mirror of https://github.com/Mai-with-u/MaiBot.git
feat：记忆遗忘和动态合并阈值
SengokuCola
parent
4d5456ed4b
commit
e0aa745ba4
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@ -772,7 +772,7 @@ class DefaultReplyer:
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continue
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timing_logs.append(f"{chinese_name}: {duration:.1f}s")
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if duration > 8:
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if duration > 12:
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logger.warning(f"回复生成前信息获取耗时过长: {chinese_name} 耗时: {duration:.1f}s，请使用更快的模型")
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logger.info(f"回复准备: {'; '.join(timing_logs)}; {almost_zero_str} <0.1s")
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@ -679,7 +679,7 @@ class PrivateReplyer:
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continue
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timing_logs.append(f"{chinese_name}: {duration:.1f}s")
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if duration > 8:
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if duration > 12:
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logger.warning(f"回复生成前信息获取耗时过长: {chinese_name} 耗时: {duration:.1f}s，请使用更快的模型")
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logger.info(f"回复准备: {'; '.join(timing_logs)}; {almost_zero_str} <0.1s")
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@ -41,6 +41,78 @@ class MemoryChest:
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self.running_content_list = {} # {chat_id: {"content": running_content, "last_update_time": timestamp, "create_time": timestamp}}
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self.fetched_memory_list = [] # [(chat_id, (question, answer, timestamp)), ...]
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def remove_one_memory_by_age_weight(self) -> bool:
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"""
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删除一条记忆：按“越老/越新更易被删”的权重随机选择（老=较小id，新=较大id）。
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返回：是否删除成功
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"""
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try:
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memories = list(MemoryChestModel.select())
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if not memories:
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return False
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# 排除锁定项
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candidates = [m for m in memories if not getattr(m, "locked", False)]
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if not candidates:
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return False
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# 按 id 排序，使用 id 近似时间顺序（小 -> 老，大 -> 新）
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candidates.sort(key=lambda m: m.id)
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n = len(candidates)
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if n == 1:
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MemoryChestModel.delete().where(MemoryChestModel.id == candidates[0].id).execute()
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logger.info(f"[记忆管理] 已删除一条记忆(权重抽样)：{candidates[0].title}")
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return True
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# 计算U型权重：中间最低，两端最高
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# r ∈ [0,1] 为位置归一化，w = 0.1 + 0.9 * (abs(r-0.5)*2)**1.5
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weights = []
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for idx, _m in enumerate(candidates):
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r = idx / (n - 1)
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w = 0.1 + 0.9 * (abs(r - 0.5) * 2) ** 1.5
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weights.append(w)
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import random as _random
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selected = _random.choices(candidates, weights=weights, k=1)[0]
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MemoryChestModel.delete().where(MemoryChestModel.id == selected.id).execute()
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logger.info(f"[记忆管理] 已删除一条记忆(权重抽样)：{selected.title}")
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return True
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except Exception as e:
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logger.error(f"[记忆管理] 按年龄权重删除记忆时出错: {e}")
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return False
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def _compute_merge_similarity_threshold(self) -> float:
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"""
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根据当前记忆数量占比动态计算合并相似度阈值。
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规则：占比越高，阈值越低。
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- < 60%: 0.80（更严格，避免早期误合并）
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- < 80%: 0.70
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- < 100%: 0.60
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- < 120%: 0.50
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- >= 120%: 0.45（最宽松，加速收敛）
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"""
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try:
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current_count = MemoryChestModel.select().count()
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max_count = max(1, int(global_config.memory.max_memory_number))
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percentage = current_count / max_count
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if percentage < 0.6:
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return 0.70
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elif percentage < 0.8:
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return 0.60
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elif percentage < 1.0:
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return 0.50
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elif percentage < 1.2:
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return 0.40
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else:
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return 0.35
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except Exception:
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# 发生异常时使用保守阈值
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return 0.70
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async def build_running_content(self, chat_id: str = None) -> str:
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"""
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构建记忆仓库的运行内容
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@ -446,19 +518,22 @@ class MemoryChest:
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logger.warning("未提供chat_id，无法进行记忆匹配")
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return [], []
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# 使用相似度匹配查找最相似的记忆
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# 动态计算相似度阈值（占比越高阈值越低）
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dynamic_threshold = self._compute_merge_similarity_threshold()
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# 使用相似度匹配查找最相似的记忆（基于动态阈值）
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similar_memory = find_most_similar_memory_by_chat_id(
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target_title=memory_title,
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target_chat_id=chat_id,
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similarity_threshold=0.5 # 相似度阈值
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similarity_threshold=dynamic_threshold
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)
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if similar_memory:
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selected_title, selected_content, similarity = similar_memory
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logger.info(f"为 '{memory_title}' 找到相似记忆: '{selected_title}' (相似度: {similarity:.3f})")
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logger.info(f"为 '{memory_title}' 找到相似记忆: '{selected_title}' (相似度: {similarity:.3f} 阈值: {dynamic_threshold:.2f})")
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return [selected_title], [selected_content]
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else:
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logger.info(f"为 '{memory_title}' 未找到相似度 >= 0.7 的记忆")
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logger.info(f"为 '{memory_title}' 未找到相似度 >= {dynamic_threshold:.2f} 的记忆")
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return [], []
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except Exception as e:
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@ -8,7 +8,6 @@ from src.memory_system.Memory_chest import global_memory_chest
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from src.common.logger import get_logger
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from src.common.database.database_model import MemoryChest as MemoryChestModel
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from src.config.config import global_config
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from src.memory_system.memory_utils import get_all_titles
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logger = get_logger("memory")
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@ -56,14 +55,14 @@ class MemoryManagementTask(AsyncTask):
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current_count = self._get_memory_count()
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percentage = current_count / self.max_memory_number
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if percentage < 0.5:
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if percentage < 0.6:
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# 小于50%，每600秒执行一次
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return 3600
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elif percentage < 0.7:
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elif percentage < 0.8:
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# 大于等于50%，每300秒执行一次
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return 1800
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elif percentage < 0.9:
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# 大于等于70%，每120秒执行一次
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elif percentage < 1.0:
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# 大于等于100%，每120秒执行一次
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return 300
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elif percentage < 1.2:
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return 30
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@ -93,6 +92,22 @@ class MemoryManagementTask(AsyncTask):
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percentage = current_count / self.max_memory_number
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logger.info(f"当前记忆数量: {current_count}/{self.max_memory_number} ({percentage:.1%})")
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# 当占比 > 1.6 时，持续删除直到占比 <= 1.6（越老/越新更易被删）
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if percentage > 1.6:
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logger.info("记忆过多，开始遗忘记忆")
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while True:
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if percentage <= 1.6:
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break
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removed = global_memory_chest.remove_one_memory_by_age_weight()
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if not removed:
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logger.warning("没有可删除的记忆，停止连续删除")
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break
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# 重新计算占比
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current_count = self._get_memory_count()
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percentage = current_count / self.max_memory_number
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logger.info(f"遗忘进度: 当前 {current_count}/{self.max_memory_number} ({percentage:.1%})")
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logger.info("遗忘记忆结束")
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# 如果记忆数量为0，跳过执行
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if current_count < 10:
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return
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