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better 更好的记忆抽取策略，并且移除了无用选项

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This commit is contained in:
SengokuCola
2025-03-21 14:37:19 +08:00
parent e0a7bf7e99
commit 6c3afa84c4
14 changed files with 547 additions and 1282 deletions
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79
src/plugins/memory_system/memory.py
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@@ -18,6 +18,7 @@ from ..chat.utils import (
)
from ..models.utils_model import LLM_request
from src.common.logger import get_module_logger, LogConfig, MEMORY_STYLE_CONFIG
from src.plugins.memory_system.sample_distribution import MemoryBuildScheduler
# 定义日志配置
memory_config = LogConfig(
@@ -195,19 +196,9 @@ class Hippocampus:
return hash(f"{nodes[0]}:{nodes[1]}")
def random_get_msg_snippet(self, target_timestamp: float, chat_size: int, max_memorized_time_per_msg: int) -> list:
"""随机抽取一段时间内的消息片段
Args:
- target_timestamp: 目标时间戳
- chat_size: 抽取的消息数量
- max_memorized_time_per_msg: 每条消息的最大记忆次数
Returns:
- list: 抽取出的消息记录列表
"""
try_count = 0
# 最多尝试三次抽取
while try_count < 3:
# 最多尝试2次抽取
while try_count < 2:
messages = get_closest_chat_from_db(length=chat_size, timestamp=target_timestamp)
if messages:
# 检查messages是否均没有达到记忆次数限制
@@ -224,54 +215,37 @@ class Hippocampus:
)
return messages
try_count += 1
# 三次尝试均失败
return None
def get_memory_sample(self, chat_size=20, time_frequency=None):
"""获取记忆样本
Returns:
list: 消息记录列表，每个元素是一个消息记录字典列表
"""
def get_memory_sample(self):
# 硬编码：每条消息最大记忆次数
# 如有需求可写入global_config
if time_frequency is None:
time_frequency = {"near": 2, "mid": 4, "far": 3}
max_memorized_time_per_msg = 3
current_timestamp = datetime.datetime.now().timestamp()
# 创建双峰分布的记忆调度器
scheduler = MemoryBuildScheduler(
n_hours1=global_config.memory_build_distribution[0], # 第一个分布均值（4小时前）
std_hours1=global_config.memory_build_distribution[1], # 第一个分布标准差
weight1=global_config.memory_build_distribution[2], # 第一个分布权重 60%
n_hours2=global_config.memory_build_distribution[3], # 第二个分布均值（24小时前）
std_hours2=global_config.memory_build_distribution[4], # 第二个分布标准差
weight2=global_config.memory_build_distribution[5], # 第二个分布权重 40%
total_samples=global_config.build_memory_sample_num # 总共生成10个时间点
)
# 生成时间戳数组
timestamps = scheduler.get_timestamp_array()
logger.debug(f"生成的时间戳数组: {timestamps}")
chat_samples = []
# 短期：1h 中期：4h 长期：24h
logger.debug("正在抽取短期消息样本")
for i in range(time_frequency.get("near")):
random_time = current_timestamp - random.randint(1, 3600)
messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
for timestamp in timestamps:
messages = self.random_get_msg_snippet(timestamp, global_config.build_memory_sample_length, max_memorized_time_per_msg)
if messages:
logger.debug(f"成功抽取短期消息样本{len(messages)}条")
time_diff = (datetime.datetime.now().timestamp() - timestamp) / 3600
logger.debug(f"成功抽取 {time_diff:.1f} 小时前的消息样本，共{len(messages)}条")
chat_samples.append(messages)
else:
logger.warning(f"第{i}次短期消息样本抽取失败")
logger.debug("正在抽取中期消息样本")
for i in range(time_frequency.get("mid")):
random_time = current_timestamp - random.randint(3600, 3600 * 4)
messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
if messages:
logger.debug(f"成功抽取中期消息样本{len(messages)}条")
chat_samples.append(messages)
else:
logger.warning(f"第{i}次中期消息样本抽取失败")
logger.debug("正在抽取长期消息样本")
for i in range(time_frequency.get("far")):
random_time = current_timestamp - random.randint(3600 * 4, 3600 * 24)
messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
if messages:
logger.debug(f"成功抽取长期消息样本{len(messages)}条")
chat_samples.append(messages)
else:
logger.warning(f"第{i}次长期消息样本抽取失败")
logger.warning(f"时间戳 {timestamp} 的消息样本抽取失败")
return chat_samples
@@ -372,9 +346,8 @@ class Hippocampus:
)
return topic_num
async def operation_build_memory(self, chat_size=20):
time_frequency = {"near": 1, "mid": 4, "far": 4}
memory_samples = self.get_memory_sample(chat_size, time_frequency)
async def operation_build_memory(self):
memory_samples = self.get_memory_sample()
for i, messages in enumerate(memory_samples, 1):
all_topics = []