feat：增加了工作记忆

src/chat/focus_chat/expressors/default_expressor.py

@@ -127,7 +127,7 @@ class DefaultExpressor:
             reply=anchor_message,  # 回复的是锚点消息
             thinking_start_time=thinking_time_point,
         )
-        logger.debug(f"创建思考消息thinking_message：{thinking_message}")
+        # logger.debug(f"创建思考消息thinking_message：{thinking_message}")
 
         await self.heart_fc_sender.register_thinking(thinking_message)
 
@@ -244,7 +244,7 @@ class DefaultExpressor:
                     # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
                     content, reasoning_content, model_name = await self.express_model.generate_response(prompt)
 
-                    logger.info(f"{self.log_prefix}\nPrompt:\n{prompt}\n---------------------------\n")
+                    # logger.info(f"{self.log_prefix}\nPrompt:\n{prompt}\n---------------------------\n")
 
                     logger.info(f"想要表达：{in_mind_reply}")
                     logger.info(f"理由：{reason}")

src/chat/focus_chat/heartFC_chat.py

@@ -22,6 +22,7 @@ from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
 from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
 from src.chat.focus_chat.memory_activator import MemoryActivator
 from src.chat.focus_chat.info_processors.base_processor import BaseProcessor
+from src.chat.focus_chat.info_processors.self_processor import SelfProcessor
 from src.chat.focus_chat.planners.planner import ActionPlanner
 from src.chat.focus_chat.planners.action_manager import ActionManager
 from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
@@ -154,6 +155,7 @@ class HeartFChatting:
         self.processors.append(MindProcessor(subheartflow_id=self.stream_id))
         self.processors.append(ToolProcessor(subheartflow_id=self.stream_id))
         self.processors.append(WorkingMemoryProcessor(subheartflow_id=self.stream_id))
+        self.processors.append(SelfProcessor(subheartflow_id=self.stream_id))
         logger.info(f"{self.log_prefix} 已注册默认处理器: {[p.__class__.__name__ for p in self.processors]}")
 
     async def start(self):
@@ -331,6 +333,7 @@ class HeartFChatting:
                         f"{self.log_prefix} 处理器 {processor_name} 执行失败，耗时 (自并行开始): {duration_since_parallel_start:.2f}秒. 错误: {e}",
                         exc_info=True,
                     )
+                    traceback.print_exc()
                     # 即使出错，也认为该任务结束了，已从 pending_tasks 中移除
 
             if pending_tasks:

src/chat/focus_chat/info/self_info.py

@@ -0,0 +1,41 @@
+from typing import Dict, Any
+from dataclasses import dataclass, field
+from .info_base import InfoBase
+
+
+@dataclass
+class SelfInfo(InfoBase):
+    """思维信息类
+
+    用于存储和管理当前思维状态的信息。
+
+    Attributes:
+        type (str): 信息类型标识符，默认为 "mind"
+        data (Dict[str, Any]): 包含 current_mind 的数据字典
+    """
+
+    type: str = "self"
+
+    def get_self_info(self) -> str:
+        """获取当前思维状态
+
+        Returns:
+            str: 当前思维状态
+        """
+        return self.get_info("self_info") or ""
+
+    def set_self_info(self, self_info: str) -> None:
+        """设置当前思维状态
+
+        Args:
+            self_info: 要设置的思维状态
+        """
+        self.data["self_info"] = self_info
+        
+    def get_processed_info(self) -> str:
+        """获取处理后的信息
+
+        Returns:
+            str: 处理后的信息
+        """
+        return self.get_self_info()

src/chat/focus_chat/info/workingmemory_info.py

@@ -0,0 +1,90 @@
+from typing import Dict, Optional, List
+from dataclasses import dataclass
+from .info_base import InfoBase
+
+
+@dataclass
+class WorkingMemoryInfo(InfoBase):
+
+    type: str = "workingmemory"
+    
+    processed_info:str = ""
+
+    def set_talking_message(self, message: str) -> None:
+        """设置说话消息
+
+        Args:
+            message (str): 说话消息内容
+        """
+        self.data["talking_message"] = message
+
+    def set_working_memory(self, working_memory: List[str]) -> None:
+        """设置工作记忆
+
+        Args:
+            working_memory (str): 工作记忆内容
+        """
+        self.data["working_memory"] = working_memory
+    
+    def add_working_memory(self, working_memory: str) -> None:
+        """添加工作记忆
+
+        Args:
+            working_memory (str): 工作记忆内容
+        """
+        working_memory_list = self.data.get("working_memory", [])
+        # print(f"working_memory_list: {working_memory_list}")
+        working_memory_list.append(working_memory)
+        # print(f"working_memory_list: {working_memory_list}")
+        self.data["working_memory"] = working_memory_list
+    
+    def get_working_memory(self) -> List[str]:
+        """获取工作记忆
+
+        Returns:
+            List[str]: 工作记忆内容
+        """
+        return self.data.get("working_memory", [])
+
+    def get_type(self) -> str:
+        """获取信息类型
+
+        Returns:
+            str: 当前信息对象的类型标识符
+        """
+        return self.type
+
+    def get_data(self) -> Dict[str, str]:
+        """获取所有信息数据
+
+        Returns:
+            Dict[str, str]: 包含所有信息数据的字典
+        """
+        return self.data
+
+    def get_info(self, key: str) -> Optional[str]:
+        """获取特定属性的信息
+
+        Args:
+            key: 要获取的属性键名
+
+        Returns:
+            Optional[str]: 属性值，如果键不存在则返回 None
+        """
+        return self.data.get(key)
+    
+    def get_processed_info(self) -> Dict[str, str]:
+        """获取处理后的信息
+
+        Returns:
+            Dict[str, str]: 处理后的信息数据
+        """
+        all_memory = self.get_working_memory()
+        # print(f"all_memory: {all_memory}")
+        memory_str = ""
+        for memory in all_memory:
+            memory_str += f"{memory}\n"
+                
+        self.processed_info = memory_str
+        
+        return self.processed_info

src/chat/focus_chat/info_processors/self_processor.py

@@ -0,0 +1,172 @@
+from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
+from src.chat.heart_flow.observation.observation import Observation
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+import time
+import traceback
+from src.common.logger_manager import get_logger
+from src.individuality.individuality import Individuality
+import random
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.utils.json_utils import safe_json_dumps
+from src.chat.message_receive.chat_stream import chat_manager
+import difflib
+from src.chat.person_info.relationship_manager import relationship_manager
+from .base_processor import BaseProcessor
+from src.chat.focus_chat.info.mind_info import MindInfo
+from typing import List, Optional
+from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
+from typing import Dict
+from src.chat.focus_chat.info.info_base import InfoBase
+from src.chat.focus_chat.info.self_info import SelfInfo
+
+logger = get_logger("processor")
+
+
+def init_prompt():
+    indentify_prompt = """
+你的名字是{bot_name},你是一个AI，但是你伪装成了一个人类，你的人格是，{prompt_personality}。
+你对外的形象是一只橙色的鱼，头上有绿色的树叶，你用的头像也是这个。
+
+{relation_prompt}
+{memory_str}
+
+现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容：
+{chat_observe_info}
+
+现在请你根据现有的信息，思考自我认同
+1. 你是一个什么样的人,你和群里的人关系如何
+2. 思考有没有人提到你，或者图片与你有关
+3. 你的自我认同是否有助于你的回答，如果你需要自我相关的信息来帮你参与聊天，请输出，否则请输出十个字以内的简短自我认同
+4. 一般情况下不用输出自我认同，只需要输出十几个字的简短自我认同就好，除非有明显需要自我认同的场景
+
+"""
+    Prompt(indentify_prompt, "indentify_prompt")
+
+
+
+class SelfProcessor(BaseProcessor):
+    log_prefix = "自我认同"
+
+    def __init__(self, subheartflow_id: str):
+        super().__init__()
+
+        self.subheartflow_id = subheartflow_id
+
+        self.llm_model = LLMRequest(
+            model=global_config.llm_sub_heartflow,
+            temperature=global_config.llm_sub_heartflow["temp"],
+            max_tokens=800,
+            request_type="self_identify",
+        )
+
+        name = chat_manager.get_stream_name(self.subheartflow_id)
+        self.log_prefix = f"[{name}] "
+
+
+    async def process_info(
+        self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
+    ) -> List[InfoBase]:
+        """处理信息对象
+
+        Args:
+            *infos: 可变数量的InfoBase类型的信息对象
+
+        Returns:
+            List[InfoBase]: 处理后的结构化信息列表
+        """
+        self_info_str = await self.self_indentify(observations, running_memorys)
+        
+        if self_info_str:
+            self_info = SelfInfo()
+            self_info.set_self_info(self_info_str)
+        else:
+            self_info = None
+            return None
+
+        return [self_info]
+
+    async def self_indentify(
+        self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None
+    ):
+        """
+        在回复前进行思考，生成内心想法并收集工具调用结果
+
+        参数:
+            observations: 观察信息
+
+        返回:
+            如果return_prompt为False:
+                tuple: (current_mind, past_mind) 当前想法和过去的想法列表
+            如果return_prompt为True:
+                tuple: (current_mind, past_mind, prompt) 当前想法、过去的想法列表和使用的prompt
+        """
+
+
+        memory_str = ""
+        if running_memorys:
+            memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
+            for running_memory in running_memorys:
+                memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
+
+
+        if observations is None:
+            observations = []
+        for observation in observations:
+            if isinstance(observation, ChattingObservation):
+                # 获取聊天元信息
+                is_group_chat = observation.is_group_chat
+                chat_target_info = observation.chat_target_info
+                chat_target_name = "对方"  # 私聊默认名称
+                if not is_group_chat and chat_target_info:
+                    # 优先使用person_name，其次user_nickname，最后回退到默认值
+                    chat_target_name = (
+                        chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or chat_target_name
+                    )
+                # 获取聊天内容
+                chat_observe_info = observation.get_observe_info()
+                person_list = observation.person_list
+            if isinstance(observation, HFCloopObservation):
+                hfcloop_observe_info = observation.get_observe_info()
+
+
+        individuality = Individuality.get_instance()
+        personality_block = individuality.get_prompt(x_person=2, level=2)
+
+        relation_prompt = ""
+        for person in person_list:
+            relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
+
+
+        prompt = (await global_prompt_manager.get_prompt_async("indentify_prompt")).format(
+            bot_name=individuality.name,
+            prompt_personality=personality_block,
+            memory_str=memory_str,
+            relation_prompt=relation_prompt,
+            time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            chat_observe_info=chat_observe_info,
+        )
+
+
+        content = ""
+        try:
+            content, _ = await self.llm_model.generate_response_async(prompt=prompt)
+            if not content:
+                logger.warning(f"{self.log_prefix} LLM返回空结果，自我识别失败。")
+        except Exception as e:
+            # 处理总体异常
+            logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
+            logger.error(traceback.format_exc())
+            content = "自我识别过程中出现错误"
+
+        if content == 'None':
+            content = ""
+        # 记录初步思考结果
+        logger.debug(f"{self.log_prefix} 自我识别prompt: \n{prompt}\n")
+        logger.info(f"{self.log_prefix} 自我识别结果: {content}")
+
+        return content
+
+
+
+init_prompt()

src/chat/focus_chat/info_processors/tool_processor.py

@@ -11,8 +11,8 @@ from src.chat.person_info.relationship_manager import relationship_manager
 from .base_processor import BaseProcessor
 from typing import List, Optional, Dict
 from src.chat.heart_flow.observation.observation import Observation
-from src.chat.heart_flow.observation.working_observation import WorkingObservation
 from src.chat.focus_chat.info.structured_info import StructuredInfo
+from src.chat.heart_flow.observation.structure_observation import StructureObservation
 
 logger = get_logger("processor")
 
@@ -24,9 +24,6 @@ def init_prompt():
     tool_executor_prompt = """
 你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
 
-你要在群聊中扮演以下角色：
-{prompt_personality}
-
 你当前的额外信息：
 {memory_str}
 
@@ -70,6 +67,8 @@ class ToolProcessor(BaseProcessor):
             list: 处理后的结构化信息列表
         """
 
+        working_infos = []
+        
         if observations:
             for observation in observations:
                 if isinstance(observation, ChattingObservation):
@@ -77,7 +76,7 @@ class ToolProcessor(BaseProcessor):
 
             # 更新WorkingObservation中的结构化信息
             for observation in observations:
-                if isinstance(observation, WorkingObservation):
+                if isinstance(observation, StructureObservation):
                     for structured_info in result:
                         logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}")
                         observation.add_structured_info(structured_info)
@@ -86,6 +85,7 @@ class ToolProcessor(BaseProcessor):
                     logger.debug(f"{self.log_prefix} 获取更新后WorkingObservation中的结构化信息: {working_infos}")
 
         structured_info = StructuredInfo()
+        if working_infos:
             for working_info in working_infos:
                 structured_info.set_info(working_info.get("type"), working_info.get("content"))
 
@@ -148,7 +148,7 @@ class ToolProcessor(BaseProcessor):
             # chat_target_name=chat_target_name,
             is_group_chat=is_group_chat,
             # relation_prompt=relation_prompt,
-            prompt_personality=prompt_personality,
+            # prompt_personality=prompt_personality,
             # mood_info=mood_info,
             bot_name=individuality.name,
             time_now=time_now,

src/chat/focus_chat/info_processors/working_memory_processor.py

@@ -0,0 +1,247 @@
+from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
+from src.chat.heart_flow.observation.observation import Observation
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+import time
+import traceback
+from src.common.logger_manager import get_logger
+from src.individuality.individuality import Individuality
+import random
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.utils.json_utils import safe_json_dumps
+from src.chat.message_receive.chat_stream import chat_manager
+import difflib
+from src.chat.person_info.relationship_manager import relationship_manager
+from .base_processor import BaseProcessor
+from src.chat.focus_chat.info.mind_info import MindInfo
+from typing import List, Optional
+from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
+from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation
+from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+from typing import Dict
+from src.chat.focus_chat.info.info_base import InfoBase
+from json_repair import repair_json
+from src.chat.focus_chat.info.workingmemory_info import WorkingMemoryInfo
+import asyncio
+import json
+
+logger = get_logger("processor")
+
+
+def init_prompt():
+    memory_proces_prompt = """
+你的名字是{bot_name}
+
+现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容：
+{chat_observe_info}
+
+以下是你已经总结的记忆，你可以调取这些记忆来帮助你聊天，不要一次调取太多记忆，最多调取3个左右记忆：
+{memory_str}
+
+观察聊天内容和已经总结的记忆，思考是否有新内容需要总结成记忆，如果有，就输出 true，否则输出 false
+如果当前聊天记录的内容已经被总结，千万不要总结新记忆，输出false
+如果已经总结的记忆包含了当前聊天记录的内容，千万不要总结新记忆，输出false
+如果已经总结的记忆摘要,包含了当前聊天记录的内容，千万不要总结新记忆，输出false
+
+如果有相近的记忆，请合并记忆，输出merge_memory，格式为[["id1", "id2"], ["id3", "id4"],...]，你可以进行多组合并，但是每组合并只能有两个记忆id，不要输出其他内容
+
+请根据聊天内容选择你需要调取的记忆并考虑是否添加新记忆，以JSON格式输出，格式如下：
+```json
+{{
+    "selected_memory_ids": ["id1", "id2", ...],
+    "new_memory": "true" or "false",
+    "merge_memory": [["id1", "id2"], ["id3", "id4"],...]
+    
+}}
+```
+"""
+    Prompt(memory_proces_prompt, "prompt_memory_proces")
+
+
+class WorkingMemoryProcessor(BaseProcessor):
+    log_prefix = "工作记忆"
+
+    def __init__(self, subheartflow_id: str):
+        super().__init__()
+
+        self.subheartflow_id = subheartflow_id
+
+        self.llm_model = LLMRequest(
+            model=global_config.llm_sub_heartflow,
+            temperature=global_config.llm_sub_heartflow["temp"],
+            max_tokens=800,
+            request_type="working_memory",
+        )
+
+        name = chat_manager.get_stream_name(self.subheartflow_id)
+        self.log_prefix = f"[{name}] "
+
+
+
+    async def process_info(
+        self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
+    ) -> List[InfoBase]:
+        """处理信息对象
+
+        Args:
+            *infos: 可变数量的InfoBase类型的信息对象
+
+        Returns:
+            List[InfoBase]: 处理后的结构化信息列表
+        """
+        working_memory = None
+        chat_info = ""
+        try:
+            for observation in observations:
+                if isinstance(observation, WorkingMemoryObservation):
+                    working_memory = observation.get_observe_info()
+                    working_memory_obs = observation
+                if isinstance(observation, ChattingObservation):
+                    chat_info = observation.get_observe_info()
+                    # chat_info_truncate = observation.talking_message_str_truncate
+                    
+            if not working_memory:
+                logger.warning(f"{self.log_prefix} 没有找到工作记忆对象")
+                mind_info = MindInfo()
+                return [mind_info]
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 处理观察时出错: {e}")
+            logger.error(traceback.format_exc())
+            return []
+                
+        all_memory = working_memory.get_all_memories()
+        memory_prompts = []
+        for memory in all_memory:
+            memory_content = memory.data
+            memory_summary = memory.summary
+            memory_id = memory.id
+            memory_brief = memory_summary.get("brief")
+            memory_detailed = memory_summary.get("detailed")
+            memory_keypoints = memory_summary.get("keypoints")
+            memory_events = memory_summary.get("events")
+            memory_single_prompt = f"记忆id:{memory_id},记忆摘要:{memory_brief}\n"
+            memory_prompts.append(memory_single_prompt)
+                
+        memory_choose_str = "".join(memory_prompts)
+            
+        # 使用提示模板进行处理
+        prompt = (await global_prompt_manager.get_prompt_async("prompt_memory_proces")).format(
+            bot_name=global_config.BOT_NICKNAME,
+            time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            chat_observe_info=chat_info,
+            memory_str=memory_choose_str
+        )
+            
+        # 调用LLM处理记忆
+        content = ""
+        try:
+            
+            logger.debug(f"{self.log_prefix} 处理工作记忆的prompt: {prompt}")
+            
+            
+            content, _ = await self.llm_model.generate_response_async(prompt=prompt)
+            if not content:
+                logger.warning(f"{self.log_prefix} LLM返回空结果，处理工作记忆失败。")
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
+            logger.error(traceback.format_exc())
+            
+        # 解析LLM返回的JSON
+        try:
+            result = repair_json(content)
+            if isinstance(result, str):
+                result = json.loads(result)
+            if not isinstance(result, dict):
+                logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败，结果不是字典类型: {type(result)}")
+                return []
+                
+            selected_memory_ids = result.get("selected_memory_ids", [])
+            new_memory = result.get("new_memory", "")
+            merge_memory = result.get("merge_memory", [])
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败: {e}")
+            logger.error(traceback.format_exc())
+            return []
+        
+        logger.debug(f"{self.log_prefix} 解析LLM返回的JSON成功: {result}")
+        
+        # 根据selected_memory_ids，调取记忆
+        memory_str = ""
+        if selected_memory_ids:
+            for memory_id in selected_memory_ids:
+                memory = await working_memory.retrieve_memory(memory_id)
+                if memory:
+                    memory_content = memory.data
+                    memory_summary = memory.summary
+                    memory_id = memory.id
+                    memory_brief = memory_summary.get("brief")
+                    memory_detailed = memory_summary.get("detailed")
+                    memory_keypoints = memory_summary.get("keypoints")
+                    memory_events = memory_summary.get("events")
+                    for keypoint in memory_keypoints:
+                        memory_str += f"记忆要点:{keypoint}\n"
+                    for event in memory_events:
+                        memory_str += f"记忆事件:{event}\n"
+                    # memory_str += f"记忆摘要:{memory_detailed}\n"
+                    # memory_str += f"记忆主题:{memory_brief}\n"
+                
+                
+        working_memory_info = WorkingMemoryInfo()
+        if memory_str:
+            working_memory_info.add_working_memory(memory_str)
+            logger.debug(f"{self.log_prefix} 取得工作记忆: {memory_str}")
+        else:
+            logger.warning(f"{self.log_prefix} 没有找到工作记忆")
+        
+        # 根据聊天内容添加新记忆
+        if new_memory:
+            # 使用异步方式添加新记忆，不阻塞主流程
+            logger.debug(f"{self.log_prefix} {new_memory}新记忆: ")
+            asyncio.create_task(self.add_memory_async(working_memory, chat_info))
+            
+        if merge_memory:
+            for merge_pairs in merge_memory:
+                memory1 = await working_memory.retrieve_memory(merge_pairs[0])
+                memory2 = await working_memory.retrieve_memory(merge_pairs[1])
+                if memory1 and memory2:
+                    memory_str = f"记忆id:{memory1.id},记忆摘要:{memory1.summary.get('brief')}\n"
+                    memory_str += f"记忆id:{memory2.id},记忆摘要:{memory2.summary.get('brief')}\n"
+                    asyncio.create_task(self.merge_memory_async(working_memory, merge_pairs[0], merge_pairs[1]))
+                
+        return [working_memory_info]
+
+    async def add_memory_async(self, working_memory: WorkingMemory, content: str):
+        """异步添加记忆，不阻塞主流程
+        
+        Args:
+            working_memory: 工作记忆对象
+            content: 记忆内容
+        """
+        try:
+            await working_memory.add_memory(content=content, from_source="chat_text")
+            logger.debug(f"{self.log_prefix} 异步添加新记忆成功: {content[:30]}...")
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 异步添加新记忆失败: {e}")
+            logger.error(traceback.format_exc())
+            
+    async def merge_memory_async(self, working_memory: WorkingMemory, memory_id1: str, memory_id2: str):
+        """异步合并记忆，不阻塞主流程
+        
+        Args:
+            working_memory: 工作记忆对象
+            memory_str: 记忆内容
+        """
+        try:
+            merged_memory = await working_memory.merge_memory(memory_id1, memory_id2)
+            logger.debug(f"{self.log_prefix} 异步合并记忆成功: {memory_id1} 和 {memory_id2}...")
+            logger.debug(f"{self.log_prefix} 合并后的记忆梗概: {merged_memory.summary.get('brief')}")
+            logger.debug(f"{self.log_prefix} 合并后的记忆详情: {merged_memory.summary.get('detailed')}")
+            logger.debug(f"{self.log_prefix} 合并后的记忆要点: {merged_memory.summary.get('keypoints')}")
+            logger.debug(f"{self.log_prefix} 合并后的记忆事件: {merged_memory.summary.get('events')}")
+            
+        except Exception as e:
+            logger.error(f"{self.log_prefix} 异步合并记忆失败: {e}")
+            logger.error(traceback.format_exc())
+
+
+init_prompt()

src/chat/focus_chat/memory_activator.py

@@ -1,5 +1,5 @@
 from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
-from src.chat.heart_flow.observation.working_observation import WorkingObservation
+from src.chat.heart_flow.observation.structure_observation import StructureObservation
 from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
 from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
@@ -53,7 +53,7 @@ class MemoryActivator:
         for observation in observations:
             if isinstance(observation, ChattingObservation):
                 obs_info_text += observation.get_observe_info()
-            elif isinstance(observation, WorkingObservation):
+            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"

src/chat/focus_chat/planners/action_manager.py

@@ -77,10 +77,10 @@ class ActionManager:
                     if is_default:
                         self._default_actions[action_name] = action_info
 
-            logger.info(f"所有注册动作: {list(self._registered_actions.keys())}")
-            logger.info(f"默认动作: {list(self._default_actions.keys())}")
-            for action_name, action_info in self._default_actions.items():
-                logger.info(f"动作名称: {action_name}, 动作信息: {action_info}")
+            # logger.info(f"所有注册动作: {list(self._registered_actions.keys())}")
+            # logger.info(f"默认动作: {list(self._default_actions.keys())}")
+            # for action_name, action_info in self._default_actions.items():
+                # logger.info(f"动作名称: {action_name}, 动作信息: {action_info}")
 
         except Exception as e:
             logger.error(f"加载已注册动作失败: {e}")

src/chat/focus_chat/planners/actions/reply_action.py

@@ -24,13 +24,13 @@ class ReplyAction(BaseAction):
     action_description: str = "表达想法，可以只包含文本、表情或两者都有"
     action_parameters: dict[str:str] = {
         "text": "你想要表达的内容（可选）",
-        "emojis": "描述当前使用表情包的场景（可选）",
+        "emojis": "描述当前使用表情包的场景，一段话描述（可选）",
         "target": "你想要回复的原始文本内容（非必须，仅文本，不包含发送者)（可选）",
     }
     action_require: list[str] = [
         "有实质性内容需要表达",
         "有人提到你，但你还没有回应他",
-        "在合适的时候添加表情（不要总是添加）",
+        "在合适的时候添加表情（不要总是添加），表情描述要详细，描述当前场景，一段话描述",
         "如果你有明确的,要回复特定某人的某句话，或者你想回复较早的消息，请在target中指定那句话的原始文本",
         "一次只回复一个人，一次只回复一个话题,突出重点",
         "如果是自己发的消息想继续，需自然衔接",

src/chat/focus_chat/planners/planner.py

@@ -24,7 +24,8 @@ def init_prompt():
     Prompt(
         """{extra_info_block}
 
-你的名字是{bot_name},{prompt_personality}，{chat_context_description}。需要基于以下信息决定如何参与对话：
+你需要基于以下信息决定如何参与对话
+这些信息可能会有冲突，请你整合这些信息，并选择一个最合适的action：
 {chat_content_block}
 
 {mind_info_block}
@@ -92,7 +93,7 @@ class ActionPlanner:
             extra_info: list[str] = []
             for info in all_plan_info:
                 if isinstance(info, ObsInfo):
-                    logger.debug(f"{self.log_prefix} 观察信息: {info}")
+                    # logger.debug(f"{self.log_prefix} 观察信息: {info}")
                     observed_messages = info.get_talking_message()
                     observed_messages_str = info.get_talking_message_str_truncate()
                     chat_type = info.get_chat_type()
@@ -101,15 +102,16 @@ class ActionPlanner:
                     else:
                         is_group_chat = False
                 elif isinstance(info, MindInfo):
-                    logger.debug(f"{self.log_prefix} 思维信息: {info}")
+                    # logger.debug(f"{self.log_prefix} 思维信息: {info}")
                     current_mind = info.get_current_mind()
                 elif isinstance(info, CycleInfo):
-                    logger.debug(f"{self.log_prefix} 循环信息: {info}")
+                    # logger.debug(f"{self.log_prefix} 循环信息: {info}")
                     cycle_info = info.get_observe_info()
                 elif isinstance(info, StructuredInfo):
-                    logger.debug(f"{self.log_prefix} 结构化信息: {info}")
+                    # logger.debug(f"{self.log_prefix} 结构化信息: {info}")
                     structured_info = info.get_data()
                 else:
+                    logger.debug(f"{self.log_prefix} 其他信息: {info}")
                     extra_info.append(info.get_processed_info())
 
             current_available_actions = self.action_manager.get_using_actions()

src/chat/focus_chat/working_memory/memory_item.py

@@ -0,0 +1,119 @@
+from typing import Dict, Any, Type, TypeVar, Generic, List, Optional, Callable, Set, Tuple
+import time
+import uuid
+import traceback
+import random
+import string
+from json_repair import repair_json
+from rich.traceback import install
+from src.common.logger_manager import get_logger
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+
+
+class MemoryItem:
+    """记忆项类，用于存储单个记忆的所有相关信息"""
+    
+    def __init__(self, data: Any, from_source: str = "", tags: Optional[List[str]] = None):
+        """
+        初始化记忆项
+        
+        Args:
+            data: 记忆数据
+            from_source: 数据来源
+            tags: 数据标签列表
+        """
+        # 生成可读ID：时间戳_随机字符串
+        timestamp = int(time.time())
+        random_str = ''.join(random.choices(string.ascii_lowercase + string.digits, k=2))
+        self.id = f"{timestamp}_{random_str}"
+        self.data = data
+        self.data_type = type(data)
+        self.from_source = from_source
+        self.tags = set(tags) if tags else set()
+        self.timestamp = time.time()
+        # 修改summary的结构说明，用于存储可能的总结信息
+        # summary结构：{
+        #   "brief": "记忆内容主题",
+        #   "detailed": "记忆内容概括",
+        #   "keypoints": ["关键概念1", "关键概念2"],
+        #   "events": ["事件1", "事件2"]
+        # }
+        self.summary = None
+        
+        # 记忆精简次数
+        self.compress_count = 0
+        
+        # 记忆提取次数
+        self.retrieval_count = 0
+        
+        # 记忆强度 (初始为10)
+        self.memory_strength = 10.0
+        
+        # 记忆操作历史记录
+        # 格式: [(操作类型, 时间戳, 当时精简次数, 当时强度), ...]
+        self.history = [("create", self.timestamp, self.compress_count, self.memory_strength)]
+    
+    def add_tag(self, tag: str) -> None:
+        """添加标签"""
+        self.tags.add(tag)
+    
+    def remove_tag(self, tag: str) -> None:
+        """移除标签"""
+        if tag in self.tags:
+            self.tags.remove(tag)
+    
+    def has_tag(self, tag: str) -> bool:
+        """检查是否有特定标签"""
+        return tag in self.tags
+    
+    def has_all_tags(self, tags: List[str]) -> bool:
+        """检查是否有所有指定的标签"""
+        return all(tag in self.tags for tag in tags)
+    
+    def matches_source(self, source: str) -> bool:
+        """检查来源是否匹配"""
+        return self.from_source == source
+    
+    def set_summary(self, summary: Dict[str, Any]) -> None:
+        """设置总结信息"""
+        self.summary = summary
+    
+    def increase_strength(self, amount: float) -> None:
+        """增加记忆强度"""
+        self.memory_strength = min(10.0, self.memory_strength + amount)
+        # 记录操作历史
+        self.record_operation("strengthen")
+    
+    def decrease_strength(self, amount: float) -> None:
+        """减少记忆强度"""
+        self.memory_strength = max(0.1, self.memory_strength - amount)
+        # 记录操作历史
+        self.record_operation("weaken")
+    
+    def increase_compress_count(self) -> None:
+        """增加精简次数并减弱记忆强度"""
+        self.compress_count += 1
+        # 记录操作历史
+        self.record_operation("compress")
+    
+    def record_retrieval(self) -> None:
+        """记录记忆被提取的情况"""
+        self.retrieval_count += 1
+        # 提取后强度翻倍
+        self.memory_strength = min(10.0, self.memory_strength * 2)
+        # 记录操作历史
+        self.record_operation("retrieval")
+    
+    def record_operation(self, operation_type: str) -> None:
+        """记录操作历史"""
+        current_time = time.time()
+        self.history.append((operation_type, current_time, self.compress_count, self.memory_strength))
+    
+    def to_tuple(self) -> Tuple[Any, str, Set[str], float, str]:
+        """转换为元组格式（为了兼容性）"""
+        return (self.data, self.from_source, self.tags, self.timestamp, self.id)
+    
+    def is_memory_valid(self) -> bool:
+        """检查记忆是否有效（强度是否大于等于1）"""
+        return self.memory_strength >= 1.0

src/chat/focus_chat/working_memory/memory_manager.py

@@ -0,0 +1,798 @@
+from typing import Dict, Any, Type, TypeVar, Generic, List, Optional, Callable, Set, Tuple
+import time
+import uuid
+import traceback
+from json_repair import repair_json
+from rich.traceback import install
+from src.common.logger_manager import get_logger
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.chat.focus_chat.working_memory.memory_item import MemoryItem
+import json  # 添加json模块导入
+
+
+install(extra_lines=3)
+logger = get_logger("working_memory")
+
+T = TypeVar('T')
+
+
+class MemoryManager:
+    def __init__(self, chat_id: str):
+        """
+        初始化工作记忆
+        
+        Args:
+            chat_id: 关联的聊天ID，用于标识该工作记忆属于哪个聊天
+        """
+        # 关联的聊天ID
+        self._chat_id = chat_id
+        
+        # 主存储: 数据类型 -> 记忆项列表
+        self._memory: Dict[Type, List[MemoryItem]] = {}
+        
+        # ID到记忆项的映射
+        self._id_map: Dict[str, MemoryItem] = {}
+        
+        self.llm_summarizer = LLMRequest(
+            model=global_config.llm_summary,
+            temperature=0.3,
+            max_tokens=512,
+            request_type="memory_summarization"
+        )
+    
+    @property
+    def chat_id(self) -> str:
+        """获取关联的聊天ID"""
+        return self._chat_id
+    
+    @chat_id.setter
+    def chat_id(self, value: str):
+        """设置关联的聊天ID"""
+        self._chat_id = value
+    
+    def push_item(self, memory_item: MemoryItem) -> str:
+        """
+        推送一个已创建的记忆项到工作记忆中
+        
+        Args:
+            memory_item: 要存储的记忆项
+            
+        Returns:
+            记忆项的ID
+        """
+        data_type = memory_item.data_type
+        
+        # 确保存在该类型的存储列表
+        if data_type not in self._memory:
+            self._memory[data_type] = []
+        
+        # 添加到内存和ID映射
+        self._memory[data_type].append(memory_item)
+        self._id_map[memory_item.id] = memory_item
+        
+        return memory_item.id
+    
+    async def push_with_summary(self, data: T, from_source: str = "", tags: Optional[List[str]] = None) -> MemoryItem:
+        """
+        推送一段有类型的信息到工作记忆中，并自动生成总结
+        
+        Args:
+            data: 要存储的数据
+            from_source: 数据来源
+            tags: 数据标签列表
+            
+        Returns:
+            包含原始数据和总结信息的字典
+        """
+        # 如果数据是字符串类型，则先进行总结
+        if isinstance(data, str):
+            # 先生成总结
+            summary = await self.summarize_memory_item(data)
+            
+            # 准备标签
+            memory_tags = list(tags) if tags else []
+            
+            # 创建记忆项
+            memory_item = MemoryItem(data, from_source, memory_tags)
+            
+            # 将总结信息保存到记忆项中
+            memory_item.set_summary(summary)
+            
+            # 推送记忆项
+            self.push_item(memory_item)
+            
+            return memory_item
+        else:
+            # 非字符串类型，直接创建并推送记忆项
+            memory_item = MemoryItem(data, from_source, tags)
+            self.push_item(memory_item)
+            
+            return memory_item
+    
+    def get_by_id(self, memory_id: str) -> Optional[MemoryItem]:
+        """
+        通过ID获取记忆项
+        
+        Args:
+            memory_id: 记忆项ID
+            
+        Returns:
+            找到的记忆项，如果不存在则返回None
+        """
+        memory_item = self._id_map.get(memory_id)
+        if memory_item:
+            
+            # 检查记忆强度，如果小于1则删除
+            if not memory_item.is_memory_valid():
+                print(f"记忆 {memory_id} 强度过低 ({memory_item.memory_strength})，已自动移除")
+                self.delete(memory_id)
+                return None
+        
+        return memory_item
+    
+    def get_all_items(self) -> List[MemoryItem]:
+        """获取所有记忆项"""
+        return list(self._id_map.values())
+    
+    def find_items(self, 
+                data_type: Optional[Type] = None, 
+                source: Optional[str] = None,
+                tags: Optional[List[str]] = None,
+                start_time: Optional[float] = None,
+                end_time: Optional[float] = None,
+                memory_id: Optional[str] = None,
+                limit: Optional[int] = None,
+                newest_first: bool = False,
+                min_strength: float = 0.0) -> List[MemoryItem]:
+        """
+        按条件查找记忆项
+        
+        Args:
+            data_type: 要查找的数据类型
+            source: 数据来源
+            tags: 必须包含的标签列表
+            start_time: 开始时间戳
+            end_time: 结束时间戳
+            memory_id: 特定记忆项ID
+            limit: 返回结果的最大数量
+            newest_first: 是否按最新优先排序
+            min_strength: 最小记忆强度
+            
+        Returns:
+            符合条件的记忆项列表
+        """
+        # 如果提供了特定ID，直接查找
+        if memory_id:
+            item = self.get_by_id(memory_id)
+            return [item] if item else []
+        
+        results = []
+        
+        # 确定要搜索的类型列表
+        types_to_search = [data_type] if data_type else list(self._memory.keys())
+        
+        # 对每个类型进行搜索
+        for typ in types_to_search:
+            if typ not in self._memory:
+                continue
+                
+            # 获取该类型的所有项目
+            items = self._memory[typ]
+            
+            # 如果需要最新优先，则反转遍历顺序
+            if newest_first:
+                items_to_check = list(reversed(items))
+            else:
+                items_to_check = items
+                
+            # 遍历项目
+            for item in items_to_check:
+                # 检查来源是否匹配
+                if source is not None and not item.matches_source(source):
+                    continue
+                    
+                # 检查标签是否匹配
+                if tags is not None and not item.has_all_tags(tags):
+                    continue
+                
+                # 检查时间范围
+                if start_time is not None and item.timestamp < start_time:
+                    continue
+                if end_time is not None and item.timestamp > end_time:
+                    continue
+                
+                # 检查记忆强度
+                if min_strength > 0 and item.memory_strength < min_strength:
+                    continue
+                
+                # 所有条件都满足，添加到结果中
+                results.append(item)
+                
+                # 如果达到限制数量，提前返回
+                if limit is not None and len(results) >= limit:
+                    return results
+                    
+        return results
+    
+    async def summarize_memory_item(self, content: str) -> Dict[str, Any]:
+        """
+        使用LLM总结记忆项
+        
+        Args:
+            content: 需要总结的内容
+            
+        Returns:
+            包含总结、概括、关键概念和事件的字典
+        """
+        prompt = f"""请对以下内容进行总结，总结成记忆，输出四部分：
+1. 记忆内容主题（精简，20字以内）：让用户可以一眼看出记忆内容是什么
+2. 记忆内容概括（200字以内）：让用户可以了解记忆内容的大致内容
+3. 关键概念和知识（keypoints）：多条，提取关键的概念、知识点和关键词，要包含对概念的解释
+4. 事件描述（events）：多条，描述谁（人物）在什么时候（时间）做了什么（事件）
+
+内容：
+{content}
+
+请按以下JSON格式输出：
+```json
+{{
+  "brief": "记忆内容主题（20字以内）",
+  "detailed": "记忆内容概括（200字以内）",
+  "keypoints": [
+    "概念1：解释",
+    "概念2：解释",
+    ...
+  ],
+  "events": [
+    "事件1：谁在什么时候做了什么",
+    "事件2：谁在什么时候做了什么",
+    ...
+  ]
+}}
+```
+请确保输出是有效的JSON格式，不要添加任何额外的说明或解释。
+"""
+        default_summary = {
+            "brief": "主题未知的记忆",
+            "detailed": "大致内容未知的记忆",
+            "keypoints": ["未知的概念"],
+            "events": ["未知的事件"]
+        }
+        
+        try:
+            # 调用LLM生成总结
+            response, _ = await self.llm_summarizer.generate_response_async(prompt)
+            
+            # 使用repair_json解析响应
+            try:
+                # 使用repair_json修复JSON格式
+                fixed_json_string = repair_json(response)
+                
+                # 如果repair_json返回的是字符串，需要解析为Python对象
+                if isinstance(fixed_json_string, str):
+                    try:
+                        json_result = json.loads(fixed_json_string)
+                    except json.JSONDecodeError as decode_error:
+                        logger.error(f"JSON解析错误: {str(decode_error)}")
+                        return default_summary
+                else:
+                    # 如果repair_json直接返回了字典对象，直接使用
+                    json_result = fixed_json_string
+                
+                # 进行额外的类型检查
+                if not isinstance(json_result, dict):
+                    logger.error(f"修复后的JSON不是字典类型: {type(json_result)}")
+                    return default_summary
+                
+                # 确保所有必要字段都存在且类型正确
+                if "brief" not in json_result or not isinstance(json_result["brief"], str):
+                    json_result["brief"] = "主题未知的记忆"
+                
+                if "detailed" not in json_result or not isinstance(json_result["detailed"], str):
+                    json_result["detailed"] = "大致内容未知的记忆"
+                
+                # 处理关键概念
+                if "keypoints" not in json_result or not isinstance(json_result["keypoints"], list):
+                    json_result["keypoints"] = ["未知的概念"]
+                else:
+                    # 确保keypoints中的每个项目都是字符串
+                    json_result["keypoints"] = [
+                        str(point) for point in json_result["keypoints"] 
+                        if point is not None
+                    ]
+                    if not json_result["keypoints"]:
+                        json_result["keypoints"] = ["未知的概念"]
+                
+                # 处理事件
+                if "events" not in json_result or not isinstance(json_result["events"], list):
+                    json_result["events"] = ["未知的事件"]
+                else:
+                    # 确保events中的每个项目都是字符串
+                    json_result["events"] = [
+                        str(event) for event in json_result["events"] 
+                        if event is not None
+                    ]
+                    if not json_result["events"]:
+                        json_result["events"] = ["未知的事件"]
+                
+                # 兼容旧版，将keypoints和events合并到key_points中
+                json_result["key_points"] = json_result["keypoints"] + json_result["events"]
+                
+                return json_result
+                
+            except Exception as json_error:
+                logger.error(f"JSON处理失败: {str(json_error)}，将使用默认摘要")
+                # 返回默认结构
+                return default_summary
+            
+        except Exception as e:
+            # 出错时返回简单的结构
+            logger.error(f"生成总结时出错: {str(e)}")
+            return default_summary
+    
+    async def refine_memory(self, 
+                        memory_id: str,
+                        requirements: str = "") -> Dict[str, Any]:
+        """
+        对记忆进行精简操作，根据要求修改要点、总结和概括
+        
+        Args:
+            memory_id: 记忆ID
+            requirements: 精简要求，描述如何修改记忆，包括可能需要移除的要点
+        
+        Returns:
+            修改后的记忆总结字典
+        """
+        # 获取指定ID的记忆项
+        logger.info(f"精简记忆: {memory_id}")
+        memory_item = self.get_by_id(memory_id)
+        if not memory_item:
+            raise ValueError(f"未找到ID为{memory_id}的记忆项")
+        
+        # 增加精简次数
+        memory_item.increase_compress_count()
+        
+        summary = memory_item.summary
+        
+        # 使用LLM根据要求对总结、概括和要点进行精简修改
+        prompt = f"""
+请根据以下要求，对记忆内容的主题、概括、关键概念和事件进行精简，模拟记忆的遗忘过程：
+要求：{requirements}
+你可以随机对关键概念和事件进行压缩，模糊或者丢弃，修改后，同样修改主题和概括
+
+目前主题：{summary["brief"]}
+
+目前概括：{summary["detailed"]}
+
+目前关键概念：
+{chr(10).join([f"- {point}" for point in summary.get("keypoints", [])])}
+
+目前事件：
+{chr(10).join([f"- {point}" for point in summary.get("events", [])])}
+
+请生成修改后的主题、概括、关键概念和事件，遵循以下格式：
+```json
+{{
+    "brief": "修改后的主题（20字以内）",
+    "detailed": "修改后的概括（200字以内）",
+    "keypoints": [
+        "修改后的概念1：解释",
+        "修改后的概念2：解释"
+    ],
+    "events": [
+        "修改后的事件1：谁在什么时候做了什么",
+        "修改后的事件2：谁在什么时候做了什么"
+    ]
+}}
+```
+请确保输出是有效的JSON格式，不要添加任何额外的说明或解释。
+"""
+        # 检查summary中是否有旧版结构，转换为新版结构
+        if "keypoints" not in summary and "events" not in summary and "key_points" in summary:
+            # 尝试区分key_points中的keypoints和events
+            # 简单地将前半部分视为keypoints，后半部分视为events
+            key_points = summary.get("key_points", [])
+            halfway = len(key_points) // 2
+            summary["keypoints"] = key_points[:halfway] or ["未知的概念"]
+            summary["events"] = key_points[halfway:] or ["未知的事件"]
+        
+        # 定义默认的精简结果
+        default_refined = {
+            "brief": summary["brief"],
+            "detailed": summary["detailed"],
+            "keypoints": summary.get("keypoints", ["未知的概念"])[:1],  # 默认只保留第一个关键概念
+            "events": summary.get("events", ["未知的事件"])[:1]  # 默认只保留第一个事件
+        }
+        
+        try:
+            # 调用LLM修改总结、概括和要点
+            response, _ = await self.llm_summarizer.generate_response_async(prompt)
+            logger.info(f"精简记忆响应: {response}")
+            # 使用repair_json处理响应
+            try:
+                # 修复JSON格式
+                fixed_json_string = repair_json(response)
+                
+                # 将修复后的字符串解析为Python对象
+                if isinstance(fixed_json_string, str):
+                    try:
+                        refined_data = json.loads(fixed_json_string)
+                    except json.JSONDecodeError as decode_error:
+                        logger.error(f"JSON解析错误: {str(decode_error)}")
+                        refined_data = default_refined
+                else:
+                    # 如果repair_json直接返回了字典对象，直接使用
+                    refined_data = fixed_json_string
+                
+                # 确保是字典类型
+                if not isinstance(refined_data, dict):
+                    logger.error(f"修复后的JSON不是字典类型: {type(refined_data)}")
+                    refined_data = default_refined
+                
+                # 更新总结、概括
+                summary["brief"] = refined_data.get("brief", "主题未知的记忆")
+                summary["detailed"] = refined_data.get("detailed", "大致内容未知的记忆")
+                
+                # 更新关键概念
+                keypoints = refined_data.get("keypoints", [])
+                if isinstance(keypoints, list) and keypoints:
+                    # 确保所有关键概念都是字符串
+                    summary["keypoints"] = [str(point) for point in keypoints if point is not None]
+                else:
+                    # 如果keypoints不是列表或为空，使用默认值
+                    summary["keypoints"] = ["主要概念已遗忘"]
+                
+                # 更新事件
+                events = refined_data.get("events", [])
+                if isinstance(events, list) and events:
+                    # 确保所有事件都是字符串
+                    summary["events"] = [str(event) for event in events if event is not None]
+                else:
+                    # 如果events不是列表或为空，使用默认值
+                    summary["events"] = ["事件细节已遗忘"]
+                
+                # 兼容旧版，维护key_points
+                summary["key_points"] = summary["keypoints"] + summary["events"]
+                
+            except Exception as e:
+                logger.error(f"精简记忆出错: {str(e)}")
+                traceback.print_exc()
+                
+                # 出错时使用简化的默认精简
+                summary["brief"] = summary["brief"] + " (已简化)"
+                summary["keypoints"] = summary.get("keypoints", ["未知的概念"])[:1]
+                summary["events"] = summary.get("events", ["未知的事件"])[:1]
+                summary["key_points"] = summary["keypoints"] + summary["events"]
+                    
+        except Exception as e:
+            logger.error(f"精简记忆调用LLM出错: {str(e)}")
+            traceback.print_exc()
+            
+        # 更新原记忆项的总结
+        memory_item.set_summary(summary)
+        
+        return memory_item
+    
+    def decay_memory(self, memory_id: str, decay_factor: float = 0.8) -> bool:
+        """
+        使单个记忆衰减
+        
+        Args:
+            memory_id: 记忆ID
+            decay_factor: 衰减因子(0-1之间)
+            
+        Returns:
+            是否成功衰减
+        """
+        memory_item = self.get_by_id(memory_id)
+        if not memory_item:
+            return False
+        
+        # 计算衰减量（当前强度 * (1-衰减因子)）
+        old_strength = memory_item.memory_strength
+        decay_amount = old_strength * (1 - decay_factor)
+        
+        # 更新强度
+        memory_item.memory_strength = decay_amount
+        
+        return True
+    
+    
+    def delete(self, memory_id: str) -> bool:
+        """
+        删除指定ID的记忆项
+        
+        Args:
+            memory_id: 要删除的记忆项ID
+            
+        Returns:
+            是否成功删除
+        """
+        if memory_id not in self._id_map:
+            return False
+        
+        # 获取要删除的项
+        item = self._id_map[memory_id]
+        
+        # 从内存中删除
+        data_type = item.data_type
+        if data_type in self._memory:
+            self._memory[data_type] = [i for i in self._memory[data_type] if i.id != memory_id]
+            
+        # 从ID映射中删除
+        del self._id_map[memory_id]
+        
+        return True
+    
+    def clear(self, data_type: Optional[Type] = None) -> None:
+        """
+        清除记忆中的数据
+        
+        Args:
+            data_type: 要清除的数据类型，如果为None则清除所有数据
+        """
+        if data_type is None:
+            # 清除所有数据
+            self._memory.clear()
+            self._id_map.clear()
+        elif data_type in self._memory:
+            # 清除指定类型的数据
+            for item in self._memory[data_type]:
+                if item.id in self._id_map:
+                    del self._id_map[item.id]
+            del self._memory[data_type]
+    
+    async def merge_memories(self, memory_id1: str, memory_id2: str, reason: str, delete_originals: bool = True) -> MemoryItem:
+        """
+        合并两个记忆项
+        
+        Args:
+            memory_id1: 第一个记忆项ID
+            memory_id2: 第二个记忆项ID
+            reason: 合并原因
+            delete_originals: 是否删除原始记忆，默认为True
+            
+        Returns:
+            包含合并后的记忆信息的字典
+        """
+        # 获取两个记忆项
+        memory_item1 = self.get_by_id(memory_id1)
+        memory_item2 = self.get_by_id(memory_id2)
+        
+        if not memory_item1 or not memory_item2:
+            raise ValueError("无法找到指定的记忆项")
+        
+        content1 = memory_item1.data
+        content2 = memory_item2.data
+        
+        # 获取记忆的摘要信息（如果有）
+        summary1 = memory_item1.summary
+        summary2 = memory_item2.summary
+        
+        # 构建合并提示
+        prompt = f"""
+请根据以下原因，将两段记忆内容有机合并成一段新的记忆内容。
+合并时保留两段记忆的重要信息，避免重复，确保生成的内容连贯、自然。
+
+合并原因：{reason}
+"""
+        
+        # 如果有摘要信息，添加到提示中
+        if summary1:
+            prompt += f"记忆1主题：{summary1['brief']}\n"
+            prompt += f"记忆1概括：{summary1['detailed']}\n"
+            
+            if "keypoints" in summary1:
+                prompt += f"记忆1关键概念：\n" + "\n".join([f"- {point}" for point in summary1['keypoints']]) + "\n\n"
+            
+            if "events" in summary1:
+                prompt += f"记忆1事件：\n" + "\n".join([f"- {point}" for point in summary1['events']]) + "\n\n"
+            elif "key_points" in summary1:
+                prompt += f"记忆1要点：\n" + "\n".join([f"- {point}" for point in summary1['key_points']]) + "\n\n"
+            
+        if summary2:
+            prompt += f"记忆2主题：{summary2['brief']}\n"
+            prompt += f"记忆2概括：{summary2['detailed']}\n"
+            
+            if "keypoints" in summary2:
+                prompt += f"记忆2关键概念：\n" + "\n".join([f"- {point}" for point in summary2['keypoints']]) + "\n\n"
+            
+            if "events" in summary2:
+                prompt += f"记忆2事件：\n" + "\n".join([f"- {point}" for point in summary2['events']]) + "\n\n"
+            elif "key_points" in summary2:
+                prompt += f"记忆2要点：\n" + "\n".join([f"- {point}" for point in summary2['key_points']]) + "\n\n"
+        
+        # 添加记忆原始内容
+        prompt += f"""
+记忆1原始内容：
+{content1}
+
+记忆2原始内容：
+{content2}
+
+请按以下JSON格式输出合并结果：
+```json
+{{
+    "content": "合并后的记忆内容文本（尽可能保留原信息，但去除重复）",
+    "brief": "合并后的主题（20字以内）",
+    "detailed": "合并后的概括（200字以内）",
+    "keypoints": [
+        "合并后的概念1：解释",
+        "合并后的概念2：解释",
+        "合并后的概念3：解释"
+    ],
+    "events": [
+        "合并后的事件1：谁在什么时候做了什么",
+        "合并后的事件2：谁在什么时候做了什么"
+    ]
+}}
+```
+请确保输出是有效的JSON格式，不要添加任何额外的说明或解释。
+"""
+        
+        # 默认合并结果
+        default_merged = {
+            "content": f"{content1}\n\n{content2}",
+            "brief": f"合并：{summary1['brief']} + {summary2['brief']}",
+            "detailed": f"合并了两个记忆：{summary1['detailed']} 以及 {summary2['detailed']}",
+            "keypoints": [],
+            "events": []
+        }
+        
+        # 合并旧版key_points
+        if "key_points" in summary1:
+            default_merged["keypoints"].extend(summary1.get("keypoints", []))
+            default_merged["events"].extend(summary1.get("events", []))
+            # 如果没有新的结构，尝试从旧结构分离
+            if not default_merged["keypoints"] and not default_merged["events"] and "key_points" in summary1:
+                key_points = summary1["key_points"]
+                halfway = len(key_points) // 2
+                default_merged["keypoints"].extend(key_points[:halfway])
+                default_merged["events"].extend(key_points[halfway:])
+                
+        if "key_points" in summary2:
+            default_merged["keypoints"].extend(summary2.get("keypoints", []))
+            default_merged["events"].extend(summary2.get("events", []))
+            # 如果没有新的结构，尝试从旧结构分离
+            if not default_merged["keypoints"] and not default_merged["events"] and "key_points" in summary2:
+                key_points = summary2["key_points"]
+                halfway = len(key_points) // 2
+                default_merged["keypoints"].extend(key_points[:halfway])
+                default_merged["events"].extend(key_points[halfway:])
+        
+        # 确保列表不为空
+        if not default_merged["keypoints"]:
+            default_merged["keypoints"] = ["合并的关键概念"]
+        if not default_merged["events"]:
+            default_merged["events"] = ["合并的事件"]
+        
+        # 添加key_points兼容
+        default_merged["key_points"] = default_merged["keypoints"] + default_merged["events"]
+        
+        try:
+            # 调用LLM合并记忆
+            response, _ = await self.llm_summarizer.generate_response_async(prompt)
+            
+            # 处理LLM返回的合并结果
+            try:
+                # 修复JSON格式
+                fixed_json_string = repair_json(response)
+                
+                # 将修复后的字符串解析为Python对象
+                if isinstance(fixed_json_string, str):
+                    try:
+                        merged_data = json.loads(fixed_json_string)
+                    except json.JSONDecodeError as decode_error:
+                        logger.error(f"JSON解析错误: {str(decode_error)}")
+                        merged_data = default_merged
+                else:
+                    # 如果repair_json直接返回了字典对象，直接使用
+                    merged_data = fixed_json_string
+                
+                # 确保是字典类型
+                if not isinstance(merged_data, dict):
+                    logger.error(f"修复后的JSON不是字典类型: {type(merged_data)}")
+                    merged_data = default_merged
+                
+                # 确保所有必要字段都存在且类型正确
+                if "content" not in merged_data or not isinstance(merged_data["content"], str):
+                    merged_data["content"] = default_merged["content"]
+                
+                if "brief" not in merged_data or not isinstance(merged_data["brief"], str):
+                    merged_data["brief"] = default_merged["brief"]
+                
+                if "detailed" not in merged_data or not isinstance(merged_data["detailed"], str):
+                    merged_data["detailed"] = default_merged["detailed"]
+                
+                # 处理关键概念
+                if "keypoints" not in merged_data or not isinstance(merged_data["keypoints"], list):
+                    merged_data["keypoints"] = default_merged["keypoints"]
+                else:
+                    # 确保keypoints中的每个项目都是字符串
+                    merged_data["keypoints"] = [
+                        str(point) for point in merged_data["keypoints"] 
+                        if point is not None
+                    ]
+                    if not merged_data["keypoints"]:
+                        merged_data["keypoints"] = ["合并的关键概念"]
+                
+                # 处理事件
+                if "events" not in merged_data or not isinstance(merged_data["events"], list):
+                    merged_data["events"] = default_merged["events"]
+                else:
+                    # 确保events中的每个项目都是字符串
+                    merged_data["events"] = [
+                        str(event) for event in merged_data["events"] 
+                        if event is not None
+                    ]
+                    if not merged_data["events"]:
+                        merged_data["events"] = ["合并的事件"]
+                
+                # 添加key_points兼容
+                merged_data["key_points"] = merged_data["keypoints"] + merged_data["events"]
+                
+            except Exception as e:
+                logger.error(f"合并记忆时处理JSON出错: {str(e)}")
+                traceback.print_exc()
+                merged_data = default_merged
+        except Exception as e:
+            logger.error(f"合并记忆调用LLM出错: {str(e)}")
+            traceback.print_exc()
+            merged_data = default_merged
+        
+        # 创建新的记忆项
+        # 合并记忆项的标签
+        merged_tags = memory_item1.tags.union(memory_item2.tags)
+        
+        # 取两个记忆项中更强的来源
+        merged_source = memory_item1.from_source if memory_item1.memory_strength >= memory_item2.memory_strength else memory_item2.from_source
+        
+        # 创建新的记忆项
+        merged_memory = MemoryItem(
+            data=merged_data["content"],
+            from_source=merged_source,
+            tags=list(merged_tags)
+        )
+        
+        # 设置合并后的摘要
+        summary = {
+            "brief": merged_data["brief"],
+            "detailed": merged_data["detailed"],
+            "keypoints": merged_data["keypoints"],
+            "events": merged_data["events"],
+            "key_points": merged_data["key_points"]
+        }
+        merged_memory.set_summary(summary)
+        
+        # 记忆强度取两者最大值
+        merged_memory.memory_strength = max(memory_item1.memory_strength, memory_item2.memory_strength)
+        
+        # 添加到存储中
+        self.push_item(merged_memory)
+        
+        # 如果需要，删除原始记忆
+        if delete_originals:
+            self.delete(memory_id1)
+            self.delete(memory_id2)
+        
+        return merged_memory
+            
+    def delete_earliest_memory(self) -> bool:
+        """
+        删除最早的记忆项
+        
+        Returns:
+            是否成功删除
+        """
+        # 获取所有记忆项
+        all_memories = self.get_all_items()
+        
+        if not all_memories:
+            return False
+            
+        # 按时间戳排序，找到最早的记忆项
+        earliest_memory = min(all_memories, key=lambda item: item.timestamp)
+        
+        # 删除最早的记忆项
+        return self.delete(earliest_memory.id)

src/chat/focus_chat/working_memory/test/memory_file_loader.py

@@ -0,0 +1,169 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import asyncio
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+
+from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+from src.chat.focus_chat.working_memory.memory_item import MemoryItem
+from src.common.logger_manager import get_logger
+
+logger = get_logger("memory_loader")
+
+class MemoryFileLoader:
+    """从文件加载记忆内容的工具类"""
+    
+    def __init__(self, working_memory: WorkingMemory):
+        """
+        初始化记忆文件加载器
+        
+        Args:
+            working_memory: 工作记忆实例
+        """
+        self.working_memory = working_memory
+    
+    async def load_from_directory(self, 
+                                directory_path: str, 
+                                file_pattern: str = "*.txt",
+                                common_tags: List[str] = None,
+                                source_prefix: str = "文件") -> List[MemoryItem]:
+        """
+        从指定目录加载符合模式的文件作为记忆
+        
+        Args:
+            directory_path: 目录路径
+            file_pattern: 文件模式（默认为*.txt）
+            common_tags: 所有记忆共有的标签
+            source_prefix: 来源前缀
+            
+        Returns:
+            加载的记忆项列表
+        """
+        directory = Path(directory_path)
+        if not directory.exists() or not directory.is_dir():
+            logger.error(f"目录不存在或不是有效目录: {directory_path}")
+            return []
+        
+        # 获取文件列表
+        files = list(directory.glob(file_pattern))
+        if not files:
+            logger.warning(f"在目录 {directory_path} 中没有找到符合 {file_pattern} 的文件")
+            return []
+        
+        logger.info(f"在目录 {directory_path} 中找到 {len(files)} 个符合条件的文件")
+        
+        # 加载文件内容为记忆
+        loaded_memories = []
+        for file_path in files:
+            try:
+                memory_item = await self._load_single_file(
+                    file_path=str(file_path),
+                    common_tags=common_tags,
+                    source_prefix=source_prefix
+                )
+                if memory_item:
+                    loaded_memories.append(memory_item)
+                    logger.info(f"成功加载记忆: {file_path.name}")
+                
+            except Exception as e:
+                logger.error(f"加载文件 {file_path} 失败: {str(e)}")
+        
+        logger.info(f"完成加载，共加载了 {len(loaded_memories)} 个记忆")
+        return loaded_memories
+    
+    async def _load_single_file(self, 
+                              file_path: str, 
+                              common_tags: Optional[List[str]] = None,
+                              source_prefix: str = "文件") -> Optional[MemoryItem]:
+        """
+        加载单个文件作为记忆
+        
+        Args:
+            file_path: 文件路径
+            common_tags: 记忆共有的标签
+            source_prefix: 来源前缀
+            
+        Returns:
+            记忆项，加载失败则返回None
+        """
+        try:
+            # 读取文件内容
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+            
+            if not content.strip():
+                logger.warning(f"文件 {file_path} 内容为空")
+                return None
+            
+            # 准备标签和来源
+            file_name = os.path.basename(file_path)
+            tags = list(common_tags) if common_tags else []
+            tags.append(file_name)  # 添加文件名作为标签
+            
+            source = f"{source_prefix}_{file_name}"
+            
+            # 添加到工作记忆
+            memory = await self.working_memory.add_memory(
+                content=content,
+                from_source=source,
+                tags=tags
+            )
+            
+            return memory
+            
+        except Exception as e:
+            logger.error(f"加载文件 {file_path} 失败: {str(e)}")
+            return None
+
+
+async def main():
+    """示例使用"""
+    # 初始化工作记忆
+    chat_id = "demo_chat"
+    working_memory = WorkingMemory(chat_id=chat_id)
+    
+    try:
+        # 初始化加载器
+        loader = MemoryFileLoader(working_memory)
+        
+        # 加载当前目录中的txt文件
+        current_dir = Path(__file__).parent
+        memories = await loader.load_from_directory(
+            directory_path=str(current_dir),
+            file_pattern="*.txt",
+            common_tags=["测试数据", "自动加载"],
+            source_prefix="测试文件"
+        )
+        
+        # 显示加载结果
+        print(f"共加载了 {len(memories)} 个记忆")
+        
+        # 获取并显示所有记忆的概要
+        all_memories = working_memory.memory_manager.get_all_items()
+        for memory in all_memories:
+            print("\n" + "=" * 40)
+            print(f"记忆ID: {memory.id}")
+            print(f"来源: {memory.from_source}")
+            print(f"标签: {', '.join(memory.tags)}")
+            
+            if memory.summary:
+                print(f"\n主题: {memory.summary.get('brief', '无主题')}")
+                print(f"概述: {memory.summary.get('detailed', '无概述')}")
+                print("\n要点:")
+                for point in memory.summary.get('key_points', []):
+                    print(f"- {point}")
+            else:
+                print("\n无摘要信息")
+            
+            print("=" * 40)
+    
+    finally:
+        # 关闭工作记忆
+        await working_memory.shutdown()
+
+
+if __name__ == "__main__":
+    # 运行示例
+    asyncio.run(main()) 

src/chat/focus_chat/working_memory/test/run_memory_tests.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import asyncio
+import os
+import sys
+from pathlib import Path
+
+# 添加项目根目录到系统路径
+current_dir = Path(__file__).parent
+project_root = current_dir.parent.parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+
+async def test_load_memories_from_files():
+    """测试从文件加载记忆的功能"""
+    print("开始测试从文件加载记忆...")
+    
+    # 初始化工作记忆
+    chat_id = "test_memory_load"
+    working_memory = WorkingMemory(chat_id=chat_id, max_memories_per_chat=10, auto_decay_interval=60)
+    
+    try:
+        # 获取测试文件列表
+        test_dir = Path(__file__).parent
+        test_files = [
+            os.path.join(test_dir, f)
+            for f in os.listdir(test_dir)
+            if f.endswith(".txt")
+        ]
+        
+        print(f"找到 {len(test_files)} 个测试文件")
+        
+        # 从每个文件加载记忆
+        for file_path in test_files:
+            file_name = os.path.basename(file_path)
+            print(f"从文件 {file_name} 加载记忆...")
+            
+            # 读取文件内容
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+            
+            # 添加记忆
+            memory = await working_memory.add_memory(
+                content=content,
+                from_source=f"文件_{file_name}",
+                tags=["测试文件", file_name]
+            )
+            
+            print(f"已添加记忆: ID={memory.id}")
+            if memory.summary:
+                print(f"记忆概要: {memory.summary.get('brief', '无概要')}")
+                print(f"记忆要点: {', '.join(memory.summary.get('key_points', ['无要点']))}")
+            print("-" * 50)
+        
+        # 获取所有记忆
+        all_memories = working_memory.memory_manager.get_all_items()
+        print(f"\n成功加载 {len(all_memories)} 个记忆")
+        
+        # 测试检索记忆
+        if all_memories:
+            print("\n测试检索第一个记忆...")
+            first_memory = all_memories[0]
+            retrieved = await working_memory.retrieve_memory(first_memory.id)
+            
+            if retrieved:
+                print(f"成功检索记忆: ID={retrieved.id}")
+                print(f"检索后强度: {retrieved.memory_strength} (初始为10.0)")
+                print(f"检索次数: {retrieved.retrieval_count}")
+            else:
+                print("检索失败")
+        
+        # 测试记忆衰减
+        print("\n测试记忆衰减...")
+        for memory in all_memories:
+            print(f"记忆 {memory.id} 衰减前强度: {memory.memory_strength}")
+        
+        await working_memory.decay_all_memories(decay_factor=0.5)
+        
+        all_memories_after = working_memory.memory_manager.get_all_items()
+        for memory in all_memories_after:
+            print(f"记忆 {memory.id} 衰减后强度: {memory.memory_strength}")
+    
+    finally:
+        # 关闭工作记忆
+        await working_memory.shutdown()
+        print("\n测试完成，已关闭工作记忆")
+
+if __name__ == "__main__":
+    # 运行测试
+    asyncio.run(test_load_memories_from_files()) 

src/chat/focus_chat/working_memory/test/simulate_real_usage.py

@@ -0,0 +1,197 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import asyncio
+import os
+import sys
+import time
+import random
+from pathlib import Path
+from datetime import datetime
+
+# 添加项目根目录到系统路径
+current_dir = Path(__file__).parent
+project_root = current_dir.parent.parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+from src.chat.focus_chat.working_memory.memory_item import MemoryItem
+from src.common.logger_manager import get_logger
+
+logger = get_logger("real_usage_simulation")
+
+class WorkingMemorySimulator:
+    """模拟工作记忆的真实使用场景"""
+    
+    def __init__(self, chat_id="real_usage_test", cycle_interval=20):
+        """
+        初始化模拟器
+        
+        Args:
+            chat_id: 聊天ID
+            cycle_interval: 循环间隔时间(秒)
+        """
+        self.chat_id = chat_id
+        self.cycle_interval = cycle_interval
+        self.working_memory = WorkingMemory(chat_id=chat_id, max_memories_per_chat=20, auto_decay_interval=60)
+        self.cycle_count = 0
+        self.running = False
+        
+        # 获取测试文件路径
+        self.test_files = self._get_test_files()
+        if not self.test_files:
+            raise FileNotFoundError("找不到测试文件，请确保test目录中有.txt文件")
+            
+        # 存储所有添加的记忆ID
+        self.memory_ids = []
+        
+    async def start(self, total_cycles=5):
+        """
+        开始模拟循环
+        
+        Args:
+            total_cycles: 总循环次数，设为None表示无限循环
+        """
+        self.running = True
+        logger.info(f"开始模拟真实使用场景，循环间隔: {self.cycle_interval}秒")
+        
+        try:
+            while self.running and (total_cycles is None or self.cycle_count < total_cycles):
+                self.cycle_count += 1
+                logger.info(f"\n===== 开始第 {self.cycle_count} 次循环 =====")
+                
+                # 执行一次循环
+                await self._run_one_cycle()
+                
+                # 如果还有更多循环，则等待
+                if self.running and (total_cycles is None or self.cycle_count < total_cycles):
+                    wait_time = self.cycle_interval
+                    logger.info(f"等待 {wait_time} 秒后开始下一循环...")
+                    await asyncio.sleep(wait_time)
+                
+            logger.info(f"模拟完成，共执行了 {self.cycle_count} 次循环")
+            
+        except KeyboardInterrupt:
+            logger.info("接收到中断信号，停止模拟")
+        except Exception as e:
+            logger.error(f"模拟过程中出错: {str(e)}", exc_info=True)
+        finally:
+            # 关闭工作记忆
+            await self.working_memory.shutdown()
+    
+    def stop(self):
+        """停止模拟循环"""
+        self.running = False
+        logger.info("正在停止模拟...")
+    
+    async def _run_one_cycle(self):
+        """运行一次完整循环：先检索记忆，再添加新记忆"""
+        start_time = time.time()
+        
+        # 1. 先检索已有记忆（如果有）
+        await self._retrieve_memories()
+        
+        # 2. 添加新记忆
+        await self._add_new_memory()
+        
+        # 3. 显示工作记忆状态
+        await self._show_memory_status()
+        
+        # 计算循环耗时
+        cycle_duration = time.time() - start_time
+        logger.info(f"第 {self.cycle_count} 次循环完成，耗时: {cycle_duration:.2f}秒")
+    
+    async def _retrieve_memories(self):
+        """检索现有记忆"""
+        # 如果有已保存的记忆ID，随机选择1-3个进行检索
+        if self.memory_ids:
+            num_to_retrieve = min(len(self.memory_ids), random.randint(1, 3))
+            retrieval_ids = random.sample(self.memory_ids, num_to_retrieve)
+            
+            logger.info(f"正在检索 {num_to_retrieve} 条记忆...")
+            
+            for memory_id in retrieval_ids:
+                memory = await self.working_memory.retrieve_memory(memory_id)
+                if memory:
+                    logger.info(f"成功检索记忆 ID: {memory_id}")
+                    logger.info(f"  - 强度: {memory.memory_strength:.2f}，检索次数: {memory.retrieval_count}")
+                    if memory.summary:
+                        logger.info(f"  - 主题: {memory.summary.get('brief', '无主题')}")
+                else:
+                    logger.warning(f"记忆 ID: {memory_id} 不存在或已被移除")
+                    # 从ID列表中移除
+                    if memory_id in self.memory_ids:
+                        self.memory_ids.remove(memory_id)
+        else:
+            logger.info("当前没有可检索的记忆")
+    
+    async def _add_new_memory(self):
+        """添加新记忆"""
+        # 随机选择一个测试文件作为记忆内容
+        file_path = random.choice(self.test_files)
+        file_name = os.path.basename(file_path)
+        
+        try:
+            # 读取文件内容
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+                
+            # 添加时间戳，模拟不同内容
+            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            content_with_timestamp = f"[{timestamp}] {content}"
+            
+            # 添加记忆
+            logger.info(f"正在添加新记忆，来源: {file_name}")
+            memory = await self.working_memory.add_memory(
+                content=content_with_timestamp,
+                from_source=f"模拟_{file_name}",
+                tags=["模拟测试", f"循环{self.cycle_count}", file_name]
+            )
+            
+            # 保存记忆ID
+            self.memory_ids.append(memory.id)
+            
+            # 显示记忆信息
+            logger.info(f"已添加新记忆 ID: {memory.id}")
+            if memory.summary:
+                logger.info(f"记忆主题: {memory.summary.get('brief', '无主题')}")
+                logger.info(f"记忆要点: {', '.join(memory.summary.get('key_points', ['无要点'])[:2])}...")
+                
+        except Exception as e:
+            logger.error(f"添加记忆失败: {str(e)}")
+    
+    async def _show_memory_status(self):
+        """显示当前工作记忆状态"""
+        all_memories = self.working_memory.memory_manager.get_all_items()
+        
+        logger.info(f"\n当前工作记忆状态:")
+        logger.info(f"记忆总数: {len(all_memories)}")
+        
+        # 按强度排序
+        sorted_memories = sorted(all_memories, key=lambda x: x.memory_strength, reverse=True)
+        
+        logger.info("记忆强度排名 (前5项):")
+        for i, memory in enumerate(sorted_memories[:5], 1):
+            logger.info(f"{i}. ID: {memory.id}, 强度: {memory.memory_strength:.2f}, "
+                      f"检索次数: {memory.retrieval_count}, "
+                      f"主题: {memory.summary.get('brief', '无主题') if memory.summary else '无摘要'}")
+    
+    def _get_test_files(self):
+        """获取测试文件列表"""
+        test_dir = Path(__file__).parent
+        return [
+            os.path.join(test_dir, f)
+            for f in os.listdir(test_dir)
+            if f.endswith(".txt")
+        ]
+
+async def main():
+    """主函数"""
+    # 创建模拟器
+    simulator = WorkingMemorySimulator(cycle_interval=20)  # 设置20秒的循环间隔
+    
+    # 设置运行5个循环
+    await simulator.start(total_cycles=5)
+
+if __name__ == "__main__":
+    asyncio.run(main()) 

src/chat/focus_chat/working_memory/test/test_decay_removal.py

@@ -0,0 +1,323 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import asyncio
+import os
+import sys
+import time
+from pathlib import Path
+
+# 添加项目根目录到系统路径
+current_dir = Path(__file__).parent
+project_root = current_dir.parent.parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+from src.chat.focus_chat.working_memory.test.memory_file_loader import MemoryFileLoader
+from src.common.logger_manager import get_logger
+
+logger = get_logger("memory_decay_test")
+
+async def test_manual_decay_until_removal():
+    """测试手动衰减直到记忆被自动移除"""
+    print("\n===== 测试手动衰减直到记忆被自动移除 =====")
+    
+    # 初始化工作记忆，设置较大的衰减间隔，避免自动衰减影响测试
+    chat_id = "decay_test_manual"
+    working_memory = WorkingMemory(chat_id=chat_id, max_memories_per_chat=10, auto_decay_interval=3600)
+    
+    try:
+        # 创建加载器并加载测试文件
+        loader = MemoryFileLoader(working_memory)
+        test_dir = current_dir
+        
+        # 加载第一个测试文件作为记忆
+        memories = await loader.load_from_directory(
+            directory_path=str(test_dir),
+            file_pattern="test1.txt",  # 只加载test1.txt
+            common_tags=["测试", "衰减", "自动移除"],
+            source_prefix="衰减测试"
+        )
+        
+        if not memories:
+            print("未能加载记忆文件，测试结束")
+            return
+            
+        # 获取加载的记忆
+        memory = memories[0]
+        memory_id = memory.id
+        print(f"已加载测试记忆，ID: {memory_id}")
+        print(f"初始强度: {memory.memory_strength}")
+        if memory.summary:
+            print(f"记忆主题: {memory.summary.get('brief', '无主题')}")
+        
+        # 执行多次衰减，直到记忆被移除
+        decay_count = 0
+        decay_factor = 0.5  # 每次衰减为原来的一半
+        
+        while True:
+            # 获取当前记忆
+            current_memory = working_memory.memory_manager.get_by_id(memory_id)
+            
+            # 如果记忆已被移除，退出循环
+            if current_memory is None:
+                print(f"记忆已在第 {decay_count} 次衰减后被自动移除!")
+                break
+                
+            # 输出当前强度
+            print(f"衰减 {decay_count} 次后强度: {current_memory.memory_strength}")
+            
+            # 执行衰减
+            await working_memory.decay_all_memories(decay_factor=decay_factor)
+            decay_count += 1
+            
+            # 输出衰减后的详细信息
+            after_memory = working_memory.memory_manager.get_by_id(memory_id)
+            if after_memory:
+                print(f"第 {decay_count} 次衰减结果: 强度={after_memory.memory_strength}，压缩次数={after_memory.compress_count}")
+                if after_memory.summary:
+                    print(f"记忆概要: {after_memory.summary.get('brief', '无概要')}")
+                    print(f"记忆要点数量: {len(after_memory.summary.get('key_points', []))}")
+            else:
+                print(f"第 {decay_count} 次衰减结果: 记忆已被移除")
+                
+            # 防止无限循环
+            if decay_count > 20:
+                print("达到最大衰减次数(20)，退出测试。")
+                break
+                
+            # 短暂等待
+            await asyncio.sleep(0.5)
+        
+        # 验证记忆是否真的被移除
+        all_memories = working_memory.memory_manager.get_all_items()
+        print(f"剩余记忆数量: {len(all_memories)}")
+        if len(all_memories) == 0:
+            print("测试通过: 记忆在强度低于阈值后被成功移除。")
+        else:
+            print("测试失败: 记忆应该被移除但仍然存在。")
+            
+    finally:
+        await working_memory.shutdown()
+
+async def test_auto_decay():
+    """测试自动衰减功能"""
+    print("\n===== 测试自动衰减功能 =====")
+    
+    # 初始化工作记忆，设置短的衰减间隔，便于测试
+    chat_id = "decay_test_auto"
+    decay_interval = 3  # 3秒
+    working_memory = WorkingMemory(chat_id=chat_id, max_memories_per_chat=10, auto_decay_interval=decay_interval)
+    
+    try:
+        # 创建加载器并加载测试文件
+        loader = MemoryFileLoader(working_memory)
+        test_dir = current_dir
+        
+        # 加载第二个测试文件作为记忆
+        memories = await loader.load_from_directory(
+            directory_path=str(test_dir),
+            file_pattern="test1.txt",  # 只加载test2.txt
+            common_tags=["测试", "自动衰减"],
+            source_prefix="自动衰减测试"
+        )
+        
+        if not memories:
+            print("未能加载记忆文件，测试结束")
+            return
+            
+        # 获取加载的记忆
+        memory = memories[0]
+        memory_id = memory.id
+        print(f"已加载测试记忆，ID: {memory_id}")
+        print(f"初始强度: {memory.memory_strength}")
+        if memory.summary:
+            print(f"记忆主题: {memory.summary.get('brief', '无主题')}")
+            print(f"记忆概要: {memory.summary.get('detailed', '无概要')}")
+            print(f"记忆要点: {memory.summary.get('keypoints', '无要点')}")
+            print(f"记忆事件: {memory.summary.get('events', '无事件')}")
+        # 观察自动衰减
+        print(f"等待自动衰减任务执行 (间隔 {decay_interval} 秒)...")
+        
+        for i in range(3):  # 观察3次自动衰减
+            # 等待自动衰减发生
+            await asyncio.sleep(decay_interval + 1)  # 多等1秒确保任务执行
+            
+            # 获取当前记忆
+            current_memory = working_memory.memory_manager.get_by_id(memory_id)
+            
+            # 如果记忆已被移除，退出循环
+            if current_memory is None:
+                print(f"记忆已在第 {i+1} 次自动衰减后被移除!")
+                break
+                
+            # 输出当前强度和详细信息
+            print(f"第 {i+1} 次自动衰减后强度: {current_memory.memory_strength}")
+            print(f"自动衰减详细结果: 压缩次数={current_memory.compress_count}, 提取次数={current_memory.retrieval_count}")
+            if current_memory.summary:
+                print(f"记忆概要: {current_memory.summary.get('brief', '无概要')}")
+        
+        print(f"\n自动衰减测试结束。")
+        
+        # 验证自动衰减是否发生
+        final_memory = working_memory.memory_manager.get_by_id(memory_id)
+        if final_memory is None:
+            print("记忆已被自动衰减移除。")
+        elif final_memory.memory_strength < memory.memory_strength:
+            print(f"自动衰减有效：初始强度 {memory.memory_strength} -> 最终强度 {final_memory.memory_strength}")
+            print(f"衰减历史记录: {final_memory.history}")
+        else:
+            print("测试失败：记忆强度未减少，自动衰减可能未生效。")
+            
+    finally:
+        await working_memory.shutdown()
+
+async def test_decay_and_retrieval_balance():
+    """测试记忆衰减和检索的平衡"""
+    print("\n===== 测试记忆衰减和检索的平衡 =====")
+    
+    # 初始化工作记忆
+    chat_id = "decay_retrieval_balance"
+    working_memory = WorkingMemory(chat_id=chat_id, max_memories_per_chat=10, auto_decay_interval=60)
+    
+    try:
+        # 创建加载器并加载测试文件
+        loader = MemoryFileLoader(working_memory)
+        test_dir = current_dir
+        
+        # 加载第三个测试文件作为记忆
+        memories = await loader.load_from_directory(
+            directory_path=str(test_dir),
+            file_pattern="test3.txt",  # 只加载test3.txt
+            common_tags=["测试", "衰减", "检索"],
+            source_prefix="平衡测试"
+        )
+        
+        if not memories:
+            print("未能加载记忆文件，测试结束")
+            return
+            
+        # 获取加载的记忆
+        memory = memories[0]
+        memory_id = memory.id
+        print(f"已加载测试记忆，ID: {memory_id}")
+        print(f"初始强度: {memory.memory_strength}")
+        if memory.summary:
+            print(f"记忆主题: {memory.summary.get('brief', '无主题')}")
+        
+        # 先衰减几次
+        print("\n开始衰减：")
+        for i in range(3):
+            await working_memory.decay_all_memories(decay_factor=0.5)
+            current = working_memory.memory_manager.get_by_id(memory_id)
+            if current:
+                print(f"衰减 {i+1} 次后强度: {current.memory_strength}")
+                print(f"衰减详细信息: 压缩次数={current.compress_count}, 历史操作数={len(current.history)}")
+                if current.summary:
+                    print(f"记忆概要: {current.summary.get('brief', '无概要')}")
+            else:
+                print(f"记忆已在第 {i+1} 次衰减后被移除。")
+                break
+        
+        # 如果记忆还存在，则检索几次增强它
+        current = working_memory.memory_manager.get_by_id(memory_id)
+        if current:
+            print("\n开始检索增强：")
+            for i in range(2):
+                retrieved = await working_memory.retrieve_memory(memory_id)
+                print(f"检索 {i+1} 次后强度: {retrieved.memory_strength}")
+                print(f"检索后详细信息: 提取次数={retrieved.retrieval_count}, 历史记录长度={len(retrieved.history)}")
+            
+            # 再次衰减几次，测试是否会被移除
+            print("\n再次衰减：")
+            for i in range(5):
+                await working_memory.decay_all_memories(decay_factor=0.5)
+                current = working_memory.memory_manager.get_by_id(memory_id)
+                if current:
+                    print(f"最终衰减 {i+1} 次后强度: {current.memory_strength}")
+                    print(f"衰减详细结果: 压缩次数={current.compress_count}")
+                else:
+                    print(f"记忆已在最终衰减第 {i+1} 次后被移除。")
+                    break
+        
+        print("\n测试结束。")
+            
+    finally:
+        await working_memory.shutdown()
+
+async def test_multi_memories_decay():
+    """测试多条记忆同时衰减"""
+    print("\n===== 测试多条记忆同时衰减 =====")
+    
+    # 初始化工作记忆
+    chat_id = "multi_decay_test"
+    working_memory = WorkingMemory(chat_id=chat_id, max_memories_per_chat=10, auto_decay_interval=60)
+    
+    try:
+        # 创建加载器并加载所有测试文件
+        loader = MemoryFileLoader(working_memory)
+        test_dir = current_dir
+        
+        # 加载所有测试文件作为记忆
+        memories = await loader.load_from_directory(
+            directory_path=str(test_dir),
+            file_pattern="*.txt",
+            common_tags=["测试", "多记忆衰减"],
+            source_prefix="多记忆测试"
+        )
+        
+        if not memories or len(memories) < 2:
+            print("未能加载足够的记忆文件，测试结束")
+            return
+        
+        # 显示已加载的记忆
+        print(f"已加载 {len(memories)} 条记忆:")
+        for idx, mem in enumerate(memories):
+            print(f"{idx+1}. ID: {mem.id}, 强度: {mem.memory_strength}, 来源: {mem.from_source}")
+            if mem.summary:
+                print(f"   主题: {mem.summary.get('brief', '无主题')}")
+        
+        # 进行多次衰减测试
+        print("\n开始多记忆衰减测试:")
+        for decay_round in range(5):
+            # 执行衰减
+            await working_memory.decay_all_memories(decay_factor=0.5)
+            
+            # 获取并显示所有记忆
+            all_memories = working_memory.memory_manager.get_all_items()
+            print(f"\n第 {decay_round+1} 次衰减后，剩余记忆数量: {len(all_memories)}")
+            
+            for idx, mem in enumerate(all_memories):
+                print(f"{idx+1}. ID: {mem.id}, 强度: {mem.memory_strength}, 压缩次数: {mem.compress_count}")
+                if mem.summary:
+                    print(f"   概要: {mem.summary.get('brief', '无概要')[:30]}...")
+            
+            # 如果所有记忆都被移除，退出循环
+            if not all_memories:
+                print("所有记忆已被移除，测试结束。")
+                break
+            
+            # 等待一下
+            await asyncio.sleep(0.5)
+        
+        print("\n多记忆衰减测试结束。")
+    
+    finally:
+        await working_memory.shutdown()
+
+async def main():
+    """运行所有测试"""
+    # 测试手动衰减直到移除
+    await test_manual_decay_until_removal()
+    
+    # 测试自动衰减
+    await test_auto_decay()
+    
+    # 测试衰减和检索的平衡
+    await test_decay_and_retrieval_balance()
+    
+    # 测试多条记忆同时衰减
+    await test_multi_memories_decay()
+
+if __name__ == "__main__":
+    asyncio.run(main()) 

src/chat/focus_chat/working_memory/test/test_working_memory.py

@@ -0,0 +1,121 @@
+import asyncio
+import os
+import unittest
+from typing import List, Dict, Any
+from pathlib import Path
+
+from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+from src.chat.focus_chat.working_memory.memory_item import MemoryItem
+
+class TestWorkingMemory(unittest.TestCase):
+    """工作记忆测试类"""
+    
+    def setUp(self):
+        """测试前准备"""
+        self.chat_id = "test_chat_123"
+        self.working_memory = WorkingMemory(chat_id=self.chat_id, max_memories_per_chat=10, auto_decay_interval=60)
+        self.test_dir = Path(__file__).parent
+        
+    def tearDown(self):
+        """测试后清理"""
+        loop = asyncio.get_event_loop()
+        loop.run_until_complete(self.working_memory.shutdown())
+        
+    def test_init(self):
+        """测试初始化"""
+        self.assertEqual(self.working_memory.max_memories_per_chat, 10)
+        self.assertEqual(self.working_memory.auto_decay_interval, 60)
+        
+    def test_add_memory_from_files(self):
+        """从文件添加记忆"""
+        loop = asyncio.get_event_loop()
+        test_files = self._get_test_files()
+        
+        # 添加记忆
+        memories = []
+        for file_path in test_files:
+            content = self._read_file_content(file_path)
+            file_name = os.path.basename(file_path)
+            source = f"test_file_{file_name}"
+            tags = ["测试", f"文件_{file_name}"]
+            
+            memory = loop.run_until_complete(
+                self.working_memory.add_memory(
+                    content=content,
+                    from_source=source,
+                    tags=tags
+                )
+            )
+            memories.append(memory)
+            
+        # 验证记忆数量
+        all_items = self.working_memory.memory_manager.get_all_items()
+        self.assertEqual(len(all_items), len(test_files))
+        
+        # 验证每个记忆的内容和标签
+        for i, memory in enumerate(memories):
+            file_name = os.path.basename(test_files[i])
+            retrieved_memory = loop.run_until_complete(
+                self.working_memory.retrieve_memory(memory.id)
+            )
+            
+            self.assertIsNotNone(retrieved_memory)
+            self.assertTrue(retrieved_memory.has_tag("测试"))
+            self.assertTrue(retrieved_memory.has_tag(f"文件_{file_name}"))
+            self.assertEqual(retrieved_memory.from_source, f"test_file_{file_name}")
+            
+            # 验证检索后强度增加
+            self.assertGreater(retrieved_memory.memory_strength, 10.0)  # 原始强度为10.0，检索后增加1.5倍
+            self.assertEqual(retrieved_memory.retrieval_count, 1)
+        
+    def test_decay_memories(self):
+        """测试记忆衰减"""
+        loop = asyncio.get_event_loop()
+        test_files = self._get_test_files()[:1]  # 只使用一个文件测试衰减
+        
+        # 添加记忆
+        for file_path in test_files:
+            content = self._read_file_content(file_path)
+            loop.run_until_complete(
+                self.working_memory.add_memory(
+                    content=content,
+                    from_source="decay_test",
+                    tags=["衰减测试"]
+                )
+            )
+            
+        # 获取添加后的记忆项
+        all_items_before = self.working_memory.memory_manager.get_all_items()
+        self.assertEqual(len(all_items_before), 1)
+        
+        # 记录原始强度
+        original_strength = all_items_before[0].memory_strength
+        
+        # 执行衰减
+        loop.run_until_complete(
+            self.working_memory.decay_all_memories(decay_factor=0.5)
+        )
+        
+        # 获取衰减后的记忆项
+        all_items_after = self.working_memory.memory_manager.get_all_items()
+        
+        # 验证强度衰减
+        self.assertEqual(len(all_items_after), 1)
+        self.assertLess(all_items_after[0].memory_strength, original_strength)
+    
+    def _get_test_files(self) -> List[str]:
+        """获取测试文件列表"""
+        test_dir = self.test_dir
+        return [
+            os.path.join(test_dir, f)
+            for f in os.listdir(test_dir)
+            if f.endswith(".txt")
+        ]
+    
+    def _read_file_content(self, file_path: str) -> str:
+        """读取文件内容"""
+        with open(file_path, "r", encoding="utf-8") as f:
+            return f.read()
+
+if __name__ == "__main__":
+    unittest.main() 

src/chat/focus_chat/working_memory/working_memory.py

@@ -0,0 +1,197 @@
+from typing import Dict, List, Any, Optional
+import asyncio
+import random
+from datetime import datetime
+from src.common.logger_manager import get_logger
+from src.chat.focus_chat.working_memory.memory_manager import MemoryManager, MemoryItem
+
+logger = get_logger(__name__)
+
+# 问题是我不知道这个manager是不是需要和其他manager统一管理，因为这个manager是从属于每一个聊天流，都有自己的定时任务
+
+class WorkingMemory:
+    """
+    工作记忆，负责协调和运作记忆
+    从属于特定的流，用chat_id来标识
+    """
+    
+    def __init__(self,chat_id:str , max_memories_per_chat: int = 10, auto_decay_interval: int = 60):
+        """
+        初始化工作记忆管理器
+        
+        Args:
+            max_memories_per_chat: 每个聊天的最大记忆数量
+            auto_decay_interval: 自动衰减记忆的时间间隔(秒)
+        """
+        self.memory_manager = MemoryManager(chat_id)
+        
+        # 记忆容量上限
+        self.max_memories_per_chat = max_memories_per_chat
+        
+        # 自动衰减间隔
+        self.auto_decay_interval = auto_decay_interval
+        
+        # 衰减任务
+        self.decay_task = None
+        
+        # 启动自动衰减任务
+        self._start_auto_decay()
+    
+    def _start_auto_decay(self):
+        """启动自动衰减任务"""
+        if self.decay_task is None:
+            self.decay_task = asyncio.create_task(self._auto_decay_loop())
+    
+    async def _auto_decay_loop(self):
+        """自动衰减循环"""
+        while True:
+            await asyncio.sleep(self.auto_decay_interval)
+            try:
+                await self.decay_all_memories()
+            except Exception as e:
+                print(f"自动衰减记忆时出错: {str(e)}")
+                
+    
+    async def add_memory(self, 
+                        content: Any, 
+                        from_source: str = "", 
+                        tags: Optional[List[str]] = None): 
+        """
+        添加一段记忆到指定聊天
+        
+        Args:
+            content: 记忆内容
+            from_source: 数据来源
+            tags: 数据标签列表
+            
+        Returns:
+            包含记忆信息的字典
+        """
+        memory = await self.memory_manager.push_with_summary(content, from_source, tags)
+        if len(self.memory_manager.get_all_items()) > self.max_memories_per_chat:
+            self.remove_earliest_memory()
+        
+        return memory
+    
+    def remove_earliest_memory(self):
+        """
+        删除最早的记忆
+        """
+        return self.memory_manager.delete_earliest_memory()
+    
+    async def retrieve_memory(self, memory_id: str) -> Optional[MemoryItem]:
+        """
+        检索记忆
+        
+        Args:
+            chat_id: 聊天ID
+            memory_id: 记忆ID
+            
+        Returns:
+            检索到的记忆项，如果不存在则返回None
+        """
+        memory_item = self.memory_manager.get_by_id(memory_id)
+        if memory_item:
+            memory_item.retrieval_count += 1
+            memory_item.increase_strength(5)
+            return memory_item
+        return None
+
+    
+    async def decay_all_memories(self, decay_factor: float = 0.5):
+        """
+        对所有聊天的所有记忆进行衰减
+        衰减：对记忆进行refine压缩，强度会变为原先的0.5
+        
+        Args:
+            decay_factor: 衰减因子(0-1之间)
+        """
+        logger.debug(f"开始对所有记忆进行衰减，衰减因子: {decay_factor}")
+        
+        all_memories = self.memory_manager.get_all_items()
+        
+        for memory_item in all_memories:
+            # 如果压缩完小于1会被删除
+            memory_id = memory_item.id
+            self.memory_manager.decay_memory(memory_id, decay_factor)
+            if memory_item.memory_strength < 1:
+                self.memory_manager.delete(memory_id)
+                continue
+            # 计算衰减量
+            if memory_item.memory_strength < 5:
+                await self.memory_manager.refine_memory(memory_id, f"由于时间过去了{self.auto_decay_interval}秒，记忆变的模糊，所以需要压缩")
+                
+    async def merge_memory(self, memory_id1: str, memory_id2: str) -> MemoryItem:
+        """合并记忆
+        
+        Args:
+            memory_str: 记忆内容
+        """
+        return await self.memory_manager.merge_memories(memory_id1 = memory_id1, memory_id2 = memory_id2,reason = "两端记忆有重复的内容")
+    
+    
+    
+    # 暂时没用，先留着
+    async def simulate_memory_blur(self, chat_id: str, blur_rate: float = 0.2):
+        """
+        模拟记忆模糊过程，随机选择一部分记忆进行精简
+        
+        Args:
+            chat_id: 聊天ID
+            blur_rate: 模糊比率(0-1之间)，表示有多少比例的记忆会被精简
+        """
+        memory = self.get_memory(chat_id)
+        
+        # 获取所有字符串类型且有总结的记忆
+        all_summarized_memories = []
+        for type_items in memory._memory.values():
+            for item in type_items:
+                if isinstance(item.data, str) and hasattr(item, 'summary') and item.summary:
+                    all_summarized_memories.append(item)
+        
+        if not all_summarized_memories:
+            return
+        
+        # 计算要模糊的记忆数量
+        blur_count = max(1, int(len(all_summarized_memories) * blur_rate))
+        
+        # 随机选择要模糊的记忆
+        memories_to_blur = random.sample(all_summarized_memories, min(blur_count, len(all_summarized_memories)))
+        
+        # 对选中的记忆进行精简
+        for memory_item in memories_to_blur:
+            try:
+                # 根据记忆强度决定模糊程度
+                if memory_item.memory_strength > 7:
+                    requirement = "保留所有重要信息，仅略微精简"
+                elif memory_item.memory_strength > 4:
+                    requirement = "保留核心要点，适度精简细节"
+                else:
+                    requirement = "只保留最关键的1-2个要点，大幅精简内容"
+                
+                # 进行精简
+                await memory.refine_memory(memory_item.id, requirement)
+                print(f"已模糊记忆 {memory_item.id}，强度: {memory_item.memory_strength}, 要求: {requirement}")
+                
+            except Exception as e:
+                print(f"模糊记忆 {memory_item.id} 时出错: {str(e)}")
+
+
+    
+    async def shutdown(self) -> None:
+        """关闭管理器，停止所有任务"""
+        if self.decay_task and not self.decay_task.done():
+            self.decay_task.cancel()
+            try:
+                await self.decay_task
+            except asyncio.CancelledError:
+                pass 
+    
+    def get_all_memories(self) -> List[MemoryItem]:
+        """
+        获取所有记忆项目
+        
+        Returns:
+            List[MemoryItem]: 当前工作记忆中的所有记忆项目列表
+        """
+        return self.memory_manager.get_all_items() 

src/chat/heart_flow/observation/chatting_observation.py

@@ -120,12 +120,12 @@ class ChattingObservation(Observation):
         for message in reverse_talking_message:
             if message["processed_plain_text"] == text:
                 find_msg = message
-                logger.debug(f"找到的锚定消息：find_msg: {find_msg}")
+                # logger.debug(f"找到的锚定消息：find_msg: {find_msg}")
                 break
             else:
                 similarity = difflib.SequenceMatcher(None, text, message["processed_plain_text"]).ratio()
                 msg_list.append({"message": message, "similarity": similarity})
-            logger.debug(f"对锚定消息检查：message: {message['processed_plain_text']},similarity: {similarity}")
+            # logger.debug(f"对锚定消息检查：message: {message['processed_plain_text']},similarity: {similarity}")
         if not find_msg:
             if msg_list:
                 msg_list.sort(key=lambda x: x["similarity"], reverse=True)

src/chat/heart_flow/observation/memory_observation.py

@@ -1,55 +0,0 @@
-from src.chat.heart_flow.observation.observation import Observation
-from datetime import datetime
-from src.common.logger_manager import get_logger
-import traceback
-
-# Import the new utility function
-from src.chat.memory_system.Hippocampus import HippocampusManager
-import jieba
-from typing import List
-
-logger = get_logger("memory")
-
-
-class MemoryObservation(Observation):
-    def __init__(self, observe_id):
-        super().__init__(observe_id)
-        self.observe_info: str = ""
-        self.context: str = ""
-        self.running_memory: List[dict] = []
-
-    def get_observe_info(self):
-        for memory in self.running_memory:
-            self.observe_info += f"{memory['topic']}:{memory['content']}\n"
-        return self.observe_info
-
-    async def observe(self):
-        # ---------- 2. 获取记忆 ----------
-        try:
-            # 从聊天内容中提取关键词
-            chat_words = set(jieba.cut(self.context))
-            # 过滤掉停用词和单字词
-            keywords = [word for word in chat_words if len(word) > 1]
-            # 去重并限制数量
-            keywords = list(set(keywords))[:5]
-
-            logger.debug(f"取的关键词: {keywords}")
-
-            # 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
-            )
-
-            logger.debug(f"获取到的记忆: {related_memory}")
-
-            if related_memory:
-                for topic, memory in related_memory:
-                    # 将记忆添加到 running_memory
-                    self.running_memory.append(
-                        {"topic": topic, "content": memory, "timestamp": datetime.now().isoformat()}
-                    )
-                    logger.debug(f"添加新记忆: {topic} - {memory}")
-
-        except Exception as e:
-            logger.error(f"观察 记忆时出错: {e}")
-            logger.error(traceback.format_exc())

src/chat/heart_flow/observation/structure_observation.py

@@ -0,0 +1,32 @@
+from datetime import datetime
+from src.common.logger_manager import get_logger
+
+# Import the new utility function
+
+logger = get_logger("observation")
+
+
+# 所有观察的基类
+class StructureObservation:
+    def __init__(self, observe_id):
+        self.observe_info = ""
+        self.observe_id = observe_id
+        self.last_observe_time = datetime.now().timestamp()  # 初始化为当前时间
+        self.history_loop = []
+        self.structured_info = []
+
+    def get_observe_info(self):
+        return self.structured_info
+
+    def add_structured_info(self, structured_info: dict):
+        self.structured_info.append(structured_info)
+
+    async def observe(self):
+        observed_structured_infos = []
+        for structured_info in self.structured_info:
+            if structured_info.get("ttl") > 0:
+                structured_info["ttl"] -= 1
+            observed_structured_infos.append(structured_info)
+            logger.debug(f"观察到结构化信息仍旧在: {structured_info}")
+
+        self.structured_info = observed_structured_infos

src/chat/heart_flow/observation/working_observation.py

@@ -2,33 +2,33 @@
 # 外部世界可以是某个聊天 不同平台的聊天 也可以是任意媒体
 from datetime import datetime
 from src.common.logger_manager import get_logger
-
+from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+from src.chat.focus_chat.working_memory.memory_item import MemoryItem
+from typing import List
 # Import the new utility function
 
 logger = get_logger("observation")
 
 
 # 所有观察的基类
-class WorkingObservation:
-    def __init__(self, observe_id):
+class WorkingMemoryObservation:
+    def __init__(self, observe_id, working_memory: WorkingMemory):
         self.observe_info = ""
         self.observe_id = observe_id
-        self.last_observe_time = datetime.now().timestamp()  # 初始化为当前时间
-        self.history_loop = []
-        self.structured_info = []
+        self.last_observe_time = datetime.now().timestamp()
+        
+        self.working_memory = working_memory
+        
+        self.retrieved_working_memory = []
 
     def get_observe_info(self):
-        return self.structured_info
+        return self.working_memory
 
-    def add_structured_info(self, structured_info: dict):
-        self.structured_info.append(structured_info)
+    def add_retrieved_working_memory(self, retrieved_working_memory: List[MemoryItem]):
+        self.retrieved_working_memory.append(retrieved_working_memory)
+        
+    def get_retrieved_working_memory(self):
+        return self.retrieved_working_memory
 
     async def observe(self):
-        observed_structured_infos = []
-        for structured_info in self.structured_info:
-            if structured_info.get("ttl") > 0:
-                structured_info["ttl"] -= 1
-            observed_structured_infos.append(structured_info)
-            logger.debug(f"观察到结构化信息仍旧在: {structured_info}")
-
-        self.structured_info = observed_structured_infos
+        pass

src/common/logger.py

@@ -629,22 +629,22 @@ PROCESSOR_STYLE_CONFIG = {
 
 PLANNER_STYLE_CONFIG = {
     "advanced": {
-        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #36DEFF>规划器</fg #36DEFF> | <fg #36DEFF>{message}</fg #36DEFF>",
+        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #4DCDFF>规划器</fg #4DCDFF> | <fg #4DCDFF>{message}</fg #4DCDFF>",
         "file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 规划器 | {message}",
     },
     "simple": {
-        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #36DEFF>规划器</fg #36DEFF> | <fg #36DEFF>{message}</fg #36DEFF>",
+        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #4DCDFF>规划器</fg #4DCDFF> | <fg #4DCDFF>{message}</fg #4DCDFF>",
         "file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 规划器 | {message}",
     },
 }
 
 ACTION_TAKEN_STYLE_CONFIG = {
     "advanced": {
-        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #22DAFF>动作</fg #22DAFF> | <fg #22DAFF>{message}</fg #22DAFF>",
+        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #FFA01F>动作</fg #FFA01F> | <fg #FFA01F>{message}</fg #FFA01F>",
         "file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 动作 | {message}",
     },
     "simple": {
-        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #22DAFF>动作</fg #22DAFF> | <fg #22DAFF>{message}</fg #22DAFF>",
+        "console_format": "<level>{time:HH:mm:ss}</level> | <fg #FFA01F>动作</fg #FFA01F> | <fg #FFA01F>{message}</fg #FFA01F>",
         "file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 动作 | {message}",
     },
 }