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chore: refurish python code by applying Pylint linter rules (#8322)

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This commit is contained in:
Bowen Liang
2024-09-13 22:42:08 +08:00
committed by GitHub
parent 1ab81b4972
commit a1104ab97e
126 changed files with 253 additions and 272 deletions
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12
api/core/model_runtime/entities/model_entities.py
View File

@@ -27,17 +27,17 @@ class ModelType(Enum):
:return: model type
"""
if origin_model_type == "text-generation" or origin_model_type == cls.LLM.value:
if origin_model_type in {"text-generation", cls.LLM.value}:
return cls.LLM
elif origin_model_type == "embeddings" or origin_model_type == cls.TEXT_EMBEDDING.value:
elif origin_model_type in {"embeddings", cls.TEXT_EMBEDDING.value}:
return cls.TEXT_EMBEDDING
elif origin_model_type == "reranking" or origin_model_type == cls.RERANK.value:
elif origin_model_type in {"reranking", cls.RERANK.value}:
return cls.RERANK
elif origin_model_type == "speech2text" or origin_model_type == cls.SPEECH2TEXT.value:
elif origin_model_type in {"speech2text", cls.SPEECH2TEXT.value}:
return cls.SPEECH2TEXT
elif origin_model_type == "tts" or origin_model_type == cls.TTS.value:
elif origin_model_type in {"tts", cls.TTS.value}:
return cls.TTS
elif origin_model_type == "text2img" or origin_model_type == cls.TEXT2IMG.value:
elif origin_model_type in {"text2img", cls.TEXT2IMG.value}:
return cls.TEXT2IMG
elif origin_model_type == cls.MODERATION.value:
return cls.MODERATION

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

@@ -494,7 +494,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
mime_type = data_split[0].replace("data:", "")
base64_data = data_split[1]
if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
raise ValueError(
f"Unsupported image type {mime_type}, "
f"only support image/jpeg, image/png, image/gif, and image/webp"

4
api/core/model_runtime/model_providers/azure_openai/tts/tts.py
View File

@@ -85,14 +85,14 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
for i in range(len(sentences))
]
for future in futures:
yield from future.result().__enter__().iter_bytes(1024)
yield from future.result().__enter__().iter_bytes(1024) # noqa:PLC2801
else:
response = client.audio.speech.with_streaming_response.create(
model=model, voice=voice, response_format="mp3", input=content_text.strip()
)
yield from response.__enter__().iter_bytes(1024)
yield from response.__enter__().iter_bytes(1024) # noqa:PLC2801
except Exception as ex:
raise InvokeBadRequestError(str(ex))

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

@@ -454,7 +454,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
base64_data = data_split[1]
image_content = base64.b64decode(base64_data)
if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
raise ValueError(
f"Unsupported image type {mime_type}, "
f"only support image/jpeg, image/png, image/gif, and image/webp"
@@ -886,16 +886,16 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
if error_code == "AccessDeniedException":
return InvokeAuthorizationError(error_msg)
elif error_code in ["ResourceNotFoundException", "ValidationException"]:
elif error_code in {"ResourceNotFoundException", "ValidationException"}:
return InvokeBadRequestError(error_msg)
elif error_code in ["ThrottlingException", "ServiceQuotaExceededException"]:
elif error_code in {"ThrottlingException", "ServiceQuotaExceededException"}:
return InvokeRateLimitError(error_msg)
elif error_code in [
elif error_code in {
"ModelTimeoutException",
"ModelErrorException",
"InternalServerException",
"ModelNotReadyException",
]:
}:
return InvokeServerUnavailableError(error_msg)
elif error_code == "ModelStreamErrorException":
return InvokeConnectionError(error_msg)

8
api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py
View File

@@ -186,16 +186,16 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
if error_code == "AccessDeniedException":
return InvokeAuthorizationError(error_msg)
elif error_code in ["ResourceNotFoundException", "ValidationException"]:
elif error_code in {"ResourceNotFoundException", "ValidationException"}:
return InvokeBadRequestError(error_msg)
elif error_code in ["ThrottlingException", "ServiceQuotaExceededException"]:
elif error_code in {"ThrottlingException", "ServiceQuotaExceededException"}:
return InvokeRateLimitError(error_msg)
elif error_code in [
elif error_code in {
"ModelTimeoutException",
"ModelErrorException",
"InternalServerException",
"ModelNotReadyException",
]:
}:
return InvokeServerUnavailableError(error_msg)
elif error_code == "ModelStreamErrorException":
return InvokeConnectionError(error_msg)

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

@@ -6,10 +6,10 @@ from collections.abc import Generator
from typing import Optional, Union, cast
import google.ai.generativelanguage as glm
import google.api_core.exceptions as exceptions
import google.generativeai as genai
import google.generativeai.client as client
import requests
from google.api_core import exceptions
from google.generativeai import client
from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory
from google.generativeai.types.content_types import to_part
from PIL import Image

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

@@ -77,7 +77,7 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
if "huggingfacehub_api_type" not in credentials:
raise CredentialsValidateFailedError("Huggingface Hub Endpoint Type must be provided.")
if credentials["huggingfacehub_api_type"] not in ("inference_endpoints", "hosted_inference_api"):
if credentials["huggingfacehub_api_type"] not in {"inference_endpoints", "hosted_inference_api"}:
raise CredentialsValidateFailedError("Huggingface Hub Endpoint Type is invalid.")
if "huggingfacehub_api_token" not in credentials:
@@ -94,7 +94,7 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
credentials["huggingfacehub_api_token"], model
)
if credentials["task_type"] not in ("text2text-generation", "text-generation"):
if credentials["task_type"] not in {"text2text-generation", "text-generation"}:
raise CredentialsValidateFailedError(
"Huggingface Hub Task Type must be one of text2text-generation, text-generation."
)

2
api/core/model_runtime/model_providers/huggingface_tei/tei_helper.py
View File

@@ -75,7 +75,7 @@ class TeiHelper:
if len(model_type.keys()) < 1:
raise RuntimeError("model_type is empty")
model_type = list(model_type.keys())[0]
if model_type not in ["embedding", "reranker"]:
if model_type not in {"embedding", "reranker"}:
raise RuntimeError(f"invalid model_type: {model_type}")
max_input_length = response_json.get("max_input_length", 512)

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

@@ -100,9 +100,9 @@ class MinimaxChatCompletion:
return self._handle_chat_generate_response(response)
def _handle_error(self, code: int, msg: str):
if code == 1000 or code == 1001 or code == 1013 or code == 1027:
if code in {1000, 1001, 1013, 1027}:
raise InternalServerError(msg)
elif code == 1002 or code == 1039:
elif code in {1002, 1039}:
raise RateLimitReachedError(msg)
elif code == 1004:
raise InvalidAuthenticationError(msg)

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

@@ -105,9 +105,9 @@ class MinimaxChatCompletionPro:
return self._handle_chat_generate_response(response)
def _handle_error(self, code: int, msg: str):
if code == 1000 or code == 1001 or code == 1013 or code == 1027:
if code in {1000, 1001, 1013, 1027}:
raise InternalServerError(msg)
elif code == 1002 or code == 1039:
elif code in {1002, 1039}:
raise RateLimitReachedError(msg)
elif code == 1004:
raise InvalidAuthenticationError(msg)

2
api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py
View File

@@ -114,7 +114,7 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
raise CredentialsValidateFailedError("Invalid api key")
def _handle_error(self, code: int, msg: str):
if code == 1000 or code == 1001:
if code in {1000, 1001}:
raise InternalServerError(msg)
elif code == 1002:
raise RateLimitReachedError(msg)

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

@@ -125,7 +125,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
model_mode = self.get_model_mode(base_model, credentials)
# transform response format
if "response_format" in model_parameters and model_parameters["response_format"] in ["JSON", "XML"]:
if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
stop = stop or []
if model_mode == LLMMode.CHAT:
# chat model

4
api/core/model_runtime/model_providers/openai/tts/tts.py
View File

@@ -89,14 +89,14 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
for i in range(len(sentences))
]
for future in futures:
yield from future.result().__enter__().iter_bytes(1024)
yield from future.result().__enter__().iter_bytes(1024) # noqa:PLC2801
else:
response = client.audio.speech.with_streaming_response.create(
model=model, voice=voice, response_format="mp3", input=content_text.strip()
)
yield from response.__enter__().iter_bytes(1024)
yield from response.__enter__().iter_bytes(1024) # noqa:PLC2801
except Exception as ex:
raise InvokeBadRequestError(str(ex))

1
api/core/model_runtime/model_providers/openrouter/llm/llm.py
View File

@@ -12,7 +12,6 @@ class OpenRouterLargeLanguageModel(OAIAPICompatLargeLanguageModel):
credentials["endpoint_url"] = "https://openrouter.ai/api/v1"
credentials["mode"] = self.get_model_mode(model).value
credentials["function_calling_type"] = "tool_call"
return
def _invoke(
self,

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

@@ -154,7 +154,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
)
for key, value in input_properties:
if key not in ["system_prompt", "prompt"] and "stop" not in key:
if key not in {"system_prompt", "prompt"} and "stop" not in key:
value_type = value.get("type")
if not value_type:

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

@@ -86,7 +86,7 @@ class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
)
for input_property in input_properties:
if input_property[0] in ("text", "texts", "inputs"):
if input_property[0] in {"text", "texts", "inputs"}:
text_input_key = input_property[0]
return text_input_key
@@ -96,7 +96,7 @@ class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
def _generate_embeddings_by_text_input_key(
client: ReplicateClient, replicate_model_version: str, text_input_key: str, texts: list[str]
) -> list[list[float]]:
if text_input_key in ("text", "inputs"):
if text_input_key in {"text", "inputs"}:
embeddings = []
for text in texts:
result = client.run(replicate_model_version, input={text_input_key: text})

8
api/core/model_runtime/model_providers/tongyi/llm/llm.py
View File

@@ -89,7 +89,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
:param tools: tools for tool calling
:return:
"""
if model in ["qwen-turbo-chat", "qwen-plus-chat"]:
if model in {"qwen-turbo-chat", "qwen-plus-chat"}:
model = model.replace("-chat", "")
if model == "farui-plus":
model = "qwen-farui-plus"
@@ -157,7 +157,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
mode = self.get_model_mode(model, credentials)
if model in ["qwen-turbo-chat", "qwen-plus-chat"]:
if model in {"qwen-turbo-chat", "qwen-plus-chat"}:
model = model.replace("-chat", "")
extra_model_kwargs = {}
@@ -201,7 +201,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
:param prompt_messages: prompt messages
:return: llm response
"""
if response.status_code != 200 and response.status_code != HTTPStatus.OK:
if response.status_code not in {200, HTTPStatus.OK}:
raise ServiceUnavailableError(response.message)
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
@@ -240,7 +240,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
full_text = ""
tool_calls = []
for index, response in enumerate(responses):
if response.status_code != 200 and response.status_code != HTTPStatus.OK:
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}"

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

@@ -93,7 +93,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
"""
Code block mode wrapper for invoking large language model
"""
if "response_format" in model_parameters and model_parameters["response_format"] in ["JSON", "XML"]:
if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
stop = stop or []
self._transform_chat_json_prompts(
model=model,

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

@@ -5,7 +5,6 @@ import logging
from collections.abc import Generator
from typing import Optional, Union, cast
import google.api_core.exceptions as exceptions
import google.auth.transport.requests
import vertexai.generative_models as glm
from anthropic import AnthropicVertex, Stream
@@ -17,6 +16,7 @@ from anthropic.types import (
MessageStopEvent,
MessageStreamEvent,
)
from google.api_core import exceptions
from google.cloud import aiplatform
from google.oauth2 import service_account
from PIL import Image
@@ -346,7 +346,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
mime_type = data_split[0].replace("data:", "")
base64_data = data_split[1]
if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
raise ValueError(
f"Unsupported image type {mime_type}, "
f"only support image/jpeg, image/png, image/gif, and image/webp"

3
api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/auth.py
View File

@@ -96,7 +96,6 @@ class Signer:
signing_key = Signer.get_signing_secret_key_v4(credentials.sk, md.date, md.region, md.service)
sign = Util.to_hex(Util.hmac_sha256(signing_key, signing_str))
request.headers["Authorization"] = Signer.build_auth_header_v4(sign, md, credentials)
return
@staticmethod
def hashed_canonical_request_v4(request, meta):
@@ -105,7 +104,7 @@ class Signer:
signed_headers = {}
for key in request.headers:
if key in ["Content-Type", "Content-Md5", "Host"] or key.startswith("X-"):
if key in {"Content-Type", "Content-Md5", "Host"} or key.startswith("X-"):
signed_headers[key.lower()] = request.headers[key]
if "host" in signed_headers:

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

@@ -69,7 +69,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
"""
Code block mode wrapper for invoking large language model
"""
if "response_format" in model_parameters and model_parameters["response_format"] in ["JSON", "XML"]:
if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
response_format = model_parameters["response_format"]
stop = stop or []
self._transform_json_prompts(

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

@@ -103,7 +103,7 @@ class XinferenceHelper:
model_handle_type = "embedding"
elif response_json.get("model_type") == "audio":
model_handle_type = "audio"
if model_family and model_family in ["ChatTTS", "CosyVoice", "FishAudio"]:
if model_family and model_family in {"ChatTTS", "CosyVoice", "FishAudio"}:
model_ability.append("text-to-audio")
else:
model_ability.append("audio-to-text")

21
api/core/model_runtime/model_providers/zhipuai/llm/llm.py
View File

@@ -186,10 +186,10 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
new_prompt_messages: list[PromptMessage] = []
for prompt_message in prompt_messages:
copy_prompt_message = prompt_message.copy()
if copy_prompt_message.role in [PromptMessageRole.USER, PromptMessageRole.SYSTEM, PromptMessageRole.TOOL]:
if copy_prompt_message.role in {PromptMessageRole.USER, PromptMessageRole.SYSTEM, PromptMessageRole.TOOL}:
if isinstance(copy_prompt_message.content, list):
# check if model is 'glm-4v'
if model not in ("glm-4v", "glm-4v-plus"):
if model not in {"glm-4v", "glm-4v-plus"}:
# not support list message
continue
# get image and
@@ -209,10 +209,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
):
new_prompt_messages[-1].content += "\n\n" + copy_prompt_message.content
else:
if (
copy_prompt_message.role == PromptMessageRole.USER
or copy_prompt_message.role == PromptMessageRole.TOOL
):
if copy_prompt_message.role in {PromptMessageRole.USER, PromptMessageRole.TOOL}:
new_prompt_messages.append(copy_prompt_message)
elif copy_prompt_message.role == PromptMessageRole.SYSTEM:
new_prompt_message = SystemPromptMessage(content=copy_prompt_message.content)
@@ -226,7 +223,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
else:
new_prompt_messages.append(copy_prompt_message)
if model == "glm-4v" or model == "glm-4v-plus":
if model in {"glm-4v", "glm-4v-plus"}:
params = self._construct_glm_4v_parameter(model, new_prompt_messages, model_parameters)
else:
params = {"model": model, "messages": [], **model_parameters}
@@ -270,11 +267,11 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
# chatglm model
for prompt_message in new_prompt_messages:
# merge system message to user message
if (
prompt_message.role == PromptMessageRole.SYSTEM
or prompt_message.role == PromptMessageRole.TOOL
or prompt_message.role == PromptMessageRole.USER
):
if prompt_message.role in {
PromptMessageRole.SYSTEM,
PromptMessageRole.TOOL,
PromptMessageRole.USER,
}:
if len(params["messages"]) > 0 and params["messages"][-1]["role"] == "user":
params["messages"][-1]["content"] += "\n\n" + prompt_message.content
else:

5
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/__init__.py
View File

@@ -1,5 +1,4 @@
from __future__ import annotations
from .fine_tuning_job import FineTuningJob as FineTuningJob
from .fine_tuning_job import ListOfFineTuningJob as ListOfFineTuningJob
from .fine_tuning_job_event import FineTuningJobEvent as FineTuningJobEvent
from .fine_tuning_job import FineTuningJob, ListOfFineTuningJob
from .fine_tuning_job_event import FineTuningJobEvent

4
api/core/model_runtime/schema_validators/common_validator.py
View File

@@ -75,7 +75,7 @@ class CommonValidator:
if not isinstance(value, str):
raise ValueError(f"Variable {credential_form_schema.variable} should be string")
if credential_form_schema.type in [FormType.SELECT, FormType.RADIO]:
if credential_form_schema.type in {FormType.SELECT, FormType.RADIO}:
# If the value is in options, no validation is performed
if credential_form_schema.options:
if value not in [option.value for option in credential_form_schema.options]:
@@ -83,7 +83,7 @@ class CommonValidator:
if credential_form_schema.type == FormType.SWITCH:
# If the value is not in ['true', 'false'], an exception is thrown
if value.lower() not in ["true", "false"]:
if value.lower() not in {"true", "false"}:
raise ValueError(f"Variable {credential_form_schema.variable} should be true or false")
value = True if value.lower() == "true" else False