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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
parent 2e718b85e9
commit dcb72e0067
58 changed files with 123 additions and 136 deletions
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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
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@@ -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
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@@ -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]

2
api/core/model_runtime/model_providers/volcengine_maas/volc_sdk/base/service.py
View File

@@ -150,7 +150,7 @@ class Request:
self.headers = OrderedDict()
self.query = OrderedDict()
self.body = ''
self.form = dict()
self.form = {}
self.connection_timeout = 0
self.socket_timeout = 0

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

@@ -147,7 +147,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
return self._get_num_tokens_by_gpt2(text)
if is_completion_model:
return sum([tokens(str(message.content)) for message in messages])
return sum(tokens(str(message.content)) for message in messages)
tokens_per_message = 3
tokens_per_name = 1

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

@@ -18,7 +18,7 @@ class _CommonZhipuaiAI:
"""
credentials_kwargs = {
"api_key": credentials['api_key'] if 'api_key' in credentials else
credentials['zhipuai_api_key'] if 'zhipuai_api_key' in credentials else None,
credentials.get("zhipuai_api_key"),
}
return credentials_kwargs