feat: add batch run beta component

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

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

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

@@ -0,0 +1,93 @@
+from langflow.custom import Component
+from langflow.field_typing import LanguageModel
+from langflow.io import (
+    DataFrameInput,
+    HandleInput,
+    MultilineInput,
+    Output,
+    StrInput,
+)
+from langflow.schema import DataFrame
+from langflow.schema.message import Message
+
+
+class BatchRunComponent(Component):
+    display_name = "Batch Run"
+    description = "Runs a language model over each row of a DataFrame’s text column and returns a DataFrame containing one model response per row."
+    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 items 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 list of processed messages returned by the model, one per row in the chosen DataFrame column.",
+        ),
+    ]
+
+    def run_batch(self) -> DataFrame:
+        """For each row in df[column_name], combine that text with system_message
+        and invoke the model, returning a list of responses as Langflow Messages.
+        """
+        # Retrieve inputs
+        model: LanguageModel = self.model
+        system_msg: str = self.system_message or ""
+        df: DataFrame = self.df
+        col_name: str = self.column_name or "text"
+
+        if col_name not in df.columns:
+            raise ValueError(f"Column '{col_name}' not found in the DataFrame.")
+
+        # We'll treat each row's text as a user message
+        user_texts = df[col_name].astype(str).tolist()
+
+        results: list[Message] = []
+
+        for text in user_texts:
+            # Build conversation array: system + user
+            conversation = []
+            if system_msg:
+                conversation.append({"role": "system", "content": system_msg})
+            conversation.append({"role": "user", "content": text})
+
+            # Invoke the model
+            response = model.invoke(conversation)
+
+            # Convert response to a Langflow Message
+            if hasattr(response, "content"):
+                # If the model returns an object with .content (e.g. AIMessage)
+                new_message = Message(text=response.content)
+            else:
+                # Otherwise assume it's raw text or a dict
+                new_message = Message(text=str(response))
+
+            results.append(new_message)
+
+        return DataFrame(results)