feat(expression): 添加解析聊天流配置和获取相关聊天ID的功能以支持共享组训练

src/chat/express/expression_learner.py

@@ -1,3 +1,4 @@
+import hashlib
 import os
 import time
 from datetime import datetime
@@ -126,6 +127,55 @@ class ExpressionLearner:
         self.min_learning_interval = 300  # 最短学习时间间隔（秒）
         self._chat_name_initialized = False
 
+    @staticmethod
+    def _parse_stream_config_to_chat_id(stream_config_str: str) -> str | None:
+        """解析'platform:id:type'为chat_id（与get_stream_id一致）"""
+        try:
+            parts = stream_config_str.split(":")
+            if len(parts) != 3:
+                return None
+            platform = parts[0]
+            id_str = parts[1]
+            stream_type = parts[2]
+            is_group = stream_type == "group"
+            if is_group:
+                components = [platform, str(id_str)]
+            else:
+                components = [platform, str(id_str), "private"]
+            key = "_".join(components)
+            return hashlib.md5(key.encode()).hexdigest()
+        except Exception:
+            return None
+
+    def get_related_chat_ids(self) -> list[str]:
+        """根据expression.rules配置，获取与当前chat_id相关的所有chat_id（包括自身）
+        
+        用于共享组功能：同一共享组内的聊天流可以共享学习到的表达方式
+        """
+        if global_config is None:
+            return [self.chat_id]
+        rules = global_config.expression.rules
+        current_group = None
+
+        # 找到当前chat_id所在的组
+        for rule in rules:
+            if rule.chat_stream_id and self._parse_stream_config_to_chat_id(rule.chat_stream_id) == self.chat_id:
+                current_group = rule.group
+                break
+
+        # 始终包含当前 chat_id（确保至少能查到自己的数据）
+        related_chat_ids = [self.chat_id]
+
+        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
+
     async def _initialize_chat_name(self):
         """异步初始化chat_name"""
         if not self._chat_name_initialized:
@@ -540,20 +590,27 @@ class ExpressionLearner:
                 # 提交后清除相关缓存
                 await session.commit()
 
-            # 清除该chat_id的表达方式缓存
+            # 🔥 清除共享组内所有 chat_id 的表达方式缓存
             from src.common.database.optimization.cache_manager import get_cache
             from src.common.database.utils.decorators import generate_cache_key
             cache = await get_cache()
-            await cache.delete(generate_cache_key("chat_expressions", chat_id))
             
-            # 🔥 训练 StyleLearner
+            # 获取共享组内所有 chat_id 并清除其缓存
+            related_chat_ids = self.get_related_chat_ids()
+            for related_id in related_chat_ids:
+                await cache.delete(generate_cache_key("chat_expressions", related_id))
+            if len(related_chat_ids) > 1:
+                logger.debug(f"已清除共享组内 {len(related_chat_ids)} 个 chat_id 的表达方式缓存")
+
+            # 🔥 训练 StyleLearner（支持共享组）
             # 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.debug(f"开始训练 StyleLearner: 源chat_id={chat_id}, 共享组包含 {len(related_chat_ids)} 个chat_id, 样本数={len(expr_list)}")
 
-                    logger.debug(f"开始训练 StyleLearner: chat_id={chat_id}, 样本数={len(expr_list)}")
+                    # 为每个共享组内的 chat_id 训练其 StyleLearner
+                    for target_chat_id in related_chat_ids:
+                        learner = style_learner_manager.get_learner(target_chat_id)
                         
                         # 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
@@ -567,19 +624,28 @@ class ExpressionLearner:
                             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']}"
-                    )
+                                logger.warning(f"训练失败 (target={target_chat_id}): {situation} -> {style}")
 
                         # 保存模型
                         if learner.save(style_learner_manager.model_save_path):
-                        logger.debug(f"StyleLearner 模型保存成功: {chat_id}")
+                            logger.debug(f"StyleLearner 模型保存成功: {target_chat_id}")
                         else:
-                        logger.error(f"StyleLearner 模型保存失败: {chat_id}")
+                            logger.error(f"StyleLearner 模型保存失败: {target_chat_id}")
+
+                        if target_chat_id == chat_id:
+                            # 只为源 chat_id 记录详细日志
+                            logger.info(
+                                f"StyleLearner 训练完成 (源): {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']}"
+                            )
+                        else:
+                            logger.debug(
+                                f"StyleLearner 训练完成 (共享组成员 {target_chat_id}): {success_count}/{len(expr_list)} 成功"
+                            )
+
+                    if len(related_chat_ids) > 1:
+                        logger.info(f"共享组内共 {len(related_chat_ids)} 个 StyleLearner 已同步训练")
 
                 except Exception as e:
                     logger.error(f"训练 StyleLearner 失败: {e}")