Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

src/chat/express/exprssion_learner.py

@@ -119,7 +119,7 @@ class ExpressionLearner:
         min_len = min(len(s1), len(s2))
         if min_len < 5:
             return False
-        same = sum(1 for a, b in zip(s1, s2) if a == b)
+        same = sum(1 for a, b in zip(s1, s2, strict=False) if a == b)
         return same / min_len > 0.8
 
     async def learn_and_store_expression(self) -> List[Tuple[str, str, str]]:

src/chat/heart_flow/heartflow_message_processor.py

@@ -120,7 +120,6 @@ class HeartFCMessageReceiver:
             chat_mood = mood_manager.get_mood_by_chat_id(subheartflow.chat_id)
             asyncio.create_task(chat_mood.update_mood_by_message(message, interested_rate))
 
-
             # 7. 日志记录
             mes_name = chat.group_info.group_name if chat.group_info else "私聊"
             # current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))

src/chat/knowledge/embedding_store.py

@@ -59,7 +59,7 @@ EMBEDDING_SIM_THRESHOLD = 0.99
 
 def cosine_similarity(a, b):
     # 计算余弦相似度
-    dot = sum(x * y for x, y in zip(a, b))
+    dot = sum(x * y for x, y in zip(a, b, strict=False))
     norm_a = math.sqrt(sum(x * x for x in a))
     norm_b = math.sqrt(sum(x * x for x in b))
     if norm_a == 0 or norm_b == 0:
@@ -285,7 +285,7 @@ class EmbeddingStore:
         distances = list(distances.flatten())
         result = [
             (self.idx2hash[str(int(idx))], float(sim))
-            for (idx, sim) in zip(indices, distances)
+            for (idx, sim) in zip(indices, distances, strict=False)
             if idx in range(len(self.idx2hash))
         ]
 

src/chat/memory_system/Hippocampus.py

@@ -819,7 +819,7 @@ class EntorhinalCortex:
         timestamps = sample_scheduler.get_timestamp_array()
         # 使用 translate_timestamp_to_human_readable 并指定 mode="normal"
         readable_timestamps = [translate_timestamp_to_human_readable(ts, mode="normal") for ts in timestamps]
-        for _, readable_timestamp in zip(timestamps, readable_timestamps):
+        for _, readable_timestamp in zip(timestamps, readable_timestamps, strict=False):
             logger.debug(f"回忆往事: {readable_timestamp}")
         chat_samples = []
         for timestamp in timestamps:

src/chat/planner_actions/action_modifier.py

@@ -299,7 +299,7 @@ class ActionModifier:
                 task_results = await asyncio.gather(*tasks, return_exceptions=True)
 
                 # 处理结果并更新缓存
-                for _, (action_name, result) in enumerate(zip(task_names, task_results)):
+                for _, (action_name, result) in enumerate(zip(task_names, task_results, strict=False)):
                     if isinstance(result, Exception):
                         logger.error(f"{self.log_prefix}LLM判定action {action_name} 时出错: {result}")
                         results[action_name] = False

src/chat/replyer/default_generator.py

@@ -845,7 +845,7 @@ def weighted_sample_no_replacement(items, weights, k) -> list:
         2. 不会重复选中同一个元素
     """
     selected = []
-    pool = list(zip(items, weights))
+    pool = list(zip(items, weights, strict=False))
     for _ in range(min(k, len(pool))):
         total = sum(w for _, w in pool)
         r = random.uniform(0, total)

src/chat/utils/typo_generator.py

@@ -363,7 +363,7 @@ class ChineseTypoGenerator:
             else:
                 # 处理多字词的单字替换
                 word_result = []
-                for _, (char, py) in enumerate(zip(word, word_pinyin)):
+                for _, (char, py) in enumerate(zip(word, word_pinyin, strict=False)):
                     # 词中的字替换概率降低
                     word_error_rate = self.error_rate * (0.7 ** (len(word) - 1))
 

src/config/config_base.py

@@ -94,7 +94,7 @@ class ConfigBase:
                     raise TypeError(
                         f"Expected {len(field_type_args)} items for {field_type.__name__}, got {len(value)}"
                     )
-                return tuple(cls._convert_field(item, arg) for item, arg in zip(value, field_type_args))
+                return tuple(cls._convert_field(item, arg) for item, arg in zip(value, field_type_args, strict=False))
 
         if field_origin_type is dict:
             # 检查提供的value是否为dict

src/mais4u/mais4u_chat/s4u_prompt.py

@@ -247,7 +247,7 @@ def weighted_sample_no_replacement(items, weights, k) -> list:
         2. 不会重复选中同一个元素
     """
     selected = []
-    pool = list(zip(items, weights))
+    pool = list(zip(items, weights, strict=False))
     for _ in range(min(k, len(pool))):
         total = sum(w for _, w in pool)
         r = random.uniform(0, total)

src/plugins/built_in/core_actions/no_reply.py

@@ -142,7 +142,6 @@ class NoReplyAction(BaseAction):
             )
             return False, f"不回复动作执行失败: {e}"
 
-
     @classmethod
     def reset_consecutive_count(cls):
         """重置连续计数器"""

src/tools/not_using/get_knowledge.py

@@ -54,7 +54,7 @@ class SearchKnowledgeTool(BaseTool):
     @staticmethod
     def _cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
         """计算两个向量之间的余弦相似度"""
-        dot_product = sum(p * q for p, q in zip(vec1, vec2))
+        dot_product = sum(p * q for p, q in zip(vec1, vec2, strict=False))
         magnitude1 = math.sqrt(sum(p * p for p in vec1))
         magnitude2 = math.sqrt(sum(q * q for q in vec2))
         if magnitude1 == 0 or magnitude2 == 0: