style: run ruff

src/backend/base/langflow/components/embeddings/AstraVectorize.py

@@ -1,6 +1,6 @@
 from typing import Any
 from langflow.custom import Component
-from langflow.inputs.inputs import DictInput, SecretStrInput, StrInput, MessageTextInput
+from langflow.inputs.inputs import DictInput, SecretStrInput, MessageTextInput
 from langflow.template.field.base import Output
 
 
@@ -14,30 +14,30 @@ class AstraVectorize(Component):
         MessageTextInput(
             name="provider",
             display_name="Provider name",
-            info='The embedding provider to use.',
+            info="The embedding provider to use.",
         ),
         MessageTextInput(
             name="model_name",
             display_name="Model name",
-            info='The embedding model to use.',
+            info="The embedding model to use.",
         ),
         DictInput(
             name="authentication",
             display_name="Authentication",
-            info='Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization.',
+            info="Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization.",
-            is_list=True
+            is_list=True,
         ),
         SecretStrInput(
             name="provider_api_key",
             display_name="Provider API Key",
-            info='An alternative to the Astra Authentication that let you use directly the API key of the provider.'
+            info="An alternative to the Astra Authentication that let you use directly the API key of the provider.",
         ),
         DictInput(
             name="model_parameters",
             display_name="Model parameters",
-            info='Additional model parameters.',
+            info="Additional model parameters.",
             advanced=True,
-            is_list=True
+            is_list=True,
         ),
     ]
     outputs = [
@@ -51,7 +51,7 @@ class AstraVectorize(Component):
                 "provider": self.provider,
                 "modelName": self.model_name,
                 "authentication": self.authentication,
-                "parameters": self.model_parameters
+                "parameters": self.model_parameters,
             },
-            "collection_embedding_api_key": self.provider_api_key
+            "collection_embedding_api_key": self.provider_api_key,
         }

src/backend/base/langflow/components/helpers/IDGenerator.py

@@ -1,5 +1,5 @@
 import uuid
-from typing import Optional, Any,
+from typing import Any, Optional
 
 from langflow.custom import CustomComponent
 from langflow.schema.dotdict import dotdict
@@ -10,10 +10,7 @@ class UUIDGeneratorComponent(CustomComponent):
     description = "Generates a unique ID."
 
     def update_build_config(  # type: ignore
-        self,
+        self, build_config: dotdict, field_value: Any, field_name: Optional[str] = None
-        build_config: dotdict,
-        field_value: Any,
-        field_name: Optional[str] = None,
     ):
         if field_name == "unique_id":
             build_config[field_name]["value"] = str(uuid.uuid4())

src/backend/base/langflow/components/prototypes/__init__.py

@@ -1,4 +1,4 @@
-µfrom .ConditionalRouter import ConditionalRouterComponent
+from .ConditionalRouter import ConditionalRouterComponent
 from .FlowTool import FlowToolComponent
 from .Listen import ListenComponent
 from .Notify import NotifyComponent

src/backend/base/langflow/components/vectorstores/AstraDB.py

@@ -159,10 +159,12 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             embedding_dict = {"embedding": self.embedding}
         else:
             from astrapy.info import CollectionVectorServiceOptions
+
             dict_options = self.embedding.get("collection_vector_service_options", {})
-            dict_options["authentication"] = {k: v for k, v in dict_options.get("authentication", {}).items() if k and v}
+            dict_options["authentication"] = {
-            dict_options["parameters"] = {k: v for k, v in dict_options.get("parameters", {}).items() if
+                k: v for k, v in dict_options.get("authentication", {}).items() if k and v
-                                              k and v}
+            }
+            dict_options["parameters"] = {k: v for k, v in dict_options.get("parameters", {}).items() if k and v}
             embedding_dict = {
                 "collection_vector_service_options": CollectionVectorServiceOptions.from_dict(dict_options),
                 "collection_embedding_api_key": self.embedding.get("collection_embedding_api_key"),