feat: add batch run beta component (#5489)

* feat: add batch run beta component

* [autofix.ci] apply automated fixes

* Update batch_run to run async using Runnable

* [autofix.ci] apply automated fixes

* refactor: streamline BatchRunComponent by removing unused synchronous model invocation and enhancing async processing

- Consolidated imports and improved type checking for LanguageModel.
- Simplified the run_batch method by directly using the model's asynchronous capabilities.
- Enhanced error handling for missing DataFrame columns.
- Cleaned up comments and improved code readability.

* refactor: update BatchRunComponent to use Runnable for improved async processing

- Added future annotations for better type hinting.
- Replaced LanguageModel with Runnable in type checking and method implementation.
- Enhanced code clarity and maintainability by consolidating imports.

* test: add unit tests for BatchRunComponent functionality

- Introduced a new test suite for BatchRunComponent to validate its behavior.
- Added tests for successful batch runs with and without system messages.
- Implemented tests for handling invalid column names and empty DataFrames.
- Included a test to ensure non-string columns are converted to strings during processing.

* refactor: rename useful.py to mock_language_model.py and update imports

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>

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

@@ -7,6 +7,7 @@ from .store_message import MessageStoreComponent
 from .structured_output import StructuredOutputComponent
 
 __all__ = [
+    "BatchRunComponent",
     "CreateListComponent",
     "CurrentDateComponent",
     "IDGeneratorComponent",

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

@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from langflow.custom import Component
+from langflow.io import DataFrameInput, HandleInput, MultilineInput, Output, StrInput
+from langflow.schema import DataFrame
+
+if TYPE_CHECKING:
+    from langchain_core.runnables import Runnable
+
+
+class BatchRunComponent(Component):
+    display_name = "Batch Run"
+    description = (
+        "Runs a language model over each row of a DataFrame's text column and returns a new "
+        "DataFrame with two columns: 'text_input' (the original text) and 'model_response' "
+        "containing the model's response."
+    )
+    icon = "List"
+    beta = True
+
+    inputs = [
+        HandleInput(
+            name="model",
+            display_name="Language Model",
+            info="Connect the 'Language Model' output from your LLM component here.",
+            input_types=["LanguageModel"],
+        ),
+        MultilineInput(
+            name="system_message",
+            display_name="System Message",
+            info="Multi-line system instruction for all rows in the DataFrame.",
+            required=False,
+        ),
+        DataFrameInput(
+            name="df",
+            display_name="DataFrame",
+            info="The DataFrame whose column (specified by 'column_name') we'll treat as text messages.",
+        ),
+        StrInput(
+            name="column_name",
+            display_name="Column Name",
+            info="The name of the DataFrame column to treat as text messages. Default='text'.",
+            value="text",
+        ),
+    ]
+
+    outputs = [
+        Output(
+            display_name="Batch Results",
+            name="batch_results",
+            method="run_batch",
+            info="A DataFrame with two columns: 'text_input' and 'model_response'.",
+        ),
+    ]
+
+    async def run_batch(self) -> DataFrame:
+        """For each row in df[column_name], combine that text with system_message, then invoke the model asynchronously.
+
+        Returns a new DataFrame of the same length, with columns 'text_input' and 'model_response'.
+        """
+        model: Runnable = self.model
+        system_msg = self.system_message or ""
+        df: DataFrame = self.df
+        col_name = self.column_name or "text"
+
+        if col_name not in df.columns:
+            msg = f"Column '{col_name}' not found in the DataFrame."
+            raise ValueError(msg)
+
+        # Convert the specified column to a list of strings
+        user_texts = df[col_name].astype(str).tolist()
+
+        # Prepare the batch of conversations
+        conversations = [
+            [{"role": "system", "content": system_msg}, {"role": "user", "content": text}]
+            if system_msg
+            else [{"role": "user", "content": text}]
+            for text in user_texts
+        ]
+        model = model.with_config(
+            {
+                "run_name": self.display_name,
+                "project_name": self.get_project_name(),
+                "callbacks": self.get_langchain_callbacks(),
+            }
+        )
+
+        responses = await model.abatch(conversations)
+
+        # Build the final data, each row has 'text_input' + 'model_response'
+        rows = []
+        for original_text, response in zip(user_texts, responses, strict=False):
+            resp_text = response.content if hasattr(response, "content") else str(response)
+
+            row = {"text_input": original_text, "model_response": resp_text}
+            rows.append(row)
+
+        # Convert to a new DataFrame
+        return DataFrame(rows)  # Langflow DataFrame from a list of dicts