chore: apply ruff's pyflakes linter rules (#2420)

api/controllers/console/app/app.py

@@ -133,8 +133,8 @@ class AppListApi(Resource):
 
                 if not model_instance:
                     raise ProviderNotInitializeError(
-                        f"No Default System Reasoning Model available. Please configure "
-                        f"in the Settings -> Model Provider.")
+                        "No Default System Reasoning Model available. Please configure "
+                        "in the Settings -> Model Provider.")
                 else:
                     model_config_dict["model"]["provider"] = model_instance.provider
                     model_config_dict["model"]["name"] = model_instance.model

api/controllers/console/datasets/datasets.py

@@ -288,8 +288,8 @@ class DatasetIndexingEstimateApi(Resource):
                                                                   args['indexing_technique'])
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
         elif args['info_list']['data_source_type'] == 'notion_import':
@@ -304,8 +304,8 @@ class DatasetIndexingEstimateApi(Resource):
                                                                     args['indexing_technique'])
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
         else:

api/controllers/console/datasets/datasets_document.py

@@ -296,8 +296,8 @@ class DatasetInitApi(Resource):
                 )
             except InvokeAuthorizationError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
 
@@ -372,8 +372,8 @@ class DocumentIndexingEstimateApi(DocumentResource):
                                                                       'English', dataset_id)
                 except LLMBadRequestError:
                     raise ProviderNotInitializeError(
-                        f"No Embedding Model available. Please configure a valid provider "
-                        f"in the Settings -> Model Provider.")
+                        "No Embedding Model available. Please configure a valid provider "
+                        "in the Settings -> Model Provider.")
                 except ProviderTokenNotInitError as ex:
                     raise ProviderNotInitializeError(ex.description)
 
@@ -442,8 +442,8 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                                                                   'English', dataset_id)
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
         elif dataset.data_source_type == 'notion_import':
@@ -456,8 +456,8 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                                                                     None, 'English', dataset_id)
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
         else:

api/controllers/console/datasets/datasets_segments.py

@@ -143,8 +143,8 @@ class DatasetDocumentSegmentApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
 
@@ -234,8 +234,8 @@ class DatasetDocumentSegmentAddApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
         try:
@@ -286,8 +286,8 @@ class DatasetDocumentSegmentUpdateApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
             # check segment

api/controllers/console/datasets/hit_testing.py

@@ -76,8 +76,8 @@ class HitTestingApi(Resource):
             raise ProviderModelCurrentlyNotSupportError()
         except LLMBadRequestError:
             raise ProviderNotInitializeError(
-                f"No Embedding Model or Reranking Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
+                "No Embedding Model or Reranking Model available. Please configure a valid provider "
+                "in the Settings -> Model Provider.")
         except InvokeError as e:
             raise CompletionRequestError(e.description)
         except ValueError as e:

api/controllers/console/explore/parameter.py

@@ -78,7 +78,7 @@ class ExploreAppMetaApi(InstalledAppResource):
         # get all tools
         tools = agent_config.get('tools', [])
         url_prefix = (current_app.config.get("CONSOLE_API_URL")
-                  + f"/console/api/workspaces/current/tool-provider/builtin/")
+                  + "/console/api/workspaces/current/tool-provider/builtin/")
         for tool in tools:
             keys = list(tool.keys())
             if len(keys) >= 4:

api/controllers/files/image_preview.py

@@ -41,7 +41,7 @@ class WorkspaceWebappLogoApi(Resource):
         webapp_logo_file_id = custom_config.get('replace_webapp_logo') if custom_config is not None else None
 
         if not webapp_logo_file_id:
-            raise NotFound(f'webapp logo is not found')
+            raise NotFound('webapp logo is not found')
 
         try:
             generator, mimetype = FileService.get_public_image_preview(

api/controllers/files/tool_files.py

@@ -32,7 +32,7 @@ class ToolFilePreviewApi(Resource):
             )
 
             if not result:
-                raise NotFound(f'file is not found')
+                raise NotFound('file is not found')
             
             generator, mimetype = result
         except Exception:

api/controllers/service_api/app/app.py

@@ -78,7 +78,7 @@ class AppMetaApi(AppApiResource):
         # get all tools
         tools = agent_config.get('tools', [])
         url_prefix = (current_app.config.get("CONSOLE_API_URL")
-                  + f"/console/api/workspaces/current/tool-provider/builtin/")
+                  + "/console/api/workspaces/current/tool-provider/builtin/")
         for tool in tools:
             keys = list(tool.keys())
             if len(keys) >= 4:

api/controllers/service_api/dataset/segment.py

@@ -46,8 +46,8 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
         # validate args
@@ -90,8 +90,8 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
 
@@ -182,8 +182,8 @@ class DatasetSegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    f"No Embedding Model available. Please configure a valid provider "
-                    f"in the Settings -> Model Provider.")
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider.")
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
             # check segment

api/controllers/web/app.py

@@ -77,7 +77,7 @@ class AppMeta(WebApiResource):
         # get all tools
         tools = agent_config.get('tools', [])
         url_prefix = (current_app.config.get("CONSOLE_API_URL")
-                  + f"/console/api/workspaces/current/tool-provider/builtin/")
+                  + "/console/api/workspaces/current/tool-provider/builtin/")
         for tool in tools:
             keys = list(tool.keys())
             if len(keys) >= 4:

api/core/app_runner/assistant_app_runner.py

@@ -38,7 +38,7 @@ class AssistantApplicationRunner(AppRunner):
         """
         app_record = db.session.query(App).filter(App.id == application_generate_entity.app_id).first()
         if not app_record:
-            raise ValueError(f"App not found")
+            raise ValueError("App not found")
 
         app_orchestration_config = application_generate_entity.app_orchestration_config_entity
 

api/core/app_runner/basic_app_runner.py

@@ -35,7 +35,7 @@ class BasicApplicationRunner(AppRunner):
         """
         app_record = db.session.query(App).filter(App.id == application_generate_entity.app_id).first()
         if not app_record:
-            raise ValueError(f"App not found")
+            raise ValueError("App not found")
 
         app_orchestration_config = application_generate_entity.app_orchestration_config_entity
 

api/core/features/assistant_base_runner.py

@@ -134,7 +134,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                 result += f"result link: {response.message}. please tell user to check it."
             elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
                  response.type == ToolInvokeMessage.MessageType.IMAGE:
-                result += f"image has been created and sent to user already, you should tell user to check it now."
+                result += "image has been created and sent to user already, you should tell user to check it now."
             else:
                 result += f"tool response: {response.message}."
 

api/core/features/assistant_cot_runner.py

@@ -238,7 +238,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
 
                             message_file_ids = [message_file.id for message_file, _ in message_files]
                         except ToolProviderCredentialValidationError as e:
-                            error_response = f"Please check your tool provider credentials"
+                            error_response = "Please check your tool provider credentials"
                         except (
                             ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
                         ) as e:
@@ -473,7 +473,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         next_iteration = agent_prompt_message.next_iteration
 
         if not isinstance(first_prompt, str) or not isinstance(next_iteration, str):
-            raise ValueError(f"first_prompt or next_iteration is required in CoT agent mode")
+            raise ValueError("first_prompt or next_iteration is required in CoT agent mode")
         
         # check instruction, tools, and tool_names slots
         if not first_prompt.find("{{instruction}}") >= 0:

api/core/features/assistant_fc_runner.py

@@ -277,7 +277,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                             message_file_ids.append(message_file.id)
                             
                     except ToolProviderCredentialValidationError as e:
-                        error_response = f"Please check your tool provider credentials"
+                        error_response = "Please check your tool provider credentials"
                     except (
                         ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
                     ) as e:

api/core/index/vector_index/vector_index.py

@@ -26,7 +26,7 @@ class VectorIndex:
             vector_type = self._dataset.index_struct_dict['type']
 
         if not vector_type:
-            raise ValueError(f"Vector store must be specified.")
+            raise ValueError("Vector store must be specified.")
 
         if vector_type == "weaviate":
             from core.index.vector_index.weaviate_vector_index import WeaviateConfig, WeaviateVectorIndex

api/core/model_manager.py

@@ -63,7 +63,7 @@ class ModelInstance:
         :return: full response or stream response chunk generator result
         """
         if not isinstance(self.model_type_instance, LargeLanguageModel):
-            raise Exception(f"Model type instance is not LargeLanguageModel")
+            raise Exception("Model type instance is not LargeLanguageModel")
 
         self.model_type_instance = cast(LargeLanguageModel, self.model_type_instance)
         return self.model_type_instance.invoke(
@@ -88,7 +88,7 @@ class ModelInstance:
         :return: embeddings result
         """
         if not isinstance(self.model_type_instance, TextEmbeddingModel):
-            raise Exception(f"Model type instance is not TextEmbeddingModel")
+            raise Exception("Model type instance is not TextEmbeddingModel")
 
         self.model_type_instance = cast(TextEmbeddingModel, self.model_type_instance)
         return self.model_type_instance.invoke(
@@ -112,7 +112,7 @@ class ModelInstance:
         :return: rerank result
         """
         if not isinstance(self.model_type_instance, RerankModel):
-            raise Exception(f"Model type instance is not RerankModel")
+            raise Exception("Model type instance is not RerankModel")
 
         self.model_type_instance = cast(RerankModel, self.model_type_instance)
         return self.model_type_instance.invoke(
@@ -135,7 +135,7 @@ class ModelInstance:
         :return: false if text is safe, true otherwise
         """
         if not isinstance(self.model_type_instance, ModerationModel):
-            raise Exception(f"Model type instance is not ModerationModel")
+            raise Exception("Model type instance is not ModerationModel")
 
         self.model_type_instance = cast(ModerationModel, self.model_type_instance)
         return self.model_type_instance.invoke(
@@ -155,7 +155,7 @@ class ModelInstance:
         :return: text for given audio file
         """
         if not isinstance(self.model_type_instance, Speech2TextModel):
-            raise Exception(f"Model type instance is not Speech2TextModel")
+            raise Exception("Model type instance is not Speech2TextModel")
 
         self.model_type_instance = cast(Speech2TextModel, self.model_type_instance)
         return self.model_type_instance.invoke(
@@ -176,7 +176,7 @@ class ModelInstance:
         :return: text for given audio file
         """
         if not isinstance(self.model_type_instance, TTSModel):
-            raise Exception(f"Model type instance is not TTSModel")
+            raise Exception("Model type instance is not TTSModel")
 
         self.model_type_instance = cast(TTSModel, self.model_type_instance)
         return self.model_type_instance.invoke(

api/core/model_runtime/callbacks/logging_callback.py

@@ -30,7 +30,7 @@ class LoggingCallback(Callback):
         """
         self.print_text("\n[on_llm_before_invoke]\n", color='blue')
         self.print_text(f"Model: {model}\n", color='blue')
-        self.print_text(f"Parameters:\n", color='blue')
+        self.print_text("Parameters:\n", color='blue')
         for key, value in model_parameters.items():
             self.print_text(f"\t{key}: {value}\n", color='blue')
 
@@ -38,7 +38,7 @@ class LoggingCallback(Callback):
             self.print_text(f"\tstop: {stop}\n", color='blue')
 
         if tools:
-            self.print_text(f"\tTools:\n", color='blue')
+            self.print_text("\tTools:\n", color='blue')
             for tool in tools:
                 self.print_text(f"\t\t{tool.name}\n", color='blue')
 
@@ -47,7 +47,7 @@ class LoggingCallback(Callback):
         if user:
             self.print_text(f"User: {user}\n", color='blue')
 
-        self.print_text(f"Prompt messages:\n", color='blue')
+        self.print_text("Prompt messages:\n", color='blue')
         for prompt_message in prompt_messages:
             if prompt_message.name:
                 self.print_text(f"\tname: {prompt_message.name}\n", color='blue')
@@ -101,7 +101,7 @@ class LoggingCallback(Callback):
         self.print_text(f"Content: {result.message.content}\n", color='yellow')
 
         if result.message.tool_calls:
-            self.print_text(f"Tool calls:\n", color='yellow')
+            self.print_text("Tool calls:\n", color='yellow')
             for tool_call in result.message.tool_calls:
                 self.print_text(f"\t{tool_call.id}\n", color='yellow')
                 self.print_text(f"\t{tool_call.function.name}\n", color='yellow')

api/core/model_runtime/model_providers/baichuan/llm/llm.py

@@ -110,7 +110,7 @@ class BaichuanLarguageModel(LargeLanguageModel):
                  stop: List[str] | None = None, stream: bool = True, user: str | None = None) \
             -> LLMResult | Generator:
         if tools is not None and len(tools) > 0:
-            raise InvokeBadRequestError(f"Baichuan model doesn't support tools")
+            raise InvokeBadRequestError("Baichuan model doesn't support tools")
         
         instance = BaichuanModel(
             api_key=credentials['api_key'],