重构整个插件系统，尝试恢复可启动性，新增插件系统maibot-plugin-sdk依赖
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DrSmoothl
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src/services/generator_service.py
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256
src/services/generator_service.py
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"""
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回复器服务模块
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提供回复器相关的核心功能。
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"""
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import traceback
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import time
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from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
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from rich.traceback import install
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from src.chat.logger.plan_reply_logger import PlanReplyLogger
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from src.chat.message_receive.chat_manager import BotChatSession
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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.chat.replyer.replyer_manager import replyer_manager
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from src.chat.utils.utils import process_llm_response
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from src.common.data_models.message_data_model import ReplySetModel
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from src.common.logger import get_logger
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from src.core.types import ActionInfo
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if TYPE_CHECKING:
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.data_models.info_data_model import ActionPlannerInfo
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from src.common.data_models.llm_data_model import LLMGenerationDataModel
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install(extra_lines=3)
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logger = get_logger("generator_service")
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# =============================================================================
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# 回复器获取函数
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# =============================================================================
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def get_replyer(
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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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"""获取回复器对象"""
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if not chat_id and not chat_stream:
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raise ValueError("chat_stream 和 chat_id 不可均为空")
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try:
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logger.debug(f"[GeneratorService] 正在获取回复器，chat_id: {chat_id}, chat_stream: {'有' if chat_stream else '无'}")
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return replyer_manager.get_replyer(
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chat_stream=chat_stream,
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chat_id=chat_id,
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request_type=request_type,
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)
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except Exception as e:
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logger.error(f"[GeneratorService] 获取回复器时发生意外错误: {e}", exc_info=True)
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traceback.print_exc()
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return None
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# =============================================================================
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# 回复生成函数
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# =============================================================================
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async def generate_reply(
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chat_stream: Optional[BotChatSession] = None,
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chat_id: Optional[str] = None,
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action_data: Optional[Dict[str, Any]] = None,
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reply_message: Optional["DatabaseMessages"] = None,
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think_level: int = 1,
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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["ActionPlannerInfo"]] = None,
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unknown_words: Optional[List[str]] = None,
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enable_tool: bool = False,
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enable_splitter: bool = True,
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enable_chinese_typo: bool = True,
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request_type: str = "generator_api",
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from_plugin: bool = True,
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reply_time_point: Optional[float] = None,
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) -> Tuple[bool, Optional["LLMGenerationDataModel"]]:
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"""生成回复"""
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try:
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if reply_time_point is None:
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reply_time_point = time.time()
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logger.debug("[GeneratorService] 开始生成回复")
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replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
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if not replyer:
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logger.error("[GeneratorService] 无法获取回复器")
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return False, None
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if action_data:
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if not extra_info:
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extra_info = action_data.get("extra_info", "")
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if not reply_reason:
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reply_reason = action_data.get("reason", "")
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if unknown_words is None:
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uw = action_data.get("unknown_words")
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if isinstance(uw, list):
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cleaned: List[str] = []
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for item in uw:
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if isinstance(item, str):
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s = item.strip()
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if s:
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cleaned.append(s)
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if cleaned:
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unknown_words = cleaned
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success, llm_response = await replyer.generate_reply_with_context(
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extra_info=extra_info,
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available_actions=available_actions,
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chosen_actions=chosen_actions,
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enable_tool=enable_tool,
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reply_message=reply_message,
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reply_reason=reply_reason,
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unknown_words=unknown_words,
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think_level=think_level,
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from_plugin=from_plugin,
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stream_id=chat_stream.session_id if chat_stream else chat_id,
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reply_time_point=reply_time_point,
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log_reply=False,
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)
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if not success:
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logger.warning("[GeneratorService] 回复生成失败")
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return False, None
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reply_set: Optional[ReplySetModel] = None
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if content := llm_response.content:
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processed_response = process_llm_response(content, enable_splitter, enable_chinese_typo)
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llm_response.processed_output = processed_response
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reply_set = ReplySetModel()
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for text in processed_response:
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reply_set.add_text_content(text)
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llm_response.reply_set = reply_set
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logger.debug(f"[GeneratorService] 回复生成成功，生成了 {len(reply_set) if reply_set else 0} 个回复项")
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try:
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PlanReplyLogger.log_reply(
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chat_id=chat_stream.session_id if chat_stream else (chat_id or ""),
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prompt=llm_response.prompt or "",
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output=llm_response.content,
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processed_output=llm_response.processed_output,
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model=llm_response.model,
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timing=llm_response.timing,
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reasoning=llm_response.reasoning,
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think_level=think_level,
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success=True,
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)
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except Exception:
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logger.exception("[GeneratorService] 记录reply日志失败")
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return success, llm_response
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except ValueError as ve:
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raise ve
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except UserWarning as uw:
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logger.warning(f"[GeneratorService] 中断了生成: {uw}")
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return False, None
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except Exception as e:
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logger.error(f"[GeneratorService] 生成回复时出错: {e}")
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logger.error(traceback.format_exc())
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return False, None
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async def rewrite_reply(
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chat_stream: Optional[BotChatSession] = None,
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reply_data: Optional[Dict[str, Any]] = None,
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chat_id: Optional[str] = None,
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enable_splitter: bool = True,
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enable_chinese_typo: bool = True,
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raw_reply: str = "",
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reason: str = "",
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reply_to: str = "",
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request_type: str = "generator_api",
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) -> Tuple[bool, Optional["LLMGenerationDataModel"]]:
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"""重写回复"""
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try:
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replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
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if not replyer:
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logger.error("[GeneratorService] 无法获取回复器")
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return False, None
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logger.info("[GeneratorService] 开始重写回复")
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if reply_data:
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raw_reply = raw_reply or reply_data.get("raw_reply", "")
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reason = reason or reply_data.get("reason", "")
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reply_to = reply_to or reply_data.get("reply_to", "")
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success, llm_response = await replyer.rewrite_reply_with_context(
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raw_reply=raw_reply,
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reason=reason,
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reply_to=reply_to,
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)
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reply_set: Optional[ReplySetModel] = None
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if success and llm_response and (content := llm_response.content):
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reply_set = process_human_text(content, enable_splitter, enable_chinese_typo)
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llm_response.reply_set = reply_set
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if success:
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logger.info(f"[GeneratorService] 重写回复成功，生成了 {len(reply_set) if reply_set else 0} 个回复项")
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else:
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logger.warning("[GeneratorService] 重写回复失败")
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return success, llm_response
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except ValueError as ve:
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raise ve
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except Exception as e:
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logger.error(f"[GeneratorService] 重写回复时出错: {e}")
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return False, None
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def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> Optional[ReplySetModel]:
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"""将文本处理为更拟人化的文本"""
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if not isinstance(content, str):
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raise ValueError("content 必须是字符串类型")
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try:
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reply_set = ReplySetModel()
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processed_response = process_llm_response(content, enable_splitter, enable_chinese_typo)
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for text in processed_response:
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reply_set.add_text_content(text)
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return reply_set
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except Exception as e:
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logger.error(f"[GeneratorService] 处理人形文本时出错: {e}")
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return None
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async def generate_response_custom(
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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 = "generator_api",
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prompt: str = "",
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) -> Optional[str]:
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replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
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if not replyer:
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logger.error("[GeneratorService] 无法获取回复器")
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return None
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try:
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logger.debug("[GeneratorService] 开始生成自定义回复")
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response, _, _, _ = await replyer.llm_generate_content(prompt)
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if response:
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logger.debug("[GeneratorService] 自定义回复生成成功")
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return response
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else:
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logger.warning("[GeneratorService] 自定义回复生成失败")
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return None
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except Exception as e:
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logger.error(f"[GeneratorService] 生成自定义回复时出错: {e}")
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return None
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