feat：修改统计，分离emoji动作

src/chat/focus_chat/hfc_performance_logger.py

@@ -42,7 +42,6 @@ class HFCPerformanceLogger:
                 "total_time": cycle_data.get("total_time", 0),
                 "step_times": cycle_data.get("step_times", {}),
                 "processor_time_costs": cycle_data.get("processor_time_costs", {}),  # 前处理器时间
-                "post_processor_time_costs": cycle_data.get("post_processor_time_costs", {}),  # 后处理器时间
                 "reasoning": cycle_data.get("reasoning", ""),
                 "success": cycle_data.get("success", False),
             }
@@ -60,12 +59,7 @@ class HFCPerformanceLogger:
                 f"time={record['total_time']:.2f}s",
             ]
 
-            # 添加后处理器时间信息到日志
-            if record["post_processor_time_costs"]:
-                post_processor_stats = ", ".join(
-                    [f"{name}: {time_cost:.3f}s" for name, time_cost in record["post_processor_time_costs"].items()]
-                )
-                log_parts.append(f"post_processors=({post_processor_stats})")
+
 
             logger.debug(f"记录HFC循环数据: {', '.join(log_parts)}")
 

src/chat/focus_chat/hfc_version_manager.py

@@ -20,7 +20,7 @@ class HFCVersionManager:
     """HFC版本号管理器"""
 
     # 默认版本号
-    DEFAULT_VERSION = "v4.0.0"
+    DEFAULT_VERSION = "v5.0.0"
 
     # 当前运行时版本号
     _current_version: Optional[str] = None

src/chat/focus_chat/memory_activator.py

@@ -46,9 +46,12 @@ def init_prompt():
     # --- Group Chat Prompt ---
     memory_activator_prompt = """
     你是一个记忆分析器，你需要根据以下信息来进行回忆
-    以下是一场聊天中的信息，请根据这些信息，总结出几个关键词作为记忆回忆的触发词
+    以下是一段聊天记录，请根据这些信息，总结出几个关键词作为记忆回忆的触发词
     
+    聊天记录:
     {obs_info_text}
+    你想要回复的消息:
+    {target_message}
     
     历史关键词（请避免重复提取这些关键词）：
     {cached_keywords}
@@ -69,12 +72,12 @@ class MemoryActivator:
         self.summary_model = LLMRequest(
             model=global_config.model.memory_summary,
             temperature=0.7,
-            request_type="focus.memory_activator",
+            request_type="memory_activator",
         )
         self.running_memory = []
         self.cached_keywords = set()  # 用于缓存历史关键词
 
-    async def activate_memory_with_chat_history(self, chat_id, target_message, chat_history_prompt) -> List[Dict]:
+    async def activate_memory_with_chat_history(self, target_message, chat_history_prompt) -> List[Dict]:
         """
         激活记忆
 
@@ -88,23 +91,13 @@ class MemoryActivator:
         if not global_config.memory.enable_memory:
             return []
 
-        # obs_info_text = ""
-        # for observation in observations:
-        #     if isinstance(observation, ChattingObservation):
-        #         obs_info_text += observation.talking_message_str_truncate_short
-        #     elif isinstance(observation, StructureObservation):
-        #         working_info = observation.get_observe_info()
-        #         for working_info_item in working_info:
-        #             obs_info_text += f"{working_info_item['type']}: {working_info_item['content']}\n"
-
-        # logger.info(f"回忆待检索内容：obs_info_text: {obs_info_text}")
-
         # 将缓存的关键词转换为字符串，用于prompt
         cached_keywords_str = ", ".join(self.cached_keywords) if self.cached_keywords else "暂无历史关键词"
 
         prompt = await global_prompt_manager.format_prompt(
             "memory_activator_prompt",
             obs_info_text=chat_history_prompt,
+            target_message=target_message,
             cached_keywords=cached_keywords_str,
         )
 
@@ -130,9 +123,6 @@ class MemoryActivator:
         related_memory = await hippocampus_manager.get_memory_from_topic(
             valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
         )
-        # related_memory = await hippocampus_manager.get_memory_from_text(
-        #     text=obs_info_text, max_memory_num=5, max_memory_length=2, max_depth=3, fast_retrieval=False
-        # )
 
         logger.info(f"获取到的记忆: {related_memory}")