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chore: skip unnecessary key checks prior to accessing a dictionary (#4497)

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Bowen Liang 2024-05-19 18:30:45 +08:00 committed by GitHub
parent aa13d14019
commit 04ad46dd31
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30 changed files with 45 additions and 44 deletions
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2
api/core/app/app_config/common/sensitive_word_avoidance/manager.py
View File

@ -11,7 +11,7 @@ class SensitiveWordAvoidanceConfigManager:
if not sensitive_word_avoidance_dict:
return None
if 'enabled' in sensitive_word_avoidance_dict and sensitive_word_avoidance_dict['enabled']:
if sensitive_word_avoidance_dict.get('enabled'):
return SensitiveWordAvoidanceEntity(
type=sensitive_word_avoidance_dict.get('type'),
config=sensitive_word_avoidance_dict.get('config'),

2
api/core/app/app_config/features/file_upload/manager.py
View File

@ -14,7 +14,7 @@ class FileUploadConfigManager:
"""
file_upload_dict = config.get('file_upload')
if file_upload_dict:
if 'image' in file_upload_dict and file_upload_dict['image']:
if file_upload_dict.get('image'):
if 'enabled' in file_upload_dict['image'] and file_upload_dict['image']['enabled']:
image_config = {
'number_limits': file_upload_dict['image']['number_limits'],

2
api/core/app/app_config/features/more_like_this/manager.py
View File

@ -9,7 +9,7 @@ class MoreLikeThisConfigManager:
more_like_this = False
more_like_this_dict = config.get('more_like_this')
if more_like_this_dict:
if 'enabled' in more_like_this_dict and more_like_this_dict['enabled']:
if more_like_this_dict.get('enabled'):
more_like_this = True
return more_like_this

2
api/core/app/app_config/features/retrieval_resource/manager.py
View File

@ -4,7 +4,7 @@ class RetrievalResourceConfigManager:
show_retrieve_source = False
retriever_resource_dict = config.get('retriever_resource')
if retriever_resource_dict:
if 'enabled' in retriever_resource_dict and retriever_resource_dict['enabled']:
if retriever_resource_dict.get('enabled'):
show_retrieve_source = True
return show_retrieve_source

2
api/core/app/app_config/features/speech_to_text/manager.py
View File

@ -9,7 +9,7 @@ class SpeechToTextConfigManager:
speech_to_text = False
speech_to_text_dict = config.get('speech_to_text')
if speech_to_text_dict:
if 'enabled' in speech_to_text_dict and speech_to_text_dict['enabled']:
if speech_to_text_dict.get('enabled'):
speech_to_text = True
return speech_to_text

2
api/core/app/app_config/features/suggested_questions_after_answer/manager.py
View File

@ -9,7 +9,7 @@ class SuggestedQuestionsAfterAnswerConfigManager:
suggested_questions_after_answer = False
suggested_questions_after_answer_dict = config.get('suggested_questions_after_answer')
if suggested_questions_after_answer_dict:
if 'enabled' in suggested_questions_after_answer_dict and suggested_questions_after_answer_dict['enabled']:
if suggested_questions_after_answer_dict.get('enabled'):
suggested_questions_after_answer = True
return suggested_questions_after_answer

2
api/core/app/app_config/features/text_to_speech/manager.py
View File

@ -12,7 +12,7 @@ class TextToSpeechConfigManager:
text_to_speech = False
text_to_speech_dict = config.get('text_to_speech')
if text_to_speech_dict:
if 'enabled' in text_to_speech_dict and text_to_speech_dict['enabled']:
if text_to_speech_dict.get('enabled'):
text_to_speech = TextToSpeechEntity(
enabled=text_to_speech_dict.get('enabled'),
voice=text_to_speech_dict.get('voice'),

2
api/core/app/apps/advanced_chat/app_generator.py
View File

@ -66,7 +66,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
conversation = self._get_conversation_by_user(app_model, args.get('conversation_id'), user)
# parse files
files = args['files'] if 'files' in args and args['files'] else []
files = args['files'] if args.get('files') else []
message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
file_extra_config = FileUploadConfigManager.convert(workflow.features_dict, is_vision=False)
if file_extra_config:

2
api/core/app/apps/agent_chat/app_generator.py
View File

@ -83,7 +83,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
)
# parse files
files = args['files'] if 'files' in args and args['files'] else []
files = args['files'] if args.get('files') else []
message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
file_extra_config = FileUploadConfigManager.convert(override_model_config_dict or app_model_config.to_dict())
if file_extra_config:

2
api/core/app/apps/chat/app_generator.py
View File

@ -80,7 +80,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
)
# parse files
files = args['files'] if 'files' in args and args['files'] else []
files = args['files'] if args.get('files') else []
message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
file_extra_config = FileUploadConfigManager.convert(override_model_config_dict or app_model_config.to_dict())
if file_extra_config:

2
api/core/app/apps/completion/app_generator.py
View File

@ -75,7 +75,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
)
# parse files
files = args['files'] if 'files' in args and args['files'] else []
files = args['files'] if args.get('files') else []
message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
file_extra_config = FileUploadConfigManager.convert(override_model_config_dict or app_model_config.to_dict())
if file_extra_config:

2
api/core/app/apps/workflow/app_generator.py
View File

@ -49,7 +49,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
inputs = args['inputs']
# parse files
files = args['files'] if 'files' in args and args['files'] else []
files = args['files'] if args.get('files') else []
message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
file_extra_config = FileUploadConfigManager.convert(workflow.features_dict, is_vision=False)
if file_extra_config:

4
api/core/docstore/dataset_docstore.py
View File

@ -121,13 +121,13 @@ class DatasetDocumentStore:
enabled=False,
created_by=self._user_id,
)
if 'answer' in doc.metadata and doc.metadata['answer']:
if doc.metadata.get('answer'):
segment_document.answer = doc.metadata.pop('answer', '')
db.session.add(segment_document)
else:
segment_document.content = doc.page_content
if 'answer' in doc.metadata and doc.metadata['answer']:
if doc.metadata.get('answer'):
segment_document.answer = doc.metadata.pop('answer', '')
segment_document.index_node_hash = doc.metadata['doc_hash']
segment_document.word_count = len(doc.page_content)

2
api/core/indexing_runner.py
View File

@ -418,7 +418,7 @@ class IndexingRunner:
if separator:
separator = separator.replace('\\n', '\n')
if 'chunk_overlap' in segmentation and segmentation['chunk_overlap']:
if segmentation.get('chunk_overlap'):
chunk_overlap = segmentation['chunk_overlap']
else:
chunk_overlap = 0

4
api/core/model_runtime/model_providers/anthropic/llm/llm.py
View File

@ -146,7 +146,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
"""
Code block mode wrapper for invoking large language model
"""
if 'response_format' in model_parameters and model_parameters['response_format']:
if model_parameters.get('response_format'):
stop = stop or []
# chat model
self._transform_chat_json_prompts(
@ -408,7 +408,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
"max_retries": 1,
}
if 'anthropic_api_url' in credentials and credentials['anthropic_api_url']:
if credentials.get('anthropic_api_url'):
credentials['anthropic_api_url'] = credentials['anthropic_api_url'].rstrip('/')
credentials_kwargs['base_url'] = credentials['anthropic_api_url']

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

@ -89,7 +89,7 @@ class BaichuanModel:
# save stop reason temporarily
stop_reason = ''
for choice in choices:
if 'finish_reason' in choice and choice['finish_reason']:
if choice.get('finish_reason'):
stop_reason = choice['finish_reason']
if len(choice['delta']['content']) == 0:

4
api/core/model_runtime/model_providers/minimax/llm/chat_completion_pro.py
View File

@ -43,7 +43,7 @@ class MinimaxChatCompletionPro:
if 'top_p' in model_parameters and type(model_parameters['top_p']) == float:
extra_kwargs['top_p'] = model_parameters['top_p']
if 'plugin_web_search' in model_parameters and model_parameters['plugin_web_search']:
if model_parameters.get('plugin_web_search'):
extra_kwargs['plugins'] = [
'plugin_web_search'
]
@ -158,7 +158,7 @@ class MinimaxChatCompletionPro:
self._handle_error(code, msg)
# final chunk
if data['reply'] or 'usage' in data and data['usage']:
if data['reply'] or data.get('usage'):
total_tokens = data['usage']['total_tokens']
minimax_message = MinimaxMessage(
role=MinimaxMessage.Role.ASSISTANT.value,

2
api/core/model_runtime/model_providers/openai/_common.py
View File

@ -25,7 +25,7 @@ class _CommonOpenAI:
"max_retries": 1,
}
if 'openai_api_base' in credentials and credentials['openai_api_base']:
if credentials.get('openai_api_base'):
credentials['openai_api_base'] = credentials['openai_api_base'].rstrip('/')
credentials_kwargs['base_url'] = credentials['openai_api_base'] + '/v1'

2
api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py
View File

@ -180,7 +180,7 @@ class OpenLLMGenerate:
completion_usage += len(token_ids)
message = OpenLLMGenerateMessage(content=text, role=OpenLLMGenerateMessage.Role.ASSISTANT.value)
if 'finish_reason' in choice and choice['finish_reason']:
if choice.get('finish_reason'):
finish_reason = choice['finish_reason']
prompt_token_ids = data['prompt_token_ids']
message.stop_reason = finish_reason

2
api/core/rag/retrieval/dataset_retrieval.py
View File

@ -124,7 +124,7 @@ class DatasetRetrieval:
document_score_list = {}
for item in all_documents:
if 'score' in item.metadata and item.metadata['score']:
if item.metadata.get('score'):
document_score_list[item.metadata['doc_id']] = item.metadata['score']
document_context_list = []

2
api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.py
View File

@ -70,7 +70,7 @@ class StableDiffusionTool(BuiltinTool):
if not base_url:
return self.create_text_message('Please input base_url')
if 'model' in tool_parameters and tool_parameters['model']:
if tool_parameters.get('model'):
self.runtime.credentials['model'] = tool_parameters['model']
model = self.runtime.credentials.get('model', None)

2
api/core/tools/provider/builtin/wolframalpha/tools/wolframalpha.py
View File

@ -48,7 +48,7 @@ class WolframAlphaTool(BuiltinTool):
if 'success' not in response_data['queryresult'] or response_data['queryresult']['success'] != True:
query_result = response_data.get('queryresult', {})
if 'error' in query_result and query_result['error']:
if query_result.get('error'):
if 'msg' in query_result['error']:
if query_result['error']['msg'] == 'Invalid appid':
raise ToolProviderCredentialValidationError('Invalid appid')

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

@ -79,7 +79,7 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
document_score_list = {}
for item in all_documents:
if 'score' in item.metadata and item.metadata['score']:
if item.metadata.get('score'):
document_score_list[item.metadata['doc_id']] = item.metadata['score']
document_context_list = []

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

@ -87,7 +87,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
document_score_list = {}
if dataset.indexing_technique != "economy":
for item in documents:
if 'score' in item.metadata and item.metadata['score']:
if item.metadata.get('score'):
document_score_list[item.metadata['doc_id']] = item.metadata['score']
document_context_list = []
index_node_ids = [document.metadata['doc_id'] for document in documents]

10
api/core/tools/utils/web_reader_tool.py
View File

@ -132,17 +132,17 @@ def extract_using_readabilipy(html):
}
# Populate article fields from readability fields where present
if input_json:
if "title" in input_json and input_json["title"]:
if input_json.get("title"):
article_json["title"] = input_json["title"]
if "byline" in input_json and input_json["byline"]:
if input_json.get("byline"):
article_json["byline"] = input_json["byline"]
if "date" in input_json and input_json["date"]:
if input_json.get("date"):
article_json["date"] = input_json["date"]
if "content" in input_json and input_json["content"]:
if input_json.get("content"):
article_json["content"] = input_json["content"]
article_json["plain_content"] = plain_content(article_json["content"], False, False)
article_json["plain_text"] = extract_text_blocks_as_plain_text(article_json["plain_content"])
if "textContent" in input_json and input_json["textContent"]:
if input_json.get("textContent"):
article_json["plain_text"] = input_json["textContent"]
article_json["plain_text"] = re.sub(r'\n\s*\n', '\n', article_json["plain_text"])

2
api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
View File

@ -143,7 +143,7 @@ class KnowledgeRetrievalNode(BaseNode):
if all_documents:
document_score_list = {}
for item in all_documents:
if 'score' in item.metadata and item.metadata['score']:
if item.metadata.get('score'):
document_score_list[item.metadata['doc_id']] = item.metadata['score']
index_node_ids = [document.metadata['doc_id'] for document in all_documents]

1
api/pyproject.toml
View File

@ -13,6 +13,7 @@ select = [
"I001", # unsorted-imports
"I002", # missing-required-import
"UP", # pyupgrade rules
"RUF019", # unnecessary-key-check
]
ignore = [
"F403", # undefined-local-with-import-star

2
api/services/annotation_service.py
View File

@ -31,7 +31,7 @@ class AppAnnotationService:
if not app:
raise NotFound("App not found")
if 'message_id' in args and args['message_id']:
if args.get('message_id'):
message_id = str(args['message_id'])
# get message info
message = db.session.query(Message).filter(

4
api/services/app_service.py
View File

@ -47,10 +47,10 @@ class AppService:
elif args['mode'] == 'channel':
filters.append(App.mode == AppMode.CHANNEL.value)
if 'name' in args and args['name']:
if args.get('name'):
name = args['name'][:30]
filters.append(App.name.ilike(f'%{name}%'))
if 'tag_ids' in args and args['tag_ids']:
if args.get('tag_ids'):
target_ids = TagService.get_target_ids_by_tag_ids('app',
tenant_id,
args['tag_ids'])

16
api/services/dataset_service.py
View File

@ -569,7 +569,7 @@ class DocumentService:
documents = []
batch = time.strftime('%Y%m%d%H%M%S') + str(random.randint(100000, 999999))
if 'original_document_id' in document_data and document_data["original_document_id"]:
if document_data.get("original_document_id"):
document = DocumentService.update_document_with_dataset_id(dataset, document_data, account)
documents.append(document)
else:
@ -750,10 +750,10 @@ class DocumentService:
if document.display_status != 'available':
raise ValueError("Document is not available")
# update document name
if 'name' in document_data and document_data['name']:
if document_data.get('name'):
document.name = document_data['name']
# save process rule
if 'process_rule' in document_data and document_data['process_rule']:
if document_data.get('process_rule'):
process_rule = document_data["process_rule"]
if process_rule["mode"] == "custom":
dataset_process_rule = DatasetProcessRule(
@ -773,7 +773,7 @@ class DocumentService:
db.session.commit()
document.dataset_process_rule_id = dataset_process_rule.id
# update document data source
if 'data_source' in document_data and document_data['data_source']:
if document_data.get('data_source'):
file_name = ''
data_source_info = {}
if document_data["data_source"]["type"] == "upload_file":
@ -871,7 +871,7 @@ class DocumentService:
embedding_model.model
)
dataset_collection_binding_id = dataset_collection_binding.id
if 'retrieval_model' in document_data and document_data['retrieval_model']:
if document_data.get('retrieval_model'):
retrieval_model = document_data['retrieval_model']
else:
default_retrieval_model = {
@ -921,9 +921,9 @@ class DocumentService:
and ('process_rule' not in args and not args['process_rule']):
raise ValueError("Data source or Process rule is required")
else:
if 'data_source' in args and args['data_source']:
if args.get('data_source'):
DocumentService.data_source_args_validate(args)
if 'process_rule' in args and args['process_rule']:
if args.get('process_rule'):
DocumentService.process_rule_args_validate(args)
@classmethod
@ -1266,7 +1266,7 @@ class SegmentService:
if segment.content == content:
if document.doc_form == 'qa_model':
segment.answer = args['answer']
if 'keywords' in args and args['keywords']:
if args.get('keywords'):
segment.keywords = args['keywords']
segment.enabled = True
segment.disabled_at = None