better:优化了表达方式采样

plugins/ChatFrequency/plugin.py

@@ -112,26 +112,40 @@ class BetterFrequencyPlugin(BasePlugin):
     # 配置节描述
     config_section_descriptions = {
         "plugin": "插件基本信息",
-        "frequency": "频率控制配置"
+        "frequency": "频率控制配置",
+        "features": "功能开关配置"
     }
 
     # 配置Schema定义
     config_schema: dict = {
         "plugin": {
             "name": ConfigField(type=str, default="better_frequency_plugin", description="插件名称"),
-            "version": ConfigField(type=str, default="1.0.0", description="插件版本"),
+            "version": ConfigField(type=str, default="1.0.1", description="插件版本"),
             "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
         },
         "frequency": {
             "default_talk_adjust": ConfigField(type=float, default=1.0, description="默认talk_frequency调整值"),
             "max_adjust_value": ConfigField(type=float, default=1.0, description="最大调整值"),
             "min_adjust_value": ConfigField(type=float, default=0.0, description="最小调整值"),
+        },
+        "features": {
+            "enable_frequency_adjust_action": ConfigField(type=bool, default=False, description="是否启用频率调节动作（FrequencyAdjustAction）"),
+            "enable_commands": ConfigField(type=bool, default=True, description="是否启用命令功能（/chat命令）"),
         }
     }
 
     def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
-        return [
+        components = []
-            (SetTalkFrequencyCommand.get_command_info(), SetTalkFrequencyCommand),
+        
-            (ShowFrequencyCommand.get_command_info(), ShowFrequencyCommand),
+        # 根据配置决定是否注册命令组件
-            (FrequencyAdjustAction.get_action_info(), FrequencyAdjustAction),
+        if self.config.get("features", {}).get("enable_commands", True):
-        ]
+            components.extend([
+                (SetTalkFrequencyCommand.get_command_info(), SetTalkFrequencyCommand),
+                (ShowFrequencyCommand.get_command_info(), ShowFrequencyCommand),
+            ])
+        
+        # 根据配置决定是否注册频率调节动作组件
+        if self.config.get("features", {}).get("enable_frequency_adjust_action", True):
+            components.append((FrequencyAdjustAction.get_action_info(), FrequencyAdjustAction))
+        
+        return components

src/express/express_utils.py

@@ -0,0 +1,93 @@
+import re
+import difflib
+import random
+from datetime import datetime
+from typing import Optional, List, Dict
+from collections import defaultdict
+
+
+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:
+        str: 过滤后的内容
+    """
+    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[picid:...]格式的图片ID
+    content = re.sub(r'\[picid:[^\]]*\]', '', 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
+    使用SequenceMatcher计算相似度
+    
+    Args:
+        text1: 第一个文本
+        text2: 第二个文本
+        
+    Returns:
+        float: 相似度值，范围0-1
+    """
+    return difflib.SequenceMatcher(None, text1, text2).ratio()
+
+
+def format_create_date(timestamp: float) -> str:
+    """
+    将时间戳格式化为可读的日期字符串
+    
+    Args:
+        timestamp: 时间戳
+        
+    Returns:
+        str: 格式化后的日期字符串
+    """
+    try:
+        return datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
+    except (ValueError, OSError):
+        return "未知时间"
+
+
+def weighted_sample(population: List[Dict], k: int) -> List[Dict]:
+    """
+    随机抽样函数
+    
+    Args:
+        population: 总体数据列表
+        k: 需要抽取的数量
+        
+    Returns:
+        List[Dict]: 抽取的数据列表
+    """
+    if not population or k <= 0:
+        return []
+
+    if len(population) <= k:
+        return population.copy()
+
+    # 使用随机抽样
+    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

src/express/expression_learner.py

@@ -18,6 +18,7 @@ from src.chat.utils.chat_message_builder import (
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.express.style_learner import style_learner_manager
+from src.express.express_utils import filter_message_content, calculate_similarity, format_create_date
 from json_repair import repair_json
 
 
@@ -26,24 +27,6 @@ from json_repair import repair_json
 logger = get_logger("expressor")
 
 
-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
-    使用SequenceMatcher计算相似度
-    """
-    return difflib.SequenceMatcher(None, text1, text2).ratio()
-
-
-def format_create_date(timestamp: float) -> str:
-    """
-    将时间戳格式化为可读的日期字符串
-    """
-    try:
-        return datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
-    except (ValueError, OSError):
-        return "未知时间"
-
-
 def init_prompt() -> None:
     learn_style_prompt = """
 {chat_str}
@@ -457,7 +440,7 @@ class ExpressionLearner:
                 continue
             
             prev_original_idx = bare_lines[pos - 1][0]
-            up_content = self._filter_message_content(random_msg[prev_original_idx].processed_plain_text or "")
+            up_content = filter_message_content(random_msg[prev_original_idx].processed_plain_text or "")
             if not up_content:
                 # 上一句为空，跳过该表达
                 continue
@@ -499,30 +482,6 @@ class ExpressionLearner:
             expressions.append((situation, style))
         return expressions
 
-    def _filter_message_content(self, content: 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:
-            str: 过滤后的内容
-        """
-        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[picid:...]格式的图片ID
-        content = re.sub(r'\[picid:[^\]]*\]', '', content)
-        # 移除[表情包：...]格式的内容
-        content = re.sub(r'\[表情包：[^\]]*\]', '', content)
-        
-        return content.strip()
-
     def _build_bare_lines(self, messages: List) -> List[Tuple[int, str]]:
         """
         为每条消息构建精简文本列表，保留到原消息索引的映射
@@ -537,7 +496,7 @@ class ExpressionLearner:
         
         for idx, msg in enumerate(messages):
             content = msg.processed_plain_text or ""
-            content = self._filter_message_content(content)
+            content = filter_message_content(content)
             # 即使content为空也要记录，防止错位
             bare_lines.append((idx, content))
                 

src/express/expression_selector.py

@@ -2,6 +2,7 @@ import json
 import time
 import random
 import hashlib
+import re
 
 from typing import List, Dict, Optional, Any, Tuple
 from json_repair import repair_json
@@ -12,6 +13,7 @@ from src.common.logger import get_logger
 from src.common.database.database_model import Expression
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.express.style_learner import style_learner_manager
+from src.express.express_utils import filter_message_content, weighted_sample
 
 logger = get_logger("expression_selector")
 
@@ -44,29 +46,6 @@ def init_prompt():
     Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
 
 
-def weighted_sample(population: List[Dict], k: int) -> List[Dict]:
-    """随机抽样"""
-    if not population or k <= 0:
-        return []
-
-    if len(population) <= k:
-        return population.copy()
-
-    # 使用随机抽样
-    selected = []
-    population_copy = population.copy()
-
-    for _ in range(k):
-        if not population_copy:
-            break
-
-        # 随机选择一个元素
-        chosen_idx = random.randint(0, len(population_copy) - 1)
-        selected.append(population_copy.pop(chosen_idx))
-
-    return selected
-
-
 class ExpressionSelector:
     def __init__(self):
         self.llm_model = LLMRequest(
@@ -149,6 +128,9 @@ class ExpressionSelector:
             List[Dict[str, Any]]: 预测的表达方式列表
         """
         try:
+            # 过滤目标消息内容，移除回复、表情包等特殊格式
+            filtered_target_message = filter_message_content(target_message)
+            
             # 支持多chat_id合并预测
             related_chat_ids = self.get_related_chat_ids(chat_id)
             
@@ -160,7 +142,7 @@ class ExpressionSelector:
                 try:
                     # 使用 style_learner 预测最合适的风格
                     best_style, scores = style_learner_manager.predict_style(
-                        related_chat_id, target_message, top_k=total_num
+                        related_chat_id, filtered_target_message, top_k=total_num
                     )
                     
                     if best_style and scores:
@@ -186,7 +168,7 @@ class ExpressionSelector:
                                     "source_id": expr.chat_id,
                                     "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time,
                                     "prediction_score": scores.get(best_style, 0.0),
-                                    "prediction_input": target_message
+                                    "prediction_input": filtered_target_message
                                 })
                             else:
                                 logger.warning(f"为聊天室 {related_chat_id} 预测表达方式失败: {best_style} 没有找到对应的表达方式")

src/express/expressor_model/tokenizer.py

@@ -8,6 +8,8 @@ except Exception:
     _HAS_JIEBA = False
 
 _WORD_RE = re.compile(r"[A-Za-z0-9_]+")
+# 匹配纯符号的正则表达式
+_SYMBOL_RE = re.compile(r'^[^\w\u4e00-\u9fff]+$')
 
 def simple_en_tokenize(text: str) -> List[str]:
     return _WORD_RE.findall(text.lower())
@@ -25,4 +27,5 @@ class Tokenizer:
             toks = [t.strip().lower() for t in jieba.cut(text) if t.strip()]
         else:
             toks = simple_en_tokenize(text)
-        return [t for t in toks if t not in self.stopwords]
+        # 过滤掉纯符号和停用词
+        return [t for t in toks if t not in self.stopwords and not _SYMBOL_RE.match(t)]