ref：移除一些荣誉模块，新建maisaka回复器
This commit is contained in:
SengokuCola
@@ -1,7 +1,8 @@
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from typing import Dict
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import asyncio
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import traceback
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from typing import Dict
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from src.chat.message_receive.chat_manager import chat_manager
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from src.common.logger import get_logger
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from src.maisaka.runtime import MaisakaHeartFlowChatting
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@@ -10,46 +11,38 @@ logger = get_logger("heartflow")
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class HeartflowManager:
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"""主心流协调器。
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当前群聊统一使用 Maisaka runtime 作为消息核心循环实现。
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"""
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"""管理 session 级别的 Maisaka 心流实例。"""
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def __init__(self) -> None:
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"""初始化心流聊天实例缓存。"""
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self.heartflow_chat_list: Dict[str, MaisakaHeartFlowChatting] = {}
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self._chat_create_locks: Dict[str, asyncio.Lock] = {}
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async def get_or_create_heartflow_chat(self, session_id: str) -> MaisakaHeartFlowChatting:
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"""获取或创建群聊心流实例。
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Args:
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session_id: 聊天会话 ID。
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Returns:
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MaisakaHeartFlowChatting: 当前会话绑定的 Maisaka runtime。
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"""
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"""获取或创建指定会话对应的 Maisaka runtime。"""
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try:
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if chat := self.heartflow_chat_list.get(session_id):
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return chat
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chat_session = chat_manager.get_session_by_session_id(session_id)
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if not chat_session:
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raise ValueError(f"未找到 session_id={session_id} 的聊天流")
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new_chat = MaisakaHeartFlowChatting(session_id=session_id)
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await new_chat.start()
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self.heartflow_chat_list[session_id] = new_chat
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return new_chat
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except Exception as e:
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logger.error(f"创建心流聊天 {session_id} 失败: {e}", exc_info=True)
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create_lock = self._chat_create_locks.setdefault(session_id, asyncio.Lock())
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async with create_lock:
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if chat := self.heartflow_chat_list.get(session_id):
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return chat
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chat_session = chat_manager.get_session_by_session_id(session_id)
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if not chat_session:
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raise ValueError(f"未找到 session_id={session_id} 对应的聊天流")
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new_chat = MaisakaHeartFlowChatting(session_id=session_id)
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await new_chat.start()
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self.heartflow_chat_list[session_id] = new_chat
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return new_chat
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except Exception as exc:
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logger.error(f"创建心流聊天 {session_id} 失败: {exc}", exc_info=True)
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traceback.print_exc()
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raise e
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raise
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def adjust_talk_frequency(self, session_id: str, frequency: float) -> None:
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"""调整指定聊天流的说话频率。
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Args:
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session_id: 聊天会话 ID。
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frequency: 目标频率系数。
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"""
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"""调整指定聊天流的说话频率。"""
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chat = self.heartflow_chat_list.get(session_id)
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if chat:
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chat.adjust_talk_frequency(frequency)
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256
src/chat/replyer/maisaka_generator.py
Normal file
256
src/chat/replyer/maisaka_generator.py
Normal file
@@ -0,0 +1,256 @@
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple
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import random
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import time
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from src.chat.message_receive.chat_manager import BotChatSession
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from src.chat.message_receive.message import SessionMessage
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from src.common.data_models.reply_generation_data_models import (
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GenerationMetrics,
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LLMCompletionResult,
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ReplyGenerationResult,
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)
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from src.common.logger import get_logger
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from src.common.prompt_i18n import load_prompt
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from src.config.config import global_config
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from src.core.types import ActionInfo
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from src.services.llm_service import LLMServiceClient
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from src.maisaka.message_adapter import (
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get_message_kind,
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get_message_role,
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get_message_text,
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is_perception_message,
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parse_speaker_content,
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)
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logger = get_logger("maisaka_replyer")
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class MaisakaReplyGenerator:
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"""Maisaka 可见回复生成器。"""
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def __init__(
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self,
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chat_stream: Optional[BotChatSession] = None,
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request_type: str = "maisaka_replyer",
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) -> None:
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self.chat_stream = chat_stream
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self.request_type = request_type
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self.express_model = LLMServiceClient(
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task_name="replyer",
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request_type=request_type,
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)
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self._personality_prompt = self._build_personality_prompt()
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def _build_personality_prompt(self) -> str:
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"""构建回复器使用的人设描述。"""
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try:
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bot_name = global_config.bot.nickname
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alias_names = global_config.bot.alias_names
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bot_aliases = f"，也有人叫你{','.join(alias_names)}" if alias_names else ""
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prompt_personality = global_config.personality.personality
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if (
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hasattr(global_config.personality, "states")
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and global_config.personality.states
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and hasattr(global_config.personality, "state_probability")
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and global_config.personality.state_probability > 0
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and random.random() < global_config.personality.state_probability
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):
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prompt_personality = random.choice(global_config.personality.states)
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return f"你的名字是{bot_name}{bot_aliases}，你{prompt_personality};"
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except Exception as exc:
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logger.warning(f"Failed to build Maisaka personality prompt: {exc}")
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return "你的名字是麦麦，你是一个活泼可爱的 AI 助手。"
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@staticmethod
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def _normalize_content(content: str, limit: int = 500) -> str:
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normalized = " ".join((content or "").split())
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if len(normalized) > limit:
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return normalized[:limit] + "..."
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return normalized
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@staticmethod
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def _format_message_time(message: SessionMessage) -> str:
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return message.timestamp.strftime("%H:%M:%S")
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@staticmethod
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def _extract_visible_assistant_reply(message: SessionMessage) -> str:
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if is_perception_message(message):
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return ""
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return ""
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def _extract_guided_bot_reply(self, message: SessionMessage) -> str:
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speaker_name, body = parse_speaker_content(get_message_text(message).strip())
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bot_nickname = global_config.bot.nickname.strip() or "Bot"
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if speaker_name == bot_nickname:
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return self._normalize_content(body.strip())
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return ""
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@staticmethod
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def _split_user_message_segments(raw_content: str) -> list[tuple[Optional[str], str]]:
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"""按说话人拆分用户消息。"""
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segments: list[tuple[Optional[str], str]] = []
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current_speaker: Optional[str] = None
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current_lines: list[str] = []
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for raw_line in raw_content.splitlines():
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speaker_name, content_body = parse_speaker_content(raw_line)
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if speaker_name is not None:
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if current_lines:
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segments.append((current_speaker, "\n".join(current_lines)))
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current_speaker = speaker_name
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current_lines = [content_body]
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continue
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current_lines.append(raw_line)
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if current_lines:
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segments.append((current_speaker, "\n".join(current_lines)))
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return segments
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def _format_chat_history(self, messages: list[SessionMessage]) -> str:
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"""格式化回复器使用的可见聊天历史。"""
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bot_nickname = global_config.bot.nickname.strip() or "Bot"
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parts: list[str] = []
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for message in messages:
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role = get_message_role(message)
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timestamp = self._format_message_time(message)
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if role == "user":
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guided_reply = self._extract_guided_bot_reply(message)
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if guided_reply:
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parts.append(f"{timestamp} {bot_nickname}(you): {guided_reply}")
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continue
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raw_content = get_message_text(message)
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for speaker_name, content_body in self._split_user_message_segments(raw_content):
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content = self._normalize_content(content_body)
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if not content:
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continue
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visible_speaker = speaker_name or global_config.maisaka.user_name.strip() or "User"
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parts.append(f"{timestamp} {visible_speaker}: {content}")
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continue
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if role == "assistant":
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visible_reply = self._extract_visible_assistant_reply(message)
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if visible_reply:
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parts.append(f"{timestamp} {bot_nickname}(you): {visible_reply}")
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return "\n".join(parts)
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def _build_prompt(self, chat_history: List[SessionMessage], reply_reason: str) -> str:
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"""构建 Maisaka replyer 提示词。"""
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current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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formatted_history = self._format_chat_history(chat_history)
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try:
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system_prompt = load_prompt(
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"maidairy_replyer",
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bot_name=global_config.bot.nickname,
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identity=self._personality_prompt,
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reply_style=global_config.personality.reply_style,
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)
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except Exception:
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system_prompt = "你是一个友好的 AI 助手，请根据用户的想法生成自然的回复。"
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user_prompt = (
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f"当前时间：{current_time}\n\n"
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f"【聊天记录】\n{formatted_history}\n\n"
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f"【你的想法】\n{reply_reason}\n\n"
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"现在，你说："
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)
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return f"System: {system_prompt}\n\nUser: {user_prompt}"
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async def generate_reply_with_context(
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self,
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extra_info: str = "",
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reply_reason: str = "",
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available_actions: Optional[Dict[str, ActionInfo]] = None,
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chosen_actions: Optional[List[object]] = None,
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enable_tool: bool = True,
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from_plugin: bool = True,
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stream_id: Optional[str] = None,
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reply_message: Optional[SessionMessage] = None,
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reply_time_point: Optional[float] = None,
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think_level: int = 1,
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unknown_words: Optional[List[str]] = None,
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log_reply: bool = True,
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chat_history: Optional[List[SessionMessage]] = None,
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) -> Tuple[bool, ReplyGenerationResult]:
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"""结合上下文生成 Maisaka 的最终可见回复。"""
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del available_actions
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del chosen_actions
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del enable_tool
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del extra_info
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del from_plugin
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del log_reply
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del reply_time_point
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del think_level
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del unknown_words
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result = ReplyGenerationResult()
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if not reply_reason or chat_history is None:
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result.error_message = "reply_reason or chat_history is empty"
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return False, result
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logger.info(
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f"Maisaka replyer start: stream_id={stream_id} reply_reason={reply_reason!r} "
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f"history_size={len(chat_history)} target_message_id="
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f"{reply_message.message_id if reply_message else None}"
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)
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filtered_history = [
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message
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for message in chat_history
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if get_message_role(message) != "system" and get_message_kind(message) != "perception"
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]
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prompt = self._build_prompt(filtered_history, reply_reason)
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result.completion.request_prompt = prompt
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if global_config.debug.show_replyer_prompt:
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logger.info(f"\nMaisaka replyer prompt:\n{prompt}\n")
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started_at = time.perf_counter()
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try:
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generation_result = await self.express_model.generate_response(prompt)
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except Exception as exc:
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logger.exception("Maisaka replyer call failed")
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result.error_message = str(exc)
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result.metrics = GenerationMetrics(
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overall_ms=round((time.perf_counter() - started_at) * 1000, 2),
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)
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return False, result
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response_text = (generation_result.response or "").strip()
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result.success = bool(response_text)
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result.completion = LLMCompletionResult(
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request_prompt=prompt,
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response_text=response_text,
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reasoning_text=generation_result.reasoning or "",
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model_name=generation_result.model_name or "",
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tool_calls=generation_result.tool_calls or [],
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)
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result.metrics = GenerationMetrics(
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overall_ms=round((time.perf_counter() - started_at) * 1000, 2),
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)
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if global_config.debug.show_replyer_reasoning and result.completion.reasoning_text:
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logger.info(f"Maisaka replyer reasoning:\n{result.completion.reasoning_text}")
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if not result.success:
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result.error_message = "replyer returned empty content"
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logger.warning("Maisaka replyer returned empty content")
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return False, result
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logger.info(
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f"Maisaka replyer success: response_text={response_text!r} "
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f"overall_ms={result.metrics.overall_ms}"
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)
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result.text_fragments = [response_text]
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return True, result
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@@ -1,65 +1,82 @@
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from typing import Dict, Optional
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from typing import TYPE_CHECKING, Any, Dict, Optional
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from src.common.logger import get_logger
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from src.chat.message_receive.chat_manager import BotChatSession, chat_manager as _chat_manager
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from src.chat.replyer.group_generator import DefaultReplyer
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from src.chat.replyer.private_generator import PrivateReplyer
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from src.common.logger import get_logger
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if TYPE_CHECKING:
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from src.chat.replyer.group_generator import DefaultReplyer
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from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator
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from src.chat.replyer.private_generator import PrivateReplyer
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logger = get_logger("ReplyerManager")
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class ReplyerManager:
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def __init__(self):
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self._repliers: Dict[str, DefaultReplyer | PrivateReplyer] = {}
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"""统一管理不同类型的回复生成器。"""
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def __init__(self) -> None:
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self._repliers: Dict[str, Any] = {}
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def get_replyer(
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self,
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chat_stream: Optional[BotChatSession] = None,
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chat_id: Optional[str] = None,
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request_type: str = "replyer",
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) -> Optional[DefaultReplyer | PrivateReplyer]:
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"""
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获取或创建回复器实例。
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model_configs 仅在首次为某个 chat_id/stream_id 创建实例时有效。
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后续调用将返回已缓存的实例，忽略 model_configs 参数。
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"""
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replyer_type: str = "default",
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) -> Optional["DefaultReplyer | MaisakaReplyGenerator | PrivateReplyer"]:
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"""按会话和 replyer 类型获取实例。"""
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stream_id = chat_stream.session_id if chat_stream else chat_id
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if not stream_id:
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logger.warning("[ReplyerManager] 缺少 stream_id，无法获取回复器。")
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logger.warning("[ReplyerManager] 缺少 stream_id，无法获取 replyer")
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return None
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# 如果已有缓存实例，直接返回
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if stream_id in self._repliers:
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logger.debug(f"[ReplyerManager] 为 stream_id '{stream_id}' 返回已存在的回复器实例。")
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return self._repliers[stream_id]
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cache_key = f"{replyer_type}:{stream_id}"
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if cache_key in self._repliers:
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logger.info(f"[ReplyerManager] 命中缓存 replyer: cache_key={cache_key}")
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return self._repliers[cache_key]
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# 如果没有缓存，则创建新实例（首次初始化）
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logger.debug(f"[ReplyerManager] 为 stream_id '{stream_id}' 创建新的回复器实例并缓存。")
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target_stream = chat_stream
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target_stream = chat_stream or _chat_manager.get_session_by_session_id(stream_id)
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if not target_stream:
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target_stream = _chat_manager.get_session_by_session_id(stream_id)
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if not target_stream:
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logger.warning(f"[ReplyerManager] 未找到 stream_id='{stream_id}' 的聊天流，无法创建回复器。")
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logger.warning(f"[ReplyerManager] 未找到会话，stream_id={stream_id}")
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return None
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# model_configs 只在此时（初始化时）生效
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if target_stream.is_group_session:
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replyer = DefaultReplyer(
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chat_stream=target_stream,
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request_type=request_type,
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)
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else:
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replyer = PrivateReplyer(
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chat_stream=target_stream,
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request_type=request_type,
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)
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logger.info(
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f"[ReplyerManager] 开始创建 replyer: cache_key={cache_key}, "
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f"replyer_type={replyer_type}, is_group_session={target_stream.is_group_session}"
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)
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self._repliers[stream_id] = replyer
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try:
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if replyer_type == "maisaka":
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logger.info("[ReplyerManager] importing MaisakaReplyGenerator")
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from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator
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replyer = MaisakaReplyGenerator(
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chat_stream=target_stream,
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request_type=request_type,
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)
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elif target_stream.is_group_session:
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logger.info("[ReplyerManager] importing DefaultReplyer")
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from src.chat.replyer.group_generator import DefaultReplyer
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replyer = DefaultReplyer(
|
||||
chat_stream=target_stream,
|
||||
request_type=request_type,
|
||||
)
|
||||
else:
|
||||
logger.info("[ReplyerManager] importing PrivateReplyer")
|
||||
from src.chat.replyer.private_generator import PrivateReplyer
|
||||
|
||||
replyer = PrivateReplyer(
|
||||
chat_stream=target_stream,
|
||||
request_type=request_type,
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(f"[ReplyerManager] 创建 replyer 失败: cache_key={cache_key}")
|
||||
raise
|
||||
|
||||
self._repliers[cache_key] = replyer
|
||||
logger.info(f"[ReplyerManager] replyer 创建完成: cache_key={cache_key}")
|
||||
return replyer
|
||||
|
||||
|
||||
# 创建一个全局实例
|
||||
replyer_manager = ReplyerManager()
|
||||
|
||||
Reference in New Issue
Block a user