feat：精简升级工作记忆模块

src/chat/focus_chat/info_processors/working_memory_processor.py

@@ -31,18 +31,13 @@ def init_prompt():
 以下是你已经总结的记忆摘要，你可以调取这些记忆查看内容来帮助你聊天，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
+观察聊天内容和已经总结的记忆，思考如果有相近的记忆，请合并记忆，输出merge_memory，
+合并记忆的格式为[["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",
+    "selected_memory_ids": ["id1", "id2", ...]
     "merge_memory": [["id1", "id2"], ["id3", "id4"],...]
 }}
 ```
@@ -81,120 +76,158 @@ class WorkingMemoryProcessor(BaseProcessor):
             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
+                    chat_obs = observation
+                    # 检查是否有待压缩内容
+            if chat_obs.compressor_prompt:
+                logger.debug(f"{self.log_prefix} 压缩聊天记忆")
+                await self.compress_chat_memory(working_memory, chat_obs)
 
-            if not working_memory:
-                logger.debug(f"{self.log_prefix} 没有找到工作记忆对象")
-                mind_info = MindInfo()
-                return [mind_info]
+            all_memory = working_memory.get_all_memories()
+            if not all_memory:
+                logger.debug(f"{self.log_prefix} 目前没有工作记忆，跳过提取")
+                return []
+
+            memory_prompts = []
+            for memory in all_memory:
+                memory_id = memory.id
+                memory_brief = memory.brief
+                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:
+                content, _ = await self.llm_model.generate_response_async(prompt=prompt)
+                
+                print(f"prompt: {prompt}---------------------------------")
+                print(f"content: {content}---------------------------------")   
+                
+                if not content:
+                    logger.warning(f"{self.log_prefix} LLM返回空结果，处理工作记忆失败。")
+                    return []
+            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())
+                return []
+
+            # 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", [])
+                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,selected_memory_ids: {selected_memory_ids}, merge_memory: {merge_memory}")
+
+            # 根据selected_memory_ids，调取记忆
+            memory_str = ""
+            selected_ids = set(selected_memory_ids)  # 转换为集合以便快速查找
+            
+            # 遍历所有记忆
+            for memory in all_memory:
+                if memory.id in selected_ids:
+                    # 选中的记忆显示详细内容
+                    memory = await working_memory.retrieve_memory(memory.id)
+                    if memory:
+                        memory_str += f"{memory.summary}\n"
+                else:
+                    # 未选中的记忆显示梗概
+                    memory_str += f"{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.debug(f"{self.log_prefix} 没有找到工作记忆")
+
+            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:
+                        asyncio.create_task(self.merge_memory_async(working_memory, merge_pairs[0], merge_pairs[1]))
+
+            return [working_memory_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_summary = memory.summary
-            memory_id = memory.id
-            memory_brief = memory_summary.get("brief")
-            memory_points = memory_summary.get("points", [])
-            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,
-        )
-
-        # print(f"prompt: {prompt}")
-
-        # 调用LLM处理记忆
-        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，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_summary = memory.summary
-                    memory_id = memory.id
-                    memory_brief = memory_summary.get("brief")
-                    memory_points = memory_summary.get("points", [])
-                    for point in memory_points:
-                        memory_str += f"{point}\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.debug(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
+    async def compress_chat_memory(self, working_memory: WorkingMemory, obs: ChattingObservation):
+        """压缩聊天记忆
 
         Args:
             working_memory: 工作记忆对象
-            content: 记忆内容
+            obs: 聊天观察对象
         """
         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]}...")
+            summary_result, _ = await self.llm_model.generate_response_async(obs.compressor_prompt)
+            if not summary_result:
+                logger.debug(f"{self.log_prefix} 压缩聊天记忆失败: 没有生成摘要")
+                return
+            
+            print(f"compressor_prompt: {obs.compressor_prompt}")
+            print(f"summary_result: {summary_result}")
+            
+            # 修复并解析JSON
+            try:
+                fixed_json = repair_json(summary_result)
+                summary_data = json.loads(fixed_json)
+                
+                if not isinstance(summary_data, dict):
+                    logger.error(f"{self.log_prefix} 解析压缩结果失败: 不是有效的JSON对象")
+                    return
+                
+                theme = summary_data.get("theme", "")
+                content = summary_data.get("content", "")
+                
+                if not theme or not content:
+                    logger.error(f"{self.log_prefix} 解析压缩结果失败: 缺少必要字段")
+                    return
+                
+                # 创建新记忆
+                await working_memory.add_memory(
+                    from_source="chat_compress",
+                    summary=content,
+                    brief=theme
+                )
+                
+                logger.debug(f"{self.log_prefix} 压缩聊天记忆成功: {theme} - {content}")
+                
+            except Exception as e:
+                logger.error(f"{self.log_prefix} 解析压缩结果失败: {e}")
+                logger.error(traceback.format_exc())
+                return
+            
+            # 清理压缩状态
+            obs.compressor_prompt = ""
+            obs.oldest_messages = []
+            obs.oldest_messages_str = ""
+            
         except Exception as e:
-            logger.error(f"{self.log_prefix} 异步添加新记忆失败: {e}")
+            logger.error(f"{self.log_prefix} 压缩聊天记忆失败: {e}")
             logger.error(traceback.format_exc())
 
     async def merge_memory_async(self, working_memory: WorkingMemory, memory_id1: str, memory_id2: str):
@@ -202,13 +235,13 @@ class WorkingMemoryProcessor(BaseProcessor):
 
         Args:
             working_memory: 工作记忆对象
-            memory_str: 记忆内容
+            memory_id1: 第一个记忆ID
+            memory_id2: 第二个记忆ID
         """
         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} 合并后的记忆要点: {merged_memory.summary.get('points')}")
+            logger.debug(f"{self.log_prefix} 合并后的记忆梗概: {merged_memory.brief}")
+            logger.debug(f"{self.log_prefix} 合并后的记忆内容: {merged_memory.summary}")
 
         except Exception as e:
             logger.error(f"{self.log_prefix} 异步合并记忆失败: {e}")

src/chat/focus_chat/working_memory/memory_item.py

@@ -7,12 +7,12 @@ import string
 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 = "", brief: str = ""):
+    def __init__(self, summary: str, from_source: str = "", brief: str = ""):
         """
         初始化记忆项
 
         Args:
-            data: 记忆数据
+            summary: 记忆内容概括
             from_source: 数据来源
             brief: 记忆内容主题
         """
@@ -20,18 +20,12 @@ class MemoryItem:
         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.brief = brief
         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：{
-        #   "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
+        
+        # 记忆内容概括
+        self.summary = summary
 
         # 记忆精简次数
         self.compress_count = 0
@@ -50,10 +44,6 @@ class MemoryItem:
         """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)
@@ -85,9 +75,9 @@ class MemoryItem:
         current_time = time.time()
         self.history.append((operation_type, current_time, self.compress_count, self.memory_strength))
 
-    def to_tuple(self) -> Tuple[Any, str, float, str]:
+    def to_tuple(self) -> Tuple[str, str, float, str]:
         """转换为元组格式（为了兼容性）"""
-        return (self.data, self.from_source, self.timestamp, self.id)
+        return (self.summary, self.from_source, 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）"""

src/chat/focus_chat/working_memory/memory_manager.py

@@ -26,8 +26,8 @@ class MemoryManager:
         # 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]] = {}
+        # 记忆项列表
+        self._memories: List[MemoryItem] = []
 
         # ID到记忆项的映射
         self._id_map: Dict[str, MemoryItem] = {}
@@ -58,51 +58,12 @@ class MemoryManager:
         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._memories.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 = "") -> 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
-
-        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)
-
-            # 创建记忆项
-            memory_item = MemoryItem(data, from_source, brief=summary.get("brief", ""))
-
-            # 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)
-            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
@@ -129,7 +90,6 @@ class MemoryManager:
 
     def find_items(
         self,
-        data_type: Optional[Type] = None,
         source: Optional[str] = None,
         start_time: Optional[float] = None,
         end_time: Optional[float] = None,
@@ -142,7 +102,6 @@ class MemoryManager:
         按条件查找记忆项
 
         Args:
-            data_type: 要查找的数据类型
             source: 数据来源
             start_time: 开始时间戳
             end_time: 结束时间戳
@@ -161,49 +120,41 @@ class MemoryManager:
 
         results = []
 
-        # 确定要搜索的类型列表
-        types_to_search = [data_type] if data_type else list(self._memory.keys())
+        # 获取所有项目
+        items = self._memories
 
-        # 对每个类型进行搜索
-        for typ in types_to_search:
-            if typ not in self._memory:
+        # 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
 
-            # 获取该类型的所有项目
-            items = self._memory[typ]
+            # 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
 
-            # 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
+            # 检查记忆强度
+            if min_strength > 0 and item.memory_strength < min_strength:
+                continue
 
-            # 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
+            # 所有条件都满足，添加到结果中
+            results.append(item)
 
-                # 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
+            # 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]:
+    async def summarize_memory_item(self, content: str) -> Dict[str, str]:
         """
         使用LLM总结记忆项
 
@@ -211,11 +162,11 @@ class MemoryManager:
             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
+            包含brief和summary的字典
         """
         prompt = f"""请对以下内容进行总结，总结成记忆，输出两部分：
 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. content：一到三条，包含关键的概念、事件，每条都要包含解释或描述，谁在什么时候干了什么
+2. 记忆内容概括：对内容进行概括，保留重要信息，200字以内
 
 内容：
 {content}
@@ -223,16 +174,13 @@ class MemoryManager:
 请按以下JSON格式输出：
 {{
   "brief": "记忆内容主题",
-  "points": [
-    "内容",
-    "内容"
-  ]
+  "summary": "记忆内容概括"
 }}
 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",
-            "points": ["未知的要点"],
+            "summary": "无法概括的记忆内容",
         }
 
         try:
@@ -264,132 +212,19 @@ class MemoryManager:
                 if "brief" not in json_result or not isinstance(json_result["brief"], str):
                     json_result["brief"] = "主题未知的记忆"
 
-                # 处理关键要点
-                if "points" not in json_result or not isinstance(json_result["points"], list):
-                    json_result["points"] = ["未知的要点"]
-                else:
-                    # 确保points中的每个项目都是字符串
-                    json_result["points"] = [str(point) for point in json_result["points"] if point is not None]
-                    if not json_result["points"]:
-                        json_result["points"] = ["未知的要点"]
+                if "summary" not in json_result or not isinstance(json_result["summary"], str):
+                    json_result["summary"] = "无法概括的记忆内容"
 
                 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，对记忆内容的主题和关键要点进行精简，模拟记忆的遗忘过程：
-    # 要求：{requirements}
-    # 你可以随机对关键要点进行压缩，模糊或者丢弃，修改后，同样修改主题
-
-    # 目前主题：{summary["brief"]}
-
-    # 目前关键要点：
-    # {chr(10).join([f"- {point}" for point in summary.get("points", [])])}
-
-    # 请生成修改后的主题和关键要点，遵循以下格式：
-    # ```json
-    # {{
-    #     "brief": "修改后的主题（20字以内）",
-    #     "points": [
-    #         "修改后的要点",
-    #         "修改后的要点"
-    #     ]
-    # }}
-    # ```
-    # 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。
-    # """
-    #         # 定义默认的精简结果
-    #         default_refined = {
-    #             "brief": summary["brief"],
-    #             "points": summary.get("points", ["未知的要点"])[:1],  # 默认只保留第一个要点
-    #         }
-
-    #         try:
-    #             # 调用LLM修改总结、概括和要点
-    #             response, _ = await self.llm_summarizer.generate_response_async(prompt)
-    #             logger.debug(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
-    #                 summary["brief"] = refined_data.get("brief", "主题未知的记忆")
-
-    #                 # 更新关键要点
-    #                 points = refined_data.get("points", [])
-    #                 if isinstance(points, list) and points:
-    #                     # 确保所有要点都是字符串
-    #                     summary["points"] = [str(point) for point in points if point is not None]
-    #                 else:
-    #                     # 如果points不是列表或为空，使用默认值
-    #                     summary["points"] = ["主要要点已遗忘"]
-
-    #             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["points"] = summary.get("points", ["未知的要点"])[:1]
-
-    #         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
@@ -431,32 +266,17 @@ class MemoryManager:
         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]
+        self._memories = [i for i in self._memories 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]
+    def clear(self) -> None:
+        """清除所有记忆"""
+        self._memories.clear()
+        self._id_map.clear()
 
     async def merge_memories(
         self, memory_id1: str, memory_id2: str, reason: str, delete_originals: bool = True
@@ -471,7 +291,7 @@ class MemoryManager:
             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
+            合并后的记忆项
         """
         # 获取两个记忆项
         memory_item1 = self.get_by_id(memory_id1)
@@ -480,58 +300,33 @@ class MemoryManager:
         if not memory_item1 or not memory_item2:
             raise ValueError("无法找到指定的记忆项")
 
-        # 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"
+记忆1主题：{memory_item1.brief}
+记忆1内容：{memory_item1.summary}
 
-            prompt += "记忆1关键要点：\n" + "\n".join([f"- {point}" for point in summary1.get("points", [])]) + "\n\n"
+记忆2主题：{memory_item2.brief}
+记忆2内容：{memory_item2.summary}
 
-        if summary2:
-            prompt += f"记忆2主题：{summary2['brief']}\n"
-            prompt += "记忆2关键要点：\n" + "\n".join([f"- {point}" for point in summary2.get("points", [])]) + "\n\n"
-
-        prompt += """
 请按以下JSON格式输出合并结果：
-```json
-{
+{{
     "brief": "合并后的主题（20字以内）",
-    "points": [
-        "合并后的要点",
-        "合并后的要点"
-    ]
-}
-```
+    "summary": "合并后的内容概括（200字以内）"
+}}
 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 = {
-            "brief": f"合并：{summary1['brief']} + {summary2['brief']}",
-            "points": [],
+            "brief": f"合并：{memory_item1.brief} + {memory_item2.brief}",
+            "summary": f"合并的记忆：{memory_item1.summary}\n{memory_item2.summary}",
         }
 
-        # 合并points
-        if "points" in summary1:
-            default_merged["points"].extend(summary1["points"])
-        if "points" in summary2:
-            default_merged["points"].extend(summary2["points"])
-
-        # 确保列表不为空
-        if not default_merged["points"]:
-            default_merged["points"] = ["合并的要点"]
-
         try:
             # 调用LLM合并记忆
             response, _ = await self.llm_summarizer.generate_response_async(prompt)
@@ -560,14 +355,8 @@ class MemoryManager:
                 if "brief" not in merged_data or not isinstance(merged_data["brief"], str):
                     merged_data["brief"] = default_merged["brief"]
 
-                # 处理关键要点
-                if "points" not in merged_data or not isinstance(merged_data["points"], list):
-                    merged_data["points"] = default_merged["points"]
-                else:
-                    # 确保points中的每个项目都是字符串
-                    merged_data["points"] = [str(point) for point in merged_data["points"] if point is not None]
-                    if not merged_data["points"]:
-                        merged_data["points"] = ["合并的要点"]
+                if "summary" not in merged_data or not isinstance(merged_data["summary"], str):
+                    merged_data["summary"] = default_merged["summary"]
 
             except Exception as e:
                 logger.error(f"合并记忆时处理JSON出错: {str(e)}")
@@ -586,15 +375,8 @@ class MemoryManager:
             else memory_item2.from_source
         )
 
-        # 创建新的记忆项，使用空字符串作为data
-        merged_memory = MemoryItem(data="", from_source=merged_source, brief=merged_data["brief"])
-
-        # 设置合并后的摘要
-        summary = {
-            "brief": merged_data["brief"],
-            "points": merged_data["points"],
-        }
-        merged_memory.set_summary(summary)
+        # 创建新的记忆项
+        merged_memory = MemoryItem(summary=merged_data["summary"], from_source=merged_source, brief=merged_data["brief"])
 
         # 记忆强度取两者最大值
         merged_memory.memory_strength = max(memory_item1.memory_strength, memory_item2.memory_strength)

src/chat/focus_chat/working_memory/working_memory.py

@@ -54,18 +54,25 @@ class WorkingMemory:
             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 = ""):
+    async def add_memory(self, summary: Any, from_source: str = "",brief: str = ""):
         """
         添加一段记忆到指定聊天
 
         Args:
-            content: 记忆内容
+            summary: 记忆内容
             from_source: 数据来源
 
         Returns:
-            包含记忆信息的字典
+            记忆项
         """
-        memory = await self.memory_manager.push_with_summary(content, from_source)
+        # 如果是字符串类型，生成总结
+
+        memory = MemoryItem(summary, from_source, brief)
+
+        # 添加到管理器
+        self.memory_manager.push_item(memory)
+
+        # 如果超过最大记忆数量，删除最早的记忆
         if len(self.memory_manager.get_all_items()) > self.max_memories_per_chat:
             self.remove_earliest_memory()