feat: Add ruff rules for error messages (EM) (#3978)

Add ruff rules for error messages (EM)

src/backend/base/langflow/__main__.py

@@ -403,7 +403,8 @@ def superuser(
             if result:
                 typer.echo("Default folder created successfully.")
             else:
-                raise RuntimeError("Could not create default folder.")
+                msg = "Could not create default folder."
+                raise RuntimeError(msg)
             typer.echo("Superuser created successfully.")
 
         else:

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

@@ -135,7 +135,8 @@ async def build_graph_from_data(flow_id: str, payload: dict, **kwargs):
     for vertex_id in graph._has_session_id_vertices:
         vertex = graph.get_vertex(vertex_id)
         if vertex is None:
-            raise ValueError(f"Vertex {vertex_id} not found")
+            msg = f"Vertex {vertex_id} not found"
+            raise ValueError(msg)
         if not vertex._raw_params.get("session_id"):
             vertex.update_raw_params({"session_id": flow_id}, overwrite=True)
 
@@ -150,7 +151,8 @@ async def build_graph_from_db_no_cache(flow_id: str, session: Session):
     """Build and cache the graph."""
     flow: Flow | None = session.get(Flow, flow_id)
     if not flow or not flow.data:
-        raise ValueError("Invalid flow ID")
+        msg = "Invalid flow ID"
+        raise ValueError(msg)
     return await build_graph_from_data(flow_id, flow.data, flow_name=flow.name, user_id=str(flow.user_id))
 
 
@@ -260,4 +262,5 @@ async def cascade_delete_flow(session: Session, flow: Flow):
         session.exec(delete(VertexBuildTable).where(VertexBuildTable.flow_id == flow.id))  # type: ignore
         session.exec(delete(Flow).where(Flow.id == flow.id))  # type: ignore
     except Exception as e:
-        raise RuntimeError(f"Unable to cascade delete flow: ${flow.id}", e)
+        msg = f"Unable to cascade delete flow: ${flow.id}"
+        raise RuntimeError(msg, e)

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

@@ -340,7 +340,8 @@ async def build_flow(
             vertex_build_response_json = vertex_build_response.model_dump_json()
             build_data = json.loads(vertex_build_response_json)
         except Exception as exc:
-            raise ValueError(f"Error serializing vertex build response: {exc}") from exc
+            msg = f"Error serializing vertex build response: {exc}"
+            raise ValueError(msg) from exc
         event_manager.on_end_vertex(data={"build_data": build_data})
         await client_consumed_queue.get()
         if vertex_build_response.valid:
@@ -652,13 +653,15 @@ async def build_vertex_stream(
                 cache = await chat_service.get_cache(flow_id_str)
                 if not cache:
                     # If there's no cache
-                    raise ValueError(f"No cache found for {flow_id_str}.")
+                    msg = f"No cache found for {flow_id_str}."
+                    raise ValueError(msg)
                 else:
                     graph = cache.get("result")
 
                 vertex: InterfaceVertex = graph.get_vertex(vertex_id)
                 if not hasattr(vertex, "stream"):
-                    raise ValueError(f"Vertex {vertex_id} does not support streaming")
+                    msg = f"Vertex {vertex_id} does not support streaming"
+                    raise ValueError(msg)
                 if isinstance(vertex._built_result, str) and vertex._built_result:
                     stream_data = StreamData(
                         event="message",
@@ -691,7 +694,8 @@ async def build_vertex_stream(
                     )
                     yield str(stream_data)
                 else:
-                    raise ValueError(f"No result found for vertex {vertex_id}")
+                    msg = f"No result found for vertex {vertex_id}"
+                    raise ValueError(msg)
 
             except Exception as exc:
                 logger.exception(f"Error building Component: {exc}")

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

@@ -94,17 +94,15 @@ def validate_input_and_tweaks(input_request: SimplifiedAPIRequest):
                 has_input_value = value.get("input_value") is not None
                 input_value_is_chat = input_request.input_value is not None and input_request.input_type == "chat"
                 if has_input_value and input_value_is_chat:
-                    raise InvalidChatInputException(
+                    msg = "If you pass an input_value to the chat input, you cannot pass a tweak with the same name."
-                        "If you pass an input_value to the chat input, you cannot pass a tweak with the same name."
+                    raise InvalidChatInputException(msg)
-                    )
         elif "Text Input" in key or "TextInput" in key:
             if isinstance(value, dict):
                 has_input_value = value.get("input_value") is not None
                 input_value_is_text = input_request.input_value is not None and input_request.input_type == "text"
                 if has_input_value and input_value_is_text:
-                    raise InvalidChatInputException(
+                    msg = "If you pass an input_value to the text input, you cannot pass a tweak with the same name."
-                        "If you pass an input_value to the text input, you cannot pass a tweak with the same name."
+                    raise InvalidChatInputException(msg)
-                    )
 
 
 async def simple_run_flow(
@@ -120,7 +118,8 @@ async def simple_run_flow(
         user_id = api_key_user.id if api_key_user else None
         flow_id_str = str(flow.id)
         if flow.data is None:
-            raise ValueError(f"Flow {flow_id_str} has no data")
+            msg = f"Flow {flow_id_str} has no data"
+            raise ValueError(msg)
         graph_data = flow.data.copy()
         graph_data = process_tweaks(graph_data, input_request.tweaks or {}, stream=stream)
         graph = Graph.from_payload(graph_data, flow_id=flow_id_str, user_id=str(user_id), flow_name=flow.name)
@@ -331,8 +330,9 @@ async def webhook_run_flow(
         data = await request.body()
         if not data:
             logger.error("Request body is empty")
+            msg = "Request body is empty. You should provide a JSON payload containing the flow ID."
             raise ValueError(
-                "Request body is empty. You should provide a JSON payload containing the flow ID.",
+                msg,
             )
 
         # get all webhook components in the flow
@@ -448,7 +448,8 @@ async def experimental_run_flow(
             session_data = await session_service.load_session(session_id, flow_id=flow_id_str)
             graph, artifacts = session_data if session_data else (None, None)
             if graph is None:
-                raise ValueError(f"Session {session_id} not found")
+                msg = f"Session {session_id} not found"
+                raise ValueError(msg)
         else:
             # Get the flow that matches the flow_id and belongs to the user
             # flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first()
@@ -456,10 +457,12 @@ async def experimental_run_flow(
                 select(Flow).where(Flow.id == flow_id_str).where(Flow.user_id == api_key_user.id)
             ).first()
             if flow is None:
-                raise ValueError(f"Flow {flow_id_str} not found")
+                msg = f"Flow {flow_id_str} not found"
+                raise ValueError(msg)
 
             if flow.data is None:
-                raise ValueError(f"Flow {flow_id_str} has no data")
+                msg = f"Flow {flow_id_str} has no data"
+                raise ValueError(msg)
             graph_data = flow.data
             graph_data = process_tweaks(graph_data, tweaks or {})
             graph = Graph.from_payload(graph_data, flow_id=flow_id_str)

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

@@ -110,7 +110,8 @@ class ChatResponse(ChatMessage):
     @classmethod
     def validate_message_type(cls, v):
         if v not in ["start", "stream", "end", "error", "info", "file"]:
-            raise ValueError("type must be start, stream, end, error, info, or file")
+            msg = "type must be start, stream, end, error, info, or file"
+            raise ValueError(msg)
         return v
 
 
@@ -134,7 +135,8 @@ class FileResponse(ChatMessage):
     @classmethod
     def validate_data_type(cls, v):
         if v not in ["image", "csv"]:
-            raise ValueError("data_type must be image or csv")
+            msg = "data_type must be image or csv"
+            raise ValueError(msg)
         return v
 
 

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

@@ -67,9 +67,11 @@ class LCAgentComponent(Component):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)
 
     def get_agent_kwargs(self, flatten: bool = False) -> dict:
         base = {
@@ -102,7 +104,8 @@ class LCAgentComponent(Component):
         )
         self.status = result
         if "output" not in result:
-            raise ValueError("Output key not found in result. Tried 'output'.")
+            msg = "Output key not found in result. Tried 'output'."
+            raise ValueError(msg)
 
         return cast(str, result.get("output"))
 
@@ -148,7 +151,8 @@ class LCToolsAgentComponent(LCAgentComponent):
         )
         self.status = result
         if "output" not in result:
-            raise ValueError("Output key not found in result. Tried 'output'.")
+            msg = "Output key not found in result. Tried 'output'."
+            raise ValueError(msg)
 
         return cast(str, result.get("output"))
 

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

@@ -46,7 +46,8 @@ class BaseCrewComponent(Component):
         return self.tasks, self.agents
 
     def build_crew(self) -> Crew:
-        raise NotImplementedError("build_crew must be implemented in subclasses")
+        msg = "build_crew must be implemented in subclasses"
+        raise NotImplementedError(msg)
 
     def get_task_callback(
         self,

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

@@ -12,6 +12,8 @@ class LCChainComponent(Component):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)

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

@@ -49,7 +49,8 @@ def parse_curl_command(curl_command):
     tokens = shlex.split(normalize_newlines(curl_command))
     tokens = [token for token in tokens if token and token != " "]
     if tokens and "curl" not in tokens[0]:
-        raise ValueError("Invalid curl command")
+        msg = "Invalid curl command"
+        raise ValueError(msg)
     args_template = {
         "command": None,
         "url": None,

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

@@ -53,7 +53,8 @@ def retrieve_file_paths(
 ) -> 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.")
+        msg = f"Path {path} must exist and be a directory."
+        raise ValueError(msg)
 
     def match_types(p: Path) -> bool:
         return any(p.suffix == f".{t}" for t in types) if types else True
@@ -83,7 +84,8 @@ def partition_file_to_data(file_path: str, silent_errors: bool) -> Data | None:
         elements = partition(file_path)
     except Exception as e:
         if not silent_errors:
-            raise ValueError(f"Error loading file {file_path}: {e}") from e
+            msg = f"Error loading file {file_path}: {e}"
+            raise ValueError(msg) from e
         return None
 
     # Create a Data
@@ -147,7 +149,8 @@ def parse_text_file_to_data(file_path: str, silent_errors: bool) -> Data | None:
             text = ET.tostring(xml_element, encoding="unicode")
     except Exception as e:
         if not silent_errors:
-            raise ValueError(f"Error loading file {file_path}: {e}") from e
+            msg = f"Error loading file {file_path}: {e}"
+            raise ValueError(msg) from e
         return None
 
     record = Data(data={"file_path": file_path, "text": text})

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

@@ -15,9 +15,12 @@ class LCEmbeddingsModel(Component):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)
 
     def build_embeddings(self) -> Embeddings:
-        raise NotImplementedError("You must implement the build_embeddings method in your class.")
+        msg = "You must implement the build_embeddings method in your class."
+        raise NotImplementedError(msg)

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

@@ -22,7 +22,8 @@ class ChatComponent(Component):
             flow_id=self.graph.flow_id,
         )
         if len(messages) > 1:
-            raise ValueError("Only one message can be stored at a time.")
+            msg = "Only one message can be stored at a time."
+            raise ValueError(msg)
         stored_message = messages[0]
         if hasattr(self, "_event_manager") and self._event_manager and stored_message.id:
             if not isinstance(message.text, str):
@@ -55,7 +56,8 @@ class ChatComponent(Component):
     def _stream_message(self, message: Message, message_id: str) -> str:
         iterator = message.text
         if not isinstance(iterator, AsyncIterator | Iterator):
-            raise ValueError("The message must be an iterator or an async iterator.")
+            msg = "The message must be an iterator or an async iterator."
+            raise ValueError(msg)
 
         if isinstance(iterator, AsyncIterator):
             return run_until_complete(self._handle_async_iterator(iterator, message, message_id))

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

@@ -19,9 +19,11 @@ class LCToolComponent(Component):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)
 
     @abstractmethod
     def run_model(self) -> Data | list[Data]:

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

@@ -22,9 +22,11 @@ class LCChatMemoryComponent(Component):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)
 
     def build_base_memory(self) -> BaseChatMemory:
         return ConversationBufferMemory(chat_memory=self.build_message_history())

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

@@ -48,9 +48,11 @@ class LCModelComponent(Component):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)
 
     def text_response(self) -> Message:
         input_value = self.input_value
@@ -145,7 +147,8 @@ class LCModelComponent(Component):
     ):
         messages: list[BaseMessage] = []
         if not input_value and not system_message:
-            raise ValueError("The message you want to send to the model is empty.")
+            msg = "The message you want to send to the model is empty."
+            raise ValueError(msg)
         system_message_added = False
         if input_value:
             if isinstance(input_value, Message):

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

@@ -127,14 +127,16 @@ def validate_prompt(prompt_template: str, silent_errors: bool = False) -> list[s
     # Check if there are invalid characters in the input_variables
     input_variables = _check_input_variables(input_variables)
     if any(var in _INVALID_NAMES for var in input_variables):
-        raise ValueError(f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. ")
+        msg = f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. "
+        raise ValueError(msg)
 
     try:
         PromptTemplate(template=prompt_template, input_variables=input_variables)
     except Exception as exc:
         logger.error(f"Invalid prompt: {exc}")
         if not silent_errors:
-            raise ValueError(f"Invalid prompt: {exc}") from exc
+            msg = f"Invalid prompt: {exc}"
+            raise ValueError(msg) from exc
 
     return input_variables
 

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

@@ -14,9 +14,11 @@ class LCTextSplitterComponent(LCDocumentTransformerComponent):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)
 
     def build_document_transformer(self) -> BaseDocumentTransformer:
         return self.build_text_splitter()

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

@@ -35,7 +35,8 @@ class ComponentTool(BaseTool):
             results, _ = self.component(**kwargs)
             return results
         except Exception as e:
-            raise ToolException(f"Error running {self.name}: {e}")
+            msg = f"Error running {self.name}: {e}"
+            raise ToolException(msg)
 
 
 ComponentTool.update_forward_refs()

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

@@ -33,7 +33,8 @@ class FlowTool(BaseTool):
         elif self.graph is not None:
             return build_schema_from_inputs(self.name, get_flow_inputs(self.graph))
         else:
-            raise ToolException("No input schema available.")
+            msg = "No input schema available."
+            raise ToolException(msg)
 
     def _run(
         self,
@@ -45,9 +46,8 @@ class FlowTool(BaseTool):
         if len(args_names) == len(args):
             kwargs = {arg["arg_name"]: arg_value for arg, arg_value in zip(args_names, args)}
         elif len(args_names) != len(args) and len(args) != 0:
-            raise ToolException(
+            msg = "Number of arguments does not match the number of inputs. Pass keyword arguments instead."
-                "Number of arguments does not match the number of inputs. Pass keyword arguments instead."
+            raise ToolException(msg)
-            )
         tweaks = {arg["component_name"]: kwargs[arg["arg_name"]] for arg in args_names}
 
         run_outputs = run_until_complete(
@@ -72,16 +72,16 @@ class FlowTool(BaseTool):
         """Validate the inputs."""
 
         if len(args) > 0 and len(args) != len(args_names):
-            raise ToolException(
+            msg = "Number of positional arguments does not match the number of inputs. Pass keyword arguments instead."
-                "Number of positional arguments does not match the number of inputs. Pass keyword arguments instead."
+            raise ToolException(msg)
-            )
 
         if len(args) == len(args_names):
             kwargs = {arg_name["arg_name"]: arg_value for arg_name, arg_value in zip(args_names, args)}
 
         missing_args = [arg["arg_name"] for arg in args_names if arg["arg_name"] not in kwargs]
         if missing_args:
-            raise ToolException(f"Missing required arguments: {', '.join(missing_args)}")
+            msg = f"Missing required arguments: {', '.join(missing_args)}"
+            raise ToolException(msg)
 
         return kwargs
 

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

@@ -47,10 +47,11 @@ class LCVectorStoreComponent(Component):
         if hasattr(cls, "build_vector_store"):
             method = cls.build_vector_store
             if not hasattr(method, "_is_cached_vector_store_checked"):
-                raise TypeError(
+                msg = (
                     f"The method 'build_vector_store' in class {cls.__name__} "
                     "must be decorated with @check_cached_vector_store"
                 )
+                raise TypeError(msg)
 
     trace_type = "retriever"
     outputs = [
@@ -81,9 +82,11 @@ class LCVectorStoreComponent(Component):
         output_names = [output.name for output in self.outputs]
         for method_name in required_output_methods:
             if method_name not in output_names:
-                raise ValueError(f"Output with name '{method_name}' must be defined.")
+                msg = f"Output with name '{method_name}' must be defined."
+                raise ValueError(msg)
             elif not hasattr(self, method_name):
-                raise ValueError(f"Method '{method_name}' must be defined.")
+                msg = f"Method '{method_name}' must be defined."
+                raise ValueError(msg)
 
     def search_with_vector_store(
         self,
@@ -112,7 +115,8 @@ class LCVectorStoreComponent(Component):
         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:
-            raise ValueError("Invalid inputs provided.")
+            msg = "Invalid inputs provided."
+            raise ValueError(msg)
         data = docs_to_data(docs)
         self.status = data
         return data
@@ -136,7 +140,8 @@ class LCVectorStoreComponent(Component):
                 self.status = "Retriever built successfully."
             return retriever
         else:
-            raise ValueError(f"Vector Store {vector_store.__class__.__name__} does not have an as_retriever method.")
+            msg = f"Vector Store {vector_store.__class__.__name__} does not have an as_retriever method."
+            raise ValueError(msg)
 
     def search_documents(self) -> list[Data]:
         """
@@ -175,4 +180,5 @@ class LCVectorStoreComponent(Component):
         """
         Builds the Vector Store object.
         """
-        raise NotImplementedError("build_vector_store method must be implemented.")
+        msg = "build_vector_store method must be implemented."
+        raise NotImplementedError(msg)

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

@@ -38,7 +38,8 @@ class OpenAIToolsAgentComponent(LCToolsAgentComponent):
 
     def create_agent_runnable(self):
         if "input" not in self.user_prompt:
-            raise ValueError("Prompt must contain 'input' key.")
+            msg = "Prompt must contain 'input' key."
+            raise ValueError(msg)
         messages = [
             ("system", self.system_prompt),
             ("placeholder", "{chat_history}"),

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

@@ -33,7 +33,8 @@ class ToolCallingAgentComponent(LCToolsAgentComponent):
 
     def create_agent_runnable(self):
         if "input" not in self.user_prompt:
-            raise ValueError("Prompt must contain 'input' key.")
+            msg = "Prompt must contain 'input' key."
+            raise ValueError(msg)
         messages = [
             ("system", self.system_prompt),
             ("placeholder", "{chat_history}"),

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

@@ -55,7 +55,8 @@ Question: {input}
 
     def create_agent_runnable(self):
         if "input" not in self.user_prompt:
-            raise ValueError("Prompt must contain 'input' key.")
+            msg = "Prompt must contain 'input' key."
+            raise ValueError(msg)
         messages = [
             ("system", self.system_prompt),
             ("placeholder", "{chat_history}"),

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

@@ -25,7 +25,8 @@ class GetEnvVar(Component):
 
     def process_inputs(self) -> Message:
         if self.env_var_name not in os.environ:
-            raise Exception(f"Environment variable {self.env_var_name} not set")
+            msg = f"Environment variable {self.env_var_name} not set"
+            raise Exception(msg)
         else:
             message = Message(text=os.environ[self.env_var_name])
             return message

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

@@ -92,4 +92,5 @@ class AssistantsRun(Component):
             return message
         except Exception as e:
             print(e)
-            raise Exception(f"Error running assistant: {e}")
+            msg = f"Error running assistant: {e}"
+            raise Exception(msg)

src/backend/base/langflow/components/chains/SQLGenerator.py

@@ -34,14 +34,16 @@ class SQLGeneratorComponent(LCChainComponent):
             prompt_template = None
 
         if self.top_k < 1:
-            raise ValueError("Top K must be greater than 0.")
+            msg = "Top K must be greater than 0."
+            raise ValueError(msg)
 
         if not prompt_template:
             sql_query_chain = create_sql_query_chain(llm=self.llm, db=self.db, k=self.top_k)
         else:
             # Check if {question} is in the prompt
             if "{question}" not in prompt_template.template or "question" not in prompt_template.input_variables:
-                raise ValueError("Prompt must contain `{question}` to be used with Natural Language to SQL.")
+                msg = "Prompt must contain `{question}` to be used with Natural Language to SQL."
+                raise ValueError(msg)
             sql_query_chain = create_sql_query_chain(llm=self.llm, db=self.db, prompt=prompt_template, k=self.top_k)
         query_writer: Runnable = sql_query_chain | {"query": lambda x: x.replace("SQLQuery:", "").strip()}
         response = query_writer.invoke(

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

@@ -93,7 +93,8 @@ class APIRequestComponent(Component):
                 build_config["body"]["value"] = {}
         except Exception as exc:
             logger.error(f"Error parsing curl: {exc}")
-            raise ValueError(f"Error parsing curl: {exc}")
+            msg = f"Error parsing curl: {exc}"
+            raise ValueError(msg)
         return build_config
 
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
@@ -112,7 +113,8 @@ class APIRequestComponent(Component):
     ) -> Data:
         method = method.upper()
         if method not in ["GET", "POST", "PATCH", "PUT", "DELETE"]:
-            raise ValueError(f"Unsupported method: {method}")
+            msg = f"Unsupported method: {method}"
+            raise ValueError(msg)
 
         if isinstance(body, str) and body:
             try:
@@ -120,7 +122,8 @@ class APIRequestComponent(Component):
             except Exception as e:
                 logger.error(f"Error decoding JSON data: {e}")
                 body = None
-                raise ValueError(f"Error decoding JSON data: {e}")
+                msg = f"Error decoding JSON data: {e}"
+                raise ValueError(msg)
 
         data = body if body else None
 

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

@@ -33,16 +33,19 @@ class FileComponent(Component):
 
     def load_file(self) -> Data:
         if not self.path:
-            raise ValueError("Please, upload a file to use this component.")
+            msg = "Please, upload a file to use this component."
+            raise ValueError(msg)
         resolved_path = self.resolve_path(self.path)
         silent_errors = self.silent_errors
 
         extension = Path(resolved_path).suffix[1:].lower()
 
         if extension == "doc":
-            raise ValueError("doc files are not supported. Please save as .docx")
+            msg = "doc files are not supported. Please save as .docx"
+            raise ValueError(msg)
         if extension not in TEXT_FILE_TYPES:
-            raise ValueError(f"Unsupported file type: {extension}")
+            msg = f"Unsupported file type: {extension}"
+            raise ValueError(msg)
 
         data = parse_text_file_to_data(resolved_path, silent_errors)
         self.status = data if data else "No data"

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

@@ -95,7 +95,8 @@ class GmailLoaderComponent(Component):
                     if name == "From":
                         from_email = values["value"]
                 if from_email is None:
-                    raise ValueError("From email not found.")
+                    msg = "From email not found."
+                    raise ValueError(msg)
 
                 if "parts" in msg["payload"]:
                     parts = msg["payload"]["parts"]
@@ -113,7 +114,8 @@ class GmailLoaderComponent(Component):
                             additional_kwargs={"sender": from_email},
                         )
                         return message
-                raise ValueError("No plain text part found in the email.")
+                msg = "No plain text part found in the email."
+                raise ValueError(msg)
 
             def _get_message_data(self, service: Any, message: Any) -> ChatSession:
                 msg = service.users().messages().get(userId="me", id=message["id"]).execute()
@@ -141,7 +143,8 @@ class GmailLoaderComponent(Component):
                                 if message_id == in_reply_to:
                                     response_email = message
                     if response_email is None:
-                        raise ValueError("Response email not found in the thread.")
+                        msg = "Response email not found in the thread."
+                        raise ValueError(msg)
                     starter_content = self._extract_email_content(response_email)
                     return ChatSession(messages=[starter_content, message_content])
                 else:
@@ -172,7 +175,8 @@ class GmailLoaderComponent(Component):
         try:
             token_info = json.loads(json_string)
         except JSONDecodeError as e:
-            raise ValueError("Invalid JSON string") from e
+            msg = "Invalid JSON string"
+            raise ValueError(msg) from e
 
         creds = Credentials.from_authorized_user_info(token_info)
 
@@ -182,11 +186,11 @@ class GmailLoaderComponent(Component):
         try:
             docs = loader.load()
         except RefreshError as e:
-            raise ValueError(
+            msg = "Authentication error: Unable to refresh authentication token. Please try to reauthenticate."
-                "Authentication error: Unable to refresh authentication token. Please try to reauthenticate."
+            raise ValueError(msg) from e
-            ) from e
         except Exception as e:
-            raise ValueError(f"Error loading documents: {e}") from e
+            msg = f"Error loading documents: {e}"
+            raise ValueError(msg) from e
 
         # Return the loaded documents
         self.status = docs

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

@@ -44,7 +44,8 @@ class GoogleDriveComponent(Component):
                 if self.creds:
                     return self.creds
                 else:
-                    raise ValueError("No credentials provided.")
+                    msg = "No credentials provided."
+                    raise ValueError(msg)
 
             class Config:
                 arbitrary_types_allowed = True
@@ -53,7 +54,8 @@ class GoogleDriveComponent(Component):
 
         document_ids = [self.document_id]
         if len(document_ids) != 1:
-            raise ValueError("Expected a single document ID")
+            msg = "Expected a single document ID"
+            raise ValueError(msg)
 
         # TODO: Add validation to check if the document ID is valid
 
@@ -61,7 +63,8 @@ class GoogleDriveComponent(Component):
         try:
             token_info = json.loads(json_string)
         except JSONDecodeError as e:
-            raise ValueError("Invalid JSON string") from e
+            msg = "Invalid JSON string"
+            raise ValueError(msg) from e
 
         # Initialize the custom loader with the provided credentials and document IDs
         loader = CustomGoogleDriveLoader(
@@ -73,11 +76,11 @@ class GoogleDriveComponent(Component):
             docs = loader.load()
         # catch google.auth.exceptions.RefreshError
         except RefreshError as e:
-            raise ValueError(
+            msg = "Authentication error: Unable to refresh authentication token. Please try to reauthenticate."
-                "Authentication error: Unable to refresh authentication token. Please try to reauthenticate."
+            raise ValueError(msg) from e
-            ) from e
         except Exception as e:
-            raise ValueError(f"Error loading documents: {e}") from e
+            msg = f"Error loading documents: {e}"
+            raise ValueError(msg) from e
 
         assert len(docs) == 1, "Expected a single document to be loaded."
 

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

@@ -65,7 +65,8 @@ class URLComponent(Component):
         )
 
         if not url_regex.match(string):
-            raise ValueError(f"Invalid URL: {string}")
+            msg = f"Invalid URL: {string}"
+            raise ValueError(msg)
 
         return string
 

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

@@ -125,9 +125,8 @@ class ChatLiteLLMModelComponent(LCModelComponent):
             litellm.drop_params = True
             litellm.set_verbose = self.verbose
         except ImportError:
-            raise ChatLiteLLMException(
+            msg = "Could not import litellm python package. " "Please install it with `pip install litellm`"
-                "Could not import litellm python package. " "Please install it with `pip install litellm`"
+            raise ChatLiteLLMException(msg)
-            )
         # Remove empty keys
         if "" in self.kwargs:
             del self.kwargs[""]
@@ -136,9 +135,11 @@ class ChatLiteLLMModelComponent(LCModelComponent):
         # Report missing fields for Azure provider
         if self.provider == "Azure":
             if "api_base" not in self.kwargs:
-                raise Exception("Missing api_base on kwargs")
+                msg = "Missing api_base on kwargs"
+                raise Exception(msg)
             if "api_version" not in self.model_kwargs:
-                raise Exception("Missing api_version on model_kwargs")
+                msg = "Missing api_version on model_kwargs"
+                raise Exception(msg)
         output = ChatLiteLLM(
             model=f"{self.provider.lower()}/{self.model}",
             client=None,

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

@@ -40,7 +40,8 @@ class ExtractKeyFromDataComponent(CustomComponent):
                 extracted_keys[key] = getattr(data, key)
             except AttributeError:
                 if not silent_error:
-                    raise KeyError(f"The key '{key}' does not exist in the data.")
+                    msg = f"The key '{key}' does not exist in the data."
+                    raise KeyError(msg)
         return_data = Data(data=extracted_keys)
         self.status = return_data
         return return_data

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

@@ -45,7 +45,8 @@ class SubFlowComponent(CustomComponent):
             try:
                 flow_data = self.get_flow(field_value)
                 if not flow_data:
-                    raise ValueError(f"Flow {field_value} not found.")
+                    msg = f"Flow {field_value} not found."
+                    raise ValueError(msg)
                 graph = Graph.from_payload(flow_data.data["data"])
                 # Get all inputs from the graph
                 inputs = get_flow_inputs(graph)

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

@@ -164,7 +164,8 @@ class AssemblyAILeMUR(Component):
                 max_output_size=self.max_output_size,
             )
         else:
-            raise ValueError(f"Endpoint not supported: {endpoint}")
+            msg = f"Endpoint not supported: {endpoint}"
+            raise ValueError(msg)
 
         return result.dict()
 
@@ -178,4 +179,5 @@ class AssemblyAILeMUR(Component):
         elif model_name == "claude3_sonnet":
             return aai.LemurModel.claude3_sonnet
         else:
-            raise ValueError(f"Model name not supported: {model_name}")
+            msg = f"Model name not supported: {model_name}"
+            raise ValueError(msg)

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

@@ -77,6 +77,7 @@ class AzureOpenAIEmbeddingsComponent(LCModelComponent):
                 dimensions=self.dimensions or None,
             )
         except Exception as e:
-            raise ValueError(f"Could not connect to AzureOpenAIEmbeddings API: {str(e)}") from e
+            msg = f"Could not connect to AzureOpenAIEmbeddings API: {str(e)}"
+            raise ValueError(msg) from e
 
         return embeddings

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

@@ -32,7 +32,8 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
 
     def build_embeddings(self) -> Embeddings:
         if not self.api_key:
-            raise ValueError("API Key is required")
+            msg = "API Key is required"
+            raise ValueError(msg)
 
         class HotaGoogleGenerativeAIEmbeddings(GoogleGenerativeAIEmbeddings):
             def __init__(self, *args, **kwargs):
@@ -85,7 +86,8 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
                             BatchEmbedContentsRequest(requests=requests, model=self.model)
                         )
                     except Exception as e:
-                        raise GoogleGenerativeAIError(f"Error embedding content: {e}") from e
+                        msg = f"Error embedding content: {e}"
+                        raise GoogleGenerativeAIError(msg) from e
                     embeddings.extend([list(np.pad(e.values, (0, 768), "constant")) for e in result.embeddings])
                 return embeddings
 

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

@@ -46,22 +46,25 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(LCEmbeddingsModel):
     def validate_inference_endpoint(self, inference_endpoint: str) -> bool:
         parsed_url = urlparse(inference_endpoint)
         if not all([parsed_url.scheme, parsed_url.netloc]):
-            raise ValueError(
+            msg = (
                 f"Invalid inference endpoint format: '{self.inference_endpoint}'. "
                 "Please ensure the URL includes both a scheme (e.g., 'http://' or 'https://') and a domain name. "
                 "Example: 'http://localhost:8080' or 'https://api.example.com'"
             )
+            raise ValueError(msg)
 
         try:
             response = requests.get(f"{inference_endpoint}/health", timeout=5)
         except requests.RequestException:
-            raise ValueError(
+            msg = (
                 f"Inference endpoint '{inference_endpoint}' is not responding. "
                 "Please ensure the URL is correct and the service is running."
             )
+            raise ValueError(msg)
 
         if response.status_code != 200:
-            raise ValueError(f"HuggingFace health check failed: {response.status_code}")
+            msg = f"HuggingFace health check failed: {response.status_code}"
+            raise ValueError(msg)
         # returning True to solve linting error
         return True
 
@@ -86,11 +89,13 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(LCEmbeddingsModel):
             self.validate_inference_endpoint(api_url)
             api_key = SecretStr("DummyAPIKeyForLocalDeployment")
         elif not self.api_key:
-            raise ValueError("API Key is required for non-local inference endpoints")
+            msg = "API Key is required for non-local inference endpoints"
+            raise ValueError(msg)
         else:
             api_key = SecretStr(self.api_key)
 
         try:
             return self.create_huggingface_embeddings(api_key, api_url, self.model_name)
         except Exception as e:
-            raise ValueError("Could not connect to HuggingFace Inference API.") from e
+            msg = "Could not connect to HuggingFace Inference API."
+            raise ValueError(msg) from e

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

@@ -43,7 +43,8 @@ class MistralAIEmbeddingsComponent(LCModelComponent):
 
     def build_embeddings(self) -> Embeddings:
         if not self.mistral_api_key:
-            raise ValueError("Mistral API Key is required")
+            msg = "Mistral API Key is required"
+            raise ValueError(msg)
 
         api_key = SecretStr(self.mistral_api_key)
 

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

@@ -51,14 +51,16 @@ class NVIDIAEmbeddingsComponent(LCEmbeddingsModel):
                 build_config["model"]["options"] = ids
                 build_config["model"]["value"] = ids[0]
             except Exception as e:
-                raise ValueError(f"Error getting model names: {e}")
+                msg = f"Error getting model names: {e}"
+                raise ValueError(msg)
         return build_config
 
     def build_embeddings(self) -> Embeddings:
         try:
             from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
         except ImportError:
-            raise ImportError("Please install langchain-nvidia-ai-endpoints to use the Nvidia model.")
+            msg = "Please install langchain-nvidia-ai-endpoints to use the Nvidia model."
+            raise ImportError(msg)
         try:
             output = NVIDIAEmbeddings(
                 model=self.model,
@@ -67,5 +69,6 @@ class NVIDIAEmbeddingsComponent(LCEmbeddingsModel):
                 nvidia_api_key=self.nvidia_api_key,
             )  # type: ignore
         except Exception as e:
-            raise ValueError(f"Could not connect to NVIDIA API. Error: {e}") from e
+            msg = f"Could not connect to NVIDIA API. Error: {e}"
+            raise ValueError(msg) from e
         return output

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

@@ -43,5 +43,6 @@ class OllamaEmbeddingsComponent(LCModelComponent):
                 temperature=self.temperature,
             )  # type: ignore
         except Exception as e:
-            raise ValueError("Could not connect to Ollama API.") from e
+            msg = "Could not connect to Ollama API."
+            raise ValueError(msg) from e
         return output

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

@@ -39,9 +39,8 @@ class VertexAIEmbeddingsComponent(LCModelComponent):
         try:
             from langchain_google_vertexai import VertexAIEmbeddings
         except ImportError:
-            raise ImportError(
+            msg = "Please install the langchain-google-vertexai package to use the VertexAIEmbeddings component."
-                "Please install the langchain-google-vertexai package to use the VertexAIEmbeddings component."
+            raise ImportError(msg)
-            )
 
         from google.oauth2 import service_account
 

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

@@ -62,7 +62,8 @@ class ParseJSONDataComponent(Component):
                 try:
                     to_filter_as_dict.append(json.loads(repair_json(f)))
                 except JSONDecodeError as e:
-                    raise ValueError(f"Invalid JSON: {e}")
+                    msg = f"Invalid JSON: {e}"
+                    raise ValueError(msg)
 
         full_filter_str = json.dumps(to_filter_as_dict)
 

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

@@ -57,9 +57,10 @@ class FirecrawlCrawlApi(CustomComponent):
         try:
             from firecrawl.firecrawl import FirecrawlApp  # type: ignore
         except ImportError:
-            raise ImportError(
+            msg = (
                 "Could not import firecrawl integration package. " "Please install it with `pip install firecrawl-py`."
             )
+            raise ImportError(msg)
         if crawlerOptions:
             crawler_options_dict = crawlerOptions.__dict__["data"]["text"]
         else:

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

@@ -50,9 +50,10 @@ class FirecrawlScrapeApi(CustomComponent):
         try:
             from firecrawl.firecrawl import FirecrawlApp  # type: ignore
         except ImportError:
-            raise ImportError(
+            msg = (
                 "Could not import firecrawl integration package. " "Please install it with `pip install firecrawl-py`."
             )
+            raise ImportError(msg)
         if extractorOptions:
             extractor_options_dict = extractorOptions.__dict__["data"]["text"]
         else:

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

@@ -43,6 +43,7 @@ class JSONDocumentBuilder(CustomComponent):
         elif isinstance(document, Document):
             documents = Document(page_content=orjson_dumps({key: document.page_content}, indent_2=False))
         else:
-            raise TypeError(f"Expected Document or list of Documents, got {type(document)}")
+            msg = f"Expected Document or list of Documents, got {type(document)}"
+            raise TypeError(msg)
         self.repr_value = documents
         return documents

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

@@ -110,9 +110,11 @@ class SpiderTool(Component):
             elif self.mode == "crawl":
                 result = app.crawl_url(self.url, parameters)
             else:
-                raise ValueError(f"Invalid mode: {self.mode}. Must be 'scrape' or 'crawl'.")
+                msg = f"Invalid mode: {self.mode}. Must be 'scrape' or 'crawl'."
+                raise ValueError(msg)
         except Exception as e:
-            raise Exception(f"Error: {str(e)}")
+            msg = f"Error: {str(e)}"
+            raise Exception(msg)
 
         records = []
 

src/backend/base/langflow/components/memories/AstraDBChatMemory.py

@@ -53,10 +53,11 @@ class AstraDBChatMemory(LCChatMemoryComponent):
         try:
             from langchain_astradb.chat_message_histories import AstraDBChatMessageHistory
         except ImportError:
-            raise ImportError(
+            msg = (
                 "Could not import langchain Astra DB integration package. "
                 "Please install it with `pip install langchain-astradb`."
             )
+            raise ImportError(msg)
 
         memory = AstraDBChatMessageHistory(
             session_id=self.session_id,

src/backend/base/langflow/components/memories/CassandraChatMemory.py

@@ -55,9 +55,8 @@ class CassandraChatMemory(LCChatMemoryComponent):
         try:
             import cassio
         except ImportError:
-            raise ImportError(
+            msg = "Could not import cassio integration package. " "Please install it with `pip install cassio`."
-                "Could not import cassio integration package. " "Please install it with `pip install cassio`."
+            raise ImportError(msg)
-            )
 
         from uuid import UUID
 

src/backend/base/langflow/components/memories/ZepChatMemory.py

@@ -35,9 +35,8 @@ class ZepChatMemory(LCChatMemoryComponent):
 
             zep_python.zep_client.API_BASE_PATH = self.api_base_path
         except ImportError:
-            raise ImportError(
+            msg = "Could not import zep-python package. " "Please install it with `pip install zep-python`."
-                "Could not import zep-python package. " "Please install it with `pip install zep-python`."
+            raise ImportError(msg)
-            )
 
         zep_client = ZepClient(api_url=self.url, api_key=self.api_key)
         return ZepChatMessageHistory(session_id=self.session_id, zep_client=zep_client)

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

@@ -69,7 +69,8 @@ class AmazonBedrockComponent(LCModelComponent):
         try:
             from langchain_aws import ChatBedrock
         except ImportError:
-            raise ImportError("langchain_aws is not installed. Please install it with `pip install langchain_aws`.")
+            msg = "langchain_aws is not installed. Please install it with `pip install langchain_aws`."
+            raise ImportError(msg)
         if self.aws_access_key:
             import boto3  # type: ignore
 
@@ -103,5 +104,6 @@ class AmazonBedrockComponent(LCModelComponent):
                 streaming=self.stream,
             )
         except Exception as e:
-            raise ValueError("Could not connect to AmazonBedrock API.") from e
+            msg = "Could not connect to AmazonBedrock API."
+            raise ValueError(msg) from e
         return output  # type: ignore

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

@@ -63,9 +63,8 @@ class AnthropicModelComponent(LCModelComponent):
         try:
             from langchain_anthropic.chat_models import ChatAnthropic
         except ImportError:
-            raise ImportError(
+            msg = "langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`."
-                "langchain_anthropic is not installed. Please install it with `pip install langchain_anthropic`."
+            raise ImportError(msg)
-            )
         model = self.model
         anthropic_api_key = self.anthropic_api_key
         max_tokens = self.max_tokens
@@ -82,7 +81,8 @@ class AnthropicModelComponent(LCModelComponent):
                 streaming=self.stream,
             )
         except Exception as e:
-            raise ValueError("Could not connect to Anthropic API.") from e
+            msg = "Could not connect to Anthropic API."
+            raise ValueError(msg) from e
 
         return output  # type: ignore
 

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

@@ -78,6 +78,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
                 streaming=stream,
             )
         except Exception as e:
-            raise ValueError(f"Could not connect to AzureOpenAI API: {str(e)}") from e
+            msg = f"Could not connect to AzureOpenAI API: {str(e)}"
+            raise ValueError(msg) from e
 
         return output  # type: ignore

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

@@ -97,6 +97,7 @@ class QianfanChatEndpointComponent(LCModelComponent):
                 endpoint=endpoint,
             )
         except Exception as e:
-            raise ValueError("Could not connect to Baidu Qianfan API.") from e
+            msg = "Could not connect to Baidu Qianfan API."
+            raise ValueError(msg) from e
 
         return output  # type: ignore

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

@@ -63,7 +63,8 @@ class GoogleGenerativeAIComponent(LCModelComponent):
         try:
             from langchain_google_genai import ChatGoogleGenerativeAI
         except ImportError:
-            raise ImportError("The 'langchain_google_genai' package is required to use the Google Generative AI model.")
+            msg = "The 'langchain_google_genai' package is required to use the Google Generative AI model."
+            raise ImportError(msg)
 
         google_api_key = self.google_api_key
         model = self.model

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

@@ -113,6 +113,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
                 repetition_penalty=repetition_penalty,
             )
         except Exception as e:
-            raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
+            msg = "Could not connect to HuggingFace Endpoints API."
+            raise ValueError(msg) from e
 
         return llm

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

@@ -65,14 +65,16 @@ class NVIDIAModelComponent(LCModelComponent):
                 build_config["model_name"]["options"] = ids
                 build_config["model_name"]["value"] = ids[0]
             except Exception as e:
-                raise ValueError(f"Error getting model names: {e}")
+                msg = f"Error getting model names: {e}"
+                raise ValueError(msg)
         return build_config
 
     def build_model(self) -> LanguageModel:  # type: ignore[type-var]
         try:
             from langchain_nvidia_ai_endpoints import ChatNVIDIA
         except ImportError:
-            raise ImportError("Please install langchain-nvidia-ai-endpoints to use the NVIDIA model.")
+            msg = "Please install langchain-nvidia-ai-endpoints to use the NVIDIA model."
+            raise ImportError(msg)
         nvidia_api_key = self.nvidia_api_key
         temperature = self.temperature
         model_name: str = self.model_name

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

@@ -67,7 +67,8 @@ class ChatOllamaComponent(LCModelComponent):
                 model_names = [model["name"] for model in data.get("models", [])]
                 return model_names
         except Exception as e:
-            raise ValueError("Could not retrieve models. Please, make sure Ollama is running.") from e
+            msg = "Could not retrieve models. Please, make sure Ollama is running."
+            raise ValueError(msg) from e
 
     inputs = LCModelComponent._base_inputs + [
         StrInput(
@@ -261,6 +262,7 @@ class ChatOllamaComponent(LCModelComponent):
         try:
             output = ChatOllama(**llm_params)  # type: ignore
         except Exception as e:
-            raise ValueError("Could not initialize Ollama LLM.") from e
+            msg = "Could not initialize Ollama LLM."
+            raise ValueError(msg) from e
 
         return output  # type: ignore

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

@@ -42,9 +42,8 @@ class ChatVertexAIComponent(LCModelComponent):
         try:
             from langchain_google_vertexai import ChatVertexAI
         except ImportError:
-            raise ImportError(
+            msg = "Please install the langchain-google-vertexai package to use the VertexAIEmbeddings component."
-                "Please install the langchain-google-vertexai package to use the VertexAIEmbeddings component."
+            raise ImportError(msg)
-            )
         location = self.location or None
         if self.credentials:
             from google.cloud import aiplatform

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

@@ -110,7 +110,8 @@ class LangChainHubPromptComponent(Component):
 
         # Check if the api key is provided
         if not self.langchain_api_key:
-            raise ValueError("Please provide a LangChain API Key")
+            msg = "Please provide a LangChain API Key"
+            raise ValueError(msg)
 
         # Pull the prompt from LangChain Hub
         prompt_data = langchain.hub.pull(self.langchain_hub_prompt, api_key=self.langchain_api_key)

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

@@ -50,7 +50,8 @@ class CreateDataComponent(Component):
             existing_fields = {}
             if field_value_int > 15:
                 build_config["number_of_fields"]["value"] = 15
-                raise ValueError("Number of fields cannot exceed 15. Try using a Component to combine two Data.")
+                msg = "Number of fields cannot exceed 15. Try using a Component to combine two Data."
+                raise ValueError(msg)
             if len(build_config) > len(default_keys):
                 # back up the existing template fields
                 for key in build_config.copy():
@@ -99,4 +100,5 @@ class CreateDataComponent(Component):
         data_keys = self.get_data().keys()
         if self.text_key not in data_keys and self.text_key != "":
             formatted_data_keys = ", ".join(data_keys)
-            raise ValueError(f"Text Key: '{self.text_key}' not found in the Data keys: '{formatted_data_keys}'")
+            msg = f"Text Key: '{self.text_key}' not found in the Data keys: '{formatted_data_keys}'"
+            raise ValueError(msg)

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

@@ -74,11 +74,13 @@ class FlowToolComponent(LCToolComponent):
     def build_tool(self) -> Tool:
         FlowTool.update_forward_refs()
         if "flow_name" not in self._attributes or not self._attributes["flow_name"]:
-            raise ValueError("Flow name is required")
+            msg = "Flow name is required"
+            raise ValueError(msg)
         flow_name = self._attributes["flow_name"]
         flow_data = self.get_flow(flow_name)
         if not flow_data:
-            raise ValueError("Flow not found.")
+            msg = "Flow not found."
+            raise ValueError(msg)
         graph = Graph.from_payload(flow_data.data["data"])
         try:
             graph.set_run_id(self.graph.run_id)

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

@@ -48,9 +48,8 @@ class JSONCleaner(Component):
         try:
             from json_repair import repair_json  # type: ignore
         except ImportError:
-            raise ImportError(
+            msg = "Could not import the json_repair package." "Please install it with `pip install json_repair`."
-                "Could not import the json_repair package." "Please install it with `pip install json_repair`."
+            raise ImportError(msg)
-            )
 
         """Clean the input JSON string based on provided options and return the cleaned JSON string."""
         json_str = self.json_str
@@ -62,7 +61,8 @@ class JSONCleaner(Component):
             start = json_str.find("{")
             end = json_str.rfind("}")
             if start == -1 or end == -1:
-                raise ValueError("Invalid JSON string: Missing '{' or '}'")
+                msg = "Invalid JSON string: Missing '{' or '}'"
+                raise ValueError(msg)
             json_str = json_str[start : end + 1]
 
             if remove_control_chars:
@@ -78,7 +78,8 @@ class JSONCleaner(Component):
             self.status = result
             return Message(text=result)
         except Exception as e:
-            raise ValueError(f"Error cleaning JSON string: {str(e)}")
+            msg = f"Error cleaning JSON string: {str(e)}"
+            raise ValueError(msg)
 
     def _remove_control_characters(self, s: str) -> str:
         """Remove control characters from the string."""
@@ -94,4 +95,5 @@ class JSONCleaner(Component):
             json.loads(s)
             return s
         except json.JSONDecodeError as e:
-            raise ValueError(f"Invalid JSON string: {str(e)}")
+            msg = f"Invalid JSON string: {str(e)}"
+            raise ValueError(msg)

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

@@ -49,7 +49,8 @@ class RunFlowComponent(Component):
 
     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")
+            msg = "Flow name is required"
+            raise ValueError(msg)
         flow_name = self._attributes["flow_name"]
 
         results: list[RunOutputs | None] = await self.run_flow(

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

@@ -118,7 +118,8 @@ class RunnableExecComponent(Component):
     async def build_executor(self) -> Message:
         input_dict, status = self.get_input_dict(self.runnable, self.input_key, self.input_value)
         if not isinstance(self.runnable, AgentExecutor):
-            raise ValueError("The runnable must be an AgentExecutor")
+            msg = "The runnable must be an AgentExecutor"
+            raise ValueError(msg)
 
         if self.use_stream:
             return self.astream_events(input_dict)

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

@@ -48,7 +48,8 @@ class SQLExecutorComponent(CustomComponent):
         try:
             database = SQLDatabase.from_uri(database_url)
         except Exception as e:
-            raise ValueError(f"An error occurred while connecting to the database: {e}")
+            msg = f"An error occurred while connecting to the database: {e}"
+            raise ValueError(msg)
         try:
             tool = QuerySQLDataBaseTool(db=database)
             result = tool.run(query, include_columns=include_columns)

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

@@ -38,7 +38,8 @@ class SelectDataComponent(Component):
 
         # Validate that the selected index is within bounds
         if selected_index < 0 or selected_index >= len(self.data_list):
-            raise ValueError(f"Selected index {selected_index} is out of range.")
+            msg = f"Selected index {selected_index} is out of range."
+            raise ValueError(msg)
 
         # Return the selected Data object
         selected_data = self.data_list[selected_index]

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

@@ -39,7 +39,8 @@ class SubFlowComponent(Component):
             try:
                 flow_data = self.get_flow(field_value)
                 if not flow_data:
-                    raise ValueError(f"Flow {field_value} not found.")
+                    msg = f"Flow {field_value} not found."
+                    raise ValueError(msg)
                 graph = Graph.from_payload(flow_data.data["data"])
                 # Get all inputs from the graph
                 inputs = get_flow_inputs(graph)

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

@@ -56,7 +56,8 @@ class UpdateDataComponent(Component):
             existing_fields = {}
             if field_value_int > 15:
                 build_config["number_of_fields"]["value"] = 15
-                raise ValueError("Number of fields cannot exceed 15. Try using a Component to combine two Data.")
+                msg = "Number of fields cannot exceed 15. Try using a Component to combine two Data."
+                raise ValueError(msg)
             if len(build_config) > len(default_keys):
                 # back up the existing template fields
                 for key in build_config.copy():
@@ -105,4 +106,5 @@ class UpdateDataComponent(Component):
         """This function validates that the Text Key is one of the keys in the Data"""
         data_keys = data.data.keys()
         if self.text_key not in data_keys and self.text_key != "":
-            raise ValueError(f"Text Key: {self.text_key} not found in the Data keys: {','.join(data_keys)}")
+            msg = f"Text Key: {self.text_key} not found in the Data keys: {','.join(data_keys)}"
+            raise ValueError(msg)

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

@@ -48,5 +48,6 @@ class AmazonKendraRetrieverComponent(CustomComponent):
                 user_context=user_context,
             )  # type: ignore
         except Exception as e:
-            raise ValueError("Could not connect to AmazonKendra API.") from e
+            msg = "Could not connect to AmazonKendra API."
+            raise ValueError(msg) from e
         return cast(Retriever, output)

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

@@ -82,4 +82,5 @@ class CohereRerankComponent(LCVectorStoreComponent):
 
     @check_cached_vector_store
     def build_vector_store(self) -> VectorStore:
-        raise NotImplementedError("Cohere Rerank does not support vector stores.")
+        msg = "Cohere Rerank does not support vector stores."
+        raise NotImplementedError(msg)

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

@@ -25,5 +25,6 @@ class MetalRetrieverComponent(CustomComponent):
         try:
             metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)
         except Exception as e:
-            raise ValueError("Could not connect to Metal API.") from e
+            msg = "Could not connect to Metal API."
+            raise ValueError(msg) from e
         return cast(Retriever, MetalRetriever(client=metal, params=params or {}))

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

@@ -55,14 +55,16 @@ class NvidiaRerankComponent(LCVectorStoreComponent):
                 build_config["model"]["options"] = ids
                 build_config["model"]["value"] = ids[0]
             except Exception as e:
-                raise ValueError(f"Error getting model names: {e}")
+                msg = f"Error getting model names: {e}"
+                raise ValueError(msg)
         return build_config
 
     def build_model(self):
         try:
             from langchain_nvidia_ai_endpoints import NVIDIARerank
         except ImportError:
-            raise ImportError("Please install langchain-nvidia-ai-endpoints to use the NVIDIA model.")
+            msg = "Please install langchain-nvidia-ai-endpoints to use the NVIDIA model."
+            raise ImportError(msg)
         return NVIDIARerank(api_key=self.api_key, model=self.model, base_url=self.base_url)
 
     def build_base_retriever(self) -> Retriever:  # type: ignore[type-var]
@@ -79,4 +81,5 @@ class NvidiaRerankComponent(LCVectorStoreComponent):
 
     @check_cached_vector_store
     def build_vector_store(self) -> VectorStore:
-        raise NotImplementedError("NVIDIA Rerank does not support vector stores.")
+        msg = "NVIDIA Rerank does not support vector stores."
+        raise NotImplementedError(msg)

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

@@ -66,7 +66,8 @@ class SelfQueryRetrieverComponent(Component):
         elif isinstance(self.query, str):
             input_text = self.query
         else:
-            raise ValueError(f"Query type {type(self.query)} not supported.")
+            msg = f"Query type {type(self.query)} not supported."
+            raise ValueError(msg)
 
         documents = self_query_retriever.invoke(input=input_text, config={"callbacks": self.get_langchain_callbacks()})
         data = [Data.from_document(document) for document in documents]

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

@@ -51,7 +51,8 @@ class VectaraSelfQueryRetriverComponent(CustomComponent):
         for meta in metadata_field_info:
             meta_obj = json.loads(meta)
             if "name" not in meta_obj or "description" not in meta_obj or "type" not in meta_obj:
-                raise Exception("Incorrect metadata field info format.")
+                msg = "Incorrect metadata field info format."
+                raise Exception(msg)
             attribute_info = AttributeInfo(
                 name=meta_obj["name"],
                 description=meta_obj["description"],

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

@@ -67,7 +67,8 @@ class GleanSearchAPIComponent(LCToolComponent):
             results = self._search_api_results(query, **kwargs)
 
             if len(results) == 0:
-                raise AssertionError("No good Glean Search Result was found")
+                msg = "No good Glean Search Result was found"
+                raise AssertionError(msg)
 
             return results
 

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

@@ -39,5 +39,6 @@ class GoogleSearchAPIComponent(LCToolComponent):
         try:
             from langchain_google_community import GoogleSearchAPIWrapper  # type: ignore
         except ImportError:
-            raise ImportError("Please install langchain-google-community to use GoogleSearchAPIWrapper.")
+            msg = "Please install langchain-google-community to use GoogleSearchAPIWrapper."
+            raise ImportError(msg)
         return GoogleSearchAPIWrapper(google_api_key=self.google_api_key, google_cse_id=self.google_cse_id, k=self.k)

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

@@ -175,7 +175,8 @@ class PythonCodeStructuredTool(LCToolComponent):
             field_name = attr.split("|")[1]
             func_arg = self._find_arg(named_functions, func_name, field_name)
             if func_arg is None:
-                raise Exception(f"Failed to find arg: {field_name}")
+                msg = f"Failed to find arg: {field_name}"
+                raise Exception(msg)
 
             field_annotation = func_arg["annotation"]
             field_description = self._get_value(self._attributes[attr], str)
@@ -250,7 +251,8 @@ class PythonCodeStructuredTool(LCToolComponent):
             func = {"name": node.name, "args": []}
             for arg in node.args.args:
                 if arg.lineno != arg.end_lineno:
-                    raise Exception("Multiline arguments are not supported")
+                    msg = "Multiline arguments are not supported"
+                    raise Exception(msg)
 
                 func_arg = {
                     "name": arg.arg,

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

@@ -54,14 +54,16 @@ class PythonREPLToolComponent(LCToolComponent):
         elif isinstance(global_imports, list):
             modules = global_imports
         else:
-            raise ValueError("global_imports must be either a string or a list")
+            msg = "global_imports must be either a string or a list"
+            raise ValueError(msg)
 
         for module in modules:
             try:
                 imported_module = importlib.import_module(module)
                 global_dict[imported_module.__name__] = imported_module
             except ImportError:
-                raise ImportError(f"Could not import module {module}")
+                msg = f"Could not import module {module}"
+                raise ImportError(msg)
         return global_dict
 
     def build_tool(self) -> Tool:

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

@@ -89,7 +89,8 @@ class SearXNGToolComponent(LCToolComponent):
             @staticmethod
             def search(query: str, categories: list[str] = []) -> list:
                 if not SearxSearch._categories and not categories:
-                    raise ValueError("No categories provided.")
+                    msg = "No categories provided."
+                    raise ValueError(msg)
                 all_categories = SearxSearch._categories + list(set(categories) - set(SearxSearch._categories))
                 try:
                     url = f"{SearxSearch._url}/"

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

@@ -367,10 +367,11 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             from langchain_astradb import AstraDBVectorStore
             from langchain_astradb.utils.astradb import SetupMode
         except ImportError:
-            raise ImportError(
+            msg = (
                 "Could not import langchain Astra DB integration package. "
                 "Please install it with `pip install langchain-astradb`."
             )
+            raise ImportError(msg)
 
         try:
             if not self.setup_mode:
@@ -378,7 +379,8 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
 
             setup_mode_value = SetupMode[self.setup_mode.upper()]
         except KeyError:
-            raise ValueError(f"Invalid setup mode: {self.setup_mode}")
+            msg = f"Invalid setup mode: {self.setup_mode}"
+            raise ValueError(msg)
 
         if self.embedding:
             embedding_dict = {"embedding": self.embedding}
@@ -423,7 +425,8 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
         try:
             vector_store = AstraDBVectorStore(**vector_store_kwargs)
         except Exception as e:
-            raise ValueError(f"Error initializing AstraDBVectorStore: {str(e)}") from e
+            msg = f"Error initializing AstraDBVectorStore: {str(e)}"
+            raise ValueError(msg) from e
 
         self._add_documents_to_vector_store(vector_store)
 
@@ -435,14 +438,16 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
-                raise ValueError("Vector Store Inputs must be Data objects.")
+                msg = "Vector Store Inputs must be Data objects."
+                raise ValueError(msg)
 
         if documents:
             logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
             try:
                 vector_store.add_documents(documents)
             except Exception as e:
-                raise ValueError(f"Error adding documents to AstraDBVectorStore: {str(e)}") from e
+                msg = f"Error adding documents to AstraDBVectorStore: {str(e)}"
+                raise ValueError(msg) from e
         else:
             logger.debug("No documents to add to the Vector Store.")
 
@@ -481,7 +486,8 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
 
                 docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)
             except Exception as e:
-                raise ValueError(f"Error performing search in AstraDBVectorStore: {str(e)}") from e
+                msg = f"Error performing search in AstraDBVectorStore: {str(e)}"
+                raise ValueError(msg) from e
 
             logger.debug(f"Retrieved documents: {len(docs)}")
 

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

@@ -137,9 +137,8 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
             import cassio
             from langchain_community.utilities.cassandra import SetupMode
         except ImportError:
-            raise ImportError(
+            msg = "Could not import cassio integration package. " "Please install it with `pip install cassio`."
-                "Could not import cassio integration package. " "Please install it with `pip install cassio`."
+            raise ImportError(msg)
-            )
 
         from uuid import UUID
 
@@ -235,10 +234,11 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
                 docs = vector_store.search(query=self.search_query, search_type=search_type, **search_args)
             except KeyError as e:
                 if "content" in str(e):
-                    raise ValueError(
+                    msg = (
                         "You should ingest data through Langflow (or LangChain) to query it in Langflow. "
                         "Your collection does not contain a field name 'content'."
                     )
+                    raise ValueError(msg)
                 else:
                     raise e
 
@@ -262,7 +262,8 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
                 args["filter"] = clean_filter
         if self.body_search:
             if not self.enable_body_search:
-                raise ValueError("You should enable body search when creating the table to search the body field.")
+                msg = "You should enable body search when creating the table to search the body field."
+                raise ValueError(msg)
             args["body_search"] = self.body_search
         return args
 

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

@@ -126,9 +126,8 @@ class CassandraGraphVectorStoreComponent(LCVectorStoreComponent):
             import cassio
             from langchain_community.utilities.cassandra import SetupMode
         except ImportError:
-            raise ImportError(
+            msg = "Could not import cassio integration package. " "Please install it with `pip install cassio`."
-                "Could not import cassio integration package. " "Please install it with `pip install cassio`."
+            raise ImportError(msg)
-            )
 
         database_ref = self.database_ref
 
@@ -214,10 +213,11 @@ class CassandraGraphVectorStoreComponent(LCVectorStoreComponent):
                 docs = vector_store.search(query=self.search_query, search_type=search_type, **search_args)
             except KeyError as e:
                 if "content" in str(e):
-                    raise ValueError(
+                    msg = (
                         "You should ingest data through Langflow (or LangChain) to query it in Langflow. "
                         "Your collection does not contain a field name 'content'."
-                    ) from e
+                    )
+                    raise ValueError(msg) from e
                 else:
                     raise e
 

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

@@ -107,9 +107,10 @@ class ChromaVectorStoreComponent(LCVectorStoreComponent):
             from chromadb import Client
             from langchain_chroma import Chroma
         except ImportError:
-            raise ImportError(
+            msg = (
                 "Could not import Chroma integration package. " "Please install it with `pip install langchain-chroma`."
             )
+            raise ImportError(msg)
         # Chroma settings
         chroma_settings = None
         client = None
@@ -163,7 +164,8 @@ class ChromaVectorStoreComponent(LCVectorStoreComponent):
                 if _input not in _stored_documents_without_id:
                     documents.append(_input.to_lc_document())
             else:
-                raise ValueError("Vector Store Inputs must be Data objects.")
+                msg = "Vector Store Inputs must be Data objects."
+                raise ValueError(msg)
 
         if documents and self.embedding is not None:
             logger.debug(f"Adding {len(documents)} documents to the Vector Store.")

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

@@ -72,16 +72,18 @@ class ClickhouseVectorStoreComponent(LCVectorStoreComponent):
         try:
             import clickhouse_connect  # type: ignore
         except ImportError as e:
-            raise ImportError(
+            msg = (
                 "Failed to import Clickhouse dependencies. "
                 "Install it using `pip install langflow[clickhouse-connect] --pre`"
-            ) from e
+            )
+            raise ImportError(msg) from e
 
         try:
             client = clickhouse_connect.get_client(host=self.host, username=self.username, password=self.password)
             client.command("SELECT 1")
         except Exception as e:
-            raise ValueError(f"Failed to connect to Clickhouse: {e}")
+            msg = f"Failed to connect to Clickhouse: {e}"
+            raise ValueError(msg)
 
         documents = []
         for _input in self.ingest_data or []:

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

@@ -48,9 +48,8 @@ class CouchbaseVectorStoreComponent(LCVectorStoreComponent):
             from couchbase.cluster import Cluster  # type: ignore
             from couchbase.options import ClusterOptions  # type: ignore
         except ImportError as e:
-            raise ImportError(
+            msg = "Failed to import Couchbase dependencies. Install it using `pip install langflow[couchbase] --pre`"
-                "Failed to import Couchbase dependencies. Install it using `pip install langflow[couchbase] --pre`"
+            raise ImportError(msg) from e
-            ) from e
 
         try:
             auth = PasswordAuthenticator(self.couchbase_username, self.couchbase_password)
@@ -59,7 +58,8 @@ class CouchbaseVectorStoreComponent(LCVectorStoreComponent):
 
             cluster.wait_until_ready(timedelta(seconds=5))
         except Exception as e:
-            raise ValueError(f"Failed to connect to Couchbase: {e}")
+            msg = f"Failed to connect to Couchbase: {e}"
+            raise ValueError(msg)
 
         documents = []
         for _input in self.ingest_data or []:

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

@@ -62,7 +62,8 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
         Builds the FAISS object.
         """
         if not self.persist_directory:
-            raise ValueError("Folder path is required to save the FAISS index.")
+            msg = "Folder path is required to save the FAISS index."
+            raise ValueError(msg)
         path = self.resolve_path(self.persist_directory)
 
         documents = []
@@ -83,7 +84,8 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
         Search for documents in the FAISS vector store.
         """
         if not self.persist_directory:
-            raise ValueError("Folder path is required to load the FAISS index.")
+            msg = "Folder path is required to load the FAISS index."
+            raise ValueError(msg)
         path = self.resolve_path(self.persist_directory)
 
         vector_store = FAISS.load_local(
@@ -94,7 +96,8 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
         )
 
         if not vector_store:
-            raise ValueError("Failed to load the FAISS index.")
+            msg = "Failed to load the FAISS index."
+            raise ValueError(msg)
 
         logger.debug(f"Search input: {self.search_query}")
         logger.debug(f"Number of results: {self.number_of_results}")

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

@@ -181,18 +181,18 @@ class HCDVectorStoreComponent(LCVectorStoreComponent):
             from langchain_astradb import AstraDBVectorStore
             from langchain_astradb.utils.astradb import SetupMode
         except ImportError:
-            raise ImportError(
+            msg = (
                 "Could not import langchain Astra DB integration package. "
                 "Please install it with `pip install langchain-astradb`."
             )
+            raise ImportError(msg)
 
         try:
             from astrapy.authentication import UsernamePasswordTokenProvider
             from astrapy.constants import Environment
         except ImportError:
-            raise ImportError(
+            msg = "Could not import astrapy integration package. " "Please install it with `pip install astrapy`."
-                "Could not import astrapy integration package. " "Please install it with `pip install astrapy`."
+            raise ImportError(msg)
-            )
 
         try:
             if not self.setup_mode:
@@ -200,7 +200,8 @@ class HCDVectorStoreComponent(LCVectorStoreComponent):
 
             setup_mode_value = SetupMode[self.setup_mode.upper()]
         except KeyError:
-            raise ValueError(f"Invalid setup mode: {self.setup_mode}")
+            msg = f"Invalid setup mode: {self.setup_mode}"
+            raise ValueError(msg)
 
         if not isinstance(self.embedding, dict):
             embedding_dict = {"embedding": self.embedding}
@@ -246,7 +247,8 @@ class HCDVectorStoreComponent(LCVectorStoreComponent):
         try:
             vector_store = AstraDBVectorStore(**vector_store_kwargs)
         except Exception as e:
-            raise ValueError(f"Error initializing AstraDBVectorStore: {str(e)}") from e
+            msg = f"Error initializing AstraDBVectorStore: {str(e)}"
+            raise ValueError(msg) from e
 
         self._add_documents_to_vector_store(vector_store)
         return vector_store
@@ -257,14 +259,16 @@ class HCDVectorStoreComponent(LCVectorStoreComponent):
             if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
-                raise ValueError("Vector Store Inputs must be Data objects.")
+                msg = "Vector Store Inputs must be Data objects."
+                raise ValueError(msg)
 
         if documents:
             logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
             try:
                 vector_store.add_documents(documents)
             except Exception as e:
-                raise ValueError(f"Error adding documents to AstraDBVectorStore: {str(e)}") from e
+                msg = f"Error adding documents to AstraDBVectorStore: {str(e)}"
+                raise ValueError(msg) from e
         else:
             logger.debug("No documents to add to the Vector Store.")
 
@@ -302,7 +306,8 @@ class HCDVectorStoreComponent(LCVectorStoreComponent):
 
                 docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)
             except Exception as e:
-                raise ValueError(f"Error performing search in AstraDBVectorStore: {str(e)}") from e
+                msg = f"Error performing search in AstraDBVectorStore: {str(e)}"
+                raise ValueError(msg) from e
 
             logger.debug(f"Retrieved documents: {len(docs)}")
 

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

@@ -74,9 +74,10 @@ class MilvusVectorStoreComponent(LCVectorStoreComponent):
         try:
             from langchain_milvus.vectorstores import Milvus as LangchainMilvus
         except ImportError:
-            raise ImportError(
+            msg = (
                 "Could not import Milvus integration package. " "Please install it with `pip install langchain-milvus`."
             )
+            raise ImportError(msg)
         self.connection_args.update(uri=self.uri, token=self.password)
         milvus_store = LangchainMilvus(
             embedding_function=self.embedding,

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

@@ -39,13 +39,15 @@ class MongoVectorStoreComponent(LCVectorStoreComponent):
         try:
             from pymongo import MongoClient
         except ImportError:
-            raise ImportError("Please install pymongo to use MongoDB Atlas Vector Store")
+            msg = "Please install pymongo to use MongoDB Atlas Vector Store"
+            raise ImportError(msg)
 
         try:
             mongo_client: MongoClient = MongoClient(self.mongodb_atlas_cluster_uri)
             collection = mongo_client[self.db_name][self.collection_name]
         except Exception as e:
-            raise ValueError(f"Failed to connect to MongoDB Atlas: {e}")
+            msg = f"Failed to connect to MongoDB Atlas: {e}"
+            raise ValueError(msg)
 
         documents = []
         for _input in self.ingest_data or []:

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

@@ -85,7 +85,8 @@ class QdrantVectorStoreComponent(LCVectorStoreComponent):
                 documents.append(_input)
 
         if not isinstance(self.embedding, Embeddings):
-            raise ValueError("Invalid embedding object")
+            msg = "Invalid embedding object"
+            raise ValueError(msg)
 
         if documents:
             qdrant = Qdrant.from_documents(documents, embedding=self.embedding, **qdrant_kwargs)

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

@@ -58,7 +58,8 @@ class RedisVectorStoreComponent(LCVectorStoreComponent):
 
         if not documents:
             if self.schema is None:
-                raise ValueError("If no documents are provided, a schema must be provided.")
+                msg = "If no documents are provided, a schema must be provided."
+                raise ValueError(msg)
             redis_vs = Redis.from_existing_index(
                 embedding=self.embedding,
                 index_name=self.redis_index_name,

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

@@ -59,7 +59,8 @@ class VectaraVectorStoreComponent(LCVectorStoreComponent):
         try:
             from langchain_community.vectorstores import Vectara
         except ImportError:
-            raise ImportError("Could not import Vectara. Please install it with `pip install langchain-community`.")
+            msg = "Could not import Vectara. Please install it with `pip install langchain-community`."
+            raise ImportError(msg)
 
         vectara = Vectara(
             vectara_customer_id=self.vectara_customer_id,

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

@@ -50,7 +50,8 @@ class WeaviateVectorStoreComponent(LCVectorStoreComponent):
             client = weaviate.Client(url=self.url)
 
         if self.index_name != self.index_name.capitalize():
-            raise ValueError(f"Weaviate requires the index name to be capitalized. Use: {self.index_name.capitalize()}")
+            msg = f"Weaviate requires the index name to be capitalized. Use: {self.index_name.capitalize()}"
+            raise ValueError(msg)
 
         documents = []
         for _input in self.ingest_data or []:

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

@@ -137,7 +137,8 @@ class VectaraRagComponent(Component):
             from langchain_community.vectorstores import Vectara
             from langchain_community.vectorstores.vectara import RerankConfig, SummaryConfig, VectaraQueryConfig
         except ImportError:
-            raise ImportError("Could not import Vectara. Please install it with `pip install langchain-community`.")
+            msg = "Could not import Vectara. Please install it with `pip install langchain-community`."
+            raise ImportError(msg)
 
         vectara = Vectara(self.vectara_customer_id, self.vectara_corpus_id, self.vectara_api_key)
         rerank_config = RerankConfig(self.reranker, self.reranker_k, self.diversity_bias)

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

@@ -13,7 +13,8 @@ def validate_icon(value: str, *args, **kwargs):
     elif not value.startswith(":") or not value.endswith(":"):
         # emoji should have both starting and ending colons
         # so if one of them is missing, we will raise
-        raise ValueError(f"Invalid emoji. {value} is not a valid emoji.")
+        msg = f"Invalid emoji. {value} is not a valid emoji."
+        raise ValueError(msg)
 
     emoji_value = emoji.emojize(value, variant="emoji_type")
     if value == emoji_value:

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

@@ -66,7 +66,8 @@ class CodeParser:
         self.cache: TTLCache = TTLCache(maxsize=1024, ttl=60)
         if isinstance(code, type):
             if not inspect.isclass(code):
-                raise ValueError("The provided code must be a class.")
+                msg = "The provided code must be a class."
+                raise ValueError(msg)
             # If the code is a class, get its source code
             code = inspect.getsource(code)
         self.code = code

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

@@ -138,11 +138,13 @@ class Component(CustomComponent):
         try:
             module = inspect.getmodule(self.__class__)
             if module is None:
-                raise ValueError("Could not find module for class")
+                msg = "Could not find module for class"
+                raise ValueError(msg)
             class_code = inspect.getsource(module)
             self._code = class_code
         except OSError:
-            raise ValueError(f"Could not find source code for {self.__class__.__name__}")
+            msg = f"Could not find source code for {self.__class__.__name__}"
+            raise ValueError(msg)
 
     def set(self, **kwargs):
         """
@@ -209,7 +211,8 @@ class Component(CustomComponent):
         """
         if name in self._inputs:
             return self._inputs[name]
-        raise ValueError(f"Input {name} not found in {self.__class__.__name__}")
+        msg = f"Input {name} not found in {self.__class__.__name__}"
+        raise ValueError(msg)
 
     def get_output(self, name: str) -> Any:
         """
@@ -226,20 +229,23 @@ class Component(CustomComponent):
         """
         if name in self._outputs_map:
             return self._outputs_map[name]
-        raise ValueError(f"Output {name} not found in {self.__class__.__name__}")
+        msg = f"Output {name} not found in {self.__class__.__name__}"
+        raise ValueError(msg)
 
     def set_on_output(self, name: str, **kwargs):
         output = self.get_output(name)
         for key, value in kwargs.items():
             if not hasattr(output, key):
-                raise ValueError(f"Output {name} does not have a method {key}")
+                msg = f"Output {name} does not have a method {key}"
+                raise ValueError(msg)
             setattr(output, key, value)
 
     def set_output_value(self, name: str, value: Any):
         if name in self._outputs_map:
             self._outputs_map[name].value = value
         else:
-            raise ValueError(f"Output {name} not found in {self.__class__.__name__}")
+            msg = f"Output {name} not found in {self.__class__.__name__}"
+            raise ValueError(msg)
 
     def map_outputs(self, outputs: list[Output]):
         """
@@ -256,7 +262,8 @@ class Component(CustomComponent):
         """
         for output in outputs:
             if output.name is None:
-                raise ValueError("Output name cannot be None.")
+                msg = "Output name cannot be None."
+                raise ValueError(msg)
             # Deepcopy is required to avoid modifying the original component;
             # allows each instance of each component to modify its own output
             self._outputs_map[output.name] = deepcopy(output)
@@ -274,7 +281,8 @@ class Component(CustomComponent):
         """
         for input_ in inputs:
             if input_.name is None:
-                raise ValueError("Input name cannot be None.")
+                msg = "Input name cannot be None."
+                raise ValueError(msg)
             self._inputs[input_.name] = deepcopy(input_)
 
     def validate(self, params: dict):
@@ -303,7 +311,8 @@ class Component(CustomComponent):
         output = next((output for output in self._outputs_map.values() if output.method == method.__name__), None)
         if output is None:
             method_name = method.__name__ if hasattr(method, "__name__") else str(method)
-            raise ValueError(f"Output with method {method_name} not found")
+            msg = f"Output with method {method_name} not found"
+            raise ValueError(msg)
         return output
 
     def _inherits_from_component(self, method: Callable):
@@ -340,13 +349,15 @@ class Component(CustomComponent):
                         matching_pairs.append((output, input_))
         if len(matching_pairs) > 1:
             matching_pairs_str = self._build_error_string_from_matching_pairs(matching_pairs)
-            raise ValueError(
+            msg = (
                 f"There are multiple outputs from {value.__class__.__name__} "
                 f"that can connect to inputs in {self.__class__.__name__}: {matching_pairs_str}"
             )
+            raise ValueError(msg)
         output, input_ = matching_pairs[0]
         if not isinstance(output.method, str):
-            raise ValueError(f"Method {output.method} is not a valid output of {value.__class__.__name__}")
+            msg = f"Method {output.method} is not a valid output of {value.__class__.__name__}"
+            raise ValueError(msg)
         return getattr(value, output.method)
 
     def _process_connection_or_parameter(self, key, value):
@@ -361,9 +372,8 @@ class Component(CustomComponent):
             try:
                 self._method_is_valid_output(value)
             except ValueError:
-                raise ValueError(
+                msg = f"Method {value.__name__} is not a valid output of {value.__self__.__class__.__name__}"
-                    f"Method {value.__name__} is not a valid output of {value.__self__.__class__.__name__}"
+                raise ValueError(msg)
-                )
             self._connect_to_component(key, value, _input)
         else:
             self._set_parameter_or_attribute(key, value)
@@ -416,10 +426,11 @@ class Component(CustomComponent):
     def _set_parameter_or_attribute(self, key, value):
         if isinstance(value, Component):
             methods = ", ".join([f"'{output.method}'" for output in value.outputs])
-            raise ValueError(
+            msg = (
                 f"You set {value.display_name} as value for `{key}`. "
                 f"You should pass one of the following: {methods}"
             )
+            raise ValueError(msg)
         self._set_input_value(key, value)
         self._parameters[key] = value
         self._attributes[key] = value
@@ -453,26 +464,28 @@ class Component(CustomComponent):
             return self.__dict__[f"_{name}"]
         if name.startswith("_") and name[1:] in BACKWARDS_COMPATIBLE_ATTRIBUTES:
             return self.__dict__[name]
-        raise AttributeError(f"{name} not found in {self.__class__.__name__}")
+        msg = f"{name} not found in {self.__class__.__name__}"
+        raise AttributeError(msg)
 
     def _set_input_value(self, name: str, value: Any):
         if name in self._inputs:
             input_value = self._inputs[name].value
             if isinstance(input_value, Component):
                 methods = ", ".join([f"'{output.method}'" for output in input_value.outputs])
-                raise ValueError(
+                msg = (
                     f"You set {input_value.display_name} as value for `{name}`. "
                     f"You should pass one of the following: {methods}"
                 )
+                raise ValueError(msg)
             if callable(input_value):
-                raise ValueError(
+                msg = f"Input {name} is connected to {input_value.__self__.display_name}.{input_value.__name__}"
-                    f"Input {name} is connected to {input_value.__self__.display_name}.{input_value.__name__}"
+                raise ValueError(msg)
-                )
             self._inputs[name].value = value
             if hasattr(self._inputs[name], "load_from_db"):
                 self._inputs[name].load_from_db = False
         else:
-            raise ValueError(f"Input {name} not found in {self.__class__.__name__}")
+            msg = f"Input {name} not found in {self.__class__.__name__}"
+            raise ValueError(msg)
 
     def _validate_outputs(self):
         # Raise Error if some rule isn't met
@@ -489,10 +502,12 @@ class Component(CustomComponent):
             except KeyError:
                 close_match = find_closest_match(name, list(template.keys()))
                 if close_match:
-                    raise ValueError(
+                    msg = (
                         f"Parameter '{name}' not found in {self.__class__.__name__}. " f"Did you mean '{close_match}'?"
                     )
-                raise ValueError(f"Parameter {name} not found in {self.__class__.__name__}. ")
+                    raise ValueError(msg)
+                msg = f"Parameter {name} not found in {self.__class__.__name__}. "
+                raise ValueError(msg)
 
     def _get_method_return_type(self, method_name: str) -> list[str]:
         method = getattr(self, method_name)
@@ -569,10 +584,11 @@ class Component(CustomComponent):
         _attributes = {}
         for key, value in params.items():
             if key in self.__dict__ and value != getattr(self, key):
-                raise ValueError(
+                msg = (
                     f"{self.__class__.__name__} defines an input parameter named '{key}' "
                     f"that is a reserved word and cannot be used."
                 )
+                raise ValueError(msg)
             _attributes[key] = value
         for key, input_obj in self._inputs.items():
             if key not in _attributes:
@@ -634,7 +650,8 @@ class Component(CustomComponent):
                     or output.name in self._vertex.edges_source_names
                 ):
                     if output.method is None:
-                        raise ValueError(f"Output {output.name} does not have a method defined.")
+                        msg = f"Output {output.name} does not have a method defined."
+                        raise ValueError(msg)
                     self._current_output = output.name
                     method: Callable = getattr(self, output.method)
                     if output.cache and output.value != UNDEFINED:

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

@@ -112,39 +112,48 @@ class CustomComponent(BaseComponent):
 
     def update_state(self, name: str, value: Any):
         if not self._vertex:
-            raise ValueError("Vertex is not set")
+            msg = "Vertex is not set"
+            raise ValueError(msg)
         try:
             self._vertex.graph.update_state(name=name, record=value, caller=self._vertex.id)
         except Exception as e:
-            raise ValueError(f"Error updating state: {e}")
+            msg = f"Error updating state: {e}"
+            raise ValueError(msg)
 
     def stop(self, output_name: str | None = None):
         if not output_name and self._vertex and len(self._vertex.outputs) == 1:
             output_name = self._vertex.outputs[0]["name"]
         elif not output_name:
-            raise ValueError("You must specify an output name to call stop")
+            msg = "You must specify an output name to call stop"
+            raise ValueError(msg)
         if not self._vertex:
-            raise ValueError("Vertex is not set")
+            msg = "Vertex is not set"
+            raise ValueError(msg)
         try:
             self.graph.mark_branch(vertex_id=self._vertex.id, output_name=output_name, state="INACTIVE")
         except Exception as e:
-            raise ValueError(f"Error stopping {self.display_name}: {e}")
+            msg = f"Error stopping {self.display_name}: {e}"
+            raise ValueError(msg)
 
     def append_state(self, name: str, value: Any):
         if not self._vertex:
-            raise ValueError("Vertex is not set")
+            msg = "Vertex is not set"
+            raise ValueError(msg)
         try:
             self._vertex.graph.append_state(name=name, record=value, caller=self._vertex.id)
         except Exception as e:
-            raise ValueError(f"Error appending state: {e}")
+            msg = f"Error appending state: {e}"
+            raise ValueError(msg)
 
     def get_state(self, name: str):
         if not self._vertex:
-            raise ValueError("Vertex is not set")
+            msg = "Vertex is not set"
+            raise ValueError(msg)
         try:
             return self._vertex.graph.get_state(name=name)
         except Exception as e:
-            raise ValueError(f"Error getting state: {e}")
+            msg = f"Error getting state: {e}"
+            raise ValueError(msg)
 
     @staticmethod
     def resolve_path(path: str) -> str:
@@ -270,14 +279,16 @@ class CustomComponent(BaseComponent):
                         try:
                             data_dict[key] = model_dump[key]
                         except KeyError:
-                            raise ValueError(f"Key {key} not found in {item}")
+                            msg = f"Key {key} not found in {item}"
+                            raise ValueError(msg)
 
             elif isinstance(item, str):
                 data_dict = {"text": item}
             elif isinstance(item, dict):
                 data_dict = item.copy()
             else:
-                raise ValueError(f"Invalid data type: {type(item)}")
+                msg = f"Invalid data type: {type(item)}"
+                raise ValueError(msg)
 
             data_objects.append(Data(data=data_dict))
 
@@ -416,7 +427,8 @@ class CustomComponent(BaseComponent):
 
         def get_variable(name: str, field: str):
             if hasattr(self, "_user_id") and not self.user_id:
-                raise ValueError(f"User id is not set for {self.__class__.__name__}")
+                msg = f"User id is not set for {self.__class__.__name__}"
+                raise ValueError(msg)
             variable_service = get_variable_service()  # Get service instance
             # Retrieve and decrypt the variable by name for the current user
             with session_scope() as session:
@@ -436,7 +448,8 @@ class CustomComponent(BaseComponent):
             List[str]: The names of the variables for the current user.
         """
         if hasattr(self, "_user_id") and not self.user_id:
-            raise ValueError(f"User id is not set for {self.__class__.__name__}")
+            msg = f"User id is not set for {self.__class__.__name__}"
+            raise ValueError(msg)
         variable_service = get_variable_service()
 
         with session_scope() as session:
@@ -469,7 +482,8 @@ class CustomComponent(BaseComponent):
 
     async def load_flow(self, flow_id: str, tweaks: dict | None = None) -> Graph:
         if not self.user_id:
-            raise ValueError("Session is invalid")
+            msg = "Session is invalid"
+            raise ValueError(msg)
         return await load_flow(user_id=str(self._user_id), flow_id=flow_id, tweaks=tweaks)
 
     async def run_flow(
@@ -492,11 +506,13 @@ class CustomComponent(BaseComponent):
 
     def list_flows(self) -> list[Data]:
         if not self.user_id:
-            raise ValueError("Session is invalid")
+            msg = "Session is invalid"
+            raise ValueError(msg)
         try:
             return list_flows(user_id=str(self._user_id))
         except Exception as e:
-            raise ValueError(f"Error listing flows: {e}")
+            msg = f"Error listing flows: {e}"
+            raise ValueError(msg)
 
     def build(self, *args: Any, **kwargs: Any) -> Any:
         """

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

@@ -124,7 +124,8 @@ class DirectoryReader:
         Walk through the directory path and return a list of all .py files.
         """
         if not (safe_path := self.get_safe_path()):
-            raise CustomComponentPathValueError(f"The path needs to start with '{self.base_path}'.")
+            msg = f"The path needs to start with '{self.base_path}'."
+            raise CustomComponentPathValueError(msg)
 
         file_list = []
         safe_path_obj = Path(safe_path)