diff --git a/src/backend/base/langflow/components/outputs/chat.py b/src/backend/base/langflow/components/outputs/chat.py
index e33bfb0ff..6dee38275 100644
--- a/src/backend/base/langflow/components/outputs/chat.py
+++ b/src/backend/base/langflow/components/outputs/chat.py
@@ -196,7 +196,11 @@ class ChatOutput(ChatComponent):
                     data = data.replace(r"^\s*$", "", regex=True)
                     # Replace multiple newlines with a single newline
                     data = data.replace(r"\n+", "\n", regex=True)
-                return data.to_markdown(index=False)
+                return (
+                    data.replace(r"\|", r"\\|", regex=True)
+                    .applymap(lambda x: (str(x).replace("\n", "<br/>") if isinstance(x, str) else x))
+                    .to_markdown(index=False)
+                )
             return str(data)
         except (ValueError, TypeError, AttributeError) as e:
             msg = f"Error converting data: {e!s}"
diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py
index 6c0c6aebb..5db1acac8 100644
--- a/src/backend/base/langflow/initial_setup/setup.py
+++ b/src/backend/base/langflow/initial_setup/setup.py
@@ -536,9 +536,9 @@ def create_new_project(
     session.add(db_flow)
 
 
-async def get_all_flows_similar_to_project(session, folder_id):
+async def get_all_flows_similar_to_project(session: AsyncSession, folder_id: UUID) -> list[Flow]:
     stmt = select(Folder).options(selectinload(Folder.flows)).where(Folder.id == folder_id)
-    return (await session.exec(stmt)).first().flows
+    return list((await session.exec(stmt)).first().flows)
 
 
 async def delete_start_projects(session, folder_id) -> None:
@@ -785,7 +785,8 @@ async def create_or_update_starter_projects(all_types_dict: dict, *, do_create:
                     # We also need to update the project data in the file
                     await update_project_file(project_path, project, updated_project_data)
             if do_create and project_name and project_data:
-                for existing_project in await get_all_flows_similar_to_project(session, new_folder.id):
+                existing_flows = await get_all_flows_similar_to_project(session, new_folder.id)
+                for existing_project in existing_flows:
                     await session.delete(existing_project)
 
                 create_new_project(
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json
index 135948bd7..1858a1893 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json	
@@ -745,7 +745,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json
index 2e764bcb0..05f4580b0 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json	
@@ -694,7 +694,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json
index 0509a5c0d..eaa5f2491 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json	
@@ -701,7 +701,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "advanced": true,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json
index 0f8888bce..e3d3e1396 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json	
@@ -1128,7 +1128,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json
index e802d8105..66bbe28df 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json	
@@ -556,7 +556,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json
index e58fa5b82..07481c7f6 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json	
@@ -634,7 +634,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Gmail Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Gmail Agent.json
index a63b2704a..8e98f9896 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Gmail Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Gmail Agent.json	
@@ -1197,7 +1197,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json
index 6cef65e21..d2f44d54f 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Graph Vector Store RAG.json	
@@ -2471,7 +2471,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json
index fce64d441..0e550bb18 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json	
@@ -601,7 +601,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json
index 97f69c824..08a7aa575 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
@@ -1125,7 +1125,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json b/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json
index 0c899959a..73964e4e0 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json
@@ -1387,7 +1387,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json
index c8f1ec648..45e89d181 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
@@ -592,7 +592,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json
index 511cf9000..2580f5508 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json	
@@ -1258,7 +1258,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -1559,7 +1559,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -2241,7 +2241,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json
index 9de062c75..afb559474 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
@@ -544,7 +544,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json b/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json
index 0f4335468..4fb4cdc8d 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json	
@@ -863,7 +863,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json
index 007249b0d..c1e0a944f 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json	
@@ -1426,7 +1426,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json
index fbb808a3a..606d9799e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json	
@@ -561,7 +561,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json
index 41784ab0a..423979744 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json	
@@ -3085,7 +3085,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json
index ee77c2987..efd7e1f2c 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json	
@@ -649,7 +649,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json
index f5035fd16..9875e31d3 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
@@ -462,7 +462,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json
index 16f425abb..a1260c56e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json	
@@ -3986,7 +3986,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json
index f58a71ef7..5da2cc715 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json	
@@ -1151,7 +1151,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json
index cf7cefbeb..8efa2c164 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
@@ -613,7 +613,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json
index 288624787..b3a607702 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json	
@@ -786,7 +786,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json
index 3610a4f08..8861d04b4 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
@@ -1287,7 +1287,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json
index b0e8fd76c..a3107898e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json	
@@ -2212,7 +2212,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return data.to_markdown(index=False)\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def _safe_convert(self, data: Any) -> str:\n        \"\"\"Safely convert input data to string.\"\"\"\n        try:\n            if isinstance(data, str):\n                return data\n            if isinstance(data, Message):\n                return data.get_text()\n            if isinstance(data, Data):\n                if data.get_text() is None:\n                    msg = \"Empty Data object\"\n                    raise ValueError(msg)\n                return data.get_text()\n            if isinstance(data, DataFrame):\n                if self.clean_data:\n                    # Remove empty rows\n                    data = data.dropna(how=\"all\")\n                    # Remove empty lines in each cell\n                    data = data.replace(r\"^\\s*$\", \"\", regex=True)\n                    # Replace multiple newlines with a single newline\n                    data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n                return (\n                    data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n                    .applymap(lambda x: (str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x))\n                    .to_markdown(index=False)\n                )\n            return str(data)\n        except (ValueError, TypeError, AttributeError) as e:\n            msg = f\"Error converting data: {e!s}\"\n            raise ValueError(msg) from e\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return self._safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json
index ee0d38eb9..1c6b04e1a 100644
--- a/src/frontend/package-lock.json
+++ b/src/frontend/package-lock.json
@@ -81,6 +81,7 @@
         "react18-json-view": "^0.2.8",
         "reactflow": "^11.11.3",
         "rehype-mathjax": "^4.0.3",
+        "rehype-raw": "^6.1.1",
         "remark-gfm": "3.0.1",
         "remark-math": "^6.0.0",
         "shadcn-ui": "^0.9.4",
@@ -836,7 +837,6 @@
     },
     "node_modules/@clack/prompts/node_modules/is-unicode-supported": {
       "version": "1.3.0",
-      "extraneous": true,
       "inBundle": true,
       "license": "MIT",
       "engines": {
@@ -1761,6 +1761,7 @@
       "version": "1.0.11",
       "resolved": "https://registry.npmjs.org/@mapbox/node-pre-gyp/-/node-pre-gyp-1.0.11.tgz",
       "integrity": "sha512-Yhlar6v9WQgUp/He7BdgzOz8lqMQ8sU+jkCq7Wx8Myc5YFJLbEe7lgui/V7G1qB1DJykHSGwreceSaD60Y0PUQ==",
+      "license": "BSD-3-Clause",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -1782,6 +1783,7 @@
       "version": "6.0.2",
       "resolved": "https://registry.npmjs.org/agent-base/-/agent-base-6.0.2.tgz",
       "integrity": "sha512-RZNwNclF7+MS/8bDg70amg32dyeZGZxiDuQmZxKLAlQjr3jGyLx+4Kkk58UO7D2QdgFIQCovuSuZESne6RG6XQ==",
+      "license": "MIT",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -1795,6 +1797,7 @@
       "version": "5.0.1",
       "resolved": "https://registry.npmjs.org/https-proxy-agent/-/https-proxy-agent-5.0.1.tgz",
       "integrity": "sha512-dFcAjpTQFgoLMzC2VwU+C/CbS7uRL0lWmxDITmqm7C+7F0Odmj6s9l6alZc6AELXhrnggM2CeWSXHGOdX2YtwA==",
+      "license": "MIT",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -1809,6 +1812,7 @@
       "version": "3.1.0",
       "resolved": "https://registry.npmjs.org/make-dir/-/make-dir-3.1.0.tgz",
       "integrity": "sha512-g3FeP20LNwhALb/6Cz6Dd4F2ngze0jz7tbzrD2wAV+o9FeNHe4rL+yK2md0J/fiSf1sa1ADhXqi5+oVwOM/eGw==",
+      "license": "MIT",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -1825,6 +1829,7 @@
       "version": "6.3.1",
       "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
       "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==",
+      "license": "ISC",
       "optional": true,
       "peer": true,
       "bin": {
@@ -5034,6 +5039,7 @@
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/@tootallnate/once/-/once-2.0.0.tgz",
       "integrity": "sha512-XCuKFP5PS55gnMVu3dty8KPatLqUoy/ZYzDzAGCQ8JNFCkLXzmI7vNHCR+XpbZaMWQK/vQubr7PkYq8g470J/A==",
+      "license": "MIT",
       "engines": {
         "node": ">= 10"
       }
@@ -5459,7 +5465,8 @@
     "node_modules/@types/mathjax": {
       "version": "0.0.37",
       "resolved": "https://registry.npmjs.org/@types/mathjax/-/mathjax-0.0.37.tgz",
-      "integrity": "sha512-y0WSZBtBNQwcYipTU/BhgeFu1EZNlFvUNCmkMXV9kBQZq7/o5z82dNVyH3yy2Xv5zzeNeQoHSL4Xm06+EQiH+g=="
+      "integrity": "sha512-y0WSZBtBNQwcYipTU/BhgeFu1EZNlFvUNCmkMXV9kBQZq7/o5z82dNVyH3yy2Xv5zzeNeQoHSL4Xm06+EQiH+g==",
+      "license": "MIT"
     },
     "node_modules/@types/mdast": {
       "version": "3.0.15",
@@ -5488,6 +5495,12 @@
       "integrity": "sha512-dISoDXWWQwUquiKsyZ4Ng+HX2KsPL7LyHKHQwgGFEA3IaKac4Obd+h2a/a6waisAoepJlBcx9paWqjA8/HVjCw==",
       "peer": true
     },
+    "node_modules/@types/parse5": {
+      "version": "6.0.3",
+      "resolved": "https://registry.npmjs.org/@types/parse5/-/parse5-6.0.3.tgz",
+      "integrity": "sha512-SuT16Q1K51EAVPz1K29DJ/sXjhSQ0zjvsypYJ6tlwVsRV9jwW5Adq2ch8Dq8kDBCkYnELS7N7VNCSB5nC56t/g==",
+      "license": "MIT"
+    },
     "node_modules/@types/prop-types": {
       "version": "15.7.14",
       "resolved": "https://registry.npmjs.org/@types/prop-types/-/prop-types-15.7.14.tgz",
@@ -5746,12 +5759,14 @@
       "version": "2.0.6",
       "resolved": "https://registry.npmjs.org/abab/-/abab-2.0.6.tgz",
       "integrity": "sha512-j2afSsaIENvHZN2B8GOpF566vZ5WVk5opAiMTvWgaQT8DkbOqsTfvNAvHoRGU2zzP8cPoqys+xHTRDWW8L+/BA==",
-      "deprecated": "Use your platform's native atob() and btoa() methods instead"
+      "deprecated": "Use your platform's native atob() and btoa() methods instead",
+      "license": "BSD-3-Clause"
     },
     "node_modules/abbrev": {
       "version": "1.1.1",
       "resolved": "https://registry.npmjs.org/abbrev/-/abbrev-1.1.1.tgz",
       "integrity": "sha512-nne9/IiQ/hzIhY6pdDnbBtz7DjPTKrY00P/zvPSm5pOFkl6xuGrGnXn/VtTNNfNtAfZ9/1RtehkszU9qcTii0Q==",
+      "license": "ISC",
       "optional": true,
       "peer": true
     },
@@ -5787,6 +5802,7 @@
       "version": "7.0.1",
       "resolved": "https://registry.npmjs.org/acorn-globals/-/acorn-globals-7.0.1.tgz",
       "integrity": "sha512-umOSDSDrfHbTNPuNpC2NSnnA3LUrqpevPb4T9jRx4MagXNS0rs+gwiTcAvqCRmsD6utzsrzNt+ebm00SNWiC3Q==",
+      "license": "MIT",
       "dependencies": {
         "acorn": "^8.1.0",
         "acorn-walk": "^8.0.2"
@@ -5805,6 +5821,7 @@
       "version": "8.3.4",
       "resolved": "https://registry.npmjs.org/acorn-walk/-/acorn-walk-8.3.4.tgz",
       "integrity": "sha512-ueEepnujpqee2o5aIYnvHU6C0A42MNdsIDeqy5BydrkuC5R1ZuUFnm27EeFJGoEHJQgn3uleRvmTXaJgfXbt4g==",
+      "license": "MIT",
       "dependencies": {
         "acorn": "^8.11.0"
       },
@@ -5967,6 +5984,7 @@
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/aproba/-/aproba-2.0.0.tgz",
       "integrity": "sha512-lYe4Gx7QT+MKGbDsA+Z+he/Wtef0BiwDOlK/XkBrdfsh9J/jPPXbX0tE9x9cl27Tmu5gg3QUbUrQYa/y+KOHPQ==",
+      "license": "ISC",
       "optional": true,
       "peer": true
     },
@@ -5995,6 +6013,7 @@
       "resolved": "https://registry.npmjs.org/are-we-there-yet/-/are-we-there-yet-2.0.0.tgz",
       "integrity": "sha512-Ci/qENmwHnsYo9xKIcUJN5LeDKdJ6R1Z1j9V/J5wyq8nh/mYPEpIKJbBZXtZjG04HiK7zV/p6Vs9952MrMeUIw==",
       "deprecated": "This package is no longer supported.",
+      "license": "ISC",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -6761,6 +6780,7 @@
       "version": "1.1.3",
       "resolved": "https://registry.npmjs.org/color-support/-/color-support-1.1.3.tgz",
       "integrity": "sha512-qiBjkpbMLO/HL68y+lh4q0/O1MZFj2RX6X/KmMa3+gJD3z+WwI1ZzDHysvqHGS3mP6mznPckpXmw1nI9cJjyRg==",
+      "license": "ISC",
       "optional": true,
       "peer": true,
       "bin": {
@@ -6858,6 +6878,7 @@
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/console-control-strings/-/console-control-strings-1.1.0.tgz",
       "integrity": "sha512-ty/fTekppD2fIwRvnZAVdeOiGd1c7YXEixbgJTNzqcxJWKQnjJ/V1bNEEE6hygpM3WjwHFUVK6HTjWSzV4a8sQ==",
+      "license": "ISC",
       "optional": true,
       "peer": true
     },
@@ -6965,12 +6986,14 @@
     "node_modules/cssom": {
       "version": "0.5.0",
       "resolved": "https://registry.npmjs.org/cssom/-/cssom-0.5.0.tgz",
-      "integrity": "sha512-iKuQcq+NdHqlAcwUY0o/HL69XQrUaQdMjmStJ8JFmUaiiQErlhrmuigkg/CU4E2J0IyUKUrMAgl36TvN67MqTw=="
+      "integrity": "sha512-iKuQcq+NdHqlAcwUY0o/HL69XQrUaQdMjmStJ8JFmUaiiQErlhrmuigkg/CU4E2J0IyUKUrMAgl36TvN67MqTw==",
+      "license": "MIT"
     },
     "node_modules/cssstyle": {
       "version": "2.3.0",
       "resolved": "https://registry.npmjs.org/cssstyle/-/cssstyle-2.3.0.tgz",
       "integrity": "sha512-AZL67abkUzIuvcHqk7c09cezpGNcxUxU4Ioi/05xHk4DQeTkWmGYftIE6ctU6AEt+Gn4n1lDStOtj7FKycP71A==",
+      "license": "MIT",
       "dependencies": {
         "cssom": "~0.3.6"
       },
@@ -6981,7 +7004,8 @@
     "node_modules/cssstyle/node_modules/cssom": {
       "version": "0.3.8",
       "resolved": "https://registry.npmjs.org/cssom/-/cssom-0.3.8.tgz",
-      "integrity": "sha512-b0tGHbfegbhPJpxpiBPU2sCkigAqtM9O121le6bbOlgyV+NyGyCmVfJ6QW9eRjz8CpNfWEOYBIMIGRYkLwsIYg=="
+      "integrity": "sha512-b0tGHbfegbhPJpxpiBPU2sCkigAqtM9O121le6bbOlgyV+NyGyCmVfJ6QW9eRjz8CpNfWEOYBIMIGRYkLwsIYg==",
+      "license": "MIT"
     },
     "node_modules/csstype": {
       "version": "3.1.3",
@@ -7096,6 +7120,7 @@
       "version": "3.0.2",
       "resolved": "https://registry.npmjs.org/data-urls/-/data-urls-3.0.2.tgz",
       "integrity": "sha512-Jy/tj3ldjZJo63sVAvg6LHt2mHvl4V6AgRAmNDtLdm7faqtsx+aJG42rsyCo9JCoRVKwPFzKlIPx3DIibwSIaQ==",
+      "license": "MIT",
       "dependencies": {
         "abab": "^2.0.6",
         "whatwg-mimetype": "^3.0.0",
@@ -7109,6 +7134,7 @@
       "version": "3.0.0",
       "resolved": "https://registry.npmjs.org/tr46/-/tr46-3.0.0.tgz",
       "integrity": "sha512-l7FvfAHlcmulp8kr+flpQZmVwtu7nfRV7NZujtN0OqES8EL4O4e0qqzL0DC5gAvx/ZC/9lk6rhcUwYvkBnBnYA==",
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       "integrity": "sha512-vVqVZQyf3WLx2Shd0qJ9xuvqgAyKPLAiqITEtqW0oIUjzo3PePDd6fW9iFz30ef7Ysp/oiWqbhszeGWW2T6Gzw==",
+      "license": "MIT",
       "optional": true,
       "peer": true,
       "bin": {
@@ -14849,6 +15066,7 @@
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz",
       "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==",
+      "license": "ISC",
       "optional": true,
       "peer": true
     },
@@ -14936,6 +15154,7 @@
       "version": "4.1.4",
       "resolved": "https://registry.npmjs.org/tough-cookie/-/tough-cookie-4.1.4.tgz",
       "integrity": "sha512-Loo5UUvLD9ScZ6jh8beX1T6sO1w2/MpCRpEP7V280GKMVUQ0Jzar2U3UJPsrdbziLEMMhu3Ujnq//rhiFuIeag==",
+      "license": "BSD-3-Clause",
       "dependencies": {
         "psl": "^1.1.33",
         "punycode": "^2.1.1",
@@ -14950,6 +15169,7 @@
       "version": "0.2.0",
       "resolved": "https://registry.npmjs.org/universalify/-/universalify-0.2.0.tgz",
       "integrity": "sha512-CJ1QgKmNg3CwvAv/kOFmtnEN05f0D/cn9QntgNOQlQF9dgvVTHj3t+8JPdjqawCHk7V/KA+fbUqzZ9XWhcqPUg==",
+      "license": "MIT",
       "engines": {
         "node": ">= 4.0.0"
       }
@@ -14963,6 +15183,7 @@
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/trim-lines/-/trim-lines-3.0.1.tgz",
       "integrity": "sha512-kRj8B+YHZCc9kQYdWfJB2/oUl9rA99qbowYYBtr4ui4mZyAQ2JpvVBd/6U2YloATfqBhBTSMhTpgBHtU0Mf3Rg==",
+      "license": "MIT",
       "funding": {
         "type": "github",
         "url": "https://github.com/sponsors/wooorm"
@@ -15214,6 +15435,7 @@
       "version": "4.0.1",
       "resolved": "https://registry.npmjs.org/unist-util-find-after/-/unist-util-find-after-4.0.1.tgz",
       "integrity": "sha512-QO/PuPMm2ERxC6vFXEPtmAutOopy5PknD+Oq64gGwxKtk4xwo9Z97t9Av1obPmGU0IyTa6EKYUfTrK2QJS3Ozw==",
+      "license": "MIT",
       "dependencies": {
         "@types/unist": "^2.0.0",
         "unist-util-is": "^5.0.0"
@@ -15227,6 +15449,7 @@
       "version": "2.0.1",
       "resolved": "https://registry.npmjs.org/unist-util-generated/-/unist-util-generated-2.0.1.tgz",
       "integrity": "sha512-qF72kLmPxAw0oN2fwpWIqbXAVyEqUzDHMsbtPvOudIlUzXYFIeQIuxXQCRCFh22B7cixvU0MG7m3MW8FTq/S+A==",
+      "license": "MIT",
       "funding": {
         "type": "opencollective",
         "url": "https://opencollective.com/unified"
@@ -15248,6 +15471,7 @@
       "version": "4.0.4",
       "resolved": "https://registry.npmjs.org/unist-util-position/-/unist-util-position-4.0.4.tgz",
       "integrity": "sha512-kUBE91efOWfIVBo8xzh/uZQ7p9ffYRtUbMRZBNFYwf0RK8koUMx6dGUfwylLOKmaT2cs4wSW96QoYUSXAyEtpg==",
+      "license": "MIT",
       "dependencies": {
         "@types/unist": "^2.0.0"
       },
@@ -15454,6 +15678,7 @@
       "version": "1.5.10",
       "resolved": "https://registry.npmjs.org/url-parse/-/url-parse-1.5.10.tgz",
       "integrity": "sha512-WypcfiRhfeUP9vvF0j6rw0J3hrWrw6iZv3+22h6iRMJ/8z1Tj6XfLP4DsUix5MhMPnXpiHDoKyoZ/bdCkwBCiQ==",
+      "license": "MIT",
       "dependencies": {
         "querystringify": "^2.1.1",
         "requires-port": "^1.0.0"
@@ -15565,6 +15790,20 @@
         "url": "https://opencollective.com/unified"
       }
     },
+    "node_modules/vfile-location": {
+      "version": "4.1.0",
+      "resolved": "https://registry.npmjs.org/vfile-location/-/vfile-location-4.1.0.tgz",
+      "integrity": "sha512-YF23YMyASIIJXpktBa4vIGLJ5Gs88UB/XePgqPmTa7cDA+JeO3yclbpheQYCHjVHBn/yePzrXuygIL+xbvRYHw==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/unist": "^2.0.0",
+        "vfile": "^5.0.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/unified"
+      }
+    },
     "node_modules/vfile-message": {
       "version": "3.1.4",
       "resolved": "https://registry.npmjs.org/vfile-message/-/vfile-message-3.1.4.tgz",
@@ -15685,6 +15924,7 @@
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/w3c-xmlserializer/-/w3c-xmlserializer-4.0.0.tgz",
       "integrity": "sha512-d+BFHzbiCx6zGfz0HyQ6Rg69w9k19nviJspaj4yNscGjrHu94sVP+aRm75yEbCh+r2/yR+7q6hux9LVtbuTGBw==",
+      "license": "MIT",
       "dependencies": {
         "xml-name-validator": "^4.0.0"
       },
@@ -15723,6 +15963,7 @@
       "version": "2.0.1",
       "resolved": "https://registry.npmjs.org/web-namespaces/-/web-namespaces-2.0.1.tgz",
       "integrity": "sha512-bKr1DkiNa2krS7qxNtdrtHAmzuYGFQLiQ13TsorsdT6ULTkPLKuu5+GsFpDlg6JFjUTwX2DyhMPG2be8uPrqsQ==",
+      "license": "MIT",
       "funding": {
         "type": "github",
         "url": "https://github.com/sponsors/wooorm"
@@ -15755,6 +15996,7 @@
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/whatwg-encoding/-/whatwg-encoding-2.0.0.tgz",
       "integrity": "sha512-p41ogyeMUrw3jWclHWTQg1k05DSVXPLcVxRTYsXUk+ZooOCZLcoYgPZ/HL/D/N+uQPOtcp1me1WhBEaX02mhWg==",
+      "license": "MIT",
       "dependencies": {
         "iconv-lite": "0.6.3"
       },
@@ -15771,6 +16013,7 @@
       "version": "3.0.0",
       "resolved": "https://registry.npmjs.org/whatwg-mimetype/-/whatwg-mimetype-3.0.0.tgz",
       "integrity": "sha512-nt+N2dzIutVRxARx1nghPKGv1xHikU7HKdfafKkLNLindmPU/ch3U31NOCGGA/dmPcmb1VlofO0vnKAcsm0o/Q==",
+      "license": "MIT",
       "engines": {
         "node": ">=12"
       }
@@ -15807,6 +16050,7 @@
       "version": "1.1.5",
       "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.5.tgz",
       "integrity": "sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg==",
+      "license": "ISC",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -15817,6 +16061,7 @@
       "version": "8.0.0",
       "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
       "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==",
+      "license": "MIT",
       "optional": true,
       "peer": true
     },
@@ -15824,6 +16069,7 @@
       "version": "4.2.3",
       "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
       "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==",
+      "license": "MIT",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -15839,6 +16085,7 @@
       "version": "6.0.1",
       "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
       "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
+      "license": "MIT",
       "optional": true,
       "peer": true,
       "dependencies": {
@@ -16019,6 +16266,7 @@
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/xml-name-validator/-/xml-name-validator-4.0.0.tgz",
       "integrity": "sha512-ICP2e+jsHvAj2E2lIHxa5tjXRlKDJo4IdvPvCXbXQGdzSfmSpNVyIKMvoZHjDY9DP0zV17iI85o90vRFXNccRw==",
+      "license": "Apache-2.0",
       "engines": {
         "node": ">=12"
       }
@@ -16026,7 +16274,8 @@
     "node_modules/xmlchars": {
       "version": "2.2.0",
       "resolved": "https://registry.npmjs.org/xmlchars/-/xmlchars-2.2.0.tgz",
-      "integrity": "sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw=="
+      "integrity": "sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw==",
+      "license": "MIT"
     },
     "node_modules/xmldom-sre": {
       "version": "0.1.31",
diff --git a/src/frontend/package.json b/src/frontend/package.json
index 93c8805bf..2f465674f 100644
--- a/src/frontend/package.json
+++ b/src/frontend/package.json
@@ -76,6 +76,7 @@
     "react18-json-view": "^0.2.8",
     "reactflow": "^11.11.3",
     "rehype-mathjax": "^4.0.3",
+    "rehype-raw": "^6.1.1",
     "remark-gfm": "3.0.1",
     "remark-math": "^6.0.0",
     "shadcn-ui": "^0.9.4",
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatMessage/components/edit-message.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatMessage/components/edit-message.tsx
index 3fca1bbac..ef0e31158 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatMessage/components/edit-message.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatMessage/components/edit-message.tsx
@@ -1,8 +1,8 @@
-import { cn } from "@/utils/utils";
-
 import { EMPTY_OUTPUT_SEND_MESSAGE } from "@/constants/constants";
+import { cn } from "@/utils/utils";
 import Markdown from "react-markdown";
 import rehypeMathjax from "rehype-mathjax";
+import rehypeRaw from "rehype-raw";
 import remarkGfm from "remark-gfm";
 import CodeTabsComponent from "../../../../../../components/core/codeTabsComponent/ChatCodeTabComponent";
 
@@ -22,9 +22,9 @@ export const MarkdownField = ({
   return (
     <div className="w-full items-baseline gap-2">
       <Markdown
-        remarkPlugins={[remarkGfm]}
+        remarkPlugins={[remarkGfm as any]}
         linkTarget="_blank"
-        rehypePlugins={[rehypeMathjax]}
+        rehypePlugins={[rehypeMathjax, rehypeRaw]}
         className={cn(
           "markdown prose flex w-fit max-w-full flex-col items-baseline text-[14px] font-normal word-break-break-word dark:prose-invert",
           isEmpty ? "text-muted-foreground" : "text-primary",
diff --git a/src/frontend/src/style/classes.css b/src/frontend/src/style/classes.css
index 5a43000d5..3334c5223 100644
--- a/src/frontend/src/style/classes.css
+++ b/src/frontend/src/style/classes.css
@@ -282,3 +282,7 @@ input[type="search"]::-webkit-search-cancel-button {
 .react-flow__background pattern circle {
   fill: hsl(var(--canvas-dot)) !important;
 }
+
+.markdown td {
+  min-width: 78px;
+}