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

Merge remote-tracking branch 'upstream/debug' into debug

Browse Source
This commit is contained in:
tcmofashi
2025-03-06 00:09:33 +08:00
parent 19e467febe a70f76c819
commit 91d8ca80cf
12 changed files with 266 additions and 254 deletions
Download Patch File Download Diff File Expand all files Collapse all files

33
src/plugins/chat/__init__.py
View File

@@ -13,6 +13,7 @@ from .willing_manager import willing_manager
from nonebot.rule import to_me
from .bot import chat_bot
from .emoji_manager import emoji_manager
import time
# 获取驱动器
@@ -86,31 +87,27 @@ async def _(bot: Bot):
async def _(bot: Bot, event: GroupMessageEvent, state: T_State):
await chat_bot.handle_message(event, bot)
'''
@scheduler.scheduled_job("interval", seconds=300000, id="monitor_relationships")
async def monitor_relationships():
"""每15秒打印一次关系数据"""
relationship_manager.print_all_relationships()
'''
# 添加build_memory定时任务
@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
async def build_memory_task():
"""每30秒执行一次记忆构建"""
print("\033[1;32m[记忆构建]\033[0m 开始构建记忆...")
await hippocampus.operation_build_memory(chat_size=30)
print("\033[1;32m[记忆构建]\033[0m 记忆构建完成")
print("\033[1;32m[记忆构建]\033[0m -------------------------------------------开始构建记忆-------------------------------------------")
start_time = time.time()
await hippocampus.operation_build_memory(chat_size=20)
end_time = time.time()
print(f"\033[1;32m[记忆构建]\033[0m -------------------------------------------记忆构建完成：耗时: {end_time - start_time:.2f} 秒-------------------------------------------")
@scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory")
async def forget_memory_task():
"""每30秒执行一次记忆构建"""
print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
await hippocampus.operation_forget_topic(percentage=0.1)
print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
# print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
# await hippocampus.operation_forget_topic(percentage=0.1)
# print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval + 10, id="build_memory")
async def build_memory_task():
@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval + 10, id="merge_memory")
async def merge_memory_task():
"""每30秒执行一次记忆构建"""
print("\033[1;32m[记忆整合]\033[0m 开始整合")
await hippocampus.operation_merge_memory(percentage=0.1)
print("\033[1;32m[记忆整合]\033[0m 记忆整合完成")
# print("\033[1;32m[记忆整合]\033[0m 开始整合")
# await hippocampus.operation_merge_memory(percentage=0.1)
# print("\033[1;32m[记忆整合]\033[0m 记忆整合完成")

8
src/plugins/chat/bot.py
View File

@@ -69,11 +69,9 @@ class ChatBot:
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time))
identifier=topic_identifier.identify_topic()
if global_config.topic_extract=='llm':
topic=await identifier(message.processed_plain_text)
else:
topic=identifier(message.detailed_plain_text)
topic=await topic_identifier.identify_topic_llm(message.processed_plain_text)
# topic1 = topic_identifier.identify_topic_jieba(message.processed_plain_text)
# topic2 = await topic_identifier.identify_topic_llm(message.processed_plain_text)

2
src/plugins/chat/config.py
View File

@@ -26,7 +26,7 @@ class BotConfig:
talk_frequency_down_groups = set()
ban_user_id = set()
build_memory_interval: int = 60 # 记忆构建间隔（秒）
build_memory_interval: int = 30 # 记忆构建间隔（秒）
forget_memory_interval: int = 300 # 记忆遗忘间隔（秒）
EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟）
EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟）

6
src/plugins/chat/llm_generator.py
View File

@@ -95,7 +95,11 @@ class ResponseGenerator:
# return None
# 生成回复
content, reasoning_content = await model.generate_response(prompt)
try:
content, reasoning_content = await model.generate_response(prompt)
except Exception as e:
print(f"生成回复时出错: {e}")
return None
# 保存到数据库
self._save_to_db(

14
src/plugins/chat/prompt_builder.py
View File

@@ -36,7 +36,9 @@ class PromptBuilder:
memory_prompt = ''
start_time = time.time() # 记录开始时间
topic = topic_identifier.identify_topic_jieba(message_txt)
# topic = await topic_identifier.identify_topic_llm(message_txt)
topic = topic_identifier.identify_topic_snownlp(message_txt)
# print(f"\033[1;32m[pb主题识别]\033[0m 主题: {topic}")
all_first_layer_items = [] # 存储所有第一层记忆
@@ -64,15 +66,7 @@ class PromptBuilder:
if overlap:
# print(f"\033[1;32m[前额叶]\033[0m 发现主题 '{current_topic}' 和 '{other_topic}' 有共同的第二层记忆: {overlap}")
overlapping_second_layer.update(overlap)
# 合并所有需要的记忆
# if all_first_layer_items:
# print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆1: {all_first_layer_items}")
# if overlapping_second_layer:
# print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆2: {list(overlapping_second_layer)}")
# 使用集合去重
# 从每个来源随机选择2条记忆（如果有的话）
selected_first_layer = random.sample(all_first_layer_items, min(2, len(all_first_layer_items))) if all_first_layer_items else []
selected_second_layer = random.sample(list(overlapping_second_layer), min(2, len(overlapping_second_layer))) if overlapping_second_layer else []

62
src/plugins/chat/topic_identifier.py
View File

@@ -15,16 +15,6 @@ class TopicIdentifier:
self.llm_client = LLM_request(model=global_config.llm_topic_extract)
self.select=global_config.topic_extract
def identify_topic(self):
if self.select=='jieba':
return self.identify_topic_jieba
elif self.select=='snownlp':
return self.identify_topic_snownlp
elif self.select=='llm':
return self.identify_topic_llm
else:
return self.identify_topic_snownlp
async def identify_topic_llm(self, text: str) -> Optional[List[str]]:
"""识别消息主题，返回主题列表"""
@@ -48,56 +38,10 @@ class TopicIdentifier:
# 解析主题字符串为列表
topic_list = [t.strip() for t in topic.split(",") if t.strip()]
print(f"\033[1;32m[主题识别]\033[0m 主题: {topic_list}")
return topic_list if topic_list else None
def identify_topic_jieba(self, text: str) -> Optional[str]:
"""使用jieba识别主题"""
words = jieba.lcut(text)
# 去除停用词和标点符号
stop_words = {
'的', '了', '和', '是', '就', '都', '而', '及', '与', '这', '那', '但', '然', '却',
'因为', '所以', '如果', '虽然', '一个', '我', '你', '他', '她', '它', '我们', '你们',
'他们', '在', '有', '个', '把', '被', '让', '给', '从', '向', '到', '又', '也', '很',
'啊', '吧', '呢', '吗', '呀', '哦', '哈', '么', '嘛', '啦', '哎', '唉', '哇', '嗯',
'哼', '哪', '什么', '怎么', '为什么', '怎样', '如何', '什么样', '这样', '那样', '这么',
'那么', '多少', '几', '谁', '哪里', '哪儿', '什么时候', '何时', '为何', '怎么办',
'怎么样', '这些', '那些', '一些', '一点', '一下', '一直', '一定', '一般', '一样',
'一会儿', '一边', '一起',
# 添加更多量词
'个', '只', '条', '张', '片', '块', '本', '册', '页', '幅', '面', '篇', '份',
'朵', '颗', '粒', '座', '幢', '栋', '间', '层', '家', '户', '位', '名', '群',
'双', '对', '打', '副', '套', '批', '组', '串', '包', '箱', '袋', '瓶', '罐',
# 添加更多介词
'按', '按照', '把', '被', '比', '比如', '除', '除了', '当', '对', '对于',
'根据', '关于', '跟', '和', '将', '经', '经过', '靠', '连', '论', '通过',
'同', '往', '为', '为了', '围绕', '于', '由', '由于', '与', '在', '沿', '沿着',
'依', '依照', '以', '因', '因为', '用', '由', '与', '自', '自从'
}
# 过滤掉停用词和标点符号,只保留名词和动词
filtered_words = []
for word in words:
if word not in stop_words and not word.strip() in {
'。', '，', '、', '：', '；', '！', '？', '"', '"', ''', ''',
'（', '）', '【', '】', '《', '》', '…', '—', '·', '、', '~',
'～', '+', '=', '-', '/', '\\', '|', '*', '#', '@', '$', '%',
'^', '&', '[', ']', '{', '}', '<', '>', '`', '_', '.', ',',
';', ':', '\'', '"', '(', ')', '?', '!', '±', '×', '÷', '≠',
'≈', '∈', '∉', '⊆', '⊇', '⊂', '⊃', '∪', '∩', '∧', '∨'
}:
filtered_words.append(word)
# 统计词频
word_freq = {}
for word in filtered_words:
word_freq[word] = word_freq.get(word, 0) + 1
# 按词频排序，取前3个
sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
top_words = [word for word, freq in sorted_words[:3]]
return top_words if top_words else None
def identify_topic_snownlp(self, text: str) -> Optional[List[str]]:
"""使用 SnowNLP 进行主题识别
@@ -113,7 +57,7 @@ class TopicIdentifier:
try:
s = SnowNLP(text)
# 提取前3个关键词作为主题
keywords = s.keywords(3)
keywords = s.keywords(5)
return keywords if keywords else None
except Exception as e:
print(f"\033[1;31m[错误]\033[0m SnowNLP 处理失败: {str(e)}")

44
src/plugins/chat/utils.py
View File

@@ -75,13 +75,11 @@ def cosine_similarity(v1, v2):
norm2 = np.linalg.norm(v2)
return dot_product / (norm1 * norm2)
def calculate_information_content(text):
def calculate_information_content(text):
"""计算文本的信息量（熵）"""
# 统计字符频率
char_count = Counter(text)
total_chars = len(text)
# 计算熵
entropy = 0
for count in char_count.values():
probability = count / total_chars
@@ -90,27 +88,37 @@ def calculate_information_content(text):
return entropy
def get_cloest_chat_from_db(db, length: int, timestamp: str):
# 从数据库中根据时间戳获取离其最近的聊天记录
"""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 = ''
closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出
# print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
if closest_record:
if closest_record and closest_record.get('memorized', 0) < 4:
closest_time = closest_record['time']
group_id = closest_record['group_id'] # 获取groupid
# 获取该时间戳之后的length条消息，且groupid相同
chat_record = list(db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length))
for record in chat_record:
time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
try:
displayname="[(%s)%s]%s" % (record["user_id"],record["user_nickname"],record["user_cardname"])
except:
displayname=record["user_nickname"] or "用户" + str(record["user_id"])
chat_text += f'[{time_str}] {displayname}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息
chat_records = list(db.db.messages.find(
{"time": {"$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
current_memorized = record.get('memorized', 0)
if current_memorized > 3:
# print(f"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
return [] # 如果没有找到记录，返回空列表
print(f"消息已读取3次，跳过")
return ''
def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
"""从数据库获取群组最近的消息记录

4
src/plugins/chat/willing_manager.py
View File

@@ -52,8 +52,8 @@ class WillingManager:
reply_probability = reply_probability / 3.5
reply_probability = min(reply_probability, 1)
if reply_probability < 0.1:
reply_probability = 0.1
if reply_probability < 0:
reply_probability = 0
return reply_probability
def change_reply_willing_sent(self, group_id: int):