""" 回复器API模块 提供回复器相关功能，采用标准Python包设计模式 使用方式： from src.plugin_system.apis import generator_api replyer = generator_api.get_replyer(chat_stream) success, reply_set, _ = await generator_api.generate_reply(chat_stream, action_data, reasoning) """ import traceback from typing import Tuple, Any, Dict, List, Optional from rich.traceback import install from src.common.logger import get_logger from src.chat.replyer.default_generator import DefaultReplyer from src.chat.message_receive.chat_stream import ChatStream from src.chat.utils.utils import process_llm_response from src.chat.replyer.replyer_manager import replyer_manager from src.plugin_system.base.component_types import ActionInfo install(extra_lines=3) logger = get_logger("generator_api") # ============================================================================= # 回复器获取API函数 # ============================================================================= def get_replyer( chat_stream: Optional[ChatStream] = None, chat_id: Optional[str] = None, model_configs: Optional[List[Dict[str, Any]]] = None, request_type: str = "replyer", ) -> Optional[DefaultReplyer]: """获取回复器对象 优先使用chat_stream，如果没有则使用chat_id直接查找。 使用 ReplyerManager 来管理实例，避免重复创建。 Args: chat_stream: 聊天流对象（优先） chat_id: 聊天ID（实际上就是stream_id） model_configs: 模型配置列表 request_type: 请求类型 Returns: Optional[DefaultReplyer]: 回复器对象，如果获取失败则返回None Raises: ValueError: chat_stream 和 chat_id 均为空 """ if not chat_id and not chat_stream: raise ValueError("chat_stream 和 chat_id 不可均为空") try: logger.debug(f"[GeneratorAPI] 正在获取回复器，chat_id: {chat_id}, chat_stream: {'有' if chat_stream else '无'}") return replyer_manager.get_replyer( chat_stream=chat_stream, chat_id=chat_id, model_configs=model_configs, request_type=request_type, ) except Exception as e: logger.error(f"[GeneratorAPI] 获取回复器时发生意外错误: {e}", exc_info=True) traceback.print_exc() return None # ============================================================================= # 回复生成API函数 # ============================================================================= async def generate_reply( chat_stream: Optional[ChatStream] = None, chat_id: Optional[str] = None, action_data: Optional[Dict[str, Any]] = None, reply_to: str = "", extra_info: str = "", available_actions: Optional[Dict[str, ActionInfo]] = None, enable_tool: bool = False, enable_splitter: bool = True, enable_chinese_typo: bool = True, return_prompt: bool = False, model_configs: Optional[List[Dict[str, Any]]] = None, request_type: str = "", enable_timeout: bool = False, ) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]: """生成回复 Args: chat_stream: 聊天流对象（优先） chat_id: 聊天ID（备用） action_data: 动作数据 enable_splitter: 是否启用消息分割器 enable_chinese_typo: 是否启用错字生成器 return_prompt: 是否返回提示词 Returns: Tuple[bool, List[Tuple[str, Any]], Optional[str]]: (是否成功, 回复集合, 提示词) """ try: # 获取回复器 replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs, request_type=request_type) if not replyer: logger.error("[GeneratorAPI] 无法获取回复器") return False, [], None logger.debug("[GeneratorAPI] 开始生成回复") if not reply_to: reply_to = action_data.get("reply_to", "") if not extra_info and action_data: extra_info = action_data.get("extra_info", "") # 调用回复器生成回复 success, content, prompt = await replyer.generate_reply_with_context( reply_to=reply_to, extra_info=extra_info, available_actions=available_actions, enable_timeout=enable_timeout, enable_tool=enable_tool, ) reply_set = [] if content: reply_set = await process_human_text(content, enable_splitter, enable_chinese_typo) if success: logger.debug(f"[GeneratorAPI] 回复生成成功，生成了 {len(reply_set)} 个回复项") else: logger.warning("[GeneratorAPI] 回复生成失败") if return_prompt: return success, reply_set, prompt else: return success, reply_set, None except ValueError as ve: raise ve except Exception as e: logger.error(f"[GeneratorAPI] 生成回复时出错: {e}") logger.error(traceback.format_exc()) return False, [], None async def rewrite_reply( chat_stream: Optional[ChatStream] = None, reply_data: Optional[Dict[str, Any]] = None, chat_id: Optional[str] = None, enable_splitter: bool = True, enable_chinese_typo: bool = True, model_configs: Optional[List[Dict[str, Any]]] = None, raw_reply: str = "", reason: str = "", reply_to: str = "", ) -> Tuple[bool, List[Tuple[str, Any]]]: """重写回复 Args: chat_stream: 聊天流对象（优先） reply_data: 回复数据字典（备用，当其他参数缺失时从此获取） chat_id: 聊天ID（备用） enable_splitter: 是否启用消息分割器 enable_chinese_typo: 是否启用错字生成器 model_configs: 模型配置列表 raw_reply: 原始回复内容 reason: 回复原因 reply_to: 回复对象 Returns: Tuple[bool, List[Tuple[str, Any]]]: (是否成功, 回复集合) """ try: # 获取回复器 replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs) if not replyer: logger.error("[GeneratorAPI] 无法获取回复器") return False, [] logger.info("[GeneratorAPI] 开始重写回复") # 如果参数缺失，从reply_data中获取 if reply_data: raw_reply = raw_reply or reply_data.get("raw_reply", "") reason = reason or reply_data.get("reason", "") reply_to = reply_to or reply_data.get("reply_to", "") # 调用回复器重写回复 success, content = await replyer.rewrite_reply_with_context( raw_reply=raw_reply, reason=reason, reply_to=reply_to, ) reply_set = [] if content: reply_set = await process_human_text(content, enable_splitter, enable_chinese_typo) if success: logger.info(f"[GeneratorAPI] 重写回复成功，生成了 {len(reply_set)} 个回复项") else: logger.warning("[GeneratorAPI] 重写回复失败") return success, reply_set except ValueError as ve: raise ve except Exception as e: logger.error(f"[GeneratorAPI] 重写回复时出错: {e}") return False, [] async def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> List[Tuple[str, Any]]: """将文本处理为更拟人化的文本 Args: content: 文本内容 enable_splitter: 是否启用消息分割器 enable_chinese_typo: 是否启用错字生成器 """ if not isinstance(content, str): raise ValueError("content 必须是字符串类型") try: processed_response = process_llm_response(content, enable_splitter, enable_chinese_typo) reply_set = [] for text in processed_response: reply_seg = ("text", text) reply_set.append(reply_seg) return reply_set except Exception as e: logger.error(f"[GeneratorAPI] 处理人形文本时出错: {e}") return []