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feat：现支持两种独立的回复模式，推理模型和心流

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SengokuCola
2025-04-01 22:59:35 +08:00
parent cb547828fe
commit 02710a77ef
11 changed files with 1030 additions and 483 deletions
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347
src/plugins/chat/bot.py
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@@ -6,14 +6,14 @@ from ..memory_system.Hippocampus import HippocampusManager
from ..moods.moods import MoodManager # 导入情绪管理器
from ..config.config import global_config
from .emoji_manager import emoji_manager # 导入表情包管理器
from .llm_generator import ResponseGenerator
from ..chat_module.reasoning_chat.reasoning_generator import ResponseGenerator
from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
from .chat_stream import chat_manager
from .message_sender import message_manager # 导入新的消息管理器
from ..relationship.relationship_manager import relationship_manager
from ..storage.storage import MessageStorage
from ..storage.storage import MessageStorage # 修改导入路径
from .utils import is_mentioned_bot_in_message, get_recent_group_detailed_plain_text
from .utils_image import image_path_to_base64
from ..willing.willing_manager import willing_manager # 导入意愿管理器
@@ -21,6 +21,8 @@ from ..message import UserInfo, Seg
from src.heart_flow.heartflow import heartflow
from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
from ..chat_module.think_flow_chat.think_flow_chat import ThinkFlowChat
from ..chat_module.reasoning_chat.reasoning_chat import ReasoningChat
# 定义日志配置
chat_config = LogConfig(
@@ -41,333 +43,42 @@ class ChatBot:
self._started = False
self.mood_manager = MoodManager.get_instance() # 获取情绪管理器单例
self.mood_manager.start_mood_update() # 启动情绪更新
self.think_flow_chat = ThinkFlowChat()
self.reasoning_chat = ReasoningChat()
async def _ensure_started(self):
"""确保所有任务已启动"""
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
2. 记忆激活
3. 意愿激活
4. 生成回复并发送
5. 更新关系
6. 更新情绪
根据global_config.response_mode选择不同的回复模式：
1. heart_flow模式：使用思维流系统进行回复
- 包含思维流状态管理
- 在回复前进行观察和状态更新
- 回复后更新思维流状态
2. reasoning模式：使用推理系统进行回复
- 直接使用意愿管理器计算回复概率
- 没有思维流相关的状态管理
- 更简单直接的回复逻辑
两种模式都包含：
- 消息过滤
- 记忆激活
- 意愿计算
- 消息生成和发送
- 表情包处理
- 性能计时
"""
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
userinfo = message.message_info.user_info
messageinfo = message.message_info
if groupinfo.group_id not in global_config.talk_allowed_groups:
return
# 消息过滤，涉及到config有待更新
# 创建聊天流
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform,
user_info=userinfo,
group_info=groupinfo,
)
message.update_chat_stream(chat)
# 创建 心流与chat的观察
heartflow.create_subheartflow(chat.stream_id)
await message.process()
# 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(
message.raw_message, chat, userinfo
):
return
await self.storage.store_message(message, chat)
timer1 = time.time()
interested_rate = 0
interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
message.processed_plain_text, fast_retrieval=True
)
timer2 = time.time()
timing_results["记忆激活"] = timer2 - timer1
is_mentioned = is_mentioned_bot_in_message(message)
if global_config.enable_think_flow:
current_willing_old = willing_manager.get_willing(chat_stream=chat)
current_willing_new = (heartflow.get_subheartflow(chat.stream_id).current_state.willing - 5) / 4
print(f"旧回复意愿：{current_willing_old}，新回复意愿：{current_willing_new}")
current_willing = (current_willing_old + current_willing_new) / 2
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
current_willing = willing_manager.get_willing(chat_stream=chat)
willing_manager.set_willing(chat.stream_id, current_willing)
timer1 = time.time()
reply_probability = await willing_manager.change_reply_willing_received(
chat_stream=chat,
is_mentioned_bot=is_mentioned,
config=global_config,
is_emoji=message.is_emoji,
interested_rate=interested_rate,
sender_id=str(message.message_info.user_info.user_id),
)
timer2 = time.time()
timing_results["意愿激活"] = timer2 - timer1
# 神秘的消息流数据结构处理
if chat.group_info:
mes_name = chat.group_info.group_name
else:
mes_name = "私聊"
# 打印收到的信息的信息
current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time))
logger.info(
f"[{current_time}][{mes_name}]"
f"{chat.user_info.user_nickname}:"
f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]"
)
if message.message_info.additional_config:
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()
thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
timer2 = time.time()
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？")
return
# 发送消息
timer1 = time.time()
await self._send_response_messages(message, chat, response_set, thinking_id)
timer2 = time.time()
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()
timing_results["处理表情包"] = timer2 - timer1
timer1 = time.time()
await self._update_using_response(message, response_set)
timer2 = time.time()
timing_results["更新心流"] = timer2 - timer1
# 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
stream_id = message.chat_stream.stream_id
chat_talking_prompt = ""
if stream_id:
chat_talking_prompt = get_recent_group_detailed_plain_text(
stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
)
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)
thinking_message = None
# logger.info(f"开始发送消息准备")
for msg in container.messages:
if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
thinking_message = msg
container.messages.remove(msg)
break
if not thinking_message:
logger.warning("未找到对应的思考消息，可能已超时被移除")
return
# logger.info(f"开始发送消息")
thinking_start_time = thinking_message.thinking_start_time
message_set = MessageSet(chat, thinking_id)
mark_head = False
for msg in response_set:
message_segment = Seg(type="text", data=msg)
bot_message = MessageSending(
message_id=thinking_id,
chat_stream=chat,
bot_user_info=UserInfo(
user_id=global_config.BOT_QQ,
user_nickname=global_config.BOT_NICKNAME,
platform=message.message_info.platform,
),
sender_info=message.message_info.user_info,
message_segment=message_segment,
reply=message,
is_head=not mark_head,
is_emoji=False,
thinking_start_time=thinking_start_time,
)
if not mark_head:
mark_head = True
message_set.add_message(bot_message)
# logger.info(f"开始添加发送消息")
message_manager.add_message(message_set)
async def _handle_emoji(self, message, chat, response):
"""处理表情包
Args:
message: 接收到的消息
chat: 聊天流对象
response: 生成的回复
"""
if random() < global_config.emoji_chance:
emoji_raw = await emoji_manager.get_emoji_for_text(response)
if emoji_raw:
emoji_path, description = emoji_raw
emoji_cq = image_path_to_base64(emoji_path)
thinking_time_point = round(message.message_info.time, 2)
message_segment = Seg(type="emoji", data=emoji_cq)
bot_message = MessageSending(
message_id="mt" + str(thinking_time_point),
chat_stream=chat,
bot_user_info=UserInfo(
user_id=global_config.BOT_QQ,
user_nickname=global_config.BOT_NICKNAME,
platform=message.message_info.platform,
),
sender_info=message.message_info.user_info,
message_segment=message_segment,
reply=message,
is_head=False,
is_emoji=True,
)
message_manager.add_message(bot_message)
async def _update_emotion_and_relationship(self, message, chat, response, raw_content):
"""更新情绪和关系
Args:
message: 接收到的消息
chat: 聊天流对象
response: 生成的回复
raw_content: 原始内容
"""
stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text)
logger.debug(f"为 '{response}' 立场为：{stance} 获取到的情感标签为：{emotion}")
await relationship_manager.calculate_update_relationship_value(chat_stream=chat, label=emotion, stance=stance)
self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
def _check_ban_words(self, text: str, chat, userinfo) -> bool:
"""检查消息中是否包含过滤词
Args:
text: 要检查的文本
chat: 聊天流对象
userinfo: 用户信息对象
Returns:
bool: 如果包含过滤词返回True，否则返回False
"""
for word in global_config.ban_words:
if word in text:
logger.info(
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
)
logger.info(f"[过滤词识别]消息中含有{word}，filtered")
return True
return False
def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
"""检查消息是否匹配过滤正则表达式
Args:
text: 要检查的文本
chat: 聊天流对象
userinfo: 用户信息对象
Returns:
bool: 如果匹配过滤正则返回True，否则返回False
"""
for pattern in global_config.ban_msgs_regex:
if re.search(pattern, text):
logger.info(
f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
)
logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
return True
return False
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
# 创建全局ChatBot实例