From 4c42c90879322313169f3bb4698eccba047bc44a Mon Sep 17 00:00:00 2001
From: SengokuCola <1026294844@qq.com>
Date: Mon, 31 Mar 2025 22:34:52 +0800
Subject: [PATCH] =?UTF-8?q?better:=E4=BC=98=E5=8C=96=E5=9B=9E=E5=A4=8D?=
=?UTF-8?q?=E9=80=BB=E8=BE=91=EF=BC=8C=E7=8E=B0=E5=9C=A8=E5=9B=9E=E5=A4=8D?=
=?UTF-8?q?=E5=89=8D=E4=BC=9A=E5=85=88=E6=80=9D=E8=80=83=EF=BC=8C=E7=A7=BB?=
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=?UTF-8?q?=E5=A4=8D=E4=B8=AD=E7=9A=84=E4=BD=BF=E7=94=A8=EF=BC=8C=E4=BC=98?=
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=?UTF-8?q?=EF=BC=8C=E4=BC=98=E5=8C=96=E6=80=9D=E8=80=83=E6=97=B6=E9=97=B4?=
=?UTF-8?q?=E8=AE=A1=E7=AE=97=E9=80=BB=E8=BE=91=EF=BC=8C=E6=B7=BB=E5=8A=A0?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
src/heart_flow/heartflow.py | 2 +-
src/heart_flow/observation.py | 6 +-
src/heart_flow/sub_heartflow.py | 64 ++++++++--
src/plugins/chat/bot.py | 115 ++++++++++--------
src/plugins/chat/llm_generator.py | 28 +++--
src/plugins/chat/message_sender.py | 10 +-
src/plugins/chat/prompt_builder.py | 50 +-------
src/plugins/chat/utils_image.py | 4 +-
src/plugins/config/config.py | 10 +-
src/plugins/memory_system/Hippocampus.py | 23 +++-
.../memory_system/sample_distribution.py | 15 ++-
src/plugins/models/utils_model.py | 67 +++++-----
src/plugins/schedule/schedule_generator.py | 28 +++--
template/bot_config_template.toml | 25 ++--
14 files changed, 254 insertions(+), 193 deletions(-)
diff --git a/src/heart_flow/heartflow.py b/src/heart_flow/heartflow.py
index 8637d2071..c34def599 100644
--- a/src/heart_flow/heartflow.py
+++ b/src/heart_flow/heartflow.py
@@ -106,7 +106,7 @@ class Heartflow:
self.current_mind = reponse
logger.info(f"麦麦的总体脑内状态：{self.current_mind}")
# logger.info("麦麦想了想，当前活动:")
- await bot_schedule.move_doing(self.current_mind)
+ # await bot_schedule.move_doing(self.current_mind)
for _, subheartflow in self._subheartflows.items():
subheartflow.main_heartflow_info = reponse
diff --git a/src/heart_flow/observation.py b/src/heart_flow/observation.py
index fb84ea5c4..93057c5ab 100644
--- a/src/heart_flow/observation.py
+++ b/src/heart_flow/observation.py
@@ -52,9 +52,9 @@ class ChattingObservation(Observation):
new_messages_str = ""
for msg in new_messages:
if "detailed_plain_text" in msg:
- new_messages_str += f"{msg['detailed_plain_text']}\n"
+ new_messages_str += f"{msg['detailed_plain_text']}"
- print(f"new_messages_str：{new_messages_str}")
+ # print(f"new_messages_str：{new_messages_str}")
# 将新消息添加到talking_message，同时保持列表长度不超过20条
self.talking_message.extend(new_messages)
@@ -112,7 +112,7 @@ class ChattingObservation(Observation):
# 基于已经有的talking_summary，和新的talking_message，生成一个summary
# print(f"更新聊天总结：{self.talking_summary}")
prompt = ""
- prompt = f"你正在参与一个qq群聊的讨论，这个群之前在聊的内容是：{self.observe_info}\n"
+ prompt = f"你正在参与一个qq群聊的讨论，你记得这个群之前在聊的内容是：{self.observe_info}\n"
prompt += f"现在群里的群友们产生了新的讨论，有了新的发言，具体内容如下：{new_messages_str}\n"
prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，
以及聊天中的一些重要信息，记得不要分点，不要太长，精简的概括成一段文本\n"""
diff --git a/src/heart_flow/sub_heartflow.py b/src/heart_flow/sub_heartflow.py
index 8989e3b64..b8de5e754 100644
--- a/src/heart_flow/sub_heartflow.py
+++ b/src/heart_flow/sub_heartflow.py
@@ -87,13 +87,10 @@ class SubHeartflow:
self.is_active = True
self.last_active_time = current_time # 更新最后激活时间
- observation = self.observations[0]
- await observation.observe()
-
self.current_state.update_current_state_info()
- await self.do_a_thinking()
- await self.judge_willing()
+ # await self.do_a_thinking()
+ # await self.judge_willing()
await asyncio.sleep(global_config.sub_heart_flow_update_interval)
# 检查是否超过10分钟没有激活
@@ -107,7 +104,7 @@ class SubHeartflow:
observation = self.observations[0]
chat_observe_info = observation.observe_info
- print(f"chat_observe_info：{chat_observe_info}")
+ # print(f"chat_observe_info：{chat_observe_info}")
# 调取记忆
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
@@ -144,8 +141,57 @@ class SubHeartflow:
self.current_mind = reponse
logger.debug(f"prompt:\n{prompt}\n")
logger.info(f"麦麦的脑内状态：{self.current_mind}")
+
+ async def do_observe(self):
+ observation = self.observations[0]
+ await observation.observe()
+
+ async def do_thinking_before_reply(self, message_txt):
+ current_thinking_info = self.current_mind
+ mood_info = self.current_state.mood
+ mood_info = "你很生气，很愤怒"
+ observation = self.observations[0]
+ chat_observe_info = observation.observe_info
+ # print(f"chat_observe_info：{chat_observe_info}")
- async def do_after_reply(self, reply_content, chat_talking_prompt):
+ # 调取记忆
+ related_memory = await HippocampusManager.get_instance().get_memory_from_text(
+ text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
+ )
+
+ if related_memory:
+ related_memory_info = ""
+ for memory in related_memory:
+ related_memory_info += memory[1]
+ else:
+ related_memory_info = ""
+
+ # print(f"相关记忆：{related_memory_info}")
+
+ schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
+
+ prompt = ""
+ # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
+ prompt += f"你{self.personality_info}\n"
+ prompt += f"你刚刚在做的事情是：{schedule_info}\n"
+ if related_memory_info:
+ prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
+ prompt += f"刚刚你的想法是{current_thinking_info}。\n"
+ prompt += "-----------------------------------\n"
+ prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
+ prompt += f"你现在{mood_info}\n"
+ prompt += f"你注意到有人刚刚说：{message_txt}\n"
+ prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
+ prompt += "记得结合上述的消息，要记得维持住你的人设，注意自己的名字，关注有人刚刚说的内容，不要思考太多:"
+ reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
+
+ self.update_current_mind(reponse)
+
+ self.current_mind = reponse
+ logger.debug(f"prompt:\n{prompt}\n")
+ logger.info(f"麦麦的思考前脑内状态：{self.current_mind}")
+
+ async def do_thinking_after_reply(self, reply_content, chat_talking_prompt):
print("麦麦回复之后脑袋转起来了")
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -155,10 +201,10 @@ class SubHeartflow:
message_new_info = chat_talking_prompt
reply_info = reply_content
- schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
+ # schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
prompt = ""
- prompt += f"你现在正在做的事情是：{schedule_info}\n"
+ # prompt += f"你现在正在做的事情是：{schedule_info}\n"
prompt += f"你{self.personality_info}\n"
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
prompt += f"刚刚你的想法是{current_thinking_info}。"
diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py
index e01a928d5..ac6d4d2c9 100644
--- a/src/plugins/chat/bot.py
+++ b/src/plugins/chat/bot.py
@@ -47,6 +47,39 @@ class ChatBot:
if not self._started:
self._started = True
+ async def _create_thinking_message(self, message, chat, userinfo, messageinfo):
+ """创建思考消息
+
+ Args:
+ message: 接收到的消息
+ chat: 聊天流对象
+ userinfo: 用户信息对象
+ messageinfo: 消息信息对象
+
+ Returns:
+ str: thinking_id
+ """
+ bot_user_info = UserInfo(
+ user_id=global_config.BOT_QQ,
+ user_nickname=global_config.BOT_NICKNAME,
+ platform=messageinfo.platform,
+ )
+
+ thinking_time_point = round(time.time(), 2)
+ thinking_id = "mt" + str(thinking_time_point)
+ thinking_message = MessageThinking(
+ message_id=thinking_id,
+ chat_stream=chat,
+ bot_user_info=bot_user_info,
+ reply=message,
+ thinking_start_time=thinking_time_point,
+ )
+
+ message_manager.add_message(thinking_message)
+ willing_manager.change_reply_willing_sent(chat)
+
+ return thinking_id
+
async def message_process(self, message_data: str) -> None:
"""处理转化后的统一格式消息
1. 过滤消息
@@ -56,6 +89,8 @@ class ChatBot:
5. 更新关系
6. 更新情绪
"""
+ timing_results = {} # 用于收集所有计时结果
+ response_set = None # 初始化response_set变量
message = MessageRecv(message_data)
groupinfo = message.message_info.group_info
@@ -75,10 +110,7 @@ class ChatBot:
# 创建 心流与chat的观察
heartflow.create_subheartflow(chat.stream_id)
- timer1 = time.time()
await message.process()
- timer2 = time.time()
- logger.debug(f"2消息处理时间: {timer2 - timer1}秒")
# 过滤词/正则表达式过滤
if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
@@ -94,7 +126,7 @@ class ChatBot:
message.processed_plain_text, fast_retrieval=True
)
timer2 = time.time()
- logger.debug(f"3记忆激活时间: {timer2 - timer1}秒")
+ timing_results["记忆激活"] = timer2 - timer1
is_mentioned = is_mentioned_bot_in_message(message)
@@ -118,7 +150,7 @@ class ChatBot:
sender_id=str(message.message_info.user_info.user_id),
)
timer2 = time.time()
- logger.debug(f"4计算意愿激活时间: {timer2 - timer1}秒")
+ timing_results["意愿激活"] = timer2 - timer1
# 神秘的消息流数据结构处理
if chat.group_info:
@@ -138,12 +170,30 @@ class ChatBot:
if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys():
reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"]
+ do_reply = False
# 开始组织语言
if random() < reply_probability:
+ do_reply = True
+
timer1 = time.time()
- response_set, thinking_id = await self._generate_response_from_message(message, chat, userinfo, messageinfo)
+ thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
timer2 = time.time()
- logger.info(f"5生成回复时间: {timer2 - timer1}秒")
+ timing_results["创建思考消息"] = timer2 - timer1
+
+ timer1 = time.time()
+ await heartflow.get_subheartflow(chat.stream_id).do_observe()
+ timer2 = time.time()
+ timing_results["观察"] = timer2 - timer1
+
+ timer1 = time.time()
+ await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(message.processed_plain_text)
+ timer2 = time.time()
+ timing_results["思考前脑内状态"] = timer2 - timer1
+
+ timer1 = time.time()
+ response_set = await self.gpt.generate_response(message)
+ timer2 = time.time()
+ timing_results["生成回复"] = timer2 - timer1
if not response_set:
logger.info("为什么生成回复失败？")
@@ -153,56 +203,25 @@ class ChatBot:
timer1 = time.time()
await self._send_response_messages(message, chat, response_set, thinking_id)
timer2 = time.time()
- logger.info(f"7发送消息时间: {timer2 - timer1}秒")
+ timing_results["发送消息"] = timer2 - timer1
# 处理表情包
timer1 = time.time()
await self._handle_emoji(message, chat, response_set)
timer2 = time.time()
- logger.debug(f"8处理表情包时间: {timer2 - timer1}秒")
+ timing_results["处理表情包"] = timer2 - timer1
timer1 = time.time()
await self._update_using_response(message, response_set)
timer2 = time.time()
- logger.info(f"6更新htfl时间: {timer2 - timer1}秒")
+ timing_results["更新心流"] = timer2 - timer1
- # 更新情绪和关系
- # await self._update_emotion_and_relationship(message, chat, response_set)
-
- async def _generate_response_from_message(self, message, chat, userinfo, messageinfo):
- """生成回复内容
-
- Args:
- message: 接收到的消息
- chat: 聊天流对象
- userinfo: 用户信息对象
- messageinfo: 消息信息对象
-
- Returns:
- tuple: (response, raw_content) 回复内容和原始内容
- """
- bot_user_info = UserInfo(
- user_id=global_config.BOT_QQ,
- user_nickname=global_config.BOT_NICKNAME,
- platform=messageinfo.platform,
- )
-
- thinking_time_point = round(time.time(), 2)
- thinking_id = "mt" + str(thinking_time_point)
- thinking_message = MessageThinking(
- message_id=thinking_id,
- chat_stream=chat,
- bot_user_info=bot_user_info,
- reply=message,
- thinking_start_time=thinking_time_point,
- )
-
- message_manager.add_message(thinking_message)
- willing_manager.change_reply_willing_sent(chat)
-
- response_set = await self.gpt.generate_response(message)
-
- return response_set, thinking_id
+ # 在最后统一输出所有计时结果
+ if do_reply:
+ timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
+ trigger_msg = message.processed_plain_text
+ response_msg = " ".join(response_set) if response_set else "无回复"
+ logger.info(f"触发消息: {trigger_msg[:20]}... | 生成消息: {response_msg[:20]}... | 性能计时: {timing_str}")
async def _update_using_response(self, message, response_set):
# 更新心流状态
@@ -213,7 +232,7 @@ class ChatBot:
stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
)
- await heartflow.get_subheartflow(stream_id).do_after_reply(response_set, chat_talking_prompt)
+ await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(response_set, chat_talking_prompt)
async def _send_response_messages(self, message, chat, response_set, thinking_id):
container = message_manager.get_container(chat.stream_id)
diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py
index f551dcca7..fa37c0382 100644
--- a/src/plugins/chat/llm_generator.py
+++ b/src/plugins/chat/llm_generator.py
@@ -30,7 +30,7 @@ class ResponseGenerator:
request_type="response",
)
self.model_normal = LLM_request(
- model=global_config.llm_normal, temperature=0.7, max_tokens=3000, request_type="response"
+ model=global_config.llm_normal, temperature=0.8, max_tokens=256, request_type="response"
)
self.model_sum = LLM_request(
@@ -42,20 +42,26 @@ class ResponseGenerator:
async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
"""根据当前模型类型选择对应的生成函数"""
# 从global_config中获取模型概率值并选择模型
- if random.random() < global_config.MODEL_R1_PROBABILITY:
- self.current_model_type = "深深地"
- current_model = self.model_reasoning
- else:
- self.current_model_type = "浅浅的"
- current_model = self.model_normal
+ # if random.random() < global_config.MODEL_R1_PROBABILITY:
+ # self.current_model_type = "深深地"
+ # current_model = self.model_reasoning
+ # else:
+ # self.current_model_type = "浅浅的"
+ # current_model = self.model_normal
+ # logger.info(
+ # f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
+ # ) # noqa: E501
+
+
logger.info(
- f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
- ) # noqa: E501
+ f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
+ )
+ current_model = self.model_normal
model_response = await self._generate_response_with_model(message, current_model)
- print(f"raw_content: {model_response}")
+ # print(f"raw_content: {model_response}")
if model_response:
logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
@@ -126,8 +132,6 @@ class ResponseGenerator:
"user": sender_name,
"message": message.processed_plain_text,
"model": self.current_model_name,
- # 'reasoning_check': reasoning_content_check,
- # 'response_check': content_check,
"reasoning": reasoning_content,
"response": content,
"prompt": prompt,
diff --git a/src/plugins/chat/message_sender.py b/src/plugins/chat/message_sender.py
index 5d8c07e0b..f18257c17 100644
--- a/src/plugins/chat/message_sender.py
+++ b/src/plugins/chat/message_sender.py
@@ -188,11 +188,11 @@ class MessageManager:
# print(message_earliest.is_head)
# print(message_earliest.update_thinking_time())
# print(message_earliest.is_private_message())
- # thinking_time = message_earliest.update_thinking_time()
- # print(thinking_time)
+ thinking_time = message_earliest.update_thinking_time()
+ print(thinking_time)
if (
message_earliest.is_head
- and message_earliest.update_thinking_time() > 50
+ and message_earliest.update_thinking_time() > 8
and not message_earliest.is_private_message() # 避免在私聊时插入reply
):
logger.debug(f"设置回复消息{message_earliest.processed_plain_text}")
@@ -215,11 +215,11 @@ class MessageManager:
try:
# print(msg.is_head)
- # print(msg.update_thinking_time())
+ print(msg.update_thinking_time())
# print(msg.is_private_message())
if (
msg.is_head
- and msg.update_thinking_time() > 50
+ and msg.update_thinking_time() > 8
and not msg.is_private_message() # 避免在私聊时插入reply
):
logger.debug(f"设置回复消息{msg.processed_plain_text}")
diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py
index 499aaa5fe..cc048fc70 100644
--- a/src/plugins/chat/prompt_builder.py
+++ b/src/plugins/chat/prompt_builder.py
@@ -24,27 +24,9 @@ class PromptBuilder:
async def _build_prompt(
self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
) -> tuple[str, str]:
- # 关系（载入当前聊天记录里部分人的关系）
- # who_chat_in_group = [chat_stream]
- # who_chat_in_group += get_recent_group_speaker(
- # stream_id,
- # (chat_stream.user_info.user_id, chat_stream.user_info.platform),
- # limit=global_config.MAX_CONTEXT_SIZE,
- # )
-
- # outer_world_info = outer_world.outer_world_info
-
+
current_mind_info = heartflow.get_subheartflow(stream_id).current_mind
- # relation_prompt = ""
- # for person in who_chat_in_group:
- # relation_prompt += relationship_manager.build_relationship_info(person)
-
- # relation_prompt_all = (
- # f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
- # f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
- # )
-
# 开始构建prompt
# 心情
@@ -71,25 +53,6 @@ class PromptBuilder:
chat_talking_prompt = chat_talking_prompt
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
- # 使用新的记忆获取方法
- memory_prompt = ""
- start_time = time.time()
-
- # 调用 hippocampus 的 get_relevant_memories 方法
- relevant_memories = await HippocampusManager.get_instance().get_memory_from_text(
- text=message_txt, max_memory_num=3, max_memory_length=2, max_depth=2, fast_retrieval=False
- )
- memory_str = ""
- for _topic, memories in relevant_memories:
- memory_str += f"{memories}\n"
-
- if relevant_memories:
- # 格式化记忆内容
- memory_prompt = f"你回忆起：\n{memory_str}\n"
-
- end_time = time.time()
- logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
-
# 类型
if chat_in_group:
chat_target = "你正在qq群里聊天，下面是群里在聊的内容："
@@ -146,19 +109,18 @@ class PromptBuilder:
涉及政治敏感以及违法违规的内容请规避。"""
logger.info("开始构建prompt")
+
prompt = f"""
{prompt_info}
-{memory_prompt}
-你刚刚脑子里在想：
-{current_mind_info}
-
{chat_target}
{chat_talking_prompt}
-现在"{sender_name}"说的:{message_txt}。引起了你的注意,{mood_prompt}\n
+你刚刚脑子里在想：
+{current_mind_info}
+现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
-请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，
+请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话
请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
{moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。"""
diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py
index e74ce2890..729c8e1f8 100644
--- a/src/plugins/chat/utils_image.py
+++ b/src/plugins/chat/utils_image.py
@@ -32,7 +32,7 @@ class ImageManager:
self._ensure_description_collection()
self._ensure_image_dir()
self._initialized = True
- self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=1000, request_type="image")
+ self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300, request_type="image")
def _ensure_image_dir(self):
"""确保图像存储目录存在"""
@@ -171,7 +171,7 @@ class ImageManager:
# 调用AI获取描述
prompt = (
- "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。"
+ "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多100个字。"
)
description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
diff --git a/src/plugins/config/config.py b/src/plugins/config/config.py
index a4a38dc1a..be031f0e6 100644
--- a/src/plugins/config/config.py
+++ b/src/plugins/config/config.py
@@ -231,7 +231,7 @@ class BotConfig:
# 模型配置
llm_reasoning: Dict[str, str] = field(default_factory=lambda: {})
- llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {})
+ # llm_reasoning_minor: Dict[str, str] = field(default_factory=lambda: {})
llm_normal: Dict[str, str] = field(default_factory=lambda: {})
llm_topic_judge: Dict[str, str] = field(default_factory=lambda: {})
llm_summary_by_topic: Dict[str, str] = field(default_factory=lambda: {})
@@ -370,9 +370,9 @@ class BotConfig:
response_config = parent["response"]
config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
- config.MODEL_R1_DISTILL_PROBABILITY = response_config.get(
- "model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY
- )
+ # config.MODEL_R1_DISTILL_PROBABILITY = response_config.get(
+ # "model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY
+ # )
config.max_response_length = response_config.get("max_response_length", config.max_response_length)
def willing(parent: dict):
@@ -397,7 +397,7 @@ class BotConfig:
config_list = [
"llm_reasoning",
- "llm_reasoning_minor",
+ # "llm_reasoning_minor",
"llm_normal",
"llm_topic_judge",
"llm_summary_by_topic",
diff --git a/src/plugins/memory_system/Hippocampus.py b/src/plugins/memory_system/Hippocampus.py
index aff35f002..717cebe17 100644
--- a/src/plugins/memory_system/Hippocampus.py
+++ b/src/plugins/memory_system/Hippocampus.py
@@ -697,6 +697,11 @@ class ParahippocampalGyrus:
start_time = time.time()
logger.info("[遗忘] 开始检查数据库...")
+ # 验证百分比参数
+ if not 0 <= percentage <= 1:
+ logger.warning(f"[遗忘] 无效的遗忘百分比: {percentage}, 使用默认值 0.005")
+ percentage = 0.005
+
all_nodes = list(self.memory_graph.G.nodes())
all_edges = list(self.memory_graph.G.edges())
@@ -704,11 +709,21 @@ class ParahippocampalGyrus:
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))
- check_edges_count = max(1, int(len(all_edges) * percentage))
+ # 确保至少检查1个节点和边，且不超过总数
+ check_nodes_count = max(1, min(len(all_nodes), int(len(all_nodes) * percentage)))
+ check_edges_count = max(1, min(len(all_edges), int(len(all_edges) * percentage)))
- nodes_to_check = random.sample(all_nodes, check_nodes_count)
- edges_to_check = random.sample(all_edges, check_edges_count)
+ # 只有在有足够的节点和边时才进行采样
+ if len(all_nodes) >= check_nodes_count and len(all_edges) >= check_edges_count:
+ try:
+ nodes_to_check = random.sample(all_nodes, check_nodes_count)
+ edges_to_check = random.sample(all_edges, check_edges_count)
+ except ValueError as e:
+ logger.error(f"[遗忘] 采样错误: {str(e)}")
+ return
+ else:
+ logger.info("[遗忘] 没有足够的节点或边进行遗忘操作")
+ return
# 使用列表存储变化信息
edge_changes = {
diff --git a/src/plugins/memory_system/sample_distribution.py b/src/plugins/memory_system/sample_distribution.py
index 29218d21f..5dae2f266 100644
--- a/src/plugins/memory_system/sample_distribution.py
+++ b/src/plugins/memory_system/sample_distribution.py
@@ -58,8 +58,18 @@ class MemoryBuildScheduler:
weight2 (float): 第二个分布的权重
total_samples (int): 要生成的总时间点数量
"""
+ # 验证参数
+ if total_samples <= 0:
+ raise ValueError("total_samples 必须大于0")
+ if weight1 < 0 or weight2 < 0:
+ raise ValueError("权重必须为非负数")
+ if std_hours1 < 0 or std_hours2 < 0:
+ raise ValueError("标准差必须为非负数")
+
# 归一化权重
total_weight = weight1 + weight2
+ if total_weight == 0:
+ raise ValueError("权重总和不能为0")
self.weight1 = weight1 / total_weight
self.weight2 = weight2 / total_weight
@@ -73,12 +83,11 @@ class MemoryBuildScheduler:
def generate_time_samples(self):
"""生成混合分布的时间采样点"""
# 根据权重计算每个分布的样本数
- samples1 = int(self.total_samples * self.weight1)
- samples2 = self.total_samples - samples1
+ samples1 = max(1, int(self.total_samples * self.weight1))
+ samples2 = max(1, self.total_samples - samples1) # 确保 samples2 至少为1
# 生成两个正态分布的小时偏移
hours_offset1 = np.random.normal(loc=self.n_hours1, scale=self.std_hours1, size=samples1)
-
hours_offset2 = np.random.normal(loc=self.n_hours2, scale=self.std_hours2, size=samples2)
# 合并两个分布的偏移
diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py
index 51f34a077..263e11618 100644
--- a/src/plugins/models/utils_model.py
+++ b/src/plugins/models/utils_model.py
@@ -285,39 +285,46 @@ class LLM_request:
usage = None # 初始化usage变量，避免未定义错误
async for line_bytes in response.content:
- line = line_bytes.decode("utf-8").strip()
- if not line:
- continue
- if line.startswith("data:"):
- data_str = line[5:].strip()
- if data_str == "[DONE]":
- break
- try:
- chunk = json.loads(data_str)
- if flag_delta_content_finished:
- chunk_usage = chunk.get("usage", None)
- if chunk_usage:
- usage = chunk_usage # 获取token用量
- else:
- delta = chunk["choices"][0]["delta"]
- delta_content = delta.get("content")
- if delta_content is None:
- delta_content = ""
- accumulated_content += delta_content
- # 检测流式输出文本是否结束
- finish_reason = chunk["choices"][0].get("finish_reason")
- if delta.get("reasoning_content", None):
- reasoning_content += delta["reasoning_content"]
- if finish_reason == "stop":
+ try:
+ line = line_bytes.decode("utf-8").strip()
+ if not line:
+ continue
+ if line.startswith("data:"):
+ data_str = line[5:].strip()
+ if data_str == "[DONE]":
+ break
+ try:
+ chunk = json.loads(data_str)
+ if flag_delta_content_finished:
chunk_usage = chunk.get("usage", None)
if chunk_usage:
- usage = chunk_usage
- break
- # 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
- flag_delta_content_finished = True
+ usage = chunk_usage # 获取token用量
+ else:
+ delta = chunk["choices"][0]["delta"]
+ delta_content = delta.get("content")
+ if delta_content is None:
+ delta_content = ""
+ accumulated_content += delta_content
+ # 检测流式输出文本是否结束
+ finish_reason = chunk["choices"][0].get("finish_reason")
+ if delta.get("reasoning_content", None):
+ reasoning_content += delta["reasoning_content"]
+ if finish_reason == "stop":
+ chunk_usage = chunk.get("usage", None)
+ if chunk_usage:
+ usage = chunk_usage
+ break
+ # 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
+ flag_delta_content_finished = True
- except Exception as e:
- logger.exception(f"解析流式输出错误: {str(e)}")
+ except Exception as e:
+ logger.exception(f"解析流式输出错误: {str(e)}")
+ except GeneratorExit:
+ logger.warning("流式输出被中断")
+ break
+ except Exception as e:
+ logger.error(f"处理流式输出时发生错误: {str(e)}")
+ break
content = accumulated_content
think_match = re.search(r"(.*?)", content, re.DOTALL)
if think_match:
diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py
index ae46ae8c2..a6a312624 100644
--- a/src/plugins/schedule/schedule_generator.py
+++ b/src/plugins/schedule/schedule_generator.py
@@ -176,21 +176,27 @@ class ScheduleGenerator:
logger.warning(f"未找到{today_str}的日程记录")
async def move_doing(self, mind_thinking: str = ""):
- current_time = datetime.datetime.now()
- if mind_thinking:
- doing_prompt = self.construct_doing_prompt(current_time, mind_thinking)
- else:
- doing_prompt = self.construct_doing_prompt(current_time)
+ try:
+ current_time = datetime.datetime.now()
+ if mind_thinking:
+ doing_prompt = self.construct_doing_prompt(current_time, mind_thinking)
+ else:
+ doing_prompt = self.construct_doing_prompt(current_time)
- # print(doing_prompt)
- doing_response, _ = await self.llm_scheduler_doing.generate_response_async(doing_prompt)
- self.today_done_list.append((current_time, doing_response))
+ doing_response, _ = await self.llm_scheduler_doing.generate_response_async(doing_prompt)
+ self.today_done_list.append((current_time, doing_response))
- await self.update_today_done_list()
+ await self.update_today_done_list()
- logger.info(f"当前活动: {doing_response}")
+ logger.info(f"当前活动: {doing_response}")
- return doing_response
+ return doing_response
+ except GeneratorExit:
+ logger.warning("日程生成被中断")
+ return "日程生成被中断"
+ except Exception as e:
+ logger.error(f"生成日程时发生错误: {str(e)}")
+ return "生成日程时发生错误"
async def get_task_from_time_to_time(self, start_time: str, end_time: str):
"""获取指定时间范围内的任务列表
diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml
index d952aaa4f..5a13710e5 100644
--- a/template/bot_config_template.toml
+++ b/template/bot_config_template.toml
@@ -1,5 +1,5 @@
[inner]
-version = "1.0.2"
+version = "1.0.3"
#以下是给开发人员阅读的，一般用户不需要阅读
@@ -53,7 +53,7 @@ schedule_temperature = 0.5 # 日程表温度，建议0.5-1.0
nonebot-qq="http://127.0.0.1:18002/api/message"
[heartflow] # 注意：可能会消耗大量token，请谨慎开启
-enable = false
+enable = false #该选项未启用
sub_heart_flow_update_interval = 60 # 子心流更新频率，间隔 单位秒
sub_heart_flow_freeze_time = 120 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
sub_heart_flow_stop_time = 600 # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
@@ -63,9 +63,9 @@ heart_flow_update_interval = 300 # 心流更新频率，间隔 单位秒
[message]
-max_context_size = 15 # 麦麦获得的上文数量，建议15，太短太长都会导致脑袋尖尖
+max_context_size = 12 # 麦麦获得的上文数量，建议12，太短太长都会导致脑袋尖尖
emoji_chance = 0.2 # 麦麦使用表情包的概率
-thinking_timeout = 120 # 麦麦最长思考时间，超过这个时间的思考会放弃
+thinking_timeout = 60 # 麦麦最长思考时间，超过这个时间的思考会放弃
max_response_length = 256 # 麦麦回答的最大token数
ban_words = [
# "403","张三"
@@ -87,10 +87,9 @@ response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数,听
down_frequency_rate = 3 # 降低回复频率的群组回复意愿降低系数 除法
emoji_response_penalty = 0.1 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
-[response]
-model_r1_probability = 0.8 # 麦麦回答时选择主要回复模型1 模型的概率
-model_v3_probability = 0.1 # 麦麦回答时选择次要回复模型2 模型的概率
-model_r1_distill_probability = 0.1 # 麦麦回答时选择次要回复模型3 模型的概率
+[response] #这些选项已无效
+model_r1_probability = 0 # 麦麦回答时选择主要回复模型1 模型的概率
+model_v3_probability = 1.0 # 麦麦回答时选择次要回复模型2 模型的概率
[emoji]
check_interval = 15 # 检查破损表情包的时间间隔(分钟)
@@ -159,22 +158,16 @@ enable_friend_chat = false # 是否启用好友聊天
# stream = : 用于指定模型是否是使用流式输出
# 如果不指定，则该项是 False
-[model.llm_reasoning] #回复模型1 主要回复模型
+[model.llm_reasoning] #暂时未使用
name = "Pro/deepseek-ai/DeepSeek-R1"
# name = "Qwen/QwQ-32B"
provider = "SILICONFLOW"
pri_in = 4 #模型的输入价格（非必填，可以记录消耗）
pri_out = 16 #模型的输出价格（非必填，可以记录消耗）
-[model.llm_reasoning_minor] #回复模型3 次要回复模型
-name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
-provider = "SILICONFLOW"
-pri_in = 1.26 #模型的输入价格（非必填，可以记录消耗）
-pri_out = 1.26 #模型的输出价格（非必填，可以记录消耗）
-
#非推理模型
-[model.llm_normal] #V3 回复模型2 次要回复模型
+[model.llm_normal] #V3 回复模型1 主要回复模型
name = "Pro/deepseek-ai/DeepSeek-V3"
provider = "SILICONFLOW"
pri_in = 2 #模型的输入价格（非必填，可以记录消耗）