

@@ -114,9 +114,12 @@ class GoalAnalyzer:
 }}"""
 
         logger.debug(f"发送到LLM的提示词: {prompt}")
+        try:
             content, _ = await self.llm.generate_response_async(prompt)
             logger.debug(f"LLM原始返回内容: {content}")
-
+        except Exception as e:
+            logger.error(f"分析对话目标时出错: {str(e)}")
+            content = ""
         # 使用简化函数提取JSON内容
         success, result = get_items_from_json(
             content, "goal", "reasoning", required_types={"goal": str, "reasoning": str}

src/plugins/chat/emoji_manager.py

@@ -340,6 +340,9 @@ class EmojiManager:
 
                 if description is not None:
                     embedding = await get_embedding(description, request_type="emoji")
+                    if not embedding:
+                        logger.error("获取消息嵌入向量失败")
+                        raise ValueError("获取消息嵌入向量失败")
                     # 准备数据库记录
                     emoji_record = {
                         "filename": filename,

src/plugins/chat/utils.py

@@ -79,7 +79,13 @@ async def get_embedding(text, request_type="embedding"):
     """获取文本的embedding向量"""
     llm = LLM_request(model=global_config.embedding, request_type=request_type)
     # return llm.get_embedding_sync(text)
-    return await llm.get_embedding(text)
+    try:
+        embedding = await llm.get_embedding(text)
+    except Exception as e:
+        logger.error(f"获取embedding失败: {str(e)}")
+        embedding = None
+    return embedding
+
 
 
 async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:

src/plugins/memory_system/Hippocampus.py

@@ -1316,15 +1316,24 @@ class HippocampusManager:
         """从文本中获取相关记忆的公共接口"""
         if not self._initialized:
             raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
-        return await self._hippocampus.get_memory_from_text(
+        try:
-            text, max_memory_num, max_memory_length, max_depth, fast_retrieval
+            response = await self._hippocampus.get_memory_from_text(text, max_memory_num, max_memory_length, max_depth, fast_retrieval)
-        )
+        except Exception as e:
+            logger.error(f"文本激活记忆失败: {e}")
+            response = []
+        return response
+        
 
     async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float:
         """从文本中获取激活值的公共接口"""
         if not self._initialized:
             raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
-        return await self._hippocampus.get_activate_from_text(text, max_depth, fast_retrieval)
+        try:
+            response = await self._hippocampus.get_activate_from_text(text, max_depth, fast_retrieval)
+        except Exception as e:
+            logger.error(f"文本产生激活值失败: {e}")
+            response = 0.0
+        return response
 
     def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
         """从关键词获取相关记忆的公共接口"""

src/plugins/schedule/schedule_generator.py

@@ -121,7 +121,11 @@ class ScheduleGenerator:
             self.today_done_list = []
         if not self.today_schedule_text:
             logger.info(f"{today.strftime('%Y-%m-%d')}的日程不存在，准备生成新的日程")
+            try:
                 self.today_schedule_text = await self.generate_daily_schedule(target_date=today)
+            except Exception as e:
+                logger.error(f"生成日程时发生错误: {str(e)}")
+                self.today_schedule_text = ""
 
         self.save_today_schedule_to_db()
 

src/plugins/topic_identify/topic_identifier.py

@@ -29,10 +29,13 @@ class TopicIdentifier:
 消息内容：{text}"""
 
         # 使用 LLM_request 类进行请求
+        try:
             topic, _, _ = await self.llm_topic_judge.generate_response(prompt)
-
+        except Exception as e:
+            logger.error(f"LLM 请求topic失败: {e}")
+            return None
         if not topic:
-            logger.error("LLM API 返回为空")
+            logger.error("LLM 得到的topic为空")
             return None
 
         # 直接在这里处理主题解析