fix: ruff
This commit is contained in:
tcmofashi
@@ -121,7 +121,7 @@ class WorkingMemoryProcessor(BaseProcessor):
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)
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print(f"prompt: {prompt}")
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# 调用LLM处理记忆
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content = ""
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try:
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@@ -194,7 +194,7 @@ class PluginAction(BaseAction):
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# 获取锚定消息（如果有）
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observations = self._services.get("observations", [])
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# 查找 ChattingObservation 实例
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chatting_observation = None
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for obs in observations:
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@@ -217,14 +217,14 @@ class ActionPlanner(BasePlanner):
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action_data[key] = value
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action_data["identity"] = self_info
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extra_info_block = "\n".join(extra_info)
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extra_info_block += f"\n{structured_info}"
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if extra_info or structured_info:
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extra_info_block = f"以下是一些额外的信息，现在请你阅读以下内容，进行决策\n{extra_info_block}\n以上是一些额外的信息，现在请你阅读以下内容，进行决策"
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else:
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extra_info_block = ""
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action_data["extra_info_block"] = extra_info_block
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# 对于reply动作不需要额外处理，因为相关字段已经在上面的循环中添加到action_data
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@@ -263,9 +263,6 @@ class ActionPlanner(BasePlanner):
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)
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action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
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plan_result = {
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"action_result": action_result,
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@@ -1,4 +1,4 @@
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from typing import Dict, Any, List, Optional, Set, Tuple
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from typing import Dict, Any, Tuple
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import time
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import random
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import string
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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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@@ -1,6 +1,5 @@
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from typing import List, Any, Optional
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import asyncio
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import random
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from src.common.logger_manager import get_logger
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from src.chat.focus_chat.working_memory.memory_manager import MemoryManager, MemoryItem
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