Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox_Bot into dev

src/chat/express/express_utils.py

@@ -0,0 +1,254 @@
+"""
+表达系统工具函数
+提供消息过滤、文本相似度计算、加权随机抽样等功能
+"""
+import difflib
+import random
+import re
+from typing import Any, Dict, List, Optional
+
+from src.common.logger import get_logger
+
+logger = get_logger("express_utils")
+
+
+def filter_message_content(content: Optional[str]) -> str:
+    """
+    过滤消息内容，移除回复、@、图片等格式
+
+    Args:
+        content: 原始消息内容
+
+    Returns:
+        过滤后的纯文本内容
+    """
+    if not content:
+        return ""
+
+    # 移除以[回复开头、]结尾的部分，包括后面的"，说："部分
+    content = re.sub(r"\[回复.*?\]，说：\s*", "", content)
+    # 移除@<...>格式的内容
+    content = re.sub(r"@<[^>]*>", "", content)
+    # 移除[图片:...]格式的图片ID
+    content = re.sub(r"\[图片:[^\]]*\]", "", content)
+    # 移除[表情包：...]格式的内容
+    content = re.sub(r"\[表情包：[^\]]*\]", "", content)
+
+    return content.strip()
+
+
+def calculate_similarity(text1: str, text2: str) -> float:
+    """
+    计算两个文本的相似度，返回0-1之间的值
+
+    Args:
+        text1: 第一个文本
+        text2: 第二个文本
+
+    Returns:
+        相似度值 (0-1)
+    """
+    return difflib.SequenceMatcher(None, text1, text2).ratio()
+
+
+def weighted_sample(population: List[Dict], k: int, weight_key: Optional[str] = None) -> List[Dict]:
+    """
+    加权随机抽样函数
+
+    Args:
+        population: 待抽样的数据列表
+        k: 抽样数量
+        weight_key: 权重字段名，如果为None则等概率抽样
+
+    Returns:
+        抽样结果列表
+    """
+    if not population or k <= 0:
+        return []
+
+    if len(population) <= k:
+        return population.copy()
+
+    # 如果指定了权重字段
+    if weight_key and all(weight_key in item for item in population):
+        try:
+            # 获取权重
+            weights = [float(item.get(weight_key, 1.0)) for item in population]
+            # 使用random.choices进行加权抽样
+            return random.choices(population, weights=weights, k=k)
+        except (ValueError, TypeError) as e:
+            logger.warning(f"加权抽样失败，使用等概率抽样: {e}")
+
+    # 等概率抽样
+    selected = []
+    population_copy = population.copy()
+
+    for _ in range(k):
+        if not population_copy:
+            break
+        # 随机选择一个元素
+        idx = random.randint(0, len(population_copy) - 1)
+        selected.append(population_copy.pop(idx))
+
+    return selected
+
+
+def normalize_text(text: str) -> str:
+    """
+    标准化文本，移除多余空白字符
+
+    Args:
+        text: 输入文本
+
+    Returns:
+        标准化后的文本
+    """
+    # 替换多个连续空白字符为单个空格
+    text = re.sub(r"\s+", " ", text)
+    return text.strip()
+
+
+def extract_keywords(text: str, max_keywords: int = 10) -> List[str]:
+    """
+    简单的关键词提取（基于词频）
+
+    Args:
+        text: 输入文本
+        max_keywords: 最大关键词数量
+
+    Returns:
+        关键词列表
+    """
+    if not text:
+        return []
+
+    try:
+        import jieba.analyse
+
+        # 使用TF-IDF提取关键词
+        keywords = jieba.analyse.extract_tags(text, topK=max_keywords)
+        return keywords
+    except ImportError:
+        logger.warning("jieba未安装，无法提取关键词")
+        # 简单分词
+        words = text.split()
+        return words[:max_keywords]
+
+
+def format_expression_pair(situation: str, style: str, index: Optional[int] = None) -> str:
+    """
+    格式化表达方式对
+
+    Args:
+        situation: 情境
+        style: 风格
+        index: 序号（可选）
+
+    Returns:
+        格式化后的字符串
+    """
+    if index is not None:
+        return f'{index}. 当"{situation}"时，使用"{style}"'
+    else:
+        return f'当"{situation}"时，使用"{style}"'
+
+
+def parse_expression_pair(text: str) -> Optional[tuple[str, str]]:
+    """
+    解析表达方式对文本
+
+    Args:
+        text: 格式化的表达方式对文本
+
+    Returns:
+        (situation, style) 或 None
+    """
+    # 匹配格式：当"..."时，使用"..."
+    match = re.search(r'当"(.+?)"时，使用"(.+?)"', text)
+    if match:
+        return match.group(1), match.group(2)
+    return None
+
+
+def batch_filter_duplicates(expressions: List[Dict[str, Any]], key_fields: List[str]) -> List[Dict[str, Any]]:
+    """
+    批量去重表达方式
+
+    Args:
+        expressions: 表达方式列表
+        key_fields: 用于去重的字段名列表
+
+    Returns:
+        去重后的表达方式列表
+    """
+    seen = set()
+    unique_expressions = []
+
+    for expr in expressions:
+        # 构建去重key
+        key_values = tuple(expr.get(field, "") for field in key_fields)
+
+        if key_values not in seen:
+            seen.add(key_values)
+            unique_expressions.append(expr)
+
+    return unique_expressions
+
+
+def calculate_time_weight(last_active_time: float, current_time: float, half_life_days: int = 30) -> float:
+    """
+    根据时间计算权重（时间衰减）
+
+    Args:
+        last_active_time: 最后活跃时间戳
+        current_time: 当前时间戳
+        half_life_days: 半衰期天数
+
+    Returns:
+        权重值 (0-1)
+    """
+    time_diff_days = (current_time - last_active_time) / 86400  # 转换为天数
+    if time_diff_days < 0:
+        return 1.0
+
+    # 使用指数衰减公式
+    decay_rate = 0.693 / half_life_days  # ln(2) / half_life
+    weight = max(0.01, min(1.0, 2 ** (-decay_rate * time_diff_days)))
+
+    return weight
+
+
+def merge_expressions_from_multiple_chats(
+    expressions_dict: Dict[str, List[Dict[str, Any]]], max_total: int = 100
+) -> List[Dict[str, Any]]:
+    """
+    合并多个聊天室的表达方式
+
+    Args:
+        expressions_dict: {chat_id: [expressions]}
+        max_total: 最大合并数量
+
+    Returns:
+        合并后的表达方式列表
+    """
+    all_expressions = []
+
+    # 收集所有表达方式
+    for chat_id, expressions in expressions_dict.items():
+        for expr in expressions:
+            # 添加source_id标识
+            expr_with_source = expr.copy()
+            expr_with_source["source_id"] = chat_id
+            all_expressions.append(expr_with_source)
+
+    # 按count或last_active_time排序
+    if all_expressions and "count" in all_expressions[0]:
+        all_expressions.sort(key=lambda x: x.get("count", 0), reverse=True)
+    elif all_expressions and "last_active_time" in all_expressions[0]:
+        all_expressions.sort(key=lambda x: x.get("last_active_time", 0), reverse=True)
+
+    # 去重（基于situation和style）
+    all_expressions = batch_filter_duplicates(all_expressions, ["situation", "style"])
+
+    # 限制数量
+    return all_expressions[:max_total]

src/chat/express/expression_learner.py

@@ -16,6 +16,9 @@ from src.common.logger import get_logger
 from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 
+# 导入 StyleLearner 管理器
+from .style_learner import style_learner_manager
+
 MAX_EXPRESSION_COUNT = 300
 DECAY_DAYS = 30  # 30天衰减到0.01
 DECAY_MIN = 0.01  # 最小衰减值
@@ -43,17 +46,29 @@ def init_prompt() -> None:
 3. 语言风格包含特殊内容和情感
 4. 思考有没有特殊的梗，一并总结成语言风格
 5. 例子仅供参考，请严格根据群聊内容总结!!!
-注意：总结成如下格式的规律，总结的内容要详细，但具有概括性：
-例如：当"AAAAA"时，可以"BBBBB", AAAAA代表某个具体的场景，不超过20个字。BBBBB代表对应的语言风格，特定句式或表达方式，不超过20个字。
+
+**重要：必须严格按照以下格式输出，每行一条规律：**
+当"xxx"时，使用"xxx"
+
+格式说明：
+- 必须以"当"开头
+- 场景描述用双引号包裹，不超过20个字
+- 必须包含"使用"或"可以"
+- 表达风格用双引号包裹，不超过20个字
+- 每条规律独占一行
 
 例如：
 当"对某件事表示十分惊叹，有些意外"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"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂"
-当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
+当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"时，使用"懂的都懂"
+当"涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
 
-请注意：不要总结你自己（SELF）的发言
-现在请你概括
+注意：
+1. 不要总结你自己（SELF）的发言
+2. 如果聊天内容中没有明显的特殊风格，请只输出1-2条最明显的特点
+3. 不要输出其他解释性文字，只输出符合格式的规律
+
+现在请你概括：
 """
     Prompt(learn_style_prompt, "learn_style_prompt")
 
@@ -65,16 +80,28 @@ def init_prompt() -> None:
 2.不要涉及具体的人名，只考虑语法和句法特点,
 3.语法和句法特点要包括，句子长短（具体字数），有何种语病，如何拆分句子。
 4. 例子仅供参考，请严格根据群聊内容总结!!!
-总结成如下格式的规律，总结的内容要简洁，不浮夸：
-当"xxx"时，可以"xxx"
+
+**重要：必须严格按照以下格式输出，每行一条规律：**
+当"xxx"时，使用"xxx"
+
+格式说明：
+- 必须以"当"开头
+- 场景描述用双引号包裹
+- 必须包含"使用"或"可以"
+- 句法特点用双引号包裹
+- 每条规律独占一行
 
 例如：
 当"表达观点较复杂"时，使用"省略主语(3-6个字)"的句法
 当"不用详细说明的一般表达"时，使用"非常简洁的句子"的句法
 当"需要单纯简单的确认"时，使用"单字或几个字的肯定(1-2个字)"的句法
 
-注意不要总结你自己（SELF）的发言
-现在请你概括
+注意：
+1. 不要总结你自己（SELF）的发言
+2. 如果聊天内容中没有明显的句法特点，请只输出1-2条最明显的特点
+3. 不要输出其他解释性文字，只输出符合格式的规律
+
+现在请你概括：
 """
     Prompt(learn_grammar_prompt, "learn_grammar_prompt")
 
@@ -405,6 +432,44 @@ class ExpressionLearner:
                     for expr in exprs[: len(exprs) - MAX_EXPRESSION_COUNT]:
                         await session.delete(expr)
 
+            # 🔥 训练 StyleLearner
+            # 只对 style 类型的表达方式进行训练（grammar 不需要训练到模型）
+            if type == "style":
+                try:
+                    # 获取 StyleLearner 实例
+                    learner = style_learner_manager.get_learner(chat_id)
+                    
+                    logger.info(f"开始训练 StyleLearner: chat_id={chat_id}, 样本数={len(expr_list)}")
+                    
+                    # 为每个学习到的表达方式训练模型
+                    # 使用 situation 作为输入，style 作为目标
+                    # 这是最符合语义的方式：场景 -> 表达方式
+                    success_count = 0
+                    for expr in expr_list:
+                        situation = expr["situation"]
+                        style = expr["style"]
+                        
+                        # 训练映射关系: situation -> style
+                        if learner.learn_mapping(situation, style):
+                            success_count += 1
+                        else:
+                            logger.warning(f"训练失败: {situation} -> {style}")
+                    
+                    logger.info(
+                        f"StyleLearner 训练完成: {success_count}/{len(expr_list)} 成功, "
+                        f"当前风格总数={len(learner.get_all_styles())}, "
+                        f"总样本数={learner.learning_stats['total_samples']}"
+                    )
+                    
+                    # 保存模型
+                    if learner.save(style_learner_manager.model_save_path):
+                        logger.info(f"StyleLearner 模型保存成功: {chat_id}")
+                    else:
+                        logger.error(f"StyleLearner 模型保存失败: {chat_id}")
+                        
+                except Exception as e:
+                    logger.error(f"训练 StyleLearner 失败: {e}", exc_info=True)
+
             return learnt_expressions
         return None
 
@@ -455,9 +520,17 @@ class ExpressionLearner:
             logger.error(f"学习{type_str}失败: {e}")
             return None
 
+        if not response or not response.strip():
+            logger.warning(f"LLM返回空响应，无法学习{type_str}")
+            return None
+
         logger.debug(f"学习{type_str}的response: {response}")
 
         expressions: list[tuple[str, str, str]] = self.parse_expression_response(response, chat_id)
+        
+        if not expressions:
+            logger.warning(f"从LLM响应中未能解析出任何{type_str}。请检查LLM输出格式是否正确。")
+            logger.info(f"LLM完整响应:\n{response}")
 
         return expressions, chat_id
 
@@ -465,31 +538,100 @@ class ExpressionLearner:
     def parse_expression_response(response: str, chat_id: str) -> list[tuple[str, str, str]]:
         """
         解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组
+        支持多种引号格式："" 和 ""
         """
         expressions: list[tuple[str, str, str]] = []
-        for line in response.splitlines():
+        failed_lines = []
+        
+        for line_num, line in enumerate(response.splitlines(), 1):
             line = line.strip()
             if not line:
                 continue
+            
+            # 替换中文引号为英文引号，便于统一处理
+            line_normalized = line.replace('"', '"').replace('"', '"').replace("'", '"').replace("'", '"')
+            
             # 查找"当"和下一个引号
-            idx_when = line.find('当"')
+            idx_when = line_normalized.find('当"')
             if idx_when == -1:
-                continue
-            idx_quote1 = idx_when + 1
-            idx_quote2 = line.find('"', idx_quote1 + 1)
-            if idx_quote2 == -1:
-                continue
-            situation = line[idx_quote1 + 1 : idx_quote2]
-            # 查找"使用"
-            idx_use = line.find('使用"', idx_quote2)
+                # 尝试不带引号的格式: 当xxx时
+                idx_when = line_normalized.find('当')
+                if idx_when == -1:
+                    failed_lines.append((line_num, line, "找不到'当'关键字"))
+                    continue
+                    
+                # 提取"当"和"时"之间的内容
+                idx_shi = line_normalized.find('时', idx_when)
+                if idx_shi == -1:
+                    failed_lines.append((line_num, line, "找不到'时'关键字"))
+                    continue
+                situation = line_normalized[idx_when + 1:idx_shi].strip('"\'""')
+                search_start = idx_shi
+            else:
+                idx_quote1 = idx_when + 1
+                idx_quote2 = line_normalized.find('"', idx_quote1 + 1)
+                if idx_quote2 == -1:
+                    failed_lines.append((line_num, line, "situation部分引号不匹配"))
+                    continue
+                situation = line_normalized[idx_quote1 + 1 : idx_quote2]
+                search_start = idx_quote2
+            
+            # 查找"使用"或"可以"
+            idx_use = line_normalized.find('使用"', search_start)
             if idx_use == -1:
+                idx_use = line_normalized.find('可以"', search_start)
+                if idx_use == -1:
+                    # 尝试不带引号的格式
+                    idx_use = line_normalized.find('使用', search_start)
+                    if idx_use == -1:
+                        idx_use = line_normalized.find('可以', search_start)
+                        if idx_use == -1:
+                            failed_lines.append((line_num, line, "找不到'使用'或'可以'关键字"))
+                            continue
+                    
+                    # 提取剩余部分作为style
+                    style = line_normalized[idx_use + 2:].strip('"\'""，。')
+                    if not style:
+                        failed_lines.append((line_num, line, "style部分为空"))
+                        continue
+                else:
+                    idx_quote3 = idx_use + 2
+                    idx_quote4 = line_normalized.find('"', idx_quote3 + 1)
+                    if idx_quote4 == -1:
+                        # 如果没有结束引号，取到行尾
+                        style = line_normalized[idx_quote3 + 1:].strip('"\'""')
+                    else:
+                        style = line_normalized[idx_quote3 + 1 : idx_quote4]
+            else:
+                idx_quote3 = idx_use + 2
+                idx_quote4 = line_normalized.find('"', idx_quote3 + 1)
+                if idx_quote4 == -1:
+                    # 如果没有结束引号，取到行尾
+                    style = line_normalized[idx_quote3 + 1:].strip('"\'""')
+                else:
+                    style = line_normalized[idx_quote3 + 1 : idx_quote4]
+            
+            # 清理并验证
+            situation = situation.strip()
+            style = style.strip()
+            
+            if not situation or not style:
+                failed_lines.append((line_num, line, f"situation或style为空: situation='{situation}', style='{style}'"))
                 continue
-            idx_quote3 = idx_use + 2
-            idx_quote4 = line.find('"', idx_quote3 + 1)
-            if idx_quote4 == -1:
-                continue
-            style = line[idx_quote3 + 1 : idx_quote4]
+            
             expressions.append((chat_id, situation, style))
+        
+        # 记录解析失败的行
+        if failed_lines:
+            logger.warning(f"解析表达方式时有 {len(failed_lines)} 行失败:")
+            for line_num, line, reason in failed_lines[:5]:  # 只显示前5个
+                logger.warning(f"  行{line_num}: {reason}")
+                logger.debug(f"    原文: {line}")
+        
+        if not expressions:
+            logger.warning(f"LLM返回了内容但无法解析任何表达方式。响应预览:\n{response[:500]}")
+        else:
+            logger.debug(f"成功解析 {len(expressions)} 个表达方式")
         return expressions
 
 
@@ -522,12 +664,12 @@ class ExpressionLearnerManager:
             os.path.join(base_dir, "learnt_grammar"),
         ]
 
-        try:
-            for directory in directories_to_create:
+        for directory in directories_to_create:
+            try:
                 os.makedirs(directory, exist_ok=True)
-            logger.debug(f"确保目录存在: {directory}")
-        except Exception as e:
-            logger.error(f"创建目录失败 {directory}: {e}")
+                logger.debug(f"确保目录存在: {directory}")
+            except Exception as e:
+                logger.error(f"创建目录失败 {directory}: {e}")
 
     @staticmethod
     async def _auto_migrate_json_to_db():

src/chat/express/expression_selector.py

@@ -15,6 +15,10 @@ from src.common.logger import get_logger
 from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 
+# 导入StyleLearner管理器和情境提取器
+from .situation_extractor import situation_extractor
+from .style_learner import style_learner_manager
+
 logger = get_logger("expression_selector")
 
 
@@ -127,17 +131,18 @@ class ExpressionSelector:
                 current_group = rule.group
                 break
 
-        if not current_group:
-            return [chat_id]
+        # 🔥 始终包含当前 chat_id（确保至少能查到自己的数据）
+        related_chat_ids = [chat_id]
 
-        # 找出同一组的所有chat_id
-        related_chat_ids = []
-        for rule in rules:
-            if rule.group == current_group and rule.chat_stream_id:
-                if chat_id_candidate := self._parse_stream_config_to_chat_id(rule.chat_stream_id):
-                    related_chat_ids.append(chat_id_candidate)
+        if current_group:
+            # 找出同一组的所有chat_id
+            for rule in rules:
+                if rule.group == current_group and rule.chat_stream_id:
+                    if chat_id_candidate := self._parse_stream_config_to_chat_id(rule.chat_stream_id):
+                        if chat_id_candidate not in related_chat_ids:
+                            related_chat_ids.append(chat_id_candidate)
 
-        return related_chat_ids if related_chat_ids else [chat_id]
+        return related_chat_ids
 
     async def get_random_expressions(
         self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
@@ -236,6 +241,287 @@ class ExpressionSelector:
                     )
                 await session.commit()
 
+    async def select_suitable_expressions(
+        self,
+        chat_id: str,
+        chat_history: list | str,
+        target_message: str | None = None,
+        max_num: int = 10,
+        min_num: int = 5,
+    ) -> list[dict[str, Any]]:
+        """
+        统一的表达方式选择入口，根据配置自动选择模式
+        
+        Args:
+            chat_id: 聊天ID
+            chat_history: 聊天历史（列表或字符串）
+            target_message: 目标消息
+            max_num: 最多返回数量
+            min_num: 最少返回数量
+            
+        Returns:
+            选中的表达方式列表
+        """
+        # 转换chat_history为字符串
+        if isinstance(chat_history, list):
+            chat_info = "\n".join([f"{msg.get('sender', 'Unknown')}: {msg.get('content', '')}" for msg in chat_history])
+        else:
+            chat_info = chat_history
+        
+        # 根据配置选择模式
+        mode = global_config.expression.mode
+        logger.debug(f"[ExpressionSelector] 使用模式: {mode}")
+        
+        if mode == "exp_model":
+            return await self._select_expressions_model_only(
+                chat_id=chat_id,
+                chat_info=chat_info,
+                target_message=target_message,
+                max_num=max_num,
+                min_num=min_num
+            )
+        else:  # classic mode
+            return await self._select_expressions_classic(
+                chat_id=chat_id,
+                chat_info=chat_info,
+                target_message=target_message,
+                max_num=max_num,
+                min_num=min_num
+            )
+    
+    async def _select_expressions_classic(
+        self,
+        chat_id: str,
+        chat_info: str,
+        target_message: str | None = None,
+        max_num: int = 10,
+        min_num: int = 5,
+    ) -> list[dict[str, Any]]:
+        """经典模式：随机抽样 + LLM评估"""
+        logger.debug(f"[Classic模式] 使用LLM评估表达方式")
+        return await self.select_suitable_expressions_llm(
+            chat_id=chat_id,
+            chat_info=chat_info,
+            max_num=max_num,
+            min_num=min_num,
+            target_message=target_message
+        )
+    
+    async def _select_expressions_model_only(
+        self,
+        chat_id: str,
+        chat_info: str,
+        target_message: str | None = None,
+        max_num: int = 10,
+        min_num: int = 5,
+    ) -> list[dict[str, Any]]:
+        """模型预测模式：先提取情境，再使用StyleLearner预测表达风格"""
+        logger.debug(f"[Exp_model模式] 使用情境提取 + StyleLearner预测表达方式")
+        
+        # 检查是否允许在此聊天流中使用表达
+        if not self.can_use_expression_for_chat(chat_id):
+            logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
+            return []
+        
+        # 步骤1: 提取聊天情境
+        situations = await situation_extractor.extract_situations(
+            chat_history=chat_info,
+            target_message=target_message,
+            max_situations=3
+        )
+        
+        if not situations:
+            logger.warning(f"无法提取聊天情境，回退到经典模式")
+            return await self._select_expressions_classic(
+                chat_id=chat_id,
+                chat_info=chat_info,
+                target_message=target_message,
+                max_num=max_num,
+                min_num=min_num
+            )
+        
+        logger.info(f"[Exp_model模式] 步骤1完成 - 提取到 {len(situations)} 个情境: {situations}")
+        
+        # 步骤2: 使用 StyleLearner 为每个情境预测合适的表达方式
+        learner = style_learner_manager.get_learner(chat_id)
+        
+        all_predicted_styles = {}
+        for i, situation in enumerate(situations, 1):
+            logger.debug(f"[Exp_model模式] 步骤2.{i} - 为情境预测风格: {situation}")
+            best_style, scores = learner.predict_style(situation, top_k=max_num)
+            
+            if best_style and scores:
+                logger.debug(f"  预测结果: best={best_style}, scores数量={len(scores)}")
+                # 合并分数（取最高分）
+                for style, score in scores.items():
+                    if style not in all_predicted_styles or score > all_predicted_styles[style]:
+                        all_predicted_styles[style] = score
+            else:
+                logger.debug(f"  该情境未返回预测结果")
+        
+        if not all_predicted_styles:
+            logger.warning(f"[Exp_model模式] StyleLearner未返回预测结果（可能模型未训练），回退到经典模式")
+            return await self._select_expressions_classic(
+                chat_id=chat_id,
+                chat_info=chat_info,
+                target_message=target_message,
+                max_num=max_num,
+                min_num=min_num
+            )
+        
+        # 将分数字典转换为列表格式 [(style, score), ...]
+        predicted_styles = sorted(all_predicted_styles.items(), key=lambda x: x[1], reverse=True)
+        
+        logger.info(f"[Exp_model模式] 步骤2完成 - 预测到 {len(predicted_styles)} 个风格, Top3: {predicted_styles[:3]}")
+        
+        # 步骤3: 根据预测的风格从数据库获取表达方式
+        logger.debug(f"[Exp_model模式] 步骤3 - 从数据库查询表达方式")
+        expressions = await self.get_model_predicted_expressions(
+            chat_id=chat_id,
+            predicted_styles=predicted_styles,
+            max_num=max_num
+        )
+        
+        if not expressions:
+            logger.warning(f"[Exp_model模式] 未找到匹配预测风格的表达方式，回退到经典模式")
+            return await self._select_expressions_classic(
+                chat_id=chat_id,
+                chat_info=chat_info,
+                target_message=target_message,
+                max_num=max_num,
+                min_num=min_num
+            )
+        
+        logger.info(f"[Exp_model模式] 成功! 返回 {len(expressions)} 个表达方式")
+        return expressions
+    
+    async def get_model_predicted_expressions(
+        self,
+        chat_id: str,
+        predicted_styles: list[tuple[str, float]],
+        max_num: int = 10
+    ) -> list[dict[str, Any]]:
+        """
+        根据StyleLearner预测的风格获取表达方式
+        
+        Args:
+            chat_id: 聊天ID
+            predicted_styles: 预测的风格列表，格式: [(style, score), ...]
+            max_num: 最多返回数量
+            
+        Returns:
+            表达方式列表
+        """
+        if not predicted_styles:
+            return []
+        
+        # 提取风格名称（前3个最佳匹配）
+        style_names = [style for style, _ in predicted_styles[:min(3, len(predicted_styles))]]
+        logger.debug(f"预测最佳风格: {style_names[0] if style_names else 'None'}, Top3分数: {predicted_styles[:3]}")
+        
+        # 🔥 使用 get_related_chat_ids 获取所有相关的 chat_id（支持共享表达方式）
+        related_chat_ids = self.get_related_chat_ids(chat_id)
+        logger.info(f"查询相关的chat_ids ({len(related_chat_ids)}个): {related_chat_ids}")
+        
+        async with get_db_session() as session:
+            # 🔍 先检查数据库中实际有哪些 chat_id 的数据
+            db_chat_ids_result = await session.execute(
+                select(Expression.chat_id)
+                .where(Expression.type == "style")
+                .distinct()
+            )
+            db_chat_ids = [cid for cid in db_chat_ids_result.scalars()]
+            logger.info(f"数据库中有表达方式的chat_ids ({len(db_chat_ids)}个): {db_chat_ids}")
+            
+            # 获取所有相关 chat_id 的表达方式（用于模糊匹配）
+            all_expressions_result = await session.execute(
+                select(Expression)
+                .where(Expression.chat_id.in_(related_chat_ids))
+                .where(Expression.type == "style")
+            )
+            all_expressions = list(all_expressions_result.scalars())
+            
+            logger.info(f"配置的相关chat_id的表达方式数量: {len(all_expressions)}")
+            
+            # 🔥 智能回退：如果相关 chat_id 没有数据，尝试查询所有 chat_id
+            if not all_expressions:
+                logger.info(f"相关chat_id没有数据，尝试从所有chat_id查询")
+                all_expressions_result = await session.execute(
+                    select(Expression)
+                    .where(Expression.type == "style")
+                )
+                all_expressions = list(all_expressions_result.scalars())
+                logger.debug(f"数据库中所有表达方式数量: {len(all_expressions)}")
+            
+            if not all_expressions:
+                logger.warning(f"数据库中完全没有任何表达方式，需要先学习")
+                return []
+            
+            # 🔥 使用模糊匹配而不是精确匹配
+            # 计算每个预测style与数据库style的相似度
+            from difflib import SequenceMatcher
+            
+            matched_expressions = []
+            for expr in all_expressions:
+                db_style = expr.style or ""
+                max_similarity = 0.0
+                best_predicted = ""
+                
+                # 与每个预测的style计算相似度
+                for predicted_style, pred_score in predicted_styles[:20]:  # 考虑前20个预测
+                    # 计算字符串相似度
+                    similarity = SequenceMatcher(None, predicted_style, db_style).ratio()
+                    
+                    # 也检查包含关系（如果一个是另一个的子串，给更高分）
+                    if len(predicted_style) >= 2 and len(db_style) >= 2:
+                        if predicted_style in db_style or db_style in predicted_style:
+                            similarity = max(similarity, 0.7)
+                    
+                    if similarity > max_similarity:
+                        max_similarity = similarity
+                        best_predicted = predicted_style
+                
+                # 🔥 降低阈值到30%，因为StyleLearner预测质量较差
+                if max_similarity >= 0.3:  # 30%相似度阈值
+                    matched_expressions.append((expr, max_similarity, expr.count, best_predicted))
+            
+            if not matched_expressions:
+                # 收集数据库中的style样例用于调试
+                all_styles = [e.style for e in all_expressions[:10]]
+                logger.warning(
+                    f"数据库中没有找到匹配的表达方式（相似度阈值30%）:\n"
+                    f"  预测的style (前3个): {style_names}\n"
+                    f"  数据库中存在的style样例: {all_styles}\n"
+                    f"  提示: StyleLearner预测质量差，建议重新训练或使用classic模式"
+                )
+                return []
+            
+            # 按照相似度*count排序，选择最佳匹配
+            matched_expressions.sort(key=lambda x: x[1] * (x[2] ** 0.5), reverse=True)
+            expressions_objs = [e[0] for e in matched_expressions[:max_num]]
+            
+            # 显示最佳匹配的详细信息
+            top_matches = [f"{e[3]}->{e[0].style}({e[1]:.2f})" for e in matched_expressions[:3]]
+            logger.info(
+                f"模糊匹配成功: 找到 {len(expressions_objs)} 个表达方式\n"
+                f"  相似度范围: {matched_expressions[0][1]:.2f} ~ {matched_expressions[min(len(matched_expressions)-1, max_num-1)][1]:.2f}\n"
+                f"  Top3匹配: {top_matches}"
+            )
+            
+            # 转换为字典格式
+            expressions = []
+            for expr in expressions_objs:
+                expressions.append({
+                    "situation": expr.situation or "",
+                    "style": expr.style or "",
+                    "type": expr.type or "style",
+                    "count": float(expr.count) if expr.count else 0.0,
+                    "last_active_time": expr.last_active_time or 0.0
+                })
+            
+            logger.debug(f"从数据库获取了 {len(expressions)} 个表达方式")
+            return expressions
+
     async def select_suitable_expressions_llm(
         self,
         chat_id: str,

src/chat/express/expressor_model/__init__.py

@@ -0,0 +1,9 @@
+"""
+表达模型包
+包含基于Online Naive Bayes的机器学习模型
+"""
+from .model import ExpressorModel
+from .online_nb import OnlineNaiveBayes
+from .tokenizer import Tokenizer
+
+__all__ = ["ExpressorModel", "OnlineNaiveBayes", "Tokenizer"]

src/chat/express/expressor_model/model.py

@@ -0,0 +1,216 @@
+"""
+基于Online Naive Bayes的表达模型
+支持候选表达的动态添加和在线学习
+"""
+import os
+import pickle
+from collections import Counter, defaultdict
+from typing import Dict, Optional, Tuple
+
+from src.common.logger import get_logger
+
+from .online_nb import OnlineNaiveBayes
+from .tokenizer import Tokenizer
+
+logger = get_logger("expressor.model")
+
+
+class ExpressorModel:
+    """直接使用朴素贝叶斯精排（可在线学习）"""
+
+    def __init__(
+        self, alpha: float = 0.5, beta: float = 0.5, gamma: float = 1.0, vocab_size: int = 200000, use_jieba: bool = True
+    ):
+        """
+        Args:
+            alpha: 词频平滑参数
+            beta: 类别先验平滑参数
+            gamma: 衰减因子
+            vocab_size: 词汇表大小
+            use_jieba: 是否使用jieba分词
+        """
+        # 初始化分词器
+        self.tokenizer = Tokenizer(stopwords=set(), use_jieba=use_jieba)
+
+        # 初始化在线朴素贝叶斯模型
+        self.nb = OnlineNaiveBayes(alpha=alpha, beta=beta, gamma=gamma, vocab_size=vocab_size)
+
+        # 候选表达管理
+        self._candidates: Dict[str, str] = {}  # cid -> text (style)
+        self._situations: Dict[str, str] = {}  # cid -> situation (不参与计算)
+
+        logger.info(
+            f"ExpressorModel初始化完成 (alpha={alpha}, beta={beta}, gamma={gamma}, vocab_size={vocab_size}, use_jieba={use_jieba})"
+        )
+
+    def add_candidate(self, cid: str, text: str, situation: Optional[str] = None):
+        """
+        添加候选文本和对应的situation
+
+        Args:
+            cid: 候选ID
+            text: 表达文本 (style)
+            situation: 情境文本
+        """
+        self._candidates[cid] = text
+        if situation is not None:
+            self._situations[cid] = situation
+
+        # 确保在nb模型中初始化该候选的计数
+        if cid not in self.nb.cls_counts:
+            self.nb.cls_counts[cid] = 0.0
+        if cid not in self.nb.token_counts:
+            self.nb.token_counts[cid] = defaultdict(float)
+
+    def predict(self, text: str, k: int = None) -> Tuple[Optional[str], Dict[str, float]]:
+        """
+        直接对所有候选进行朴素贝叶斯评分
+
+        Args:
+            text: 查询文本
+            k: 返回前k个候选，如果为None则返回所有
+
+        Returns:
+            (最佳候选ID, 所有候选的分数字典)
+        """
+        # 1. 分词
+        toks = self.tokenizer.tokenize(text)
+        if not toks or not self._candidates:
+            return None, {}
+
+        # 2. 计算词频
+        tf = Counter(toks)
+        all_cids = list(self._candidates.keys())
+
+        # 3. 批量评分
+        scores = self.nb.score_batch(tf, all_cids)
+
+        if not scores:
+            return None, {}
+
+        # 4. 根据k参数限制返回的候选数量
+        if k is not None and k > 0:
+            sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+            limited_scores = dict(sorted_scores[:k])
+            best = sorted_scores[0][0] if sorted_scores else None
+            return best, limited_scores
+        else:
+            best = max(scores.items(), key=lambda x: x[1])[0]
+            return best, scores
+
+    def update_positive(self, text: str, cid: str):
+        """
+        更新正反馈学习
+
+        Args:
+            text: 输入文本
+            cid: 目标类别ID
+        """
+        toks = self.tokenizer.tokenize(text)
+        if not toks:
+            return
+
+        tf = Counter(toks)
+        self.nb.update_positive(tf, cid)
+
+    def decay(self, factor: Optional[float] = None):
+        """
+        应用知识衰减
+
+        Args:
+            factor: 衰减因子，如果为None则使用模型配置的gamma
+        """
+        self.nb.decay(factor)
+
+    def get_candidate_info(self, cid: str) -> Tuple[Optional[str], Optional[str]]:
+        """
+        获取候选信息
+
+        Args:
+            cid: 候选ID
+
+        Returns:
+            (style文本, situation文本)
+        """
+        style = self._candidates.get(cid)
+        situation = self._situations.get(cid)
+        return style, situation
+
+    def get_all_candidates(self) -> Dict[str, Tuple[str, str]]:
+        """
+        获取所有候选
+
+        Returns:
+            {cid: (style, situation)}
+        """
+        result = {}
+        for cid in self._candidates.keys():
+            style, situation = self.get_candidate_info(cid)
+            result[cid] = (style, situation)
+        return result
+
+    def save(self, path: str):
+        """
+        保存模型到文件
+
+        Args:
+            path: 保存路径
+        """
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+
+        data = {
+            "candidates": self._candidates,
+            "situations": self._situations,
+            "nb_cls_counts": dict(self.nb.cls_counts),
+            "nb_token_counts": {k: dict(v) for k, v in self.nb.token_counts.items()},
+            "nb_alpha": self.nb.alpha,
+            "nb_beta": self.nb.beta,
+            "nb_gamma": self.nb.gamma,
+            "nb_V": self.nb.V,
+        }
+
+        with open(path, "wb") as f:
+            pickle.dump(data, f)
+
+        logger.info(f"模型已保存到 {path}")
+
+    def load(self, path: str):
+        """
+        从文件加载模型
+
+        Args:
+            path: 加载路径
+        """
+        if not os.path.exists(path):
+            logger.warning(f"模型文件不存在: {path}")
+            return
+
+        with open(path, "rb") as f:
+            data = pickle.load(f)
+
+        self._candidates = data["candidates"]
+        self._situations = data["situations"]
+
+        # 恢复nb模型的参数
+        self.nb.alpha = data["nb_alpha"]
+        self.nb.beta = data["nb_beta"]
+        self.nb.gamma = data["nb_gamma"]
+        self.nb.V = data["nb_V"]
+
+        # 恢复统计数据
+        self.nb.cls_counts = defaultdict(float, data["nb_cls_counts"])
+        self.nb.token_counts = defaultdict(lambda: defaultdict(float))
+        for cid, tc in data["nb_token_counts"].items():
+            self.nb.token_counts[cid] = defaultdict(float, tc)
+
+        logger.info(f"模型已从 {path} 加载")
+
+    def get_stats(self) -> Dict:
+        """获取模型统计信息"""
+        nb_stats = self.nb.get_stats()
+        return {
+            "n_candidates": len(self._candidates),
+            "n_classes": nb_stats["n_classes"],
+            "n_tokens": nb_stats["n_tokens"],
+            "total_counts": nb_stats["total_counts"],
+        }

src/chat/express/expressor_model/online_nb.py

@@ -0,0 +1,142 @@
+"""
+在线朴素贝叶斯分类器
+支持增量学习和知识衰减
+"""
+import math
+from collections import Counter, defaultdict
+from typing import Dict, List, Optional
+
+from src.common.logger import get_logger
+
+logger = get_logger("expressor.online_nb")
+
+
+class OnlineNaiveBayes:
+    """在线朴素贝叶斯分类器"""
+
+    def __init__(self, alpha: float = 0.5, beta: float = 0.5, gamma: float = 1.0, vocab_size: int = 200000):
+        """
+        Args:
+            alpha: 词频平滑参数
+            beta: 类别先验平滑参数
+            gamma: 衰减因子 (0-1之间，1表示不衰减)
+            vocab_size: 词汇表大小
+        """
+        self.alpha = alpha
+        self.beta = beta
+        self.gamma = gamma
+        self.V = vocab_size
+
+        # 类别统计
+        self.cls_counts: Dict[str, float] = defaultdict(float)  # cid -> total token count
+        self.token_counts: Dict[str, Dict[str, float]] = defaultdict(
+            lambda: defaultdict(float)
+        )  # cid -> term -> count
+
+        # 缓存
+        self._logZ: Dict[str, float] = {}  # cache log(∑counts + Vα)
+
+    def score_batch(self, tf: Counter, cids: List[str]) -> Dict[str, float]:
+        """
+        批量计算候选的贝叶斯分数
+
+        Args:
+            tf: 查询文本的词频Counter
+            cids: 候选类别ID列表
+
+        Returns:
+            每个候选的分数字典
+        """
+        total_cls = sum(self.cls_counts.values())
+        n_cls = max(1, len(self.cls_counts))
+        denom_prior = math.log(total_cls + self.beta * n_cls)
+
+        out: Dict[str, float] = {}
+        for cid in cids:
+            # 计算先验概率 log P(c)
+            prior = math.log(self.cls_counts[cid] + self.beta) - denom_prior
+            s = prior
+
+            # 计算似然概率 log P(w|c)
+            logZ = self._logZ_c(cid)
+            tc = self.token_counts[cid]
+
+            for term, qtf in tf.items():
+                num = tc.get(term, 0.0) + self.alpha
+                s += qtf * (math.log(num) - logZ)
+
+            out[cid] = s
+        return out
+
+    def update_positive(self, tf: Counter, cid: str):
+        """
+        正反馈更新
+
+        Args:
+            tf: 词频Counter
+            cid: 类别ID
+        """
+        inc = 0.0
+        tc = self.token_counts[cid]
+
+        # 更新词频统计
+        for term, c in tf.items():
+            tc[term] += float(c)
+            inc += float(c)
+
+        # 更新类别统计
+        self.cls_counts[cid] += inc
+        self._invalidate(cid)
+
+    def decay(self, factor: Optional[float] = None):
+        """
+        知识衰减（遗忘机制）
+
+        Args:
+            factor: 衰减因子，如果为None则使用self.gamma
+        """
+        g = self.gamma if factor is None else factor
+        if g >= 1.0:
+            return
+
+        # 对所有统计进行衰减
+        for cid in list(self.cls_counts.keys()):
+            self.cls_counts[cid] *= g
+            for term in list(self.token_counts[cid].keys()):
+                self.token_counts[cid][term] *= g
+            self._invalidate(cid)
+
+        logger.debug(f"应用知识衰减，衰减因子: {g}")
+
+    def _logZ_c(self, cid: str) -> float:
+        """
+        计算归一化因子logZ
+
+        Args:
+            cid: 类别ID
+
+        Returns:
+            log(Z_c)
+        """
+        if cid not in self._logZ:
+            Z = self.cls_counts[cid] + self.V * self.alpha
+            self._logZ[cid] = math.log(max(Z, 1e-12))
+        return self._logZ[cid]
+
+    def _invalidate(self, cid: str):
+        """
+        使缓存失效
+
+        Args:
+            cid: 类别ID
+        """
+        if cid in self._logZ:
+            del self._logZ[cid]
+
+    def get_stats(self) -> Dict:
+        """获取统计信息"""
+        return {
+            "n_classes": len(self.cls_counts),
+            "n_tokens": sum(len(tc) for tc in self.token_counts.values()),
+            "total_counts": sum(self.cls_counts.values()),
+        }

src/chat/express/expressor_model/tokenizer.py

@@ -0,0 +1,62 @@
+"""
+文本分词器，支持中文Jieba分词
+"""
+from typing import List
+
+from src.common.logger import get_logger
+
+logger = get_logger("expressor.tokenizer")
+
+
+class Tokenizer:
+    """文本分词器，支持中文Jieba分词"""
+
+    def __init__(self, stopwords: set = None, use_jieba: bool = True):
+        """
+        Args:
+            stopwords: 停用词集合
+            use_jieba: 是否使用jieba分词
+        """
+        self.stopwords = stopwords or set()
+        self.use_jieba = use_jieba
+
+        if use_jieba:
+            try:
+                import jieba
+
+                jieba.initialize()
+                logger.info("Jieba分词器初始化成功")
+            except ImportError:
+                logger.warning("Jieba未安装，将使用字符级分词")
+                self.use_jieba = False
+
+    def tokenize(self, text: str) -> List[str]:
+        """
+        分词并返回token列表
+
+        Args:
+            text: 输入文本
+
+        Returns:
+            token列表
+        """
+        if not text:
+            return []
+
+        # 使用jieba分词
+        if self.use_jieba:
+            try:
+                import jieba
+
+                tokens = list(jieba.cut(text))
+            except Exception as e:
+                logger.warning(f"Jieba分词失败，使用字符级分词: {e}")
+                tokens = list(text)
+        else:
+            # 简单按字符分词
+            tokens = list(text)
+
+        # 过滤停用词和空字符串
+        tokens = [token.strip() for token in tokens if token.strip() and token not in self.stopwords]
+
+        return tokens

src/chat/express/situation_extractor.py

@@ -0,0 +1,162 @@
+"""
+情境提取器
+从聊天历史中提取当前的情境（situation），用于 StyleLearner 预测
+"""
+from typing import Optional
+
+from src.chat.utils.prompt import Prompt, global_prompt_manager
+from src.common.logger import get_logger
+from src.config.config import global_config, model_config
+from src.llm_models.utils_model import LLMRequest
+
+logger = get_logger("situation_extractor")
+
+
+def init_prompt():
+    situation_extraction_prompt = """
+以下是正在进行的聊天内容：
+{chat_history}
+
+你的名字是{bot_name}{target_message_info}
+
+请分析当前聊天的情境特征，提取出最能描述当前情境的1-3个关键场景描述。
+
+场景描述应该：
+1. 简洁明了（每个不超过20个字）
+2. 聚焦情绪、话题、氛围
+3. 不涉及具体人名
+4. 类似于"表示惊讶"、"讨论游戏"、"表达赞同"这样的格式
+
+请以纯文本格式输出，每行一个场景描述，不要有序号、引号或其他格式：
+
+例如：
+表示惊讶和意外
+讨论技术问题
+表达友好的赞同
+
+现在请提取当前聊天的情境：
+"""
+    Prompt(situation_extraction_prompt, "situation_extraction_prompt")
+
+
+class SituationExtractor:
+    """情境提取器，从聊天历史中提取当前情境"""
+    
+    def __init__(self):
+        self.llm_model = LLMRequest(
+            model_set=model_config.model_task_config.utils_small,
+            request_type="expression.situation_extractor"
+        )
+    
+    async def extract_situations(
+        self,
+        chat_history: list | str,
+        target_message: Optional[str] = None,
+        max_situations: int = 3
+    ) -> list[str]:
+        """
+        从聊天历史中提取情境
+        
+        Args:
+            chat_history: 聊天历史（列表或字符串）
+            target_message: 目标消息（可选）
+            max_situations: 最多提取的情境数量
+            
+        Returns:
+            情境描述列表
+        """
+        # 转换chat_history为字符串
+        if isinstance(chat_history, list):
+            chat_info = "\n".join([
+                f"{msg.get('sender', 'Unknown')}: {msg.get('content', '')}" 
+                for msg in chat_history
+            ])
+        else:
+            chat_info = chat_history
+        
+        # 构建目标消息信息
+        if target_message:
+            target_message_info = f"，现在你想要回复消息：{target_message}"
+        else:
+            target_message_info = ""
+        
+        # 构建 prompt
+        try:
+            prompt = (await global_prompt_manager.get_prompt_async("situation_extraction_prompt")).format(
+                bot_name=global_config.bot.nickname,
+                chat_history=chat_info,
+                target_message_info=target_message_info
+            )
+            
+            # 调用 LLM
+            response, _ = await self.llm_model.generate_response_async(
+                prompt=prompt,
+                temperature=0.3
+            )
+            
+            if not response or not response.strip():
+                logger.warning("LLM返回空响应，无法提取情境")
+                return []
+            
+            # 解析响应
+            situations = self._parse_situations(response, max_situations)
+            
+            if situations:
+                logger.debug(f"提取到 {len(situations)} 个情境: {situations}")
+            else:
+                logger.warning(f"无法从LLM响应中解析出情境。响应:\n{response}")
+            
+            return situations
+            
+        except Exception as e:
+            logger.error(f"提取情境失败: {e}")
+            return []
+    
+    @staticmethod
+    def _parse_situations(response: str, max_situations: int) -> list[str]:
+        """
+        解析 LLM 返回的情境描述
+        
+        Args:
+            response: LLM 响应
+            max_situations: 最多返回的情境数量
+            
+        Returns:
+            情境描述列表
+        """
+        situations = []
+        
+        for line in response.splitlines():
+            line = line.strip()
+            if not line:
+                continue
+            
+            # 移除可能的序号、引号等
+            line = line.lstrip('0123456789.、-*>）)】] \t"\'""''')
+            line = line.rstrip('"\'""''')
+            line = line.strip()
+            
+            if not line:
+                continue
+            
+            # 过滤掉明显不是情境描述的内容
+            if len(line) > 30:  # 太长
+                continue
+            if len(line) < 2:   # 太短
+                continue
+            if any(keyword in line.lower() for keyword in ['例如', '注意', '请', '分析', '总结']):
+                continue
+            
+            situations.append(line)
+            
+            if len(situations) >= max_situations:
+                break
+        
+        return situations
+
+
+# 初始化 prompt
+init_prompt()
+
+# 全局单例
+situation_extractor = SituationExtractor()

src/chat/express/style_learner.py

@@ -0,0 +1,425 @@
+"""
+风格学习引擎
+基于ExpressorModel实现的表达风格学习和预测系统
+支持多聊天室独立建模和在线学习
+"""
+import os
+import time
+from typing import Dict, List, Optional, Tuple
+
+from src.common.logger import get_logger
+
+from .expressor_model import ExpressorModel
+
+logger = get_logger("expressor.style_learner")
+
+
+class StyleLearner:
+    """单个聊天室的表达风格学习器"""
+
+    def __init__(self, chat_id: str, model_config: Optional[Dict] = None):
+        """
+        Args:
+            chat_id: 聊天室ID
+            model_config: 模型配置
+        """
+        self.chat_id = chat_id
+        self.model_config = model_config or {
+            "alpha": 0.5,
+            "beta": 0.5,
+            "gamma": 0.99,  # 衰减因子，支持遗忘
+            "vocab_size": 200000,
+            "use_jieba": True,
+        }
+
+        # 初始化表达模型
+        self.expressor = ExpressorModel(**self.model_config)
+
+        # 动态风格管理
+        self.max_styles = 2000  # 每个chat_id最多2000个风格
+        self.style_to_id: Dict[str, str] = {}  # style文本 -> style_id
+        self.id_to_style: Dict[str, str] = {}  # style_id -> style文本
+        self.id_to_situation: Dict[str, str] = {}  # style_id -> situation文本
+        self.next_style_id = 0
+
+        # 学习统计
+        self.learning_stats = {
+            "total_samples": 0,
+            "style_counts": {},
+            "last_update": time.time(),
+        }
+
+        logger.info(f"StyleLearner初始化成功: chat_id={chat_id}")
+
+    def add_style(self, style: str, situation: Optional[str] = None) -> bool:
+        """
+        动态添加一个新的风格
+
+        Args:
+            style: 风格文本
+            situation: 情境文本
+
+        Returns:
+            是否添加成功
+        """
+        try:
+            # 检查是否已存在
+            if style in self.style_to_id:
+                return True
+
+            # 检查是否超过最大限制
+            if len(self.style_to_id) >= self.max_styles:
+                logger.warning(f"已达到最大风格数量限制 ({self.max_styles})")
+                return False
+
+            # 生成新的style_id
+            style_id = f"style_{self.next_style_id}"
+            self.next_style_id += 1
+
+            # 添加到映射
+            self.style_to_id[style] = style_id
+            self.id_to_style[style_id] = style
+            if situation:
+                self.id_to_situation[style_id] = situation
+
+            # 添加到expressor模型
+            self.expressor.add_candidate(style_id, style, situation)
+
+            # 初始化统计
+            self.learning_stats["style_counts"][style_id] = 0
+
+            logger.debug(f"添加风格成功: {style_id} -> {style}")
+            return True
+
+        except Exception as e:
+            logger.error(f"添加风格失败: {e}")
+            return False
+
+    def learn_mapping(self, up_content: str, style: str) -> bool:
+        """
+        学习一个up_content到style的映射
+
+        Args:
+            up_content: 前置内容
+            style: 目标风格
+
+        Returns:
+            是否学习成功
+        """
+        try:
+            # 如果style不存在，先添加它
+            if style not in self.style_to_id:
+                if not self.add_style(style):
+                    return False
+
+            # 获取style_id
+            style_id = self.style_to_id[style]
+
+            # 使用正反馈学习
+            self.expressor.update_positive(up_content, style_id)
+
+            # 更新统计
+            self.learning_stats["total_samples"] += 1
+            self.learning_stats["style_counts"][style_id] += 1
+            self.learning_stats["last_update"] = time.time()
+
+            logger.debug(f"学习映射成功: {up_content[:20]}... -> {style}")
+            return True
+
+        except Exception as e:
+            logger.error(f"学习映射失败: {e}")
+            return False
+
+    def predict_style(self, up_content: str, top_k: int = 5) -> Tuple[Optional[str], Dict[str, float]]:
+        """
+        根据up_content预测最合适的style
+
+        Args:
+            up_content: 前置内容
+            top_k: 返回前k个候选
+
+        Returns:
+            (最佳style文本, 所有候选的分数字典)
+        """
+        try:
+            # 先检查是否有训练数据
+            if not self.style_to_id:
+                logger.debug(f"StyleLearner还没有任何训练数据: chat_id={self.chat_id}")
+                return None, {}
+            
+            best_style_id, scores = self.expressor.predict(up_content, k=top_k)
+
+            if best_style_id is None:
+                logger.debug(f"ExpressorModel未返回预测结果: chat_id={self.chat_id}, up_content={up_content[:50]}...")
+                return None, {}
+
+            # 将style_id转换为style文本
+            best_style = self.id_to_style.get(best_style_id)
+            
+            if best_style is None:
+                logger.warning(
+                    f"style_id无法转换为style文本: style_id={best_style_id}, "
+                    f"已知的id_to_style数量={len(self.id_to_style)}"
+                )
+                return None, {}
+
+            # 转换所有分数
+            style_scores = {}
+            for sid, score in scores.items():
+                style_text = self.id_to_style.get(sid)
+                if style_text:
+                    style_scores[style_text] = score
+                else:
+                    logger.warning(f"跳过无法转换的style_id: {sid}")
+            
+            logger.debug(
+                f"预测成功: up_content={up_content[:30]}..., "
+                f"best_style={best_style}, top3_scores={list(style_scores.items())[:3]}"
+            )
+
+            return best_style, style_scores
+
+        except Exception as e:
+            logger.error(f"预测style失败: {e}", exc_info=True)
+            return None, {}
+
+    def get_style_info(self, style: str) -> Tuple[Optional[str], Optional[str]]:
+        """
+        获取style的完整信息
+
+        Args:
+            style: 风格文本
+
+        Returns:
+            (style_id, situation)
+        """
+        style_id = self.style_to_id.get(style)
+        if not style_id:
+            return None, None
+
+        situation = self.id_to_situation.get(style_id)
+        return style_id, situation
+
+    def get_all_styles(self) -> List[str]:
+        """
+        获取所有风格列表
+
+        Returns:
+            风格文本列表
+        """
+        return list(self.style_to_id.keys())
+
+    def apply_decay(self, factor: Optional[float] = None):
+        """
+        应用知识衰减
+
+        Args:
+            factor: 衰减因子
+        """
+        self.expressor.decay(factor)
+        logger.debug(f"应用知识衰减: chat_id={self.chat_id}")
+
+    def save(self, base_path: str) -> bool:
+        """
+        保存学习器到文件
+
+        Args:
+            base_path: 基础保存路径
+
+        Returns:
+            是否保存成功
+        """
+        try:
+            # 创建保存目录
+            save_dir = os.path.join(base_path, self.chat_id)
+            os.makedirs(save_dir, exist_ok=True)
+
+            # 保存expressor模型
+            model_path = os.path.join(save_dir, "expressor_model.pkl")
+            self.expressor.save(model_path)
+
+            # 保存映射关系和统计信息
+            import pickle
+
+            meta_path = os.path.join(save_dir, "meta.pkl")
+            meta_data = {
+                "style_to_id": self.style_to_id,
+                "id_to_style": self.id_to_style,
+                "id_to_situation": self.id_to_situation,
+                "next_style_id": self.next_style_id,
+                "learning_stats": self.learning_stats,
+            }
+
+            with open(meta_path, "wb") as f:
+                pickle.dump(meta_data, f)
+
+            logger.info(f"StyleLearner保存成功: {save_dir}")
+            return True
+
+        except Exception as e:
+            logger.error(f"保存StyleLearner失败: {e}")
+            return False
+
+    def load(self, base_path: str) -> bool:
+        """
+        从文件加载学习器
+
+        Args:
+            base_path: 基础加载路径
+
+        Returns:
+            是否加载成功
+        """
+        try:
+            save_dir = os.path.join(base_path, self.chat_id)
+
+            # 检查目录是否存在
+            if not os.path.exists(save_dir):
+                logger.debug(f"StyleLearner保存目录不存在: {save_dir}")
+                return False
+
+            # 加载expressor模型
+            model_path = os.path.join(save_dir, "expressor_model.pkl")
+            if os.path.exists(model_path):
+                self.expressor.load(model_path)
+
+            # 加载映射关系和统计信息
+            import pickle
+
+            meta_path = os.path.join(save_dir, "meta.pkl")
+            if os.path.exists(meta_path):
+                with open(meta_path, "rb") as f:
+                    meta_data = pickle.load(f)
+
+                self.style_to_id = meta_data["style_to_id"]
+                self.id_to_style = meta_data["id_to_style"]
+                self.id_to_situation = meta_data["id_to_situation"]
+                self.next_style_id = meta_data["next_style_id"]
+                self.learning_stats = meta_data["learning_stats"]
+
+            logger.info(f"StyleLearner加载成功: {save_dir}")
+            return True
+
+        except Exception as e:
+            logger.error(f"加载StyleLearner失败: {e}")
+            return False
+
+    def get_stats(self) -> Dict:
+        """获取统计信息"""
+        model_stats = self.expressor.get_stats()
+        return {
+            "chat_id": self.chat_id,
+            "n_styles": len(self.style_to_id),
+            "total_samples": self.learning_stats["total_samples"],
+            "last_update": self.learning_stats["last_update"],
+            "model_stats": model_stats,
+        }
+
+
+class StyleLearnerManager:
+    """多聊天室表达风格学习管理器"""
+
+    def __init__(self, model_save_path: str = "data/expression/style_models"):
+        """
+        Args:
+            model_save_path: 模型保存路径
+        """
+        self.learners: Dict[str, StyleLearner] = {}
+        self.model_save_path = model_save_path
+
+        # 确保保存目录存在
+        os.makedirs(model_save_path, exist_ok=True)
+
+        logger.info(f"StyleLearnerManager初始化成功, 模型保存路径: {model_save_path}")
+
+    def get_learner(self, chat_id: str, model_config: Optional[Dict] = None) -> StyleLearner:
+        """
+        获取或创建指定chat_id的学习器
+
+        Args:
+            chat_id: 聊天室ID
+            model_config: 模型配置
+
+        Returns:
+            StyleLearner实例
+        """
+        if chat_id not in self.learners:
+            # 创建新的学习器
+            learner = StyleLearner(chat_id, model_config)
+
+            # 尝试加载已保存的模型
+            learner.load(self.model_save_path)
+
+            self.learners[chat_id] = learner
+
+        return self.learners[chat_id]
+
+    def learn_mapping(self, chat_id: str, up_content: str, style: str) -> bool:
+        """
+        学习一个映射关系
+
+        Args:
+            chat_id: 聊天室ID
+            up_content: 前置内容
+            style: 目标风格
+
+        Returns:
+            是否学习成功
+        """
+        learner = self.get_learner(chat_id)
+        return learner.learn_mapping(up_content, style)
+
+    def predict_style(self, chat_id: str, up_content: str, top_k: int = 5) -> Tuple[Optional[str], Dict[str, float]]:
+        """
+        预测最合适的风格
+
+        Args:
+            chat_id: 聊天室ID
+            up_content: 前置内容
+            top_k: 返回前k个候选
+
+        Returns:
+            (最佳style, 分数字典)
+        """
+        learner = self.get_learner(chat_id)
+        return learner.predict_style(up_content, top_k)
+
+    def save_all(self) -> bool:
+        """
+        保存所有学习器
+
+        Returns:
+            是否全部保存成功
+        """
+        success = True
+        for chat_id, learner in self.learners.items():
+            if not learner.save(self.model_save_path):
+                success = False
+
+        logger.info(f"保存所有StyleLearner {'成功' if success else '部分失败'}")
+        return success
+
+    def apply_decay_all(self, factor: Optional[float] = None):
+        """
+        对所有学习器应用知识衰减
+
+        Args:
+            factor: 衰减因子
+        """
+        for learner in self.learners.values():
+            learner.apply_decay(factor)
+
+        logger.info(f"对所有StyleLearner应用知识衰减")
+
+    def get_all_stats(self) -> Dict[str, Dict]:
+        """
+        获取所有学习器的统计信息
+
+        Returns:
+            {chat_id: stats}
+        """
+        return {chat_id: learner.get_stats() for chat_id, learner in self.learners.items()}
+
+
+# 全局单例
+style_learner_manager = StyleLearnerManager()

src/chat/message_manager/distribution_manager.py

@@ -46,6 +46,9 @@ class StreamLoopManager:
         # 状态控制
         self.is_running = False
 
+        # 每个流的上一次间隔值（用于日志去重）
+        self._last_intervals: dict[str, float] = {}
+
         logger.info(f"流循环管理器初始化完成 (最大并发流数: {self.max_concurrent_streams})")
 
     async def start(self) -> None:
@@ -285,7 +288,11 @@ class StreamLoopManager:
                     interval = await self._calculate_interval(stream_id, has_messages)
 
                     # 6. sleep等待下次检查
-                    logger.info(f"流 {stream_id} 等待 {interval:.2f}s")
+                    # 只在间隔发生变化时输出日志，避免刷屏
+                    last_interval = self._last_intervals.get(stream_id)
+                    if last_interval is None or abs(interval - last_interval) > 0.01:
+                        logger.info(f"流 {stream_id} 等待周期变化: {interval:.2f}s")
+                        self._last_intervals[stream_id] = interval
                     await asyncio.sleep(interval)
 
                 except asyncio.CancelledError:
@@ -316,6 +323,9 @@ class StreamLoopManager:
             except Exception as e:
                 logger.debug(f"释放自适应流处理槽位失败: {e}")
 
+            # 清理间隔记录
+            self._last_intervals.pop(stream_id, None)
+
             logger.info(f"流循环结束: {stream_id}")
 
     async def _get_stream_context(self, stream_id: str) -> Any | None:

src/chat/planner_actions/action_manager.py

@@ -5,6 +5,7 @@ from typing import Any
 
 from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
 from src.chat.utils.timer_calculator import Timer
+from src.common.data_models.database_data_model import DatabaseMessages
 from src.common.logger import get_logger
 from src.config.config import global_config
 from src.person_info.person_info import get_person_info_manager
@@ -142,7 +143,7 @@ class ChatterActionManager:
         self,
         action_name: str,
         chat_id: str,
-        target_message: dict | None = None,
+        target_message: dict | DatabaseMessages | None = None,
         reasoning: str = "",
         action_data: dict | None = None,
         thinking_id: str | None = None,
@@ -262,9 +263,15 @@ class ChatterActionManager:
                         from_plugin=False,
                     )
                     if not success or not response_set:
-                        logger.info(
-                            f"对 {target_message.get('processed_plain_text') if target_message else '未知消息'} 的回复生成失败"
-                        )
+                        # 安全地获取 processed_plain_text
+                        if isinstance(target_message, DatabaseMessages):
+                            msg_text = target_message.processed_plain_text or "未知消息"
+                        elif target_message:
+                            msg_text = target_message.get("processed_plain_text", "未知消息")
+                        else:
+                            msg_text = "未知消息"
+                        
+                        logger.info(f"对 {msg_text} 的回复生成失败")
                         return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
                 except asyncio.CancelledError:
                     logger.debug(f"{log_prefix} 并行执行：回复生成任务已被取消")
@@ -322,8 +329,11 @@ class ChatterActionManager:
 
             # 获取目标消息ID
             target_message_id = None
-            if target_message and isinstance(target_message, dict):
-                target_message_id = target_message.get("message_id")
+            if target_message:
+                if isinstance(target_message, DatabaseMessages):
+                    target_message_id = target_message.message_id
+                elif isinstance(target_message, dict):
+                    target_message_id = target_message.get("message_id")
             elif action_data and isinstance(action_data, dict):
                 target_message_id = action_data.get("target_message_id")
 
@@ -488,14 +498,19 @@ class ChatterActionManager:
         person_info_manager = get_person_info_manager()
 
         # 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
-        platform = action_message.get("chat_info_platform")
-        if platform is None:
-            platform = getattr(chat_stream, "platform", "unknown")
+        if isinstance(action_message, DatabaseMessages):
+            platform = action_message.chat_info.platform
+            user_id = action_message.user_info.user_id
+        else:
+            platform = action_message.get("chat_info_platform")
+            if platform is None:
+                platform = getattr(chat_stream, "platform", "unknown")
+            user_id = action_message.get("user_id", "")
 
         # 获取用户信息并生成回复提示
         person_id = person_info_manager.get_person_id(
             platform,
-            action_message.get("user_id", ""),
+            user_id,
         )
         person_name = await person_info_manager.get_value(person_id, "person_name")
         action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
@@ -565,7 +580,14 @@ class ChatterActionManager:
 
         # 根据新消息数量决定是否需要引用回复
         reply_text = ""
-        is_proactive_thinking = (message_data.get("message_type") == "proactive_thinking") if message_data else True
+        # 检查是否为主动思考消息
+        if isinstance(message_data, DatabaseMessages):
+            # DatabaseMessages 对象没有 message_type 字段，默认为 False
+            is_proactive_thinking = False
+        elif message_data:
+            is_proactive_thinking = message_data.get("message_type") == "proactive_thinking"
+        else:
+            is_proactive_thinking = True
 
         logger.debug(f"[send_response] message_data: {message_data}")
 

src/chat/replyer/default_generator.py

@@ -27,6 +27,7 @@ from src.chat.utils.prompt import Prompt, global_prompt_manager
 from src.chat.utils.prompt_params import PromptParameters
 from src.chat.utils.timer_calculator import Timer
 from src.chat.utils.utils import get_chat_type_and_target_info
+from src.common.data_models.database_data_model import DatabaseMessages
 from src.common.logger import get_logger
 from src.config.config import global_config, model_config
 from src.individuality.individuality import get_individuality
@@ -474,10 +475,13 @@ class DefaultReplyer:
         style_habits = []
         grammar_habits = []
 
-        # 使用从处理器传来的选中表达方式
-        # LLM模式：调用LLM选择5-10个，然后随机选5个
-        selected_expressions = await expression_selector.select_suitable_expressions_llm(
-            self.chat_stream.stream_id, chat_history, max_num=8, min_num=2, target_message=target
+        # 使用统一的表达方式选择入口（支持classic和exp_model模式）
+        selected_expressions = await expression_selector.select_suitable_expressions(
+            chat_id=self.chat_stream.stream_id,
+            chat_history=chat_history,
+            target_message=target,
+            max_num=8,
+            min_num=2
         )
 
         if selected_expressions:
@@ -1208,7 +1212,7 @@ class DefaultReplyer:
         extra_info: str = "",
         available_actions: dict[str, ActionInfo] | None = None,
         enable_tool: bool = True,
-        reply_message: dict[str, Any] | None = None,
+        reply_message: dict[str, Any] | DatabaseMessages | None = None,
     ) -> str:
         """
         构建回复器上下文
@@ -1250,10 +1254,24 @@ class DefaultReplyer:
             if reply_message is None:
                 logger.warning("reply_message 为 None，无法构建prompt")
                 return ""
-            platform = reply_message.get("chat_info_platform")
+            
+            # 统一处理 DatabaseMessages 对象和字典
+            if isinstance(reply_message, DatabaseMessages):
+                platform = reply_message.chat_info.platform
+                user_id = reply_message.user_info.user_id
+                user_nickname = reply_message.user_info.user_nickname
+                user_cardname = reply_message.user_info.user_cardname
+                processed_plain_text = reply_message.processed_plain_text
+            else:
+                platform = reply_message.get("chat_info_platform")
+                user_id = reply_message.get("user_id")
+                user_nickname = reply_message.get("user_nickname")
+                user_cardname = reply_message.get("user_cardname")
+                processed_plain_text = reply_message.get("processed_plain_text")
+            
             person_id = person_info_manager.get_person_id(
                 platform,  # type: ignore
-                reply_message.get("user_id"),  # type: ignore
+                user_id,  # type: ignore
             )
             person_name = await person_info_manager.get_value(person_id, "person_name")
 
@@ -1262,22 +1280,22 @@ class DefaultReplyer:
                 # 尝试从reply_message获取用户名
                 await person_info_manager.first_knowing_some_one(
                     platform,  # type: ignore
-                    reply_message.get("user_id"),  # type: ignore
-                    reply_message.get("user_nickname") or "",
-                    reply_message.get("user_cardname") or "",
+                    user_id,  # type: ignore
+                    user_nickname or "",
+                    user_cardname or "",
                 )
 
             # 检查是否是bot自己的名字，如果是则替换为"(你)"
             bot_user_id = str(global_config.bot.qq_account)
             current_user_id = await person_info_manager.get_value(person_id, "user_id")
-            current_platform = reply_message.get("chat_info_platform")
+            current_platform = platform
 
             if current_user_id == bot_user_id and current_platform == global_config.bot.platform:
                 sender = f"{person_name}(你)"
             else:
                 # 如果不是bot自己，直接使用person_name
                 sender = person_name
-            target = reply_message.get("processed_plain_text")
+            target = processed_plain_text
 
         # 最终的空值检查，确保sender和target不为None
         if sender is None:
@@ -1611,15 +1629,22 @@ class DefaultReplyer:
         raw_reply: str,
         reason: str,
         reply_to: str,
-        reply_message: dict[str, Any] | None = None,
+        reply_message: dict[str, Any] | DatabaseMessages | None = None,
     ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
         is_group_chat = bool(chat_stream.group_info)
 
         if reply_message:
-            sender = reply_message.get("sender")
-            target = reply_message.get("target")
+            if isinstance(reply_message, DatabaseMessages):
+                # 从 DatabaseMessages 对象获取 sender 和 target
+                # 注意: DatabaseMessages 没有直接的 sender/target 字段
+                # 需要根据实际情况构造
+                sender = reply_message.user_info.user_nickname or reply_message.user_info.user_id
+                target = reply_message.processed_plain_text or ""
+            else:
+                sender = reply_message.get("sender")
+                target = reply_message.get("target")
         else:
             sender, target = self._parse_reply_target(reply_to)
 
@@ -1891,42 +1916,64 @@ class DefaultReplyer:
             logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
             return f"你完全不认识{sender}，不理解ta的相关信息。"
 
-        # 使用统一评分API获取关系信息
+        # 使用 RelationshipFetcher 获取完整关系信息（包含新字段）
         try:
-            from src.plugin_system.apis.scoring_api import scoring_api
+            from src.person_info.relationship_fetcher import relationship_fetcher_manager
 
-            # 获取用户信息以获取真实的user_id
-            user_info = await person_info_manager.get_values(person_id, ["user_id", "platform"])
-            user_id = user_info.get("user_id", "unknown")
+            # 获取 chat_id
+            chat_id = self.chat_stream.stream_id
 
-            # 从统一API获取关系数据
-            relationship_data = await scoring_api.get_user_relationship_data(user_id)
-            if relationship_data:
-                relationship_text = relationship_data.get("relationship_text", "")
-                relationship_score = relationship_data.get("relationship_score", 0.3)
+            # 获取 RelationshipFetcher 实例
+            relationship_fetcher = relationship_fetcher_manager.get_fetcher(chat_id)
 
-                # 构建丰富的关系信息描述
-                if relationship_text:
-                    # 转换关系分数为描述性文本
-                    if relationship_score >= 0.8:
-                        relationship_level = "非常亲密的朋友"
-                    elif relationship_score >= 0.6:
-                        relationship_level = "好朋友"
-                    elif relationship_score >= 0.4:
-                        relationship_level = "普通朋友"
-                    elif relationship_score >= 0.2:
-                        relationship_level = "认识的人"
-                    else:
-                        relationship_level = "陌生人"
+            # 构建用户关系信息（包含别名、偏好关键词等新字段）
+            user_relation_info = await relationship_fetcher.build_relation_info(person_id, points_num=5)
 
-                    return f"你与{sender}的关系：{relationship_level}（关系分：{relationship_score:.2f}/1.0）。{relationship_text}"
-                else:
-                    return f"你与{sender}是初次见面，关系分：{relationship_score:.2f}/1.0。"
+            # 构建聊天流印象信息
+            stream_impression = await relationship_fetcher.build_chat_stream_impression(chat_id)
+
+            # 组合两部分信息
+            if user_relation_info and stream_impression:
+                return "\n\n".join([user_relation_info, stream_impression])
+            elif user_relation_info:
+                return user_relation_info
+            elif stream_impression:
+                return stream_impression
             else:
                 return f"你完全不认识{sender}，这是第一次互动。"
 
         except Exception as e:
             logger.error(f"获取关系信息失败: {e}")
+            # 降级到基本信息
+            try:
+                from src.plugin_system.apis.scoring_api import scoring_api
+
+                user_info = await person_info_manager.get_values(person_id, ["user_id", "platform"])
+                user_id = user_info.get("user_id", "unknown")
+
+                relationship_data = await scoring_api.get_user_relationship_data(user_id)
+                if relationship_data:
+                    relationship_text = relationship_data.get("relationship_text", "")
+                    relationship_score = relationship_data.get("relationship_score", 0.3)
+
+                    if relationship_text:
+                        if relationship_score >= 0.8:
+                            relationship_level = "非常亲密的朋友"
+                        elif relationship_score >= 0.6:
+                            relationship_level = "好朋友"
+                        elif relationship_score >= 0.4:
+                            relationship_level = "普通朋友"
+                        elif relationship_score >= 0.2:
+                            relationship_level = "认识的人"
+                        else:
+                            relationship_level = "陌生人"
+
+                        return f"你与{sender}的关系：{relationship_level}（关系分：{relationship_score:.2f}/1.0）。{relationship_text}"
+                    else:
+                        return f"你与{sender}是初次见面，关系分：{relationship_score:.2f}/1.0。"
+            except Exception:
+                pass
+
             return f"你与{sender}是普通朋友关系。"
 
     async def _store_chat_memory_async(self, reply_to: str, reply_message: dict[str, Any] | None = None):

src/chat/utils/prompt.py

@@ -606,11 +606,11 @@ class Prompt:
                     recent_messages, replace_bot_name=True, timestamp_mode="normal", truncate=True
                 )
 
-            # 使用LLM选择与当前情景匹配的表达习惯
+            # 使用统一的表达方式选择入口（支持classic和exp_model模式）
             expression_selector = ExpressionSelector(self.parameters.chat_id)
-            selected_expressions = await expression_selector.select_suitable_expressions_llm(
+            selected_expressions = await expression_selector.select_suitable_expressions(
                 chat_id=self.parameters.chat_id,
-                chat_info=chat_history,
+                chat_history=chat_history,
                 target_message=self.parameters.target,
             )
 
@@ -1109,8 +1109,18 @@ class Prompt:
             logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
             return f"你完全不认识{sender}，不理解ta的相关信息。"
 
-        # 使用关系提取器构建关系信息
-        return await relationship_fetcher.build_relation_info(person_id, points_num=5)
+        # 使用关系提取器构建用户关系信息和聊天流印象
+        user_relation_info = await relationship_fetcher.build_relation_info(person_id, points_num=5)
+        stream_impression = await relationship_fetcher.build_chat_stream_impression(chat_id)
+        
+        # 组合两部分信息
+        info_parts = []
+        if user_relation_info:
+            info_parts.append(user_relation_info)
+        if stream_impression:
+            info_parts.append(stream_impression)
+        
+        return "\n\n".join(info_parts) if info_parts else ""
 
     def _get_default_result_for_task(self, task_name: str) -> dict[str, Any]:
         """为超时或失败的异步构建任务提供一个安全的默认返回值.