ref: Apply ruff rule TC006 (#5088)

Apply ruff rule TC006

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

@@ -17,7 +17,6 @@ from langflow.io import BoolInput, HandleInput, IntInput, MessageTextInput
 from langflow.memory import delete_message
 from langflow.schema import Data
 from langflow.schema.content_block import ContentBlock
-from langflow.schema.log import SendMessageFunctionType
 from langflow.schema.message import Message
 from langflow.template import Output
 from langflow.utils.constants import MESSAGE_SENDER_AI
@@ -25,6 +24,8 @@ from langflow.utils.constants import MESSAGE_SENDER_AI
 if TYPE_CHECKING:
     from langchain_core.messages import BaseMessage
 
+    from langflow.schema.log import SendMessageFunctionType
+
 
 DEFAULT_TOOLS_DESCRIPTION = "A helpful assistant with access to the following tools:"
 DEFAULT_AGENT_NAME = "Agent ({tools_names})"
@@ -163,7 +164,7 @@ class LCAgentComponent(Component):
                     version="v2",
                 ),
                 agent_message,
-                cast(SendMessageFunctionType, self.send_message),
+                cast("SendMessageFunctionType", self.send_message),
             )
         except ExceptionWithMessageError as e:
             msg_id = e.agent_message.id

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

@@ -177,7 +177,7 @@ class BaseCrewComponent(Component):
             _id = self._vertex.id if self._vertex else self.display_name
             if isinstance(agent_output, AgentFinish):
                 messages = agent_output.messages
-                self.log(cast(dict, messages[0].to_json()), name=f"Finish (Agent: {_id})")
+                self.log(cast("dict", messages[0].to_json()), name=f"Finish (Agent: {_id})")
             elif isinstance(agent_output, list):
                 _messages_dict = {f"Action {i}": action.messages for i, (action, _) in enumerate(agent_output)}
                 # Serialize the messages with to_json() to avoid issues with circular references

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

@@ -31,7 +31,7 @@ class ChatComponent(Component):
             self.status = messages
             self._send_messages_events(messages)
 
-        return cast(str | Message, message_text)
+        return cast("str | Message", message_text)
 
     def _create_message(self, input_value, sender, sender_name, files, session_id) -> Message:
         if isinstance(input_value, Data):

src/backend/base/langflow/components/cohere/cohere_rerank.py

@@ -75,7 +75,7 @@ class CohereRerankComponent(LCVectorStoreComponent):
             user_agent=self.user_agent,
         )
         retriever = ContextualCompressionRetriever(base_compressor=cohere_reranker, base_retriever=self.retriever)
-        return cast(Retriever, retriever)
+        return cast("Retriever", retriever)
 
     async def search_documents(self) -> list[Data]:  # type: ignore[override]
         retriever = self.build_base_retriever()

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

@@ -1,14 +1,16 @@
-from typing import cast
+from typing import TYPE_CHECKING, cast
 
 from pydantic import BaseModel, Field, create_model
 
 from langflow.base.models.chat_result import get_chat_result
 from langflow.custom import Component
-from langflow.field_typing.constants import LanguageModel
 from langflow.helpers.base_model import build_model_from_schema
 from langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput
 from langflow.schema.data import Data
 
+if TYPE_CHECKING:
+    from langflow.field_typing.constants import LanguageModel
+
 
 class StructuredOutputComponent(Component):
     display_name = "Structured Output"
@@ -94,7 +96,7 @@ class StructuredOutputComponent(Component):
         else:
             output_model = _output_model
         try:
-            llm_with_structured_output = cast(LanguageModel, self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]
+            llm_with_structured_output = cast("LanguageModel", self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]
 
         except NotImplementedError as exc:
             msg = f"{self.llm.__class__.__name__} does not support structured output."

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

@@ -63,7 +63,7 @@ class ChatVertexAIComponent(LCModelComponent):
             credentials = None
 
         return cast(
-            LanguageModel,
+            "LanguageModel",
             ChatVertexAI(
                 credentials=credentials,
                 location=location,

src/backend/base/langflow/components/nvidia/nvidia_rerank.py

@@ -83,7 +83,7 @@ class NvidiaRerankComponent(LCVectorStoreComponent):
     def build_base_retriever(self) -> Retriever:  # type: ignore[type-var]
         nvidia_reranker = self.build_model()
         retriever = ContextualCompressionRetriever(base_compressor=nvidia_reranker, base_retriever=self.retriever)
-        return cast(Retriever, retriever)
+        return cast("Retriever", retriever)
 
     async def search_documents(self) -> list[Data]:  # type: ignore[override]
         retriever = self.build_base_retriever()

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

@@ -51,4 +51,4 @@ class AmazonKendraRetrieverComponent(CustomComponent):
         except Exception as e:
             msg = "Could not connect to AmazonKendra API."
             raise ValueError(msg) from e
-        return cast(Retriever, output)
+        return cast("Retriever", output)

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

@@ -28,4 +28,4 @@ class MetalRetrieverComponent(CustomComponent):
         except Exception as e:
             msg = "Could not connect to Metal API."
             raise ValueError(msg) from e
-        return cast(Retriever, MetalRetriever(client=metal, params=params or {}))
+        return cast("Retriever", MetalRetriever(client=metal, params=params or {}))

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

@@ -44,4 +44,4 @@ class BingSearchAPIComponent(LCToolComponent):
             )
         else:
             wrapper = BingSearchAPIWrapper(bing_subscription_key=self.bing_subscription_key)
-        return cast(Tool, BingSearchResults(api_wrapper=wrapper, num_results=self.k))
+        return cast("Tool", BingSearchResults(api_wrapper=wrapper, num_results=self.k))

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

@@ -37,7 +37,7 @@ class WikipediaAPIComponent(LCToolComponent):
 
     def build_tool(self) -> Tool:
         wrapper = self._build_wrapper()
-        return cast(Tool, WikipediaQueryRun(api_wrapper=wrapper))
+        return cast("Tool", WikipediaQueryRun(api_wrapper=wrapper))
 
     def _build_wrapper(self) -> WikipediaAPIWrapper:
         return WikipediaAPIWrapper(

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

@@ -60,7 +60,7 @@ class VectaraSelfQueryRetriverComponent(CustomComponent):
             metadata_field_obj.append(attribute_info)
 
         return cast(
-            Retriever,
+            "Retriever",
             SelfQueryRetriever.from_llm(
                 llm, vectorstore, document_content_description, metadata_field_obj, verbose=True
             ),

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

@@ -22,7 +22,7 @@ class Edge:
         self.is_cycle = False
         if data := edge.get("data", {}):
             self._source_handle = data.get("sourceHandle", {})
-            self._target_handle = cast(TargetHandleDict, data.get("targetHandle", {}))
+            self._target_handle = cast("TargetHandleDict", data.get("targetHandle", {}))
             self.source_handle: SourceHandle = SourceHandle(**self._source_handle)
             if isinstance(self._target_handle, dict):
                 try:

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

@@ -8,7 +8,6 @@ import queue
 import threading
 import uuid
 from collections import defaultdict, deque
-from collections.abc import Generator, Iterable
 from datetime import datetime, timezone
 from functools import partial
 from itertools import chain
@@ -43,6 +42,8 @@ from langflow.services.deps import get_chat_service, get_tracing_service
 from langflow.utils.async_helpers import run_until_complete
 
 if TYPE_CHECKING:
+    from collections.abc import Generator, Iterable
+
     from langflow.api.v1.schemas import InputValueRequest
     from langflow.custom.custom_component.component import Component
     from langflow.events.event_manager import EventManager
@@ -1728,7 +1729,7 @@ class Graph:
             edges.add(new_edge)
         if self.vertices and not edges:
             logger.warning("Graph has vertices but no edges")
-        return list(cast(Iterable[CycleEdge], edges))
+        return list(cast("Iterable[CycleEdge]", edges))
 
     def build_edge(self, edge: EdgeData) -> CycleEdge | Edge:
         source = self.get_vertex(edge["source"])

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

@@ -129,7 +129,7 @@ class ComponentVertex(Vertex):
                     if output.value is UNDEFINED:
                         result = self.results[edge.source_handle.name]
                     else:
-                        result = cast(Any, output.value)
+                        result = cast("Any", output.value)
                 except NoComponentInstanceError:
                     result = self.results[edge.source_handle.name]
                 break

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

@@ -106,7 +106,7 @@ async def run_flow(
     inputs_components = []
     types = []
     for input_dict in inputs:
-        inputs_list.append({INPUT_FIELD_NAME: cast(str, input_dict.get("input_value"))})
+        inputs_list.append({INPUT_FIELD_NAME: cast("str", input_dict.get("input_value"))})
         inputs_components.append(input_dict.get("components", []))
         types.append(input_dict.get("type", "chat"))
 

src/backend/base/langflow/processing/process.py

@@ -175,10 +175,10 @@ def process_tweaks(
     :return: The modified graph_data dictionary.
     :raises ValueError: If the input is not in the expected format.
     """
-    tweaks_dict = cast(dict[str, Any], tweaks.model_dump()) if not isinstance(tweaks, dict) else tweaks
+    tweaks_dict = cast("dict[str, Any]", tweaks.model_dump()) if not isinstance(tweaks, dict) else tweaks
     if "stream" not in tweaks_dict:
         tweaks_dict |= {"stream": stream}
-    nodes = validate_input(graph_data, cast(dict[str, str | dict[str, Any]], tweaks_dict))
+    nodes = validate_input(graph_data, cast("dict[str, str | dict[str, Any]]", tweaks_dict))
     nodes_map = {node.get("id"): node for node in nodes}
     nodes_display_name_map = {node.get("data", {}).get("node", {}).get("display_name"): node for node in nodes}
 

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

@@ -97,7 +97,7 @@ class Data(BaseModel):
             Data: The converted Data.
         """
         data: dict = {"text": message.content}
-        data["metadata"] = cast(dict, message.to_json())
+        data["metadata"] = cast("dict", message.to_json())
         return cls(data=data, text_key="text")
 
     def __add__(self, other: "Data") -> "Data":

src/backend/base/langflow/schema/dataframe.py

@@ -70,7 +70,7 @@ class DataFrame(pandas_DataFrame):
         if isinstance(data, Data):
             data = data.data
         new_df = self._constructor([data])
-        return cast(DataFrame, pd.concat([self, new_df], ignore_index=True))
+        return cast("DataFrame", pd.concat([self, new_df], ignore_index=True))
 
     def add_rows(self, data: list[dict | Data]) -> "DataFrame":
         """Adds multiple rows to the dataset.
@@ -88,7 +88,7 @@ class DataFrame(pandas_DataFrame):
             else:
                 processed_data.append(item)
         new_df = self._constructor(processed_data)
-        return cast(DataFrame, pd.concat([self, new_df], ignore_index=True))
+        return cast("DataFrame", pd.concat([self, new_df], ignore_index=True))
 
     @property
     def _constructor(self):

src/backend/base/langflow/services/tracing/langwatch.py

@@ -162,15 +162,15 @@ class LangWatchTracer(BaseTracer):
             if "prompt" in value:
                 prompt = value.load_lc_prompt()
                 if len(prompt.input_variables) == 0 and all(isinstance(m, BaseMessage) for m in prompt.messages):
-                    value = langchain_messages_to_chat_messages([cast(list[BaseMessage], prompt.messages)])
+                    value = langchain_messages_to_chat_messages([cast("list[BaseMessage]", prompt.messages)])
                 else:
-                    value = cast(dict, value.load_lc_prompt())
+                    value = cast("dict", value.load_lc_prompt())
             elif value.sender:
                 value = langchain_message_to_chat_message(value.to_lc_message())
             else:
-                value = cast(dict, value.to_lc_document())
+                value = cast("dict", value.to_lc_document())
         elif isinstance(value, Data):
-            value = cast(dict, value.to_lc_document())
+            value = cast("dict", value.to_lc_document())
         return value
 
     def get_langchain_callback(self) -> BaseCallbackHandler | None:

src/backend/base/langflow/template/template/base.py

@@ -80,7 +80,7 @@ class Template(BaseModel):
         if field is None:
             msg = f"Field {field_name} not found in template {self.type_name}"
             raise ValueError(msg)
-        return cast(Input, field)
+        return cast("Input", field)
 
     def update_field(self, field_name: str, field: Input) -> None:
         """Updates the field with the given name."""