diff --git a/app/service/search_image_with_text/service.py b/app/service/search_image_with_text/service.py
index 98f6ac4..712050f 100644
--- a/app/service/search_image_with_text/service.py
+++ b/app/service/search_image_with_text/service.py
@@ -7,60 +7,60 @@ 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"))
 # client = chromadb.Client(Settings(is_persistent=True, persist_directory="./service/search_image_with_text/vector_db"))
 # client = chromadb.Client(Settings(is_persistent=True, persist_directory="D:/workspace/AiDLab/vector_db"))
 # 创建集合
-embedding_fn = OllamaEmbeddingFunction(url="http://localhost:11434/api/embeddings", model_name="mxbai-embed-large")
+embedding_fn = OllamaEmbeddingFunction(url="http://10.1.1.240:11434/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):