diff --git a/zzb_data_prod/db_service.py b/zzb_data_prod/db_service.py
index f3738b5..690645c 100644
--- a/zzb_data_prod/db_service.py
+++ b/zzb_data_prod/db_service.py
@@ -399,13 +399,10 @@ def insert_table_from_vector_mul_process(parent_table_pages,file_id,file_name,re
             distance = record[2]
             ori_measure_id = record[3]
             measure_id = record[4]
-            measure_vector = redis_service.read_from_redis(redis_client,ori_measure_id)
-            
+            measure_vector = redis_service.read_from_redis(redis_client, ori_measure_id, redis_key="measure_config_new")
            
             measure_list = ast.literal_eval(measure_vector)
-
-            filter_str = 'file_id == "'+file_id+'"'
-            res = vector_storage.search_similar_vectors(measure_vector, top_k=3, similarity_threshold=distance)
+            res = vector_storage.search_similar_vectors(measure_list, top_k=3, similarity_threshold=distance)
             # 返回格式为 [(similarity, {metadata}), ...]
             for i in range(len(res)):
                 vector_distance = float(res[i][0])
diff --git a/zzb_data_prod/redis_service.py b/zzb_data_prod/redis_service.py
index 0bec300..46b57e1 100644
--- a/zzb_data_prod/redis_service.py
+++ b/zzb_data_prod/redis_service.py
@@ -5,12 +5,12 @@ def read_from_file_and_write_to_redis(redis_client,ori_measure_id,measure_vector
     redis_client.hset('measure_config',ori_measure_id, measure_vector)
 
 # 从 Redis 中读取数据
-def read_from_redis(redis_client,ori_measure_id):
+def read_from_redis(redis_client, ori_measure_id, redis_key="measure_config"):
     # 获取所有键
-    return redis_client.hget('measure_config',ori_measure_id).decode()
+    return redis_client.hget(redis_key, ori_measure_id).decode()
     
 if __name__ == "__main__":
     redis_client = redis.Redis(host='192.168.0.175', port=6379, password='Xgf_redis', db=6)
 
-    value = read_from_redis(redis_client,"bb3cf43f3dba147373c706c6567b5a")
+    value = read_from_redis(redis_client,"7c2dc91c1df9ab657b24e070431b401d", "measure_config_new")
     print(value)
diff --git a/zzb_data_prod/redis_update.py b/zzb_data_prod/redis_update.py
new file mode 100644
index 0000000..734a617
--- /dev/null
+++ b/zzb_data_prod/redis_update.py
@@ -0,0 +1,46 @@
+import redis
+import utils
+import mysql.connector
+from config import * 
+
+def insert_measure_vector(conn,cursor):
+    
+    redis_client = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, db=6)
+    # redis_client = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, db=6)
+    # 执行SQL语句，更新数据
+    select_query = '''
+                SELECT ori_measure_id,ori_measure_name,measure_name FROM measure_config_half_year where year='2025'
+                '''
+    # select_query = '''
+    #                 SELECT ori_measure_id,ori_measure_name,measure_name FROM measure_config where year='2024'
+    #                '''
+    cursor.execute(select_query)
+    records = cursor.fetchall()
+    print(records[:8])
+    #return 
+    index = 1
+    for record in records:
+        if redis_client.hexists('measure_config_new', record[0]):
+            measure_vector = redis_client.hget('measure_config_new', record[0])
+        else:
+            print('新增指标',record[1])
+            vector = utils.embed_with_str_local(record[1])
+            measure_vector = str(vector)
+        #print(f'新增指标{index} 对应归一化指标为{record[2]}',record[1])
+        #index += 1
+        #vector = utils.embed_with_str_local(record[1])
+        #measure_vector = str(vector)
+        redis_client.hset('measure_config_new', record[0], measure_vector)
+    redis_client.close()
+    conn.close()
+
+if __name__ == "__main__":
+    conn = mysql.connector.connect(
+        host=MYSQL_HOST,
+        user=MYSQL_USER,
+        password=MYSQL_PASSWORD,
+        database="financial_report_prod"
+    )
+    cursor = conn.cursor()
+    
+    insert_measure_vector(conn,cursor)
diff --git a/zzb_data_prod/vector_storage.py b/zzb_data_prod/vector_storage.py
index 9da1ed2..9cc0747 100644
--- a/zzb_data_prod/vector_storage.py
+++ b/zzb_data_prod/vector_storage.py
@@ -108,7 +108,7 @@ class VectorStorage:
     def search_similar_vectors(self, query_vector: np.ndarray, 
                               top_k: int = 10, similarity_threshold: float = 0.0) -> List[Tuple[float, Dict[str, Any]]]:
         """搜索相似向量"""
-        query_vector = query_vector.astype(np.float32)
+        query_vector = np.asarray(query_vector).astype(np.float32)
         current_count = self.data_count[0]
         if current_count == 0:
             return []