fix：调整目录结构，优化hfc prompt，移除日程，移除动态和llm判断willing模式，

src/chat/focus_chat/memory_activator.py

@@ -0,0 +1,105 @@
+from src.heart_flow.observation.chatting_observation import ChattingObservation
+from src.heart_flow.observation.working_observation import WorkingObservation
+from src.heart_flow.observation.hfcloop_observation import HFCloopObservation
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.common.logger_manager import get_logger
+from src.chat.utils.prompt_builder import Prompt
+from datetime import datetime
+from src.chat.memory_system.Hippocampus import HippocampusManager
+from typing import List, Dict
+
+
+logger = get_logger("memory_activator")
+
+
+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会议
+    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
+    
+    {obs_info_text}
+    
+    请输出一个json格式，包含以下字段：
+    {{
+        "keywords": ["关键词1", "关键词2", "关键词3",......]
+    }}
+    不要输出其他多余内容，只输出json格式就好
+    """
+
+    Prompt(memory_activator_prompt, "memory_activator_prompt")
+
+
+class MemoryActivator:
+    def __init__(self):
+        self.summary_model = LLMRequest(
+            model=global_config.llm_summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
+        )
+        self.running_memory = []
+
+    async def activate_memory(self, observations) -> List[Dict]:
+        """
+        激活记忆
+
+        Args:
+            observations: 现有的进行观察后的 观察列表
+
+        Returns:
+            List[Dict]: 激活的记忆列表
+        """
+        obs_info_text = ""
+        for observation in observations:
+            if isinstance(observation, ChattingObservation):
+                obs_info_text += observation.get_observe_info()
+            elif isinstance(observation, WorkingObservation):
+                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"
+            elif isinstance(observation, HFCloopObservation):
+                obs_info_text += observation.get_observe_info()
+
+        # prompt = await global_prompt_manager.format_prompt(
+        #     "memory_activator_prompt",
+        #     obs_info_text=obs_info_text,
+        # )
+
+        # logger.debug(f"prompt: {prompt}")
+
+        # response = await self.summary_model.generate_response(prompt)
+
+        # logger.debug(f"response: {response}")
+
+        # # 只取response的第一个元素（字符串）
+        # response_str = response[0]
+        # keywords = list(get_keywords_from_json(response_str))
+
+        # #调用记忆系统获取相关记忆
+        # related_memory = await HippocampusManager.get_instance().get_memory_from_topic(
+        #     valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
+        # )
+        related_memory = await HippocampusManager.get_instance().get_memory_from_text(
+            text=obs_info_text, max_memory_num=3, max_memory_length=2, max_depth=3, fast_retrieval=True
+        )
+
+        logger.debug(f"获取到的记忆: {related_memory}")
+
+        # 激活时，所有已有记忆的duration+1，达到3则移除
+        for m in self.running_memory[:]:
+            m["duration"] = m.get("duration", 1) + 1
+        self.running_memory = [m for m in self.running_memory if m["duration"] < 3]
+
+        if related_memory:
+            for topic, memory in related_memory:
+                # 检查是否已存在相同topic和content的记忆
+                exists = any(m["topic"] == topic and m["content"] == memory for m in self.running_memory)
+                if not exists:
+                    self.running_memory.append(
+                        {"topic": topic, "content": memory, "timestamp": datetime.now().isoformat(), "duration": 1}
+                    )
+                    logger.debug(f"添加新记忆: {topic} - {memory}")
+
+        return self.running_memory
+
+
+init_prompt()