better：优化工作记忆
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SengokuCola
@@ -162,7 +162,7 @@ class WorkingMemoryProcessor(BaseProcessor):
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memory_brief = memory_summary.get("brief")
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memory_points = memory_summary.get("points", [])
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for point in memory_points:
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memory_str += f"记忆要点:{point}\n"
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memory_str += f"{point}\n"
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working_memory_info = WorkingMemoryInfo()
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if memory_str:
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@@ -286,110 +286,110 @@ class MemoryManager:
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logger.error(f"生成总结时出错: {str(e)}")
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return default_summary
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async def refine_memory(self, memory_id: str, requirements: str = "") -> Dict[str, Any]:
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"""
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对记忆进行精简操作，根据要求修改要点、总结和概括
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# async def refine_memory(self, memory_id: str, requirements: str = "") -> Dict[str, Any]:
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# """
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# 对记忆进行精简操作，根据要求修改要点、总结和概括
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Args:
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memory_id: 记忆ID
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requirements: 精简要求，描述如何修改记忆，包括可能需要移除的要点
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# Args:
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# memory_id: 记忆ID
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# requirements: 精简要求，描述如何修改记忆，包括可能需要移除的要点
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Returns:
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修改后的记忆总结字典
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"""
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# 获取指定ID的记忆项
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logger.info(f"精简记忆: {memory_id}")
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memory_item = self.get_by_id(memory_id)
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if not memory_item:
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raise ValueError(f"未找到ID为{memory_id}的记忆项")
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# Returns:
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# 修改后的记忆总结字典
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# """
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# # 获取指定ID的记忆项
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# logger.info(f"精简记忆: {memory_id}")
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# memory_item = self.get_by_id(memory_id)
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# if not memory_item:
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# raise ValueError(f"未找到ID为{memory_id}的记忆项")
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# 增加精简次数
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memory_item.increase_compress_count()
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# # 增加精简次数
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# memory_item.increase_compress_count()
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summary = memory_item.summary
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# summary = memory_item.summary
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# 使用LLM根据要求对总结、概括和要点进行精简修改
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prompt = f"""
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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：
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要求：{requirements}
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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
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# # 使用LLM根据要求对总结、概括和要点进行精简修改
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# prompt = f"""
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# 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：
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# 要求：{requirements}
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# 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
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目前主题：{summary["brief"]}
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# 目前主题：{summary["brief"]}
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目前关键要点：
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{chr(10).join([f"- {point}" for point in summary.get("points", [])])}
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# 目前关键要点：
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# {chr(10).join([f"- {point}" for point in summary.get("points", [])])}
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请生成修改后的主题和关键要点，遵循以下格式：
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```json
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{{
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"brief": "修改后的主题（20字以内）",
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"points": [
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"修改后的要点",
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"修改后的要点"
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]
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}}
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```
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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。
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"""
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# 定义默认的精简结果
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default_refined = {
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"brief": summary["brief"],
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"points": summary.get("points", ["未知的要点"])[:1], # 默认只保留第一个要点
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}
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# 请生成修改后的主题和关键要点，遵循以下格式：
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# ```json
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# {{
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# "brief": "修改后的主题（20字以内）",
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# "points": [
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# "修改后的要点",
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# "修改后的要点"
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# ]
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# }}
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# ```
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# 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。
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# """
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# # 定义默认的精简结果
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# default_refined = {
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# "brief": summary["brief"],
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# "points": summary.get("points", ["未知的要点"])[:1], # 默认只保留第一个要点
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# }
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try:
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# 调用LLM修改总结、概括和要点
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response, _ = await self.llm_summarizer.generate_response_async(prompt)
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logger.debug(f"精简记忆响应: {response}")
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# 使用repair_json处理响应
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try:
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# 修复JSON格式
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fixed_json_string = repair_json(response)
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# try:
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# # 调用LLM修改总结、概括和要点
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# response, _ = await self.llm_summarizer.generate_response_async(prompt)
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# logger.debug(f"精简记忆响应: {response}")
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# # 使用repair_json处理响应
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# try:
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# # 修复JSON格式
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# fixed_json_string = repair_json(response)
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# 将修复后的字符串解析为Python对象
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if isinstance(fixed_json_string, str):
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try:
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refined_data = json.loads(fixed_json_string)
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except json.JSONDecodeError as decode_error:
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logger.error(f"JSON解析错误: {str(decode_error)}")
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refined_data = default_refined
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else:
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# 如果repair_json直接返回了字典对象，直接使用
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refined_data = fixed_json_string
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# # 将修复后的字符串解析为Python对象
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# if isinstance(fixed_json_string, str):
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# try:
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# refined_data = json.loads(fixed_json_string)
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# except json.JSONDecodeError as decode_error:
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# logger.error(f"JSON解析错误: {str(decode_error)}")
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# refined_data = default_refined
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# else:
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# # 如果repair_json直接返回了字典对象，直接使用
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# refined_data = fixed_json_string
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# 确保是字典类型
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if not isinstance(refined_data, dict):
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logger.error(f"修复后的JSON不是字典类型: {type(refined_data)}")
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refined_data = default_refined
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# # 确保是字典类型
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# if not isinstance(refined_data, dict):
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# logger.error(f"修复后的JSON不是字典类型: {type(refined_data)}")
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# refined_data = default_refined
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# 更新总结
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summary["brief"] = refined_data.get("brief", "主题未知的记忆")
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# # 更新总结
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# summary["brief"] = refined_data.get("brief", "主题未知的记忆")
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# 更新关键要点
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points = refined_data.get("points", [])
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if isinstance(points, list) and points:
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# 确保所有要点都是字符串
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summary["points"] = [str(point) for point in points if point is not None]
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else:
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# 如果points不是列表或为空，使用默认值
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summary["points"] = ["主要要点已遗忘"]
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# # 更新关键要点
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# points = refined_data.get("points", [])
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# if isinstance(points, list) and points:
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# # 确保所有要点都是字符串
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# summary["points"] = [str(point) for point in points if point is not None]
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# else:
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# # 如果points不是列表或为空，使用默认值
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# summary["points"] = ["主要要点已遗忘"]
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except Exception as e:
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logger.error(f"精简记忆出错: {str(e)}")
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traceback.print_exc()
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# except Exception as e:
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# logger.error(f"精简记忆出错: {str(e)}")
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# traceback.print_exc()
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# 出错时使用简化的默认精简
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summary["brief"] = summary["brief"] + " (已简化)"
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summary["points"] = summary.get("points", ["未知的要点"])[:1]
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# # 出错时使用简化的默认精简
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# summary["brief"] = summary["brief"] + " (已简化)"
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# summary["points"] = summary.get("points", ["未知的要点"])[:1]
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except Exception as e:
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logger.error(f"精简记忆调用LLM出错: {str(e)}")
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traceback.print_exc()
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# except Exception as e:
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# logger.error(f"精简记忆调用LLM出错: {str(e)}")
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# traceback.print_exc()
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# 更新原记忆项的总结
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memory_item.set_summary(summary)
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# # 更新原记忆项的总结
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# memory_item.set_summary(summary)
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return memory_item
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# return memory_item
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def decay_memory(self, memory_id: str, decay_factor: float = 0.8) -> bool:
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"""
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@@ -112,10 +112,10 @@ class WorkingMemory:
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self.memory_manager.delete(memory_id)
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continue
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# 计算衰减量
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if memory_item.memory_strength < 5:
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await self.memory_manager.refine_memory(
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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"
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)
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# if memory_item.memory_strength < 5:
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# await self.memory_manager.refine_memory(
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# 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"
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# )
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async def merge_memory(self, memory_id1: str, memory_id2: str) -> MemoryItem:
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"""合并记忆
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@@ -127,51 +127,6 @@ class WorkingMemory:
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memory_id1=memory_id1, memory_id2=memory_id2, reason="两端记忆有重复的内容"
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)
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# 暂时没用，先留着
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async def simulate_memory_blur(self, chat_id: str, blur_rate: float = 0.2):
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"""
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模拟记忆模糊过程，随机选择一部分记忆进行精简
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Args:
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chat_id: 聊天ID
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blur_rate: 模糊比率(0-1之间)，表示有多少比例的记忆会被精简
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"""
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memory = self.get_memory(chat_id)
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# 获取所有字符串类型且有总结的记忆
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all_summarized_memories = []
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for type_items in memory._memory.values():
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for item in type_items:
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if isinstance(item.data, str) and hasattr(item, "summary") and item.summary:
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all_summarized_memories.append(item)
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if not all_summarized_memories:
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return
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# 计算要模糊的记忆数量
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blur_count = max(1, int(len(all_summarized_memories) * blur_rate))
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# 随机选择要模糊的记忆
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memories_to_blur = random.sample(all_summarized_memories, min(blur_count, len(all_summarized_memories)))
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# 对选中的记忆进行精简
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for memory_item in memories_to_blur:
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try:
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# 根据记忆强度决定模糊程度
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if memory_item.memory_strength > 7:
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requirement = "保留所有重要信息，仅略微精简"
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elif memory_item.memory_strength > 4:
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requirement = "保留核心要点，适度精简细节"
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else:
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requirement = "只保留最关键的1-2个要点，大幅精简内容"
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# 进行精简
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await memory.refine_memory(memory_item.id, requirement)
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print(f"已模糊记忆 {memory_item.id}，强度: {memory_item.memory_strength}, 要求: {requirement}")
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except Exception as e:
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print(f"模糊记忆 {memory_item.id} 时出错: {str(e)}")
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async def shutdown(self) -> None:
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"""关闭管理器，停止所有任务"""
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if self.decay_task and not self.decay_task.done():
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