mirror of https://github.com/Mai-with-u/MaiBot.git
682 lines
26 KiB
Python
682 lines
26 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 get_chat_manager
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from src.common.logger import get_logger
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from src.llm_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 get_emoji_manager
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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.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.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
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import time
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import random
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from datetime import datetime
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import re
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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import numpy as np
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logger = get_logger("replyer")
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def init_prompt():
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Prompt(
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"""
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{expression_habits_block}
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{extra_info_block}
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{relation_info_block}
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{time_block}
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{chat_target}
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{chat_info}
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{reply_target_block}
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{identity}
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你需要使用合适的语言习惯和句法，参考聊天内容，组织一条日常且口语化的回复。注意不要复读你说过的话。
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{config_expression_style}，请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容。
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{keywords_reaction_prompt}
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请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。
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不要浮夸，不要夸张修辞，只输出一条回复就好。
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现在，你说：
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""",
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"default_replyer_prompt",
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)
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Prompt(
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"""
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{expression_habits_block}
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{extra_info_block}
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{time_block}
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{chat_target}
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{chat_info}
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现在"{sender_name}"说的:{target_message}。引起了你的注意，你想要发言或者回复这条消息。
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{identity}，
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你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。注意不要复读你说过的话。
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{config_expression_style}，请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容。
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{keywords_reaction_prompt}
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请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。
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不要浮夸，不要夸张修辞，只输出一条回复就好。
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现在，你说：
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""",
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"default_replyer_private_prompt",
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)
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class DefaultReplyer:
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def __init__(self, chat_stream: ChatStream):
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self.log_prefix = "replyer"
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# TODO: API-Adapter修改标记
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self.express_model = LLMRequest(
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model=global_config.model.replyer_1,
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request_type="focus.replyer",
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)
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self.heart_fc_sender = HeartFCSender()
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self.chat_id = chat_stream.stream_id
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self.chat_stream = chat_stream
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self.is_group_chat, self.chat_target_info = 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_account,
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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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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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return None
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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 = [] # 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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sent_msg_list = []
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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.reply(
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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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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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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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traceback.print_exc()
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return False, None
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# --- 回复器 (Replier) 的定义 --- #
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async def deal_emoji(
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self,
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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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cycle_timers: dict,
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) -> Optional[List[str]]:
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"""
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表情动作处理类
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"""
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await self._create_thinking_message(anchor_message, thinking_id)
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try:
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has_sent_something = False
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sent_msg_list = []
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reply = []
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with Timer("选择表情", cycle_timers):
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emoji_keyword = action_data.get("description", [])
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emoji_base64, _description, emotion = await self._choose_emoji(emoji_keyword)
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if emoji_base64:
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# logger.info(f"选择表情: {_description}")
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reply.append(("emoji", emoji_base64))
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else:
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logger.warning(f"{self.log_prefix} 没有找到合适表情")
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if reply:
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with Timer("发送表情", 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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else:
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logger.warning(f"{self.log_prefix} 表情发送失败")
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if not has_sent_something:
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logger.warning(f"{self.log_prefix} 表情发送失败")
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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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traceback.print_exc()
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return False, None
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async def reply(
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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.model.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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reply_to = action_data.get("reply_to", "none")
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sender = ""
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targer = ""
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if ":" in reply_to or "：" in reply_to:
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# 使用正则表达式匹配中文或英文冒号
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parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1)
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if len(parts) == 2:
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sender = parts[0].strip()
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targer = parts[1].strip()
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identity = action_data.get("identity", "")
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extra_info_block = action_data.get("extra_info_block", "")
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relation_info_block = action_data.get("relation_info_block", "")
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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 self.build_prompt_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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identity=identity,
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extra_info_block=extra_info_block,
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relation_info_block=relation_info_block,
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reason=reason,
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sender_name=sender, # Pass determined name
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target_message=targer,
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config_expression_style=global_config.expression.expression_style,
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action_data=action_data, # 传递action_data
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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}Prompt:\n{prompt}\n")
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content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt)
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# logger.info(f"prompt: {prompt}")
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logger.info(f"最终回复: {content}")
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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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async def build_prompt_focus(
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self,
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reason,
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chat_stream,
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sender_name,
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# in_mind_reply,
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extra_info_block,
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relation_info_block,
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identity,
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target_message,
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config_expression_style,
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action_data=None,
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# stuation,
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) -> str:
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is_group_chat = bool(chat_stream.group_info)
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=chat_stream.stream_id,
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timestamp=time.time(),
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limit=global_config.focus_chat.observation_context_size,
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)
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chat_talking_prompt = build_readable_messages(
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message_list_before_now,
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replace_bot_name=True,
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merge_messages=False,
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timestamp_mode="normal_no_YMD",
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read_mark=0.0,
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truncate=True,
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show_actions=True,
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)
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style_habbits = []
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grammar_habbits = []
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# 使用从处理器传来的选中表达方式
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selected_expressions = action_data.get("selected_expressions", []) if action_data else []
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if selected_expressions:
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logger.info(f"{self.log_prefix} 使用处理器选中的{len(selected_expressions)}个表达方式")
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for expr in selected_expressions:
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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expr_type = expr.get("type", "style")
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if expr_type == "grammar":
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grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
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else:
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style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
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else:
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logger.debug(f"{self.log_prefix} 没有从处理器获得表达方式，将使用空的表达方式")
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# 不再在replyer中进行随机选择，全部交给处理器处理
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style_habbits_str = "\n".join(style_habbits)
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grammar_habbits_str = "\n".join(grammar_habbits)
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# 动态构建expression habits块
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expression_habits_block = ""
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if style_habbits_str.strip():
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expression_habits_block += f"你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中：\n{style_habbits_str}\n\n"
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if grammar_habbits_str.strip():
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expression_habits_block += f"请你根据情景使用以下句法：\n{grammar_habbits_str}\n"
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# 关键词检测与反应
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keywords_reaction_prompt = ""
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try:
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# 处理关键词规则
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for rule in global_config.keyword_reaction.keyword_rules:
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if any(keyword in target_message for keyword in rule.keywords):
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logger.info(f"检测到关键词规则：{rule.keywords}，触发反应：{rule.reaction}")
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keywords_reaction_prompt += f"{rule.reaction}，"
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# 处理正则表达式规则
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for rule in global_config.keyword_reaction.regex_rules:
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for pattern_str in rule.regex:
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try:
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pattern = re.compile(pattern_str)
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if result := pattern.search(target_message):
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reaction = rule.reaction
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for name, content in result.groupdict().items():
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reaction = reaction.replace(f"[{name}]", content)
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logger.info(f"匹配到正则表达式：{pattern_str}，触发反应：{reaction}")
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keywords_reaction_prompt += reaction + "，"
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break
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except re.error as e:
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logger.error(f"正则表达式编译错误: {pattern_str}, 错误信息: {str(e)}")
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continue
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except Exception as e:
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logger.error(f"关键词检测与反应时发生异常: {str(e)}", exc_info=True)
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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# logger.debug("开始构建 focus prompt")
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if sender_name:
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reply_target_block = (
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f"现在{sender_name}说的:{target_message}。引起了你的注意，你想要在群里发言或者回复这条消息。"
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)
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elif target_message:
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reply_target_block = f"现在{target_message}引起了你的注意，你想要在群里发言或者回复这条消息。"
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else:
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reply_target_block = "现在，你想要在群里发言或者回复消息。"
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# --- Choose template based on chat type ---
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if is_group_chat:
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template_name = "default_replyer_prompt"
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# Group specific formatting variables (already fetched or default)
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chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1")
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# chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2")
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prompt = await global_prompt_manager.format_prompt(
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template_name,
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expression_habits_block=expression_habits_block,
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chat_target=chat_target_1,
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chat_info=chat_talking_prompt,
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extra_info_block=extra_info_block,
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relation_info_block=relation_info_block,
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time_block=time_block,
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reply_target_block=reply_target_block,
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# bot_name=global_config.bot.nickname,
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# prompt_personality="",
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# reason=reason,
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# in_mind_reply=in_mind_reply,
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keywords_reaction_prompt=keywords_reaction_prompt,
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identity=identity,
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target_message=target_message,
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sender_name=sender_name,
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config_expression_style=config_expression_style,
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)
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else: # Private chat
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template_name = "default_replyer_private_prompt"
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chat_target_1 = "你正在和人私聊"
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prompt = await global_prompt_manager.format_prompt(
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template_name,
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expression_habits_block=expression_habits_block,
|
||
chat_target=chat_target_1,
|
||
chat_info=chat_talking_prompt,
|
||
extra_info_block=extra_info_block,
|
||
relation_info_block=relation_info_block,
|
||
time_block=time_block,
|
||
reply_target_block=reply_target_block,
|
||
# bot_name=global_config.bot.nickname,
|
||
# prompt_personality="",
|
||
# reason=reason,
|
||
# in_mind_reply=in_mind_reply,
|
||
keywords_reaction_prompt=keywords_reaction_prompt,
|
||
identity=identity,
|
||
target_message=target_message,
|
||
sender_name=sender_name,
|
||
config_expression_style=config_expression_style,
|
||
)
|
||
|
||
return prompt
|
||
|
||
# --- 发送器 (Sender) --- #
|
||
|
||
async def send_response_messages(
|
||
self,
|
||
anchor_message: Optional[MessageRecv],
|
||
response_set: List[Tuple[str, str]],
|
||
thinking_id: str = "",
|
||
display_message: str = "",
|
||
) -> Optional[MessageSending]:
|
||
"""发送回复消息 (尝试锚定到 anchor_message)，使用 HeartFCSender"""
|
||
chat = self.chat_stream
|
||
chat_id = self.chat_id
|
||
if chat is None:
|
||
logger.error(f"{self.log_prefix} 无法发送回复，chat_stream 为空。")
|
||
return None
|
||
if not anchor_message:
|
||
logger.error(f"{self.log_prefix} 无法发送回复，anchor_message 为空。")
|
||
return None
|
||
|
||
stream_name = get_chat_manager().get_stream_name(chat_id) or chat_id # 获取流名称用于日志
|
||
|
||
# 检查思考过程是否仍在进行，并获取开始时间
|
||
if thinking_id:
|
||
# print(f"thinking_id: {thinking_id}")
|
||
thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(chat_id, thinking_id)
|
||
else:
|
||
print("thinking_id is None")
|
||
# thinking_id = "ds" + str(round(time.time(), 2))
|
||
thinking_start_time = time.time()
|
||
|
||
if thinking_start_time is None:
|
||
logger.error(f"[{stream_name}]replyer思考过程未找到或已结束，无法发送回复。")
|
||
return None
|
||
|
||
mark_head = False
|
||
# first_bot_msg: Optional[MessageSending] = None
|
||
reply_message_ids = [] # 记录实际发送的消息ID
|
||
|
||
sent_msg_list = []
|
||
|
||
for i, msg_text in enumerate(response_set):
|
||
# 为每个消息片段生成唯一ID
|
||
type = msg_text[0]
|
||
data = msg_text[1]
|
||
|
||
if global_config.experimental.debug_show_chat_mode and type == "text":
|
||
data += "ᶠ"
|
||
|
||
part_message_id = f"{thinking_id}_{i}"
|
||
message_segment = Seg(type=type, data=data)
|
||
|
||
if type == "emoji":
|
||
is_emoji = True
|
||
else:
|
||
is_emoji = False
|
||
reply_to = not mark_head
|
||
|
||
bot_message = await self._build_single_sending_message(
|
||
anchor_message=anchor_message,
|
||
message_id=part_message_id,
|
||
message_segment=message_segment,
|
||
display_message=display_message,
|
||
reply_to=reply_to,
|
||
is_emoji=is_emoji,
|
||
thinking_id=thinking_id,
|
||
thinking_start_time=thinking_start_time,
|
||
)
|
||
|
||
try:
|
||
if not mark_head:
|
||
mark_head = True
|
||
# first_bot_msg = bot_message # 保存第一个成功发送的消息对象
|
||
typing = False
|
||
else:
|
||
typing = True
|
||
|
||
if type == "emoji":
|
||
typing = False
|
||
|
||
if anchor_message.raw_message:
|
||
set_reply = True
|
||
else:
|
||
set_reply = False
|
||
sent_msg = await self.heart_fc_sender.send_message(
|
||
bot_message, has_thinking=True, typing=typing, set_reply=set_reply
|
||
)
|
||
|
||
reply_message_ids.append(part_message_id) # 记录我们生成的ID
|
||
|
||
sent_msg_list.append((type, sent_msg))
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix}发送回复片段 {i} ({part_message_id}) 时失败: {e}")
|
||
traceback.print_exc()
|
||
# 这里可以选择是继续发送下一个片段还是中止
|
||
|
||
# 在尝试发送完所有片段后，完成原始的 thinking_id 状态
|
||
try:
|
||
await self.heart_fc_sender.complete_thinking(chat_id, thinking_id)
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix}完成思考状态 {thinking_id} 时出错: {e}")
|
||
|
||
return sent_msg_list
|
||
|
||
async def _choose_emoji(self, send_emoji: str):
|
||
"""
|
||
选择表情，根据send_emoji文本选择表情，返回表情base64
|
||
"""
|
||
emoji_base64 = ""
|
||
description = ""
|
||
emoji_raw = await get_emoji_manager().get_emoji_for_text(send_emoji)
|
||
if emoji_raw:
|
||
emoji_path, description, _emotion = emoji_raw
|
||
emoji_base64 = image_path_to_base64(emoji_path)
|
||
return emoji_base64, description, _emotion
|
||
else:
|
||
return None, None, None
|
||
|
||
async def _build_single_sending_message(
|
||
self,
|
||
anchor_message: MessageRecv,
|
||
message_id: str,
|
||
message_segment: Seg,
|
||
reply_to: bool,
|
||
is_emoji: bool,
|
||
thinking_id: str,
|
||
thinking_start_time: float,
|
||
display_message: str,
|
||
) -> MessageSending:
|
||
"""构建单个发送消息"""
|
||
|
||
bot_user_info = UserInfo(
|
||
user_id=global_config.bot.qq_account,
|
||
user_nickname=global_config.bot.nickname,
|
||
platform=self.chat_stream.platform,
|
||
)
|
||
|
||
# await anchor_message.process()
|
||
|
||
bot_message = MessageSending(
|
||
message_id=message_id, # 使用片段的唯一ID
|
||
chat_stream=self.chat_stream,
|
||
bot_user_info=bot_user_info,
|
||
sender_info=anchor_message.message_info.user_info,
|
||
message_segment=message_segment,
|
||
reply=anchor_message, # 回复原始锚点
|
||
is_head=reply_to,
|
||
is_emoji=is_emoji,
|
||
thinking_start_time=thinking_start_time, # 传递原始思考开始时间
|
||
display_message=display_message,
|
||
)
|
||
|
||
return bot_message
|
||
|
||
|
||
def weighted_sample_no_replacement(items, weights, k) -> list:
|
||
"""
|
||
加权且不放回地随机抽取k个元素。
|
||
|
||
参数：
|
||
items: 待抽取的元素列表
|
||
weights: 每个元素对应的权重（与items等长，且为正数）
|
||
k: 需要抽取的元素个数
|
||
返回：
|
||
selected: 按权重加权且不重复抽取的k个元素组成的列表
|
||
|
||
如果 items 中的元素不足 k 个，就只会返回所有可用的元素
|
||
|
||
实现思路：
|
||
每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。
|
||
这样保证了：
|
||
1. count越大被选中概率越高
|
||
2. 不会重复选中同一个元素
|
||
"""
|
||
selected = []
|
||
pool = list(zip(items, weights))
|
||
for _ in range(min(k, len(pool))):
|
||
total = sum(w for _, w in pool)
|
||
r = random.uniform(0, total)
|
||
upto = 0
|
||
for idx, (item, weight) in enumerate(pool):
|
||
upto += weight
|
||
if upto >= r:
|
||
selected.append(item)
|
||
pool.pop(idx)
|
||
break
|
||
return selected
|
||
|
||
|
||
def find_similar_expressions(input_text: str, expressions: List[Dict], top_k: int = 3) -> List[Dict]:
|
||
"""使用TF-IDF和余弦相似度找出与输入文本最相似的top_k个表达方式"""
|
||
if not expressions:
|
||
return []
|
||
|
||
# 准备文本数据
|
||
texts = [expr["situation"] for expr in expressions]
|
||
texts.append(input_text) # 添加输入文本
|
||
|
||
# 使用TF-IDF向量化
|
||
vectorizer = TfidfVectorizer()
|
||
tfidf_matrix = vectorizer.fit_transform(texts)
|
||
|
||
# 计算余弦相似度
|
||
similarity_matrix = cosine_similarity(tfidf_matrix)
|
||
|
||
# 获取输入文本的相似度分数（最后一行）
|
||
scores = similarity_matrix[-1][:-1] # 排除与自身的相似度
|
||
|
||
# 获取top_k的索引
|
||
top_indices = np.argsort(scores)[::-1][:top_k]
|
||
|
||
# 获取相似表达
|
||
similar_exprs = []
|
||
for idx in top_indices:
|
||
if scores[idx] > 0: # 只保留有相似度的
|
||
similar_exprs.append(expressions[idx])
|
||
|
||
return similar_exprs
|
||
|
||
|
||
init_prompt()
|