mirror of https://github.com/Mai-with-u/MaiBot.git
549 lines
22 KiB
Python
549 lines
22 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.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 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.utils.info_catcher import info_catcher_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.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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from src.chat.focus_chat.expressors.exprssion_learner import expression_learner
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import random
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logger = get_logger("expressor")
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def init_prompt():
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Prompt(
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"""
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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：
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{style_habbits}
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你现在正在群里聊天，以下是群里正在进行的聊天内容：
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{chat_info}
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以上是聊天内容，你需要了解聊天记录中的内容
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{chat_target}
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你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{target_message}"引起了你的注意，对这句话，你想表达：{in_mind_reply},原因是：{reason}。你现在要思考怎么回复
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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。
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请你根据情景使用以下句法：
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{grammar_habbits}
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回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，你可以完全重组回复，保留最基本的表达含义就好，但注意回复要简短，但重组后保持语意通顺。
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回复不要浮夸，不要用夸张修辞，平淡一些。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 )，只输出一条回复就好。
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现在，你说：
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""",
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"default_expressor_prompt",
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)
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Prompt(
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"""
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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：
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{style_habbits}
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你现在正在群里聊天，以下是群里正在进行的聊天内容：
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{chat_info}
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以上是聊天内容，你需要了解聊天记录中的内容
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{chat_target}
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你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{target_message}"引起了你的注意，对这句话，你想表达：{in_mind_reply},原因是：{reason}。你现在要思考怎么回复
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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。
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请你根据情景使用以下句法：
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{grammar_habbits}
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回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，你可以完全重组回复，保留最基本的表达含义就好，但注意回复要简短，但重组后保持语意通顺。
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回复不要浮夸，不要用夸张修辞，平淡一些。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 )，只输出一条回复就好。
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现在，你说：
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""",
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"default_expressor_private_prompt", # New template for private FOCUSED chat
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)
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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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# TODO: API-Adapter修改标记
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self.express_model = LLMRequest(
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model=global_config.model.focus_expressor,
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# temperature=global_config.model.focus_expressor["temp"],
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max_tokens=256,
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request_type="focus_expressor",
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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_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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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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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 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.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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# 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 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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reason=reason,
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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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# TODO: API-Adapter修改标记
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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}||理由：{reason}")
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logger.info(f"最终回复: {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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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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target_message,
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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 = await 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=True,
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timestamp_mode="relative",
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read_mark=0.0,
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truncate=True,
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)
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(
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learnt_style_expressions,
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learnt_grammar_expressions,
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personality_expressions,
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) = await expression_learner.get_expression_by_chat_id(chat_stream.stream_id)
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style_habbits = []
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grammar_habbits = []
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# 1. learnt_expressions加权随机选3条
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if learnt_style_expressions:
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weights = [expr["count"] for expr in learnt_style_expressions]
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selected_learnt = weighted_sample_no_replacement(learnt_style_expressions, weights, 3)
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for expr in selected_learnt:
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
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# 2. learnt_grammar_expressions加权随机选3条
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if learnt_grammar_expressions:
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weights = [expr["count"] for expr in learnt_grammar_expressions]
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selected_learnt = weighted_sample_no_replacement(learnt_grammar_expressions, weights, 3)
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for expr in selected_learnt:
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
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# 3. personality_expressions随机选1条
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if personality_expressions:
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expr = random.choice(personality_expressions)
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
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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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logger.debug("开始构建 focus prompt")
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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_expressor_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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style_habbits=style_habbits_str,
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grammar_habbits=grammar_habbits_str,
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chat_target=chat_target_1,
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chat_info=chat_talking_prompt,
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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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target_message=target_message,
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)
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else: # Private chat
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template_name = "default_expressor_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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style_habbits=style_habbits_str,
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grammar_habbits=grammar_habbits_str,
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chat_target=chat_target_1,
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chat_info=chat_talking_prompt,
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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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target_message=target_message,
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)
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return prompt
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# --- 发送器 (Sender) --- #
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async def send_response_messages(
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self,
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anchor_message: Optional[MessageRecv],
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response_set: List[Tuple[str, str]],
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thinking_id: str = "",
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display_message: 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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if thinking_id:
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thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(chat_id, thinking_id)
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else:
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thinking_id = "ds" + str(round(time.time(), 2))
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thinking_start_time = time.time()
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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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if global_config.experimental.debug_show_chat_mode and type == "text":
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data += "ᶠ"
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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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display_message=display_message,
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reply_to=reply_to,
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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 = ""
|
||
emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji)
|
||
if emoji_raw:
|
||
emoji_path, _description = emoji_raw
|
||
emoji_base64 = image_path_to_base64(emoji_path)
|
||
return emoji_base64
|
||
|
||
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,
|
||
)
|
||
|
||
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
|
||
|
||
|
||
init_prompt()
|