Refactor code and fix bugs

src/backend/base/langflow/__main__.py

@@ -9,9 +9,9 @@ from typing import Optional
 import httpx
 import typer
 from dotenv import load_dotenv
-from multiprocess import (
+from multiprocess import (  # noqa
-    Process,  # type: ignore
+    Process,  # noqa
-    cpu_count,  # type: ignore
+    cpu_count,  # noqa; type: ignore
 )
 from rich import box
 from rich import print as rprint

src/backend/base/langflow/alembic/helpers/flow.py

@@ -69,6 +69,8 @@ async def run_flow(
     flow_name: Optional[str] = None,
     user_id: Optional[str] = None,
 ) -> Any:
+    if not user_id:
+        raise ValueError("Session is invalid")
     graph = await load_flow(user_id, flow_id, flow_name, tweaks)
 
     if inputs is None:

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

@@ -1,6 +1,6 @@
-from typing import List, Union
+from typing import List, Optional, Union
 
-from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
+from langchain.agents.agent import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent, RunnableMultiActionAgent
 from langchain_core.runnables import Runnable
 
 from langflow.field_typing import BaseMemory, Text, Tool
@@ -44,12 +44,14 @@ class LCAgentComponent(CustomComponent):
         inputs: str,
         input_variables: list[str],
         tools: List[Tool],
-        memory: BaseMemory = None,
+        memory: Optional[BaseMemory] = None,
         handle_parsing_errors: bool = True,
         output_key: str = "output",
     ) -> Text:
         if isinstance(agent, AgentExecutor):
             runnable = agent
+        elif isinstance(agent, Runnable):
+            runnable = RunnableMultiActionAgent(runnable=agent, stream_runnable=False)
         else:
             runnable = AgentExecutor.from_agent_and_tools(
                 agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors

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

@@ -4,7 +4,7 @@ from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.language_models.llms import LLM
 from langchain_core.messages import HumanMessage, SystemMessage
 
-from langflow.interface.custom.custom_component import CustomComponent
+from langflow.custom import CustomComponent
 
 
 class LCModelComponent(CustomComponent):
@@ -35,10 +35,10 @@ class LCModelComponent(CustomComponent):
         self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
     ):
         messages = []
-        if system_message:
-            messages.append(SystemMessage(system_message))
         if input_value:
             messages.append(HumanMessage(input_value))
+        if system_message:
+            messages.append(SystemMessage(system_message))
         if stream:
             result = runnable.stream(messages)
         else:

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

@@ -1,6 +1,6 @@
 import asyncio
 from collections import defaultdict
-from typing import TYPE_CHECKING, Coroutine, List
+from typing import TYPE_CHECKING, Awaitable, Callable, List
 
 if TYPE_CHECKING:
     from langflow.graph.graph.base import Graph
@@ -55,7 +55,7 @@ class RunnableVerticesManager:
     async def get_next_runnable_vertices(
         self,
         lock: asyncio.Lock,
-        set_cache_coro: Coroutine,
+        set_cache_coro: Callable[["Graph", asyncio.Lock], Awaitable[None]],
         graph: "Graph",
         vertex: "Vertex",
     ):

src/backend/base/langflow/helpers/flow.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Any, Callable, Coroutine, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Coroutine, List, Optional, Tuple, Union
 
 from pydantic.v1 import BaseModel, Field, create_model
 from sqlmodel import select
@@ -69,6 +69,8 @@ async def run_flow(
     flow_name: Optional[str] = None,
     user_id: Optional[str] = None,
 ) -> Any:
+    if not user_id:
+        raise ValueError("Session is invalid")
     graph = await load_flow(user_id, flow_id, flow_name, tweaks)
 
     if inputs is None:
@@ -77,7 +79,7 @@ async def run_flow(
     inputs_components = []
     types = []
     for input_dict in inputs:
-        inputs_list.append({INPUT_FIELD_NAME: input_dict.get("input_value")})
+        inputs_list.append({INPUT_FIELD_NAME: input_dict.get("input_value", "")})
         inputs_components.append(input_dict.get("components", []))
         types.append(input_dict.get("type", []))
 
@@ -138,12 +140,12 @@ async def flow_function({func_args}):
 """
 
     compiled_func = compile(func_body, "<string>", "exec")
-    local_scope = {}
+    local_scope: dict = {}
     exec(compiled_func, globals(), local_scope)
     return local_scope["flow_function"]
 
 
-def build_function_and_schema(flow_record: Record, graph: "Graph") -> Tuple[Callable, BaseModel]:
+def build_function_and_schema(flow_record: Record, graph: "Graph") -> Tuple[Coroutine, BaseModel]:
     """
     Builds a dynamic function and schema for a given flow.
 
@@ -178,7 +180,7 @@ def get_flow_inputs(graph: "Graph") -> List["Vertex"]:
     return inputs
 
 
-def build_schema_from_inputs(name: str, inputs: List[tuple[str, str, str]]) -> BaseModel:
+def build_schema_from_inputs(name: str, inputs: List["Vertex"]) -> BaseModel:
     """
     Builds a schema from the given inputs.
 

src/backend/base/langflow/services/factory.py

@@ -23,7 +23,7 @@ class ServiceFactory:
         raise self.service_class(*args, **kwargs)
 
 
-def hash_factory(factory: ServiceFactory) -> str:
+def hash_factory(factory: Type[ServiceFactory]) -> str:
     return factory.service_class.__name__
 
 
@@ -38,7 +38,7 @@ def hash_infer_service_types_args(factory_class: Type[ServiceFactory], available
 
 
 @cached(cache=LRUCache(maxsize=10), key=hash_infer_service_types_args)
-def infer_service_types(factory_class: Type[ServiceFactory], available_services=None) -> "ServiceType":
+def infer_service_types(factory_class: Type[ServiceFactory], available_services=None) -> list["ServiceType"]:
     create_method = factory_class.create
     type_hints = get_type_hints(create_method, globalns=available_services)
     service_types = []

src/backend/base/langflow/services/settings/auth.py

@@ -1,5 +1,6 @@
 import secrets
 from pathlib import Path
+from typing import Literal
 
 from loguru import logger
 from passlib.context import CryptContext
@@ -14,7 +15,7 @@ class AuthSettings(BaseSettings):
     # Login settings
     CONFIG_DIR: str
     SECRET_KEY: SecretStr = Field(
-        default=None,
+        default="",
         description="Secret key for JWT. If not provided, a random one will be generated.",
         frozen=False,
     )
@@ -33,13 +34,13 @@ class AuthSettings(BaseSettings):
     SUPERUSER: str = DEFAULT_SUPERUSER
     SUPERUSER_PASSWORD: str = DEFAULT_SUPERUSER_PASSWORD
 
-    REFRESH_SAME_SITE: str = "none"
+    REFRESH_SAME_SITE: Literal["lax", "strict", "none"] = "none"
     """The SameSite attribute of the refresh token cookie."""
     REFRESH_SECURE: bool = True
     """The Secure attribute of the refresh token cookie."""
     REFRESH_HTTPONLY: bool = True
     """The HttpOnly attribute of the refresh token cookie."""
-    ACCESS_SAME_SITE: str = "none"
+    ACCESS_SAME_SITE: Literal["lax", "strict", "none"] = "none"
     """The SameSite attribute of the access token cookie."""
     ACCESS_SECURE: bool = True
     """The Secure attribute of the access token cookie."""

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

@@ -1,8 +1,8 @@
 from typing import List, Union
 
-from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
+from langchain.agents import (AgentExecutor, BaseMultiActionAgent,
 
-from langflow import CustomComponent
+from langflow.custom import CustomComponent
 from langflow.field_typing import BaseMemory, Text, Tool
 
 

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

@@ -1,3 +0,0 @@
-from .model import LCModelComponent
-
-__all__ = ["LCModelComponent"]

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

@@ -1,48 +0,0 @@
-from typing import Optional
-
-from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_core.language_models.llms import LLM
-from langchain_core.messages import HumanMessage, SystemMessage
-
-from langflow import CustomComponent
-
-
-class LCModelComponent(CustomComponent):
-    display_name: str = "Model Name"
-    description: str = "Model Description"
-
-    def get_result(self, runnable: LLM, stream: bool, input_value: str):
-        """
-        Retrieves the result from the output of a Runnable object.
-
-        Args:
-            output (Runnable): The output object to retrieve the result from.
-            stream (bool): Indicates whether to use streaming or invocation mode.
-            input_value (str): The input value to pass to the output object.
-
-        Returns:
-            The result obtained from the output object.
-        """
-        if stream:
-            result = runnable.stream(input_value)
-        else:
-            message = runnable.invoke(input_value)
-            result = message.content if hasattr(message, "content") else message
-            self.status = result
-        return result
-
-    def get_chat_result(
-        self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
-    ):
-        messages = []
-        if input_value:
-            messages.append(HumanMessage(input_value))
-        if system_message:
-            messages.append(SystemMessage(system_message))
-        if stream:
-            result = runnable.stream(messages)
-        else:
-            message = runnable.invoke(messages)
-            result = message.content
-            self.status = result
-        return result

src/backend/langflow/components/experimental/FlowTool.py

@@ -1,13 +1,12 @@
 from typing import Any, List, Optional, Text
 
 from langchain_core.tools import StructuredTool
-from loguru import logger
+from langflow.custom import CustomComponent
-
-from langflow import CustomComponent
 from langflow.field_typing import Tool
 from langflow.graph.graph.base import Graph
 from langflow.helpers.flow import build_function_and_schema
 from langflow.schema.dotdict import dotdict
+from loguru import logger
 
 
 class FlowToolComponent(CustomComponent):

src/backend/langflow/components/tools/SearchAPITool.py

@@ -1,7 +1,6 @@
 from langchain_community.tools.searchapi import SearchAPIRun
 from langchain_community.utilities.searchapi import SearchApiAPIWrapper
-
+from langflow.custom import CustomComponent
-from langflow import CustomComponent
 from langflow.field_typing import Tool