gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #376 from SengokuCola/refactor

Browse Source 
Refactor
This commit is contained in:
SengokuCola
2025-03-14 15:06:30 +08:00
committed by GitHub
parent 113d591792 e17f3276a4
commit c23f60f518
15 changed files with 655 additions and 402 deletions
Download Patch File Download Diff File Expand all files Collapse all files

85
src/plugins/memory_system/memory.py
View File

@@ -8,9 +8,8 @@ import os
import jieba
import networkx as nx
from loguru import logger
from nonebot import get_driver
from ...common.database import db # 使用正确的导入语法
from ...common.database import db
from ..chat.config import global_config
from ..chat.utils import (
calculate_information_content,
@@ -20,6 +19,13 @@ from ..chat.utils import (
)
from ..models.utils_model import LLM_request
from ..utils.logger_config import setup_logger, LogModule
# 配置日志
logger = setup_logger(LogModule.MEMORY)
logger.info("初始化记忆系统")
class Memory_graph:
def __init__(self):
self.G = nx.Graph() # 使用 networkx 的图结构
@@ -518,7 +524,7 @@ class Hippocampus:
{'concept': concept},
{'$set': update_data}
)
logger.info(f"为节点 {concept} 添加缺失的时间字段")
logger.info(f"[时间更新] 节点 {concept} 添加缺失的时间字段")
# 获取时间信息(如果不存在则使用当前时间)
created_time = node.get('created_time', current_time)
@@ -551,7 +557,7 @@ class Hippocampus:
{'source': source, 'target': target},
{'$set': update_data}
)
logger.info(f"为边 {source} - {target} 添加缺失的时间字段")
logger.info(f"[时间更新] 边 {source} - {target} 添加缺失的时间字段")
# 获取时间信息(如果不存在则使用当前时间)
created_time = edge.get('created_time', current_time)
@@ -565,16 +571,27 @@ class Hippocampus:
last_modified=last_modified)
if need_update:
logger.success("已为缺失的时间字段进行补充")
logger.success("[数据库] 已为缺失的时间字段进行补充")
async def operation_forget_topic(self, percentage=0.1):
"""随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘"""
# 检查数据库是否为空
# logger.remove()
logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
# logger.info(f"- Logger名称: {logger.name}")
logger.info(f"- Logger等级: {logger.level}")
# logger.info(f"- Logger处理器: {[handler.__class__.__name__ for handler in logger.handlers]}")
# logger2 = setup_logger(LogModule.MEMORY)
# logger2.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
# logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
all_nodes = list(self.memory_graph.G.nodes())
all_edges = list(self.memory_graph.G.edges())
if not all_nodes and not all_edges:
logger.info("记忆图为空,无需进行遗忘操作")
logger.info("[遗忘] 记忆图为空,无需进行遗忘操作")
return
check_nodes_count = max(1, int(len(all_nodes) * percentage))
@@ -589,35 +606,32 @@ class Hippocampus:
current_time = datetime.datetime.now().timestamp()
# 检查并遗忘连接
logger.info("开始检查连接...")
logger.info("[遗忘] 开始检查连接...")
for source, target in edges_to_check:
edge_data = self.memory_graph.G[source][target]
last_modified = edge_data.get('last_modified')
# print(source,target)
# print(f"float(last_modified):{float(last_modified)}" )
# print(f"current_time:{current_time}")
# print(f"current_time - last_modified:{current_time - last_modified}")
if current_time - last_modified > 3600*global_config.memory_forget_time: # test
if current_time - last_modified > 3600*global_config.memory_forget_time:
current_strength = edge_data.get('strength', 1)
new_strength = current_strength - 1
if new_strength <= 0:
self.memory_graph.G.remove_edge(source, target)
edge_changes['removed'] += 1
logger.info(f"\033[1;31m[连接移除]\033[0m {source} - {target}")
logger.info(f"[遗忘] 连接移除: {source} -> {target}")
else:
edge_data['strength'] = new_strength
edge_data['last_modified'] = current_time
edge_changes['weakened'] += 1
logger.info(f"\033[1;34m[连接减弱]\033[0m {source} - {target} (强度: {current_strength} -> {new_strength})")
logger.info(f"[遗忘] 连接减弱: {source} -> {target} (强度: {current_strength} -> {new_strength})")
# 检查并遗忘话题
logger.info("开始检查节点...")
logger.info("[遗忘] 开始检查节点...")
for node in nodes_to_check:
node_data = self.memory_graph.G.nodes[node]
last_modified = node_data.get('last_modified', current_time)
if current_time - last_modified > 3600*24: # test
if current_time - last_modified > 3600*24:
memory_items = node_data.get('memory_items', [])
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
@@ -631,27 +645,22 @@ class Hippocampus:
self.memory_graph.G.nodes[node]['memory_items'] = memory_items
self.memory_graph.G.nodes[node]['last_modified'] = current_time
node_changes['reduced'] += 1
logger.info(f"\033[1;33m[记忆减少]\033[0m {node} (记忆数量: {current_count} -> {len(memory_items)})")
logger.info(f"[遗忘] 记忆减少: {node} (数量: {current_count} -> {len(memory_items)})")
else:
self.memory_graph.G.remove_node(node)
node_changes['removed'] += 1
logger.info(f"\033[1;31m[节点移除]\033[0m {node}")
logger.info(f"[遗忘] 节点移除: {node}")
if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()):
self.sync_memory_to_db()
logger.info("\n遗忘操作统计:")
logger.info(f"连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除")
logger.info(f"节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除")
logger.info("[遗忘] 统计信息:")
logger.info(f"[遗忘] 连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除")
logger.info(f"[遗忘] 节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除")
else:
logger.info("\n本次检查没有节点或连接满足遗忘条件")
logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件")
async def merge_memory(self, topic):
"""
对指定话题的记忆进行合并压缩
Args:
topic: 要合并的话题节点
"""
"""对指定话题的记忆进行合并压缩"""
# 获取节点的记忆项
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
if not isinstance(memory_items, list):
@@ -666,8 +675,8 @@ class Hippocampus:
# 拼接成文本
merged_text = "\n".join(selected_memories)
logger.debug(f"\n[合并记忆] 话题: {topic}")
logger.debug(f"选择的记忆:\n{merged_text}")
logger.debug(f"[合并] 话题: {topic}")
logger.debug(f"[合并] 选择的记忆:\n{merged_text}")
# 使用memory_compress生成新的压缩记忆
compressed_memories, _ = await self.memory_compress(selected_memories, 0.1)
@@ -679,11 +688,11 @@ class Hippocampus:
# 添加新的压缩记忆
for _, compressed_memory in compressed_memories:
memory_items.append(compressed_memory)
logger.info(f"添加压缩记忆: {compressed_memory}")
logger.info(f"[合并] 添加压缩记忆: {compressed_memory}")
# 更新节点的记忆项
self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
logger.debug(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
logger.debug(f"[合并] 完成记忆合并，当前记忆数量: {len(memory_items)}")
async def operation_merge_memory(self, percentage=0.1):
"""
@@ -813,7 +822,7 @@ class Hippocampus:
async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
"""计算输入文本对记忆的激活程度"""
logger.info(f"识别主题: {await self._identify_topics(text)}")
logger.info(f"[激活] 识别主题: {await self._identify_topics(text)}")
# 识别主题
identified_topics = await self._identify_topics(text)
@@ -824,7 +833,7 @@ class Hippocampus:
all_similar_topics = self._find_similar_topics(
identified_topics,
similarity_threshold=similarity_threshold,
debug_info="记忆激活"
debug_info="激活"
)
if not all_similar_topics:
@@ -845,7 +854,7 @@ class Hippocampus:
activation = int(score * 50 * penalty)
logger.info(
f"[记忆激活]单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
f"[激活] 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
return activation
# 计算关键词匹配率，同时考虑内容数量
@@ -872,8 +881,8 @@ class Hippocampus:
matched_topics.add(input_topic)
adjusted_sim = sim * penalty
topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
logger.info(
f"[记忆激活]主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
logger.debug(
f"[激活] 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
# 计算主题匹配率和平均相似度
topic_match = len(matched_topics) / len(identified_topics)
@@ -882,7 +891,7 @@ class Hippocampus:
# 计算最终激活值
activation = int((topic_match + average_similarities) / 2 * 100)
logger.info(
f"[记忆激活]匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
f"[激活] 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
return activation