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chore: apply flake8-comprehensions Ruff rules to improve collection comprehensions (#5652)

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Co-authored-by: -LAN- <laipz8200@outlook.com>
This commit is contained in:
Bowen Liang 2024-06-27 11:21:31 +08:00 • committed by GitHub
commit dcb72e0067
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GPG key ID: B5690EEEBB952194
58 changed files with 123 additions and 136 deletions
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2
api/core/app/app_config/easy_ui_based_app/agent/manager.py
View file

@ -40,7 +40,7 @@ class AgentConfigManager:
'provider_type': tool['provider_type'],
'provider_id': tool['provider_id'],
'tool_name': tool['tool_name'],
'tool_parameters': tool['tool_parameters'] if 'tool_parameters' in tool else {}
'tool_parameters': tool.get('tool_parameters', {})
}
agent_tools.append(AgentToolEntity(**agent_tool_properties))

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

@ -59,7 +59,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
inputs = args['inputs']
extras = {
"auto_generate_conversation_name": args['auto_generate_name'] if 'auto_generate_name' in args else False
"auto_generate_conversation_name": args.get('auto_generate_name', False)
}
# get conversation

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

@ -57,7 +57,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
inputs = args['inputs']
extras = {
"auto_generate_conversation_name": args['auto_generate_name'] if 'auto_generate_name' in args else True
"auto_generate_conversation_name": args.get('auto_generate_name', True)
}
# get conversation

2
api/core/app/apps/agent_chat/app_runner.py
View file

@ -203,7 +203,7 @@ class AgentChatAppRunner(AppRunner):
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
if set([ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL]).intersection(model_schema.features or []):
if {ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL}.intersection(model_schema.features or []):
agent_entity.strategy = AgentEntity.Strategy.FUNCTION_CALLING
conversation = db.session.query(Conversation).filter(Conversation.id == conversation.id).first()

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

@ -55,7 +55,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
inputs = args['inputs']
extras = {
"auto_generate_conversation_name": args['auto_generate_name'] if 'auto_generate_name' in args else True
"auto_generate_conversation_name": args.get('auto_generate_name', True)
}
# get conversation

4
api/core/entities/provider_configuration.py
View file

@ -66,8 +66,8 @@ class ProviderConfiguration(BaseModel):
original_provider_configurate_methods[self.provider.provider].append(configurate_method)
if original_provider_configurate_methods[self.provider.provider] == [ConfigurateMethod.CUSTOMIZABLE_MODEL]:
if (any([len(quota_configuration.restrict_models) > 0
for quota_configuration in self.system_configuration.quota_configurations])
if (any(len(quota_configuration.restrict_models) > 0
for quota_configuration in self.system_configuration.quota_configurations)
and ConfigurateMethod.PREDEFINED_MODEL not in self.provider.configurate_methods):
self.provider.configurate_methods.append(ConfigurateMethod.PREDEFINED_MODEL)

2
api/core/indexing_runner.py
View file

@ -397,7 +397,7 @@ class IndexingRunner:
document_id=dataset_document.id,
after_indexing_status="splitting",
extra_update_params={
DatasetDocument.word_count: sum([len(text_doc.page_content) for text_doc in text_docs]),
DatasetDocument.word_count: sum(len(text_doc.page_content) for text_doc in text_docs),
DatasetDocument.parsing_completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
}
)

2
api/core/model_runtime/model_providers/azure_openai/tts/tts.py
View file

@ -83,7 +83,7 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
max_workers = self._get_model_workers_limit(model, credentials)
try:
sentences = list(self._split_text_into_sentences(text=content_text, limit=word_limit))
audio_bytes_list = list()
audio_bytes_list = []
# Create a thread pool and map the function to the list of sentences
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:

10
api/core/model_runtime/model_providers/bedrock/llm/llm.py
View file

@ -175,8 +175,8 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
# - https://docs.anthropic.com/claude/reference/claude-on-amazon-bedrock
# - https://github.com/anthropics/anthropic-sdk-python
client = AnthropicBedrock(
aws_access_key=credentials.get("aws_access_key_id", None),
aws_secret_key=credentials.get("aws_secret_access_key", None),
aws_access_key=credentials.get("aws_access_key_id"),
aws_secret_key=credentials.get("aws_secret_access_key"),
aws_region=credentials["aws_region"],
)
@ -576,7 +576,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
"""
Create payload for bedrock api call depending on model provider
"""
payload = dict()
payload = {}
model_prefix = model.split('.')[0]
model_name = model.split('.')[1]
@ -648,8 +648,8 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
runtime_client = boto3.client(
service_name='bedrock-runtime',
config=client_config,
aws_access_key_id=credentials.get("aws_access_key_id", None),
aws_secret_access_key=credentials.get("aws_secret_access_key", None)
aws_access_key_id=credentials.get("aws_access_key_id"),
aws_secret_access_key=credentials.get("aws_secret_access_key")
)
model_prefix = model.split('.')[0]

6
api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py
View file

@ -49,8 +49,8 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
bedrock_runtime = boto3.client(
service_name='bedrock-runtime',
config=client_config,
aws_access_key_id=credentials.get("aws_access_key_id", None),
aws_secret_access_key=credentials.get("aws_secret_access_key", None)
aws_access_key_id=credentials.get("aws_access_key_id"),
aws_secret_access_key=credentials.get("aws_secret_access_key")
)
embeddings = []
@ -148,7 +148,7 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
"""
Create payload for bedrock api call depending on model provider
"""
payload = dict()
payload = {}
if model_prefix == "amazon":
payload['inputText'] = texts

8
api/core/model_runtime/model_providers/cohere/llm/llm.py
View file

@ -696,12 +696,10 @@ class CohereLargeLanguageModel(LargeLanguageModel):
en_US=model
),
model_type=ModelType.LLM,
features=[feature for feature in base_model_schema_features],
features=list(base_model_schema_features),
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
key: property for key, property in base_model_schema_model_properties.items()
},
parameter_rules=[rule for rule in base_model_schema_parameters_rules],
model_properties=dict(base_model_schema_model_properties.items()),
parameter_rules=list(base_model_schema_parameters_rules),
pricing=base_model_schema.pricing
)

5
api/core/model_runtime/model_providers/google/llm/llm.py
View file

@ -277,10 +277,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
type='function',
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
name=part.function_call.name,
arguments=json.dumps({
key: value
for key, value in part.function_call.args.items()
})
arguments=json.dumps(dict(part.function_call.args.items()))
)
)
]

4
api/core/model_runtime/model_providers/moonshot/llm/llm.py
View file

@ -88,9 +88,9 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
def _add_function_call(self, model: str, credentials: dict) -> None:
model_schema = self.get_model_schema(model, credentials)
if model_schema and set([
if model_schema and {
ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL
]).intersection(model_schema.features or []):
}.intersection(model_schema.features or []):
credentials['function_calling_type'] = 'tool_call'
def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:

8
api/core/model_runtime/model_providers/nvidia/llm/llm.py
View file

@ -100,10 +100,10 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
endpoint_url = credentials['endpoint_url'] if 'endpoint_url' in credentials else None
endpoint_url = credentials.get('endpoint_url')
if endpoint_url and not endpoint_url.endswith('/'):
endpoint_url += '/'
server_url = credentials['server_url'] if 'server_url' in credentials else None
server_url = credentials.get('server_url')
# prepare the payload for a simple ping to the model
data = {
@ -182,10 +182,10 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
if stream:
headers['Accept'] = 'text/event-stream'
endpoint_url = credentials['endpoint_url'] if 'endpoint_url' in credentials else None
endpoint_url = credentials.get('endpoint_url')
if endpoint_url and not endpoint_url.endswith('/'):
endpoint_url += '/'
server_url = credentials['server_url'] if 'server_url' in credentials else None
server_url = credentials.get('server_url')
data = {
"model": model,

8
api/core/model_runtime/model_providers/openai/llm/llm.py
View file

@ -1073,12 +1073,10 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
en_US=model
),
model_type=ModelType.LLM,
features=[feature for feature in base_model_schema_features],
features=list(base_model_schema_features),
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
key: property for key, property in base_model_schema_model_properties.items()
},
parameter_rules=[rule for rule in base_model_schema_parameters_rules],
model_properties=dict(base_model_schema_model_properties.items()),
parameter_rules=list(base_model_schema_parameters_rules),
pricing=base_model_schema.pricing
)

2
api/core/model_runtime/model_providers/openai/tts/tts.py
View file

@ -80,7 +80,7 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
max_workers = self._get_model_workers_limit(model, credentials)
try:
sentences = list(self._split_text_into_sentences(text=content_text, limit=word_limit))
audio_bytes_list = list()
audio_bytes_list = []
# Create a thread pool and map the function to the list of sentences
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:

15
api/core/model_runtime/model_providers/replicate/llm/llm.py
View file

@ -275,14 +275,13 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
@classmethod
def _get_parameter_type(cls, param_type: str) -> str:
if param_type == 'integer':
return 'int'
elif param_type == 'number':
return 'float'
elif param_type == 'boolean':
return 'boolean'
elif param_type == 'string':
return 'string'
type_mapping = {
'integer': 'int',
'number': 'float',
'boolean': 'boolean',
'string': 'string'
}
return type_mapping.get(param_type)
def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
messages = messages.copy() # don't mutate the original list

2
api/core/model_runtime/model_providers/tongyi/tts/tts.py
View file

@ -80,7 +80,7 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
max_workers = self._get_model_workers_limit(model, credentials)
try:
sentences = list(self._split_text_into_sentences(text=content_text, limit=word_limit))
audio_bytes_list = list()
audio_bytes_list = []
# Create a thread pool and map the function to the list of sentences
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:

5
api/core/model_runtime/model_providers/vertex_ai/llm/llm.py
View file

@ -579,10 +579,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
type='function',
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
name=part.function_call.name,
arguments=json.dumps({
key: value
for key, value in part.function_call.args.items()
})
arguments=json.dumps(dict(part.function_call.args.items()))
)
)
]

2
api/core/model_runtime/model_providers/volcengine_maas/volc_sdk/base/auth.py
View file

@ -102,7 +102,7 @@ class Signer:
body_hash = Util.sha256(request.body)
request.headers['X-Content-Sha256'] = body_hash
signed_headers = dict()
signed_headers = {}
for key in request.headers:
if key in ['Content-Type', 'Content-Md5', 'Host'] or key.startswith('X-'):
signed_headers[key.lower()] = request.headers[key]

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