修改配置文件

src/chat/heart_flow/heartFC_chat.py

@@ -109,7 +109,7 @@ class HeartFChatting:
 
         self.last_read_time = time.time() - 10
         
-        
+        self.talk_threshold = global_config.chat.talk_value
         
         self.no_reply_until_call = False
 
@@ -172,6 +172,16 @@ class HeartFChatting:
             f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒"  # type: ignore
             + (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
         )
+        
+    def get_talk_threshold(self):
+        talk_value = global_config.chat.talk_value
+        # 处理talk_value：取整数部分和小数部分
+        base_value = int(talk_value)
+        probability = talk_value - base_value
+        # 根据概率决定是否+1
+        think_len = base_value + 1 if random.random() < probability else base_value
+        self.talk_threshold = think_len
+        logger.info(f"{self.log_prefix} 思考频率阈值: {self.talk_threshold}")
 
     async def _loopbody(self):
         recent_messages_list = message_api.get_messages_by_time_in_chat(
@@ -184,7 +194,7 @@ class HeartFChatting:
             filter_command=True,
         )
 
-        if recent_messages_list:
+        if len(recent_messages_list) >= self.talk_threshold:
             
             # !处理no_reply_until_call逻辑
             if self.no_reply_until_call:
@@ -203,6 +213,7 @@ class HeartFChatting:
             await self._observe(
                 recent_messages_list=recent_messages_list,
             )
+            self.get_talk_threshold()
         else:
             # Normal模式：消息数量不足，等待
             await asyncio.sleep(0.2)

src/chat/heart_flow/heartflow_message_processor.py

@@ -6,7 +6,6 @@ import traceback
 from typing import Tuple, TYPE_CHECKING
 
 from src.config.config import global_config
-from src.chat.memory_system.Hippocampus import hippocampus_manager
 from src.chat.message_receive.message import MessageRecv
 from src.chat.message_receive.storage import MessageStorage
 from src.chat.heart_flow.heartflow import heartflow
@@ -38,16 +37,16 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
 
     is_mentioned, is_at, reply_probability_boost = is_mentioned_bot_in_message(message)
     interested_rate = 0.0
-
-    with Timer("记忆激活"):
-        interested_rate, keywords, keywords_lite = await hippocampus_manager.get_activate_from_text(
-            message.processed_plain_text,
-            max_depth=4,
-            fast_retrieval=global_config.chat.interest_rate_mode == "fast",
-        )
-        message.key_words = keywords
-        message.key_words_lite = keywords_lite
-        logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
+    keywords = []
+    # with Timer("记忆激活"):
+    #     interested_rate, keywords, keywords_lite = await hippocampus_manager.get_activate_from_text(
+    #         message.processed_plain_text,
+    #         max_depth=4,
+    #         fast_retrieval=global_config.chat.interest_rate_mode == "fast",
+    #     )
+    #     message.key_words = keywords
+    #     message.key_words_lite = keywords_lite
+    #     logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
 
     text_len = len(message.processed_plain_text)
     # 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算

src/chat/replyer/default_generator.py

@@ -25,7 +25,7 @@ from src.chat.utils.chat_message_builder import (
     replace_user_references,
 )
 from src.chat.express.expression_selector import expression_selector
-from src.chat.memory_system.memory_activator import MemoryActivator
+# from src.chat.memory_system.memory_activator import MemoryActivator
 from src.mood.mood_manager import mood_manager
 from src.person_info.person_info import Person, is_person_known
 from src.plugin_system.base.component_types import ActionInfo, EventType
@@ -143,7 +143,7 @@ class DefaultReplyer:
         self.chat_stream = chat_stream
         self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
         self.heart_fc_sender = UniversalMessageSender()
-        self.memory_activator = MemoryActivator()
+        # self.memory_activator = MemoryActivator()
 
         from src.plugin_system.core.tool_use import ToolExecutor  # 延迟导入ToolExecutor，不然会循环依赖
 
@@ -1019,9 +1019,6 @@ class DefaultReplyer:
     async def llm_generate_content(self, prompt: str):
         with Timer("LLM生成", {}):  # 内部计时器，可选保留
             # 直接使用已初始化的模型实例
-            logger.info(f"使用模型集生成回复: {', '.join(map(str, self.express_model.model_for_task.model_list))}")
-
-            logger.info(f"\n{prompt}\n")
 
             if global_config.debug.show_prompt:
                 logger.info(f"\n{prompt}\n")