feat：精简升级工作记忆模块
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SengokuCola
@@ -31,18 +31,13 @@ def init_prompt():
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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：
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{memory_str}
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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
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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
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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
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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
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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
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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，
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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
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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：
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```json
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{{
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"selected_memory_ids": ["id1", "id2", ...],
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"new_memory": "true" or "false",
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"selected_memory_ids": ["id1", "id2", ...]
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"merge_memory": [["id1", "id2"], ["id3", "id4"],...]
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}}
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```
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@@ -81,120 +76,158 @@ class WorkingMemoryProcessor(BaseProcessor):
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for observation in observations:
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if isinstance(observation, WorkingMemoryObservation):
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working_memory = observation.get_observe_info()
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# working_memory_obs = observation
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if isinstance(observation, ChattingObservation):
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chat_info = observation.get_observe_info()
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# chat_info_truncate = observation.talking_message_str_truncate
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chat_obs = observation
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# 检查是否有待压缩内容
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if chat_obs.compressor_prompt:
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logger.debug(f"{self.log_prefix} 压缩聊天记忆")
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await self.compress_chat_memory(working_memory, chat_obs)
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if not working_memory:
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logger.debug(f"{self.log_prefix} 没有找到工作记忆对象")
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mind_info = MindInfo()
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return [mind_info]
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all_memory = working_memory.get_all_memories()
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if not all_memory:
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logger.debug(f"{self.log_prefix} 目前没有工作记忆，跳过提取")
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return []
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memory_prompts = []
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for memory in all_memory:
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memory_id = memory.id
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memory_brief = memory.brief
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memory_single_prompt = f"记忆id:{memory_id},记忆摘要:{memory_brief}\n"
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memory_prompts.append(memory_single_prompt)
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memory_choose_str = "".join(memory_prompts)
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# 使用提示模板进行处理
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prompt = (await global_prompt_manager.get_prompt_async("prompt_memory_proces")).format(
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bot_name=global_config.bot.nickname,
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time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
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chat_observe_info=chat_info,
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memory_str=memory_choose_str,
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)
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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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content, _ = await self.llm_model.generate_response_async(prompt=prompt)
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print(f"prompt: {prompt}---------------------------------")
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print(f"content: {content}---------------------------------")
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if not content:
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logger.warning(f"{self.log_prefix} LLM返回空结果，处理工作记忆失败。")
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return []
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except Exception as e:
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logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
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logger.error(traceback.format_exc())
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return []
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# 解析LLM返回的JSON
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try:
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result = repair_json(content)
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if isinstance(result, str):
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result = json.loads(result)
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if not isinstance(result, dict):
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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)}")
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return []
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selected_memory_ids = result.get("selected_memory_ids", [])
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merge_memory = result.get("merge_memory", [])
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except Exception as e:
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logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败: {e}")
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logger.error(traceback.format_exc())
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return []
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logger.debug(f"{self.log_prefix} 解析LLM返回的JSON,selected_memory_ids: {selected_memory_ids}, merge_memory: {merge_memory}")
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# 根据selected_memory_ids，调取记忆
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memory_str = ""
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selected_ids = set(selected_memory_ids) # 转换为集合以便快速查找
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# 遍历所有记忆
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for memory in all_memory:
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if memory.id in selected_ids:
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# 选中的记忆显示详细内容
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memory = await working_memory.retrieve_memory(memory.id)
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if memory:
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memory_str += f"{memory.summary}\n"
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else:
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# 未选中的记忆显示梗概
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memory_str += f"{memory.brief}\n"
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working_memory_info = WorkingMemoryInfo()
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if memory_str:
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working_memory_info.add_working_memory(memory_str)
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logger.debug(f"{self.log_prefix} 取得工作记忆: {memory_str}")
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else:
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logger.debug(f"{self.log_prefix} 没有找到工作记忆")
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if merge_memory:
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for merge_pairs in merge_memory:
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memory1 = await working_memory.retrieve_memory(merge_pairs[0])
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memory2 = await working_memory.retrieve_memory(merge_pairs[1])
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if memory1 and memory2:
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asyncio.create_task(self.merge_memory_async(working_memory, merge_pairs[0], merge_pairs[1]))
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return [working_memory_info]
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except Exception as e:
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logger.error(f"{self.log_prefix} 处理观察时出错: {e}")
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logger.error(traceback.format_exc())
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return []
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all_memory = working_memory.get_all_memories()
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memory_prompts = []
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for memory in all_memory:
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memory_summary = memory.summary
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memory_id = memory.id
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memory_brief = memory_summary.get("brief")
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memory_points = memory_summary.get("points", [])
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memory_single_prompt = f"记忆id:{memory_id},记忆摘要:{memory_brief}\n"
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memory_prompts.append(memory_single_prompt)
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memory_choose_str = "".join(memory_prompts)
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# 使用提示模板进行处理
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prompt = (await global_prompt_manager.get_prompt_async("prompt_memory_proces")).format(
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bot_name=global_config.bot.nickname,
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time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
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chat_observe_info=chat_info,
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memory_str=memory_choose_str,
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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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content, _ = await self.llm_model.generate_response_async(prompt=prompt)
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if not content:
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logger.warning(f"{self.log_prefix} LLM返回空结果，处理工作记忆失败。")
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except Exception as e:
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logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
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logger.error(traceback.format_exc())
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# 解析LLM返回的JSON
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try:
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result = repair_json(content)
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if isinstance(result, str):
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result = json.loads(result)
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if not isinstance(result, dict):
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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)}")
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return []
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selected_memory_ids = result.get("selected_memory_ids", [])
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new_memory = result.get("new_memory", "")
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merge_memory = result.get("merge_memory", [])
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except Exception as e:
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logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败: {e}")
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logger.error(traceback.format_exc())
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return []
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logger.debug(f"{self.log_prefix} 解析LLM返回的JSON成功: {result}")
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# 根据selected_memory_ids，调取记忆
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memory_str = ""
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if selected_memory_ids:
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for memory_id in selected_memory_ids:
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memory = await working_memory.retrieve_memory(memory_id)
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if memory:
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memory_summary = memory.summary
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memory_id = memory.id
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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"{point}\n"
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working_memory_info = WorkingMemoryInfo()
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if memory_str:
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working_memory_info.add_working_memory(memory_str)
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logger.debug(f"{self.log_prefix} 取得工作记忆: {memory_str}")
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else:
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logger.debug(f"{self.log_prefix} 没有找到工作记忆")
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# 根据聊天内容添加新记忆
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if new_memory:
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# 使用异步方式添加新记忆，不阻塞主流程
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logger.debug(f"{self.log_prefix} {new_memory}新记忆: ")
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asyncio.create_task(self.add_memory_async(working_memory, chat_info))
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if merge_memory:
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for merge_pairs in merge_memory:
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memory1 = await working_memory.retrieve_memory(merge_pairs[0])
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memory2 = await working_memory.retrieve_memory(merge_pairs[1])
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if memory1 and memory2:
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memory_str = f"记忆id:{memory1.id},记忆摘要:{memory1.summary.get('brief')}\n"
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memory_str += f"记忆id:{memory2.id},记忆摘要:{memory2.summary.get('brief')}\n"
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asyncio.create_task(self.merge_memory_async(working_memory, merge_pairs[0], merge_pairs[1]))
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return [working_memory_info]
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async def add_memory_async(self, working_memory: WorkingMemory, content: str):
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"""异步添加记忆，不阻塞主流程
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async def compress_chat_memory(self, working_memory: WorkingMemory, obs: ChattingObservation):
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"""压缩聊天记忆
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Args:
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working_memory: 工作记忆对象
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content: 记忆内容
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obs: 聊天观察对象
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"""
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try:
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await working_memory.add_memory(content=content, from_source="chat_text")
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# logger.debug(f"{self.log_prefix} 异步添加新记忆成功: {content[:30]}...")
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summary_result, _ = await self.llm_model.generate_response_async(obs.compressor_prompt)
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if not summary_result:
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logger.debug(f"{self.log_prefix} 压缩聊天记忆失败: 没有生成摘要")
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return
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print(f"compressor_prompt: {obs.compressor_prompt}")
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print(f"summary_result: {summary_result}")
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# 修复并解析JSON
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try:
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fixed_json = repair_json(summary_result)
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summary_data = json.loads(fixed_json)
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if not isinstance(summary_data, dict):
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logger.error(f"{self.log_prefix} 解析压缩结果失败: 不是有效的JSON对象")
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return
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theme = summary_data.get("theme", "")
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content = summary_data.get("content", "")
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if not theme or not content:
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logger.error(f"{self.log_prefix} 解析压缩结果失败: 缺少必要字段")
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return
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# 创建新记忆
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await working_memory.add_memory(
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from_source="chat_compress",
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summary=content,
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brief=theme
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)
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logger.debug(f"{self.log_prefix} 压缩聊天记忆成功: {theme} - {content}")
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except Exception as e:
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logger.error(f"{self.log_prefix} 解析压缩结果失败: {e}")
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logger.error(traceback.format_exc())
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return
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# 清理压缩状态
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obs.compressor_prompt = ""
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obs.oldest_messages = []
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obs.oldest_messages_str = ""
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except Exception as e:
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logger.error(f"{self.log_prefix} 异步添加新记忆失败: {e}")
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logger.error(f"{self.log_prefix} 压缩聊天记忆失败: {e}")
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logger.error(traceback.format_exc())
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async def merge_memory_async(self, working_memory: WorkingMemory, memory_id1: str, memory_id2: str):
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@@ -202,13 +235,13 @@ class WorkingMemoryProcessor(BaseProcessor):
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Args:
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working_memory: 工作记忆对象
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memory_str: 记忆内容
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memory_id1: 第一个记忆ID
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memory_id2: 第二个记忆ID
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"""
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try:
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merged_memory = await working_memory.merge_memory(memory_id1, memory_id2)
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# logger.debug(f"{self.log_prefix} 异步合并记忆成功: {memory_id1} 和 {memory_id2}...")
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logger.debug(f"{self.log_prefix} 合并后的记忆梗概: {merged_memory.summary.get('brief')}")
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logger.debug(f"{self.log_prefix} 合并后的记忆要点: {merged_memory.summary.get('points')}")
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logger.debug(f"{self.log_prefix} 合并后的记忆梗概: {merged_memory.brief}")
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logger.debug(f"{self.log_prefix} 合并后的记忆内容: {merged_memory.summary}")
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except Exception as e:
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logger.error(f"{self.log_prefix} 异步合并记忆失败: {e}")
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