chore(lint): fix quotes for f-string formatting by bumping ruff to 0.9.x

api/.ruff.toml

@@ -53,10 +53,12 @@ ignore = [
     "FURB152", # math-constant
     "UP007", # non-pep604-annotation
     "UP032", # f-string
+    "UP045", # non-pep604-annotation-optional
     "B005", # strip-with-multi-characters
     "B006", # mutable-argument-default
     "B007", # unused-loop-control-variable
     "B026", # star-arg-unpacking-after-keyword-arg
+    "B903", # class-as-data-structure
     "B904", # raise-without-from-inside-except
     "B905", # zip-without-explicit-strict
     "N806", # non-lowercase-variable-in-function

api/configs/feature/__init__.py

@@ -146,7 +146,7 @@ class EndpointConfig(BaseSettings):
     )
 
     CONSOLE_WEB_URL: str = Field(
-        description="Base URL for the console web interface," "used for frontend references and CORS configuration",
+        description="Base URL for the console web interface,used for frontend references and CORS configuration",
         default="",
     )
 

api/configs/feature/hosted_service/__init__.py

@@ -181,7 +181,7 @@ class HostedFetchAppTemplateConfig(BaseSettings):
     """
 
     HOSTED_FETCH_APP_TEMPLATES_MODE: str = Field(
-        description="Mode for fetching app templates: remote, db, or builtin" " default to remote,",
+        description="Mode for fetching app templates: remote, db, or builtin default to remote,",
         default="remote",
     )
 

api/controllers/console/admin.py

@@ -56,7 +56,7 @@ def post(self):
 
         app = App.query.filter(App.id == args["app_id"]).first()
         if not app:
-            raise NotFound(f'App \'{args["app_id"]}\' is not found')
+            raise NotFound(f"App '{args['app_id']}' is not found")
 
         site = app.site
         if not site:

api/controllers/console/datasets/datasets.py

@@ -457,7 +457,7 @@ def post(self):
             )
         except LLMBadRequestError:
             raise ProviderNotInitializeError(
-                "No Embedding Model available. Please configure a valid provider " "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)

api/controllers/console/datasets/datasets_document.py

@@ -350,8 +350,7 @@ def post(self):
                 )
             except InvokeAuthorizationError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)
@@ -526,8 +525,7 @@ def get(self, dataset_id, batch):
                 return response.model_dump(), 200
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)

api/controllers/console/datasets/datasets_segments.py

@@ -168,8 +168,7 @@ def patch(self, dataset_id, document_id, action):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)
@@ -217,8 +216,7 @@ def post(self, dataset_id, document_id):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)
@@ -267,8 +265,7 @@ def patch(self, dataset_id, document_id, segment_id):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)
@@ -437,8 +434,7 @@ def post(self, dataset_id, document_id, segment_id):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)

api/controllers/service_api/dataset/segment.py

@@ -53,8 +53,7 @@ def post(self, tenant_id, dataset_id, document_id):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)
@@ -95,8 +94,7 @@ def get(self, tenant_id, dataset_id, document_id):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)
@@ -175,8 +173,7 @@ def post(self, tenant_id, dataset_id, document_id, segment_id):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "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)

api/core/app/apps/base_app_queue_manager.py

@@ -167,8 +167,7 @@ def _check_for_sqlalchemy_models(self, data: Any):
         else:
             if isinstance(data, DeclarativeMeta) or hasattr(data, "_sa_instance_state"):
                 raise TypeError(
-                    "Critical Error: Passing SQLAlchemy Model instances "
-                    "that cause thread safety issues is not allowed."
+                    "Critical Error: Passing SQLAlchemy Model instances that cause thread safety issues is not allowed."
                 )
 
 

api/core/app/task_pipeline/message_cycle_manage.py

@@ -145,7 +145,7 @@ def _message_file_to_stream_response(self, event: QueueMessageFileEvent) -> Opti
 
             # get extension
             if "." in message_file.url:
-                extension = f'.{message_file.url.split(".")[-1]}'
+                extension = f".{message_file.url.split('.')[-1]}"
                 if len(extension) > 10:
                     extension = ".bin"
             else:

api/core/external_data_tool/api/api.py

@@ -62,8 +62,9 @@ def query(self, inputs: dict, query: Optional[str] = None) -> str:
 
         if not api_based_extension:
             raise ValueError(
-                "[External data tool] API query failed, variable: {}, "
-                "error: api_based_extension_id is invalid".format(self.variable)
+                "[External data tool] API query failed, variable: {}, error: api_based_extension_id is invalid".format(
+                    self.variable
+                )
             )
 
         # decrypt api_key

api/core/file/models.py

@@ -90,7 +90,7 @@ def to_dict(self) -> Mapping[str, str | int | None]:
     def markdown(self) -> str:
         url = self.generate_url()
         if self.type == FileType.IMAGE:
-            text = f'![{self.filename or ""}]({url})'
+            text = f"![{self.filename or ''}]({url})"
         else:
             text = f"[{self.filename or url}]({url})"
 

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

@@ -108,7 +108,7 @@ def validate_credentials(self, model: str, credentials: dict) -> None:
         ai_model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
 
         if not ai_model_entity:
-            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
+            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
 
         try:
             client = AzureOpenAI(**self._to_credential_kwargs(credentials))

api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py

@@ -130,7 +130,7 @@ def validate_credentials(self, model: str, credentials: dict) -> None:
             raise CredentialsValidateFailedError("Base Model Name is required")
 
         if not self._get_ai_model_entity(credentials["base_model_name"], model):
-            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
+            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
 
         try:
             credentials_kwargs = self._to_credential_kwargs(credentials)

api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py

@@ -162,9 +162,9 @@ def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> Em
     @staticmethod
     def _check_endpoint_url_model_repository_name(credentials: dict, model_name: str):
         try:
-            url = f'{HUGGINGFACE_ENDPOINT_API}{credentials["huggingface_namespace"]}'
+            url = f"{HUGGINGFACE_ENDPOINT_API}{credentials['huggingface_namespace']}"
             headers = {
-                "Authorization": f'Bearer {credentials["huggingfacehub_api_token"]}',
+                "Authorization": f"Bearer {credentials['huggingfacehub_api_token']}",
                 "Content-Type": "application/json",
             }
 

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

@@ -257,8 +257,7 @@ def _handle_generate_stream_response(
         for index, response in enumerate(responses):
             if response.status_code not in {200, HTTPStatus.OK}:
                 raise ServiceUnavailableError(
-                    f"Failed to invoke model {model}, status code: {response.status_code}, "
-                    f"message: {response.message}"
+                    f"Failed to invoke model {model}, status code: {response.status_code}, message: {response.message}"
                 )
 
             resp_finish_reason = response.output.choices[0].finish_reason

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

@@ -146,7 +146,7 @@ def get_customizable_model_schema(self, model: str, credentials: dict) -> Option
             elif credentials["completion_type"] == "completion":
                 completion_type = LLMMode.COMPLETION.value
             else:
-                raise ValueError(f'completion_type {credentials["completion_type"]} is not supported')
+                raise ValueError(f"completion_type {credentials['completion_type']} is not supported")
 
         entity = AIModelEntity(
             model=model,

api/core/model_runtime/model_providers/wenxin/_common.py

@@ -41,15 +41,15 @@ def _get_access_token(api_key: str, secret_key: str) -> str:
         resp = response.json()
         if "error" in resp:
             if resp["error"] == "invalid_client":
-                raise InvalidAPIKeyError(f'Invalid API key or secret key: {resp["error_description"]}')
+                raise InvalidAPIKeyError(f"Invalid API key or secret key: {resp['error_description']}")
             elif resp["error"] == "unknown_error":
-                raise InternalServerError(f'Internal server error: {resp["error_description"]}')
+                raise InternalServerError(f"Internal server error: {resp['error_description']}")
             elif resp["error"] == "invalid_request":
-                raise BadRequestError(f'Bad request: {resp["error_description"]}')
+                raise BadRequestError(f"Bad request: {resp['error_description']}")
             elif resp["error"] == "rate_limit_exceeded":
-                raise RateLimitReachedError(f'Rate limit reached: {resp["error_description"]}')
+                raise RateLimitReachedError(f"Rate limit reached: {resp['error_description']}")
             else:
-                raise Exception(f'Unknown error: {resp["error_description"]}')
+                raise Exception(f"Unknown error: {resp['error_description']}")
 
         return resp["access_token"]
 

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

@@ -406,7 +406,7 @@ def get_customizable_model_schema(self, model: str, credentials: dict) -> Option
             elif credentials["completion_type"] == "completion":
                 completion_type = LLMMode.COMPLETION.value
             else:
-                raise ValueError(f'completion_type {credentials["completion_type"]} is not supported')
+                raise ValueError(f"completion_type {credentials['completion_type']} is not supported")
         else:
             extra_args = XinferenceHelper.get_xinference_extra_parameter(
                 server_url=credentials["server_url"],
@@ -472,7 +472,7 @@ def _generate(
         api_key = credentials.get("api_key") or "abc"
 
         client = OpenAI(
-            base_url=f'{credentials["server_url"]}/v1',
+            base_url=f"{credentials['server_url']}/v1",
             api_key=api_key,
             max_retries=int(credentials.get("max_retries") or DEFAULT_MAX_RETRIES),
             timeout=int(credentials.get("invoke_timeout") or DEFAULT_INVOKE_TIMEOUT),

api/core/rag/extractor/firecrawl/firecrawl_app.py

@@ -31,7 +31,7 @@ def scrape_url(self, url, params=None) -> dict:
                     "markdown": data.get("markdown"),
                 }
             else:
-                raise Exception(f'Failed to scrape URL. Error: {response_data["error"]}')
+                raise Exception(f"Failed to scrape URL. Error: {response_data['error']}")
 
         elif response.status_code in {402, 409, 500}:
             error_message = response.json().get("error", "Unknown error occurred")

api/core/rag/extractor/notion_extractor.py

@@ -358,8 +358,7 @@ def _get_access_token(cls, tenant_id: str, notion_workspace_id: str) -> str:
 
         if not data_source_binding:
             raise Exception(
-                f"No notion data source binding found for tenant {tenant_id} "
-                f"and notion workspace {notion_workspace_id}"
+                f"No notion data source binding found for tenant {tenant_id} and notion workspace {notion_workspace_id}"
             )
 
         return cast(str, data_source_binding.access_token)

api/core/tools/provider/builtin/aippt/tools/aippt.py

@@ -127,7 +127,7 @@ def _create_task(self, type: int, title: str, content: str, user_id: str) -> str
 
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to create task: {response.get("msg")}')
+            raise Exception(f"Failed to create task: {response.get('msg')}")
 
         return response.get("data", {}).get("id")
 
@@ -222,7 +222,7 @@ def _generate_content(self, task_id: str, model: str, user_id: str) -> str:
         elif model == "wenxin":
             response = response.json()
             if response.get("code") != 0:
-                raise Exception(f'Failed to generate content: {response.get("msg")}')
+                raise Exception(f"Failed to generate content: {response.get('msg')}")
 
             return response.get("data", "")
 
@@ -254,7 +254,7 @@ def _generate_ppt(self, task_id: str, suit_id: int, user_id: str) -> tuple[str,
 
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to generate ppt: {response.get("msg")}')
+            raise Exception(f"Failed to generate ppt: {response.get('msg')}")
 
         id = response.get("data", {}).get("id")
         cover_url = response.get("data", {}).get("cover_url")
@@ -270,7 +270,7 @@ def _generate_ppt(self, task_id: str, suit_id: int, user_id: str) -> tuple[str,
 
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to generate ppt: {response.get("msg")}')
+            raise Exception(f"Failed to generate ppt: {response.get('msg')}")
 
         export_code = response.get("data")
         if not export_code:
@@ -290,7 +290,7 @@ def _generate_ppt(self, task_id: str, suit_id: int, user_id: str) -> tuple[str,
 
             response = response.json()
             if response.get("code") != 0:
-                raise Exception(f'Failed to generate ppt: {response.get("msg")}')
+                raise Exception(f"Failed to generate ppt: {response.get('msg')}")
 
             if response.get("msg") == "导出中":
                 current_iteration += 1
@@ -343,7 +343,7 @@ def _get_api_token(cls, credentials: dict[str, str], user_id: str) -> str:
             raise Exception(f"Failed to connect to aippt: {response.text}")
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
+            raise Exception(f"Failed to connect to aippt: {response.get('msg')}")
 
         token = response.get("data", {}).get("token")
         expire = response.get("data", {}).get("time_expire")
@@ -379,7 +379,7 @@ def _get_styles(
                 if cls._style_cache[key]["expire"] < now:
                     del cls._style_cache[key]
 
-            key = f'{credentials["aippt_access_key"]}#@#{user_id}'
+            key = f"{credentials['aippt_access_key']}#@#{user_id}"
             if key in cls._style_cache:
                 return cls._style_cache[key]["colors"], cls._style_cache[key]["styles"]
 
@@ -396,19 +396,19 @@ def _get_styles(
         response = response.json()
 
         if response.get("code") != 0:
-            raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
+            raise Exception(f"Failed to connect to aippt: {response.get('msg')}")
 
         colors = [
             {
-                "id": f'id-{item.get("id")}',
+                "id": f"id-{item.get('id')}",
                 "name": item.get("name"),
                 "en_name": item.get("en_name", item.get("name")),
             }
             for item in response.get("data", {}).get("colour") or []
         ]
         styles = [
             {
-                "id": f'id-{item.get("id")}',
+                "id": f"id-{item.get('id')}",
                 "name": item.get("title"),
             }
             for item in response.get("data", {}).get("suit_style") or []
@@ -454,7 +454,7 @@ def _get_suit(self, style_id: int, colour_id: int) -> int:
         response = response.json()
 
         if response.get("code") != 0:
-            raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
+            raise Exception(f"Failed to connect to aippt: {response.get('msg')}")
 
         if len(response.get("data", {}).get("list") or []) > 0:
             return response.get("data", {}).get("list")[0].get("id")

api/core/tools/provider/builtin/aws/tools/nova_reel.py

@@ -229,8 +229,7 @@ def _handle_generation_mode(
 
         if async_mode:
             return self.create_text_message(
-                f"Video generation started.\nInvocation ARN: {invocation_arn}\n"
-                f"Video will be available at: {video_uri}"
+                f"Video generation started.\nInvocation ARN: {invocation_arn}\nVideo will be available at: {video_uri}"
             )
 
         return self._wait_for_completion(bedrock, s3_client, invocation_arn)