fix: Update type hints in BatchRunComponent to avoid lint errors (#7567)

* fix: update type hints in BatchRunComponent for improved clarity and consistency

* fix: add type casting in BatchRunComponent for improved type safety

* [autofix.ci] apply automated fixes

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

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

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, cast
 
 import toml  # type: ignore[import-untyped]
 from loguru import logger
@@ -10,8 +10,6 @@ from langflow.io import BoolInput, DataFrameInput, HandleInput, MessageTextInput
 from langflow.schema import DataFrame
 
 if TYPE_CHECKING:
-    from collections.abc import Hashable
-
     from langchain_core.runnables import Runnable
 
 
@@ -78,14 +76,14 @@ class BatchRunComponent(Component):
         ),
     ]
 
-    def _format_row_as_toml(self, row: dict[Hashable, Any]) -> str:
+    def _format_row_as_toml(self, row: dict[str, Any]) -> str:
         """Convert a dictionary (row) into a TOML-formatted string."""
         formatted_dict = {str(col): {"value": str(val)} for col, val in row.items()}
         return toml.dumps(formatted_dict)
 
     def _create_base_row(
-        self, original_row: dict[Hashable, Any], model_response: str = "", batch_index: int = -1
+        self, original_row: dict[str, Any], model_response: str = "", batch_index: int = -1
-    ) -> dict[Hashable, Any]:
+    ) -> dict[str, Any]:
         """Create a base row with original columns and additional metadata."""
         row = original_row.copy()
         row[self.output_column_name] = model_response
@@ -93,7 +91,7 @@ class BatchRunComponent(Component):
         return row
 
     def _add_metadata(
-        self, row: dict[Hashable, Any], *, success: bool = True, system_msg: str = "", error: str | None = None
+        self, row: dict[str, Any], *, success: bool = True, system_msg: str = "", error: str | None = None
     ) -> None:
         """Add metadata to a row if enabled."""
         if not self.enable_metadata:
@@ -145,7 +143,9 @@ class BatchRunComponent(Component):
             if col_name:
                 user_texts = df[col_name].astype(str).tolist()
             else:
-                user_texts = [self._format_row_as_toml(row) for row in df.to_dict(orient="records")]
+                user_texts = [
+                    self._format_row_as_toml(cast(dict[str, Any], row)) for row in df.to_dict(orient="records")
+                ]
 
             total_rows = len(user_texts)
             logger.info(f"Processing {total_rows} rows with batch run")
@@ -166,25 +166,27 @@ class BatchRunComponent(Component):
                     "callbacks": self.get_langchain_callbacks(),
                 }
             )
-
             # Process batches and track progress
-            responses_with_idx = [
+            responses_with_idx = list(
-                (idx, response)
+                zip(
-                for idx, response in zip(
+                    range(len(conversations)),
-                    range(len(conversations)), await model.abatch(list(conversations)), strict=True
+                    await model.abatch(list(conversations)),
+                    strict=True,
                 )
-            ]
+            )
 
             # Sort by index to maintain order
             responses_with_idx.sort(key=lambda x: x[0])
 
             # Build the final data with enhanced metadata
-            rows: list[dict[Hashable, Any]] = []
+            rows: list[dict[str, Any]] = []
             for idx, (original_row, response) in enumerate(
                 zip(df.to_dict(orient="records"), responses_with_idx, strict=False)
             ):
                 response_text = response[1].content if hasattr(response[1], "content") else str(response[1])
-                row = self._create_base_row(original_row, model_response=response_text, batch_index=idx)
+                row = self._create_base_row(
+                    cast(dict[str, Any], original_row), model_response=response_text, batch_index=idx
+                )
                 self._add_metadata(row, success=True, system_msg=system_msg)
                 rows.append(row)