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better:优化回复逻辑，现在回复前会先思考，移除推理模型再回复中的使用，优化心流运行逻辑，优化思考时间计算逻辑，添加错误检测

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This commit is contained in:
SengokuCola
2025-03-31 22:34:52 +08:00
parent 42b1b772ef
commit 4c42c90879
14 changed files with 254 additions and 193 deletions
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115
src/plugins/chat/bot.py
View File

@@ -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)

28
src/plugins/chat/llm_generator.py
View File

@@ -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,

10
src/plugins/chat/message_sender.py
View File

@@ -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}")

50
src/plugins/chat/prompt_builder.py
View File

@@ -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 )。"""

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

@@ -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)