resolve lint errors

libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py

@@ -128,7 +128,7 @@ def initialize_bq_vector_index(self) -> Self:
         self._have_index = self._have_index
         self._last_index_check = self._last_index_check
 
-        if (self._have_index or None) or (self._creating_index or None):
+        if self._have_index or self._creating_index:
             return self
 
         table = self._bq_client.get_table(self._full_table_id)  # type: ignore[union-attr]
@@ -567,7 +567,9 @@ def to_vertex_fs_vector_store(self, **kwargs: Any) -> Any:
             VertexFSVectorStore,
         )
 
-        base_params = self.dict(include=BaseBigQueryVectorStore.__fields__.keys())
+        base_params = self.model_dump(
+            include=set(BaseBigQueryVectorStore.model_fields.keys())
+        )
         base_params["embedding"] = self.embedding
         all_params = {**base_params, **kwargs}
         fs_obj = VertexFSVectorStore(**all_params)

libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py

@@ -99,9 +99,9 @@ def _initialize_bq_vector_index(self) -> Self:
             self.algorithm_config = utils.TreeAhConfig()
         if self.distance_measure_type is None:
             self.distance_measure_type = utils.DistanceMeasureType.DOT_PRODUCT_DISTANCE
-        if (self.online_store_name or None) is None:
+        if self.online_store_name is None:
             self.online_store_name = self.dataset_name
-        if (self.view_name or None) is None:
+        if self.view_name is None:
             self.view_name = self.table_name
 
         api_endpoint = f"{self.location}-aiplatform.googleapis.com"
@@ -513,7 +513,9 @@ def to_bq_vector_store(self, **kwargs: Any) -> Any:
             BigQueryVectorStore,
         )
 
-        base_params = self.dict(include=BaseBigQueryVectorStore.__fields__.keys())
+        base_params = self.model_dump(
+            include=set(BaseBigQueryVectorStore.model_fields.keys())
+        )
         base_params["embedding"] = self.embedding
         all_params = {**base_params, **kwargs}
         bq_obj = BigQueryVectorStore(**all_params)

libs/community/langchain_google_community/drive.py

@@ -13,7 +13,7 @@
 
 from langchain_core.document_loaders import BaseLoader
 from langchain_core.documents import Document
-from pydantic import BaseModel, root_validator, validator
+from pydantic import BaseModel, model_validator, validator
 
 SCOPES = ["https://www.googleapis.com/auth/drive.file"]
 
@@ -194,8 +194,9 @@ def _get_identity_metadata_from_id(self, id: str) -> List[str]:
 
         return authorized_identities
 
-    @root_validator
-    def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_inputs(cls, values: Dict[str, Any]) -> Any:
         """Validate that either folder_id or document_ids is set, but not both."""
         if values.get("folder_id") and (
             values.get("document_ids") or values.get("file_ids")