fix: logger初始化顺序

bot.py

@@ -149,6 +149,7 @@ if __name__ == "__main__":
     init_config()
     init_env()
     load_env()
+    load_logger()
 
     env_config = {key: os.getenv(key) for key in os.environ}
     scan_provider(env_config)

src/plugins/chat/config.py

@@ -162,7 +162,7 @@ class BotConfig:
             personality_config = parent['personality']
             personality = personality_config.get('prompt_personality')
             if len(personality) >= 2:
-                logger.info(f"载入自定义人格:{personality}")
+                logger.debug(f"载入自定义人格:{personality}")
                 config.PROMPT_PERSONALITY = personality_config.get('prompt_personality', config.PROMPT_PERSONALITY)
             logger.info(f"载入自定义日程prompt:{personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)}")
             config.PROMPT_SCHEDULE_GEN = personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)

src/plugins/memory_system/memory.py

@@ -7,6 +7,7 @@ import time
 import jieba
 import networkx as nx
 
+from loguru import logger
 from ...common.database import Database  # 使用正确的导入语法
 from ..chat.config import global_config
 from ..chat.utils import (
@@ -230,7 +231,8 @@ class Hippocampus:
 
         # 过滤topics
         filter_keywords = global_config.memory_ban_words
-        topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
+        topics = [topic.strip() for topic in
+                  topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
         filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
 
         print(f"过滤后话题: {filtered_topics}")
@@ -257,7 +259,8 @@ class Hippocampus:
         topic_by_length = text.count('\n') * compress_rate
         topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
         topic_num = int((topic_by_length + topic_by_information_content) / 2)
-        print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}")
+        print(
+            f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}")
         return topic_num
 
     async def operation_build_memory(self, chat_size=20):
@@ -551,7 +554,8 @@ class Hippocampus:
         """
         topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5))
         # print(f"话题: {topics_response[0]}")
-        topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
+        topics = [topic.strip() for topic in
+                  topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
         # print(f"话题: {topics}")
 
         return topics
@@ -655,7 +659,8 @@ class Hippocampus:
             penalty = 1.0 / (1 + math.log(content_count + 1))
 
             activation = int(score * 50 * penalty)
-            print(f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
+            print(
+                f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
             return activation
 
         # 计算关键词匹配率，同时考虑内容数量
@@ -682,7 +687,8 @@ class Hippocampus:
                     matched_topics.add(input_topic)
                     adjusted_sim = sim * penalty
                     topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
-                    print(f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
+                    print(
+                        f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
 
         # 计算主题匹配率和平均相似度
         topic_match = len(matched_topics) / len(identified_topics)
@@ -690,11 +696,13 @@ class Hippocampus:
 
         # 计算最终激活值
         activation = int((topic_match + average_similarities) / 2 * 100)
-        print(f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
+        print(
+            f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
 
         return activation
 
-    async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list:
+    async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4,
+                                    max_memory_num: int = 5) -> list:
         """根据输入文本获取相关的记忆内容"""
         # 识别主题
         identified_topics = await self._identify_topics(text)
@@ -764,4 +772,4 @@ hippocampus = Hippocampus(memory_graph)
 hippocampus.sync_memory_from_db()
 
 end_time = time.time()
-print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
+logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")