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chore: fix unnecessary string concatation in single line (#8311)

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This commit is contained in:
Bowen Liang
2024-09-13 14:24:49 +08:00
committed by GitHub
parent 08c486452f
commit 6613b8f2e0
30 changed files with 46 additions and 49 deletions
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2
api/core/app/app_config/easy_ui_based_app/prompt_template/manager.py
View File

@@ -86,7 +86,7 @@ class PromptTemplateConfigManager:
if config["prompt_type"] == PromptTemplateEntity.PromptType.ADVANCED.value:
if not config["chat_prompt_config"] and not config["completion_prompt_config"]:
raise ValueError(
"chat_prompt_config or completion_prompt_config is required " "when prompt_type is advanced"
"chat_prompt_config or completion_prompt_config is required when prompt_type is advanced"
)
model_mode_vals = [mode.value for mode in ModelMode]

2
api/core/app/app_config/easy_ui_based_app/variables/manager.py
View File

@@ -115,7 +115,7 @@ class BasicVariablesConfigManager:
pattern = re.compile(r"^(?!\d)[\u4e00-\u9fa5A-Za-z0-9_\U0001F300-\U0001F64F\U0001F680-\U0001F6FF]{1,100}$")
if pattern.match(form_item["variable"]) is None:
raise ValueError("variable in user_input_form must be a string, " "and cannot start with a number")
raise ValueError("variable in user_input_form must be a string, and cannot start with a number")
variables.append(form_item["variable"])

2
api/core/app/apps/base_app_runner.py
View File

@@ -379,7 +379,7 @@ class AppRunner:
queue_manager=queue_manager,
app_generate_entity=application_generate_entity,
prompt_messages=prompt_messages,
text="I apologize for any confusion, " "but I'm an AI assistant to be helpful, harmless, and honest.",
text="I apologize for any confusion, but I'm an AI assistant to be helpful, harmless, and honest.",
stream=application_generate_entity.stream,
)

6
api/core/app/apps/workflow_logging_callback.py
View File

@@ -84,7 +84,7 @@ class WorkflowLoggingCallback(WorkflowCallback):
if route_node_state.node_run_result:
node_run_result = route_node_state.node_run_result
self.print_text(
f"Inputs: " f"{jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
color="green",
)
self.print_text(
@@ -116,7 +116,7 @@ class WorkflowLoggingCallback(WorkflowCallback):
node_run_result = route_node_state.node_run_result
self.print_text(f"Error: {node_run_result.error}", color="red")
self.print_text(
f"Inputs: " f"" f"{jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
color="red",
)
self.print_text(
@@ -125,7 +125,7 @@ class WorkflowLoggingCallback(WorkflowCallback):
color="red",
)
self.print_text(
f"Outputs: " f"{jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}",
f"Outputs: {jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}",
color="red",
)

2
api/core/model_runtime/model_providers/__base/ai_model.py
View File

@@ -200,7 +200,7 @@ class AIModel(ABC):
except Exception as e:
model_schema_yaml_file_name = os.path.basename(model_schema_yaml_path).rstrip(".yaml")
raise Exception(
f"Invalid model schema for {provider_name}.{model_type}.{model_schema_yaml_file_name}:" f" {str(e)}"
f"Invalid model schema for {provider_name}.{model_type}.{model_schema_yaml_file_name}: {str(e)}"
)
# cache model schema

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

@@ -621,7 +621,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
desc = p_val["description"]
if "enum" in p_val:
desc += f"; Only accepts one of the following predefined options: " f"[{', '.join(p_val['enum'])}]"
desc += f"; Only accepts one of the following predefined options: [{', '.join(p_val['enum'])}]"
parameter_definitions[p_key] = ToolParameterDefinitionsValue(
description=desc, type=p_val["type"], required=required

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

@@ -96,7 +96,7 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
if credentials["task_type"] not in ("text2text-generation", "text-generation"):
raise CredentialsValidateFailedError(
"Huggingface Hub Task Type must be one of text2text-generation, " "text-generation."
"Huggingface Hub Task Type must be one of text2text-generation, text-generation."
)
client = InferenceClient(token=credentials["huggingfacehub_api_token"])
@@ -282,7 +282,7 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
valid_tasks = ("text2text-generation", "text-generation")
if model_info.pipeline_tag not in valid_tasks:
raise ValueError(f"Model {model_name} is not a valid task, " f"must be one of {valid_tasks}.")
raise ValueError(f"Model {model_name} is not a valid task, must be one of {valid_tasks}.")
except Exception as e:
raise CredentialsValidateFailedError(f"{str(e)}")

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

@@ -121,7 +121,7 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel
valid_tasks = "feature-extraction"
if model_info.pipeline_tag not in valid_tasks:
raise ValueError(f"Model {model_name} is not a valid task, " f"must be one of {valid_tasks}.")
raise ValueError(f"Model {model_name} is not a valid task, must be one of {valid_tasks}.")
except Exception as e:
raise CredentialsValidateFailedError(f"{str(e)}")

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

@@ -572,7 +572,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
label=I18nObject(en_US="Size of context window"),
type=ParameterType.INT,
help=I18nObject(
en_US="Sets the size of the context window used to generate the next token. " "(Default: 2048)"
en_US="Sets the size of the context window used to generate the next token. (Default: 2048)"
),
default=2048,
min=1,
@@ -650,7 +650,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
label=I18nObject(en_US="Format"),
type=ParameterType.STRING,
help=I18nObject(
en_US="the format to return a response in." " Currently the only accepted value is json."
en_US="the format to return a response in. Currently the only accepted value is json."
),
options=["json"],
),

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

@@ -86,7 +86,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
if model.count("/") != 1:
raise CredentialsValidateFailedError(
"Replicate Model Name must be provided, " "format: {user_name}/{model_name}"
"Replicate Model Name must be provided, format: {user_name}/{model_name}"
)
try:

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

@@ -472,7 +472,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
for p_key, p_val in properties.items():
desc = p_val["description"]
if "enum" in p_val:
desc += f"; Only accepts one of the following predefined options: " f"[{', '.join(p_val['enum'])}]"
desc += f"; Only accepts one of the following predefined options: [{', '.join(p_val['enum'])}]"
properties_definitions[p_key] = {
"description": desc,

2
api/core/rag/datasource/vdb/relyt/relyt_vector.py
View File

@@ -245,7 +245,7 @@ class RelytVector(BaseVector):
try:
from sqlalchemy.engine import Row
except ImportError:
raise ImportError("Could not import Row from sqlalchemy.engine. " "Please 'pip install sqlalchemy>=1.4'.")
raise ImportError("Could not import Row from sqlalchemy.engine. Please 'pip install sqlalchemy>=1.4'.")
filter_condition = ""
if filter is not None:

2
api/core/rag/docstore/dataset_docstore.py
View File

@@ -88,7 +88,7 @@ class DatasetDocumentStore:
# NOTE: doc could already exist in the store, but we overwrite it
if not allow_update and segment_document:
raise ValueError(
f"doc_id {doc.metadata['doc_id']} already exists. " "Set allow_update to True to overwrite."
f"doc_id {doc.metadata['doc_id']} already exists. Set allow_update to True to overwrite."
)
# calc embedding use tokens

2
api/core/rag/extractor/notion_extractor.py
View File

@@ -50,7 +50,7 @@ class NotionExtractor(BaseExtractor):
integration_token = dify_config.NOTION_INTEGRATION_TOKEN
if integration_token is None:
raise ValueError(
"Must specify `integration_token` or set environment " "variable `NOTION_INTEGRATION_TOKEN`."
"Must specify `integration_token` or set environment variable `NOTION_INTEGRATION_TOKEN`."
)
self._notion_access_token = integration_token

6
api/core/rag/splitter/text_splitter.py
View File

@@ -60,7 +60,7 @@ class TextSplitter(BaseDocumentTransformer, ABC):
"""
if chunk_overlap > chunk_size:
raise ValueError(
f"Got a larger chunk overlap ({chunk_overlap}) than chunk size " f"({chunk_size}), should be smaller."
f"Got a larger chunk overlap ({chunk_overlap}) than chunk size ({chunk_size}), should be smaller."
)
self._chunk_size = chunk_size
self._chunk_overlap = chunk_overlap
@@ -117,7 +117,7 @@ class TextSplitter(BaseDocumentTransformer, ABC):
if total + _len + (separator_len if len(current_doc) > 0 else 0) > self._chunk_size:
if total > self._chunk_size:
logger.warning(
f"Created a chunk of size {total}, " f"which is longer than the specified {self._chunk_size}"
f"Created a chunk of size {total}, which is longer than the specified {self._chunk_size}"
)
if len(current_doc) > 0:
doc = self._join_docs(current_doc, separator)
@@ -153,7 +153,7 @@ class TextSplitter(BaseDocumentTransformer, ABC):
except ImportError:
raise ValueError(
"Could not import transformers python package. " "Please install it with `pip install transformers`."
"Could not import transformers python package. Please install it with `pip install transformers`."
)
return cls(length_function=_huggingface_tokenizer_length, **kwargs)

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

@@ -14,7 +14,7 @@ class GaodeProvider(BuiltinToolProviderController):
try:
response = requests.get(
url="https://restapi.amap.com/v3/geocode/geo?address={address}&key={apikey}" "".format(
url="https://restapi.amap.com/v3/geocode/geo?address={address}&key={apikey}".format(
address=urllib.parse.quote("广东省广州市天河区广州塔"), apikey=credentials.get("api_key")
)
)

2
api/core/tools/provider/builtin/gaode/tools/gaode_weather.py
View File

@@ -27,7 +27,7 @@ class GaodeRepositoriesTool(BuiltinTool):
city_response = s.request(
method="GET",
headers={"Content-Type": "application/json; charset=utf-8"},
url="{url}/config/district?keywords={keywords}" "&subdistrict=0&extensions=base&key={apikey}" "".format(
url="{url}/config/district?keywords={keywords}&subdistrict=0&extensions=base&key={apikey}".format(
url=api_domain, keywords=city, apikey=self.runtime.credentials.get("api_key")
),
)

2
api/core/tools/provider/builtin/github/tools/github_repositories.py
View File

@@ -39,7 +39,7 @@ class GithubRepositoriesTool(BuiltinTool):
response = s.request(
method="GET",
headers=headers,
url=f"{api_domain}/search/repositories?" f"q={quote(query)}&sort=stars&per_page={top_n}&order=desc",
url=f"{api_domain}/search/repositories?q={quote(query)}&sort=stars&per_page={top_n}&order=desc",
)
response_data = response.json()
if response.status_code == 200 and isinstance(response_data.get("items"), list):

4
api/core/tools/provider/builtin/pubmed/tools/pubmed_search.py
View File

@@ -51,7 +51,7 @@ class PubMedAPIWrapper(BaseModel):
try:
# Retrieve the top-k results for the query
docs = [
f"Published: {result['pub_date']}\nTitle: {result['title']}\n" f"Summary: {result['summary']}"
f"Published: {result['pub_date']}\nTitle: {result['title']}\nSummary: {result['summary']}"
for result in self.load(query[: self.ARXIV_MAX_QUERY_LENGTH])
]
@@ -97,7 +97,7 @@ class PubMedAPIWrapper(BaseModel):
if e.code == 429 and retry < self.max_retry:
# Too Many Requests error
# wait for an exponentially increasing amount of time
print(f"Too Many Requests, " f"waiting for {self.sleep_time:.2f} seconds...")
print(f"Too Many Requests, waiting for {self.sleep_time:.2f} seconds...")
time.sleep(self.sleep_time)
self.sleep_time *= 2
retry += 1

2
api/core/tools/provider/builtin/twilio/tools/send_message.py
View File

@@ -39,7 +39,7 @@ class TwilioAPIWrapper(BaseModel):
try:
from twilio.rest import Client
except ImportError:
raise ImportError("Could not import twilio python package. " "Please install it with `pip install twilio`.")
raise ImportError("Could not import twilio python package. Please install it with `pip install twilio`.")
account_sid = values.get("account_sid")
auth_token = values.get("auth_token")
values["from_number"] = values.get("from_number")