mirror of https://github.com/Mai-with-u/MaiBot.git
243 lines
12 KiB
Python
243 lines
12 KiB
Python
import time
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import traceback
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from ..memory_system.Hippocampus import HippocampusManager
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from ...config.config import global_config
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from ..chat.message import MessageRecv
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from ..storage.storage import MessageStorage
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from ..chat.utils import is_mentioned_bot_in_message
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from ..message import Seg
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from src.heart_flow.heartflow import heartflow
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from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
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from ..chat.chat_stream import chat_manager
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from ..chat.message_buffer import message_buffer
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from ..utils.timer_calculater import Timer
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from src.plugins.person_info.relationship_manager import relationship_manager
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from .normal_chat import ReasoningChat
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# 定义日志配置
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processor_config = LogConfig(
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console_format=CHAT_STYLE_CONFIG["console_format"],
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file_format=CHAT_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("heartflow_processor", config=processor_config)
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class HeartFCProcessor:
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def __init__(self):
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self.storage = MessageStorage()
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self.normal_chat = ReasoningChat.get_instance()
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async def process_message(self, message_data: str) -> None:
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"""处理接收到的原始消息数据，完成消息解析、缓冲、过滤、存储、兴趣度计算与更新等核心流程。
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此函数是消息处理的核心入口，负责接收原始字符串格式的消息数据，并将其转化为结构化的 `MessageRecv` 对象。
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主要执行步骤包括：
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1. 解析 `message_data` 为 `MessageRecv` 对象，提取用户信息、群组信息等。
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2. 将消息加入 `message_buffer` 进行缓冲处理，以应对消息轰炸或者某些人一条消息分几次发等情况。
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3. 获取或创建对应的 `chat_stream` 和 `subheartflow` 实例，用于管理会话状态和心流。
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4. 对消息内容进行初步处理（如提取纯文本）。
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5. 应用全局配置中的过滤词和正则表达式，过滤不符合规则的消息。
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6. 查询消息缓冲结果，如果消息被缓冲器拦截（例如，判断为消息轰炸的一部分），则中止后续处理。
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7. 对于通过缓冲的消息，将其存储到 `MessageStorage` 中。
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8. 调用海马体（`HippocampusManager`）计算消息内容的记忆激活率。（这部分算法后续会进行优化）
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9. 根据是否被提及（@）和记忆激活率，计算最终的兴趣度增量。(提及的额外兴趣增幅)
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10. 使用计算出的增量更新 `InterestManager` 中对应会话的兴趣度。
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11. 记录处理后的消息信息及当前的兴趣度到日志。
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注意：此函数本身不负责生成和发送回复。回复的决策和生成逻辑被移至 `HeartFC_Chat` 类中的监控任务，
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该任务会根据 `InterestManager` 中的兴趣度变化来决定何时触发回复。
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Args:
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message_data: str: 从消息源接收到的原始消息字符串。
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"""
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timing_results = {} # 初始化 timing_results
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message = None
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try:
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message = MessageRecv(message_data)
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groupinfo = message.message_info.group_info
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userinfo = message.message_info.user_info
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messageinfo = message.message_info
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# 消息加入缓冲池
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await message_buffer.start_caching_messages(message)
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# 创建聊天流
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chat = await chat_manager.get_or_create_stream(
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platform=messageinfo.platform,
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user_info=userinfo,
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group_info=groupinfo,
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)
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# --- 确保 SubHeartflow 存在 ---
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subheartflow = await heartflow.create_subheartflow(chat.stream_id)
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if not subheartflow:
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logger.error(f"无法为 stream_id {chat.stream_id} 创建或获取 SubHeartflow，中止处理")
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return
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# --- 添加兴趣追踪启动 (现在移动到这里，确保 subheartflow 存在后启动) ---
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# 在获取到 chat 对象和确认 subheartflow 后，启动对该聊天流的兴趣监控
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await self.normal_chat.start_monitoring_interest(chat) # start_monitoring_interest 内部需要修改以适应
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# --- 结束添加 ---
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message.update_chat_stream(chat)
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await heartflow.create_subheartflow(chat.stream_id)
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await message.process()
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logger.trace(f"消息处理成功: {message.processed_plain_text}")
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# 过滤词/正则表达式过滤
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if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
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message.raw_message, chat, userinfo
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):
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return
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# 查询缓冲器结果
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buffer_result = await message_buffer.query_buffer_result(message)
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# 处理缓冲器结果 (Bombing logic)
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if not buffer_result:
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f_type = "seglist"
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if message.message_segment.type != "seglist":
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f_type = message.message_segment.type
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else:
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if (
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isinstance(message.message_segment.data, list)
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and all(isinstance(x, Seg) for x in message.message_segment.data)
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and len(message.message_segment.data) == 1
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):
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f_type = message.message_segment.data[0].type
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if f_type == "text":
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logger.debug(f"触发缓冲，消息：{message.processed_plain_text}")
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elif f_type == "image":
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logger.debug("触发缓冲，表情包/图片等待中")
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elif f_type == "seglist":
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logger.debug("触发缓冲，消息列表等待中")
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return # 被缓冲器拦截，不生成回复
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# ---- 只有通过缓冲的消息才进行存储和后续处理 ----
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# 存储消息 (使用可能被缓冲器更新过的 message)
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try:
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await self.storage.store_message(message, chat)
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logger.trace(f"存储成功 (通过缓冲后): {message.processed_plain_text}")
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except Exception as e:
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logger.error(f"存储消息失败: {e}")
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logger.error(traceback.format_exc())
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# 存储失败可能仍需考虑是否继续，暂时返回
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return
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# 激活度计算 (使用可能被缓冲器更新过的 message.processed_plain_text)
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is_mentioned, _ = is_mentioned_bot_in_message(message)
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interested_rate = 0.0 # 默认值
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try:
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with Timer("记忆激活", timing_results):
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interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
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message.processed_plain_text,
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fast_retrieval=True, # 使用更新后的文本
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)
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logger.trace(f"记忆激活率 (通过缓冲后): {interested_rate:.2f}")
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except Exception as e:
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logger.error(f"计算记忆激活率失败: {e}")
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logger.error(traceback.format_exc())
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# --- 修改：兴趣度更新逻辑 --- #
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if is_mentioned:
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interest_increase_on_mention = 2
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mentioned_boost = interest_increase_on_mention # 从配置获取提及增加值
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interested_rate += mentioned_boost
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logger.trace(f"消息提及机器人，额外增加兴趣 {mentioned_boost:.2f}")
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# 更新兴趣度 (调用 SubHeartflow 的方法)
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current_interest = 0.0 # 初始化
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try:
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# 获取当前时间，传递给 increase_interest
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current_time = time.time()
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subheartflow.interest_chatting.increase_interest(current_time, value=interested_rate)
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current_interest = subheartflow.get_interest_level() # 获取更新后的值
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logger.trace(
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f"使用激活率 {interested_rate:.2f} 更新后 (通过缓冲后)，当前兴趣度: {current_interest:.2f} (Stream: {chat.stream_id})"
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)
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# 添加到 SubHeartflow 的 interest_dict
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subheartflow.add_interest_dict_entry(message, interested_rate, is_mentioned)
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logger.trace(
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f"Message {message.message_info.message_id} added to interest dict for stream {chat.stream_id}"
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)
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except Exception as e:
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logger.error(f"更新兴趣度失败 (Stream: {chat.stream_id}): {e}")
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logger.error(traceback.format_exc())
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# --- 结束修改 --- #
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# 打印消息接收和处理信息
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mes_name = chat.group_info.group_name if chat.group_info else "私聊"
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current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
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logger.info(
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f"[{current_time}][{mes_name}]"
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f"{chat.user_info.user_nickname}:"
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f"{message.processed_plain_text}"
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f"兴趣度: {current_interest:.2f}"
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)
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try:
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is_known = await relationship_manager.is_known_some_one(
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message.message_info.platform, message.message_info.user_info.user_id
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)
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if not is_known:
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logger.info(f"首次认识用户: {message.message_info.user_info.user_nickname}")
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await relationship_manager.first_knowing_some_one(
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message.message_info.platform,
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message.message_info.user_info.user_id,
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message.message_info.user_info.user_nickname,
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message.message_info.user_info.user_cardname or message.message_info.user_info.user_nickname,
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"",
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)
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else:
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# logger.debug(f"已认识用户: {message.message_info.user_info.user_nickname}")
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if not await relationship_manager.is_qved_name(
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message.message_info.platform, message.message_info.user_info.user_id
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):
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logger.info(f"更新已认识但未取名的用户: {message.message_info.user_info.user_nickname}")
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await relationship_manager.first_knowing_some_one(
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message.message_info.platform,
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message.message_info.user_info.user_id,
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message.message_info.user_info.user_nickname,
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message.message_info.user_info.user_cardname
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or message.message_info.user_info.user_nickname,
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"",
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)
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except Exception as e:
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logger.error(f"处理认识关系失败: {e}")
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logger.error(traceback.format_exc())
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except Exception as e:
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logger.error(f"消息处理失败 (process_message V3): {e}")
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logger.error(traceback.format_exc())
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if message: # 记录失败的消息内容
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logger.error(f"失败消息原始内容: {message.raw_message}")
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def _check_ban_words(self, text: str, chat, userinfo) -> bool:
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"""检查消息中是否包含过滤词"""
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for word in global_config.ban_words:
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if word in text:
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logger.info(
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f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
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)
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logger.info(f"[过滤词识别]消息中含有{word}，filtered")
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return True
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return False
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def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
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"""检查消息是否匹配过滤正则表达式"""
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for pattern in global_config.ban_msgs_regex:
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if pattern.search(text):
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logger.info(
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f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
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)
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logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
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return True
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return False
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