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

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Bowen Liang 2024-02-08 14:11:10 +08:00 committed by GitHub
parent 1b04382a9b
commit 14a19a3da9
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34 changed files with 91 additions and 86 deletions
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4
api/controllers/console/app/app.py
View File

@ -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

8
api/controllers/console/datasets/datasets.py
View File

@ -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:

16
api/controllers/console/datasets/datasets_document.py
View File

@ -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:

12
api/controllers/console/datasets/datasets_segments.py
View File

@ -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

4
api/controllers/console/datasets/hit_testing.py
View File

@ -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:

2
api/controllers/console/explore/parameter.py
View File

@ -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:

2
api/controllers/files/image_preview.py
View File

@ -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(

2
api/controllers/files/tool_files.py
View File

@ -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:

2
api/controllers/service_api/app/app.py
View File

@ -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:

12
api/controllers/service_api/dataset/segment.py
View File

@ -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

2
api/controllers/web/app.py
View File

@ -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:

2
api/core/app_runner/assistant_app_runner.py
View File

@ -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

2
api/core/app_runner/basic_app_runner.py
View File

@ -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

2
api/core/features/assistant_base_runner.py
View File

@ -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}."

4
api/core/features/assistant_cot_runner.py
View File

@ -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:

2
api/core/features/assistant_fc_runner.py
View File

@ -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:

2
api/core/index/vector_index/vector_index.py
View File

@ -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

12
api/core/model_manager.py
View File

@ -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(

8
api/core/model_runtime/callbacks/logging_callback.py
View File

@ -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')

2
api/core/model_runtime/model_providers/baichuan/llm/llm.py
View File

@ -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'],

6
api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
View File

@ -146,16 +146,16 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
try:
json_result = response.json()
except json.JSONDecodeError as e:
raise CredentialsValidateFailedError(f'Credentials validation failed: JSON decode error')
raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error')
if (completion_type is LLMMode.CHAT
and ('object' not in json_result or json_result['object'] != 'chat.completion')):
raise CredentialsValidateFailedError(
f'Credentials validation failed: invalid response object, must be \'chat.completion\'')
'Credentials validation failed: invalid response object, must be \'chat.completion\'')
elif (completion_type is LLMMode.COMPLETION
and ('object' not in json_result or json_result['object'] != 'text_completion')):
raise CredentialsValidateFailedError(
f'Credentials validation failed: invalid response object, must be \'text_completion\'')
'Credentials validation failed: invalid response object, must be \'text_completion\'')
except CredentialsValidateFailedError:
raise
except Exception as ex:

4
api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py
View File

@ -179,11 +179,11 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
try:
json_result = response.json()
except json.JSONDecodeError as e:
raise CredentialsValidateFailedError(f'Credentials validation failed: JSON decode error')
raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error')
if 'model' not in json_result:
raise CredentialsValidateFailedError(
f'Credentials validation failed: invalid response')
'Credentials validation failed: invalid response')
except CredentialsValidateFailedError:
raise
except Exception as ex:

16
api/core/model_runtime/model_providers/wenxin/llm/ernie_bot.py
View File

@ -231,15 +231,15 @@ class ErnieBotModel(object):
# so, we just disable function calling for now.
if tools is not None and len(tools) > 0:
raise BadRequestError(f'function calling is not supported yet.')
raise BadRequestError('function calling is not supported yet.')
if stop is not None:
if len(stop) > 4:
raise BadRequestError(f'stop list should not exceed 4 items.')
raise BadRequestError('stop list should not exceed 4 items.')
for s in stop:
if len(s) > 20:
raise BadRequestError(f'stop item should not exceed 20 characters.')
raise BadRequestError('stop item should not exceed 20 characters.')
def _build_request_body(self, model: str, messages: List[ErnieMessage], stream: bool, parameters: Dict[str, Any],
tools: List[PromptMessageTool], stop: List[str], user: str) -> Dict[str, Any]:
@ -252,9 +252,9 @@ class ErnieBotModel(object):
stop: List[str], user: str) \
-> Dict[str, Any]:
if len(messages) % 2 == 0:
raise BadRequestError(f'The number of messages should be odd.')
raise BadRequestError('The number of messages should be odd.')
if messages[0].role == 'function':
raise BadRequestError(f'The first message should be user message.')
raise BadRequestError('The first message should be user message.')
"""
TODO: implement function calling
@ -264,7 +264,7 @@ class ErnieBotModel(object):
parameters: Dict[str, Any], stop: List[str], user: str) \
-> Dict[str, Any]:
if len(messages) == 0:
raise BadRequestError(f'The number of messages should not be zero.')
raise BadRequestError('The number of messages should not be zero.')
# check if the first element is system, shift it
system_message = ''
@ -273,9 +273,9 @@ class ErnieBotModel(object):
system_message = message.content
if len(messages) % 2 == 0:
raise BadRequestError(f'The number of messages should be odd.')
raise BadRequestError('The number of messages should be odd.')
if messages[0].role != 'user':
raise BadRequestError(f'The first message should be user message.')
raise BadRequestError('The first message should be user message.')
body = {
'messages': [message.to_dict() for message in messages],
'stream': stream,

2
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/_client.py
View File

@ -37,7 +37,7 @@ class ZhipuAI(HttpClient):
if base_url is None:
base_url = os.environ.get("ZHIPUAI_BASE_URL")
if base_url is None:
base_url = f"https://open.bigmodel.cn/api/paas/v4"
base_url = "https://open.bigmodel.cn/api/paas/v4"
from .__version__ import __version__
super().__init__(
version=__version__,

4
api/core/prompt/output_parser/rule_config_generator.py
View File

@ -19,11 +19,11 @@ class RuleConfigGeneratorOutputParser(BaseOutputParser):
raise ValueError("Expected 'prompt' to be a string.")
if not isinstance(parsed["variables"], list):
raise ValueError(
f"Expected 'variables' to be a list."
"Expected 'variables' to be a list."
)
if not isinstance(parsed["opening_statement"], str):
raise ValueError(
f"Expected 'opening_statement' to be a str."
"Expected 'opening_statement' to be a str."
)
return parsed
except Exception as e:

8
api/core/tools/model/tool_model_manager.py
View File

@ -39,13 +39,13 @@ class ToolModelManager:
)
if not model_instance:
raise InvokeModelError(f'Model not found')
raise InvokeModelError('Model not found')
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
if not schema:
raise InvokeModelError(f'No model schema found')
raise InvokeModelError('No model schema found')
max_tokens = schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE, None)
if max_tokens is None:
@ -69,7 +69,7 @@ class ToolModelManager:
)
if not model_instance:
raise InvokeModelError(f'Model not found')
raise InvokeModelError('Model not found')
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
@ -156,7 +156,7 @@ class ToolModelManager:
except InvokeConnectionError as e:
raise InvokeModelError(f'Invoke connection error: {e}')
except InvokeAuthorizationError as e:
raise InvokeModelError(f'Invoke authorization error')
raise InvokeModelError('Invoke authorization error')
except InvokeServerUnavailableError as e:
raise InvokeModelError(f'Invoke server unavailable error: {e}')
except Exception as e:

2
api/core/tools/provider/builtin/yahoo/tools/analytics.py
View File

@ -66,5 +66,5 @@ class YahooFinanceAnalyticsTool(BuiltinTool):
try:
return self.create_text_message(str(summary_df.to_dict()))
except (HTTPError, ReadTimeout):
return self.create_text_message(f'There is a internet connection problem. Please try again later.')
return self.create_text_message('There is a internet connection problem. Please try again later.')

2
api/core/tools/provider/builtin/yahoo/tools/news.py
View File

@ -21,7 +21,7 @@ class YahooFinanceSearchTickerTool(BuiltinTool):
try:
return self.run(ticker=query, user_id=user_id)
except (HTTPError, ReadTimeout):
return self.create_text_message(f'There is a internet connection problem. Please try again later.')
return self.create_text_message('There is a internet connection problem. Please try again later.')
def run(self, ticker: str, user_id: str) -> ToolInvokeMessage:
company = yfinance.Ticker(ticker)

2
api/core/tools/provider/builtin/yahoo/tools/ticker.py
View File

@ -20,7 +20,7 @@ class YahooFinanceSearchTickerTool(BuiltinTool):
try:
return self.create_text_message(self.run(ticker=query))
except (HTTPError, ReadTimeout):
return self.create_text_message(f'There is a internet connection problem. Please try again later.')
return self.create_text_message('There is a internet connection problem. Please try again later.')
def run(self, ticker: str) -> str:
return str(Ticker(ticker).info)

2
api/core/tools/tool/tool.py
View File

@ -221,7 +221,7 @@ class Tool(BaseModel, ABC):
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."
elif response.type == ToolInvokeMessage.MessageType.BLOB:
if len(response.message) > 114:
result += str(response.message[:114]) + '...'

2
api/libs/helper.py
View File

@ -101,7 +101,7 @@ class datetime_string(object):
datetime.strptime(value, self.format)
except ValueError:
error = ('Invalid {arg}: {val}. {arg} must be conform to the format {format}'
.format(arg=self.argument, val=value, lo=self.format))
.format(arg=self.argument, val=value, format=self.format))
raise ValueError(error)
return value

9
api/pyproject.toml
View File

@ -11,8 +11,13 @@ line-length = 120
[tool.ruff.lint]
ignore-init-module-imports = true
select = [
"F401", # unused-import
"F", # pyflakes rules
"I001", # unsorted-imports
"I002", # missing-required-import
"F811", # redefined-while-unused
]
ignore = [
"F403", # undefined-local-with-import-star
"F405", # undefined-local-with-import-star-usage
"F821", # undefined-name
"F841", # unused-variable
]

8
api/services/dataset_service.py
View File

@ -139,8 +139,8 @@ class DatasetService:
)
except LLMBadRequestError:
raise ValueError(
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 ValueError(f"The dataset in unavailable, due to: "
f"{ex.description}")
@ -176,8 +176,8 @@ class DatasetService:
filtered_data['collection_binding_id'] = dataset_collection_binding.id
except LLMBadRequestError:
raise ValueError(
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 ValueError(ex.description)

8
api/services/tools_manage_service.py
View File

@ -50,7 +50,7 @@ class ToolManageService:
:param provider: the provider dict
"""
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
+ "/console/api/workspaces/current/tool-provider/builtin/")
if 'icon' in provider:
if provider['type'] == UserToolProvider.ProviderType.BUILTIN.value:
@ -211,7 +211,7 @@ class ToolManageService:
tool_bundles, schema_type = ToolManageService.convert_schema_to_tool_bundles(schema, extra_info)
if len(tool_bundles) > 10:
raise ValueError(f'the number of apis should be less than 10')
raise ValueError('the number of apis should be less than 10')
# create db provider
db_provider = ApiToolProvider(
@ -269,7 +269,7 @@ class ToolManageService:
# try to parse schema, avoid SSRF attack
ToolManageService.parser_api_schema(schema)
except Exception as e:
raise ValueError(f'invalid schema, please check the url you provided')
raise ValueError('invalid schema, please check the url you provided')
return {
'schema': schema
@ -490,7 +490,7 @@ class ToolManageService:
try:
tool_bundles, _ = ApiBasedToolSchemaParser.auto_parse_to_tool_bundle(schema)
except Exception as e:
raise ValueError(f'invalid schema')
raise ValueError('invalid schema')
# get tool bundle
tool_bundle = next(filter(lambda tb: tb.operation_id == tool_name, tool_bundles), None)