chore: format

backend/open_webui/retrieval/vector/dbs/milvus_multitenancy.py

@@ -284,9 +284,7 @@ class MilvusClient(VectorDBBase):
                 elif isinstance(value, (int, float)):
                     expr.append(f"metadata['{key}'] == {value}")
                 else:
-                    raise TypeError(
-                        f'Unsupported Milvus filter value type for key {key!r}: {type(value).__name__}'
-                    )
+                    raise TypeError(f'Unsupported Milvus filter value type for key {key!r}: {type(value).__name__}')
 
         iterator = collection.query_iterator(
             expr=' and '.join(expr),

backend/open_webui/retrieval/vector/dbs/valkey.py

@@ -56,8 +56,7 @@ def _import_glide():
         )
     except ImportError as e:
         raise ImportError(
-            'valkey-glide-sync is required when VECTOR_DB=valkey. '
-            'Install it with: pip install valkey-glide-sync==2.3.1'
+            'valkey-glide-sync is required when VECTOR_DB=valkey. Install it with: pip install valkey-glide-sync==2.3.1'
         ) from e
     return {
         'Batch': Batch,
@@ -193,7 +192,7 @@ class ValkeyClient(VectorDBBase):
             addresses=[NodeAddress(host=host, port=port)],
             database_id=db if db else None,
             request_timeout=5000,
-            client_name="open_webui_vector_store_client",
+            client_name='open_webui_vector_store_client',
         )
         try:
             self.client = GlideClient.create(config)
@@ -206,7 +205,7 @@ class ValkeyClient(VectorDBBase):
             addresses=[NodeAddress(host=host, port=port)],
             database_id=db if db else None,
             request_timeout=10000,  # 10s — HNSW indexing can take 1-4s per vector
-            client_name="open_webui_vector_store_batch_client",
+            client_name='open_webui_vector_store_batch_client',
         )
         try:
             self.batch_client = GlideClient.create(batch_config)