diff --git a/app/service/search_image_with_text/service.py b/app/service/search_image_with_text/service.py
index 47a9dde..98f6ac4 100644
--- a/app/service/search_image_with_text/service.py
+++ b/app/service/search_image_with_text/service.py
@@ -7,10 +7,10 @@ from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaE
 from tqdm import tqdm
 
 # 读取 csv 文件
-# csv_file_path = r'D:/Files/csv/output/output.csv'
-# image_path = r'D:/images-clean'
+csv_file_path = r'D:/Files/csv/output/output.csv'
+image_path = r'D:/images-clean'
 
-# df = pd.read_csv(csv_file_path, encoding='Windows-1252')
+df = pd.read_csv(csv_file_path, encoding='Windows-1252')
 
 # 创建 Chroma 客户端
 client = chromadb.Client(Settings(is_persistent=True, persist_directory="/vector_db"))
@@ -20,47 +20,47 @@ client = chromadb.Client(Settings(is_persistent=True, persist_directory="/vector
 embedding_fn = OllamaEmbeddingFunction(url="http://localhost:11434/api/embeddings", model_name="mxbai-embed-large")
 
 
-# def create_collection():
-#     collection = client.get_or_create_collection("sub_sketches_description", embedding_function=embedding_fn)
-#
-#     # 存储数据，包括自定义属性
-#     images_description = []
-#     images_metadata = []
-#     ids = []
-#     batch_size = 41666  # 最大批量大小
-#     for index, row in tqdm(df.iterrows()):
-#         # 将图片的md5作为id
-#         with open(image_path + row['path'], 'rb') as f:
-#             image_data = f.read()
-#             md5_value = hashlib.md5(image_data).hexdigest()
-#             ids.append(md5_value)
-#         images_description.append(row['description'])
-#         images_metadata.append({
-#             "gender": row['gender'],
-#             "path": row['path']
-#         })
-#
-#         # 将数据添加到集合
-#         # 每达到 batch_size 就执行一次 upsert
-#         if len(ids) >= batch_size:
-#             collection.upsert(
-#                 ids=list(ids),
-#                 documents=images_description,
-#                 metadatas=images_metadata  # 添加自定义属性
-#             )
-#             # 清空列表以准备下一批数据
-#             ids.clear()
-#             images_description.clear()
-#             images_metadata.clear()
-#
-#     if ids:
-#         collection.upsert(
-#             ids=list(ids),
-#             documents=images_description,
-#             metadatas=images_metadata  # 添加自定义属性
-#         )
-#
-#     print("Data successfully stored in the vector database.")
+def create_collection():
+    collection = client.get_or_create_collection("sub_sketches_description", embedding_function=embedding_fn)
+
+    # 存储数据，包括自定义属性
+    images_description = []
+    images_metadata = []
+    ids = []
+    batch_size = 41666  # 最大批量大小
+    for index, row in tqdm(df.iterrows()):
+        # 将图片的md5作为id
+        with open(image_path + row['path'], 'rb') as f:
+            image_data = f.read()
+            md5_value = hashlib.md5(image_data).hexdigest()
+            ids.append(md5_value)
+        images_description.append(row['description'])
+        images_metadata.append({
+            "gender": row['gender'],
+            "path": row['path']
+        })
+
+        # 将数据添加到集合
+        # 每达到 batch_size 就执行一次 upsert
+        if len(ids) >= batch_size:
+            collection.upsert(
+                ids=list(ids),
+                documents=images_description,
+                metadatas=images_metadata  # 添加自定义属性
+            )
+            # 清空列表以准备下一批数据
+            ids.clear()
+            images_description.clear()
+            images_metadata.clear()
+
+    if ids:
+        collection.upsert(
+            ids=list(ids),
+            documents=images_description,
+            metadatas=images_metadata  # 添加自定义属性
+        )
+
+    print("Data successfully stored in the vector database.")
 
 
 def query(gender, content):