better：重整配置，分离表达，聊天模式区分

重整配置文件路径，添加更多配置选项
分离了人设表达方式和学习到的表达方式
将聊天模式区分为normal focus和auto

src/chat/focus_chat/expressors/default_expressor.py

@@ -17,7 +17,7 @@ from src.manager.mood_manager import mood_manager
 from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info
 from src.chat.message_receive.chat_stream import ChatStream
 from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
 import time
@@ -281,7 +281,6 @@ class DefaultExpressor:
         in_mind_reply,
         target_message,
     ) -> str:
-        individuality = Individuality.get_instance()
         prompt_personality = individuality.get_prompt(x_person=0, level=2)
 
         # Determine if it's a group chat
@@ -294,7 +293,7 @@ class DefaultExpressor:
         message_list_before_now = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_stream.stream_id,
             timestamp=time.time(),
-            limit=global_config.chat.observation_context_size,
+            limit=global_config.focus_chat.observation_context_size,
         )
         chat_talking_prompt = await build_readable_messages(
             message_list_before_now,

src/chat/focus_chat/expressors/exprssion_learner.py

@@ -36,24 +36,6 @@ def init_prompt() -> None:
 """
     Prompt(learn_style_prompt, "learn_style_prompt")
 
-    personality_expression_prompt = """
-{personality}
-
-请从以上人设中总结出这个角色可能的语言风格
-思考回复的特殊内容和情感
-思考有没有特殊的梗，一并总结成语言风格
-总结成如下格式的规律，总结的内容要详细，但具有概括性：
-当"xxx"时，可以"xxx", xxx不超过10个字
-
-例如：
-当"表示十分惊叹"时，使用"我嘞个xxxx"
-当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
-当"想说明某个观点，但懒得明说"，使用"懂的都懂"
-
-现在请你概括
-"""
-    Prompt(personality_expression_prompt, "personality_expression_prompt")
-
     learn_grammar_prompt = """
 {chat_str}
 
@@ -278,44 +260,6 @@ class ExpressionLearner:
             expressions.append((chat_id, situation, style))
         return expressions
 
-    async def extract_and_store_personality_expressions(self):
-        """
-        检查data/expression/personality目录，不存在则创建。
-        用peronality变量作为chat_str，调用LLM生成表达风格，解析后count=100，存储到expressions.json。
-        """
-        dir_path = os.path.join("data", "expression", "personality")
-        os.makedirs(dir_path, exist_ok=True)
-        file_path = os.path.join(dir_path, "expressions.json")
-
-        # 构建prompt
-        prompt = await global_prompt_manager.format_prompt(
-            "personality_expression_prompt",
-            personality=global_config.personality.expression_style,
-        )
-        # logger.info(f"个性表达方式提取prompt: {prompt}")
-
-        try:
-            response, _ = await self.express_learn_model.generate_response_async(prompt)
-        except Exception as e:
-            logger.error(f"个性表达方式提取失败: {e}")
-            return
-
-        logger.info(f"个性表达方式提取response: {response}")
-        # chat_id用personality
-        expressions = self.parse_expression_response(response, "personality")
-        # 转为dict并count=100
-        result = []
-        for _, situation, style in expressions:
-            result.append({"situation": situation, "style": style, "count": 100})
-        # 超过50条时随机删除多余的，只保留50条
-        if len(result) > 50:
-            remove_count = len(result) - 50
-            remove_indices = set(random.sample(range(len(result)), remove_count))
-            result = [item for idx, item in enumerate(result) if idx not in remove_indices]
-        with open(file_path, "w", encoding="utf-8") as f:
-            json.dump(result, f, ensure_ascii=False, indent=2)
-        logger.info(f"已写入{len(result)}条表达到{file_path}")
-
 
 init_prompt()