diff --git a/src/chat/focus_chat/info_processors/working_memory_processor.py b/src/chat/focus_chat/info_processors/working_memory_processor.py index e13cddbe4..a7e6ae6cd 100644 --- a/src/chat/focus_chat/info_processors/working_memory_processor.py +++ b/src/chat/focus_chat/info_processors/working_memory_processor.py @@ -162,7 +162,7 @@ class WorkingMemoryProcessor(BaseProcessor): memory_brief = memory_summary.get("brief") memory_points = memory_summary.get("points", []) for point in memory_points: - memory_str += f"记忆要点:{point}\n" + memory_str += f"{point}\n" working_memory_info = WorkingMemoryInfo() if memory_str: diff --git a/src/chat/focus_chat/working_memory/memory_manager.py b/src/chat/focus_chat/working_memory/memory_manager.py index 27f7ad0b1..21aa94707 100644 --- a/src/chat/focus_chat/working_memory/memory_manager.py +++ b/src/chat/focus_chat/working_memory/memory_manager.py @@ -286,110 +286,110 @@ class MemoryManager: 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 +# 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 +# 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") +# 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() +# # 增加精简次数 +# memory_item.increase_compress_count() - summary = memory_item.summary +# 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 +# # 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"]} +# 目前主题：{summary["brief"]} -目前关键要点： -{chr(10).join([f"- {point}" for point in summary.get("points", [])])} +# 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 - } +# 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) +# 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 +# # 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 +# # 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"] +# # 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() +# 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] +# # 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() +# 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) +# # 更新原记忆项的总结 +# memory_item.set_summary(summary) - return memory_item +# return memory_item def decay_memory(self, memory_id: str, decay_factor: float = 0.8) -> bool: """ diff --git a/src/chat/focus_chat/working_memory/working_memory.py b/src/chat/focus_chat/working_memory/working_memory.py index b06456a50..6f3510709 100644 --- a/src/chat/focus_chat/working_memory/working_memory.py +++ b/src/chat/focus_chat/working_memory/working_memory.py @@ -112,10 +112,10 @@ class WorkingMemory: 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" - ) + # 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 @@ -127,51 +127,6 @@ class WorkingMemory: 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():