chore: refurbish Python code by applying refurb linter rules (#8296)

api/controllers/console/admin.py

@@ -60,23 +60,15 @@ class InsertExploreAppListApi(Resource):
 
         site = app.site
         if not site:
-            desc = args["desc"] if args["desc"] else ""
-            copy_right = args["copyright"] if args["copyright"] else ""
-            privacy_policy = args["privacy_policy"] if args["privacy_policy"] else ""
-            custom_disclaimer = args["custom_disclaimer"] if args["custom_disclaimer"] else ""
+            desc = args["desc"] or ""
+            copy_right = args["copyright"] or ""
+            privacy_policy = args["privacy_policy"] or ""
+            custom_disclaimer = args["custom_disclaimer"] or ""
         else:
-            desc = site.description if site.description else args["desc"] if args["desc"] else ""
-            copy_right = site.copyright if site.copyright else args["copyright"] if args["copyright"] else ""
-            privacy_policy = (
-                site.privacy_policy if site.privacy_policy else args["privacy_policy"] if args["privacy_policy"] else ""
-            )
-            custom_disclaimer = (
-                site.custom_disclaimer
-                if site.custom_disclaimer
-                else args["custom_disclaimer"]
-                if args["custom_disclaimer"]
-                else ""
-            )
+            desc = site.description or args["desc"] or ""
+            copy_right = site.copyright or args["copyright"] or ""
+            privacy_policy = site.privacy_policy or args["privacy_policy"] or ""
+            custom_disclaimer = site.custom_disclaimer or args["custom_disclaimer"] or ""
 
         recommended_app = RecommendedApp.query.filter(RecommendedApp.app_id == args["app_id"]).first()
 

api/controllers/console/app/audio.py

@@ -99,14 +99,10 @@ class ChatMessageTextApi(Resource):
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
             response = AudioService.transcript_tts(app_model=app_model, text=text, message_id=message_id, voice=voice)

api/controllers/console/auth/oauth.py

@@ -101,7 +101,7 @@ def _generate_account(provider: str, user_info: OAuthUserInfo):
 
     if not account:
         # Create account
-        account_name = user_info.name if user_info.name else "Dify"
+        account_name = user_info.name or "Dify"
         account = RegisterService.register(
             email=user_info.email, name=account_name, password=None, open_id=user_info.id, provider=provider
         )

api/controllers/console/datasets/datasets.py

@@ -550,12 +550,7 @@ class DatasetApiBaseUrlApi(Resource):
     @login_required
     @account_initialization_required
     def get(self):
-        return {
-            "api_base_url": (
-                dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL else request.host_url.rstrip("/")
-            )
-            + "/v1"
-        }
+        return {"api_base_url": (dify_config.SERVICE_API_URL or request.host_url.rstrip("/")) + "/v1"}
 
 
 class DatasetRetrievalSettingApi(Resource):

api/controllers/console/explore/audio.py

@@ -86,14 +86,10 @@ class ChatTextApi(InstalledAppResource):
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
             response = AudioService.transcript_tts(app_model=app_model, message_id=message_id, voice=voice, text=text)

api/controllers/console/workspace/tool_providers.py

@@ -327,7 +327,7 @@ class ToolApiProviderPreviousTestApi(Resource):
 
         return ApiToolManageService.test_api_tool_preview(
             current_user.current_tenant_id,
-            args["provider_name"] if args["provider_name"] else "",
+            args["provider_name"] or "",
             args["tool_name"],
             args["credentials"],
             args["parameters"],

api/controllers/service_api/app/audio.py

@@ -84,14 +84,10 @@ class TextApi(Resource):
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
             response = AudioService.transcript_tts(

api/controllers/web/audio.py

@@ -83,14 +83,10 @@ class TextApi(WebApiResource):
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
 

api/core/agent/cot_agent_runner.py

@@ -256,7 +256,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                     model=model_instance.model,
                     prompt_messages=prompt_messages,
                     message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] if llm_usage["usage"] else LLMUsage.empty_usage(),
+                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
                     system_fingerprint="",
                 )
             ),

api/core/agent/fc_agent_runner.py

@@ -298,7 +298,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                     model=model_instance.model,
                     prompt_messages=prompt_messages,
                     message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] if llm_usage["usage"] else LLMUsage.empty_usage(),
+                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
                     system_fingerprint="",
                 )
             ),

api/core/app/apps/base_app_runner.py

@@ -161,7 +161,7 @@ class AppRunner:
                 app_mode=AppMode.value_of(app_record.mode),
                 prompt_template_entity=prompt_template_entity,
                 inputs=inputs,
-                query=query if query else "",
+                query=query or "",
                 files=files,
                 context=context,
                 memory=memory,
@@ -189,7 +189,7 @@ class AppRunner:
             prompt_messages = prompt_transform.get_prompt(
                 prompt_template=prompt_template,
                 inputs=inputs,
-                query=query if query else "",
+                query=query or "",
                 files=files,
                 context=context,
                 memory_config=memory_config,
@@ -238,7 +238,7 @@ class AppRunner:
                     model=app_generate_entity.model_conf.model,
                     prompt_messages=prompt_messages,
                     message=AssistantPromptMessage(content=text),
-                    usage=usage if usage else LLMUsage.empty_usage(),
+                    usage=usage or LLMUsage.empty_usage(),
                 ),
             ),
             PublishFrom.APPLICATION_MANAGER,
@@ -351,7 +351,7 @@ class AppRunner:
             tenant_id=tenant_id,
             app_config=app_generate_entity.app_config,
             inputs=inputs,
-            query=query if query else "",
+            query=query or "",
             message_id=message_id,
             trace_manager=app_generate_entity.trace_manager,
         )

api/core/extension/extensible.py

@@ -3,6 +3,7 @@ import importlib.util
 import json
 import logging
 import os
+from pathlib import Path
 from typing import Any, Optional
 
 from pydantic import BaseModel
@@ -63,8 +64,7 @@ class Extensible:
 
                     builtin_file_path = os.path.join(subdir_path, "__builtin__")
                     if os.path.exists(builtin_file_path):
-                        with open(builtin_file_path, encoding="utf-8") as f:
-                            position = int(f.read().strip())
+                        position = int(Path(builtin_file_path).read_text(encoding="utf-8").strip())
                     position_map[extension_name] = position
 
                 if (extension_name + ".py") not in file_names:

api/core/memory/token_buffer_memory.py

@@ -39,7 +39,7 @@ class TokenBufferMemory:
         )
 
         if message_limit and message_limit > 0:
-            message_limit = message_limit if message_limit <= 500 else 500
+            message_limit = min(message_limit, 500)
         else:
             message_limit = 500
 

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -449,7 +449,7 @@ if you are not sure about the structure.
                 model=real_model,
                 prompt_messages=prompt_messages,
                 message=prompt_message,
-                usage=usage if usage else LLMUsage.empty_usage(),
+                usage=usage or LLMUsage.empty_usage(),
                 system_fingerprint=system_fingerprint,
             ),
             credentials=credentials,

api/core/model_runtime/model_providers/anthropic/llm/llm.py

@@ -409,7 +409,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                     ),
                 )
             elif isinstance(chunk, ContentBlockDeltaEvent):
-                chunk_text = chunk.delta.text if chunk.delta.text else ""
+                chunk_text = chunk.delta.text or ""
                 full_assistant_content += chunk_text
 
                 # transform assistant message to prompt message

api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py

@@ -213,7 +213,7 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
                 model=real_model,
                 prompt_messages=prompt_messages,
                 message=prompt_message,
-                usage=usage if usage else LLMUsage.empty_usage(),
+                usage=usage or LLMUsage.empty_usage(),
                 system_fingerprint=system_fingerprint,
             ),
             credentials=credentials,

api/core/model_runtime/model_providers/azure_openai/llm/llm.py

@@ -225,7 +225,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 continue
 
             # transform assistant message to prompt message
-            text = delta.text if delta.text else ""
+            text = delta.text or ""
             assistant_prompt_message = AssistantPromptMessage(content=text)
 
             full_text += text
@@ -400,15 +400,13 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 continue
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else "", tool_calls=tool_calls
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ""
+            full_assistant_content += delta.delta.content or ""
 
             real_model = chunk.model
             system_fingerprint = chunk.system_fingerprint
-            completion += delta.delta.content if delta.delta.content else ""
+            completion += delta.delta.content or ""
 
             yield LLMResultChunk(
                 model=real_model,

api/core/model_runtime/model_providers/azure_openai/tts/tts.py

@@ -84,7 +84,7 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
                     )
                     for i in range(len(sentences))
                 ]
-                for index, future in enumerate(futures):
+                for future in futures:
                     yield from future.result().__enter__().iter_bytes(1024)
 
             else:

api/core/model_runtime/model_providers/bedrock/llm/llm.py

@@ -331,10 +331,10 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                 elif "contentBlockDelta" in chunk:
                     delta = chunk["contentBlockDelta"]["delta"]
                     if "text" in delta:
-                        chunk_text = delta["text"] if delta["text"] else ""
+                        chunk_text = delta["text"] or ""
                         full_assistant_content += chunk_text
                         assistant_prompt_message = AssistantPromptMessage(
-                            content=chunk_text if chunk_text else "",
+                            content=chunk_text or "",
                         )
                         index = chunk["contentBlockDelta"]["contentBlockIndex"]
                         yield LLMResultChunk(
@@ -751,7 +751,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         elif model_prefix == "cohere":
             output = response_body.get("generations")[0].get("text")
             prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-            completion_tokens = self.get_num_tokens(model, credentials, output if output else "")
+            completion_tokens = self.get_num_tokens(model, credentials, output or "")
 
         else:
             raise ValueError(f"Got unknown model prefix {model_prefix} when handling block response")
@@ -828,7 +828,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=content_delta if content_delta else "",
+                content=content_delta or "",
             )
             index += 1
 

api/core/model_runtime/model_providers/chatglm/llm/llm.py

@@ -302,11 +302,11 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
             if delta.delta.function_call:
                 function_calls = [delta.delta.function_call]
 
-            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls if function_calls else [])
+            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls or [])
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_message_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
             )
 
             if delta.finish_reason is not None:

api/core/model_runtime/model_providers/localai/llm/llm.py

@@ -511,7 +511,7 @@ class LocalAILanguageModel(LargeLanguageModel):
             delta = chunk.choices[0]
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(content=delta.text if delta.text else "", tool_calls=[])
+            assistant_prompt_message = AssistantPromptMessage(content=delta.text or "", tool_calls=[])
 
             if delta.finish_reason is not None:
                 # temp_assistant_prompt_message is used to calculate usage
@@ -578,11 +578,11 @@ class LocalAILanguageModel(LargeLanguageModel):
             if delta.delta.function_call:
                 function_calls = [delta.delta.function_call]
 
-            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls if function_calls else [])
+            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls or [])
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_message_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
             )
 
             if delta.finish_reason is not None:

api/core/model_runtime/model_providers/minimax/llm/llm.py

@@ -211,7 +211,7 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
                         index=0,
                         message=AssistantPromptMessage(content=message.content, tool_calls=[]),
                         usage=usage,
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
             elif message.function_call:
@@ -244,7 +244,7 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=0,
                         message=AssistantPromptMessage(content=message.content, tool_calls=[]),
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
 

api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py

@@ -65,7 +65,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
         inputs = []
         used_tokens = 0
 
-        for i, text in enumerate(texts):
+        for text in texts:
             # Here token count is only an approximation based on the GPT2 tokenizer
             num_tokens = self._get_num_tokens_by_gpt2(text)
 

api/core/model_runtime/model_providers/openai/llm/llm.py

@@ -508,7 +508,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 continue
 
             # transform assistant message to prompt message
-            text = delta.text if delta.text else ""
+            text = delta.text or ""
             assistant_prompt_message = AssistantPromptMessage(content=text)
 
             full_text += text
@@ -760,11 +760,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 final_tool_calls.extend(tool_calls)
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else "", tool_calls=tool_calls
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ""
+            full_assistant_content += delta.delta.content or ""
 
             if has_finish_reason:
                 final_chunk = LLMResultChunk(

api/core/model_runtime/model_providers/openai/tts/tts.py

@@ -88,7 +88,7 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
                     )
                     for i in range(len(sentences))
                 ]
-                for index, future in enumerate(futures):
+                for future in futures:
                     yield from future.result().__enter__().iter_bytes(1024)
 
             else:

api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py

@@ -179,9 +179,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
         features = []
 
         function_calling_type = credentials.get("function_calling_type", "no_call")
-        if function_calling_type in ["function_call"]:
+        if function_calling_type == "function_call":
             features.append(ModelFeature.TOOL_CALL)
-        elif function_calling_type in ["tool_call"]:
+        elif function_calling_type == "tool_call":
             features.append(ModelFeature.MULTI_TOOL_CALL)
 
         stream_function_calling = credentials.get("stream_function_calling", "supported")

api/core/model_runtime/model_providers/openllm/llm/llm.py

@@ -179,7 +179,7 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
                         index=0,
                         message=AssistantPromptMessage(content=message.content, tool_calls=[]),
                         usage=usage,
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
             else:
@@ -189,7 +189,7 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=0,
                         message=AssistantPromptMessage(content=message.content, tool_calls=[]),
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
 

api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py

@@ -106,7 +106,7 @@ class OpenLLMGenerate:
             timeout = 120
 
         data = {
-            "stop": stop if stop else [],
+            "stop": stop or [],
             "prompt": "\n".join([message.content for message in prompt_messages]),
             "llm_config": default_llm_config,
         }

api/core/model_runtime/model_providers/replicate/llm/llm.py

@@ -214,7 +214,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
 
             index += 1
 
-            assistant_prompt_message = AssistantPromptMessage(content=output if output else "")
+            assistant_prompt_message = AssistantPromptMessage(content=output or "")
 
             if index < prediction_output_length:
                 yield LLMResultChunk(

api/core/model_runtime/model_providers/sagemaker/rerank/rerank.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import operator
 from typing import Any, Optional
 
 import boto3
@@ -94,7 +95,7 @@ class SageMakerRerankModel(RerankModel):
             for idx in range(len(scores)):
                 candidate_docs.append({"content": docs[idx], "score": scores[idx]})
 
-            sorted(candidate_docs, key=lambda x: x["score"], reverse=True)
+            sorted(candidate_docs, key=operator.itemgetter("score"), reverse=True)
 
             line = 3
             rerank_documents = []

api/core/model_runtime/model_providers/sagemaker/tts/tts.py

@@ -260,7 +260,7 @@ class SageMakerText2SpeechModel(TTSModel):
                     for payload in payloads
                 ]
 
-                for index, future in enumerate(futures):
+                for future in futures:
                     resp = future.result()
                     audio_bytes = requests.get(resp.get("s3_presign_url")).content
                     for i in range(0, len(audio_bytes), 1024):

api/core/model_runtime/model_providers/spark/llm/llm.py

@@ -220,7 +220,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
                 delta = content
 
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta if delta else "",
+                content=delta or "",
             )
 
             prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)

api/core/model_runtime/model_providers/tencent/speech2text/flash_recognizer.py

@@ -1,6 +1,7 @@
 import base64
 import hashlib
 import hmac
+import operator
 import time
 
 import requests
@@ -127,7 +128,7 @@ class FlashRecognizer:
         return s
 
     def _build_req_with_signature(self, secret_key, params, header):
-        query = sorted(params.items(), key=lambda d: d[0])
+        query = sorted(params.items(), key=operator.itemgetter(0))
         signstr = self._format_sign_string(query)
         signature = self._sign(signstr, secret_key)
         header["Authorization"] = signature

api/core/model_runtime/model_providers/tongyi/llm/llm.py

@@ -4,6 +4,7 @@ import tempfile
 import uuid
 from collections.abc import Generator
 from http import HTTPStatus
+from pathlib import Path
 from typing import Optional, Union, cast
 
 from dashscope import Generation, MultiModalConversation, get_tokenizer
@@ -454,8 +455,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
 
         file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.{mime_type.split('/')[1]}")
 
-        with open(file_path, "wb") as image_file:
-            image_file.write(base64.b64decode(encoded_string))
+        Path(file_path).write_bytes(base64.b64decode(encoded_string))
 
         return f"file://{file_path}"
 

api/core/model_runtime/model_providers/upstage/llm/llm.py

@@ -368,11 +368,9 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                 final_tool_calls.extend(tool_calls)
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else "", tool_calls=tool_calls
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ""
+            full_assistant_content += delta.delta.content or ""
 
             if has_finish_reason:
                 final_chunk = LLMResultChunk(

api/core/model_runtime/model_providers/vertex_ai/llm/llm.py

@@ -231,10 +231,10 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                         ),
                     )
                 elif isinstance(chunk, ContentBlockDeltaEvent):
-                    chunk_text = chunk.delta.text if chunk.delta.text else ""
+                    chunk_text = chunk.delta.text or ""
                     full_assistant_content += chunk_text
                     assistant_prompt_message = AssistantPromptMessage(
-                        content=chunk_text if chunk_text else "",
+                        content=chunk_text or "",
                     )
                     index = chunk.index
                     yield LLMResultChunk(

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

@@ -1,5 +1,6 @@
 # coding : utf-8
 import datetime
+from itertools import starmap
 
 import pytz
 
@@ -48,7 +49,7 @@ class SignResult:
         self.authorization = ""
 
     def __str__(self):
-        return "\n".join(["{}:{}".format(*item) for item in self.__dict__.items()])
+        return "\n".join(list(starmap("{}:{}".format, self.__dict__.items())))
 
 
 class Credentials:

api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/util.py

@@ -1,5 +1,6 @@
 import hashlib
 import hmac
+import operator
 from functools import reduce
 from urllib.parse import quote
 
@@ -40,4 +41,4 @@ class Util:
             if len(hv) == 1:
                 hv = "0" + hv
             lst.append(hv)
-        return reduce(lambda x, y: x + y, lst)
+        return reduce(operator.add, lst)

api/core/model_runtime/model_providers/volcengine_maas/llm/llm.py

@@ -174,9 +174,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=index,
-                        message=AssistantPromptMessage(
-                            content=message["content"] if message["content"] else "", tool_calls=[]
-                        ),
+                        message=AssistantPromptMessage(content=message["content"] or "", tool_calls=[]),
                         usage=usage,
                         finish_reason=choice.get("finish_reason"),
                     ),
@@ -208,7 +206,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 model=model,
                 prompt_messages=prompt_messages,
                 message=AssistantPromptMessage(
-                    content=message["content"] if message["content"] else "",
+                    content=message["content"] or "",
                     tool_calls=tool_calls,
                 ),
                 usage=self._calc_response_usage(
@@ -284,7 +282,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 model=model,
                 prompt_messages=prompt_messages,
                 message=AssistantPromptMessage(
-                    content=message.content if message.content else "",
+                    content=message.content or "",
                     tool_calls=tool_calls,
                 ),
                 usage=self._calc_response_usage(

api/core/model_runtime/model_providers/wenxin/llm/llm.py

@@ -199,7 +199,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
             secret_key=credentials["secret_key"],
         )
 
-        user = user if user else "ErnieBotDefault"
+        user = user or "ErnieBotDefault"
 
         # convert prompt messages to baichuan messages
         messages = [
@@ -289,7 +289,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
                         index=0,
                         message=AssistantPromptMessage(content=message.content, tool_calls=[]),
                         usage=usage,
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
             else:
@@ -299,7 +299,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=0,
                         message=AssistantPromptMessage(content=message.content, tool_calls=[]),
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
 

api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py

@@ -85,7 +85,7 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
         api_key = credentials["api_key"]
         secret_key = credentials["secret_key"]
         embedding: TextEmbedding = self._create_text_embedding(api_key, secret_key)
-        user = user if user else "ErnieBotDefault"
+        user = user or "ErnieBotDefault"
 
         context_size = self._get_context_size(model, credentials)
         max_chunks = self._get_max_chunks(model, credentials)

api/core/model_runtime/model_providers/xinference/llm/llm.py

@@ -589,7 +589,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
         # convert tool call to assistant message tool call
         tool_calls = assistant_message.tool_calls
-        assistant_prompt_message_tool_calls = self._extract_response_tool_calls(tool_calls if tool_calls else [])
+        assistant_prompt_message_tool_calls = self._extract_response_tool_calls(tool_calls or [])
         function_call = assistant_message.function_call
         if function_call:
             assistant_prompt_message_tool_calls += [self._extract_response_function_call(function_call)]
@@ -652,7 +652,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_message_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
             )
 
             if delta.finish_reason is not None:
@@ -749,7 +749,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             delta = chunk.choices[0]
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(content=delta.text if delta.text else "", tool_calls=[])
+            assistant_prompt_message = AssistantPromptMessage(content=delta.text or "", tool_calls=[])
 
             if delta.finish_reason is not None:
                 # temp_assistant_prompt_message is used to calculate usage

api/core/model_runtime/model_providers/xinference/tts/tts.py

@@ -215,7 +215,7 @@ class XinferenceText2SpeechModel(TTSModel):
                     for i in range(len(sentences))
                 ]
 
-                for index, future in enumerate(futures):
+                for future in futures:
                     response = future.result()
                     for i in range(0, len(response), 1024):
                         yield response[i : i + 1024]

api/core/model_runtime/model_providers/zhipuai/llm/llm.py

@@ -414,10 +414,10 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_tool_calls
             )
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ""
+            full_assistant_content += delta.delta.content or ""
 
             if delta.finish_reason is not None and chunk.usage is not None:
                 completion_tokens = chunk.usage.completion_tokens

api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py

@@ -30,6 +30,8 @@ def _merge_map(map1: Mapping, map2: Mapping) -> Mapping:
     return {key: val for key, val in merged.items() if val is not None}
 
 
+from itertools import starmap
+
 from httpx._config import DEFAULT_TIMEOUT_CONFIG as HTTPX_DEFAULT_TIMEOUT
 
 ZHIPUAI_DEFAULT_TIMEOUT = httpx.Timeout(timeout=300.0, connect=8.0)
@@ -159,7 +161,7 @@ class HttpClient:
         return [(key, str_data)]
 
     def _make_multipartform(self, data: Mapping[object, object]) -> dict[str, object]:
-        items = flatten([self._object_to_formdata(k, v) for k, v in data.items()])
+        items = flatten(list(starmap(self._object_to_formdata, data.items())))
 
         serialized: dict[str, object] = {}
         for key, value in items:

api/core/ops/langfuse_trace/langfuse_trace.py

@@ -65,7 +65,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             self.generate_name_trace(trace_info)
 
     def workflow_trace(self, trace_info: WorkflowTraceInfo):
-        trace_id = trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id
+        trace_id = trace_info.workflow_app_log_id or trace_info.workflow_run_id
         user_id = trace_info.metadata.get("user_id")
         if trace_info.message_id:
             trace_id = trace_info.message_id
@@ -84,7 +84,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             )
             self.add_trace(langfuse_trace_data=trace_data)
             workflow_span_data = LangfuseSpan(
-                id=(trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id),
+                id=(trace_info.workflow_app_log_id or trace_info.workflow_run_id),
                 name=TraceTaskName.WORKFLOW_TRACE.value,
                 input=trace_info.workflow_run_inputs,
                 output=trace_info.workflow_run_outputs,
@@ -93,7 +93,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                 end_time=trace_info.end_time,
                 metadata=trace_info.metadata,
                 level=LevelEnum.DEFAULT if trace_info.error == "" else LevelEnum.ERROR,
-                status_message=trace_info.error if trace_info.error else "",
+                status_message=trace_info.error or "",
             )
             self.add_span(langfuse_span_data=workflow_span_data)
         else:
@@ -143,7 +143,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             else:
                 inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
             outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
-            created_at = node_execution.created_at if node_execution.created_at else datetime.now()
+            created_at = node_execution.created_at or datetime.now()
             elapsed_time = node_execution.elapsed_time
             finished_at = created_at + timedelta(seconds=elapsed_time)
 
@@ -172,10 +172,8 @@ class LangFuseDataTrace(BaseTraceInstance):
                     end_time=finished_at,
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
-                    status_message=trace_info.error if trace_info.error else "",
-                    parent_observation_id=(
-                        trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id
-                    ),
+                    status_message=trace_info.error or "",
+                    parent_observation_id=(trace_info.workflow_app_log_id or trace_info.workflow_run_id),
                 )
             else:
                 span_data = LangfuseSpan(
@@ -188,7 +186,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                     end_time=finished_at,
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
-                    status_message=trace_info.error if trace_info.error else "",
+                    status_message=trace_info.error or "",
                 )
 
             self.add_span(langfuse_span_data=span_data)
@@ -212,7 +210,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                     output=outputs,
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
-                    status_message=trace_info.error if trace_info.error else "",
+                    status_message=trace_info.error or "",
                     usage=generation_usage,
                 )
 
@@ -277,7 +275,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             output=message_data.answer,
             metadata=metadata,
             level=(LevelEnum.DEFAULT if message_data.status != "error" else LevelEnum.ERROR),
-            status_message=message_data.error if message_data.error else "",
+            status_message=message_data.error or "",
             usage=generation_usage,
         )
 
@@ -319,7 +317,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             end_time=trace_info.end_time,
             metadata=trace_info.metadata,
             level=(LevelEnum.DEFAULT if message_data.status != "error" else LevelEnum.ERROR),
-            status_message=message_data.error if message_data.error else "",
+            status_message=message_data.error or "",
             usage=generation_usage,
         )
 

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -82,7 +82,7 @@ class LangSmithDataTrace(BaseTraceInstance):
         langsmith_run = LangSmithRunModel(
             file_list=trace_info.file_list,
             total_tokens=trace_info.total_tokens,
-            id=trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id,
+            id=trace_info.workflow_app_log_id or trace_info.workflow_run_id,
             name=TraceTaskName.WORKFLOW_TRACE.value,
             inputs=trace_info.workflow_run_inputs,
             run_type=LangSmithRunType.tool,
@@ -94,7 +94,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             },
             error=trace_info.error,
             tags=["workflow"],
-            parent_run_id=trace_info.message_id if trace_info.message_id else None,
+            parent_run_id=trace_info.message_id or None,
         )
 
         self.add_run(langsmith_run)
@@ -133,7 +133,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             else:
                 inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
             outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
-            created_at = node_execution.created_at if node_execution.created_at else datetime.now()
+            created_at = node_execution.created_at or datetime.now()
             elapsed_time = node_execution.elapsed_time
             finished_at = created_at + timedelta(seconds=elapsed_time)
 
@@ -180,9 +180,7 @@ class LangSmithDataTrace(BaseTraceInstance):
                 extra={
                     "metadata": metadata,
                 },
-                parent_run_id=trace_info.workflow_app_log_id
-                if trace_info.workflow_app_log_id
-                else trace_info.workflow_run_id,
+                parent_run_id=trace_info.workflow_app_log_id or trace_info.workflow_run_id,
                 tags=["node_execution"],
             )
 

api/core/ops/ops_trace_manager.py

@@ -354,11 +354,11 @@ class TraceTask:
         workflow_run_inputs = json.loads(workflow_run.inputs) if workflow_run.inputs else {}
         workflow_run_outputs = json.loads(workflow_run.outputs) if workflow_run.outputs else {}
         workflow_run_version = workflow_run.version
-        error = workflow_run.error if workflow_run.error else ""
+        error = workflow_run.error or ""
 
         total_tokens = workflow_run.total_tokens
 
-        file_list = workflow_run_inputs.get("sys.file") if workflow_run_inputs.get("sys.file") else []
+        file_list = workflow_run_inputs.get("sys.file") or []
         query = workflow_run_inputs.get("query") or workflow_run_inputs.get("sys.query") or ""
 
         # get workflow_app_log_id
@@ -452,7 +452,7 @@ class TraceTask:
             message_tokens=message_tokens,
             answer_tokens=message_data.answer_tokens,
             total_tokens=message_tokens + message_data.answer_tokens,
-            error=message_data.error if message_data.error else "",
+            error=message_data.error or "",
             inputs=inputs,
             outputs=message_data.answer,
             file_list=file_list,
@@ -487,7 +487,7 @@ class TraceTask:
             workflow_app_log_id = str(workflow_app_log_data.id) if workflow_app_log_data else None
 
         moderation_trace_info = ModerationTraceInfo(
-            message_id=workflow_app_log_id if workflow_app_log_id else message_id,
+            message_id=workflow_app_log_id or message_id,
             inputs=inputs,
             message_data=message_data.to_dict(),
             flagged=moderation_result.flagged,
@@ -527,7 +527,7 @@ class TraceTask:
             workflow_app_log_id = str(workflow_app_log_data.id) if workflow_app_log_data else None
 
         suggested_question_trace_info = SuggestedQuestionTraceInfo(
-            message_id=workflow_app_log_id if workflow_app_log_id else message_id,
+            message_id=workflow_app_log_id or message_id,
             message_data=message_data.to_dict(),
             inputs=message_data.message,
             outputs=message_data.answer,
@@ -569,7 +569,7 @@ class TraceTask:
 
         dataset_retrieval_trace_info = DatasetRetrievalTraceInfo(
             message_id=message_id,
-            inputs=message_data.query if message_data.query else message_data.inputs,
+            inputs=message_data.query or message_data.inputs,
             documents=[doc.model_dump() for doc in documents],
             start_time=timer.get("start"),
             end_time=timer.get("end"),
@@ -695,8 +695,7 @@ class TraceQueueManager:
             self.start_timer()
 
     def add_trace_task(self, trace_task: TraceTask):
-        global trace_manager_timer
-        global trace_manager_queue
+        global trace_manager_timer, trace_manager_queue
         try:
             if self.trace_instance:
                 trace_task.app_id = self.app_id

api/core/prompt/simple_prompt_transform.py

@@ -112,11 +112,11 @@ class SimplePromptTransform(PromptTransform):
         for v in prompt_template_config["special_variable_keys"]:
             # support #context#, #query# and #histories#
             if v == "#context#":
-                variables["#context#"] = context if context else ""
+                variables["#context#"] = context or ""
             elif v == "#query#":
-                variables["#query#"] = query if query else ""
+                variables["#query#"] = query or ""
             elif v == "#histories#":
-                variables["#histories#"] = histories if histories else ""
+                variables["#histories#"] = histories or ""
 
         prompt_template = prompt_template_config["prompt_template"]
         prompt = prompt_template.format(variables)

api/core/rag/datasource/keyword/keyword_base.py

@@ -34,7 +34,7 @@ class BaseKeyword(ABC):
         raise NotImplementedError
 
     def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
-        for text in texts[:]:
+        for text in texts.copy():
             doc_id = text.metadata["doc_id"]
             exists_duplicate_node = self.text_exists(doc_id)
             if exists_duplicate_node:

api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py

@@ -239,7 +239,7 @@ class AnalyticdbVector(BaseVector):
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
 
-        score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
+        score_threshold = kwargs.get("score_threshold", 0.0)
         request = gpdb_20160503_models.QueryCollectionDataRequest(
             dbinstance_id=self.config.instance_id,
             region_id=self.config.region_id,
@@ -267,7 +267,7 @@ class AnalyticdbVector(BaseVector):
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
 
-        score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
+        score_threshold = kwargs.get("score_threshold", 0.0)
         request = gpdb_20160503_models.QueryCollectionDataRequest(
             dbinstance_id=self.config.instance_id,
             region_id=self.config.region_id,

api/core/rag/datasource/vdb/chroma/chroma_vector.py

@@ -92,7 +92,7 @@ class ChromaVector(BaseVector):
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         collection = self._client.get_or_create_collection(self._collection_name)
         results: QueryResult = collection.query(query_embeddings=query_vector, n_results=kwargs.get("top_k", 4))
-        score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
+        score_threshold = kwargs.get("score_threshold", 0.0)
 
         ids: list[str] = results["ids"][0]
         documents: list[str] = results["documents"][0]

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py

@@ -86,8 +86,8 @@ class ElasticSearchVector(BaseVector):
                 id=uuids[i],
                 document={
                     Field.CONTENT_KEY.value: documents[i].page_content,
-                    Field.VECTOR.value: embeddings[i] if embeddings[i] else None,
-                    Field.METADATA_KEY.value: documents[i].metadata if documents[i].metadata else {},
+                    Field.VECTOR.value: embeddings[i] or None,
+                    Field.METADATA_KEY.value: documents[i].metadata or {},
                 },
             )
         self._client.indices.refresh(index=self._collection_name)
@@ -131,7 +131,7 @@ class ElasticSearchVector(BaseVector):
 
         docs = []
         for doc, score in docs_and_scores:
-            score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             if score > score_threshold:
                 doc.metadata["score"] = score
             docs.append(doc)

api/core/rag/datasource/vdb/milvus/milvus_vector.py

@@ -141,7 +141,7 @@ class MilvusVector(BaseVector):
         for result in results[0]:
             metadata = result["entity"].get(Field.METADATA_KEY.value)
             metadata["score"] = result["distance"]
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             if result["distance"] > score_threshold:
                 doc = Document(page_content=result["entity"].get(Field.CONTENT_KEY.value), metadata=metadata)
                 docs.append(doc)

api/core/rag/datasource/vdb/myscale/myscale_vector.py

@@ -122,7 +122,7 @@ class MyScaleVector(BaseVector):
 
     def _search(self, dist: str, order: SortOrder, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
-        score_threshold = kwargs.get("score_threshold") or 0.0
+        score_threshold = kwargs.get("score_threshold", 0.0)
         where_str = (
             f"WHERE dist < {1 - score_threshold}"
             if self._metric.upper() == "COSINE" and order == SortOrder.ASC and score_threshold > 0.0

api/core/rag/datasource/vdb/opensearch/opensearch_vector.py

@@ -170,7 +170,7 @@ class OpenSearchVector(BaseVector):
                 metadata = {}
 
             metadata["score"] = hit["_score"]
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             if hit["_score"] > score_threshold:
                 doc = Document(page_content=hit["_source"].get(Field.CONTENT_KEY.value), metadata=metadata)
                 docs.append(doc)

api/core/rag/datasource/vdb/oracle/oraclevector.py

@@ -200,7 +200,7 @@ class OracleVector(BaseVector):
                 [numpy.array(query_vector)],
             )
             docs = []
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             for record in cur:
                 metadata, text, distance = record
                 score = 1 - distance
@@ -212,7 +212,7 @@ class OracleVector(BaseVector):
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
         # just not implement fetch by score_threshold now, may be later
-        score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+        score_threshold = kwargs.get("score_threshold", 0.0)
         if len(query) > 0:
             # Check which language the query is in
             zh_pattern = re.compile("[\u4e00-\u9fa5]+")

api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py

@@ -198,7 +198,7 @@ class PGVectoRS(BaseVector):
             metadata = record.meta
             score = 1 - dis
             metadata["score"] = score
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             if score > score_threshold:
                 doc = Document(page_content=record.text, metadata=metadata)
                 docs.append(doc)

api/core/rag/datasource/vdb/pgvector/pgvector.py

@@ -144,7 +144,7 @@ class PGVector(BaseVector):
                 (json.dumps(query_vector),),
             )
             docs = []
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             for record in cur:
                 metadata, text, distance = record
                 score = 1 - distance

api/core/rag/datasource/vdb/qdrant/qdrant_vector.py

@@ -339,7 +339,7 @@ class QdrantVector(BaseVector):
         for result in results:
             metadata = result.payload.get(Field.METADATA_KEY.value) or {}
             # duplicate check score threshold
-            score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             if result.score > score_threshold:
                 metadata["score"] = result.score
                 doc = Document(

api/core/rag/datasource/vdb/relyt/relyt_vector.py

@@ -230,7 +230,7 @@ class RelytVector(BaseVector):
         # Organize results.
         docs = []
         for document, score in results:
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             if 1 - score > score_threshold:
                 docs.append(document)
         return docs

api/core/rag/datasource/vdb/tencent/tencent_vector.py

@@ -153,7 +153,7 @@ class TencentVector(BaseVector):
             limit=kwargs.get("top_k", 4),
             timeout=self._client_config.timeout,
         )
-        score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
+        score_threshold = kwargs.get("score_threshold", 0.0)
         return self._get_search_res(res, score_threshold)
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:

api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py

@@ -185,7 +185,7 @@ class TiDBVector(BaseVector):
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
-        score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+        score_threshold = kwargs.get("score_threshold", 0.0)
         filter = kwargs.get("filter")
         distance = 1 - score_threshold
 

api/core/rag/datasource/vdb/vector_base.py

@@ -49,7 +49,7 @@ class BaseVector(ABC):
         raise NotImplementedError
 
     def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
-        for text in texts[:]:
+        for text in texts.copy():
             doc_id = text.metadata["doc_id"]
             exists_duplicate_node = self.text_exists(doc_id)
             if exists_duplicate_node:

api/core/rag/datasource/vdb/vector_factory.py

@@ -153,7 +153,7 @@ class Vector:
         return CacheEmbedding(embedding_model)
 
     def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
-        for text in texts[:]:
+        for text in texts.copy():
             doc_id = text.metadata["doc_id"]
             exists_duplicate_node = self.text_exists(doc_id)
             if exists_duplicate_node:

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -205,7 +205,7 @@ class WeaviateVector(BaseVector):
 
         docs = []
         for doc, score in docs_and_scores:
-            score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
+            score_threshold = kwargs.get("score_threshold", 0.0)
             # check score threshold
             if score > score_threshold:
                 doc.metadata["score"] = score

api/core/rag/extractor/blob/blob.py

@@ -12,7 +12,7 @@ import mimetypes
 from abc import ABC, abstractmethod
 from collections.abc import Generator, Iterable, Mapping
 from io import BufferedReader, BytesIO
-from pathlib import PurePath
+from pathlib import Path, PurePath
 from typing import Any, Optional, Union
 
 from pydantic import BaseModel, ConfigDict, model_validator
@@ -56,8 +56,7 @@ class Blob(BaseModel):
     def as_string(self) -> str:
         """Read data as a string."""
         if self.data is None and self.path:
-            with open(str(self.path), encoding=self.encoding) as f:
-                return f.read()
+            return Path(str(self.path)).read_text(encoding=self.encoding)
         elif isinstance(self.data, bytes):
             return self.data.decode(self.encoding)
         elif isinstance(self.data, str):
@@ -72,8 +71,7 @@ class Blob(BaseModel):
         elif isinstance(self.data, str):
             return self.data.encode(self.encoding)
         elif self.data is None and self.path:
-            with open(str(self.path), "rb") as f:
-                return f.read()
+            return Path(str(self.path)).read_bytes()
         else:
             raise ValueError(f"Unable to get bytes for blob {self}")
 

api/core/rag/extractor/extract_processor.py

@@ -68,8 +68,7 @@ class ExtractProcessor:
                         suffix = "." + re.search(r"\.(\w+)$", filename).group(1)
 
             file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
-            with open(file_path, "wb") as file:
-                file.write(response.content)
+            Path(file_path).write_bytes(response.content)
             extract_setting = ExtractSetting(datasource_type="upload_file", document_model="text_model")
             if return_text:
                 delimiter = "\n"
@@ -111,7 +110,7 @@ class ExtractProcessor:
                         )
                     elif file_extension in [".htm", ".html"]:
                         extractor = HtmlExtractor(file_path)
-                    elif file_extension in [".docx"]:
+                    elif file_extension == ".docx":
                         extractor = WordExtractor(file_path, upload_file.tenant_id, upload_file.created_by)
                     elif file_extension == ".csv":
                         extractor = CSVExtractor(file_path, autodetect_encoding=True)
@@ -143,7 +142,7 @@ class ExtractProcessor:
                         extractor = MarkdownExtractor(file_path, autodetect_encoding=True)
                     elif file_extension in [".htm", ".html"]:
                         extractor = HtmlExtractor(file_path)
-                    elif file_extension in [".docx"]:
+                    elif file_extension == ".docx":
                         extractor = WordExtractor(file_path, upload_file.tenant_id, upload_file.created_by)
                     elif file_extension == ".csv":
                         extractor = CSVExtractor(file_path, autodetect_encoding=True)

api/core/rag/extractor/helpers.py

@@ -1,6 +1,7 @@
 """Document loader helpers."""
 
 import concurrent.futures
+from pathlib import Path
 from typing import NamedTuple, Optional, cast
 
 
@@ -28,8 +29,7 @@ def detect_file_encodings(file_path: str, timeout: int = 5) -> list[FileEncoding
     import chardet
 
     def read_and_detect(file_path: str) -> list[dict]:
-        with open(file_path, "rb") as f:
-            rawdata = f.read()
+        rawdata = Path(file_path).read_bytes()
         return cast(list[dict], chardet.detect_all(rawdata))
 
     with concurrent.futures.ThreadPoolExecutor() as executor:

api/core/rag/extractor/markdown_extractor.py

@@ -1,6 +1,7 @@
 """Abstract interface for document loader implementations."""
 
 import re
+from pathlib import Path
 from typing import Optional, cast
 
 from core.rag.extractor.extractor_base import BaseExtractor
@@ -102,15 +103,13 @@ class MarkdownExtractor(BaseExtractor):
         """Parse file into tuples."""
         content = ""
         try:
-            with open(filepath, encoding=self._encoding) as f:
-                content = f.read()
+            content = Path(filepath).read_text(encoding=self._encoding)
         except UnicodeDecodeError as e:
             if self._autodetect_encoding:
                 detected_encodings = detect_file_encodings(filepath)
                 for encoding in detected_encodings:
                     try:
-                        with open(filepath, encoding=encoding.encoding) as f:
-                            content = f.read()
+                        content = Path(filepath).read_text(encoding=encoding.encoding)
                         break
                     except UnicodeDecodeError:
                         continue

api/core/rag/extractor/text_extractor.py

@@ -1,5 +1,6 @@
 """Abstract interface for document loader implementations."""
 
+from pathlib import Path
 from typing import Optional
 
 from core.rag.extractor.extractor_base import BaseExtractor
@@ -25,15 +26,13 @@ class TextExtractor(BaseExtractor):
         """Load from file path."""
         text = ""
         try:
-            with open(self._file_path, encoding=self._encoding) as f:
-                text = f.read()
+            text = Path(self._file_path).read_text(encoding=self._encoding)
         except UnicodeDecodeError as e:
             if self._autodetect_encoding:
                 detected_encodings = detect_file_encodings(self._file_path)
                 for encoding in detected_encodings:
                     try:
-                        with open(self._file_path, encoding=encoding.encoding) as f:
-                            text = f.read()
+                        text = Path(self._file_path).read_text(encoding=encoding.encoding)
                         break
                     except UnicodeDecodeError:
                         continue

api/core/rag/extractor/word_extractor.py

@@ -153,7 +153,7 @@ class WordExtractor(BaseExtractor):
             if col_index >= total_cols:
                 break
             cell_content = self._parse_cell(cell, image_map).strip()
-            cell_colspan = cell.grid_span if cell.grid_span else 1
+            cell_colspan = cell.grid_span or 1
             for i in range(cell_colspan):
                 if col_index + i < total_cols:
                     row_cells[col_index + i] = cell_content if i == 0 else ""

api/core/rag/retrieval/dataset_retrieval.py

@@ -256,7 +256,7 @@ class DatasetRetrieval:
             # get retrieval model config
             dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
             if dataset:
-                retrieval_model_config = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+                retrieval_model_config = dataset.retrieval_model or default_retrieval_model
 
                 # get top k
                 top_k = retrieval_model_config["top_k"]
@@ -410,7 +410,7 @@ class DatasetRetrieval:
                 return []
 
             # get retrieval model , if the model is not setting , using default
-            retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+            retrieval_model = dataset.retrieval_model or default_retrieval_model
 
             if dataset.indexing_technique == "economy":
                 # use keyword table query
@@ -433,9 +433,7 @@ class DatasetRetrieval:
                         reranking_model=retrieval_model.get("reranking_model", None)
                         if retrieval_model["reranking_enable"]
                         else None,
-                        reranking_mode=retrieval_model.get("reranking_mode")
-                        if retrieval_model.get("reranking_mode")
-                        else "reranking_model",
+                        reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
                         weights=retrieval_model.get("weights", None),
                     )
 
@@ -486,7 +484,7 @@ class DatasetRetrieval:
             }
 
             for dataset in available_datasets:
-                retrieval_model_config = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+                retrieval_model_config = dataset.retrieval_model or default_retrieval_model
 
                 # get top k
                 top_k = retrieval_model_config["top_k"]

api/core/tools/provider/api_tool_provider.py

@@ -106,7 +106,7 @@ class ApiToolProviderController(ToolProviderController):
                     "human": {"en_US": tool_bundle.summary or "", "zh_Hans": tool_bundle.summary or ""},
                     "llm": tool_bundle.summary or "",
                 },
-                "parameters": tool_bundle.parameters if tool_bundle.parameters else [],
+                "parameters": tool_bundle.parameters or [],
             }
         )
 

api/core/tools/provider/builtin/aws/tools/sagemaker_text_rerank.py

@@ -1,4 +1,5 @@
 import json
+import operator
 from typing import Any, Union
 
 import boto3
@@ -71,7 +72,7 @@ class SageMakerReRankTool(BuiltinTool):
                 candidate_docs[idx]["score"] = scores[idx]
 
             line = 8
-            sorted_candidate_docs = sorted(candidate_docs, key=lambda x: x["score"], reverse=True)
+            sorted_candidate_docs = sorted(candidate_docs, key=operator.itemgetter("score"), reverse=True)
 
             line = 9
             return [self.create_json_message(res) for res in sorted_candidate_docs[: self.topk]]

api/core/tools/provider/builtin/hap/tools/get_worksheet_fields.py

@@ -115,7 +115,7 @@ class GetWorksheetFieldsTool(BuiltinTool):
             fields.append(field)
             fields_list.append(
                 f"|{field['id']}|{field['name']}|{field['type']}|{field['typeId']}|{field['description']}"
-                f"|{field['options'] if field['options'] else ''}|"
+                f"|{field['options'] or ''}|"
             )
 
         fields.append(

api/core/tools/provider/builtin/hap/tools/get_worksheet_pivot_data.py

@@ -130,7 +130,7 @@ class GetWorksheetPivotDataTool(BuiltinTool):
         # ]
         rows = []
         for row in data["data"]:
-            row_data = row["rows"] if row["rows"] else {}
+            row_data = row["rows"] or {}
             row_data.update(row["columns"])
             row_data.update(row["values"])
             rows.append(row_data)

api/core/tools/provider/builtin/hap/tools/list_worksheet_records.py

@@ -113,7 +113,7 @@ class ListWorksheetRecordsTool(BuiltinTool):
                         result_text = f"Found {result['total']} rows in worksheet \"{worksheet_name}\"."
                         if result["total"] > 0:
                             result_text += (
-                                f" The following are {result['total'] if result['total'] < limit else limit}"
+                                f" The following are {min(limit, result['total'])}"
                                 f" pieces of data presented in a table format:\n\n{table_header}"
                             )
                             for row in rows:

api/core/tools/provider/builtin/searchapi/tools/youtube_transcripts.py

@@ -37,7 +37,7 @@ class SearchAPI:
         return {
             "engine": "youtube_transcripts",
             "video_id": video_id,
-            "lang": language if language else "en",
+            "lang": language or "en",
             **{key: value for key, value in kwargs.items() if value not in [None, ""]},
         }
 

api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py

@@ -160,7 +160,7 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
                 hit_callback.on_query(query, dataset.id)
 
             # get retrieval model , if the model is not setting , using default
-            retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+            retrieval_model = dataset.retrieval_model or default_retrieval_model
 
             if dataset.indexing_technique == "economy":
                 # use keyword table query
@@ -183,9 +183,7 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
                         reranking_model=retrieval_model.get("reranking_model", None)
                         if retrieval_model["reranking_enable"]
                         else None,
-                        reranking_mode=retrieval_model.get("reranking_mode")
-                        if retrieval_model.get("reranking_mode")
-                        else "reranking_model",
+                        reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
                         weights=retrieval_model.get("weights", None),
                     )
 

api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py

@@ -55,7 +55,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
             hit_callback.on_query(query, dataset.id)
 
         # get retrieval model , if the model is not setting , using default
-        retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+        retrieval_model = dataset.retrieval_model or default_retrieval_model
         if dataset.indexing_technique == "economy":
             # use keyword table query
             documents = RetrievalService.retrieve(
@@ -76,9 +76,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                     reranking_model=retrieval_model.get("reranking_model", None)
                     if retrieval_model["reranking_enable"]
                     else None,
-                    reranking_mode=retrieval_model.get("reranking_mode")
-                    if retrieval_model.get("reranking_mode")
-                    else "reranking_model",
+                    reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
                     weights=retrieval_model.get("weights", None),
                 )
             else:

api/core/tools/utils/web_reader_tool.py

@@ -8,6 +8,7 @@ import subprocess
 import tempfile
 import unicodedata
 from contextlib import contextmanager
+from pathlib import Path
 from urllib.parse import unquote
 
 import chardet
@@ -98,7 +99,7 @@ def get_url(url: str, user_agent: str = None) -> str:
         authors=a["byline"],
         publish_date=a["date"],
         top_image="",
-        text=a["plain_text"] if a["plain_text"] else "",
+        text=a["plain_text"] or "",
     )
 
     return res
@@ -117,8 +118,7 @@ def extract_using_readabilipy(html):
         subprocess.check_call(["node", "ExtractArticle.js", "-i", html_path, "-o", article_json_path])
 
     # Read output of call to Readability.parse() from JSON file and return as Python dictionary
-    with open(article_json_path, encoding="utf-8") as json_file:
-        input_json = json.loads(json_file.read())
+    input_json = json.loads(Path(article_json_path).read_text(encoding="utf-8"))
 
     # Deleting files after processing
     os.unlink(article_json_path)

api/core/tools/utils/yaml_utils.py

@@ -21,7 +21,7 @@ def load_yaml_file(file_path: str, ignore_error: bool = True, default_value: Any
         with open(file_path, encoding="utf-8") as yaml_file:
             try:
                 yaml_content = yaml.safe_load(yaml_file)
-                return yaml_content if yaml_content else default_value
+                return yaml_content or default_value
             except Exception as e:
                 raise YAMLError(f"Failed to load YAML file {file_path}: {e}")
     except Exception as e:

api/core/workflow/graph_engine/entities/graph.py

@@ -268,7 +268,7 @@ class Graph(BaseModel):
                     f"Node {graph_edge.source_node_id} is connected to the previous node, please check the graph."
                 )
 
-            new_route = route[:]
+            new_route = route.copy()
             new_route.append(graph_edge.target_node_id)
             cls._check_connected_to_previous_node(
                 route=new_route,
@@ -679,8 +679,7 @@ class Graph(BaseModel):
         all_routes_node_ids = set()
         parallel_start_node_ids: dict[str, list[str]] = {}
         for branch_node_id, node_ids in routes_node_ids.items():
-            for node_id in node_ids:
-                all_routes_node_ids.add(node_id)
+            all_routes_node_ids.update(node_ids)
 
             if branch_node_id in reverse_edge_mapping:
                 for graph_edge in reverse_edge_mapping[branch_node_id]:

api/core/workflow/nodes/code/code_node.py

@@ -74,7 +74,7 @@ class CodeNode(BaseNode):
         :return:
         """
         if not isinstance(value, str):
-            if isinstance(value, type(None)):
+            if value is None:
                 return None
             else:
                 raise ValueError(f"Output variable `{variable}` must be a string")
@@ -95,7 +95,7 @@ class CodeNode(BaseNode):
         :return:
         """
         if not isinstance(value, int | float):
-            if isinstance(value, type(None)):
+            if value is None:
                 return None
             else:
                 raise ValueError(f"Output variable `{variable}` must be a number")
@@ -182,7 +182,7 @@ class CodeNode(BaseNode):
                                 f"Output {prefix}.{output_name} is not a valid array."
                                 f" make sure all elements are of the same type."
                             )
-                elif isinstance(output_value, type(None)):
+                elif output_value is None:
                     pass
                 else:
                     raise ValueError(f"Output {prefix}.{output_name} is not a valid type.")
@@ -284,7 +284,7 @@ class CodeNode(BaseNode):
 
                     for i, value in enumerate(result[output_name]):
                         if not isinstance(value, dict):
-                            if isinstance(value, type(None)):
+                            if value is None:
                                 pass
                             else:
                                 raise ValueError(

api/core/workflow/nodes/if_else/if_else_node.py

@@ -79,7 +79,7 @@ class IfElseNode(BaseNode):
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
             inputs=node_inputs,
             process_data=process_datas,
-            edge_source_handle=selected_case_id if selected_case_id else "false",  # Use case ID or 'default'
+            edge_source_handle=selected_case_id or "false",  # Use case ID or 'default'
             outputs=outputs,
         )
 

api/core/workflow/nodes/llm/llm_node.py

@@ -580,7 +580,7 @@ class LLMNode(BaseNode):
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=node_data.prompt_template,
             inputs=inputs,
-            query=query if query else "",
+            query=query or "",
             files=files,
             context=context,
             memory_config=node_data.memory,

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -250,7 +250,7 @@ class QuestionClassifierNode(LLMNode):
         for class_ in classes:
             category = {"category_id": class_.id, "category_name": class_.name}
             categories.append(category)
-        instruction = node_data.instruction if node_data.instruction else ""
+        instruction = node_data.instruction or ""
         input_text = query
         memory_str = ""
         if memory:

api/events/event_handlers/update_app_dataset_join_when_app_model_config_updated.py

@@ -18,8 +18,7 @@ def handle(sender, **kwargs):
         added_dataset_ids = dataset_ids
     else:
         old_dataset_ids = set()
-        for app_dataset_join in app_dataset_joins:
-            old_dataset_ids.add(app_dataset_join.dataset_id)
+        old_dataset_ids.update(app_dataset_join.dataset_id for app_dataset_join in app_dataset_joins)
 
         added_dataset_ids = dataset_ids - old_dataset_ids
         removed_dataset_ids = old_dataset_ids - dataset_ids

api/events/event_handlers/update_app_dataset_join_when_app_published_workflow_updated.py

@@ -22,8 +22,7 @@ def handle(sender, **kwargs):
         added_dataset_ids = dataset_ids
     else:
         old_dataset_ids = set()
-        for app_dataset_join in app_dataset_joins:
-            old_dataset_ids.add(app_dataset_join.dataset_id)
+        old_dataset_ids.update(app_dataset_join.dataset_id for app_dataset_join in app_dataset_joins)
 
         added_dataset_ids = dataset_ids - old_dataset_ids
         removed_dataset_ids = old_dataset_ids - dataset_ids

api/extensions/storage/local_storage.py

@@ -1,6 +1,7 @@
 import os
 import shutil
 from collections.abc import Generator
+from pathlib import Path
 
 from flask import Flask
 
@@ -26,8 +27,7 @@ class LocalStorage(BaseStorage):
         folder = os.path.dirname(filename)
         os.makedirs(folder, exist_ok=True)
 
-        with open(os.path.join(os.getcwd(), filename), "wb") as f:
-            f.write(data)
+        Path(os.path.join(os.getcwd(), filename)).write_bytes(data)
 
     def load_once(self, filename: str) -> bytes:
         if not self.folder or self.folder.endswith("/"):
@@ -38,9 +38,7 @@ class LocalStorage(BaseStorage):
         if not os.path.exists(filename):
             raise FileNotFoundError("File not found")
 
-        with open(filename, "rb") as f:
-            data = f.read()
-
+        data = Path(filename).read_bytes()
         return data
 
     def load_stream(self, filename: str) -> Generator:

api/models/dataset.py

@@ -144,7 +144,7 @@ class Dataset(db.Model):
             "top_k": 2,
             "score_threshold_enabled": False,
         }
-        return self.retrieval_model if self.retrieval_model else default_retrieval_model
+        return self.retrieval_model or default_retrieval_model
 
     @property
     def tags(self):
@@ -160,7 +160,7 @@ class Dataset(db.Model):
             .all()
         )
 
-        return tags if tags else []
+        return tags or []
 
     @staticmethod
     def gen_collection_name_by_id(dataset_id: str) -> str:

api/models/model.py

@@ -118,7 +118,7 @@ class App(db.Model):
 
     @property
     def api_base_url(self):
-        return (dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL else request.host_url.rstrip("/")) + "/v1"
+        return (dify_config.SERVICE_API_URL or request.host_url.rstrip("/")) + "/v1"
 
     @property
     def tenant(self):
@@ -207,7 +207,7 @@ class App(db.Model):
             .all()
         )
 
-        return tags if tags else []
+        return tags or []
 
 
 class AppModelConfig(db.Model):
@@ -908,7 +908,7 @@ class Message(db.Model):
                     "id": message_file.id,
                     "type": message_file.type,
                     "url": url,
-                    "belongs_to": message_file.belongs_to if message_file.belongs_to else "user",
+                    "belongs_to": message_file.belongs_to or "user",
                 }
             )
 
@@ -1212,7 +1212,7 @@ class Site(db.Model):
 
     @property
     def app_base_url(self):
-        return dify_config.APP_WEB_URL if dify_config.APP_WEB_URL else request.url_root.rstrip("/")
+        return dify_config.APP_WEB_URL or request.url_root.rstrip("/")
 
 
 class ApiToken(db.Model):
@@ -1488,7 +1488,7 @@ class TraceAppConfig(db.Model):
 
     @property
     def tracing_config_dict(self):
-        return self.tracing_config if self.tracing_config else {}
+        return self.tracing_config or {}
 
     @property
     def tracing_config_str(self):

api/pyproject.toml

@@ -15,6 +15,7 @@ select = [
     "C4", # flake8-comprehensions
     "E", # pycodestyle E rules
     "F", # pyflakes rules
+    "FURB", # refurb rules
     "I", # isort rules
     "N", # pep8-naming
     "RUF019", # unnecessary-key-check
@@ -37,6 +38,8 @@ ignore = [
     "F405", # undefined-local-with-import-star-usage
     "F821", # undefined-name
     "F841", # unused-variable
+    "FURB113", # repeated-append
+    "FURB152", # math-constant
     "UP007", # non-pep604-annotation
     "UP032", # f-string
     "B005", # strip-with-multi-characters

api/services/account_service.py

@@ -544,7 +544,7 @@ class RegisterService:
         """Register account"""
         try:
             account = AccountService.create_account(
-                email=email, name=name, interface_language=language if language else languages[0], password=password
+                email=email, name=name, interface_language=language or languages[0], password=password
             )
             account.status = AccountStatus.ACTIVE.value if not status else status.value
             account.initialized_at = datetime.now(timezone.utc).replace(tzinfo=None)

api/services/app_dsl_service.py

@@ -81,13 +81,11 @@ class AppDslService:
             raise ValueError("Missing app in data argument")
 
         # get app basic info
-        name = args.get("name") if args.get("name") else app_data.get("name")
-        description = args.get("description") if args.get("description") else app_data.get("description", "")
-        icon_type = args.get("icon_type") if args.get("icon_type") else app_data.get("icon_type")
-        icon = args.get("icon") if args.get("icon") else app_data.get("icon")
-        icon_background = (
-            args.get("icon_background") if args.get("icon_background") else app_data.get("icon_background")
-        )
+        name = args.get("name") or app_data.get("name")
+        description = args.get("description") or app_data.get("description", "")
+        icon_type = args.get("icon_type") or app_data.get("icon_type")
+        icon = args.get("icon") or app_data.get("icon")
+        icon_background = args.get("icon_background") or app_data.get("icon_background")
         use_icon_as_answer_icon = app_data.get("use_icon_as_answer_icon", False)
 
         # import dsl and create app

api/services/dataset_service.py

@@ -155,7 +155,7 @@ class DatasetService:
         dataset.tenant_id = tenant_id
         dataset.embedding_model_provider = embedding_model.provider if embedding_model else None
         dataset.embedding_model = embedding_model.model if embedding_model else None
-        dataset.permission = permission if permission else DatasetPermissionEnum.ONLY_ME
+        dataset.permission = permission or DatasetPermissionEnum.ONLY_ME
         db.session.add(dataset)
         db.session.commit()
         return dataset
@@ -681,11 +681,7 @@ class DocumentService:
                         "score_threshold_enabled": False,
                     }
 
-                    dataset.retrieval_model = (
-                        document_data.get("retrieval_model")
-                        if document_data.get("retrieval_model")
-                        else default_retrieval_model
-                    )
+                    dataset.retrieval_model = document_data.get("retrieval_model") or default_retrieval_model
 
         documents = []
         batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999))

api/services/hit_testing_service.py

@@ -33,7 +33,7 @@ class HitTestingService:
 
         # get retrieval model , if the model is not setting , using default
         if not retrieval_model:
-            retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+            retrieval_model = dataset.retrieval_model or default_retrieval_model
 
         all_documents = RetrievalService.retrieve(
             retrieval_method=retrieval_model.get("search_method", "semantic_search"),
@@ -46,9 +46,7 @@ class HitTestingService:
             reranking_model=retrieval_model.get("reranking_model", None)
             if retrieval_model["reranking_enable"]
             else None,
-            reranking_mode=retrieval_model.get("reranking_mode")
-            if retrieval_model.get("reranking_mode")
-            else "reranking_model",
+            reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
             weights=retrieval_model.get("weights", None),
         )
 

api/services/model_provider_service.py

@@ -1,6 +1,7 @@
 import logging
 import mimetypes
 import os
+from pathlib import Path
 from typing import Optional, cast
 
 import requests
@@ -453,9 +454,8 @@ class ModelProviderService:
         mimetype = mimetype or "application/octet-stream"
 
         # read binary from file
-        with open(file_path, "rb") as f:
-            byte_data = f.read()
-            return byte_data, mimetype
+        byte_data = Path(file_path).read_bytes()
+        return byte_data, mimetype
 
     def switch_preferred_provider(self, tenant_id: str, provider: str, preferred_provider_type: str) -> None:
         """

api/services/recommended_app_service.py

@@ -1,6 +1,7 @@
 import json
 import logging
 from os import path
+from pathlib import Path
 from typing import Optional
 
 import requests
@@ -218,10 +219,9 @@ class RecommendedAppService:
             return cls.builtin_data
 
         root_path = current_app.root_path
-        with open(path.join(root_path, "constants", "recommended_apps.json"), encoding="utf-8") as f:
-            json_data = f.read()
-            data = json.loads(json_data)
-            cls.builtin_data = data
+        cls.builtin_data = json.loads(
+            Path(path.join(root_path, "constants", "recommended_apps.json")).read_text(encoding="utf-8")
+        )
 
         return cls.builtin_data