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Merge branch 'dev' of github.com:MaiM-with-u/MaiBot into dev

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This commit is contained in:
UnCLAS-Prommer
2025-09-13 18:56:18 +08:00
parent 9ea1b53284 43ac8bee02
commit 45cc6a7f7d
13 changed files with 124 additions and 159 deletions
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14
src/chat/express/expression_selector.py
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@@ -114,6 +114,20 @@ class ExpressionSelector:
def get_related_chat_ids(self, chat_id: str) -> List[str]:
"""根据expression_groups配置，获取与当前chat_id相关的所有chat_id（包括自身）"""
groups = global_config.expression.expression_groups
# 检查是否存在全局共享组（包含"*"的组）
global_group_exists = any("*" in group for group in groups)
if global_group_exists:
# 如果存在全局共享组，则返回所有可用的chat_id
all_chat_ids = set()
for group in groups:
for stream_config_str in group:
if chat_id_candidate := self._parse_stream_config_to_chat_id(stream_config_str):
all_chat_ids.add(chat_id_candidate)
return list(all_chat_ids) if all_chat_ids else [chat_id]
# 否则使用现有的组逻辑
for group in groups:
group_chat_ids = []
for stream_config_str in group:

2
src/chat/heart_flow/heartFC_chat.py
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@@ -188,7 +188,7 @@ class HeartFChatting:
chat_id=self.stream_id,
start_time=self.last_read_time,
end_time=time.time(),
limit=10,
limit=20,
limit_mode="latest",
filter_mai=True,
filter_command=True,

44
src/chat/heart_flow/heartflow_message_processor.py
View File

@@ -37,53 +37,13 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
is_mentioned, is_at, reply_probability_boost = is_mentioned_bot_in_message(message)
# interested_rate = 0.0
keywords = []
# with Timer("记忆激活"):
# interested_rate, keywords, keywords_lite = await hippocampus_manager.get_activate_from_text(
# message.processed_plain_text,
# max_depth=4,
# fast_retrieval=global_config.chat.interest_rate_mode == "fast",
# )
# message.key_words = keywords
# message.key_words_lite = keywords_lite
# logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
text_len = len(message.processed_plain_text)
# 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
# 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
if text_len == 0:
base_interest = 0.01 # 空消息最低兴趣度
elif text_len <= 5:
# 1-5字符：线性增长 0.01 -> 0.03
base_interest = 0.01 + (text_len - 1) * (0.03 - 0.01) / 4
elif text_len <= 10:
# 6-10字符：线性增长 0.03 -> 0.06
base_interest = 0.03 + (text_len - 5) * (0.06 - 0.03) / 5
elif text_len <= 20:
# 11-20字符：线性增长 0.06 -> 0.12
base_interest = 0.06 + (text_len - 10) * (0.12 - 0.06) / 10
elif text_len <= 30:
# 21-30字符：线性增长 0.12 -> 0.18
base_interest = 0.12 + (text_len - 20) * (0.18 - 0.12) / 10
elif text_len <= 50:
# 31-50字符：线性增长 0.18 -> 0.22
base_interest = 0.18 + (text_len - 30) * (0.22 - 0.18) / 20
elif text_len <= 100:
# 51-100字符：线性增长 0.22 -> 0.26
base_interest = 0.22 + (text_len - 50) * (0.26 - 0.22) / 50
else:
# 100+字符：对数增长 0.26 -> 0.3，增长率递减
base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901)) # 1000-99=901
# 确保在范围内
base_interest = min(max(base_interest, 0.01), 0.3)
message.interest_value = base_interest
message.interest_value = 1
message.is_mentioned = is_mentioned
message.is_at = is_at
message.reply_probability_boost = reply_probability_boost
return base_interest, keywords
return 1, keywords
class HeartFCMessageReceiver:

4
src/chat/planner_actions/planner.py
View File

@@ -453,8 +453,8 @@ class ActionPlanner:
# 调用LLM
llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
# logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
# logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
if global_config.debug.show_prompt:
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")

30
src/chat/replyer/default_generator.py
View File

@@ -306,7 +306,7 @@ class DefaultReplyer:
traceback.print_exc()
return False, llm_response
async def build_relation_info(self, sender: str, target: str):
async def build_relation_info(self, chat_content: str, sender: str, person_list: List[Person] = None):
if not global_config.relationship.enable_relationship:
return ""
@@ -322,7 +322,13 @@ class DefaultReplyer:
logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
return f"你完全不认识{sender}，不理解ta的相关信息。"
return person.build_relationship()
sender_relation = await person.build_relationship(chat_content)
others_relation = ""
for person in person_list:
person_relation = await person.build_relationship()
others_relation += person_relation
return f"{sender_relation}\n{others_relation}"
async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, List[int]]:
# sourcery skip: for-append-to-extend
@@ -748,6 +754,19 @@ class DefaultReplyer:
timestamp=time.time(),
limit=int(global_config.chat.max_context_size * 0.33),
)
person_list_short:List[Person] = []
for msg in message_list_before_short:
if global_config.bot.qq_account == msg.user_info.user_id and global_config.bot.platform == msg.user_info.platform:
continue
if reply_message and reply_message.user_info.user_id == msg.user_info.user_id and reply_message.user_info.platform == msg.user_info.platform:
continue
person = Person(platform=msg.user_info.platform, user_id=msg.user_info.user_id)
if person.is_known:
person_list_short.append(person)
for person in person_list_short:
print(person.person_name)
chat_talking_prompt_short = build_readable_messages(
message_list_before_short,
@@ -762,7 +781,7 @@ class DefaultReplyer:
self._time_and_run_task(
self.build_expression_habits(chat_talking_prompt_short, target), "expression_habits"
),
self._time_and_run_task(self.build_relation_info(sender, target), "relation_info"),
self._time_and_run_task(self.build_relation_info(chat_talking_prompt_short,sender, person_list_short), "relation_info"),
# self._time_and_run_task(self.build_memory_block(message_list_before_short, target), "memory_block"),
self._time_and_run_task(
self.build_tool_info(chat_talking_prompt_short, sender, target, enable_tool=enable_tool), "tool_info"
@@ -916,7 +935,7 @@ class DefaultReplyer:
# 并行执行2个构建任务
(expression_habits_block, _), relation_info, personality_prompt = await asyncio.gather(
self.build_expression_habits(chat_talking_prompt_half, target),
self.build_relation_info(sender, target),
self.build_relation_info(chat_talking_prompt_half, sender),
self.build_personality_prompt(),
)
@@ -1019,7 +1038,8 @@ class DefaultReplyer:
async def llm_generate_content(self, prompt: str):
with Timer("LLM生成", {}): # 内部计时器，可选保留
# 直接使用已初始化的模型实例
logger.info(f"\n{prompt}\n")
if global_config.debug.show_prompt:
logger.info(f"\n{prompt}\n")
else:

2
src/chat/utils/utils_image.py
View File

@@ -146,7 +146,7 @@ class ImageManager:
return "[表情包(GIF处理失败)]"
vlm_prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，描述一下表情包表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
detailed_description, _ = await self.vlm.generate_response_for_image(
vlm_prompt, image_base64_processed, "jpg", temperature=0.4, max_tokens=300
vlm_prompt, image_base64_processed, "jpg", temperature=0.4
)
else:
vlm_prompt = (