chore: avoid implicit optional in type annotations of method (#8727)

api/core/agent/cot_agent_runner.py

         return message
 
     def _organize_historic_prompt_messages(
-        self, current_session_messages: list[PromptMessage] = None
+        self, current_session_messages: Optional[list[PromptMessage]] = None
     ) -> list[PromptMessage]:
         """
         organize historic prompt messages

api/core/agent/cot_chat_agent_runner.py

 
         return SystemPromptMessage(content=system_prompt)
 
-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
+    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
         """
         Organize user query
         """

api/core/agent/cot_completion_agent_runner.py

 import json
+from typing import Optional
 
 from core.agent.cot_agent_runner import CotAgentRunner
 from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, UserPromptMessage
 
         return system_prompt
 
-    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] = None) -> str:
+    def _organize_historic_prompt(self, current_session_messages: Optional[list[PromptMessage]] = None) -> str:
         """
         Organize historic prompt
         """

api/core/agent/fc_agent_runner.py

 import logging
 from collections.abc import Generator
 from copy import deepcopy
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 from core.agent.base_agent_runner import BaseAgentRunner
 from core.app.apps.base_app_queue_manager import PublishFrom
         return tool_calls
 
     def _init_system_message(
-        self, prompt_template: str, prompt_messages: list[PromptMessage] = None
+        self, prompt_template: str, prompt_messages: Optional[list[PromptMessage]] = None
     ) -> list[PromptMessage]:
         """
         Initialize system message
 
         return prompt_messages
 
-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
+    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
         """
         Organize user query
         """

api/core/indexing_runner.py

         tenant_id: str,
         extract_settings: list[ExtractSetting],
         tmp_processing_rule: dict,
-        doc_form: str = None,
+        doc_form: Optional[str] = None,
         doc_language: str = "English",
-        dataset_id: str = None,
+        dataset_id: Optional[str] = None,
         indexing_technique: str = "economy",
     ) -> dict:
         """

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

         stop: Optional[list[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
-        callbacks: list[Callback] = None,
+        callbacks: Optional[list[Callback]] = None,
     ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model

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

         stop: Optional[list[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
-        callbacks: list[Callback] = None,
+        callbacks: Optional[list[Callback]] = None,
     ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model

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

         model: str,
         messages: list[PromptMessage],
         tools: Optional[list[PromptMessageTool]] = None,
-        credentials: dict = None,
+        credentials: Optional[dict] = None,
     ) -> int:
         """
         Approximate num tokens with GPT2 tokenizer.

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

         stop: Optional[list[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
-        callbacks: list[Callback] = None,
+        callbacks: Optional[list[Callback]] = None,
     ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model

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

         model: str,
         messages: list[PromptMessage],
         tools: Optional[list[PromptMessageTool]] = None,
-        credentials: dict = None,
+        credentials: Optional[dict] = None,
     ) -> int:
         """
         Approximate num tokens with GPT2 tokenizer.

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

         """
         pass
 
-    def _detect_lang_code(self, content: str, map_dict: dict = None):
+    def _detect_lang_code(self, content: str, map_dict: Optional[dict] = None):
         map_dict = {"zh": "<|zh|>", "en": "<|en|>", "ja": "<|jp|>", "zh-TW": "<|yue|>", "ko": "<|ko|>"}
 
         response = self.comprehend_client.detect_dominant_language(Text=content)

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

         stop: Optional[list[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
-        callbacks: list[Callback] = None,
+        callbacks: Optional[list[Callback]] = None,
     ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model

api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/assistant/assistant.py

         conversation_id: Optional[str] = None,
         attachments: Optional[list[assistant_create_params.AssistantAttachments]] = None,
         metadata: dict | None = None,
-        request_id: str = None,
-        user_id: str = None,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
         extra_headers: Headers | None = None,
         extra_body: Body | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     def query_support(
         self,
         *,
-        assistant_id_list: list[str] = None,
-        request_id: str = None,
-        user_id: str = None,
+        assistant_id_list: Optional[list[str]] = None,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
         extra_headers: Headers | None = None,
         extra_body: Body | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
         page: int = 1,
         page_size: int = 10,
         *,
-        request_id: str = None,
-        user_id: str = None,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
         extra_headers: Headers | None = None,
         extra_body: Body | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/files.py

 from __future__ import annotations
 
 from collections.abc import Mapping
-from typing import TYPE_CHECKING, Literal, cast
+from typing import TYPE_CHECKING, Literal, Optional, cast
 
 import httpx
 
     def create(
         self,
         *,
-        file: FileTypes = None,
-        upload_detail: list[UploadDetail] = None,
+        file: Optional[FileTypes] = None,
+        upload_detail: Optional[list[UploadDetail]] = None,
         purpose: Literal["fine-tune", "retrieval", "batch"],
-        knowledge_id: str = None,
-        sentence_size: int = None,
+        knowledge_id: Optional[str] = None,
+        sentence_size: Optional[int] = None,
         extra_headers: Headers | None = None,
         extra_body: Body | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/knowledge/document/document.py

     def create(
         self,
         *,
-        file: FileTypes = None,
+        file: Optional[FileTypes] = None,
         custom_separator: Optional[list[str]] = None,
-        upload_detail: list[UploadDetail] = None,
+        upload_detail: Optional[list[UploadDetail]] = None,
         purpose: Literal["retrieval"],
-        knowledge_id: str = None,
-        sentence_size: int = None,
+        knowledge_id: Optional[str] = None,
+        sentence_size: Optional[int] = None,
         extra_headers: Headers | None = None,
         extra_body: Body | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/videos/videos.py

         self,
         model: str,
         *,
-        prompt: str = None,
-        image_url: str = None,
+        prompt: Optional[str] = None,
+        image_url: Optional[str] = None,
         sensitive_word_check: Optional[SensitiveWordCheckRequest] | NotGiven = NOT_GIVEN,
-        request_id: str = None,
-        user_id: str = None,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
         extra_headers: Headers | None = None,
         extra_body: Body | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

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

         else:
             return None
 
-    def delete_by_uuids(self, ids: list[str] = None):
+    def delete_by_uuids(self, ids: Optional[list[str]] = None):
         """Delete by vector IDs.
 
         Args:

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

 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, Optional
 
 from configs import dify_config
 from core.embedding.cached_embedding import CacheEmbedding
 
 
 class Vector:
-    def __init__(self, dataset: Dataset, attributes: list = None):
+    def __init__(self, dataset: Dataset, attributes: Optional[list] = None):
         if attributes is None:
             attributes = ["doc_id", "dataset_id", "document_id", "doc_hash"]
         self._dataset = dataset
             case _:
                 raise ValueError(f"Vector store {vector_type} is not supported.")
 
-    def create(self, texts: list = None, **kwargs):
+    def create(self, texts: Optional[list] = None, **kwargs):
         if texts:
             embeddings = self._embeddings.embed_documents([document.page_content for document in texts])
             self._vector_processor.create(texts=texts, embeddings=embeddings, **kwargs)

api/core/rag/extractor/extract_processor.py

 import re
 import tempfile
 from pathlib import Path
-from typing import Union
+from typing import Optional, Union
 from urllib.parse import unquote
 
 from configs import dify_config
 
     @classmethod
     def extract(
-        cls, extract_setting: ExtractSetting, is_automatic: bool = False, file_path: str = None
+        cls, extract_setting: ExtractSetting, is_automatic: bool = False, file_path: Optional[str] = None
     ) -> list[Document]:
         if extract_setting.datasource_type == DatasourceType.FILE.value:
             with tempfile.TemporaryDirectory() as temp_dir:

api/core/rag/extractor/unstructured/unstructured_epub_extractor.py

 import logging
+from typing import Optional
 
 from core.rag.extractor.extractor_base import BaseExtractor
 from core.rag.models.document import Document
     def __init__(
         self,
         file_path: str,
-        api_url: str = None,
+        api_url: Optional[str] = None,
     ):
         """Initialize with file path."""
         self._file_path = file_path

api/core/tools/entities/tool_entities.py

 
         self.pool.append(variable)
 
-    def set_file(self, tool_name: str, value: str, name: str = None) -> None:
+    def set_file(self, tool_name: str, value: str, name: Optional[str] = None) -> None:
         """
         set an image variable
 

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

 import json
 from enum import Enum
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 import boto3
 
     s3_client: Any = None
     comprehend_client: Any = None
 
-    def _detect_lang_code(self, content: str, map_dict: dict = None):
+    def _detect_lang_code(self, content: str, map_dict: Optional[dict] = None):
         map_dict = {"zh": "<|zh|>", "en": "<|en|>", "ja": "<|jp|>", "zh-TW": "<|yue|>", "ko": "<|ko|>"}
 
         response = self.comprehend_client.detect_dominant_language(Text=content)

api/core/tools/tool/builtin_tool.py

+from typing import Optional
+
 from core.model_runtime.entities.llm_entities import LLMResult
 from core.model_runtime.entities.message_entities import PromptMessage, SystemPromptMessage, UserPromptMessage
 from core.tools.entities.tool_entities import ToolProviderType
 
         return result
 
-    def get_url(self, url: str, user_agent: str = None) -> str:
+    def get_url(self, url: str, user_agent: Optional[str] = None) -> str:
         """
         get url
         """

api/core/tools/tool/tool.py

         """
         return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.TEXT, message=text, save_as=save_as)
 
-    def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = "") -> ToolInvokeMessage:
+    def create_blob_message(self, blob: bytes, meta: Optional[dict] = None, save_as: str = "") -> ToolInvokeMessage:
         """
         create a blob message
 

api/core/tools/tool_manager.py

 from collections.abc import Generator
 from os import listdir, path
 from threading import Lock
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 from configs import dify_config
 from core.agent.entities import AgentToolEntity
 
     @classmethod
     def get_tool(
-        cls, provider_type: str, provider_id: str, tool_name: str, tenant_id: str = None
+        cls, provider_type: str, provider_id: str, tool_name: str, tenant_id: Optional[str] = None
     ) -> Union[BuiltinTool, ApiTool]:
         """
         get the tool

api/core/tools/utils/feishu_api_utils.py

+from typing import Optional
+
 import httpx
 
 from core.tools.errors import ToolProviderCredentialValidationError
         return res.get("tenant_access_token")
 
     def _send_request(
-        self, url: str, method: str = "post", require_token: bool = True, payload: dict = None, params: dict = None
+        self,
+        url: str,
+        method: str = "post",
+        require_token: bool = True,
+        payload: Optional[dict] = None,
+        params: Optional[dict] = None,
     ):
         headers = {
             "Content-Type": "application/json",

api/core/tools/utils/parser.py

 from json import dumps as json_dumps
 from json import loads as json_loads
 from json.decoder import JSONDecodeError
+from typing import Optional
 
 from requests import get
 from yaml import YAMLError, safe_load
 class ApiBasedToolSchemaParser:
     @staticmethod
     def parse_openapi_to_tool_bundle(
-        openapi: dict, extra_info: dict = None, warning: dict = None
+        openapi: dict, extra_info: Optional[dict], warning: Optional[dict]
     ) -> list[ApiToolBundle]:
         warning = warning if warning is not None else {}
         extra_info = extra_info if extra_info is not None else {}
 
     @staticmethod
     def parse_openapi_yaml_to_tool_bundle(
-        yaml: str, extra_info: dict = None, warning: dict = None
+        yaml: str, extra_info: Optional[dict], warning: Optional[dict]
     ) -> list[ApiToolBundle]:
         """
         parse openapi yaml to tool bundle
         return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
 
     @staticmethod
-    def parse_swagger_to_openapi(swagger: dict, extra_info: dict = None, warning: dict = None) -> dict:
+    def parse_swagger_to_openapi(swagger: dict, extra_info: Optional[dict], warning: Optional[dict]) -> dict:
         """
         parse swagger to openapi
 
 
     @staticmethod
     def parse_openai_plugin_json_to_tool_bundle(
-        json: str, extra_info: dict = None, warning: dict = None
+        json: str, extra_info: Optional[dict], warning: Optional[dict]
     ) -> list[ApiToolBundle]:
         """
         parse openapi plugin yaml to tool bundle
 
     @staticmethod
     def auto_parse_to_tool_bundle(
-        content: str, extra_info: dict = None, warning: dict = None
+        content: str, extra_info: Optional[dict], warning: Optional[dict]
     ) -> tuple[list[ApiToolBundle], str]:
         """
         auto parse to tool bundle

api/core/tools/utils/web_reader_tool.py

 import unicodedata
 from contextlib import contextmanager
 from pathlib import Path
+from typing import Optional
 from urllib.parse import unquote
 
 import chardet
     return text[cursor : cursor + max_length]
 
 
-def get_url(url: str, user_agent: str = None) -> str:
+def get_url(url: str, user_agent: Optional[str] = None) -> str:
     """Fetch URL and return the contents as a string."""
     headers = {
         "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"

api/libs/helper.py

 
 class TokenManager:
     @classmethod
-    def generate_token(cls, account: Account, token_type: str, additional_data: dict = None) -> str:
+    def generate_token(cls, account: Account, token_type: str, additional_data: Optional[dict] = None) -> str:
         old_token = cls._get_current_token_for_account(account.id, token_type)
         if old_token:
             if isinstance(old_token, bytes):

api/pyproject.toml

     "PLR0402", # manual-from-import
     "PLR1711", # useless-return
     "PLR1714", # repeated-equality-comparison
+    "RUF013", # implicit-optional
     "RUF019", # unnecessary-key-check
     "RUF100", # unused-noqa
     "RUF101", # redirected-noqa

api/services/account_service.py

         return tenant
 
     @staticmethod
-    def switch_tenant(account: Account, tenant_id: int = None) -> None:
+    def switch_tenant(account: Account, tenant_id: Optional[int] = None) -> None:
         """Switch the current workspace for the account"""
 
         # Ensure tenant_id is provided

api/services/errors/base.py

+from typing import Optional
+
+
 class BaseServiceError(Exception):
-    def __init__(self, description: str = None):
+    def __init__(self, description: Optional[str] = None):
         self.description = description

api/services/tag_service.py

 import uuid
+from typing import Optional
 
 from flask_login import current_user
 from sqlalchemy import func
 
 class TagService:
     @staticmethod
-    def get_tags(tag_type: str, current_tenant_id: str, keyword: str = None) -> list:
+    def get_tags(tag_type: str, current_tenant_id: str, keyword: Optional[str] = None) -> list:
         query = (
             db.session.query(Tag.id, Tag.type, Tag.name, func.count(TagBinding.id).label("binding_count"))
             .outerjoin(TagBinding, Tag.id == TagBinding.tag_id)

api/services/tools/api_tools_manage_service.py

 import json
 import logging
+from typing import Optional
 
 from httpx import get
 
             raise ValueError(f"invalid schema: {str(e)}")
 
     @staticmethod
-    def convert_schema_to_tool_bundles(schema: str, extra_info: dict = None) -> list[ApiToolBundle]:
+    def convert_schema_to_tool_bundles(schema: str, extra_info: Optional[dict] = None) -> list[ApiToolBundle]:
         """
         convert schema to tool bundles
 

api/services/tools/tools_transform_service.py

 
     @staticmethod
     def workflow_provider_to_user_provider(
-        provider_controller: WorkflowToolProviderController, labels: list[str] = None
+        provider_controller: WorkflowToolProviderController, labels: Optional[list[str]] = None
     ):
         """
         convert provider controller to user provider
         provider_controller: ApiToolProviderController,
         db_provider: ApiToolProvider,
         decrypt_credentials: bool = True,
-        labels: list[str] = None,
+        labels: Optional[list[str]] = None,
     ) -> UserToolProvider:
         """
         convert provider controller to user provider
     @staticmethod
     def tool_to_user_tool(
         tool: Union[ApiToolBundle, WorkflowTool, Tool],
-        credentials: dict = None,
-        tenant_id: str = None,
-        labels: list[str] = None,
+        credentials: Optional[dict] = None,
+        tenant_id: Optional[str] = None,
+        labels: Optional[list[str]] = None,
     ) -> UserTool:
         """
         convert tool to user tool

api/services/tools/workflow_tools_manage_service.py

 import json
 from datetime import datetime
+from typing import Optional
 
 from sqlalchemy import or_
 
         description: str,
         parameters: list[dict],
         privacy_policy: str = "",
-        labels: list[str] = None,
+        labels: Optional[list[str]] = None,
     ) -> dict:
         """
         Create a workflow tool.
         description: str,
         parameters: list[dict],
         privacy_policy: str = "",
-        labels: list[str] = None,
+        labels: Optional[list[str]] = None,
     ) -> dict:
         """
         Update a workflow tool.

api/tests/integration_tests/vdb/__mock/tcvectordb.py

         description: str,
         index: Index,
         embedding: Embedding = None,
-        timeout: float = None,
+        timeout: Optional[float] = None,
     ) -> Collection:
         return Collection(
             self,
 
     def collection_delete(
         self,
-        document_ids: list[str] = None,
+        document_ids: Optional[list[str]] = None,
         filter: Filter = None,
-        timeout: float = None,
+        timeout: Optional[float] = None,
     ):
         return {"code": 0, "msg": "operation success"}