[CHORE]: remove redundant-cast (#24807)

api/core/app/apps/advanced_chat/app_runner.py

                 environment_variables=self._workflow.environment_variables,
                 # Based on the definition of `VariableUnion`,
                 # `list[Variable]` can be safely used as `list[VariableUnion]` since they are compatible.
-                conversation_variables=cast(list[VariableUnion], conversation_variables),
+                conversation_variables=conversation_variables,
             )
 
             # init graph

api/core/helper/encrypter.py

 from libs import rsa
 
 
-def obfuscated_token(token: str):
+def obfuscated_token(token: str) -> str:
     if not token:
         return token
     if len(token) <= 8:

api/core/model_manager.py

         """
         if not isinstance(self.model_type_instance, LargeLanguageModel):
             raise Exception("Model type instance is not LargeLanguageModel")
-
-        self.model_type_instance = cast(LargeLanguageModel, self.model_type_instance)
         return cast(
             Union[LLMResult, Generator],
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, LargeLanguageModel):
             raise Exception("Model type instance is not LargeLanguageModel")
-
-        self.model_type_instance = cast(LargeLanguageModel, self.model_type_instance)
         return cast(
             int,
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, TextEmbeddingModel):
             raise Exception("Model type instance is not TextEmbeddingModel")
-
-        self.model_type_instance = cast(TextEmbeddingModel, self.model_type_instance)
         return cast(
             TextEmbeddingResult,
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, TextEmbeddingModel):
             raise Exception("Model type instance is not TextEmbeddingModel")
-
-        self.model_type_instance = cast(TextEmbeddingModel, self.model_type_instance)
         return cast(
             list[int],
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, RerankModel):
             raise Exception("Model type instance is not RerankModel")
-
-        self.model_type_instance = cast(RerankModel, self.model_type_instance)
         return cast(
             RerankResult,
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, ModerationModel):
             raise Exception("Model type instance is not ModerationModel")
-
-        self.model_type_instance = cast(ModerationModel, self.model_type_instance)
         return cast(
             bool,
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, Speech2TextModel):
             raise Exception("Model type instance is not Speech2TextModel")
-
-        self.model_type_instance = cast(Speech2TextModel, self.model_type_instance)
         return cast(
             str,
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, TTSModel):
             raise Exception("Model type instance is not TTSModel")
-
-        self.model_type_instance = cast(TTSModel, self.model_type_instance)
         return cast(
             Iterable[bytes],
             self._round_robin_invoke(
         """
         if not isinstance(self.model_type_instance, TTSModel):
             raise Exception("Model type instance is not TTSModel")
-
-        self.model_type_instance = cast(TTSModel, self.model_type_instance)
         return self.model_type_instance.get_tts_model_voices(
             model=self.model, credentials=self.credentials, language=language
         )

api/core/prompt/utils/prompt_message_util.py

             if isinstance(prompt_message.content, list):
                 for content in prompt_message.content:
                     if content.type == PromptMessageContentType.TEXT:
-                        content = cast(TextPromptMessageContent, content)
                         text += content.data
                     else:
                         content = cast(ImagePromptMessageContent, content)

api/core/provider_manager.py

 import json
 from collections import defaultdict
 from json import JSONDecodeError
-from typing import Any, Optional, cast
+from typing import Any, Optional
 
 from sqlalchemy import select
 from sqlalchemy.exc import IntegrityError
         for provider_entity in provider_entities:
             # handle include, exclude
             if is_filtered(
-                include_set=cast(set[str], dify_config.POSITION_PROVIDER_INCLUDES_SET),
-                exclude_set=cast(set[str], dify_config.POSITION_PROVIDER_EXCLUDES_SET),
+                include_set=dify_config.POSITION_PROVIDER_INCLUDES_SET,
+                exclude_set=dify_config.POSITION_PROVIDER_EXCLUDES_SET,
                 data=provider_entity,
                 name_func=lambda x: x.provider,
             ):

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

 import uuid
 from collections.abc import Generator, Iterable, Sequence
 from itertools import islice
-from typing import TYPE_CHECKING, Any, Optional, Union, cast
+from typing import TYPE_CHECKING, Any, Optional, Union
 
 import qdrant_client
 from flask import current_app
 
     def _reload_if_needed(self):
         if isinstance(self._client, QdrantLocal):
-            self._client = cast(QdrantLocal, self._client)
             self._client._load()
 
     @classmethod

api/core/rag/extractor/markdown_extractor.py

 
 import re
 from pathlib import Path
-from typing import Optional, cast
+from typing import Optional
 
 from core.rag.extractor.extractor_base import BaseExtractor
 from core.rag.extractor.helpers import detect_file_encodings
         markdown_tups.append((current_header, current_text))
 
         markdown_tups = [
-            (re.sub(r"#", "", cast(str, key)).strip() if key else None, re.sub(r"<.*?>", "", value))
+            (re.sub(r"#", "", key).strip() if key else None, re.sub(r"<.*?>", "", value))
             for key, value in markdown_tups
         ]
 

api/core/rag/extractor/notion_extractor.py

                 f"No notion data source binding found for tenant {tenant_id} and notion workspace {notion_workspace_id}"
             )
 
-        return cast(str, data_source_binding.access_token)
+        return data_source_binding.access_token

api/core/rag/extractor/pdf_extractor.py

 
 import contextlib
 from collections.abc import Iterator
-from typing import Optional, cast
+from typing import Optional
 
 from core.rag.extractor.blob.blob import Blob
 from core.rag.extractor.extractor_base import BaseExtractor
         plaintext_file_exists = False
         if self._file_cache_key:
             with contextlib.suppress(FileNotFoundError):
-                text = cast(bytes, storage.load(self._file_cache_key)).decode("utf-8")
+                text = storage.load(self._file_cache_key).decode("utf-8")
                 plaintext_file_exists = True
                 return [Document(page_content=text)]
         documents = list(self.load())

api/core/tools/tool_manager.py

             if controller_tools is None or len(controller_tools) == 0:
                 raise ToolProviderNotFoundError(f"workflow provider {provider_id} not found")
 
-            return cast(
-                WorkflowTool,
-                controller.get_tools(tenant_id=workflow_provider.tenant_id)[0].fork_tool_runtime(
-                    runtime=ToolRuntime(
-                        tenant_id=tenant_id,
-                        credentials={},
-                        invoke_from=invoke_from,
-                        tool_invoke_from=tool_invoke_from,
-                    )
-                ),
+            return controller.get_tools(tenant_id=workflow_provider.tenant_id)[0].fork_tool_runtime(
+                runtime=ToolRuntime(
+                    tenant_id=tenant_id,
+                    credentials={},
+                    invoke_from=invoke_from,
+                    tool_invoke_from=tool_invoke_from,
+                )
             )
         elif provider_type == ToolProviderType.APP:
             raise NotImplementedError("app provider not implemented")
                 for provider in builtin_providers:
                     # handle include, exclude
                     if is_filtered(
-                        include_set=cast(set[str], dify_config.POSITION_TOOL_INCLUDES_SET),
-                        exclude_set=cast(set[str], dify_config.POSITION_TOOL_EXCLUDES_SET),
+                        include_set=dify_config.POSITION_TOOL_INCLUDES_SET,
+                        exclude_set=dify_config.POSITION_TOOL_EXCLUDES_SET,
                         data=provider,
                         name_func=lambda x: x.identity.name,
                     ):

api/core/tools/utils/message_transformer.py

 from datetime import date, datetime
 from decimal import Decimal
 from mimetypes import guess_extension
-from typing import Optional, cast
+from typing import Optional
 from uuid import UUID
 
 import numpy as np
 
             elif message.type == ToolInvokeMessage.MessageType.JSON:
                 if isinstance(message.message, ToolInvokeMessage.JsonMessage):
-                    json_msg = cast(ToolInvokeMessage.JsonMessage, message.message)
-                    json_msg.json_object = safe_json_value(json_msg.json_object)
+                    message.message.json_object = safe_json_value(message.message.json_object)
                 yield message
             else:
                 yield message

api/core/tools/utils/model_invocation_utils.py

         db.session.commit()
 
         try:
-            response: LLMResult = cast(
-                LLMResult,
-                model_instance.invoke_llm(
-                    prompt_messages=prompt_messages,
-                    model_parameters=model_parameters,
-                    tools=[],
-                    stop=[],
-                    stream=False,
-                    user=user_id,
-                    callbacks=[],
-                ),
+            response: LLMResult = model_instance.invoke_llm(
+                prompt_messages=prompt_messages,
+                model_parameters=model_parameters,
+                tools=[],
+                stop=[],
+                stream=False,
+                user=user_id,
+                callbacks=[],
             )
         except InvokeRateLimitError as e:
             raise InvokeModelError(f"Invoke rate limit error: {e}")

api/core/tools/workflow_as_tool/tool.py

 import json
 import logging
 from collections.abc import Generator
-from typing import Any, Optional, cast
+from typing import Any, Optional
 
 from core.file import FILE_MODEL_IDENTITY, File, FileTransferMethod
 from core.tools.__base.tool import Tool
                         item = self._update_file_mapping(item)
                         file = build_from_mapping(
                             mapping=item,
-                            tenant_id=str(cast(ToolRuntime, self.runtime).tenant_id),
+                            tenant_id=str(self.runtime.tenant_id),
                         )
                         files.append(file)
             elif isinstance(value, dict) and value.get("dify_model_identity") == FILE_MODEL_IDENTITY:
                 value = self._update_file_mapping(value)
                 file = build_from_mapping(
                     mapping=value,
-                    tenant_id=str(cast(ToolRuntime, self.runtime).tenant_id),
+                    tenant_id=str(self.runtime.tenant_id),
                 )
                 files.append(file)
 

api/core/variables/variables.py

 from collections.abc import Sequence
-from typing import Annotated, TypeAlias, cast
+from typing import Annotated, TypeAlias
 from uuid import uuid4
 
 from pydantic import Discriminator, Field, Tag
 
     @property
     def log(self) -> str:
-        return cast(str, encrypter.obfuscated_token(self.value))
+        return encrypter.obfuscated_token(self.value)
 
 
 class NoneVariable(NoneSegment, Variable):

api/core/workflow/graph_engine/graph_engine.py

                         if len(sub_edge_mappings) == 0:
                             continue
 
-                        edge = cast(GraphEdge, sub_edge_mappings[0])
+                        edge = sub_edge_mappings[0]
                         if edge.run_condition is None:
                             logger.warning("Edge %s run condition is None", edge.target_node_id)
                             continue

api/core/workflow/nodes/agent/agent_node.py

                 messages=message_stream,
                 tool_info={
                     "icon": self.agent_strategy_icon,
-                    "agent_strategy": cast(AgentNodeData, self._node_data).agent_strategy_name,
+                    "agent_strategy": self._node_data.agent_strategy_name,
                 },
                 parameters_for_log=parameters_for_log,
                 user_id=self.user_id,
             current_plugin = next(
                 plugin
                 for plugin in plugins
-                if f"{plugin.plugin_id}/{plugin.name}"
-                == cast(AgentNodeData, self._node_data).agent_strategy_provider_name
+                if f"{plugin.plugin_id}/{plugin.name}" == self._node_data.agent_strategy_provider_name
             )
             icon = current_plugin.declaration.icon
         except StopIteration:

api/core/workflow/nodes/document_extractor/node.py

             encoding = "utf-8"
 
         yaml_data = yaml.safe_load_all(file_content.decode(encoding, errors="ignore"))
-        return cast(str, yaml.dump_all(yaml_data, allow_unicode=True, sort_keys=False))
+        return yaml.dump_all(yaml_data, allow_unicode=True, sort_keys=False)
     except (UnicodeDecodeError, LookupError, yaml.YAMLError) as e:
         # If decoding fails, try with utf-8 as last resort
         try:
             yaml_data = yaml.safe_load_all(file_content.decode("utf-8", errors="ignore"))
-            return cast(str, yaml.dump_all(yaml_data, allow_unicode=True, sort_keys=False))
+            return yaml.dump_all(yaml_data, allow_unicode=True, sort_keys=False)
         except (UnicodeDecodeError, yaml.YAMLError):
             raise TextExtractionError(f"Failed to decode or parse YAML file: {e}") from e
 

api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py

         """
         Run the node.
         """
-        node_data = cast(ParameterExtractorNodeData, self._node_data)
+        node_data = self._node_data
         variable = self.graph_runtime_state.variable_pool.get(node_data.query)
         query = variable.text if variable else ""
 

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

 import json
 from collections.abc import Mapping, Sequence
-from typing import TYPE_CHECKING, Any, Optional, cast
+from typing import TYPE_CHECKING, Any, Optional
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.memory.token_buffer_memory import TokenBufferMemory
         return "1"
 
     def _run(self):
-        node_data = cast(QuestionClassifierNodeData, self._node_data)
+        node_data = self._node_data
         variable_pool = self.graph_runtime_state.variable_pool
 
         # extract variables

api/core/workflow/nodes/tool/tool_node.py

 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, Optional, cast
+from typing import Any, Optional
 
 from sqlalchemy import select
 from sqlalchemy.orm import Session
         Run the tool node
         """
 
-        node_data = cast(ToolNodeData, self._node_data)
+        node_data = self._node_data
 
         # fetch tool icon
         tool_info = {

api/core/workflow/workflow_entry.py

 import time
 import uuid
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, Optional, cast
+from typing import Any, Optional
 
 from configs import dify_config
 from core.app.apps.exc import GenerateTaskStoppedError
             environment_variables=[],
         )
 
-        node_cls = cast(type[BaseNode], node_cls)
         # init workflow run state
         node: BaseNode = node_cls(
             id=str(uuid.uuid4()),

api/factories/file_factory.py

 import urllib.parse
 import uuid
 from collections.abc import Callable, Mapping, Sequence
-from typing import Any, cast
+from typing import Any
 
 import httpx
 from sqlalchemy import select
         mime_type = ""
 
     resp = ssrf_proxy.head(url, follow_redirects=True)
-    resp = cast(httpx.Response, resp)
     if resp.status_code == httpx.codes.OK:
         if content_disposition := resp.headers.get("Content-Disposition"):
             filename = str(content_disposition.split("filename=")[-1].strip('"'))

api/models/tools.py

 
     @property
     def decrypted_server_url(self) -> str:
-        return cast(str, encrypter.decrypt_token(self.tenant_id, self.server_url))
+        return encrypter.decrypt_token(self.tenant_id, self.server_url)
 
     @property
     def masked_server_url(self) -> str:

api/services/account_service.py

             account.last_active_at = naive_utc_now()
             db.session.commit()
 
-        return cast(Account, account)
+        return account
 
     @staticmethod
     def get_account_jwt_token(account: Account) -> str:
 
         db.session.commit()
 
-        return cast(Account, account)
+        return account
 
     @staticmethod
     def update_account_password(account, password, new_password):
     def get_custom_config(tenant_id: str) -> dict:
         tenant = db.get_or_404(Tenant, tenant_id)
 
-        return cast(dict, tenant.custom_config_dict)
+        return tenant.custom_config_dict
 
     @staticmethod
     def is_owner(account: Account, tenant: Tenant) -> bool:

api/services/annotation_service.py

 import uuid
-from typing import cast
+from typing import Optional
 
 import pandas as pd
 from flask_login import current_user
             if not message:
                 raise NotFound("Message Not Exists.")
 
-            annotation = message.annotation
+            annotation: Optional[MessageAnnotation] = message.annotation
             # save the message annotation
             if annotation:
                 annotation.content = args["answer"]
                 app_id,
                 annotation_setting.collection_binding_id,
             )
-        return cast(MessageAnnotation, annotation)
+        return annotation
 
     @classmethod
     def enable_app_annotation(cls, args: dict, app_id: str) -> dict:

api/tests/integration_tests/workflow/nodes/test_code.py

 import time
 import uuid
 from os import getenv
-from typing import cast
 
 import pytest
 
 from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
 from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.code.code_node import CodeNode
-from core.workflow.nodes.code.entities import CodeNodeData
 from core.workflow.system_variable import SystemVariable
 from models.enums import UserFrom
 from models.workflow import WorkflowType
         "object_validator": {"result": 1, "depth": {"depth": {"depth": 1}}},
     }
 
-    node._node_data = cast(CodeNodeData, node._node_data)
-
     # validate
     node._transform_result(result, node._node_data.outputs)
 
         ]
     }
 
-    node._node_data = cast(CodeNodeData, node._node_data)
-
     # validate
     node._transform_result(result, node._node_data.outputs)