168 lines
6.6 KiB
Python
168 lines
6.6 KiB
Python
import asyncio
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import re
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import math
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import traceback
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from typing import Tuple, TYPE_CHECKING
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from src.config.config import global_config
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from src.chat.memory_system.Hippocampus import hippocampus_manager
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from src.chat.message_receive.message import MessageRecv
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from src.chat.message_receive.storage import MessageStorage
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from src.chat.heart_flow.heartflow import heartflow
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from src.chat.utils.utils import is_mentioned_bot_in_message
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from src.chat.utils.timer_calculator import Timer
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from src.chat.utils.chat_message_builder import replace_user_references
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from src.common.logger import get_logger
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from src.mood.mood_manager import mood_manager
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from src.person_info.person_info import Person
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from src.common.database.database_model import Images
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if TYPE_CHECKING:
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from src.chat.heart_flow.heartFC_chat import HeartFChatting
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logger = get_logger("chat")
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async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
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"""计算消息的兴趣度
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Args:
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message: 待处理的消息对象
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Returns:
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Tuple[float, bool, list[str]]: (兴趣度, 是否被提及, 关键词)
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"""
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if message.is_picid:
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return 0.0, []
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is_mentioned, _ = is_mentioned_bot_in_message(message)
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interested_rate = 0.0
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with Timer("记忆激活"):
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interested_rate, keywords,keywords_lite = await hippocampus_manager.get_activate_from_text(
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message.processed_plain_text,
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max_depth= 4,
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fast_retrieval=global_config.chat.interest_rate_mode == "fast",
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)
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message.key_words = keywords
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message.key_words_lite = keywords_lite
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logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
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text_len = len(message.processed_plain_text)
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# 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
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# 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
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if text_len == 0:
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base_interest = 0.01 # 空消息最低兴趣度
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elif text_len <= 5:
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# 1-5字符：线性增长 0.01 -> 0.03
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base_interest = 0.01 + (text_len - 1) * (0.03 - 0.01) / 4
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elif text_len <= 10:
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# 6-10字符：线性增长 0.03 -> 0.06
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base_interest = 0.03 + (text_len - 5) * (0.06 - 0.03) / 5
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elif text_len <= 20:
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# 11-20字符：线性增长 0.06 -> 0.12
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base_interest = 0.06 + (text_len - 10) * (0.12 - 0.06) / 10
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elif text_len <= 30:
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# 21-30字符：线性增长 0.12 -> 0.18
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base_interest = 0.12 + (text_len - 20) * (0.18 - 0.12) / 10
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elif text_len <= 50:
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# 31-50字符：线性增长 0.18 -> 0.22
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base_interest = 0.18 + (text_len - 30) * (0.22 - 0.18) / 20
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elif text_len <= 100:
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# 51-100字符：线性增长 0.22 -> 0.26
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base_interest = 0.22 + (text_len - 50) * (0.26 - 0.22) / 50
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else:
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# 100+字符：对数增长 0.26 -> 0.3，增长率递减
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base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901)) # 1000-99=901
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# 确保在范围内
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base_interest = min(max(base_interest, 0.01), 0.3)
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interested_rate += base_interest
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if is_mentioned:
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interest_increase_on_mention = 2
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interested_rate += interest_increase_on_mention
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message.interest_value = interested_rate
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message.is_mentioned = is_mentioned
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return interested_rate, keywords
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class HeartFCMessageReceiver:
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"""心流处理器，负责处理接收到的消息并计算兴趣度"""
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def __init__(self):
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"""初始化心流处理器，创建消息存储实例"""
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self.storage = MessageStorage()
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async def process_message(self, message: MessageRecv) -> None:
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"""处理接收到的原始消息数据
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主要流程:
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1. 消息解析与初始化
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2. 消息缓冲处理
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3. 过滤检查
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4. 兴趣度计算
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5. 关系处理
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Args:
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message_data: 原始消息字符串
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"""
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try:
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# 1. 消息解析与初始化
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userinfo = message.message_info.user_info
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chat = message.chat_stream
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# 2. 兴趣度计算与更新
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interested_rate, keywords = await _calculate_interest(message)
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await self.storage.store_message(message, chat)
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heartflow_chat: HeartFChatting = await heartflow.get_or_create_heartflow_chat(chat.stream_id) # type: ignore
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# subheartflow.add_message_to_normal_chat_cache(message, interested_rate, is_mentioned)
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if global_config.mood.enable_mood:
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chat_mood = mood_manager.get_mood_by_chat_id(heartflow_chat.stream_id)
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asyncio.create_task(chat_mood.update_mood_by_message(message, interested_rate))
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# 3. 日志记录
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mes_name = chat.group_info.group_name if chat.group_info else "私聊"
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# 用这个pattern截取出id部分，picid是一个list，并替换成对应的图片描述
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picid_pattern = r"\[picid:([^\]]+)\]"
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picid_list = re.findall(picid_pattern, message.processed_plain_text)
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# 创建替换后的文本
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processed_text = message.processed_plain_text
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if picid_list:
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for picid in picid_list:
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image = Images.get_or_none(Images.image_id == picid)
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if image and image.description:
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# 将[picid:xxxx]替换成图片描述
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processed_text = processed_text.replace(f"[picid:{picid}]", f"[图片：{image.description}]")
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else:
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# 如果没有找到图片描述，则移除[picid:xxxx]标记
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processed_text = processed_text.replace(f"[picid:{picid}]", "[图片：网络不好，图片无法加载]")
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# 应用用户引用格式替换，将回复<aaa:bbb>和@<aaa:bbb>格式转换为可读格式
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processed_plain_text = replace_user_references(
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processed_text,
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message.message_info.platform, # type: ignore
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replace_bot_name=True
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)
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logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[{interested_rate:.2f}]") # type: ignore
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_ = Person.register_person(platform=message.message_info.platform, user_id=message.message_info.user_info.user_id,nickname=userinfo.user_nickname) # type: ignore
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except Exception as e:
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logger.error(f"消息处理失败: {e}")
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print(traceback.format_exc())
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