feat：对HFC进行巨大重构，采用新架构

2025-05-12 11:49:14 +08:00
parent e5a756f156
commit 05f0aaa6d7
33 changed files with 2221 additions and 1738 deletions
--- a/src/plugins/heartFC_chat/expressors/default_expressor.py
+++ b/src/plugins/heartFC_chat/expressors/default_expressor.py
@@ -0,0 +1,319 @@
+import time
+import traceback
+from typing import List, Optional, Dict, Any
+from src.plugins.chat.message import MessageRecv, MessageThinking, MessageSending
+from src.plugins.chat.message import Seg  # Local import needed after move
+from src.plugins.chat.message import UserInfo
+from src.plugins.chat.chat_stream import chat_manager
+from src.common.logger_manager import get_logger
+from src.plugins.models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.plugins.chat.utils_image import image_path_to_base64  # Local import needed after move
+from src.plugins.utils.timer_calculator import Timer  # <--- Import Timer
+from src.plugins.emoji_system.emoji_manager import emoji_manager
+from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder
+from src.plugins.heartFC_chat.heartFC_sender import HeartFCSender
+from src.plugins.chat.utils import process_llm_response
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from src.plugins.moods.moods import MoodManager
+from src.heart_flow.utils_chat import get_chat_type_and_target_info
+from src.plugins.chat.chat_stream import ChatStream
+
+logger = get_logger("expressor")
+
+
+class DefaultExpressor:
+    def __init__(self, chat_id: str):
+        self.log_prefix = "expressor"
+        self.express_model = LLMRequest(
+            model=global_config.llm_normal,
+            temperature=global_config.llm_normal["temp"],
+            max_tokens=256,
+            request_type="response_heartflow",
+        )
+        self.heart_fc_sender = HeartFCSender()
+
+        self.chat_id = chat_id
+        self.chat_stream: Optional[ChatStream] = None
+        self.is_group_chat = True
+        self.chat_target_info = None
+
+    async def initialize(self):
+        self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.chat_id)
+
+    async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]) -> Optional[str]:
+        """创建思考消息 (尝试锚定到 anchor_message)"""
+        if not anchor_message or not anchor_message.chat_stream:
+            logger.error(f"{self.log_prefix} 无法创建思考消息，缺少有效的锚点消息或聊天流。")
+            return None
+
+        chat = anchor_message.chat_stream
+        messageinfo = anchor_message.message_info
+        bot_user_info = UserInfo(
+            user_id=global_config.BOT_QQ,
+            user_nickname=global_config.BOT_NICKNAME,
+            platform=messageinfo.platform,
+        )
+        logger.debug(f"创建思考消息：{anchor_message}")
+        logger.debug(f"创建思考消息chat：{chat}")
+        logger.debug(f"创建思考消息bot_user_info：{bot_user_info}")
+        logger.debug(f"创建思考消息messageinfo：{messageinfo}")
+
+        thinking_time_point = round(time.time(), 2)
+        thinking_id = "mt" + str(thinking_time_point)
+        thinking_message = MessageThinking(
+            message_id=thinking_id,
+            chat_stream=chat,
+            bot_user_info=bot_user_info,
+            reply=anchor_message,  # 回复的是锚点消息
+            thinking_start_time=thinking_time_point,
+        )
+        logger.debug(f"创建思考消息thinking_message：{thinking_message}")
+        # Access MessageManager directly (using heart_fc_sender)
+        await self.heart_fc_sender.register_thinking(thinking_message)
+        return thinking_id
+
+    async def deal_reply(
+        self,
+        cycle_timers: dict,
+        action_data: Dict[str, Any],
+        reasoning: str,
+        anchor_message: MessageRecv,
+    ) -> tuple[bool, str]:
+        # 创建思考消息
+        thinking_id = await self._create_thinking_message(anchor_message)
+        if not thinking_id:
+            raise Exception("无法创建思考消息")
+
+        try:
+            has_sent_something = False
+
+            # 处理文本部分
+            text_part = action_data.get("text", [])
+            if text_part:
+                with Timer("生成回复", cycle_timers):
+                    # 可以保留原有的文本处理逻辑或进行适当调整
+                    reply = await self.express(
+                        in_mind_reply=text_part,
+                        anchor_message=anchor_message,
+                        thinking_id=thinking_id,
+                        reason=reasoning,
+                    )
+
+                if reply:
+                    with Timer("发送文本消息", cycle_timers):
+                        await self._send_response_messages(
+                            anchor_message=anchor_message,
+                            thinking_id=thinking_id,
+                            response_set=reply,
+                        )
+                    has_sent_something = True
+                else:
+                    logger.warning(f"{self.log_prefix} 文本回复生成失败")
+
+            # 处理表情部分
+            emoji_keyword = action_data.get("emojis", [])
+            if emoji_keyword:
+                await self._handle_emoji(anchor_message, [], emoji_keyword)
+                has_sent_something = True
+
+            if not has_sent_something:
+                logger.warning(f"{self.log_prefix} 回复动作未包含任何有效内容")
+
+            return has_sent_something, thinking_id
+
+        except Exception as e:
+            logger.error(f"回复失败: {e}")
+            return False, thinking_id
+
+        # --- 回复器 (Replier) 的定义 --- #
+
+    async def express(
+        self,
+        in_mind_reply: str,
+        reason: str,
+        anchor_message: MessageRecv,
+        thinking_id: str,
+    ) -> Optional[List[str]]:
+        """
+        回复器 (Replier): 核心逻辑，负责生成回复文本。
+        (已整合原 HeartFCGenerator 的功能)
+        """
+        try:
+            # 1. 获取情绪影响因子并调整模型温度
+            arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
+            current_temp = global_config.llm_normal["temp"] * arousal_multiplier
+            self.express_model.temperature = current_temp  # 动态调整温度
+
+            # 2. 获取信息捕捉器
+            info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
+
+            # --- Determine sender_name for private chat ---
+            sender_name_for_prompt = "某人"  # Default for group or if info unavailable
+            if not self.is_group_chat and self.chat_target_info:
+                # Prioritize person_name, then nickname
+                sender_name_for_prompt = (
+                    self.chat_target_info.get("person_name")
+                    or self.chat_target_info.get("user_nickname")
+                    or sender_name_for_prompt
+                )
+            # --- End determining sender_name ---
+
+            # 3. 构建 Prompt
+            with Timer("构建Prompt", {}):  # 内部计时器，可选保留
+                prompt = await prompt_builder.build_prompt(
+                    build_mode="focus",
+                    chat_stream=self.chat_stream,  # Pass the stream object
+                    in_mind_reply=in_mind_reply,
+                    reason=reason,
+                    current_mind_info="",
+                    structured_info="",
+                    sender_name=sender_name_for_prompt,  # Pass determined name
+                )
+
+            # 4. 调用 LLM 生成回复
+            content = None
+            reasoning_content = None
+            model_name = "unknown_model"
+            if not prompt:
+                logger.error(f"{self.log_prefix}[Replier-{thinking_id}] Prompt 构建失败，无法生成回复。")
+                return None
+
+            try:
+                with Timer("LLM生成", {}):  # 内部计时器，可选保留
+                    content, reasoning_content, model_name = await self.express_model.generate_response(prompt)
+                # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n生成回复: {content}\n")
+                # 捕捉 LLM 输出信息
+                info_catcher.catch_after_llm_generated(
+                    prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=model_name
+                )
+
+            except Exception as llm_e:
+                # 精简报错信息
+                logger.error(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成失败: {llm_e}")
+                return None  # LLM 调用失败则无法生成回复
+
+            # 5. 处理 LLM 响应
+            if not content:
+                logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成了空内容。")
+                return None
+
+            processed_response = process_llm_response(content)
+
+            if not processed_response:
+                logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] 处理后的回复为空。")
+                return None
+
+            return processed_response
+
+        except Exception as e:
+            logger.error(f"{self.log_prefix}[Replier-{thinking_id}] 回复生成意外失败: {e}")
+            traceback.print_exc()
+            return None
+
+        # --- 发送器 (Sender) --- #
+
+    async def _send_response_messages(
+        self, anchor_message: Optional[MessageRecv], response_set: List[str], thinking_id: str
+    ) -> Optional[MessageSending]:
+        """发送回复消息 (尝试锚定到 anchor_message)，使用 HeartFCSender"""
+        if not anchor_message or not anchor_message.chat_stream:
+            logger.error(f"{self.log_prefix} 无法发送回复，缺少有效的锚点消息或聊天流。")
+            return None
+
+        chat = self.chat_stream
+        chat_id = self.chat_id
+        stream_name = chat_manager.get_stream_name(chat_id) or chat_id  # 获取流名称用于日志
+
+        # 检查思考过程是否仍在进行，并获取开始时间
+        thinking_start_time = await self.heart_fc_sender.get_thinking_start_time(chat_id, thinking_id)
+
+        if thinking_start_time is None:
+            logger.warning(f"[{stream_name}] {thinking_id} 思考过程未找到或已结束，无法发送回复。")
+            return None
+
+        mark_head = False
+        first_bot_msg: Optional[MessageSending] = None
+        reply_message_ids = []  # 记录实际发送的消息ID
+        bot_user_info = UserInfo(
+            user_id=global_config.BOT_QQ,
+            user_nickname=global_config.BOT_NICKNAME,
+            platform=chat.platform,
+        )
+
+        for i, msg_text in enumerate(response_set):
+            # 为每个消息片段生成唯一ID
+            part_message_id = f"{thinking_id}_{i}"
+            message_segment = Seg(type="text", data=msg_text)
+            bot_message = MessageSending(
+                message_id=part_message_id,  # 使用片段的唯一ID
+                chat_stream=chat,
+                bot_user_info=bot_user_info,
+                sender_info=anchor_message.message_info.user_info,
+                message_segment=message_segment,
+                reply=anchor_message,  # 回复原始锚点
+                is_head=not mark_head,
+                is_emoji=False,
+                thinking_start_time=thinking_start_time,  # 传递原始思考开始时间
+            )
+            try:
+                if not mark_head:
+                    mark_head = True
+                    first_bot_msg = bot_message  # 保存第一个成功发送的消息对象
+                    await self.heart_fc_sender.type_and_send_message(bot_message, typing=False)
+                else:
+                    await self.heart_fc_sender.type_and_send_message(bot_message, typing=True)
+
+                reply_message_ids.append(part_message_id)  # 记录我们生成的ID
+
+            except Exception as e:
+                logger.error(
+                    f"{self.log_prefix}[Sender-{thinking_id}] 发送回复片段 {i} ({part_message_id}) 时失败: {e}"
+                )
+                # 这里可以选择是继续发送下一个片段还是中止
+
+        # 在尝试发送完所有片段后，完成原始的 thinking_id 状态
+        try:
+            await self.heart_fc_sender.complete_thinking(chat_id, thinking_id)
+        except Exception as e:
+            logger.error(f"{self.log_prefix}[Sender-{thinking_id}] 完成思考状态 {thinking_id} 时出错: {e}")
+
+        return first_bot_msg  # 返回第一个成功发送的消息对象
+
+    async def _handle_emoji(self, anchor_message: Optional[MessageRecv], response_set: List[str], send_emoji: str = ""):
+        """处理表情包 (尝试锚定到 anchor_message)，使用 HeartFCSender"""
+        if not anchor_message or not anchor_message.chat_stream:
+            logger.error(f"{self.log_prefix} 无法处理表情包，缺少有效的锚点消息或聊天流。")
+            return
+
+        chat = anchor_message.chat_stream
+
+        emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji)
+
+        if emoji_raw:
+            emoji_path, description = emoji_raw
+
+            emoji_cq = image_path_to_base64(emoji_path)
+            thinking_time_point = round(time.time(), 2)  # 用于唯一ID
+            message_segment = Seg(type="emoji", data=emoji_cq)
+            bot_user_info = UserInfo(
+                user_id=global_config.BOT_QQ,
+                user_nickname=global_config.BOT_NICKNAME,
+                platform=anchor_message.message_info.platform,
+            )
+            bot_message = MessageSending(
+                message_id="me" + str(thinking_time_point),  # 表情消息的唯一ID
+                chat_stream=chat,
+                bot_user_info=bot_user_info,
+                sender_info=anchor_message.message_info.user_info,
+                message_segment=message_segment,
+                reply=anchor_message,  # 回复原始锚点
+                is_head=False,  # 表情通常不是头部消息
+                is_emoji=True,
+                # 不需要 thinking_start_time
+            )
+
+            try:
+                await self.heart_fc_sender.send_and_store(bot_message)
+            except Exception as e:
+                logger.error(f"{self.log_prefix} 发送表情包 {bot_message.message_info.message_id} 时失败: {e}")