remove & fix：移除人格表达，修复过滤词失效，私聊强制focus

src/chat/express/expression_selector.py

@@ -80,14 +80,16 @@ class ExpressionSelector:
         )
 
     def get_random_expressions(
-        self, chat_id: str, style_num: int, grammar_num: int, personality_num: int
+        self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
     ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
         (
             learnt_style_expressions,
             learnt_grammar_expressions,
-            personality_expressions,
         ) = self.expression_learner.get_expression_by_chat_id(chat_id)
 
+        style_num = int(total_num * style_percentage)
+        grammar_num = int(total_num * grammar_percentage)
+
         # 按权重抽样（使用count作为权重）
         if learnt_style_expressions:
             style_weights = [expr.get("count", 1) for expr in learnt_style_expressions]
@@ -101,13 +103,7 @@ class ExpressionSelector:
         else:
             selected_grammar = []
 
-        if personality_expressions:
-            personality_weights = [expr.get("count", 1) for expr in personality_expressions]
-            selected_personality = weighted_sample(personality_expressions, personality_weights, personality_num)
-        else:
-            selected_personality = []
-
-        return selected_style, selected_grammar, selected_personality
+        return selected_style, selected_grammar
 
     def update_expressions_count_batch(self, expressions_to_update: List[Dict[str, str]], increment: float = 0.1):
         """对一批表达方式更新count值，按文件分组后一次性写入"""
@@ -174,7 +170,7 @@ class ExpressionSelector:
         """使用LLM选择适合的表达方式"""
 
         # 1. 获取35个随机表达方式（现在按权重抽取）
-        style_exprs, grammar_exprs, personality_exprs = self.get_random_expressions(chat_id, 25, 25, 10)
+        style_exprs, grammar_exprs= self.get_random_expressions(chat_id, 50, 0.5, 0.5)
 
         # 2. 构建所有表达方式的索引和情境列表
         all_expressions = []
@@ -196,13 +192,6 @@ class ExpressionSelector:
                 all_expressions.append(expr_with_type)
                 all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
 
-        # 添加personality表达方式
-        for expr in personality_exprs:
-            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                expr_with_type = expr.copy()
-                expr_with_type["type"] = "style_personality"
-                all_expressions.append(expr_with_type)
-                all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
 
         if not all_expressions:
             logger.warning("没有找到可用的表达方式")
@@ -260,7 +249,7 @@ class ExpressionSelector:
 
             # 对选中的所有表达方式，一次性更新count数
             if valid_expressions:
-                self.update_expressions_count_batch(valid_expressions, 0.003)
+                self.update_expressions_count_batch(valid_expressions, 0.006)
 
             # logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
             return valid_expressions

src/chat/express/exprssion_learner.py

@@ -76,14 +76,13 @@ class ExpressionLearner:
 
     def get_expression_by_chat_id(
         self, chat_id: str
-    ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]], List[Dict[str, str]]]:
+    ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
         """
-        获取指定chat_id的style和grammar表达方式, 同时获取全局的personality表达方式
+        获取指定chat_id的style和grammar表达方式
         返回的每个表达方式字典中都包含了source_id, 用于后续的更新操作
         """
         learnt_style_expressions = []
         learnt_grammar_expressions = []
-        personality_expressions = []
 
         # 获取style表达方式
         style_dir = os.path.join("data", "expression", "learnt_style", str(chat_id))
@@ -111,19 +110,8 @@ class ExpressionLearner:
             except Exception as e:
                 logger.error(f"读取grammar表达方式失败: {e}")
 
-        # 获取personality表达方式
-        personality_file = os.path.join("data", "expression", "personality", "expressions.json")
-        if os.path.exists(personality_file):
-            try:
-                with open(personality_file, "r", encoding="utf-8") as f:
-                    expressions = json.load(f)
-                    for expr in expressions:
-                        expr["source_id"] = "personality"  # 添加来源ID
-                        personality_expressions.append(expr)
-            except Exception as e:
-                logger.error(f"读取personality表达方式失败: {e}")
 
-        return learnt_style_expressions, learnt_grammar_expressions, personality_expressions
+        return learnt_style_expressions, learnt_grammar_expressions
 
     def is_similar(self, s1: str, s2: str) -> bool:
         """
@@ -428,11 +416,12 @@ class ExpressionLearner:
 
 init_prompt()
 
-expression_learner = None
 
+if global_config.expression.enable_expression:
+    expression_learner = None
 
-def get_expression_learner():
-    global expression_learner
-    if expression_learner is None:
-        expression_learner = ExpressionLearner()
-    return expression_learner
+    def get_expression_learner():
+        global expression_learner
+        if expression_learner is None:
+            expression_learner = ExpressionLearner()
+        return expression_learner