mirror of https://github.com/Mai-with-u/MaiBot.git
361 lines
15 KiB
Python
361 lines
15 KiB
Python
import traceback
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from typing import List, Optional, Dict, Any, Tuple
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from src.chat.message_receive.message import MessageRecv, MessageThinking, MessageSending
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from src.chat.message_receive.message import Seg # Local import needed after move
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from src.chat.message_receive.message import UserInfo
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from src.chat.message_receive.chat_stream import chat_manager
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from src.common.logger_manager import get_logger
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from src.chat.models.utils_model import LLMRequest
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from src.config.config import global_config
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from src.chat.utils.utils_image import image_path_to_base64 # Local import needed after move
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from src.chat.utils.timer_calculator import Timer # <--- Import Timer
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from src.chat.emoji_system.emoji_manager import emoji_manager
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from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
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from src.chat.focus_chat.heartFC_sender import HeartFCSender
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from src.chat.utils.utils import process_llm_response
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from src.chat.utils.info_catcher import info_catcher_manager
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from src.manager.mood_manager import mood_manager
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from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info
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from src.chat.message_receive.chat_stream import ChatStream
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from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp
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from src.plugins.group_nickname.nickname_manager import nickname_manager
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logger = get_logger("expressor")
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class DefaultExpressor:
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def __init__(self, chat_id: str):
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self.log_prefix = "expressor"
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self.express_model = LLMRequest(
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model=global_config.llm_normal,
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temperature=global_config.llm_normal["temp"],
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max_tokens=256,
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request_type="response_heartflow",
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)
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self.heart_fc_sender = HeartFCSender()
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self.chat_id = chat_id
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self.chat_stream: Optional[ChatStream] = None
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self.is_group_chat = True
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self.chat_target_info = None
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async def initialize(self):
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self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.chat_id)
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async def _create_thinking_message(self, anchor_message: Optional[MessageRecv], thinking_id: str):
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"""创建思考消息 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error(f"{self.log_prefix} 无法创建思考消息，缺少有效的锚点消息或聊天流。")
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return None
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chat = anchor_message.chat_stream
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messageinfo = anchor_message.message_info
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thinking_time_point = parse_thinking_id_to_timestamp(thinking_id)
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=messageinfo.platform,
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)
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# logger.debug(f"创建思考消息：{anchor_message}")
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# logger.debug(f"创建思考消息chat：{chat}")
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# logger.debug(f"创建思考消息bot_user_info：{bot_user_info}")
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# logger.debug(f"创建思考消息messageinfo：{messageinfo}")
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thinking_message = MessageThinking(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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reply=anchor_message, # 回复的是锚点消息
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thinking_start_time=thinking_time_point,
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)
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logger.debug(f"创建思考消息thinking_message：{thinking_message}")
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await self.heart_fc_sender.register_thinking(thinking_message)
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async def deal_reply(
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self,
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cycle_timers: dict,
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action_data: Dict[str, Any],
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reasoning: str,
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anchor_message: MessageRecv,
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thinking_id: str,
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) -> tuple[bool, Optional[List[Tuple[str, str]]]]:
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# 创建思考消息
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await self._create_thinking_message(anchor_message, thinking_id)
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reply = None # 初始化 reply，防止未定义
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try:
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has_sent_something = False
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# 处理文本部分
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text_part = action_data.get("text", [])
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if text_part:
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with Timer("生成回复", cycle_timers):
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# 可以保留原有的文本处理逻辑或进行适当调整
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reply = await self.express(
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in_mind_reply=text_part,
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anchor_message=anchor_message,
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thinking_id=thinking_id,
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reason=reasoning,
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action_data=action_data,
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)
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with Timer("选择表情", cycle_timers):
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emoji_keyword = action_data.get("emojis", [])
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emoji_base64 = await self._choose_emoji(emoji_keyword)
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if emoji_base64:
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reply.append(("emoji", emoji_base64))
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if reply:
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with Timer("发送消息", cycle_timers):
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sent_msg_list = await self._send_response_messages(
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anchor_message=anchor_message,
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thinking_id=thinking_id,
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response_set=reply,
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)
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has_sent_something = True
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# 为 trigger_nickname_analysis 准备 bot_reply 参数
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bot_reply_for_analysis = []
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if reply: # reply 是 List[Tuple[str, str]]
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for seg_type, seg_data in reply:
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if seg_type == "text": # 只取文本类型的数据
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bot_reply_for_analysis.append(seg_data)
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await nickname_manager.trigger_nickname_analysis(anchor_message, bot_reply_for_analysis, self.chat_stream)
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else:
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logger.warning(f"{self.log_prefix} 文本回复生成失败")
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if not has_sent_something:
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logger.warning(f"{self.log_prefix} 回复动作未包含任何有效内容")
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return has_sent_something, sent_msg_list
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except Exception as e:
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logger.error(f"回复失败: {e}")
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return False, None
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# --- 回复器 (Replier) 的定义 --- #
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async def express(
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self,
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in_mind_reply: str,
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reason: str,
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anchor_message: MessageRecv,
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thinking_id: str,
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action_data: Dict[str, Any],
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) -> Optional[List[str]]:
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"""
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回复器 (Replier): 核心逻辑，负责生成回复文本。
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(已整合原 HeartFCGenerator 的功能)
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"""
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try:
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# 1. 获取情绪影响因子并调整模型温度
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arousal_multiplier = mood_manager.get_arousal_multiplier()
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current_temp = float(global_config.llm_normal["temp"]) * arousal_multiplier
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self.express_model.params["temperature"] = current_temp # 动态调整温度
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# 2. 获取信息捕捉器
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info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
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# --- Determine sender_name for private chat ---
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sender_name_for_prompt = "某人" # Default for group or if info unavailable
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if not self.is_group_chat and self.chat_target_info:
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# Prioritize person_name, then nickname
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sender_name_for_prompt = (
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self.chat_target_info.get("person_name")
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or self.chat_target_info.get("user_nickname")
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or sender_name_for_prompt
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)
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# --- End determining sender_name ---
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target_message = action_data.get("target", "")
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# 3. 构建 Prompt
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with Timer("构建Prompt", {}): # 内部计时器，可选保留
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prompt = await prompt_builder.build_prompt(
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build_mode="focus",
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chat_stream=self.chat_stream, # Pass the stream object
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in_mind_reply=in_mind_reply,
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reason=reason,
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current_mind_info="",
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structured_info="",
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sender_name=sender_name_for_prompt, # Pass determined name
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target_message=target_message,
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)
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# 4. 调用 LLM 生成回复
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content = None
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reasoning_content = None
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model_name = "unknown_model"
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if not prompt:
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logger.error(f"{self.log_prefix}[Replier-{thinking_id}] Prompt 构建失败，无法生成回复。")
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return None
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try:
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with Timer("LLM生成", {}): # 内部计时器，可选保留
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# logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
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content, reasoning_content, model_name = await self.express_model.generate_response(prompt)
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logger.info(f"{self.log_prefix}\nPrompt:\n{prompt}\n---------------------------\n")
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logger.info(f"想要表达：{in_mind_reply}")
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logger.info(f"理由：{reason}")
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logger.info(f"生成回复: {content}\n")
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info_catcher.catch_after_llm_generated(
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prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=model_name
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)
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except Exception as llm_e:
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# 精简报错信息
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logger.error(f"{self.log_prefix}LLM 生成失败: {llm_e}")
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return None # LLM 调用失败则无法生成回复
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processed_response = process_llm_response(content)
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# 5. 处理 LLM 响应
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if not content:
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logger.warning(f"{self.log_prefix}LLM 生成了空内容。")
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return None
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if not processed_response:
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logger.warning(f"{self.log_prefix}处理后的回复为空。")
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return None
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reply_set = []
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for str in processed_response:
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reply_seg = ("text", str)
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reply_set.append(reply_seg)
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return reply_set
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except Exception as e:
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logger.error(f"{self.log_prefix}回复生成意外失败: {e}")
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traceback.print_exc()
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return None
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# --- 发送器 (Sender) --- #
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async def _send_response_messages(
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self, anchor_message: Optional[MessageRecv], response_set: List[Tuple[str, str]], thinking_id: str
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) -> Optional[MessageSending]:
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"""发送回复消息 (尝试锚定到 anchor_message)，使用 HeartFCSender"""
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chat = self.chat_stream
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chat_id = self.chat_id
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if chat is None:
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logger.error(f"{self.log_prefix} 无法发送回复，chat_stream 为空。")
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return None
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if not anchor_message:
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logger.error(f"{self.log_prefix} 无法发送回复，anchor_message 为空。")
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return None
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stream_name = chat_manager.get_stream_name(chat_id) or chat_id # 获取流名称用于日志
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# 检查思考过程是否仍在进行，并获取开始时间
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thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(chat_id, thinking_id)
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if thinking_start_time is None:
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logger.error(f"[{stream_name}]思考过程未找到或已结束，无法发送回复。")
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return None
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mark_head = False
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first_bot_msg: Optional[MessageSending] = None
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reply_message_ids = [] # 记录实际发送的消息ID
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sent_msg_list = []
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for i, msg_text in enumerate(response_set):
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# 为每个消息片段生成唯一ID
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type = msg_text[0]
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data = msg_text[1]
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part_message_id = f"{thinking_id}_{i}"
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message_segment = Seg(type=type, data=data)
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if type == "emoji":
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is_emoji = True
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else:
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is_emoji = False
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reply_to = not mark_head
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bot_message = await self._build_single_sending_message(
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anchor_message=anchor_message,
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message_id=part_message_id,
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message_segment=message_segment,
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reply_to=reply_to,
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is_emoji=is_emoji,
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thinking_id=thinking_id,
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)
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try:
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if not mark_head:
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mark_head = True
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_first_bot_msg = bot_message # 保存第一个成功发送的消息对象
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typing = False
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else:
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typing = True
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if type == "emoji":
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typing = False
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sent_msg = await self.heart_fc_sender.send_message(bot_message, has_thinking=True, typing=typing)
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reply_message_ids.append(part_message_id) # 记录我们生成的ID
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sent_msg_list.append((type, sent_msg))
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except Exception as e:
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logger.error(f"{self.log_prefix}发送回复片段 {i} ({part_message_id}) 时失败: {e}")
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# 这里可以选择是继续发送下一个片段还是中止
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# 在尝试发送完所有片段后，完成原始的 thinking_id 状态
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try:
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await self.heart_fc_sender.complete_thinking(chat_id, thinking_id)
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except Exception as e:
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logger.error(f"{self.log_prefix}完成思考状态 {thinking_id} 时出错: {e}")
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return sent_msg_list
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async def _choose_emoji(self, send_emoji: str):
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"""
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选择表情，根据send_emoji文本选择表情，返回表情base64
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"""
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emoji_base64 = ""
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emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji)
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if emoji_raw:
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emoji_path, _description = emoji_raw
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emoji_base64 = image_path_to_base64(emoji_path)
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return emoji_base64
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async def _build_single_sending_message(
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self,
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anchor_message: MessageRecv,
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message_id: str,
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message_segment: Seg,
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reply_to: bool,
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is_emoji: bool,
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thinking_id: str,
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) -> MessageSending:
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"""构建单个发送消息"""
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thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(self.chat_id, thinking_id)
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=self.chat_stream.platform,
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)
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bot_message = MessageSending(
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message_id=message_id, # 使用片段的唯一ID
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chat_stream=self.chat_stream,
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bot_user_info=bot_user_info,
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sender_info=anchor_message.message_info.user_info,
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message_segment=message_segment,
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reply=anchor_message, # 回复原始锚点
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is_head=reply_to,
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is_emoji=is_emoji,
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thinking_start_time=thinking_start_time, # 传递原始思考开始时间
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)
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return bot_message
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