fix：优化表达方式log和最大表达数量，优化配置文件注释

src/chat/focus_chat/expressors/exprssion_learner.py

@@ -7,10 +7,11 @@ from src.config.config import global_config
 from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_random, build_anonymous_messages
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 import os
+from src.chat.message_receive.chat_stream import chat_manager
 import json
 
 
-MAX_EXPRESSION_COUNT = 100
+MAX_EXPRESSION_COUNT = 300
 
 logger = get_logger("expressor")
 
@@ -129,9 +130,19 @@ class ExpressionLearner:
             type_str = "句法特点"
         else:
             raise ValueError(f"Invalid type: {type}")
-        logger.info(f"开始学习{type_str}...")
+        # logger.info(f"开始学习{type_str}...")
-        learnt_expressions: Optional[List[Tuple[str, str, str]]] = await self.learn_expression(type, num)
+        learnt_expressions,chat_id = await self.learn_expression(type, num)
-        logger.info(f"学习到{len(learnt_expressions) if learnt_expressions else 0}条{type_str}")
+        
+        chat_stream = chat_manager.get_stream(chat_id)
+        if chat_stream.group_info:
+            group_name = chat_stream.group_info.group_name
+        else:
+            group_name = f"{chat_stream.user_info.user_nickname}的私聊"
+        learnt_expressions_str = ""
+        for _chat_id, situation, style in learnt_expressions:
+            
+            learnt_expressions_str += f"{situation}->{style}\n"
+        logger.info(f"在 {group_name} 学习到{type_str}:\n{learnt_expressions_str}")
         # learnt_expressions: List[(chat_id, situation, style)]
 
         if not learnt_expressions:
@@ -188,7 +199,7 @@ class ExpressionLearner:
                 json.dump(old_data, f, ensure_ascii=False, indent=2)
         return learnt_expressions
 
-    async def learn_expression(self, type: str, num: int = 10) -> Optional[List[Tuple[str, str, str]]]:
+    async def learn_expression(self, type: str, num: int = 10) -> Optional[Tuple[List[Tuple[str, str, str]], str]]:
         """选择从当前到最近1小时内的随机num条消息，然后学习这些消息的表达方式
 
         Args:
@@ -233,7 +244,7 @@ class ExpressionLearner:
 
         expressions: List[Tuple[str, str, str]] = self.parse_expression_response(response, chat_id)
 
-        return expressions
+        return expressions,chat_id
 
     def parse_expression_response(self, response: str, chat_id: str) -> List[Tuple[str, str, str]]:
         """

src/chat/heart_flow/sub_heartflow.py

@@ -75,9 +75,9 @@ class SubHeartflow:
         self.log_prefix = (
             chat_manager.get_stream_name(self.subheartflow_id) or self.subheartflow_id
         )  # Keep this line or adjust if utils provides name
-        logger.debug(
+        # logger.debug(
-            f"SubHeartflow {self.chat_id} initialized: is_group={self.is_group_chat}, target_info={self.chat_target_info}"
+            # f"SubHeartflow {self.chat_id} initialized: is_group={self.is_group_chat}, target_info={self.chat_target_info}"
-        )
+        # )
 
         # 根据配置决定初始状态
         if global_config.chat.chat_mode == "focus":

template/bot_config_template.toml

@@ -109,7 +109,6 @@ planner_type = "simple" # 规划器类型，可选值：complex（复杂规划
 self_identify_processor = true # 是否启用自我识别处理器
 relation_processor = true # 是否启用关系识别处理器
 tool_use_processor = false # 是否启用工具使用处理器
-mind_processor = false # 是否启用思维处理器
 working_memory_processor = false # 是否启用工作记忆处理器，消耗量大
 
 [emoji]
@@ -135,7 +134,7 @@ consolidate_memory_interval = 2000 # 记忆整合间隔 单位秒   间隔越低
 consolidation_similarity_threshold = 0.7 # 相似度阈值
 consolidation_check_percentage = 0.01 # 检查节点比例
 
-#不希望记忆的词，已经记忆的不会受到影响
+#不希望记忆的词，已经记忆的不会受到影响，需要手动清理
 memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ]
 
 [mood] # 仅在 普通聊天 有效
@@ -143,11 +142,17 @@ mood_update_interval = 1.0 # 情绪更新间隔 单位秒
 mood_decay_rate = 0.95 # 情绪衰减率
 mood_intensity_factor = 1.0 # 情绪强度因子
 
-# 规则
+
+# keyword_rules 用于设置关键词触发的额外回复知识
+# 添加新规则方法：在 keyword_rules 数组中增加一项，格式如下：
+# { keywords = ["关键词1", "关键词2"], reaction = "触发这些关键词时的回复内容" }
+# 例如，添加一个新规则：当检测到“你好”或“hello”时回复“你好，有什么可以帮你？”
+# { keywords = ["你好", "hello"], reaction = "你好，有什么可以帮你？" }
 [keyword_reaction]
 keyword_rules = [
     { keywords = ["人机", "bot", "机器", "入机", "robot", "机器人", "ai", "AI"], reaction = "有人说你是人机或者机器人，否定这一事实，攻击或反问他。注意，只有认为你是机器人才回复，否则不要否认" },
     { keywords = ["测试关键词回复", "test"], reaction = "回答测试成功" }
+    # 在此处添加更多规则，格式同上
 ]
 
 regex_rules = [
@@ -164,7 +169,7 @@ word_replace_rate=0.006 # 整词替换概率
 [response_splitter]
 enable = true # 是否启用回复分割器
 max_length = 512 # 回复允许的最大长度
-max_sentence_num = 7 # 回复允许的最大句子数
+max_sentence_num = 8 # 回复允许的最大句子数
 enable_kaomoji_protection = false # 是否启用颜文字保护