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Merge branch 'debug' into fix/20250310-logger-optimize

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AL76
2025-03-10 02:29:34 +08:00
committed by GitHub
parent 052e67b576 5c7881133e
commit 2f2be5b3ad
7 changed files with 53 additions and 89 deletions
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4
README.md
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@@ -40,6 +40,10 @@
**交流群**: 571780722 另一个群（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
**交流群**: 1035228475 另一个群（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
**其他平台版本**
- (由 [CabLate](https://github.com/cablate) 贡献) [Telegram 与其他平台(未来可能会有)的版本](https://github.com/cablate/MaiMBot/tree/telegram) - [集中讨论串](https://github.com/SengokuCola/MaiMBot/discussions/149)
##
<div align="left">
<h2>📚 文档 ⬇️ 快速开始使用麦麦 ⬇️</h2>

2
run.py
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@@ -98,7 +98,7 @@ def install_mongodb_compass():
run_cmd(r"powershell mongodb\bin\Install-Compass.ps1")
input("按任意键启动麦麦")
input("如不需要启动此窗口可直接关闭，无需等待 Compass 安装完成")
install_mongodb_compass()
run_maimbot()
def install_napcat():

13
run_memory_vis.bat
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@@ -8,7 +8,18 @@ if errorlevel 1 (
exit /b 1
)
echo Conda 环境 "%CONDA_ENV%" 激活成功
python src/plugins/memory_system/memory_manual_build.py
set /p OPTION="请选择运行选项 (1: 运行全部绘制, 2: 运行简单绘制): "
if "%OPTION%"=="1" (
python src/plugins/memory_system/memory_manual_build.py
) else if "%OPTION%"=="2" (
python src/plugins/memory_system/draw_memory.py
) else (
echo 无效的选项
pause
exit /b 1
)
if errorlevel 1 (
echo 命令执行失败，错误代码 %errorlevel%
pause

2
src/plugins/chat/emoji_manager.py
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@@ -36,6 +36,7 @@ class EmojiManager:
self.llm_emotion_judge = LLM_request(model=global_config.llm_normal_minor, max_tokens=60,
temperature=0.8) # 更高的温度，更少的token（后续可以根据情绪来调整温度）
def _ensure_emoji_dir(self):
"""确保表情存储目录存在"""
os.makedirs(self.EMOJI_DIR, exist_ok=True)
@@ -331,3 +332,4 @@ class EmojiManager:
# 创建全局单例
emoji_manager = EmojiManager()

40
src/plugins/chat/utils.py
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@@ -99,37 +99,43 @@ def calculate_information_content(text):
def get_cloest_chat_from_db(db, length: int, timestamp: str):
"""从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数"""
chat_text = ''
"""从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数
Returns:
list: 消息记录字典列表，每个字典包含消息内容和时间信息
"""
chat_records = []
closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
if closest_record and closest_record.get('memorized', 0) < 4:
if closest_record and closest_record.get('memorized', 0) < 4:
closest_time = closest_record['time']
group_id = closest_record['group_id'] # 获取groupid
group_id = closest_record['group_id']
# 获取该时间戳之后的length条消息，且groupid相同
chat_records = list(db.db.messages.find(
records = list(db.db.messages.find(
{"time": {"$gt": closest_time}, "group_id": group_id}
).sort('time', 1).limit(length))
# 更新每条消息的memorized属性
for record in chat_records:
# 检查当前记录的memorized值
for record in records:
current_memorized = record.get('memorized', 0)
if current_memorized > 3:
# print(f"消息已读取3次，跳过")
print("消息已读取3次，跳过")
return ''
# 更新memorized值
db.db.messages.update_one(
{"_id": record["_id"]},
{"$set": {"memorized": current_memorized + 1}}
)
chat_text += record["detailed_plain_text"]
return chat_text
# print(f"消息已读取3次，跳过")
return ''
# 添加到记录列表中
chat_records.append({
'text': record["detailed_plain_text"],
'time': record["time"],
'group_id': record["group_id"]
})
return chat_records
async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:

79
src/plugins/memory_system/draw_memory.py
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@@ -201,67 +201,6 @@ def topic_what(text, topic):
prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好'
return prompt
def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False):
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
G = memory_graph.G
# 保存图到本地
nx.write_gml(G, "memory_graph.gml") # 保存为 GML 格式
# 根据连接条数或记忆数量设置节点颜色
node_colors = []
nodes = list(G.nodes()) # 获取图中实际的节点列表
if color_by_memory:
# 计算每个节点的记忆数量
memory_counts = []
for node in nodes:
memory_items = G.nodes[node].get('memory_items', [])
if isinstance(memory_items, list):
count = len(memory_items)
else:
count = 1 if memory_items else 0
memory_counts.append(count)
max_memories = max(memory_counts) if memory_counts else 1
for count in memory_counts:
# 使用不同的颜色方案：红色表示记忆多，蓝色表示记忆少
if max_memories > 0:
intensity = min(1.0, count / max_memories)
color = (intensity, 0, 1.0 - intensity) # 从蓝色渐变到红色
else:
color = (0, 0, 1) # 如果没有记忆，则为蓝色
node_colors.append(color)
else:
# 使用原来的连接数量着色方案
max_degree = max(G.degree(), key=lambda x: x[1])[1] if G.degree() else 1
for node in nodes:
degree = G.degree(node)
if max_degree > 0:
red = min(1.0, degree / max_degree)
blue = 1.0 - red
color = (red, 0, blue)
else:
color = (0, 0, 1)
node_colors.append(color)
# 绘制图形
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G, k=1, iterations=50)
nx.draw(G, pos,
with_labels=True,
node_color=node_colors,
node_size=200,
font_size=10,
font_family='SimHei',
font_weight='bold')
title = '记忆图谱可视化 - ' + ('按记忆数量着色' if color_by_memory else '按连接数量着色')
plt.title(title, fontsize=16, fontfamily='SimHei')
plt.show()
def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
@@ -280,7 +219,7 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
memory_items = H.nodes[node].get('memory_items', [])
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
degree = H.degree(node)
if memory_count < 5 or degree < 2: # 改为小于2而不是小于等于2
if memory_count < 3 or degree < 2: # 改为小于2而不是小于等于2
nodes_to_remove.append(node)
H.remove_nodes_from(nodes_to_remove)
@@ -291,7 +230,7 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
return
# 保存图到本地
nx.write_gml(H, "memory_graph.gml") # 保存为 GML 格式
# nx.write_gml(H, "memory_graph.gml") # 保存为 GML 格式
# 计算节点大小和颜色
node_colors = []
@@ -315,21 +254,23 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
# 使用指数函数使变化更明显
ratio = memory_count / max_memories
size = 500 + 5000 * (ratio ** 2) # 使用平方函数使差异更明显
size = 500 + 5000 * (ratio ) # 使用1.5次方函数使差异不那么明显
node_sizes.append(size)
# 计算节点颜色（基于连接数）
degree = H.degree(node)
# 红色分量随着度数增加而增加
red = min(1.0, degree / max_degree)
r = (degree / max_degree) ** 0.3
red = min(1.0, r)
# 蓝色分量随着度数减少而增加
blue = 1.0 - red
color = (red, 0, blue)
blue = max(0.0, 1 - red)
# blue = 1
color = (red, 0.1, blue)
node_colors.append(color)
# 绘制图形
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(H, k=1.5, iterations=50) # 增加k值使节点分布更开
pos = nx.spring_layout(H, k=1, iterations=50) # 增加k值使节点分布更开
nx.draw(H, pos,
with_labels=True,
node_color=node_colors,
@@ -339,7 +280,7 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
font_weight='bold',
edge_color='gray',
width=0.5,
alpha=0.7)
alpha=0.9)
title = '记忆图谱可视化 - 节点大小表示记忆数量，颜色表示连接数'
plt.title(title, fontsize=16, fontfamily='SimHei')

2
src/plugins/memory_system/memory_manual_build.py
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@@ -944,7 +944,7 @@ async def main():
db = Database.get_instance()
start_time = time.time()
test_pare = {'do_build_memory':True,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False}
test_pare = {'do_build_memory':False,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False}
# 创建记忆图
memory_graph = Memory_graph()