ruff fix但指定了--unsafe-fixes

src/chat/knowledge/embedding_store.py

@@ -495,7 +495,7 @@ class EmbeddingStore:
         """重新构建Faiss索引，以余弦相似度为度量"""
         # 获取所有的embedding
         array = []
-        self.idx2hash = dict()
+        self.idx2hash = {}
         for key in self.store:
             array.append(self.store[key].embedding)
             self.idx2hash[str(len(array) - 1)] = key

src/chat/knowledge/ie_process.py

@@ -33,7 +33,7 @@ def _extract_json_from_text(text: str):
         if isinstance(parsed_json, dict):
             # 如果字典只有一个键，并且值是列表，返回那个列表
             if len(parsed_json) == 1:
-                value = list(parsed_json.values())[0]
+                value = next(iter(parsed_json.values()))
                 if isinstance(value, list):
                     return value
             return parsed_json

src/chat/knowledge/kg_manager.py

@@ -91,7 +91,7 @@ class KGManager:
 
         # 加载实体计数
         ent_cnt_df = pd.read_parquet(self.ent_cnt_data_path, engine="pyarrow")
-        self.ent_appear_cnt = dict({row["hash_key"]: row["appear_cnt"] for _, row in ent_cnt_df.iterrows()})
+        self.ent_appear_cnt = {row["hash_key"]: row["appear_cnt"] for _, row in ent_cnt_df.iterrows()}
 
         # 加载KG
         self.graph = di_graph.load_from_file(self.graph_data_path)
@@ -290,7 +290,7 @@ class KGManager:
             embedding_manager: EmbeddingManager对象
         """
         # 实体之间的联系
-        node_to_node = dict()
+        node_to_node = {}
 
         # 构建实体节点之间的关系，同时统计实体出现次数
         logger.info("正在构建KG实体节点之间的关系，同时统计实体出现次数")
@@ -379,8 +379,8 @@ class KGManager:
         top_k = global_config.lpmm_knowledge.qa_ent_filter_top_k
         if len(ent_mean_scores) > top_k:
             # 从大到小排序，取后len - k个
-            ent_mean_scores = {k: v for k, v in sorted(ent_mean_scores.items(), key=lambda item: item[1], reverse=True)}
-            for ent_hash, _ in ent_mean_scores.items():
+            ent_mean_scores = dict(sorted(ent_mean_scores.items(), key=lambda item: item[1], reverse=True))
+            for ent_hash in ent_mean_scores.keys():
                 # 删除被淘汰的实体节点权重设置
                 del ent_weights[ent_hash]
         del top_k, ent_mean_scores

src/chat/knowledge/open_ie.py

@@ -124,29 +124,25 @@ class OpenIE:
 
     def extract_entity_dict(self):
         """提取实体列表"""
-        ner_output_dict = dict(
-            {
+        ner_output_dict = {
                 doc_item["idx"]: doc_item["extracted_entities"]
                 for doc_item in self.docs
                 if len(doc_item["extracted_entities"]) > 0
             }
-        )
         return ner_output_dict
 
     def extract_triple_dict(self):
         """提取三元组列表"""
-        triple_output_dict = dict(
-            {
+        triple_output_dict = {
                 doc_item["idx"]: doc_item["extracted_triples"]
                 for doc_item in self.docs
                 if len(doc_item["extracted_triples"]) > 0
             }
-        )
         return triple_output_dict
 
     def extract_raw_paragraph_dict(self):
         """提取原始段落"""
-        raw_paragraph_dict = dict({doc_item["idx"]: doc_item["passage"] for doc_item in self.docs})
+        raw_paragraph_dict = {doc_item["idx"]: doc_item["passage"] for doc_item in self.docs}
         return raw_paragraph_dict
 
 

src/chat/knowledge/utils/dyn_topk.py

@@ -18,13 +18,11 @@ def dyn_select_top_k(
     normalized_score = []
     for score_item in sorted_score:
         normalized_score.append(
-            tuple(
-                [
+            (
                     score_item[0],
                     score_item[1],
                     (score_item[1] - min_score) / (max_score - min_score),
-                ]
-            )
+                )
         )
 
     # 寻找跳变点：score变化最大的位置