168 lines
5.7 KiB
Python
168 lines
5.7 KiB
Python
from src.chat.message_receive.chat_stream import get_chat_manager
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import time
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config
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from src.chat.message_receive.message import MessageRecvS4U
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from src.mais4u.mais4u_chat.s4u_msg_processor import S4UMessageProcessor
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from src.mais4u.mais4u_chat.internal_manager import internal_manager
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from src.common.logger import get_logger
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logger = get_logger(__name__)
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def init_prompt():
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Prompt(
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"""
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你之前的内心想法是：{mind}
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{memory_block}
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{relation_info_block}
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{chat_target}
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{time_block}
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{chat_info}
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{identity}
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你刚刚在{chat_target_2},你你刚刚的心情是：{mood_state}
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---------------------
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在这样的情况下，你对上面的内容，你对 {sender} 发送的 消息 “{target}” 进行了回复
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你刚刚选择回复的内容是：{reponse}
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现在，根据你之前的想法和回复的内容，推测你现在的想法，思考你现在的想法是什么，为什么做出上面的回复内容
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请不要浮夸和夸张修辞，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 )。只输出想法：""",
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"after_response_think_prompt",
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)
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class MaiThinking:
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def __init__(self, chat_id):
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self.chat_id = chat_id
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self.chat_stream = get_chat_manager().get_stream(chat_id)
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self.platform = self.chat_stream.platform
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if self.chat_stream.group_info:
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self.is_group = True
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else:
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self.is_group = False
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self.s4u_message_processor = S4UMessageProcessor()
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self.mind = ""
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self.memory_block = ""
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self.relation_info_block = ""
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self.time_block = ""
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self.chat_target = ""
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self.chat_target_2 = ""
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self.chat_info = ""
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self.mood_state = ""
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self.identity = ""
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self.sender = ""
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self.target = ""
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self.thinking_model = LLMRequest(model_set=model_config.model_task_config.replyer_1, request_type="thinking")
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async def do_think_before_response(self):
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pass
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async def do_think_after_response(self, reponse: str):
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prompt = await global_prompt_manager.format_prompt(
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"after_response_think_prompt",
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mind=self.mind,
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reponse=reponse,
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memory_block=self.memory_block,
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relation_info_block=self.relation_info_block,
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time_block=self.time_block,
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chat_target=self.chat_target,
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chat_target_2=self.chat_target_2,
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chat_info=self.chat_info,
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mood_state=self.mood_state,
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identity=self.identity,
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sender=self.sender,
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target=self.target,
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)
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result, _ = await self.thinking_model.generate_response_async(prompt)
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self.mind = result
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logger.info(f"[{self.chat_id}] 思考前想法：{self.mind}")
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# logger.info(f"[{self.chat_id}] 思考前prompt：{prompt}")
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logger.info(f"[{self.chat_id}] 思考后想法：{self.mind}")
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msg_recv = await self.build_internal_message_recv(self.mind)
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await self.s4u_message_processor.process_message(msg_recv)
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internal_manager.set_internal_state(self.mind)
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async def do_think_when_receive_message(self):
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pass
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async def build_internal_message_recv(self, message_text: str):
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msg_id = f"internal_{time.time()}"
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message_dict = {
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"message_info": {
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"message_id": msg_id,
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"time": time.time(),
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"user_info": {
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"user_id": "internal", # 内部用户ID
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"user_nickname": "内心", # 内部昵称
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"platform": self.platform, # 平台标记为 internal
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# 其他 user_info 字段按需补充
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},
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"platform": self.platform, # 平台
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# 其他 message_info 字段按需补充
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},
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"message_segment": {
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"type": "text", # 消息类型
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"data": message_text, # 消息内容
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# 其他 segment 字段按需补充
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},
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"raw_message": message_text, # 原始消息内容
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"processed_plain_text": message_text, # 处理后的纯文本
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# 下面这些字段可选，根据 MessageRecv 需要
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"is_emoji": False,
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"has_emoji": False,
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"is_picid": False,
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"has_picid": False,
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"is_voice": False,
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"is_mentioned": False,
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"is_command": False,
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"is_internal": True,
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"priority_mode": "interest",
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"priority_info": {"message_priority": 10.0}, # 内部消息可设高优先级
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"interest_value": 1.0,
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}
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if self.is_group:
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message_dict["message_info"]["group_info"] = {
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"platform": self.platform,
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"group_id": self.chat_stream.group_info.group_id,
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"group_name": self.chat_stream.group_info.group_name,
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}
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msg_recv = MessageRecvS4U(message_dict)
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msg_recv.chat_info = self.chat_info
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msg_recv.chat_stream = self.chat_stream
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msg_recv.is_internal = True
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return msg_recv
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class MaiThinkingManager:
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def __init__(self):
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self.mai_think_list = []
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def get_mai_think(self, chat_id):
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for mai_think in self.mai_think_list:
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if mai_think.chat_id == chat_id:
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return mai_think
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mai_think = MaiThinking(chat_id)
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self.mai_think_list.append(mai_think)
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return mai_think
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mai_thinking_manager = MaiThinkingManager()
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init_prompt()
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