@@ -30,26 +30,56 @@ def is_english_letter(char: str) -> bool:
return " a " < = char . lower ( ) < = " z "
def db_message_to_str ( message_dict : dict ) - > str :
logger . debug ( f " message_dict: { message_dict } " )
time_str = time . strftime ( " % m- %d % H: % M: % S " , time . localtime ( message_dict [ " time " ] ) )
try :
name = f " [( { message_dict [ ' user_id ' ] } ) { message_dict . get ( ' user_nickname ' , ' ' ) } ] { message_dict . get ( ' user_cardname ' , ' ' ) } "
except Exception :
name = message_dict . get ( " user_nickname " , " " ) or f " 用户 { message_dict [ ' user_id ' ] } "
content = message_dict . get ( " processed_plain_text " , " " )
result = f " [ { time_str } ] { name } : { content } \n "
logger . debug ( f " result: { result } " )
def parse_platform_accounts ( platforms : list [ str ] ) - > dict [ str , str ] :
""" 解析 platforms 列表，返回平台到账号的映射
Args:
platforms: 格式为 [ " platform:account " ] 的列表，如 [ " tg:123456789 " , " wx:wxid123 " ]
Returns:
字典，键为平台名，值为账号
"" "
result = { }
for platform_entry in platforms :
if " : " in platform_entry :
platform_name , account = platform_entry . split ( " : " , 1 )
result [ platform_name . strip ( ) ] = account . strip ( )
return result
def get_current_platform_account ( platform : str , platform_accounts : dict [ str , str ] , qq_account : str ) - > str :
""" 根据当前平台获取对应的账号
Args:
platform: 当前消息的平台
platform_accounts: 从 platforms 列表解析的平台账号映射
qq_account: QQ 账号（兼容旧配置）
Returns:
当前平台对应的账号
"""
if platform == " qq " :
return qq_account
elif platform == " telegram " :
# 优先使用 tg，
return platform_accounts . get ( " tg " , " " ) or platform_accounts . get ( " telegram " , " " )
else :
# 其他平台直接使用平台名作为键
return platform_accounts . get ( platform , " " )
def is_mentioned_bot_in_message ( message : MessageRecv ) - > tuple [ bool , bool , float ] :
""" 检查消息是否提到了机器人（多平台实现） """
""" 检查消息是否提到了机器人（统一 多平台实现） """
text = message . processed_plain_text or " "
platform = getattr ( message . message_info , " platform " , " " ) or " "
qq_id = str ( getattr ( global_config . bot , " qq_account " , " " ) or " " )
tg_id = str ( getattr ( global_config . bot , " telegram_account " , " " ) or " " )
tg_uname = str ( getattr ( global_config . bot , " telegram_username " , " " ) or " " )
# 获取各平台账号
platforms_list = getattr ( global_config . bot , " platforms " , [ ] ) or [ ]
platform_accounts = parse_platform_accounts ( platforms_list )
qq_account = str ( getattr ( global_config . bot , " qq_account " , " " ) or " " )
# 获取当前平台对应的账号
current_account = get_current_platform_account ( platform , platform_accounts , qq_account )
nickname = str ( global_config . bot . nickname or " " )
alias_names = list ( getattr ( global_config . bot , " alias_names " , [ ] ) or [ ] )
@@ -94,32 +124,30 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, bool, float
is_at = True
is_mentioned = True
# 4) 文本层面 的 @ 检测（多平台）
# QQ: @<name:qq_id>
if qq_id and re . search ( rf " @<(.+?): { re . escape ( qq_id ) } > " , text ) :
is_at = True
is_mentioned = True
# Telegram: @usernam e
if platform == " telegram " and tg_uname :
if re . search ( rf " @ { re . escape ( tg_uname ) } ( \ b|$) " , text , flags = re . IGNORECASE ) :
is_at = True
is_mentioned = True
# 4) 统一 的 @ 检测逻辑
if current_account and not is_at and not is_mentioned :
if platform == " qq " :
# QQ 格式: @<name:qq_id>
if re . search ( rf " @<(.+?): { re . escape ( current_account ) } > " , text ) :
is_at = Tru e
is_mentioned = True
else :
# 其他平台格式: @username 或 @account
if re . search ( rf " @ { re . escape ( current_account ) } ( \ b|$) " , text , flags = re . IGNORECASE ) :
is_at = True
is_mentioned = True
# 5) 回复机器人检测：
# a) 通用显示文本：包含 “(你)” 或 “（你）” 的回复格式
if re . search ( r " \ [回复 .*? \ (你 \ )： " , text ) or re . search ( r " \ [回复 .*?（你）： " , text ) :
is_mentioned = True
# b) 兼容 ID 形式（ ）
if qq_id and (
re . search ( rf " \ [回复 (.+?) \ ( { re . escape ( qq_id ) } \ )： \ ]，说： " , tex t)
or re . search ( rf " \ [回复< (.+?)(?=: { re . escape ( qq_id ) } >) \ : { re . escape ( qq_id ) } > ： \ ]，说： " , text )
) :
is_mentioned = True
if tg_id and (
re . search ( rf " \ [回复 (.+?) \ ( { re . escape ( tg_id ) } \ )： \ ]，说： " , text )
or re . search ( rf " \ [回复<(.+?)(?=: { re . escape ( tg_id ) } >) \ : { re . escape ( tg_id ) } >： \ ]，说： " , text )
) :
is_mentioned = True
# 5) 统一的回复检测逻辑
if not is_mentioned :
# 通用回复格式：包含 "(你)" 或 "（你）"
if re . search ( r " \ [回复 .*? \ (你 \ )： " , text ) or re . search ( r " \ [回复 .*?（你）： " , text ) :
is_mentioned = True
# ID 形式的回复检测
elif current_accoun t:
if re . search ( rf " \ [回复 (.+?) \ ( { re . escape ( current_account ) } \ ) ： \ ]，说： " , text ) :
is_mentioned = True
elif re . search ( rf " \ [回复<(.+?)(?=: { re . escape ( current_account ) } >) \ : { re . escape ( current_account ) } >： \ ]，说： " , text ) :
is_mentioned = True
# 6) 名称/别名 提及（去除 @/回复标记后再匹配）
if not is_mentioned and keywords :
@@ -157,45 +185,6 @@ async def get_embedding(text, request_type="embedding") -> Optional[List[float]]
return embedding
def get_recent_group_speaker ( chat_stream_id : str , sender , limit : int = 12 ) - > list :
# 获取当前群聊记录内发言的人
filter_query = { " chat_id " : chat_stream_id }
sort_order = [ ( " time " , - 1 ) ]
recent_messages = find_messages ( message_filter = filter_query , sort = sort_order , limit = limit )
if not recent_messages :
return [ ]
who_chat_in_group = [ ]
for db_msg in recent_messages :
# user_info = UserInfo.from_dict(
# {
# "platform": msg_db_data["user_platform"],
# "user_id": msg_db_data["user_id"],
# "user_nickname": msg_db_data["user_nickname"],
# "user_cardname": msg_db_data.get("user_cardname", ""),
# }
# )
# if (
# (user_info.platform, user_info.user_id) != sender
# and user_info.user_id != global_config.bot.qq_account
# and (user_info.platform, user_info.user_id, user_info.user_nickname) not in who_chat_in_group
# and len(who_chat_in_group) < 5
# ): # 排除重复， ， ，
# who_chat_in_group.append((user_info.platform, user_info.user_id, user_info.user_nickname))
if (
( db_msg . user_info . platform , db_msg . user_info . user_id ) != sender
and db_msg . user_info . user_id != global_config . bot . qq_account
and ( db_msg . user_info . platform , db_msg . user_info . user_id , db_msg . user_info . user_nickname )
not in who_chat_in_group
and len ( who_chat_in_group ) < 5
) : # 排除重复， ， ，
who_chat_in_group . append (
( db_msg . user_info . platform , db_msg . user_info . user_id , db_msg . user_info . user_nickname )
)
return who_chat_in_group
def split_into_sentences_w_remove_punctuation ( text : str ) - > list [ str ] :
""" 将文本分割成句子，并根据概率合并
@@ -452,42 +441,6 @@ def calculate_typing_time(
return total_time # 加上回车时间
def cosine_similarity ( v1 , v2 ) :
""" 计算余弦相似度 """
dot_product = np . dot ( v1 , v2 )
norm1 = np . linalg . norm ( v1 )
norm2 = np . linalg . norm ( v2 )
return 0 if norm1 == 0 or norm2 == 0 else dot_product / ( norm1 * norm2 )
def text_to_vector ( text ) :
""" 将文本转换为词频向量 """
# 分词
words = jieba . lcut ( text )
return Counter ( words )
def find_similar_topics_simple ( text : str , topics : list , top_k : int = 5 ) - > list :
""" 使用简单的余弦相似度计算文本相似度 """
# 将输入文本转换为词频向量
text_vector = text_to_vector ( text )
# 计算每个主题的相似度
similarities = [ ]
for topic in topics :
topic_vector = text_to_vector ( topic )
# 获取所有唯一词
all_words = set ( text_vector . keys ( ) ) | set ( topic_vector . keys ( ) )
# 构建向量
v1 = [ text_vector . get ( word , 0 ) for word in all_words ]
v2 = [ topic_vector . get ( word , 0 ) for word in all_words ]
# 计算相似度
similarity = cosine_similarity ( v1 , v2 )
similarities . append ( ( topic , similarity ) )
# 按相似度降序排序并返回前k个
return sorted ( similarities , key = lambda x : x [ 1 ] , reverse = True ) [ : top_k ]
def truncate_message ( message : str , max_length = 20 ) - > str :
""" 截断消息，使其不超过指定长度 """
@@ -565,47 +518,6 @@ def get_western_ratio(paragraph):
return western_count / len ( alnum_chars )
def count_messages_between ( start_time : float , end_time : float , stream_id : str ) - > tuple [ int , int ] :
""" 计算两个时间点之间的消息数量和文本总长度
Args:
start_time (float): 起始时间戳 (不包含)
end_time (float): 结束时间戳 (包含)
stream_id (str): 聊天流ID
Returns:
tuple[int, int]: (消息数量, 文本总长度)
"""
count = 0
total_length = 0
# 参数校验 (可选但推荐)
if start_time > = end_time :
# logger.debug(f"开始时间 {start_time} 大于或等于结束时间 {end_time}，返回 0, 0")
return 0 , 0
if not stream_id :
logger . error ( " stream_id 不能为空 " )
return 0 , 0
# 使用message_repository中的count_messages和find_messages函数
# 构建查询条件
filter_query = { " chat_id " : stream_id , " time " : { " $gt " : start_time , " $lte " : end_time } }
try :
# 先获取消息数量
count = count_messages ( filter_query )
# 获取消息内容计算总长度
messages = find_messages ( message_filter = filter_query )
total_length = sum ( len ( msg . processed_plain_text or " " ) for msg in messages )
return count , total_length
except Exception as e :
logger . error ( f " 计算消息数量时发生意外错误: { e } " )
return 0 , 0
def translate_timestamp_to_human_readable ( timestamp : float , mode : str = " normal " ) - > str :
# sourcery skip: merge-comparisons, merge-duplicate-blocks, switch
@@ -740,65 +652,6 @@ def assign_message_ids(messages: List[DatabaseMessages]) -> List[Tuple[str, Data
return result
# def assign_message_ids_flexible(
# messages: list, prefix: str = "msg", id_length: int = 6, use_timestamp: bool = False
# ) -> list:
# """
# 为消息列表中的每个消息分配唯一的简短随机ID（ ）
# Args:
# messages: 消息列表
# prefix: ID前缀，
# id_length: ID的总长度（ ） ，
# use_timestamp: 是否在ID中包含时间戳，
# Returns:
# 包含 {'id': str, 'message': any} 格式的字典列表
# """
# result = []
# used_ids = set()
# for i, message in enumerate(messages):
# # 生成唯一的ID
# while True:
# if use_timestamp:
# # 使用时间戳的后几位 + 随机字符
# timestamp_suffix = str(int(time.time() * 1000))[-3:]
# remaining_length = id_length - 3
# random_chars = "".join(random.choices(string.ascii_lowercase + string.digits, k=remaining_length))
# message_id = f"{prefix}{timestamp_suffix}{random_chars}"
# else:
# # 使用索引 + 随机字符
# index_str = str(i + 1)
# remaining_length = max(1, id_length - len(index_str))
# random_chars = "".join(random.choices(string.ascii_lowercase + string.digits, k=remaining_length))
# message_id = f"{prefix}{index_str}{random_chars}"
# if message_id not in used_ids:
# used_ids.add(message_id)
# break
# result.append({"id": message_id, "message": message})
# return result
# 使用示例:
# messages = ["Hello", "World", "Test message"]
#
# # 基础版本
# result1 = assign_message_ids(messages)
# # 结果: [{'id': 'm1123', 'message': 'Hello'}, {'id': 'm2456', 'message': 'World'}, {'id': 'm3789', 'message': 'Test message'}]
#
# # 增强版本 - 自定义前缀和长度
# result2 = assign_message_ids_flexible(messages, prefix="chat", id_length=8)
# # 结果: [{'id': 'chat1abc2', 'message': 'Hello'}, {'id': 'chat2def3', 'message': 'World'}, {'id': 'chat3ghi4', 'message': 'Test message'}]
#
# # 增强版本 - 使用时间戳
# result3 = assign_message_ids_flexible(messages, prefix="ts", use_timestamp=True)
# # 结果: [{'id': 'ts123a1b', 'message': 'Hello'}, {'id': 'ts123c2d', 'message': 'World'}, {'id': 'ts123e3f', 'message': 'Test message'}]
def parse_keywords_string ( keywords_input ) - > list [ str ] :
# sourcery skip: use-contextlib-suppress
"""
SengokuCola