🤖 自动格式化代码 [skip ci]

bot.py

@@ -2,6 +2,7 @@ import asyncio
 import hashlib
 import os
 from dotenv import load_dotenv
+
 if os.path.exists(".env"):
     load_dotenv(".env", override=True)
     print("成功加载环境变量配置")
@@ -13,9 +14,11 @@ import platform
 import traceback
 from pathlib import Path
 from rich.traceback import install
+
 # maim_message imports for console input
 from maim_message import Seg, UserInfo, BaseMessageInfo, MessageBase
 from src.chat.message_receive.bot import chat_bot
+
 # 最早期初始化日志系统，确保所有后续模块都使用正确的日志格式
 from src.common.logger import initialize_logging, get_logger, shutdown_logging
 from src.main import MainSystem
@@ -26,7 +29,6 @@ initialize_logging()
 logger = get_logger("main")
 
 
-
 install(extra_lines=3)
 
 # 设置工作目录为脚本所在目录

src/chat/express/expression_selector.py

@@ -12,6 +12,7 @@ from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 
 logger = get_logger("expression_selector")
 
+
 def init_prompt():
     expression_evaluation_prompt = """
 你的名字是{bot_name}
@@ -42,30 +43,32 @@ def init_prompt():
 """
     Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
 
+
 def weighted_sample(population: List[Dict], weights: List[float], k: int) -> List[Dict]:
     """按权重随机抽样"""
     if not population or not weights or k <= 0:
         return []
-    
+
     if len(population) <= k:
         return population.copy()
-    
+
     # 使用累积权重的方法进行加权抽样
     selected = []
     population_copy = population.copy()
     weights_copy = weights.copy()
-    
+
     for _ in range(k):
         if not population_copy:
             break
-            
+
         # 选择一个元素
         chosen_idx = random.choices(range(len(population_copy)), weights=weights_copy)[0]
         selected.append(population_copy.pop(chosen_idx))
         weights_copy.pop(chosen_idx)
-    
+
     return selected
 
+
 class ExpressionSelector:
     def __init__(self):
         self.expression_learner = get_expression_learner()
@@ -75,7 +78,9 @@ class ExpressionSelector:
             request_type="expression.selector",
         )
 
-    def get_random_expressions(self, chat_id: str, style_num: int, grammar_num: int, personality_num: int) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
+    def get_random_expressions(
+        self, chat_id: str, style_num: int, grammar_num: int, personality_num: int
+    ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
         (
             learnt_style_expressions,
             learnt_grammar_expressions,
@@ -88,13 +93,13 @@ class ExpressionSelector:
             selected_style = weighted_sample(learnt_style_expressions, style_weights, style_num)
         else:
             selected_style = []
-            
+
         if learnt_grammar_expressions:
             grammar_weights = [expr.get("count", 1) for expr in learnt_grammar_expressions]
             selected_grammar = weighted_sample(learnt_grammar_expressions, grammar_weights, grammar_num)
         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)
@@ -102,7 +107,7 @@ class ExpressionSelector:
             selected_personality = []
 
         return selected_style, selected_grammar, selected_personality
-    
+
     def update_expression_count(self, chat_id: str, expression: Dict[str, str], multiplier: float = 1.5):
         """更新表达方式的count值"""
         if expression.get("type") == "style_personality":
@@ -117,29 +122,30 @@ class ExpressionSelector:
                 file_path = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json")
             else:
                 return
-        
+
         if not os.path.exists(file_path):
             return
-            
+
         try:
             with open(file_path, "r", encoding="utf-8") as f:
                 expressions = json.load(f)
-            
+
             # 找到匹配的表达方式并更新count
             for expr in expressions:
-                if (expr.get("situation") == expression.get("situation") and 
+                if expr.get("situation") == expression.get("situation") and expr.get("style") == expression.get(
-                    expr.get("style") == expression.get("style")):
+                    "style"
+                ):
                     expr["count"] = expr.get("count", 1) * multiplier
                     expr["last_active_time"] = time.time()
                     break
-            
+
             # 保存更新后的文件
             with open(file_path, "w", encoding="utf-8") as f:
                 json.dump(expressions, f, ensure_ascii=False, indent=2)
-                
+
         except Exception as e:
             logger.error(f"更新表达方式count失败: {e}")
-    
+
     async def select_suitable_expressions_llm(self, chat_id: str, chat_info: str) -> List[Dict[str, str]]:
         """使用LLM选择适合的表达方式"""
 
@@ -188,7 +194,7 @@ class ExpressionSelector:
         )
 
         print(prompt)
-        
+
         # 4. 调用LLM
         try:
             content, (_, _) = await self.llm_model.generate_response_async(prompt=prompt)
@@ -216,7 +222,7 @@ class ExpressionSelector:
                 if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
                     expression = all_expressions[idx - 1]  # 索引从1开始
                     valid_expressions.append(expression)
-                    
+
                     # 对选中的表达方式count数*1.5
                     self.update_expression_count(chat_id, expression, 1.5)
 
@@ -226,7 +232,7 @@ class ExpressionSelector:
         except Exception as e:
             logger.error(f"LLM处理表达方式选择时出错: {e}")
             return []
-    
+
 
 init_prompt()
 
@@ -234,10 +240,3 @@ try:
     expression_selector = ExpressionSelector()
 except Exception as e:
     print(f"ExpressionSelector初始化失败: {e}")
-
-
-
-
-
-
-

src/chat/focus_chat/info_processors/expression_selector_processor.py

@@ -24,7 +24,6 @@ class ExpressionSelectorProcessor(BaseProcessor):
         self.selection_interval = 10  # 40秒间隔
         self.cached_expressions = []  # 缓存上一次选择的表达方式
 
-
         name = get_chat_manager().get_stream_name(self.subheartflow_id)
         self.log_prefix = f"[{name}] 表达选择器"
 
@@ -72,7 +71,9 @@ class ExpressionSelectorProcessor(BaseProcessor):
 
         try:
             # LLM模式：调用LLM选择5-10个，然后随机选5个
-            selected_expressions = await expression_selector.select_suitable_expressions_llm(self.subheartflow_id, chat_info)
+            selected_expressions = await expression_selector.select_suitable_expressions_llm(
+                self.subheartflow_id, chat_info
+            )
             cache_size = len(selected_expressions) if selected_expressions else 0
             mode_desc = f"LLM模式（已缓存{cache_size}个）"
 
@@ -93,4 +94,3 @@ class ExpressionSelectorProcessor(BaseProcessor):
         except Exception as e:
             logger.error(f"{self.log_prefix} 处理表达方式选择时出错: {e}")
             return []
-

src/chat/normal_chat/normal_prompt.py

@@ -148,7 +148,7 @@ class PromptBuilder:
             read_mark=0.0,
             show_actions=True,
         )
-        
+
         message_list_before_now_half = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_stream.stream_id,
             timestamp=time.time(),
@@ -162,8 +162,10 @@ class PromptBuilder:
             read_mark=0.0,
             show_actions=True,
         )
-        
+
-        expressions = expression_selector.select_suitable_expressions_llm(chat_stream.stream_id, chat_talking_prompt_half)
+        expressions = expression_selector.select_suitable_expressions_llm(
+            chat_stream.stream_id, chat_talking_prompt_half
+        )
         style_habbits = []
         grammar_habbits = []
         if expressions:

src/individuality/individuality.py

@@ -31,7 +31,7 @@ class Individuality:
         self.name = ""
         self.bot_person_id = ""
         self.meta_info_file_path = "data/personality/meta.json"
-        
+
         self.model = LLMRequest(
             model=global_config.model.utils,
             request_type="individuality.compress",
@@ -99,9 +99,7 @@ class Individuality:
             logger.info("已将完整人设更新到bot的impression中")
 
         # 创建压缩版本的short_impression
-        asyncio.create_task(self._create_compressed_impression(
+        asyncio.create_task(self._create_compressed_impression(personality_core, personality_sides, identity_detail))
-            personality_core, personality_sides, identity_detail
-        ))
 
         asyncio.create_task(self.express_style.extract_and_store_personality_expressions())
 
@@ -374,12 +372,12 @@ class Individuality:
         self, personality_core: str, personality_sides: list, identity_detail: list
     ) -> str:
         """使用LLM创建压缩版本的impression
-        
+
         Args:
             personality_core: 核心人格
             personality_sides: 人格侧面列表
             identity_detail: 身份细节列表
-            
+
         Returns:
             str: 压缩后的impression文本
         """
@@ -387,23 +385,23 @@ class Individuality:
         compressed_parts = []
         if personality_core:
             compressed_parts.append(f"{personality_core}")
-        
+
         # 准备需要压缩的内容
         content_to_compress = []
         if personality_sides:
             content_to_compress.append(f"人格特质: {'、'.join(personality_sides)}")
         if identity_detail:
             content_to_compress.append(f"身份背景: {'、'.join(identity_detail)}")
-            
+
         if not content_to_compress:
             # 如果没有需要压缩的内容，直接返回核心人格
             result = "。".join(compressed_parts)
             return result + "。" if result else ""
-            
+
         # 使用LLM压缩其他内容
         try:
             compress_content = "、".join(content_to_compress)
-            
+
             prompt = f"""请将以下人设信息进行简洁压缩，保留主要内容，用简练的中文表达：
 
 {compress_content}
@@ -413,10 +411,10 @@ class Individuality:
 2. 尽量简洁，不超过30字
 3. 直接输出压缩后的内容，不要解释"""
 
-            response,(_,_) = await self.model.generate_response_async(
+            response, (_, _) = await self.model.generate_response_async(
                 prompt=prompt,
             )
-            
+
             if response.strip():
                 compressed_parts.append(response.strip())
                 logger.info(f"精简人格侧面: {response.strip()}")
@@ -424,15 +422,13 @@ class Individuality:
                 logger.error(f"使用LLM压缩人设时出错: {response}")
         except Exception as e:
             logger.error(f"使用LLM压缩人设时出错: {e}")
-            
+
         result = "。".join(compressed_parts)
-    
+
         # 更新short_impression字段
         if result:
             person_info_manager = get_person_info_manager()
-            await person_info_manager.update_one_field(
+            await person_info_manager.update_one_field(self.bot_person_id, "short_impression", result)
-                self.bot_person_id, "short_impression", result
-            )
             logger.info("已将压缩人设更新到bot的short_impression中")