🤖 自动格式化代码 [skip ci]

src/chat/focus_chat/heartFC_chat.py

@@ -754,14 +754,11 @@ class HeartFChatting:
         if relation_info:
             updated_action_data["relation_info"] = relation_info
 
-
         if structured_info:
             updated_action_data["structured_info"] = structured_info
 
         if all_post_plan_info:
-            logger.info(
-                f"{self.log_prefix} 后期处理完成，产生了 {len(all_post_plan_info)} 个信息项"
-            )
+            logger.info(f"{self.log_prefix} 后期处理完成，产生了 {len(all_post_plan_info)} 个信息项")
 
         # 输出详细统计信息
         if post_processor_time_costs:

src/chat/focus_chat/memory_activator.py

@@ -1,5 +1,3 @@
-from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
-from src.chat.heart_flow.observation.structure_observation import StructureObservation
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config
 from src.common.logger import get_logger
@@ -10,7 +8,6 @@ from typing import List, Dict
 import difflib
 import json
 from json_repair import repair_json
-from src.person_info.person_info import get_person_info_manager
 
 
 logger = get_logger("memory_activator")

src/chat/normal_chat/normal_chat_generator.py

@@ -1,4 +1,3 @@
-from typing import List, Optional, Union
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config
 from src.chat.message_receive.message import MessageThinking
@@ -19,8 +18,8 @@ class NormalChatGenerator:
 
         prob_first = global_config.normal_chat.normal_chat_first_probability
 
-        model_config_1['weight'] = prob_first
-        model_config_2['weight'] = 1.0 - prob_first
+        model_config_1["weight"] = prob_first
+        model_config_2["weight"] = 1.0 - prob_first
 
         self.model_configs = [model_config_1, model_config_2]
 
@@ -54,7 +53,7 @@ class NormalChatGenerator:
                 available_actions=available_actions,
                 model_configs=self.model_configs,
                 request_type="normal.replyer",
-                return_prompt=True
+                return_prompt=True,
             )
 
             if not success or not reply_set:

src/chat/replyer/default_generator.py

@@ -31,15 +31,12 @@ logger = get_logger("replyer")
 
 
 def init_prompt():
-    
     Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
     Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
     Prompt("在群里聊天", "chat_target_group2")
     Prompt("和{sender_name}私聊", "chat_target_private2")
     Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
 
-    
-    
     Prompt(
         """
 {expression_habits_block}
@@ -134,7 +131,12 @@ def init_prompt():
 
 
 class DefaultReplyer:
-    def __init__(self, chat_stream: ChatStream, model_configs: Optional[List[Dict[str, Any]]] = None, request_type: str = "focus.replyer"):
+    def __init__(
+        self,
+        chat_stream: ChatStream,
+        model_configs: Optional[List[Dict[str, Any]]] = None,
+        request_type: str = "focus.replyer",
+    ):
         self.log_prefix = "replyer"
         self.request_type = request_type
 
@@ -143,14 +145,14 @@ class DefaultReplyer:
         else:
             # 当未提供配置时，使用默认配置并赋予默认权重
             default_config = global_config.model.replyer_1.copy()
-            default_config.setdefault('weight', 1.0)
+            default_config.setdefault("weight", 1.0)
             self.express_model_configs = [default_config]
 
         if not self.express_model_configs:
             logger.warning("未找到有效的模型配置，回复生成可能会失败。")
             # 提供一个最终的回退，以防止在空列表上调用 random.choice
             fallback_config = global_config.model.replyer_1.copy()
-            fallback_config.setdefault('weight', 1.0)
+            fallback_config.setdefault("weight", 1.0)
             self.express_model_configs = [fallback_config]
 
         self.heart_fc_sender = HeartFCSender()
@@ -163,7 +165,7 @@ class DefaultReplyer:
         """使用加权随机选择来挑选一个模型配置"""
         configs = self.express_model_configs
         # 提取权重，如果模型配置中没有'weight'键，则默认为1.0
-        weights = [config.get('weight', 1.0) for config in configs]
+        weights = [config.get("weight", 1.0) for config in configs]
 
         # random.choices 返回一个列表，我们取第一个元素
         selected_config = random.choices(population=configs, weights=weights, k=1)[0]
@@ -198,18 +200,21 @@ class DefaultReplyer:
 
     async def generate_reply_with_context(
         self,
-        reply_data: Dict[str, Any] = {},
+        reply_data: Dict[str, Any] = None,
         reply_to: str = "",
         relation_info: str = "",
         structured_info: str = "",
         extra_info: str = "",
-        available_actions: List[str] = [],
-
+        available_actions: List[str] = None,
     ) -> Tuple[bool, Optional[str]]:
         """
         回复器 (Replier): 核心逻辑，负责生成回复文本。
         (已整合原 HeartFCGenerator 的功能)
         """
+        if available_actions is None:
+            available_actions = []
+        if reply_data is None:
+            reply_data = {}
         try:
             if not reply_data:
                 reply_data = {
@@ -226,7 +231,7 @@ class DefaultReplyer:
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_reply_context(
                     reply_data=reply_data,  # 传递action_data
-                    available_actions=available_actions
+                    available_actions=available_actions,
                 )
 
             # 4. 调用 LLM 生成回复
@@ -238,7 +243,9 @@ class DefaultReplyer:
                 with Timer("LLM生成", {}):  # 内部计时器，可选保留
                     # 加权随机选择一个模型配置
                     selected_model_config = self._select_weighted_model_config()
-                    logger.info(f"{self.log_prefix} 使用模型配置: {selected_model_config.get('model_name', 'N/A')} (权重: {selected_model_config.get('weight', 1.0)})")
+                    logger.info(
+                        f"{self.log_prefix} 使用模型配置: {selected_model_config.get('model_name', 'N/A')} (权重: {selected_model_config.get('weight', 1.0)})"
+                    )
 
                     express_model = LLMRequest(
                         model=selected_model_config,
@@ -262,9 +269,7 @@ class DefaultReplyer:
             traceback.print_exc()
             return False, None
 
-    async def rewrite_reply_with_context(
-        self, reply_data: Dict[str, Any]
-    ) -> Tuple[bool, Optional[str]]:
+    async def rewrite_reply_with_context(self, reply_data: Dict[str, Any]) -> Tuple[bool, Optional[str]]:
         """
         表达器 (Expressor): 核心逻辑，负责生成回复文本。
         """
@@ -291,7 +296,9 @@ class DefaultReplyer:
                 with Timer("LLM生成", {}):  # 内部计时器，可选保留
                     # 加权随机选择一个模型配置
                     selected_model_config = self._select_weighted_model_config()
-                    logger.info(f"{self.log_prefix} 使用模型配置进行重写: {selected_model_config.get('model_name', 'N/A')} (权重: {selected_model_config.get('weight', 1.0)})")
+                    logger.info(
+                        f"{self.log_prefix} 使用模型配置进行重写: {selected_model_config.get('model_name', 'N/A')} (权重: {selected_model_config.get('weight', 1.0)})"
+                    )
 
                     express_model = LLMRequest(
                         model=selected_model_config,
@@ -315,11 +322,7 @@ class DefaultReplyer:
             traceback.print_exc()
             return False, None
 
-    async def build_prompt_reply_context(
-        self,
-        reply_data=None,
-        available_actions: List[str] = []
-    ) -> str:
+    async def build_prompt_reply_context(self, reply_data=None, available_actions: List[str] = None) -> str:
         """
         构建回复器上下文
 
@@ -336,6 +339,8 @@ class DefaultReplyer:
         Returns:
             str: 构建好的上下文
         """
+        if available_actions is None:
+            available_actions = []
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
         person_info_manager = get_person_info_manager()
@@ -403,7 +408,6 @@ class DefaultReplyer:
         person_info_manager = get_person_info_manager()
         bot_person_id = person_info_manager.get_person_id("system", "bot_id")
 
-
         is_group_chat = bool(chat_stream.group_info)
 
         style_habbits = []
@@ -415,7 +419,6 @@ class DefaultReplyer:
             chat_id, chat_talking_prompt_half, max_num=12, min_num=2, target_message=target
         )
 
-
         if selected_expressions:
             logger.info(f"{self.log_prefix} 使用处理器选中的{len(selected_expressions)}个表达方式")
             for expr in selected_expressions:
@@ -450,9 +453,7 @@ class DefaultReplyer:
             # 由于无法直接访问 HeartFChatting 的 observations 列表，
             # 我们直接使用聊天记录作为上下文来激活记忆
             running_memorys = await self.memory_activator.activate_memory_with_chat_history(
-                chat_id=chat_id,
-                target_message=target,
-                chat_history_prompt=chat_talking_prompt_half
+                chat_id=chat_id, target_message=target, chat_history_prompt=chat_talking_prompt_half
             )
 
             if running_memorys:
@@ -468,7 +469,9 @@ class DefaultReplyer:
             memory_block = ""
 
         if structured_info:
-            structured_info_block = f"以下是你了解的额外信息信息，现在请你阅读以下内容，进行决策\n{structured_info}\n以上是一些额外的信息。"
+            structured_info_block = (
+                f"以下是你了解的额外信息信息，现在请你阅读以下内容，进行决策\n{structured_info}\n以上是一些额外的信息。"
+            )
         else:
             structured_info_block = ""
 
@@ -558,7 +561,6 @@ class DefaultReplyer:
         if prompt_info:
             prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
 
-
         # --- Choose template based on chat type ---
         if is_group_chat:
             template_name = "default_generator_prompt"

src/chat/replyer/replyer_manager.py

@@ -5,6 +5,7 @@ from src.common.logger import get_logger
 
 logger = get_logger("ReplyerManager")
 
+
 class ReplyerManager:
     def __init__(self):
         self._replyers: Dict[str, DefaultReplyer] = {}
@@ -14,7 +15,7 @@ class ReplyerManager:
         chat_stream: Optional[ChatStream] = None,
         chat_id: Optional[str] = None,
         model_configs: Optional[List[Dict[str, Any]]] = None,
-        request_type: str = "replyer"
+        request_type: str = "replyer",
     ) -> Optional[DefaultReplyer]:
         """
         获取或创建回复器实例。
@@ -49,10 +50,11 @@ class ReplyerManager:
         replyer = DefaultReplyer(
             chat_stream=target_stream,
             model_configs=model_configs,  # 可以是None，此时使用默认模型
-            request_type=request_type
+            request_type=request_type,
         )
         self._replyers[stream_id] = replyer
         return replyer
 
+
 # 创建一个全局实例
 replyer_manager = ReplyerManager()

src/plugin_system/apis/generator_api.py

@@ -27,7 +27,7 @@ def get_replyer(
     chat_stream: Optional[ChatStream] = None,
     chat_id: Optional[str] = None,
     model_configs: Optional[List[Dict[str, Any]]] = None,
-    request_type: str = "replyer"
+    request_type: str = "replyer",
 ) -> Optional[DefaultReplyer]:
     """获取回复器对象
 
@@ -46,10 +46,7 @@ def get_replyer(
     try:
         logger.debug(f"[GeneratorAPI] 正在获取回复器，chat_id: {chat_id}, chat_stream: {'有' if chat_stream else '无'}")
         return replyer_manager.get_replyer(
-            chat_stream=chat_stream,
-            chat_id=chat_id,
-            model_configs=model_configs,
-            request_type=request_type
+            chat_stream=chat_stream, chat_id=chat_id, model_configs=model_configs, request_type=request_type
         )
     except Exception as e:
         logger.error(f"[GeneratorAPI] 获取回复器时发生意外错误: {e}", exc_info=True)
@@ -154,9 +151,7 @@ async def rewrite_reply(
         logger.info("[GeneratorAPI] 开始重写回复")
 
         # 调用回复器重写回复
-        success, content = await replyer.rewrite_reply_with_context(
-            reply_data=reply_data or {}
-        )
+        success, content = await replyer.rewrite_reply_with_context(reply_data=reply_data or {})
 
         reply_set = await process_human_text(content, enable_splitter, enable_chinese_typo)
 
@@ -172,11 +167,7 @@ async def rewrite_reply(
         return False, []
 
 
-async def process_human_text(
-    content:str,
-    enable_splitter:bool,
-    enable_chinese_typo:bool
-) -> List[Tuple[str, Any]]:
+async def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> List[Tuple[str, Any]]:
     """将文本处理为更拟人化的文本
 
     Args: