feat: 整合reasoning模式和hfc模式，统一调控（但不是很统一）

src/plugins/chat_module/heartFC_chat/reasoning_generator.py

@@ -0,0 +1,199 @@
+from typing import List, Optional, Tuple, Union
+import random
+
+from ...models.utils_model import LLMRequest
+from ....config.config import global_config
+from ...chat.message import MessageThinking
+from .reasoning_prompt_builder import prompt_builder
+from ...chat.utils import process_llm_response
+from ...utils.timer_calculater import Timer
+from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+
+# 定义日志配置
+llm_config = LogConfig(
+    # 使用消息发送专用样式
+    console_format=LLM_STYLE_CONFIG["console_format"],
+    file_format=LLM_STYLE_CONFIG["file_format"],
+)
+
+logger = get_module_logger("llm_generator", config=llm_config)
+
+
+class ResponseGenerator:
+    def __init__(self):
+        self.model_reasoning = LLMRequest(
+            model=global_config.llm_reasoning,
+            temperature=0.7,
+            max_tokens=3000,
+            request_type="response_reasoning",
+        )
+        self.model_normal = LLMRequest(
+            model=global_config.llm_normal,
+            temperature=global_config.llm_normal["temp"],
+            max_tokens=256,
+            request_type="response_reasoning",
+        )
+
+        self.model_sum = LLMRequest(
+            model=global_config.llm_summary_by_topic, temperature=0.7, max_tokens=3000, request_type="relation"
+        )
+        self.current_model_type = "r1"  # 默认使用 R1
+        self.current_model_name = "unknown model"
+
+    async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
+        """根据当前模型类型选择对应的生成函数"""
+        # 从global_config中获取模型概率值并选择模型
+        if random.random() < global_config.model_reasoning_probability:
+            self.current_model_type = "深深地"
+            current_model = self.model_reasoning
+        else:
+            self.current_model_type = "浅浅的"
+            current_model = self.model_normal
+
+        logger.info(
+            f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
+        )  # noqa: E501
+
+        model_response = await self._generate_response_with_model(message, current_model, thinking_id)
+
+        # print(f"raw_content: {model_response}")
+
+        if model_response:
+            logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
+            model_response = await self._process_response(model_response)
+
+            return model_response
+        else:
+            logger.info(f"{self.current_model_type}思考，失败")
+            return None
+
+    async def _generate_response_with_model(self, message: MessageThinking, model: LLMRequest, thinking_id: str):
+        info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
+
+        if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
+            sender_name = (
+                f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]"
+                f"{message.chat_stream.user_info.user_cardname}"
+            )
+        elif message.chat_stream.user_info.user_nickname:
+            sender_name = f"({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}"
+        else:
+            sender_name = f"用户({message.chat_stream.user_info.user_id})"
+
+        logger.debug("开始使用生成回复-2")
+        # 构建prompt
+        with Timer() as t_build_prompt:
+            prompt = await prompt_builder._build_prompt(
+                message.chat_stream,
+                message_txt=message.processed_plain_text,
+                sender_name=sender_name,
+                stream_id=message.chat_stream.stream_id,
+            )
+        logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
+
+        try:
+            content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
+
+            info_catcher.catch_after_llm_generated(
+                prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
+            )
+
+        except Exception:
+            logger.exception("生成回复时出错")
+            return None
+
+        # 保存到数据库
+        # self._save_to_db(
+        #     message=message,
+        #     sender_name=sender_name,
+        #     prompt=prompt,
+        #     content=content,
+        #     reasoning_content=reasoning_content,
+        #     # reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
+        # )
+
+        return content
+
+    # def _save_to_db(
+    #     self,
+    #     message: MessageRecv,
+    #     sender_name: str,
+    #     prompt: str,
+    #     content: str,
+    #     reasoning_content: str,
+    # ):
+    #     """保存对话记录到数据库"""
+    #     db.reasoning_logs.insert_one(
+    #         {
+    #             "time": time.time(),
+    #             "chat_id": message.chat_stream.stream_id,
+    #             "user": sender_name,
+    #             "message": message.processed_plain_text,
+    #             "model": self.current_model_name,
+    #             "reasoning": reasoning_content,
+    #             "response": content,
+    #             "prompt": prompt,
+    #         }
+    #     )
+
+    async def _get_emotion_tags(self, content: str, processed_plain_text: str):
+        """提取情感标签，结合立场和情绪"""
+        try:
+            # 构建提示词，结合回复内容、被回复的内容以及立场分析
+            prompt = f"""
+            请严格根据以下对话内容，完成以下任务：
+            1. 判断回复者对被回复者观点的直接立场：
+            - "支持"：明确同意或强化被回复者观点
+            - "反对"：明确反驳或否定被回复者观点
+            - "中立"：不表达明确立场或无关回应
+            2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
+            3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
+            4. 考虑回复者的人格设定为{global_config.personality_core}
+
+            对话示例：
+            被回复：「A就是笨」
+            回复：「A明明很聪明」 → 反对-愤怒
+
+            当前对话：
+            被回复：「{processed_plain_text}」
+            回复：「{content}」
+
+            输出要求：
+            - 只需输出"立场-情绪"结果，不要解释
+            - 严格基于文字直接表达的对立关系判断
+            """
+
+            # 调用模型生成结果
+            result, _, _ = await self.model_sum.generate_response(prompt)
+            result = result.strip()
+
+            # 解析模型输出的结果
+            if "-" in result:
+                stance, emotion = result.split("-", 1)
+                valid_stances = ["支持", "反对", "中立"]
+                valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
+                if stance in valid_stances and emotion in valid_emotions:
+                    return stance, emotion  # 返回有效的立场-情绪组合
+                else:
+                    logger.debug(f"无效立场-情感组合:{result}")
+                    return "中立", "平静"  # 默认返回中立-平静
+            else:
+                logger.debug(f"立场-情感格式错误:{result}")
+                return "中立", "平静"  # 格式错误时返回默认值
+
+        except Exception as e:
+            logger.debug(f"获取情感标签时出错: {e}")
+            return "中立", "平静"  # 出错时返回默认值
+
+    @staticmethod
+    async def _process_response(content: str) -> Tuple[List[str], List[str]]:
+        """处理响应内容，返回处理后的内容和情感标签"""
+        if not content:
+            return None, []
+
+        processed_response = process_llm_response(content)
+
+        # print(f"得到了处理后的llm返回{processed_response}")
+
+        return processed_response