feat: Activate ruff rules UP(pyupgrade) (#3871)

* Activate ruff rules UP(pyupgrade)

* Set ruff target version

* Fix threading.Lock | None error
Due to https://github.com/python/cpython/issues/114315

* Fix Text in custom component supported typed

* Fix mypy issues

---------

Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

src/backend/base/langflow/__main__.py

@@ -4,7 +4,6 @@ import sys
 import time
 import warnings
 from pathlib import Path
-from typing import Optional
 
 import click
 import httpx
@@ -84,7 +83,7 @@ def run(
     workers: int = typer.Option(1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"),
     timeout: int = typer.Option(300, help="Worker timeout in seconds.", envvar="LANGFLOW_WORKER_TIMEOUT"),
     port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"),
-    components_path: Optional[Path] = typer.Option(
+    components_path: Path | None = typer.Option(
         Path(__file__).parent / "components",
         help="Path to the directory containing custom components.",
         envvar="LANGFLOW_COMPONENTS_PATH",
@@ -93,7 +92,7 @@ def run(
     env_file: Path = typer.Option(None, help="Path to the .env file containing environment variables."),
     log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
     log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"),
-    cache: Optional[str] = typer.Option(
+    cache: str | None = typer.Option(
         envvar="LANGFLOW_LANGCHAIN_CACHE",
         help="Type of cache to use. (InMemoryCache, SQLiteCache)",
         default=None,
@@ -161,7 +160,7 @@ def run(
         health_check_max_retries=health_check_max_retries,
     )
     # create path object if path is provided
-    static_files_dir: Optional[Path] = Path(path) if path else None
+    static_files_dir: Path | None = Path(path) if path else None
     settings_service = get_settings_service()
     settings_service.set("backend_only", backend_only)
     app = setup_app(static_files_dir=static_files_dir, backend_only=backend_only)

src/backend/base/langflow/api/utils.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import uuid
 import warnings
 from typing import TYPE_CHECKING, Any
@@ -69,7 +71,7 @@ def build_input_keys_response(langchain_object, artifacts):
     return input_keys_response
 
 
-def validate_is_component(flows: list["Flow"]):
+def validate_is_component(flows: list[Flow]):
     for flow in flows:
         if not flow.data or flow.is_component is not None:
             continue
@@ -152,7 +154,7 @@ async def build_graph_from_db_no_cache(flow_id: str, session: Session):
     return await build_graph_from_data(flow_id, flow.data, flow_name=flow.name, user_id=str(flow.user_id))
 
 
-async def build_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
+async def build_graph_from_db(flow_id: str, session: Session, chat_service: ChatService):
     graph = await build_graph_from_db_no_cache(flow_id, session)
     await chat_service.set_cache(flow_id, graph)
     return graph
@@ -160,7 +162,7 @@ async def build_graph_from_db(flow_id: str, session: Session, chat_service: "Cha
 
 async def build_and_cache_graph_from_data(
     flow_id: str,
-    chat_service: "ChatService",
+    chat_service: ChatService,
     graph_data: dict,
 ):  # -> Graph | Any:
     """Build and cache the graph."""

src/backend/base/langflow/api/v1/callback.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import TYPE_CHECKING, Any
 from uuid import UUID
 
@@ -25,7 +27,7 @@ class AsyncStreamingLLMCallbackHandleSIO(AsyncCallbackHandler):
     def __init__(self, session_id: str):
         self.chat_service = get_chat_service()
         self.client_id = session_id
-        self.socketio_service: "SocketIOService" = get_socket_service()
+        self.socketio_service: SocketIOService = get_socket_service()
         self.sid = session_id
         # self.socketio_service = self.chat_service.active_connections[self.client_id]
 

src/backend/base/langflow/api/v1/chat.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import asyncio
 import json
 import time
@@ -72,9 +74,9 @@ async def retrieve_vertices_order(
     data: Annotated[FlowDataRequest | None, Body(embed=True)] | None = None,
     stop_component_id: str | None = None,
     start_component_id: str | None = None,
-    chat_service: "ChatService" = Depends(get_chat_service),
+    chat_service: ChatService = Depends(get_chat_service),
     session=Depends(get_session),
-    telemetry_service: "TelemetryService" = Depends(get_telemetry_service),
+    telemetry_service: TelemetryService = Depends(get_telemetry_service),
 ):
     """
     Retrieve the vertices order for a given flow.
@@ -148,12 +150,12 @@ async def build_flow(
     stop_component_id: str | None = None,
     start_component_id: str | None = None,
     log_builds: bool | None = True,
-    chat_service: "ChatService" = Depends(get_chat_service),
+    chat_service: ChatService = Depends(get_chat_service),
     current_user=Depends(get_current_active_user),
-    telemetry_service: "TelemetryService" = Depends(get_telemetry_service),
+    telemetry_service: TelemetryService = Depends(get_telemetry_service),
     session=Depends(get_session),
 ):
-    async def build_graph_and_get_order() -> tuple[list[str], list[str], "Graph"]:
+    async def build_graph_and_get_order() -> tuple[list[str], list[str], Graph]:
         start_time = time.perf_counter()
         components_count = None
         try:
@@ -205,7 +207,7 @@ async def build_flow(
             logger.exception(exc)
             raise HTTPException(status_code=500, detail=str(exc)) from exc
 
-    async def _build_vertex(vertex_id: str, graph: "Graph", event_manager: "EventManager") -> VertexBuildResponse:
+    async def _build_vertex(vertex_id: str, graph: Graph, event_manager: EventManager) -> VertexBuildResponse:
         flow_id_str = str(flow_id)
 
         next_runnable_vertices = []
@@ -320,9 +322,9 @@ async def build_flow(
 
     async def build_vertices(
         vertex_id: str,
-        graph: "Graph",
+        graph: Graph,
         client_consumed_queue: asyncio.Queue,
-        event_manager: "EventManager",
+        event_manager: EventManager,
     ) -> None:
         build_task = asyncio.create_task(asyncio.to_thread(asyncio.run, _build_vertex(vertex_id, graph, event_manager)))
         try:
@@ -457,9 +459,9 @@ async def build_vertex(
     background_tasks: BackgroundTasks,
     inputs: Annotated[InputValueRequest | None, Body(embed=True)] = None,
     files: list[str] | None = None,
-    chat_service: "ChatService" = Depends(get_chat_service),
+    chat_service: ChatService = Depends(get_chat_service),
     current_user=Depends(get_current_active_user),
-    telemetry_service: "TelemetryService" = Depends(get_telemetry_service),
+    telemetry_service: TelemetryService = Depends(get_telemetry_service),
 ):
     """Build a vertex instead of the entire graph.
 
@@ -489,7 +491,7 @@ async def build_vertex(
         if not cache:
             # If there's no cache
             logger.warning(f"No cache found for {flow_id_str}. Building graph starting at {vertex_id}")
-            graph: "Graph" = await build_graph_from_db(
+            graph: Graph = await build_graph_from_db(
                 flow_id=flow_id_str, session=next(get_session()), chat_service=chat_service
             )
         else:
@@ -609,8 +611,8 @@ async def build_vertex_stream(
     flow_id: uuid.UUID,
     vertex_id: str,
     session_id: str | None = None,
-    chat_service: "ChatService" = Depends(get_chat_service),
+    chat_service: ChatService = Depends(get_chat_service),
-    session_service: "SessionService" = Depends(get_session_service),
+    session_service: SessionService = Depends(get_session_service),
 ):
     """Build a vertex instead of the entire graph.
 
@@ -650,7 +652,7 @@ async def build_vertex_stream(
                 else:
                     graph = cache.get("result")
 
-                vertex: "InterfaceVertex" = graph.get_vertex(vertex_id)
+                vertex: InterfaceVertex = graph.get_vertex(vertex_id)
                 if not hasattr(vertex, "stream"):
                     raise ValueError(f"Vertex {vertex_id} does not support streaming")
                 if isinstance(vertex._built_result, str) and vertex._built_result:

src/backend/base/langflow/api/v1/endpoints.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import time
 from asyncio import Lock
 from http import HTTPStatus
@@ -62,7 +64,7 @@ router = APIRouter(tags=["Base"])
 @router.get("/all", dependencies=[Depends(get_current_active_user)])
 async def get_all(
     settings_service=Depends(get_settings_service),
-    cache_service: "CacheService" = Depends(dependency=get_cache_service),
+    cache_service: CacheService = Depends(dependency=get_cache_service),
     force_refresh: bool = False,
 ):
     from langflow.interface.types import get_and_cache_all_types_dict
@@ -181,7 +183,7 @@ async def simplified_run_flow(
     input_request: SimplifiedAPIRequest = SimplifiedAPIRequest(),
     stream: bool = False,
     api_key_user: UserRead = Depends(api_key_security),
-    telemetry_service: "TelemetryService" = Depends(get_telemetry_service),
+    telemetry_service: TelemetryService = Depends(get_telemetry_service),
 ):
     """
     Executes a specified flow by ID with input customization, performance enhancements through caching, and optional data streaming.
@@ -290,7 +292,7 @@ async def webhook_run_flow(
     user: Annotated[User, Depends(get_user_by_flow_id_or_endpoint_name)],
     request: Request,
     background_tasks: BackgroundTasks,
-    telemetry_service: "TelemetryService" = Depends(get_telemetry_service),
+    telemetry_service: TelemetryService = Depends(get_telemetry_service),
 ):
     """
     Run a flow using a webhook request.
@@ -484,7 +486,7 @@ async def process(
     tweaks: dict | None = None,
     clear_cache: Annotated[bool, Body(embed=True)] = False,  # noqa: F821
     session_id: Annotated[None | str, Body(embed=True)] = None,  # noqa: F821
-    task_service: "TaskService" = Depends(get_task_service),
+    task_service: TaskService = Depends(get_task_service),
     api_key_user: UserRead = Depends(api_key_security),
     sync: Annotated[bool, Body(embed=True)] = True,  # noqa: F821
     session_service: SessionService = Depends(get_session_service),
@@ -633,7 +635,7 @@ def get_config():
     try:
         from langflow.services.deps import get_settings_service
 
-        settings_service: "SettingsService" = get_settings_service()  # type: ignore
+        settings_service: SettingsService = get_settings_service()  # type: ignore
         return settings_service.settings.model_dump()
     except Exception as exc:
         logger.exception(exc)

src/backend/base/langflow/base/agents/agent.py

@@ -1,5 +1,5 @@
 from abc import abstractmethod
-from typing import List, Optional, Union, cast
+from typing import cast
 
 from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
 from langchain.agents.agent import RunnableAgent
@@ -20,7 +20,7 @@ from langflow.utils.constants import MESSAGE_SENDER_AI
 
 class LCAgentComponent(Component):
     trace_type = "agent"
-    _base_inputs: List[InputTypes] = [
+    _base_inputs: list[InputTypes] = [
         MessageTextInput(name="input_value", display_name="Input"),
         BoolInput(
             name="handle_parsing_errors",
@@ -89,7 +89,7 @@ class LCAgentComponent(Component):
             }
         return {**base, "agent_executor_kwargs": agent_kwargs}
 
-    def get_chat_history_data(self) -> Optional[List[Data]]:
+    def get_chat_history_data(self) -> list[Data] | None:
         # might be overridden in subclasses
         return None
 
@@ -128,7 +128,7 @@ class LCToolsAgentComponent(LCAgentComponent):
 
     async def run_agent(
         self,
-        agent: Union[Runnable, BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
+        agent: Runnable | BaseSingleActionAgent | BaseMultiActionAgent | AgentExecutor,
     ) -> Text:
         if isinstance(agent, AgentExecutor):
             runnable = agent

src/backend/base/langflow/base/agents/callback.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any
 from uuid import UUID
 
 from langchain.callbacks.base import AsyncCallbackHandler
@@ -15,14 +15,14 @@ class AgentAsyncHandler(AsyncCallbackHandler):
 
     async def on_tool_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: dict[str, Any],
         input_str: str,
         *,
         run_id: UUID,
         parent_run_id: UUID | None = None,
-        tags: List[str] | None = None,
+        tags: list[str] | None = None,
-        metadata: Dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
-        inputs: Dict[str, Any] | None = None,
+        inputs: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> None:
         if self.log_function is None:
@@ -62,7 +62,7 @@ class AgentAsyncHandler(AsyncCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: UUID | None = None,
-        tags: List[str] | None = None,
+        tags: list[str] | None = None,
         **kwargs: Any,
     ) -> None:
         if self.log_function is None:
@@ -85,7 +85,7 @@ class AgentAsyncHandler(AsyncCallbackHandler):
         *,
         run_id: UUID,
         parent_run_id: UUID | None = None,
-        tags: List[str] | None = None,
+        tags: list[str] | None = None,
         **kwargs: Any,
     ) -> None:
         if self.log_function is None:

src/backend/base/langflow/base/agents/crewai/crew.py

@@ -1,4 +1,5 @@
-from typing import Callable, List, Tuple, Union, cast
+from collections.abc import Callable
+from typing import cast
 
 from crewai import Agent, Crew, Process, Task  # type: ignore
 from crewai.task import TaskOutput  # type: ignore
@@ -62,7 +63,7 @@ class BaseCrewComponent(Component):
     def get_step_callback(
         self,
     ) -> Callable:
-        def step_callback(agent_output: Union[AgentFinish, List[Tuple[AgentAction, str]]]):
+        def step_callback(agent_output: AgentFinish | list[tuple[AgentAction, str]]):
             _id = self._vertex.id if self._vertex else self.display_name
             if isinstance(agent_output, AgentFinish):
                 messages = agent_output.messages

src/backend/base/langflow/base/agents/utils.py

@@ -1,4 +1,5 @@
-from typing import Any, Callable, Dict, List, Optional, Sequence, Union
+from collections.abc import Callable, Sequence
+from typing import Any
 
 from langchain.agents import (
     create_json_chat_agent,
@@ -24,17 +25,17 @@ class AgentSpec(BaseModel):
             BaseLanguageModel,
             Sequence[BaseTool],
             BasePromptTemplate | ChatPromptTemplate,
-            Optional[Callable[[List[BaseTool]], str]],
+            Callable[[list[BaseTool]], str] | None,
-            Optional[Union[bool, List[str]]],
+            bool | list[str] | None,
         ],
         Any,
     ]
-    prompt: Optional[Any] = None
+    prompt: Any | None = None
-    fields: List[str]
+    fields: list[str]
-    hub_repo: Optional[str] = None
+    hub_repo: str | None = None
 
 
-def data_to_messages(data: List[Data]) -> List[BaseMessage]:
+def data_to_messages(data: list[Data]) -> list[BaseMessage]:
     """
     Convert a list of data to a list of messages.
 
@@ -51,9 +52,9 @@ def validate_and_create_xml_agent(
     llm: BaseLanguageModel,
     tools: Sequence[BaseTool],
     prompt: BasePromptTemplate,
-    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    tools_renderer: Callable[[list[BaseTool]], str] = render_text_description,
     *,
-    stop_sequence: Union[bool, List[str]] = True,
+    stop_sequence: bool | list[str] = True,
 ):
     return create_xml_agent(
         llm=llm,
@@ -68,9 +69,9 @@ def validate_and_create_openai_tools_agent(
     llm: BaseLanguageModel,
     tools: Sequence[BaseTool],
     prompt: ChatPromptTemplate,
-    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    tools_renderer: Callable[[list[BaseTool]], str] = render_text_description,
     *,
-    stop_sequence: Union[bool, List[str]] = True,
+    stop_sequence: bool | list[str] = True,
 ):
     return create_openai_tools_agent(
         llm=llm,
@@ -83,9 +84,9 @@ def validate_and_create_tool_calling_agent(
     llm: BaseLanguageModel,
     tools: Sequence[BaseTool],
     prompt: ChatPromptTemplate,
-    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    tools_renderer: Callable[[list[BaseTool]], str] = render_text_description,
     *,
-    stop_sequence: Union[bool, List[str]] = True,
+    stop_sequence: bool | list[str] = True,
 ):
     return create_tool_calling_agent(
         llm=llm,
@@ -98,9 +99,9 @@ def validate_and_create_json_chat_agent(
     llm: BaseLanguageModel,
     tools: Sequence[BaseTool],
     prompt: ChatPromptTemplate,
-    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    tools_renderer: Callable[[list[BaseTool]], str] = render_text_description,
     *,
-    stop_sequence: Union[bool, List[str]] = True,
+    stop_sequence: bool | list[str] = True,
 ):
     return create_json_chat_agent(
         llm=llm,
@@ -111,7 +112,7 @@ def validate_and_create_json_chat_agent(
     )
 
 
-AGENTS: Dict[str, AgentSpec] = {
+AGENTS: dict[str, AgentSpec] = {
     "Tool Calling Agent": AgentSpec(
         func=validate_and_create_tool_calling_agent,
         prompt=None,

src/backend/base/langflow/base/curl/parse.py

@@ -153,13 +153,13 @@ def parse_context(curl_command):
     # proxy_auth = parsed_args.proxy_user
     if parsed_args.proxy and parsed_args.proxy_user:
         proxies = {
-            "http": "http://{}@{}/".format(parsed_args.proxy_user, parsed_args.proxy),
+            "http": f"http://{parsed_args.proxy_user}@{parsed_args.proxy}/",
-            "https": "http://{}@{}/".format(parsed_args.proxy_user, parsed_args.proxy),
+            "https": f"http://{parsed_args.proxy_user}@{parsed_args.proxy}/",
         }
     elif parsed_args.proxy:
         proxies = {
-            "http": "http://{}/".format(parsed_args.proxy),
+            "http": f"http://{parsed_args.proxy}/",
-            "https": "http://{}/".format(parsed_args.proxy),
+            "https": f"http://{parsed_args.proxy}/",
         }
 
     return ParsedContext(

src/backend/base/langflow/base/data/utils.py

@@ -1,8 +1,8 @@
 import unicodedata
 import xml.etree.ElementTree as ET
+from collections.abc import Callable
 from concurrent import futures
 from pathlib import Path
-from typing import Callable, List, Optional
 
 import chardet
 import orjson
@@ -49,8 +49,8 @@ def retrieve_file_paths(
     load_hidden: bool,
     recursive: bool,
     depth: int,
-    types: List[str] = TEXT_FILE_TYPES,
+    types: list[str] = TEXT_FILE_TYPES,
-) -> List[str]:
+) -> list[str]:
     path_obj = Path(path)
     if not path_obj.exists() or not path_obj.is_dir():
         raise ValueError(f"Path {path} must exist and be a directory.")
@@ -75,7 +75,7 @@ def retrieve_file_paths(
     return file_paths
 
 
-def partition_file_to_data(file_path: str, silent_errors: bool) -> Optional[Data]:
+def partition_file_to_data(file_path: str, silent_errors: bool) -> Data | None:
     # Use the partition function to load the file
     from unstructured.partition.auto import partition  # type: ignore
 
@@ -103,7 +103,7 @@ def read_text_file(file_path: str) -> str:
         if encoding in ["Windows-1252", "Windows-1254", "MacRoman"]:
             encoding = "utf-8"
 
-    with open(file_path, "r", encoding=encoding) as f:
+    with open(file_path, encoding=encoding) as f:
         return f.read()
 
 
@@ -122,7 +122,7 @@ def parse_pdf_to_text(file_path: str) -> str:
         return "\n\n".join([page.extract_text() for page in reader.pages])
 
 
-def parse_text_file_to_data(file_path: str, silent_errors: bool) -> Optional[Data]:
+def parse_text_file_to_data(file_path: str, silent_errors: bool) -> Data | None:
     try:
         if file_path.endswith(".pdf"):
             text = parse_pdf_to_text(file_path)
@@ -171,11 +171,11 @@ def parse_text_file_to_data(file_path: str, silent_errors: bool) -> Optional[Dat
 
 
 def parallel_load_data(
-    file_paths: List[str],
+    file_paths: list[str],
     silent_errors: bool,
     max_concurrency: int,
     load_function: Callable = parse_text_file_to_data,
-) -> List[Optional[Data]]:
+) -> list[Data | None]:
     with futures.ThreadPoolExecutor(max_workers=max_concurrency) as executor:
         loaded_files = executor.map(
             lambda file_path: load_function(file_path, silent_errors),

src/backend/base/langflow/base/flow_processing/utils.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from loguru import logger
 
 from langflow.graph.schema import ResultData, RunOutputs
@@ -7,7 +5,7 @@ from langflow.schema import Data
 from langflow.schema.message import Message
 
 
-def build_data_from_run_outputs(run_outputs: RunOutputs) -> List[Data]:
+def build_data_from_run_outputs(run_outputs: RunOutputs) -> list[Data]:
     """
     Build a list of data from the given RunOutputs.
 
@@ -27,7 +25,7 @@ def build_data_from_run_outputs(run_outputs: RunOutputs) -> List[Data]:
     return data
 
 
-def build_data_from_result_data(result_data: ResultData, get_final_results_only: bool = True) -> List[Data]:
+def build_data_from_result_data(result_data: ResultData, get_final_results_only: bool = True) -> list[Data]:
     """
     Build a list of data from the given ResultData.
 
@@ -80,7 +78,7 @@ def build_data_from_result_data(result_data: ResultData, get_final_results_only:
     return data
 
 
-def format_flow_output_data(data: List[Data]) -> str:
+def format_flow_output_data(data: list[Data]) -> str:
     """
     Format the flow output data into a string.
 

src/backend/base/langflow/base/io/chat.py

@@ -1,4 +1,4 @@
-from typing import AsyncIterator, Iterator, Optional, Union
+from collections.abc import AsyncIterator, Iterator
 
 from langflow.custom import Component
 from langflow.memory import store_message
@@ -54,7 +54,7 @@ class ChatComponent(Component):
 
     def _stream_message(self, message: Message, message_id: str) -> str:
         iterator = message.text
-        if not isinstance(iterator, (AsyncIterator, Iterator)):
+        if not isinstance(iterator, AsyncIterator | Iterator):
             raise ValueError("The message must be an iterator or an async iterator.")
 
         if isinstance(iterator, AsyncIterator):
@@ -68,12 +68,12 @@ class ChatComponent(Component):
 
     def build_with_data(
         self,
-        sender: Optional[str] = "User",
+        sender: str | None = "User",
-        sender_name: Optional[str] = "User",
+        sender_name: str | None = "User",
-        input_value: Optional[Union[str, Data, Message]] = None,
+        input_value: str | Data | Message | None = None,
-        files: Optional[list[str]] = None,
+        files: list[str] | None = None,
-        session_id: Optional[str] = None,
+        session_id: str | None = None,
-        return_message: Optional[bool] = False,
+        return_message: bool | None = False,
     ) -> Message:
         message: Message | None = None
 

src/backend/base/langflow/base/langchain_utilities/model.py

@@ -1,5 +1,5 @@
 from abc import abstractmethod
-from typing import Sequence, Union
+from collections.abc import Sequence
 
 from langflow.custom import Component
 from langflow.field_typing import Tool
@@ -24,7 +24,7 @@ class LCToolComponent(Component):
                 raise ValueError(f"Method '{method_name}' must be defined.")
 
     @abstractmethod
-    def run_model(self) -> Union[Data, list[Data]]:
+    def run_model(self) -> Data | list[Data]:
         """
         Run model and return the output.
         """

src/backend/base/langflow/base/memory/memory.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from langflow.custom import CustomComponent
 from langflow.schema import Data
 from langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER
@@ -45,6 +43,6 @@ class BaseMemoryComponent(CustomComponent):
         raise NotImplementedError
 
     def add_message(
-        self, sender: str, sender_name: str, text: str, session_id: str, metadata: Optional[dict] = None, **kwargs
+        self, sender: str, sender_name: str, text: str, session_id: str, metadata: dict | None = None, **kwargs
     ):
         raise NotImplementedError

src/backend/base/langflow/base/models/model.py

@@ -1,7 +1,6 @@
 import json
 import warnings
 from abc import abstractmethod
-from typing import List, Optional, Union
 
 from langchain_core.language_models.llms import LLM
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
@@ -20,7 +19,7 @@ class LCModelComponent(Component):
     description: str = "Model Description"
     trace_type = "llm"
 
-    _base_inputs: List[InputTypes] = [
+    _base_inputs: list[InputTypes] = [
         MessageInput(name="input_value", display_name="Input"),
         MessageTextInput(
             name="system_message",
@@ -138,9 +137,9 @@ class LCModelComponent(Component):
         runnable: LanguageModel,
         stream: bool,
         input_value: str | Message,
-        system_message: Optional[str] = None,
+        system_message: str | None = None,
     ):
-        messages: list[Union[BaseMessage]] = []
+        messages: list[BaseMessage] = []
         if not input_value and not system_message:
             raise ValueError("The message you want to send to the model is empty.")
         system_message_added = False
@@ -161,7 +160,7 @@ class LCModelComponent(Component):
 
         if system_message and not system_message_added:
             messages.append(SystemMessage(content=system_message))
-        inputs: Union[list, dict] = messages or {}
+        inputs: list | dict = messages or {}
         try:
             runnable = runnable.with_config(  # type: ignore
                 {

src/backend/base/langflow/base/prompts/api_utils.py

@@ -1,5 +1,5 @@
 from collections import defaultdict
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 from fastapi import HTTPException
 from langchain_core.prompts import PromptTemplate
@@ -198,7 +198,7 @@ def update_input_variables_field(input_variables, template):
 
 
 def process_prompt_template(
-    template: str, name: str, custom_fields: Optional[Dict[str, List[str]]], frontend_node_template: Dict[str, Any]
+    template: str, name: str, custom_fields: dict[str, list[str]] | None, frontend_node_template: dict[str, Any]
 ):
     """Process and validate prompt template, update template and custom fields."""
     # Validate the prompt template and extract input variables

src/backend/base/langflow/base/tools/flow_tool.py

@@ -1,5 +1,5 @@
 import warnings
-from typing import Any, List, Optional, Type
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langchain_core.tools import BaseTool, ToolException
@@ -15,10 +15,10 @@ from langflow.utils.async_helpers import run_until_complete
 class FlowTool(BaseTool):
     name: str
     description: str
-    graph: Optional[Graph] = None
+    graph: Graph | None = None
-    flow_id: Optional[str] = None
+    flow_id: str | None = None
-    user_id: Optional[str] = None
+    user_id: str | None = None
-    inputs: List["Vertex"] = []
+    inputs: list["Vertex"] = []
     get_final_results_only: bool = True
 
     @property
@@ -26,7 +26,7 @@ class FlowTool(BaseTool):
         schema = self.get_input_schema()
         return schema.schema()["properties"]
 
-    def get_input_schema(self, config: Optional[RunnableConfig] = None) -> Type[BaseModel]:
+    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
         """The tool's input schema."""
         if self.args_schema is not None:
             return self.args_schema
@@ -68,7 +68,7 @@ class FlowTool(BaseTool):
                     data.extend(build_data_from_result_data(output, get_final_results_only=self.get_final_results_only))
         return format_flow_output_data(data)
 
-    def validate_inputs(self, args_names: List[dict[str, str]], args: Any, kwargs: Any):
+    def validate_inputs(self, args_names: list[dict[str, str]], args: Any, kwargs: Any):
         """Validate the inputs."""
 
         if len(args) > 0 and len(args) != len(args_names):

src/backend/base/langflow/base/vectorstores/model.py

@@ -1,6 +1,6 @@
 from abc import ABC, ABCMeta, abstractmethod
 from functools import wraps
-from typing import List, cast
+from typing import cast
 
 from langchain_core.documents import Document
 from loguru import logger
@@ -94,7 +94,7 @@ class LCVectorStoreComponent(Component, ABC, metaclass=EnforceCacheDecoratorMeta
         vector_store: VectorStore,
         k=10,
         **kwargs,
-    ) -> List[Data]:
+    ) -> list[Data]:
         """
         Search for data in the vector store based on the input value and search type.
 
@@ -110,7 +110,7 @@ class LCVectorStoreComponent(Component, ABC, metaclass=EnforceCacheDecoratorMeta
             ValueError: If invalid inputs are provided.
         """
 
-        docs: List[Document] = []
+        docs: list[Document] = []
         if input_value and isinstance(input_value, str) and hasattr(vector_store, "search"):
             docs = vector_store.search(query=input_value, search_type=search_type.lower(), k=k, **kwargs)
         else:
@@ -140,7 +140,7 @@ class LCVectorStoreComponent(Component, ABC, metaclass=EnforceCacheDecoratorMeta
         else:
             raise ValueError(f"Vector Store {vector_store.__class__.__name__} does not have an as_retriever method.")
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         """
         Search for documents in the vector store.
         """

src/backend/base/langflow/components/Notion/add_content_to_page.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Union
+from typing import Any
 
 import requests
 from bs4 import BeautifulSoup
@@ -54,7 +54,7 @@ class AddContentToPage(LCToolComponent):
             args_schema=self.AddContentToPageSchema,
         )
 
-    def _add_content_to_page(self, markdown_text: str, block_id: str) -> Union[Dict[str, Any], str]:
+    def _add_content_to_page(self, markdown_text: str, block_id: str) -> dict[str, Any] | str:
         try:
             html_text = markdown(markdown_text)
             soup = BeautifulSoup(html_text, "html.parser")
@@ -209,7 +209,7 @@ class AddContentToPage(LCToolComponent):
 
         return blocks
 
-    def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:
+    def create_block(self, block_type: str, content: str, **kwargs) -> dict[str, Any]:
         block: dict[str, Any] = {
             "object": "block",
             "type": block_type,

src/backend/base/langflow/components/Notion/create_page.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Union
+from typing import Any
 
 import requests
 from langchain.tools import StructuredTool
@@ -60,7 +60,7 @@ class NotionPageCreator(LCToolComponent):
             args_schema=self.NotionPageCreatorSchema,
         )
 
-    def _create_notion_page(self, database_id: str, properties_json: str) -> Union[Dict[str, Any], str]:
+    def _create_notion_page(self, database_id: str, properties_json: str) -> dict[str, Any] | str:
         if not database_id or not properties_json:
             return "Invalid input. Please provide 'database_id' and 'properties_json'."
 

src/backend/base/langflow/components/Notion/list_database_properties.py

@@ -1,5 +1,3 @@
-from typing import Dict, Union
-
 import requests
 from langchain.tools import StructuredTool
 from pydantic import BaseModel, Field
@@ -50,7 +48,7 @@ class NotionDatabaseProperties(LCToolComponent):
             args_schema=self.NotionDatabasePropertiesSchema,
         )
 
-    def _fetch_database_properties(self, database_id: str) -> Union[Dict, str]:
+    def _fetch_database_properties(self, database_id: str) -> dict | str:
         url = f"https://api.notion.com/v1/databases/{database_id}"
         headers = {
             "Authorization": f"Bearer {self.notion_secret}",

src/backend/base/langflow/components/Notion/list_pages.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 import requests
 from langchain.tools import StructuredTool
@@ -43,12 +43,12 @@ class NotionListPages(LCToolComponent):
 
     class NotionListPagesSchema(BaseModel):
         database_id: str = Field(..., description="The ID of the Notion database to query.")
-        query_json: Optional[str] = Field(
+        query_json: str | None = Field(
             default="",
             description="A JSON string containing the filters and sorts for querying the database. Leave empty for no filters or sorts.",
         )
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         result = self._query_notion_database(self.database_id, self.query_json)
 
         if isinstance(result, str):
@@ -89,7 +89,7 @@ class NotionListPages(LCToolComponent):
             args_schema=self.NotionListPagesSchema,
         )
 
-    def _query_notion_database(self, database_id: str, query_json: Optional[str] = None) -> List[Dict[str, Any]] | str:
+    def _query_notion_database(self, database_id: str, query_json: str | None = None) -> list[dict[str, Any]] | str:
         url = f"https://api.notion.com/v1/databases/{database_id}/query"
         headers = {
             "Authorization": f"Bearer {self.notion_secret}",

src/backend/base/langflow/components/Notion/list_users.py

@@ -1,5 +1,3 @@
-from typing import Dict, List
-
 import requests
 from langchain.tools import StructuredTool
 from pydantic import BaseModel
@@ -28,7 +26,7 @@ class NotionUserList(LCToolComponent):
     class NotionUserListSchema(BaseModel):
         pass
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         users = self._list_users()
         records = []
         combined_text = ""
@@ -53,7 +51,7 @@ class NotionUserList(LCToolComponent):
             args_schema=self.NotionUserListSchema,
         )
 
-    def _list_users(self) -> List[Dict]:
+    def _list_users(self) -> list[dict]:
         url = "https://api.notion.com/v1/users"
         headers = {
             "Authorization": f"Bearer {self.notion_secret}",

src/backend/base/langflow/components/Notion/search.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any
 
 import requests
 from langchain.tools import StructuredTool
@@ -49,7 +49,7 @@ class NotionSearch(LCToolComponent):
         filter_value: str = Field(default="page", description="Filter type: 'page' or 'database'.")
         sort_direction: str = Field(default="descending", description="Sort direction: 'ascending' or 'descending'.")
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         results = self._search_notion(self.query, self.filter_value, self.sort_direction)
         records = []
         combined_text = f"Results found: {len(results)}\n\n"
@@ -89,7 +89,7 @@ class NotionSearch(LCToolComponent):
 
     def _search_notion(
         self, query: str, filter_value: str = "page", sort_direction: str = "descending"
-    ) -> List[Dict[str, Any]]:
+    ) -> list[dict[str, Any]]:
         url = "https://api.notion.com/v1/search"
         headers = {
             "Authorization": f"Bearer {self.notion_secret}",

src/backend/base/langflow/components/Notion/update_page_property.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Union
+from typing import Any
 
 import requests
 from langchain.tools import StructuredTool
@@ -39,7 +39,7 @@ class NotionPageUpdate(LCToolComponent):
 
     class NotionPageUpdateSchema(BaseModel):
         page_id: str = Field(..., description="The ID of the Notion page to update.")
-        properties: Union[str, Dict[str, Any]] = Field(
+        properties: str | dict[str, Any] = Field(
             ..., description="The properties to update on the page (as a JSON string or a dictionary)."
         )
 
@@ -63,7 +63,7 @@ class NotionPageUpdate(LCToolComponent):
             args_schema=self.NotionPageUpdateSchema,
         )
 
-    def _update_notion_page(self, page_id: str, properties: Union[str, Dict[str, Any]]) -> Union[Dict[str, Any], str]:
+    def _update_notion_page(self, page_id: str, properties: str | dict[str, Any]) -> dict[str, Any] | str:
         url = f"https://api.notion.com/v1/pages/{page_id}"
         headers = {
             "Authorization": f"Bearer {self.notion_secret}",

src/backend/base/langflow/components/agents/JsonAgent.py

@@ -22,7 +22,7 @@ class JsonAgentComponent(LCAgentComponent):
 
     def build_agent(self) -> AgentExecutor:
         if self.path.endswith("yaml") or self.path.endswith("yml"):
-            with open(self.path, "r") as file:
+            with open(self.path) as file:
                 yaml_dict = yaml.load(file, Loader=yaml.FullLoader)
             spec = JsonSpec(dict_=yaml_dict)
         else:

src/backend/base/langflow/components/agents/OpenAIToolsAgent.py

@@ -1,5 +1,3 @@
-from typing import List, Optional
-
 from langchain.agents import create_openai_tools_agent
 from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate
 
@@ -35,7 +33,7 @@ class OpenAIToolsAgentComponent(LCToolsAgentComponent):
         DataInput(name="chat_history", display_name="Chat History", is_list=True, advanced=True),
     ]
 
-    def get_chat_history_data(self) -> Optional[List[Data]]:
+    def get_chat_history_data(self) -> list[Data] | None:
         return self.chat_history
 
     def create_agent_runnable(self):

src/backend/base/langflow/components/agents/OpenAPIAgent.py

@@ -24,7 +24,7 @@ class OpenAPIAgentComponent(LCAgentComponent):
 
     def build_agent(self) -> AgentExecutor:
         if self.path.endswith("yaml") or self.path.endswith("yml"):
-            with open(self.path, "r") as file:
+            with open(self.path) as file:
                 yaml_dict = yaml.load(file, Loader=yaml.FullLoader)
             spec = JsonSpec(dict_=yaml_dict)
         else:

src/backend/base/langflow/components/agents/ToolCallingAgent.py

@@ -1,5 +1,3 @@
-from typing import List, Optional
-
 from langchain.agents import create_tool_calling_agent
 from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate
 
@@ -30,7 +28,7 @@ class ToolCallingAgentComponent(LCToolsAgentComponent):
         DataInput(name="chat_history", display_name="Chat History", is_list=True, advanced=True),
     ]
 
-    def get_chat_history_data(self) -> Optional[List[Data]]:
+    def get_chat_history_data(self) -> list[Data] | None:
         return self.chat_history
 
     def create_agent_runnable(self):

src/backend/base/langflow/components/agents/XMLAgent.py

@@ -1,5 +1,3 @@
-from typing import List, Optional
-
 from langchain.agents import create_xml_agent
 from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate
 
@@ -52,7 +50,7 @@ Question: {input}
         ),
     ]
 
-    def get_chat_history_data(self) -> Optional[List[Data]]:
+    def get_chat_history_data(self) -> list[Data] | None:
         return self.chat_history
 
     def create_agent_runnable(self):

src/backend/base/langflow/components/astra_assistants/run.py

@@ -1,4 +1,4 @@
-from typing import Any, Optional
+from typing import Any
 
 from astra_assistants import patch  # type: ignore
 from openai import OpenAI
@@ -20,7 +20,7 @@ class AssistantsRun(Component):
         self,
         build_config: dotdict,
         field_value: Any,
-        field_name: Optional[str] = None,
+        field_name: str | None = None,
     ):
         if field_name == "thread_id":
             if field_value is None:

src/backend/base/langflow/components/data/APIRequest.py

@@ -1,6 +1,6 @@
 import asyncio
 import json
-from typing import Any, List, Optional
+from typing import Any
 from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
 
 import httpx
@@ -104,8 +104,8 @@ class APIRequestComponent(Component):
         client: httpx.AsyncClient,
         method: str,
         url: str,
-        headers: Optional[dict] = None,
+        headers: dict | None = None,
-        body: Optional[dict] = None,
+        body: dict | None = None,
         timeout: int = 5,
     ) -> Data:
         method = method.upper()
@@ -162,7 +162,7 @@ class APIRequestComponent(Component):
         url_parts[4] = urlencode(query)
         return urlunparse(url_parts)
 
-    async def make_requests(self) -> List[Data]:
+    async def make_requests(self) -> list[Data]:
         method = self.method
         urls = [url.strip() for url in self.urls if url.strip()]
         curl = self.curl

src/backend/base/langflow/components/data/Directory.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langflow.base.data.utils import parallel_load_data, parse_text_file_to_data, retrieve_file_paths
 from langflow.custom import Component
 from langflow.io import BoolInput, IntInput, MessageTextInput
@@ -68,7 +66,7 @@ class DirectoryComponent(Component):
         Output(display_name="Data", name="data", method="load_directory"),
     ]
 
-    def load_directory(self) -> List[Data]:
+    def load_directory(self) -> list[Data]:
         path = self.path
         types = self.types or []  # self.types is already a list due to is_list=True
         depth = self.depth

src/backend/base/langflow/components/data/Gmail.py

@@ -1,8 +1,9 @@
 import base64
 import json
 import re
+from collections.abc import Iterator
 from json.decoder import JSONDecodeError
-from typing import Any, Iterator, List, Optional
+from typing import Any
 
 from google.auth.exceptions import RefreshError
 from google.oauth2.credentials import Credentials
@@ -29,7 +30,7 @@ class GmailLoaderComponent(Component):
             display_name="JSON String of the Service Account Token",
             info="JSON string containing OAuth 2.0 access token information for service account access",
             required=True,
-            value=str("""{
+            value="""{
                 "account": "",
                 "client_id": "",
                 "client_secret": "",
@@ -41,7 +42,7 @@ class GmailLoaderComponent(Component):
                 "token": "",
                 "token_uri": "https://oauth2.googleapis.com/token",
                 "universe_domain": "googleapis.com"
-            }"""),
+            }""",
         ),
         MessageTextInput(
             name="label_ids",
@@ -66,7 +67,7 @@ class GmailLoaderComponent(Component):
     def load_emails(self) -> Data:
         class CustomGMailLoader(GMailLoader):
             def __init__(
-                self, creds: Any, n: int = 100, label_ids: Optional[List[str]] = None, raise_error: bool = False
+                self, creds: Any, n: int = 100, label_ids: list[str] | None = None, raise_error: bool = False
             ) -> None:
                 super().__init__(creds, n, raise_error)
                 self.label_ids = label_ids if label_ids is not None else ["SENT"]

src/backend/base/langflow/components/data/GoogleDrive.py

@@ -1,6 +1,5 @@
 import json
 from json.decoder import JSONDecodeError
-from typing import Optional
 
 from google.auth.exceptions import RefreshError
 from google.oauth2.credentials import Credentials
@@ -37,7 +36,7 @@ class GoogleDriveComponent(Component):
 
     def load_documents(self) -> Data:
         class CustomGoogleDriveLoader(GoogleDriveLoader):
-            creds: Optional[Credentials] = None
+            creds: Credentials | None = None
             """Credentials object to be passed directly."""
 
             def _load_credentials(self):

src/backend/base/langflow/components/data/GoogleDriveSearch.py

@@ -1,5 +1,4 @@
 import json
-from typing import List
 
 from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
@@ -146,13 +145,13 @@ class GoogleDriveSearchComponent(Component):
 
         return {"doc_urls": doc_urls, "doc_ids": doc_ids, "doc_titles_urls": doc_titles_urls, "doc_titles": doc_titles}
 
-    def search_doc_ids(self) -> List[str]:
+    def search_doc_ids(self) -> list[str]:
         return self.search_files()["doc_ids"]
 
-    def search_doc_urls(self) -> List[str]:
+    def search_doc_urls(self) -> list[str]:
         return self.search_files()["doc_urls"]
 
-    def search_doc_titles(self) -> List[str]:
+    def search_doc_titles(self) -> list[str]:
         return self.search_files()["doc_titles"]
 
     def search_data(self) -> Data:

src/backend/base/langflow/components/deactivated/DocumentsToData.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_core.documents import Document
 
 from langflow.custom import CustomComponent
@@ -16,7 +14,7 @@ class DocumentsToDataComponent(CustomComponent):
         "documents": {"display_name": "Documents"},
     }
 
-    def build(self, documents: List[Document]) -> List[Data]:
+    def build(self, documents: list[Document]) -> list[Data]:
         if isinstance(documents, Document):
             documents = [documents]
         data = [Data.from_document(document) for document in documents]

src/backend/base/langflow/components/deactivated/ListFlows.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langflow.custom import CustomComponent
 from langflow.schema import Data
 
@@ -16,7 +14,7 @@ class ListFlowsComponent(CustomComponent):
 
     def build(
         self,
-    ) -> List[Data]:
+    ) -> list[Data]:
         flows = self.list_flows()
         self.status = flows
         return flows

src/backend/base/langflow/components/deactivated/Message.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from langflow.custom import CustomComponent
 from langflow.schema.message import Message
 from langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER
@@ -28,8 +26,8 @@ class MessageComponent(CustomComponent):
     def build(
         self,
         sender: str = MESSAGE_SENDER_USER,
-        sender_name: Optional[str] = None,
+        sender_name: str | None = None,
-        session_id: Optional[str] = None,
+        session_id: str | None = None,
         text: str = "",
     ) -> Message:
         message = Message(

src/backend/base/langflow/components/deactivated/SplitText.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_text_splitters import CharacterTextSplitter
 
 from langflow.custom import Component
@@ -52,7 +50,7 @@ class SplitTextComponent(Component):
             data.append(Data(text=doc.page_content, data=doc.metadata))
         return data
 
-    def split_text(self) -> List[Data]:
+    def split_text(self) -> list[Data]:
         separator = unescape_string(self.separator)
 
         documents = []

src/backend/base/langflow/components/deactivated/SubFlow.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import Any
 
 from loguru import logger
 
@@ -22,11 +22,11 @@ class SubFlowComponent(CustomComponent):
     field_order = ["flow_name"]
     name = "SubFlow"
 
-    def get_flow_names(self) -> List[str]:
+    def get_flow_names(self) -> list[str]:
         flow_datas = self.list_flows()
         return [flow_data.data["name"] for flow_data in flow_datas]
 
-    def get_flow(self, flow_name: str) -> Optional[Data]:
+    def get_flow(self, flow_name: str) -> Data | None:
         flow_datas = self.list_flows()
         for flow_data in flow_datas:
             if flow_data.data["name"] == flow_name:
@@ -56,7 +56,7 @@ class SubFlowComponent(CustomComponent):
 
         return build_config
 
-    def add_inputs_to_build_config(self, inputs: List[Vertex], build_config: dotdict):
+    def add_inputs_to_build_config(self, inputs: list[Vertex], build_config: dotdict):
         new_fields: list[Input] = []
         for vertex in inputs:
             field = Input(
@@ -96,9 +96,9 @@ class SubFlowComponent(CustomComponent):
             },
         }
 
-    async def build(self, flow_name: str, get_final_results_only: bool = True, **kwargs) -> List[Data]:
+    async def build(self, flow_name: str, get_final_results_only: bool = True, **kwargs) -> list[Data]:
         tweaks = {key: {"input_value": value} for key, value in kwargs.items()}
-        run_outputs: List[Optional[RunOutputs]] = await self.run_flow(
+        run_outputs: list[RunOutputs | None] = await self.run_flow(
             tweaks=tweaks,
             flow_name=flow_name,
         )

src/backend/base/langflow/components/documentloaders/Confluence.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.document_loaders import ConfluenceLoader
 from langchain_community.document_loaders.confluence import ContentFormat
 
@@ -79,7 +77,7 @@ class ConfluenceComponent(Component):
         )
         return loader
 
-    def load_documents(self) -> List[Data]:
+    def load_documents(self) -> list[Data]:
         confluence = self.build_confluence()
         documents = confluence.load()
         data = [Data.from_document(doc) for doc in documents]  # Using the from_document method of Data

src/backend/base/langflow/components/documentloaders/GitLoader.py

@@ -1,6 +1,5 @@
 import re
 from pathlib import Path
-from typing import List
 
 from langchain_community.document_loaders.git import GitLoader
 
@@ -109,7 +108,7 @@ class GitLoaderComponent(Component):
         )
         return loader
 
-    def load_documents(self) -> List[Data]:
+    def load_documents(self) -> list[Data]:
         gitloader = self.build_gitloader()
         documents = list(gitloader.lazy_load())
         data = [Data.from_document(doc) for doc in documents]

src/backend/base/langflow/components/documentloaders/Unstructured.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_unstructured import UnstructuredLoader
 
 from langflow.custom import Component
@@ -47,7 +45,7 @@ class UnstructuredComponent(Component):
 
         return loader
 
-    def load_documents(self) -> List[Data]:
+    def load_documents(self) -> list[Data]:
         unstructured = self.build_unstructured()
 
         documents = unstructured.load()

src/backend/base/langflow/components/embeddings/EmbeddingSimilarity.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import numpy as np
 
 from langflow.custom import Component
@@ -33,7 +31,7 @@ class EmbeddingSimilarityComponent(Component):
     ]
 
     def compute_similarity(self) -> Data:
-        embedding_vectors: List[Data] = self.embedding_vectors
+        embedding_vectors: list[Data] = self.embedding_vectors
 
         # Assert that the list contains exactly two Data objects
         assert len(embedding_vectors) == 2, "Exactly two embedding vectors are required."

src/backend/base/langflow/components/embeddings/GoogleGenerativeAIEmbeddings.py

@@ -1,17 +1,11 @@
 # from langflow.field_typing import Data
-from typing import List, Optional
-
 import numpy as np
 
 # TODO: remove ignore once the google package is published with types
-from google.ai.generativelanguage_v1beta.types import (
+from google.ai.generativelanguage_v1beta.types import BatchEmbedContentsRequest
-    BatchEmbedContentsRequest,
-)
 from langchain_core.embeddings import Embeddings
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_google_genai._common import (
+from langchain_google_genai._common import GoogleGenerativeAIError
-    GoogleGenerativeAIError,
-)
 
 from langflow.custom import Component
 from langflow.io import MessageTextInput, Output, SecretStrInput
@@ -43,13 +37,13 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
 
             def embed_documents(
                 self,
-                texts: List[str],
+                texts: list[str],
                 *,
                 batch_size: int = 100,
-                task_type: Optional[str] = None,
+                task_type: str | None = None,
-                titles: Optional[List[str]] = None,
+                titles: list[str] | None = None,
-                output_dimensionality: Optional[int] = 1536,
+                output_dimensionality: int | None = 1536,
-            ) -> List[List[float]]:
+            ) -> list[list[float]]:
                 """Embed a list of strings. Google Generative AI currently
                 sets a max batch size of 100 strings.
 
@@ -64,7 +58,7 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
                 Returns:
                     List of embeddings, one for each text.
                 """
-                embeddings: List[List[float]] = []
+                embeddings: list[list[float]] = []
                 batch_start_index = 0
                 for batch in GoogleGenerativeAIEmbeddings._prepare_batches(texts, batch_size):
                     if titles:
@@ -95,10 +89,10 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
             def embed_query(
                 self,
                 text: str,
-                task_type: Optional[str] = None,
+                task_type: str | None = None,
-                title: Optional[str] = None,
+                title: str | None = None,
-                output_dimensionality: Optional[int] = 1536,
+                output_dimensionality: int | None = 1536,
-            ) -> List[float]:
+            ) -> list[float]:
                 """Embed a text.
 
                 Args:

src/backend/base/langflow/components/embeddings/util/AIMLEmbeddingsImpl.py

@@ -1,6 +1,5 @@
 import concurrent.futures
 import json
-from typing import List
 
 import httpx
 from langchain_core.pydantic_v1 import BaseModel, SecretStr
@@ -15,7 +14,7 @@ class AIMLEmbeddingsImpl(BaseModel, Embeddings):
     api_key: SecretStr
     model: str
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         embeddings = [None] * len(texts)
         headers = {
             "Content-Type": "application/json",
@@ -58,5 +57,5 @@ class AIMLEmbeddingsImpl(BaseModel, Embeddings):
         result_data = response.json()
         return result_data
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         return self.embed_documents([text])[0]

src/backend/base/langflow/components/helpers/FilterData.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langflow.custom import Component
 from langflow.io import DataInput, MessageTextInput, Output
 from langflow.schema import Data
@@ -31,7 +29,7 @@ class FilterDataComponent(Component):
     ]
 
     def filter_data(self) -> Data:
-        filter_criteria: List[str] = self.filter_criteria
+        filter_criteria: list[str] = self.filter_criteria
         data = self.data.data if isinstance(self.data, Data) else {}
 
         # Filter the data

src/backend/base/langflow/components/helpers/IDGenerator.py

@@ -1,5 +1,5 @@
 import uuid
-from typing import Any, Optional
+from typing import Any
 
 from langflow.custom import Component
 from langflow.io import MessageTextInput, Output
@@ -26,7 +26,7 @@ class IDGeneratorComponent(Component):
         Output(display_name="ID", name="id", method="generate_id"),
     ]
 
-    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: Optional[str] = None):
+    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
         if field_name == "unique_id":
             build_config[field_name]["value"] = str(uuid.uuid4())
         return build_config

src/backend/base/langflow/components/helpers/SplitText.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_text_splitters import CharacterTextSplitter
 
 from langflow.custom import Component
@@ -52,7 +50,7 @@ class SplitTextComponent(Component):
             data.append(Data(text=doc.page_content, data=doc.metadata))
         return data
 
-    def split_text(self) -> List[Data]:
+    def split_text(self) -> list[Data]:
         separator = unescape_string(self.separator)
 
         documents = []

src/backend/base/langflow/components/langchain_utilities/FirecrawlCrawlApi.py

@@ -1,5 +1,4 @@
 import uuid
-from typing import Optional
 
 from langflow.custom import CustomComponent
 from langflow.schema import Data
@@ -51,9 +50,9 @@ class FirecrawlCrawlApi(CustomComponent):
         api_key: str,
         url: str,
         timeout: int = 30000,
-        crawlerOptions: Optional[Data] = None,
+        crawlerOptions: Data | None = None,
-        pageOptions: Optional[Data] = None,
+        pageOptions: Data | None = None,
-        idempotency_key: Optional[str] = None,
+        idempotency_key: str | None = None,
     ) -> Data:
         try:
             from firecrawl.firecrawl import FirecrawlApp  # type: ignore

src/backend/base/langflow/components/langchain_utilities/FirecrawlScrapeApi.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from langflow.custom import CustomComponent
 from langflow.schema import Data
 
@@ -46,8 +44,8 @@ class FirecrawlScrapeApi(CustomComponent):
         api_key: str,
         url: str,
         timeout: int = 10000,
-        pageOptions: Optional[Data] = None,
+        pageOptions: Data | None = None,
-        extractorOptions: Optional[Data] = None,
+        extractorOptions: Data | None = None,
     ) -> Data:
         try:
             from firecrawl.firecrawl import FirecrawlApp  # type: ignore

src/backend/base/langflow/components/models/GroqModel.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import requests
 from langchain_groq import ChatGroq
 from pydantic.v1 import SecretStr
@@ -55,7 +53,7 @@ class GroqModel(LCModelComponent):
         ),
     ]
 
-    def get_models(self) -> List[str]:
+    def get_models(self) -> list[str]:
         api_key = self.groq_api_key
         base_url = self.groq_api_base or "https://api.groq.com"
         url = f"{base_url}/openai/v1/models"

src/backend/base/langflow/components/models/HuggingFaceModel.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional
+from typing import Any
 
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 from tenacity import retry, stop_after_attempt, wait_fixed
@@ -84,15 +84,15 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
     def create_huggingface_endpoint(
         self,
         model_id: str,
-        task: Optional[str],
+        task: str | None,
-        huggingfacehub_api_token: Optional[str],
+        huggingfacehub_api_token: str | None,
-        model_kwargs: Dict[str, Any],
+        model_kwargs: dict[str, Any],
         max_new_tokens: int,
-        top_k: Optional[int],
+        top_k: int | None,
         top_p: float,
-        typical_p: Optional[float],
+        typical_p: float | None,
-        temperature: Optional[float],
+        temperature: float | None,
-        repetition_penalty: Optional[float],
+        repetition_penalty: float | None,
     ) -> HuggingFaceEndpoint:
         retry_attempts = self.retry_attempts
         endpoint_url = self.get_api_url()

src/backend/base/langflow/components/prompts/LangChainHubPrompt.py

@@ -1,5 +1,6 @@
 import re
-from typing import List
+
+from langchain_core.prompts import HumanMessagePromptTemplate
 
 from langchain_core.prompts import HumanMessagePromptTemplate
 
@@ -55,7 +56,7 @@ class LangChainHubPromptComponent(Component):
             pattern = r"\{(.*?)\}"
 
             # Get all the custom fields
-            custom_fields: List[str] = []
+            custom_fields: list[str] = []
             full_template = ""
             for message in prompt_template:
                 # Find all matches

src/backend/base/langflow/components/prototypes/FlowTool.py

@@ -1,5 +1,5 @@
 import warnings
-from typing import Any, List, Optional
+from typing import Any
 
 from langflow.base.langchain_utilities.model import LCToolComponent
 from langflow.base.tools.flow_tool import FlowTool
@@ -19,11 +19,11 @@ class FlowToolComponent(LCToolComponent):
     name = "FlowTool"
     beta = True
 
-    def get_flow_names(self) -> List[str]:
+    def get_flow_names(self) -> list[str]:
         flow_datas = self.list_flows()
         return [flow_data.data["name"] for flow_data in flow_datas]
 
-    def get_flow(self, flow_name: str) -> Optional[Data]:
+    def get_flow(self, flow_name: str) -> Data | None:
         """
         Retrieves a flow by its name.
 

src/backend/base/langflow/components/prototypes/Notify.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from langflow.custom import CustomComponent
 from langflow.schema import Data
 
@@ -21,7 +19,7 @@ class NotifyComponent(CustomComponent):
             },
         }
 
-    def build(self, name: str, data: Optional[Data] = None, append: bool = False) -> Data:
+    def build(self, name: str, data: Data | None = None, append: bool = False) -> Data:
         if data and not isinstance(data, Data):
             if isinstance(data, str):
                 data = Data(text=data)

src/backend/base/langflow/components/prototypes/PythonFunction.py

@@ -1,4 +1,4 @@
-from typing import Callable, List
+from collections.abc import Callable
 
 from langflow.custom import Component
 from langflow.custom.utils import get_function
@@ -46,7 +46,7 @@ class PythonFunctionComponent(Component):
         func = get_function(function_code)
         return func
 
-    def execute_function(self) -> List[dotdict | str] | dotdict | str:
+    def execute_function(self) -> list[dotdict | str] | dotdict | str:
         function_code = self.function_code
 
         if not function_code:
@@ -58,7 +58,7 @@ class PythonFunctionComponent(Component):
         except Exception as e:
             return f"Error executing function: {str(e)}"
 
-    def execute_function_data(self) -> List[Data]:
+    def execute_function_data(self) -> list[Data]:
         results = self.execute_function()
         results = results if isinstance(results, list) else [results]
         data = [(Data(text=x) if isinstance(x, str) else Data(**x)) for x in results]

src/backend/base/langflow/components/prototypes/RunFlow.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import Any
 
 from langflow.base.flow_processing.utils import build_data_from_run_outputs
 from langflow.custom import Component
@@ -13,7 +13,7 @@ class RunFlowComponent(Component):
     name = "RunFlow"
     beta: bool = True
 
-    def get_flow_names(self) -> List[str]:
+    def get_flow_names(self) -> list[str]:
         flow_data = self.list_flows()
         return [flow_data.data["name"] for flow_data in flow_data]
 
@@ -47,12 +47,12 @@ class RunFlowComponent(Component):
         Output(display_name="Run Outputs", name="run_outputs", method="generate_results"),
     ]
 
-    async def generate_results(self) -> List[Data]:
+    async def generate_results(self) -> list[Data]:
         if "flow_name" not in self._attributes or not self._attributes["flow_name"]:
             raise ValueError("Flow name is required")
         flow_name = self._attributes["flow_name"]
 
-        results: List[Optional[RunOutputs]] = await self.run_flow(
+        results: list[RunOutputs | None] = await self.run_flow(
             inputs={"input_value": self.input_value}, flow_name=flow_name, tweaks=self.tweaks
         )
         if isinstance(results, list):

src/backend/base/langflow/components/prototypes/SubFlow.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import Any
 
 from loguru import logger
 
@@ -17,11 +17,11 @@ class SubFlowComponent(Component):
     name = "SubFlow"
     beta: bool = True
 
-    def get_flow_names(self) -> List[str]:
+    def get_flow_names(self) -> list[str]:
         flow_data = self.list_flows()
         return [flow_data.data["name"] for flow_data in flow_data]
 
-    def get_flow(self, flow_name: str) -> Optional[Data]:
+    def get_flow(self, flow_name: str) -> Data | None:
         flow_datas = self.list_flows()
         for flow_data in flow_datas:
             if flow_data.data["name"] == flow_name:
@@ -50,7 +50,7 @@ class SubFlowComponent(Component):
 
         return build_config
 
-    def add_inputs_to_build_config(self, inputs_vertex: List[Vertex], build_config: dotdict):
+    def add_inputs_to_build_config(self, inputs_vertex: list[Vertex], build_config: dotdict):
         new_fields: list[dotdict] = []
 
         for vertex in inputs_vertex:
@@ -81,7 +81,7 @@ class SubFlowComponent(Component):
 
     outputs = [Output(name="flow_outputs", display_name="Flow Outputs", method="generate_results")]
 
-    async def generate_results(self) -> List[Data]:
+    async def generate_results(self) -> list[Data]:
         tweaks: dict = {}
         for field in self._attributes.keys():
             if field != "flow_name":

src/backend/base/langflow/components/retrievers/AmazonKendra.py

@@ -1,4 +1,4 @@
-from typing import Optional, cast
+from typing import cast
 
 from langchain_community.retrievers import AmazonKendraRetriever
 
@@ -33,10 +33,10 @@ class AmazonKendraRetrieverComponent(CustomComponent):
         self,
         index_id: str,
         top_k: int = 3,
-        region_name: Optional[str] = None,
+        region_name: str | None = None,
-        credentials_profile_name: Optional[str] = None,
+        credentials_profile_name: str | None = None,
-        attribute_filter: Optional[dict] = None,
+        attribute_filter: dict | None = None,
-        user_context: Optional[dict] = None,
+        user_context: dict | None = None,
     ) -> Retriever:  # type: ignore[type-var]
         try:
             output = AmazonKendraRetriever(

src/backend/base/langflow/components/retrievers/CohereRerank.py

@@ -1,4 +1,4 @@
-from typing import List, cast
+from typing import cast
 
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain_cohere import CohereRerank
@@ -73,7 +73,7 @@ class CohereRerankComponent(LCVectorStoreComponent):
         retriever = ContextualCompressionRetriever(base_compressor=cohere_reranker, base_retriever=self.retriever)
         return cast(Retriever, retriever)
 
-    async def search_documents(self) -> List[Data]:  # type: ignore
+    async def search_documents(self) -> list[Data]:  # type: ignore
         retriever = self.build_base_retriever()
         documents = await retriever.ainvoke(self.search_query, config={"callbacks": self.get_langchain_callbacks()})
         data = self.to_data(documents)

src/backend/base/langflow/components/retrievers/MetalRetriever.py

@@ -1,4 +1,4 @@
-from typing import Optional, cast
+from typing import cast
 
 from langchain_community.retrievers import MetalRetriever
 from metal_sdk.metal import Metal  # type: ignore
@@ -21,7 +21,7 @@ class MetalRetrieverComponent(CustomComponent):
             "code": {"show": False},
         }
 
-    def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> Retriever:  # type: ignore[type-var]
+    def build(self, api_key: str, client_id: str, index_id: str, params: dict | None = None) -> Retriever:  # type: ignore[type-var]
         try:
             metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)
         except Exception as e:

src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from langchain.retrievers import MultiQueryRetriever
 
 from langflow.custom import CustomComponent
@@ -42,7 +40,7 @@ class MultiQueryRetrieverComponent(CustomComponent):
         self,
         llm: LanguageModel,
         retriever: BaseRetriever,
-        prompt: Optional[Text] = None,
+        prompt: Text | None = None,
         parser_key: str = "lines",
     ) -> MultiQueryRetriever:
         if not prompt:

src/backend/base/langflow/components/retrievers/NvidiaRerank.py

@@ -1,4 +1,4 @@
-from typing import Any, List, cast
+from typing import Any, cast
 
 from langchain.retrievers import ContextualCompressionRetriever
 
@@ -70,7 +70,7 @@ class NvidiaRerankComponent(LCVectorStoreComponent):
         retriever = ContextualCompressionRetriever(base_compressor=nvidia_reranker, base_retriever=self.retriever)
         return cast(Retriever, retriever)
 
-    async def search_documents(self) -> List[Data]:  # type: ignore
+    async def search_documents(self) -> list[Data]:  # type: ignore
         retriever = self.build_base_retriever()
         documents = await retriever.ainvoke(self.search_query, config={"callbacks": self.get_langchain_callbacks()})
         data = self.to_data(documents)

src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain.chains.query_constructor.base import AttributeInfo
 from langchain.retrievers.self_query.base import SelfQueryRetriever
 
@@ -53,7 +51,7 @@ class SelfQueryRetrieverComponent(Component):
         Output(display_name="Retrieved Documents", name="documents", method="retrieve_documents"),
     ]
 
-    def retrieve_documents(self) -> List[Data]:
+    def retrieve_documents(self) -> list[Data]:
         metadata_field_infos = [AttributeInfo(**value.data) for value in self.attribute_infos]
         self_query_retriever = SelfQueryRetriever.from_llm(
             llm=self.llm,

src/backend/base/langflow/components/retrievers/VectaraSelfQueryRetriver.py

@@ -1,5 +1,5 @@
 import json
-from typing import List, cast
+from typing import cast
 
 from langchain.chains.query_constructor.base import AttributeInfo
 from langchain.retrievers.self_query.base import SelfQueryRetriever
@@ -40,7 +40,7 @@ class VectaraSelfQueryRetriverComponent(CustomComponent):
         vectorstore: VectorStore,
         document_content_description: str,
         llm: LanguageModel,
-        metadata_field_info: List[str],
+        metadata_field_info: list[str],
     ) -> Retriever:  # type: ignore
         metadata_field_obj = []
 

src/backend/base/langflow/components/toolkits/ComposioAPI.py

@@ -1,4 +1,5 @@
-from typing import Any, Sequence
+from collections.abc import Sequence
+from typing import Any
 
 from composio_langchain import Action, App, ComposioToolSet  # type: ignore
 from langchain_core.tools import Tool

src/backend/base/langflow/components/toolkits/Metaphor.py

@@ -1,5 +1,3 @@
-from typing import List, Union
-
 from langchain_community.agent_toolkits.base import BaseToolkit
 from langchain_core.tools import Tool, tool
 from metaphor_python import Metaphor  # type: ignore
@@ -25,7 +23,7 @@ class MetaphorToolkit(CustomComponent):
         use_autoprompt: bool = True,
         search_num_results: int = 5,
         similar_num_results: int = 5,
-    ) -> Union[Tool, BaseToolkit]:
+    ) -> Tool | BaseToolkit:
         # If documents, then we need to create a Vectara instance using .from_documents
         client = Metaphor(api_key=metaphor_api_key)
 
@@ -35,7 +33,7 @@ class MetaphorToolkit(CustomComponent):
             return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)
 
         @tool
-        def get_contents(ids: List[str]):
+        def get_contents(ids: list[str]):
             """Get contents of a webpage.
 
             The ids passed in should be a list of ids as fetched from `search`.

src/backend/base/langflow/components/tools/BingSearchAPI.py

@@ -1,4 +1,4 @@
-from typing import List, cast
+from typing import cast
 
 from langchain_community.tools.bing_search import BingSearchResults
 from langchain_community.utilities import BingSearchAPIWrapper
@@ -24,7 +24,7 @@ class BingSearchAPIComponent(LCToolComponent):
         IntInput(name="k", display_name="Number of results", value=4, required=True),
     ]
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         if self.bing_search_url:
             wrapper = BingSearchAPIWrapper(
                 bing_search_url=self.bing_search_url, bing_subscription_key=self.bing_subscription_key

src/backend/base/langflow/components/tools/Calculator.py

@@ -1,6 +1,5 @@
 import ast
 import operator
-from typing import List
 
 from langchain.tools import StructuredTool
 from pydantic import BaseModel, Field
@@ -28,7 +27,7 @@ class CalculatorToolComponent(LCToolComponent):
     class CalculatorToolSchema(BaseModel):
         expression: str = Field(..., description="The arithmetic expression to evaluate.")
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         return self._evaluate_expression(self.expression)
 
     def build_tool(self) -> Tool:
@@ -39,7 +38,7 @@ class CalculatorToolComponent(LCToolComponent):
             args_schema=self.CalculatorToolSchema,
         )
 
-    def _evaluate_expression(self, expression: str) -> List[Data]:
+    def _evaluate_expression(self, expression: str) -> list[Data]:
         try:
             # Define the allowed operators
             operators = {

src/backend/base/langflow/components/tools/DuckDuckGoSearchRun.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any
 
 from langchain.tools import StructuredTool
 from langchain_community.tools import DuckDuckGoSearchRun
@@ -37,7 +37,7 @@ class DuckDuckGoSearchComponent(LCToolComponent):
     def build_tool(self) -> Tool:
         wrapper = self._build_wrapper()
 
-        def search_func(query: str, max_results: int = 5, max_snippet_length: int = 100) -> List[Dict[str, Any]]:
+        def search_func(query: str, max_results: int = 5, max_snippet_length: int = 100) -> list[dict[str, Any]]:
             full_results = wrapper.run(f"{query} (site:*)")
             result_list = full_results.split("\n")[:max_results]
             limited_results = []
@@ -57,7 +57,7 @@ class DuckDuckGoSearchComponent(LCToolComponent):
         self.status = "DuckDuckGo Search Tool created"
         return tool
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         tool = self.build_tool()
         results = tool.run(
             {

src/backend/base/langflow/components/tools/GleanSearchAPI.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Optional, Union
+from typing import Any
 from urllib.parse import urljoin
 
 import httpx
@@ -33,7 +33,7 @@ class GleanSearchAPIComponent(LCToolComponent):
 
         return Tool(name="glean_search_api", description="Search with the Glean API", func=wrapper.run)
 
-    def run_model(self) -> Union[Data, list[Data]]:
+    def run_model(self) -> Data | list[Data]:
         wrapper = self._build_wrapper()
 
         results = wrapper.results(
@@ -67,7 +67,7 @@ class GleanSearchAPIComponent(LCToolComponent):
                 self,
                 query: str,
                 page_size: int = 10,
-                request_options: Optional[Dict[str, Any]] = None,
+                request_options: dict[str, Any] | None = None,
             ) -> dict:
                 # Ensure there's a trailing slash
                 url = self.glean_api_url
@@ -97,7 +97,7 @@ class GleanSearchAPIComponent(LCToolComponent):
 
                 return results
 
-            def _search_api_results(self, query: str, **kwargs: Any) -> Dict[str, Any]:
+            def _search_api_results(self, query: str, **kwargs: Any) -> dict[str, Any]:
                 request_details = self._prepare_request(query, **kwargs)
 
                 response = httpx.post(

src/backend/base/langflow/components/tools/GoogleSearchAPI.py

@@ -1,5 +1,3 @@
-from typing import Union
-
 from langchain_core.tools import Tool
 
 from langflow.base.langchain_utilities.model import LCToolComponent
@@ -22,7 +20,7 @@ class GoogleSearchAPIComponent(LCToolComponent):
         IntInput(name="k", display_name="Number of results", value=4, required=True),
     ]
 
-    def run_model(self) -> Union[Data, list[Data]]:
+    def run_model(self) -> Data | list[Data]:
         wrapper = self._build_wrapper()
         results = wrapper.results(query=self.input_value, num_results=self.k)
         data = [Data(data=result, text=result["snippet"]) for result in results]

src/backend/base/langflow/components/tools/GoogleSerperAPI.py

@@ -1,5 +1,3 @@
-from typing import Union
-
 from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
 
 from langflow.base.langchain_utilities.model import LCToolComponent
@@ -22,7 +20,7 @@ class GoogleSerperAPIComponent(LCToolComponent):
         IntInput(name="k", display_name="Number of results", value=4, required=True),
     ]
 
-    def run_model(self) -> Union[Data, list[Data]]:
+    def run_model(self) -> Data | list[Data]:
         wrapper = self._build_wrapper()
         results = wrapper.results(query=self.input_value)
         list_results = results.get("organic", [])

src/backend/base/langflow/components/tools/PythonREPLTool.py

@@ -1,5 +1,4 @@
 import importlib
-from typing import List, Union
 
 from langchain.tools import StructuredTool
 from langchain_experimental.utilities import PythonREPL
@@ -46,7 +45,7 @@ class PythonREPLToolComponent(LCToolComponent):
     class PythonREPLSchema(BaseModel):
         code: str = Field(..., description="The Python code to execute.")
 
-    def get_globals(self, global_imports: Union[str, List[str]]) -> dict:
+    def get_globals(self, global_imports: str | list[str]) -> dict:
         global_dict = {}
         if isinstance(global_imports, str):
             modules = [module.strip() for module in global_imports.split(",")]
@@ -83,7 +82,7 @@ class PythonREPLToolComponent(LCToolComponent):
         self.status = f"Python REPL Tool created with global imports: {self.global_imports}"
         return tool
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         tool = self.build_tool()
         result = tool.run(self.code)
         return [Data(data={"result": result})]

src/backend/base/langflow/components/tools/SearchAPI.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 from langchain.tools import StructuredTool
 from langchain_community.utilities.searchapi import SearchApiAPIWrapper
@@ -30,7 +30,7 @@ class SearchAPIComponent(LCToolComponent):
 
     class SearchAPISchema(BaseModel):
         query: str = Field(..., description="The search query")
-        params: Optional[Dict[str, Any]] = Field(default_factory=dict, description="Additional search parameters")
+        params: dict[str, Any] | None = Field(default_factory=dict, description="Additional search parameters")
         max_results: int = Field(5, description="Maximum number of results to return")
         max_snippet_length: int = Field(100, description="Maximum length of each result snippet")
 
@@ -41,8 +41,8 @@ class SearchAPIComponent(LCToolComponent):
         wrapper = self._build_wrapper()
 
         def search_func(
-            query: str, params: Optional[Dict[str, Any]] = None, max_results: int = 5, max_snippet_length: int = 100
+            query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100
-        ) -> List[Dict[str, Any]]:
+        ) -> list[dict[str, Any]]:
             params = params or {}
             full_results = wrapper.results(query=query, **params)
             organic_results = full_results.get("organic_results", [])[:max_results]
@@ -68,7 +68,7 @@ class SearchAPIComponent(LCToolComponent):
         self.status = f"Search API Tool created with engine: {self.engine}"
         return tool
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         tool = self.build_tool()
         results = tool.run(
             {

src/backend/base/langflow/components/tools/SerpAPI.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 from langchain.tools import StructuredTool
 from langchain_community.utilities.serpapi import SerpAPIWrapper
@@ -28,7 +28,7 @@ class SerpAPIComponent(LCToolComponent):
 
     class SerpAPISchema(BaseModel):
         query: str = Field(..., description="The search query")
-        params: Optional[Dict[str, Any]] = Field(default_factory=dict, description="Additional search parameters")
+        params: dict[str, Any] | None = Field(default_factory=dict, description="Additional search parameters")
         max_results: int = Field(5, description="Maximum number of results to return")
         max_snippet_length: int = Field(100, description="Maximum length of each result snippet")
 
@@ -44,8 +44,8 @@ class SerpAPIComponent(LCToolComponent):
         wrapper = self._build_wrapper()
 
         def search_func(
-            query: str, params: Optional[Dict[str, Any]] = None, max_results: int = 5, max_snippet_length: int = 100
+            query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100
-        ) -> List[Dict[str, Any]]:
+        ) -> list[dict[str, Any]]:
             params = params or {}
             full_results = wrapper.results(query, **params)
             organic_results = full_results.get("organic_results", [])[:max_results]
@@ -71,7 +71,7 @@ class SerpAPIComponent(LCToolComponent):
         self.status = "SerpAPI Tool created"
         return tool
 
-    def run_model(self) -> List[Data]:
+    def run_model(self) -> list[Data]:
         tool = self.build_tool()
         try:
             results = tool.run(

src/backend/base/langflow/components/vectorstores/Cassandra.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.vectorstores import Cassandra
 from loguru import logger
 
@@ -219,7 +217,7 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
         else:
             return "similarity"
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         logger.debug(f"Search input: {self.search_query}")

src/backend/base/langflow/components/vectorstores/CassandraGraph.py

@@ -1,4 +1,3 @@
-from typing import List
 from uuid import UUID
 
 from langchain_community.graph_vectorstores import CassandraGraphVectorStore
@@ -197,7 +196,7 @@ class CassandraGraphVectorStoreComponent(LCVectorStoreComponent):
         else:
             return "traversal"
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         logger.debug(f"Search input: {self.search_query}")

src/backend/base/langflow/components/vectorstores/Clickhouse.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.vectorstores import Clickhouse, ClickhouseSettings
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
@@ -117,7 +115,7 @@ class ClickhouseVectorStoreComponent(LCVectorStoreComponent):
 
         return clickhouse_vs
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/Couchbase.py

@@ -1,5 +1,4 @@
 from datetime import timedelta
-from typing import List
 
 from langchain_community.vectorstores import CouchbaseVectorStore
 
@@ -92,7 +91,7 @@ class CouchbaseVectorStoreComponent(LCVectorStoreComponent):
 
         return couchbase_vs
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/FAISS.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.vectorstores import FAISS
 from loguru import logger
 
@@ -79,7 +77,7 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
 
         return faiss
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         """
         Search for documents in the FAISS vector store.
         """

src/backend/base/langflow/components/vectorstores/Milvus.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
 from langflow.helpers.data import docs_to_data
 from langflow.io import (
@@ -108,7 +106,7 @@ class MilvusVectorStoreComponent(LCVectorStoreComponent):
 
         return milvus_store
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.vectorstores import MongoDBAtlasVectorSearch
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
@@ -75,7 +73,7 @@ class MongoVectorStoreComponent(LCVectorStoreComponent):
 
         return vector_store
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         from bson import ObjectId
 
         vector_store = self.build_vector_store()

src/backend/base/langflow/components/vectorstores/Pinecone.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_pinecone import Pinecone
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
@@ -85,7 +83,7 @@ class PineconeVectorStoreComponent(LCVectorStoreComponent):
             pinecone.add_documents(documents)
         return pinecone
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/Qdrant.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain.embeddings.base import Embeddings
 from langchain_community.vectorstores import Qdrant
 
@@ -99,7 +97,7 @@ class QdrantVectorStoreComponent(LCVectorStoreComponent):
 
         return qdrant
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/Redis.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain.text_splitter import CharacterTextSplitter
 from langchain_community.vectorstores.redis import Redis
 
@@ -79,7 +77,7 @@ class RedisVectorStoreComponent(LCVectorStoreComponent):
             )
         return redis_vs
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.vectorstores import SupabaseVectorStore
 from supabase.client import Client, create_client
 
@@ -66,7 +64,7 @@ class SupabaseVectorStoreComponent(LCVectorStoreComponent):
 
         return supabase_vs
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/Upstash.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.vectorstores import UpstashVectorStore
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
@@ -114,7 +112,7 @@ class UpstashVectorStoreComponent(LCVectorStoreComponent):
 
         return upstash_vs
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/Vectara.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, List
+from typing import TYPE_CHECKING
 
 from langchain_community.vectorstores import Vectara
 from loguru import logger
@@ -93,7 +93,7 @@ class VectaraVectorStoreComponent(LCVectorStoreComponent):
             logger.debug("No documents to add to Vectara.")
             self.status = "No valid documents to add to Vectara"
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/Weaviate.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import weaviate  # type: ignore
 from langchain_community.vectorstores import Weaviate
 
@@ -70,7 +68,7 @@ class WeaviateVectorStoreComponent(LCVectorStoreComponent):
             by_text=self.search_by_text,
         )
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/components/vectorstores/pgvector.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from langchain_community.vectorstores import PGVector
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
@@ -63,7 +61,7 @@ class PGVectorStoreComponent(LCVectorStoreComponent):
 
         return pgvector
 
-    def search_documents(self) -> List[Data]:
+    def search_documents(self) -> list[Data]:
         vector_store = self.build_vector_store()
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():

src/backend/base/langflow/custom/code_parser/code_parser.py

@@ -41,7 +41,7 @@ def find_class_ast_node(class_obj):
     for node in ast.walk(tree):
         if isinstance(node, ast.ClassDef) and node.name == class_obj.__name__:
             class_node = node
-        elif isinstance(node, (ast.Import, ast.ImportFrom)):
+        elif isinstance(node, ast.Import | ast.ImportFrom):
             import_nodes.append(node)
 
     return class_node, import_nodes
@@ -275,7 +275,7 @@ class CodeParser:
                     or any(has_return(child) for child in node.handlers)
                     or any(has_return(child) for child in node.finalbody)
                 )
-            elif isinstance(node, (ast.For, ast.While)):
+            elif isinstance(node, ast.For | ast.While):
                 return any(has_return(child) for child in node.body) or any(has_return(child) for child in node.orelse)
             elif isinstance(node, ast.With):
                 return any(has_return(child) for child in node.body)
@@ -365,7 +365,7 @@ class CodeParser:
             elif isinstance(stmt, ast.AnnAssign):
                 if attr := self.parse_ann_assign(stmt):
                     class_details.attributes.append(attr)
-            elif isinstance(stmt, (ast.FunctionDef, ast.AsyncFunctionDef)):
+            elif isinstance(stmt, ast.FunctionDef | ast.AsyncFunctionDef):
                 method, is_init = self.parse_function_def(stmt)
                 if is_init:
                     class_details.init = method

src/backend/base/langflow/custom/custom_component/component.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import inspect
 from collections.abc import Callable
 from copy import deepcopy
@@ -33,7 +35,7 @@ CONFIG_ATTRIBUTES = ["_display_name", "_description", "_icon", "_name"]
 
 
 class Component(CustomComponent):
-    inputs: list["InputTypes"] = []
+    inputs: list[InputTypes] = []
     outputs: list[Output] = []
     code_class_base_inheritance: ClassVar[str] = "Component"
     _output_logs: dict[str, Log] = {}
@@ -51,7 +53,7 @@ class Component(CustomComponent):
                 config[key[1:]] = value
             else:
                 inputs[key] = value
-        self._inputs: dict[str, "InputTypes"] = {}
+        self._inputs: dict[str, InputTypes] = {}
         self._outputs_map: dict[str, Output] = {}
         self._results: dict[str, Any] = {}
         self._attributes: dict[str, Any] = {}
@@ -179,7 +181,7 @@ class Component(CustomComponent):
         """
         return await self._run()
 
-    def set_vertex(self, vertex: "Vertex"):
+    def set_vertex(self, vertex: Vertex):
         """
         Sets the vertex for the component.
 
@@ -258,7 +260,7 @@ class Component(CustomComponent):
             # allows each instance of each component to modify its own output
             self._outputs_map[output.name] = deepcopy(output)
 
-    def map_inputs(self, inputs: list["InputTypes"]):
+    def map_inputs(self, inputs: list[InputTypes]):
         """
         Maps the given inputs to the component.
 
@@ -325,7 +327,7 @@ class Component(CustomComponent):
             text += f"{output.name}[{','.join(output.types)}]->{input_.name}[{','.join(input_.input_types or [])}]\n"
         return text
 
-    def _find_matching_output_method(self, value: "Component"):
+    def _find_matching_output_method(self, value: Component):
         # get all outputs of the value component
         outputs = value._outputs_map.values()
         # check if the any of the types in the output.types matches ONLY one input in the current component
@@ -652,7 +654,7 @@ class Component(CustomComponent):
                         output.value = result
 
                     custom_repr = self.custom_repr()
-                    if custom_repr is None and isinstance(result, (dict, Data, str)):
+                    if custom_repr is None and isinstance(result, dict | Data | str):
                         custom_repr = result
                     if not isinstance(custom_repr, str):
                         custom_repr = str(custom_repr)
@@ -670,7 +672,7 @@ class Component(CustomComponent):
 
                     elif hasattr(raw, "model_dump") and raw is not None:
                         raw = raw.model_dump()
-                    if raw is None and isinstance(result, (dict, Data, str)):
+                    if raw is None and isinstance(result, dict | Data | str):
                         raw = result.data if isinstance(result, Data) else result
                     artifact_type = get_artifact_type(artifact_value, result)
                     raw, artifact_type = post_process_raw(raw, artifact_type)

src/backend/base/langflow/custom/custom_component/custom_component.py

@@ -1,6 +1,8 @@
+from __future__ import annotations
+
 from collections.abc import Callable, Sequence
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, ClassVar, Optional
+from typing import TYPE_CHECKING, Any, ClassVar
 
 import yaml
 from cachetools import TTLCache
@@ -69,7 +71,7 @@ class CustomComponent(BaseComponent):
     """The default frozen state of the component. Defaults to False."""
     build_parameters: dict | None = None
     """The build parameters of the component. Defaults to None."""
-    _vertex: Optional["Vertex"] = None
+    _vertex: Vertex | None = None
     """The edge target parameter of the component. Defaults to None."""
     _code_class_base_inheritance: ClassVar[str] = "CustomComponent"
     function_entrypoint_name: ClassVar[str] = "build"
@@ -81,7 +83,7 @@ class CustomComponent(BaseComponent):
     _outputs: list[OutputValue] = []
     _logs: list[Log] = []
     _output_logs: dict[str, Log] = {}
-    _tracing_service: Optional["TracingService"] = None
+    _tracing_service: TracingService | None = None
     _tree: dict | None = None
 
     def __init__(self, **data):
@@ -158,7 +160,7 @@ class CustomComponent(BaseComponent):
         return str(path_object)
 
     def get_full_path(self, path: str) -> str:
-        storage_svc: "StorageService" = get_storage_service()
+        storage_svc: StorageService = get_storage_service()
 
         flow_id, file_name = path.split("/", 1)
         return storage_svc.build_full_path(flow_id, file_name)
@@ -464,7 +466,7 @@ class CustomComponent(BaseComponent):
         """
         return validate.create_function(self._code, self._function_entrypoint_name)
 
-    async def load_flow(self, flow_id: str, tweaks: dict | None = None) -> "Graph":
+    async def load_flow(self, flow_id: str, tweaks: dict | None = None) -> Graph:
         if not self.user_id:
             raise ValueError("Session is invalid")
         return await load_flow(user_id=str(self._user_id), flow_id=flow_id, tweaks=tweaks)
@@ -517,7 +519,7 @@ class CustomComponent(BaseComponent):
         )
         return frontend_node
 
-    def get_langchain_callbacks(self) -> list["BaseCallbackHandler"]:
+    def get_langchain_callbacks(self) -> list[BaseCallbackHandler]:
         if self._tracing_service:
             return self._tracing_service.get_langchain_callbacks()
         return []

src/backend/base/langflow/field_typing/constants.py

@@ -1,4 +1,5 @@
-from typing import Callable, Dict, Text, TypeAlias, TypeVar, Union
+from collections.abc import Callable
+from typing import Text, TypeAlias, TypeVar
 
 from langchain.agents.agent import AgentExecutor
 from langchain.chains.base import Chain
@@ -20,7 +21,7 @@ from langchain_text_splitters import TextSplitter
 from langflow.schema.data import Data
 from langflow.schema.message import Message
 
-NestedDict: TypeAlias = Dict[str, Union[str, Dict]]
+NestedDict: TypeAlias = dict[str, str | dict]
 LanguageModel = TypeVar("LanguageModel", BaseLanguageModel, BaseLLM, BaseChatModel)
 ToolEnabledLanguageModel = TypeVar("ToolEnabledLanguageModel", BaseLanguageModel, BaseLLM, BaseChatModel)
 Retriever = TypeVar(
@@ -66,7 +67,7 @@ CUSTOM_COMPONENT_SUPPORTED_TYPES = {
     "NestedDict": NestedDict,
     "Data": Data,
     "Message": Message,
-    "Text": Text,
+    "Text": Text,  # noqa: UP019
     "Object": Object,
     "Callable": Callable,
     "LanguageModel": LanguageModel,

src/backend/base/langflow/graph/graph/base.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import asyncio
 import copy
 import json
@@ -8,7 +10,7 @@ from collections.abc import Generator, Iterable
 from datetime import datetime, timezone
 from functools import partial
 from itertools import chain
-from typing import TYPE_CHECKING, Any, Optional, cast
+from typing import TYPE_CHECKING, Any, cast
 
 import nest_asyncio
 from loguru import logger
@@ -53,8 +55,8 @@ class Graph:
 
     def __init__(
         self,
-        start: Optional["Component"] = None,
+        start: Component | None = None,
-        end: Optional["Component"] = None,
+        end: Component | None = None,
         flow_id: str | None = None,
         flow_name: str | None = None,
         description: str | None = None,
@@ -115,7 +117,7 @@ class Graph:
         self._call_order: list[str] = []
         self._snapshots: list[dict[str, Any]] = []
         try:
-            self.tracing_service: "TracingService" | None = get_tracing_service()
+            self.tracing_service: TracingService | None = get_tracing_service()
         except Exception as exc:
             logger.error(f"Error getting tracing service: {exc}")
             self.tracing_service = None
@@ -203,7 +205,7 @@ class Graph:
         self._edges = self._graph_data["edges"]
         self.initialize()
 
-    def add_component(self, component: "Component", component_id: Optional[str] = None) -> str:
+    def add_component(self, component: Component, component_id: str | None = None) -> str:
         component_id = component_id or component._id
         if component_id in self.vertex_map:
             return component_id
@@ -225,7 +227,7 @@ class Graph:
 
         return component_id
 
-    def _set_start_and_end(self, start: "Component", end: "Component"):
+    def _set_start_and_end(self, start: Component, end: Component):
         if not hasattr(start, "to_frontend_node"):
             raise TypeError(f"start must be a Component. Got {type(start)}")
         if not hasattr(end, "to_frontend_node"):
@@ -613,7 +615,7 @@ class Graph:
         stream: bool,
         session_id: str,
         fallback_to_env_vars: bool,
-    ) -> list[Optional["ResultData"]]:
+    ) -> list[ResultData | None]:
         """
         Runs the graph with the given inputs.
 
@@ -808,7 +810,7 @@ class Graph:
             "flow_name": self.flow_name,
         }
 
-    def build_graph_maps(self, edges: list[CycleEdge] | None = None, vertices: list["Vertex"] | None = None):
+    def build_graph_maps(self, edges: list[CycleEdge] | None = None, vertices: list[Vertex] | None = None):
         """
         Builds the adjacency maps for the graph.
         """
@@ -878,7 +880,7 @@ class Graph:
                 return edge
         return None
 
-    def build_parent_child_map(self, vertices: list["Vertex"]):
+    def build_parent_child_map(self, vertices: list[Vertex]):
         parent_child_map = defaultdict(list)
         for vertex in vertices:
             parent_child_map[vertex.id] = [child.id for child in self.get_successors(vertex)]
@@ -977,7 +979,7 @@ class Graph:
         flow_id: str | None = None,
         flow_name: str | None = None,
         user_id: str | None = None,
-    ) -> "Graph":
+    ) -> Graph:
         """
         Creates a graph from a payload.
 
@@ -1016,7 +1018,7 @@ class Graph:
     # both graphs have the same vertices and edges
     # but the data of the vertices might be different
 
-    def update_edges_from_vertex(self, vertex: "Vertex", other_vertex: "Vertex") -> None:
+    def update_edges_from_vertex(self, vertex: Vertex, other_vertex: Vertex) -> None:
         """Updates the edges of a vertex in the Graph."""
         new_edges = []
         for edge in self.edges:
@@ -1026,13 +1028,13 @@ class Graph:
         new_edges += other_vertex.edges
         self.edges = new_edges
 
-    def vertex_data_is_identical(self, vertex: "Vertex", other_vertex: "Vertex") -> bool:
+    def vertex_data_is_identical(self, vertex: Vertex, other_vertex: Vertex) -> bool:
         data_is_equivalent = vertex == other_vertex
         if not data_is_equivalent:
             return False
         return self.vertex_edges_are_identical(vertex, other_vertex)
 
-    def vertex_edges_are_identical(self, vertex: "Vertex", other_vertex: "Vertex") -> bool:
+    def vertex_edges_are_identical(self, vertex: Vertex, other_vertex: Vertex) -> bool:
         same_length = len(vertex.edges) == len(other_vertex.edges)
         if not same_length:
             return False
@@ -1041,7 +1043,7 @@ class Graph:
                 return False
         return True
 
-    def update(self, other: "Graph") -> "Graph":
+    def update(self, other: Graph) -> Graph:
         # Existing vertices in self graph
         existing_vertex_ids = {vertex.id for vertex in self.vertices}
         # Vertex IDs in the other graph
@@ -1091,7 +1093,7 @@ class Graph:
         self.increment_update_count()
         return self
 
-    def update_vertex_from_another(self, vertex: "Vertex", other_vertex: "Vertex") -> None:
+    def update_vertex_from_another(self, vertex: Vertex, other_vertex: Vertex) -> None:
         """
         Updates a vertex from another vertex.
 
@@ -1129,12 +1131,12 @@ class Graph:
         self.vertices.append(vertex)
         self.vertex_map[vertex.id] = vertex
 
-    def add_vertex(self, vertex: "Vertex") -> None:
+    def add_vertex(self, vertex: Vertex) -> None:
         """Adds a new vertex to the graph."""
         self._add_vertex(vertex)
         self._update_edges(vertex)
 
-    def _update_edges(self, vertex: "Vertex") -> None:
+    def _update_edges(self, vertex: Vertex) -> None:
         """Updates the edges of a vertex."""
         # Vertex has edges, so we need to update the edges
         for edge in vertex.edges:
@@ -1169,19 +1171,19 @@ class Graph:
         for vertex in self.vertices:
             vertex._build_params()
 
-    def _validate_vertex(self, vertex: "Vertex") -> bool:
+    def _validate_vertex(self, vertex: Vertex) -> bool:
         """Validates a vertex."""
         # All vertices that do not have edges are invalid
         return len(self.get_vertex_edges(vertex.id)) > 0
 
-    def get_vertex(self, vertex_id: str, silent: bool = False) -> "Vertex":
+    def get_vertex(self, vertex_id: str, silent: bool = False) -> Vertex:
         """Returns a vertex by id."""
         try:
             return self.vertex_map[vertex_id]
         except KeyError:
             raise ValueError(f"Vertex {vertex_id} not found")
 
-    def get_root_of_group_node(self, vertex_id: str) -> "Vertex":
+    def get_root_of_group_node(self, vertex_id: str) -> Vertex:
         """Returns the root of a group node."""
         if vertex_id in self.top_level_vertices:
             # Get all vertices with vertex_id as .parent_node_id
@@ -1205,7 +1207,7 @@ class Graph:
 
     async def astep(
         self,
-        inputs: Optional["InputValueRequest"] = None,
+        inputs: InputValueRequest | None = None,
         files: list[str] | None = None,
         user_id: str | None = None,
         event_manager: EventManager | None = None,
@@ -1259,7 +1261,7 @@ class Graph:
 
     def step(
         self,
-        inputs: Optional["InputValueRequest"] = None,
+        inputs: InputValueRequest | None = None,
         files: list[str] | None = None,
         user_id: str | None = None,
     ):
@@ -1382,9 +1384,9 @@ class Graph:
             or (edge.target_id == vertex_id and is_target is not False)
         ]
 
-    def get_vertices_with_target(self, vertex_id: str) -> list["Vertex"]:
+    def get_vertices_with_target(self, vertex_id: str) -> list[Vertex]:
         """Returns the vertices connected to a vertex."""
-        vertices: list["Vertex"] = []
+        vertices: list[Vertex] = []
         for edge in self.edges:
             if edge.target_id == vertex_id:
                 vertex = self.get_vertex(edge.source_id)
@@ -1393,7 +1395,7 @@ class Graph:
                 vertices.append(vertex)
         return vertices
 
-    async def process(self, fallback_to_env_vars: bool, start_component_id: str | None = None) -> "Graph":
+    async def process(self, fallback_to_env_vars: bool, start_component_id: str | None = None) -> Graph:
         """Processes the graph with vertices in each layer run in parallel."""
 
         first_layer = self.sort_vertices(start_component_id=start_component_id)
@@ -1450,7 +1452,7 @@ class Graph:
 
         return list(next_runnable_vertices)
 
-    async def get_next_runnable_vertices(self, lock: asyncio.Lock, vertex: "Vertex", cache: bool = True) -> list[str]:
+    async def get_next_runnable_vertices(self, lock: asyncio.Lock, vertex: Vertex, cache: bool = True) -> list[str]:
         v_id = vertex.id
         v_successors_ids = vertex.successors_ids
         async with lock:
@@ -1471,7 +1473,7 @@ class Graph:
         """Executes tasks in parallel, handling exceptions for each task."""
         results = []
         completed_tasks = await asyncio.gather(*tasks, return_exceptions=True)
-        vertices: list["Vertex"] = []
+        vertices: list[Vertex] = []
 
         for i, result in enumerate(completed_tasks):
             task_name = tasks[i].get_name()
@@ -1500,7 +1502,7 @@ class Graph:
         no_duplicate_results = list(set(results))
         return no_duplicate_results
 
-    def topological_sort(self) -> list["Vertex"]:
+    def topological_sort(self) -> list[Vertex]:
         """
         Performs a topological sort of the vertices in the graph.
 
@@ -1533,7 +1535,7 @@ class Graph:
 
         return list(reversed(sorted_vertices))
 
-    def generator_build(self) -> Generator["Vertex", None, None]:
+    def generator_build(self) -> Generator[Vertex, None, None]:
         """Builds each vertex in the graph and yields it."""
         sorted_vertices = self.topological_sort()
         logger.debug("There are %s vertices in the graph", len(sorted_vertices))
@@ -1543,7 +1545,7 @@ class Graph:
         """Returns the predecessors of a vertex."""
         return [self.get_vertex(source_id) for source_id in self.predecessor_map.get(vertex.id, [])]
 
-    def get_all_successors(self, vertex: "Vertex", recursive=True, flat=True, visited=None):
+    def get_all_successors(self, vertex: Vertex, recursive=True, flat=True, visited=None):
         if visited is None:
             visited = set()
 
@@ -1576,13 +1578,13 @@ class Graph:
 
         return successors_result
 
-    def get_successors(self, vertex: "Vertex") -> list["Vertex"]:
+    def get_successors(self, vertex: Vertex) -> list[Vertex]:
         """Returns the successors of a vertex."""
         return [self.get_vertex(target_id) for target_id in self.successor_map.get(vertex.id, [])]
 
-    def get_vertex_neighbors(self, vertex: "Vertex") -> dict["Vertex", int]:
+    def get_vertex_neighbors(self, vertex: Vertex) -> dict[Vertex, int]:
         """Returns the neighbors of a vertex."""
-        neighbors: dict["Vertex", int] = {}
+        neighbors: dict[Vertex, int] = {}
         for edge in self.edges:
             if edge.source_id == vertex.id:
                 neighbor = self.get_vertex(edge.target_id)
@@ -1638,7 +1640,7 @@ class Graph:
             new_edge = Edge(source, target, edge)
         return new_edge
 
-    def _get_vertex_class(self, node_type: str, node_base_type: str, node_id: str) -> type["Vertex"]:
+    def _get_vertex_class(self, node_type: str, node_base_type: str, node_id: str) -> type[Vertex]:
         """Returns the node class based on the node type."""
         # First we check for the node_base_type
         node_name = node_id.split("-")[0]
@@ -1655,9 +1657,9 @@ class Graph:
             return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_type]
         return Vertex
 
-    def _build_vertices(self) -> list["Vertex"]:
+    def _build_vertices(self) -> list[Vertex]:
         """Builds the vertices of the graph."""
-        vertices: list["Vertex"] = []
+        vertices: list[Vertex] = []
         for frontend_data in self._vertices:
             if frontend_data.get("type") == NodeTypeEnum.NoteNode:
                 continue
@@ -1730,7 +1732,7 @@ class Graph:
 
     def layered_topological_sort(
         self,
-        vertices: list["Vertex"],
+        vertices: list[Vertex],
         filter_graphs: bool = False,
     ) -> list[list[str]]:
         """Performs a layered topological sort of the vertices in the graph."""
@@ -1986,7 +1988,7 @@ class Graph:
         runnable_vertices = []
         visited = set()
 
-        def find_runnable_predecessors(predecessor: "Vertex"):
+        def find_runnable_predecessors(predecessor: Vertex):
             predecessor_id = predecessor.id
             if predecessor_id in visited:
                 return

src/backend/base/langflow/graph/graph/state_manager.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from collections.abc import Callable
 from typing import TYPE_CHECKING
 
@@ -12,7 +14,7 @@ if TYPE_CHECKING:
 class GraphStateManager:
     def __init__(self):
         try:
-            self.state_service: "StateService" = get_state_service()
+            self.state_service: StateService = get_state_service()
         except Exception as e:
             logger.debug(f"Error getting state service. Defaulting to InMemoryStateService: {e}")
             from langflow.services.state.service import InMemoryStateService