From 73d37757a443306e38cc3a785f5f094dcd85aee3 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
Date: Tue, 25 Feb 2025 10:49:28 -0300
Subject: [PATCH 1/2] refactor: replace deprecated `DataFrame.applymap` with
 `DataFrame.map`

---
 src/backend/base/langflow/components/outputs/chat.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/backend/base/langflow/components/outputs/chat.py b/src/backend/base/langflow/components/outputs/chat.py
index 6dee38275a01..e97342d1c91b 100644
--- a/src/backend/base/langflow/components/outputs/chat.py
+++ b/src/backend/base/langflow/components/outputs/chat.py
@@ -196,11 +196,15 @@ def _safe_convert(self, data: Any) -> str:
                     data = data.replace(r"^\s*$", "", regex=True)
                     # Replace multiple newlines with a single newline
                     data = data.replace(r"\n+", "\n", regex=True)
-                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)
+
+                # Replace pipe characters to avoid markdown table issues
+                processed_data = data.replace(r"\|", r"\\|", regex=True)
+
+                processed_data = processed_data.map(
+                    lambda x: str(x).replace("\n", "<br/>") if isinstance(x, str) else x
                 )
+
+                return processed_data.to_markdown(index=False)
             return str(data)
         except (ValueError, TypeError, AttributeError) as e:
             msg = f"Error converting data: {e!s}"

From d713bbf21af9da83a2c3cc109e137ea7dc300133 Mon Sep 17 00:00:00 2001
From: "autofix-ci[bot]" <114827586+autofix-ci[bot]@users.noreply.github.com>
Date: Tue, 25 Feb 2025 13:51:22 +0000
Subject: [PATCH 2/2] [autofix.ci] apply automated fixes

---
 .../Basic Prompt Chaining.json                |   2 +-
 .../starter_projects/Basic Prompting.json     |   2 +-
 .../starter_projects/Blog Writer.json         |   2 +-
 .../Custom Component Maker.json               |   2 +-
 .../starter_projects/Document Q&A.json        |   2 +-
 .../Financial Report Parser.json              |   2 +-
 .../starter_projects/Gmail Agent.json         |   2 +-
 .../Graph Vector Store RAG.json               | 648 ++++++++++++++----
 .../Image Sentiment Analysis.json             |   2 +-
 .../Instagram Copywriter.json                 |   2 +-
 .../starter_projects/LoopTemplate.json        |   2 +-
 .../starter_projects/Market Research.json     |   2 +-
 .../starter_projects/Meeting Summary.json     |   6 +-
 .../starter_projects/Memory Chatbot.json      |   2 +-
 .../starter_projects/News Aggregator.json     |   2 +-
 .../Portfolio Website Code Generator.json     |   2 +-
 .../starter_projects/Price Deal Finder.json   |   2 +-
 .../starter_projects/Research Agent.json      |   2 +-
 .../SEO Keyword Generator.json                |   2 +-
 .../starter_projects/SaaS Pricing.json        |   2 +-
 .../Sequential Tasks Agents.json              |   2 +-
 .../starter_projects/Simple Agent.json        |   2 +-
 .../Travel Planning Agents.json               |   2 +-
 .../Twitter Thread Generator.json             |   2 +-
 .../starter_projects/Vector Store RAG.json    | 194 +++---
 .../starter_projects/Youtube Analysis.json    |   2 +-
 26 files changed, 620 insertions(+), 274 deletions(-)

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 1858a1893965..2ed445dfd5c7 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 05f4580b0939..d87b98663000 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 eaa5f24911d8..68035cb6dfb3 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 e3d3e1396437..ac1fca3bb250 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 66bbe28dfa9f..8a45b0c2f524 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 07481c7f6224..e50a61cb77a6 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 8e98f9896b30..aae4abf782fa 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 4db206300a11..7fbd521a1cc8 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	
@@ -9,12 +9,16 @@
             "dataType": "OpenAIEmbeddings",
             "id": "OpenAIEmbeddings-jyvkr",
             "name": "embeddings",
-            "output_types": ["Embeddings"]
+            "output_types": [
+              "Embeddings"
+            ]
           },
           "targetHandle": {
             "fieldName": "embedding_model",
             "id": "AstraDBGraph-jr8pY",
-            "inputTypes": ["Embeddings"],
+            "inputTypes": [
+              "Embeddings"
+            ],
             "type": "other"
           }
         },
@@ -33,12 +37,16 @@
             "dataType": "ChatInput",
             "id": "ChatInput-ZCSfi",
             "name": "message",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "search_query",
             "id": "AstraDBGraph-jr8pY",
-            "inputTypes": ["Message"],
+            "inputTypes": [
+              "Message"
+            ],
             "type": "str"
           }
         },
@@ -57,12 +65,16 @@
             "dataType": "AstraDBGraph",
             "id": "AstraDBGraph-jr8pY",
             "name": "search_results",
-            "output_types": ["Data"]
+            "output_types": [
+              "Data"
+            ]
           },
           "targetHandle": {
             "fieldName": "data",
             "id": "ParseData-T6FGT",
-            "inputTypes": ["Data"],
+            "inputTypes": [
+              "Data"
+            ],
             "type": "other"
           }
         },
@@ -81,12 +93,17 @@
             "dataType": "ParseData",
             "id": "ParseData-alciW",
             "name": "text",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "context",
             "id": "Prompt-2M2d5",
-            "inputTypes": ["Message", "Text"],
+            "inputTypes": [
+              "Message",
+              "Text"
+            ],
             "type": "str"
           }
         },
@@ -105,12 +122,17 @@
             "dataType": "ChatInput",
             "id": "ChatInput-DA114",
             "name": "message",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "question",
             "id": "Prompt-2M2d5",
-            "inputTypes": ["Message", "Text"],
+            "inputTypes": [
+              "Message",
+              "Text"
+            ],
             "type": "str"
           }
         },
@@ -129,12 +151,16 @@
             "dataType": "Prompt",
             "id": "Prompt-rmO8w",
             "name": "prompt",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "OpenAIModel-a26gL",
-            "inputTypes": ["Message"],
+            "inputTypes": [
+              "Message"
+            ],
             "type": "str"
           }
         },
@@ -153,12 +179,18 @@
             "dataType": "OpenAIModel",
             "id": "OpenAIModel-LnWKb",
             "name": "text_output",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "ChatOutput-XL9ho",
-            "inputTypes": ["Data", "DataFrame", "Message"],
+            "inputTypes": [
+              "Data",
+              "DataFrame",
+              "Message"
+            ],
             "type": "str"
           }
         },
@@ -177,12 +209,17 @@
             "dataType": "URL",
             "id": "URL-qOh1r",
             "name": "data",
-            "output_types": ["Data"]
+            "output_types": [
+              "Data"
+            ]
           },
           "targetHandle": {
             "fieldName": "data_input",
             "id": "LanguageRecursiveTextSplitter-jefpx",
-            "inputTypes": ["Document", "Data"],
+            "inputTypes": [
+              "Document",
+              "Data"
+            ],
             "type": "other"
           }
         },
@@ -201,12 +238,17 @@
             "dataType": "LanguageRecursiveTextSplitter",
             "id": "LanguageRecursiveTextSplitter-KDtC3",
             "name": "data",
-            "output_types": ["Data"]
+            "output_types": [
+              "Data"
+            ]
           },
           "targetHandle": {
             "fieldName": "data_input",
             "id": "HtmlLinkExtractor-exHgk",
-            "inputTypes": ["Document", "Data"],
+            "inputTypes": [
+              "Document",
+              "Data"
+            ],
             "type": "other"
           }
         },
@@ -228,7 +270,9 @@
           "targetHandle": {
             "fieldName": "ingest_data",
             "id": "AstraDBGraph-FX0tA",
-            "inputTypes": ["Data"],
+            "inputTypes": [
+              "Data"
+            ],
             "type": "other"
           }
         },
@@ -244,12 +288,16 @@
             "dataType": "OpenAIEmbeddings",
             "id": "OpenAIEmbeddings-fcwMC",
             "name": "embeddings",
-            "output_types": ["Embeddings"]
+            "output_types": [
+              "Embeddings"
+            ]
           },
           "targetHandle": {
             "fieldName": "embedding_model",
             "id": "AstraDBGraph-FX0tA",
-            "inputTypes": ["Embeddings"],
+            "inputTypes": [
+              "Embeddings"
+            ],
             "type": "other"
           }
         },
@@ -265,12 +313,16 @@
             "dataType": "AstraDBGraph",
             "id": "AstraDBGraph-xJiDN",
             "name": "search_results",
-            "output_types": ["Data"]
+            "output_types": [
+              "Data"
+            ]
           },
           "targetHandle": {
             "fieldName": "data",
             "id": "ParseData-alciW",
-            "inputTypes": ["Data"],
+            "inputTypes": [
+              "Data"
+            ],
             "type": "other"
           }
         },
@@ -286,12 +338,16 @@
             "dataType": "OpenAIEmbeddings",
             "id": "OpenAIEmbeddings-XKhhV",
             "name": "embeddings",
-            "output_types": ["Embeddings"]
+            "output_types": [
+              "Embeddings"
+            ]
           },
           "targetHandle": {
             "fieldName": "embedding_model",
             "id": "AstraDBGraph-uza6S",
-            "inputTypes": ["Embeddings"],
+            "inputTypes": [
+              "Embeddings"
+            ],
             "type": "other"
           }
         },
@@ -307,12 +363,16 @@
             "dataType": "HtmlLinkExtractor",
             "id": "HtmlLinkExtractor-LWuvQ",
             "name": "data",
-            "output_types": ["Data"]
+            "output_types": [
+              "Data"
+            ]
           },
           "targetHandle": {
             "fieldName": "ingest_data",
             "id": "AstraDBGraph-uza6S",
-            "inputTypes": ["Data"],
+            "inputTypes": [
+              "Data"
+            ],
             "type": "other"
           }
         },
@@ -328,7 +388,9 @@
         "data": {
           "id": "ChatInput-DA114",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -362,7 +424,9 @@
                 "name": "message",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -375,7 +439,9 @@
                 "display_name": "Background Color",
                 "dynamic": false,
                 "info": "The background color of the icon.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "background_color",
@@ -395,7 +461,9 @@
                 "display_name": "Icon",
                 "dynamic": false,
                 "info": "The icon of the message.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "chat_icon",
@@ -498,7 +566,10 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": ["Machine", "User"],
+                "options": [
+                  "Machine",
+                  "User"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -514,7 +585,9 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "sender_name",
@@ -534,7 +607,9 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "session_id",
@@ -570,7 +645,9 @@
                 "display_name": "Text Color",
                 "dynamic": false,
                 "info": "The text color of the name",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "text_color",
@@ -612,7 +689,9 @@
         "data": {
           "id": "OpenAIEmbeddings-fcwMC",
           "node": {
-            "base_classes": ["Embeddings"],
+            "base_classes": [
+              "Embeddings"
+            ],
             "beta": false,
             "category": "embeddings",
             "conditional_paths": [],
@@ -658,10 +737,14 @@
                 "display_name": "Embeddings",
                 "method": "build_embeddings",
                 "name": "embeddings",
-                "required_inputs": ["openai_api_key"],
+                "required_inputs": [
+                  "openai_api_key"
+                ],
                 "selected": "Embeddings",
                 "tool_mode": true,
-                "types": ["Embeddings"],
+                "types": [
+                  "Embeddings"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -691,7 +774,9 @@
                 "display_name": "Client",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "client",
@@ -761,7 +846,9 @@
                 "display_name": "Deployment",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "deployment",
@@ -867,7 +954,9 @@
                 "display_name": "OpenAI API Base",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_api_base",
@@ -887,7 +976,9 @@
                 "display_name": "OpenAI API Key",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": true,
                 "name": "openai_api_key",
                 "password": true,
@@ -904,7 +995,9 @@
                 "display_name": "OpenAI API Type",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_api_type",
@@ -924,7 +1017,9 @@
                 "display_name": "OpenAI API Version",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_api_version",
@@ -944,7 +1039,9 @@
                 "display_name": "OpenAI Organization",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_organization",
@@ -964,7 +1061,9 @@
                 "display_name": "OpenAI Proxy",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_proxy",
@@ -1048,7 +1147,9 @@
                 "display_name": "TikToken Model Name",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "tiktoken_model_name",
@@ -1092,7 +1193,9 @@
           "display_name": "Astra DB Graph",
           "id": "AstraDBGraph-jr8pY",
           "node": {
-            "base_classes": ["Data"],
+            "base_classes": [
+              "Data"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -1138,10 +1241,16 @@
                 "display_name": "Search Results",
                 "method": "search_documents",
                 "name": "search_results",
-                "required_inputs": ["api_endpoint", "collection_name", "token"],
+                "required_inputs": [
+                  "api_endpoint",
+                  "collection_name",
+                  "token"
+                ],
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -1153,7 +1262,9 @@
                 "required_inputs": [],
                 "selected": "DataFrame",
                 "tool_mode": true,
-                "types": ["DataFrame"],
+                "types": [
+                  "DataFrame"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -1166,7 +1277,9 @@
                 "display_name": "API Endpoint",
                 "dynamic": false,
                 "info": "API endpoint URL for the Astra DB service.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": false,
                 "name": "api_endpoint",
                 "password": true,
@@ -1305,7 +1418,9 @@
                 "display_name": "Embedding Model",
                 "dynamic": false,
                 "info": "Allows an embedding model configuration.",
-                "input_types": ["Embeddings"],
+                "input_types": [
+                  "Embeddings"
+                ],
                 "list": false,
                 "name": "embedding_model",
                 "placeholder": "",
@@ -1322,7 +1437,9 @@
                 "display_name": "Ingest Data",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Data"],
+                "input_types": [
+                  "Data"
+                ],
                 "list": false,
                 "name": "ingest_data",
                 "placeholder": "",
@@ -1387,7 +1504,9 @@
                 "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "str",
-                "value": [""]
+                "value": [
+                  ""
+                ]
               },
               "metadata_indexing_include": {
                 "_input_type": "StrInput",
@@ -1415,7 +1534,11 @@
                 "dynamic": false,
                 "info": "Optional distance metric for vector comparisons in the vector store.",
                 "name": "metric",
-                "options": ["cosine", "dot_product", "euclidean"],
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1483,7 +1606,9 @@
                 "display_name": "Search Query",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -1548,7 +1673,10 @@
                 "dynamic": false,
                 "info": "Configuration mode for setting up the vector store, with options like 'Sync', or 'Off'.",
                 "name": "setup_mode",
-                "options": ["Sync", "Off"],
+                "options": [
+                  "Sync",
+                  "Off"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1582,7 +1710,9 @@
                 "display_name": "Astra DB Application Token",
                 "dynamic": false,
                 "info": "Authentication token for accessing Astra DB.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": false,
                 "name": "token",
                 "password": true,
@@ -1617,7 +1747,10 @@
           "display_name": "Parse Data",
           "id": "ParseData-alciW",
           "node": {
-            "base_classes": ["Data", "Message"],
+            "base_classes": [
+              "Data",
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -1625,7 +1758,11 @@
             "display_name": "Parse Data",
             "documentation": "",
             "edited": false,
-            "field_order": ["data", "template", "sep"],
+            "field_order": [
+              "data",
+              "template",
+              "sep"
+            ],
             "frozen": false,
             "icon": "message-square",
             "legacy": false,
@@ -1644,7 +1781,9 @@
                 "name": "text",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -1655,7 +1794,9 @@
                 "name": "data_list",
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -1686,7 +1827,9 @@
                 "display_name": "Data",
                 "dynamic": false,
                 "info": "The data to convert to text.",
-                "input_types": ["Data"],
+                "input_types": [
+                  "Data"
+                ],
                 "list": true,
                 "name": "data",
                 "placeholder": "",
@@ -1723,7 +1866,9 @@
                 "display_name": "Template",
                 "dynamic": false,
                 "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -1760,17 +1905,25 @@
         "data": {
           "id": "Prompt-rmO8w",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {
-              "template": ["context", "question"]
+              "template": [
+                "context",
+                "question"
+              ]
             },
             "description": "Create a prompt template with dynamic variables.",
             "display_name": "Prompt",
             "documentation": "",
             "edited": false,
-            "field_order": ["template", "tool_placeholder"],
+            "field_order": [
+              "template",
+              "tool_placeholder"
+            ],
             "frozen": false,
             "icon": "prompts",
             "legacy": false,
@@ -1786,7 +1939,9 @@
                 "name": "prompt",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -1819,7 +1974,10 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": ["Message", "Text"],
+                "input_types": [
+                  "Message",
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -1839,7 +1997,10 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": ["Message", "Text"],
+                "input_types": [
+                  "Message",
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -1875,7 +2036,9 @@
                 "display_name": "Tool Placeholder",
                 "dynamic": false,
                 "info": "A placeholder input for tool mode.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "tool_placeholder",
@@ -1911,7 +2074,10 @@
         "data": {
           "id": "OpenAIModel-LnWKb",
           "node": {
-            "base_classes": ["LanguageModel", "Message"],
+            "base_classes": [
+              "LanguageModel",
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -1948,7 +2114,9 @@
                 "required_inputs": [],
                 "selected": "Message",
                 "tool_mode": true,
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -1957,10 +2125,14 @@
                 "display_name": "Language Model",
                 "method": "build_model",
                 "name": "model_output",
-                "required_inputs": ["api_key"],
+                "required_inputs": [
+                  "api_key"
+                ],
                 "selected": "LanguageModel",
                 "tool_mode": true,
-                "types": ["LanguageModel"],
+                "types": [
+                  "LanguageModel"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -1973,7 +2145,9 @@
                 "display_name": "OpenAI API Key",
                 "dynamic": false,
                 "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": true,
                 "name": "api_key",
                 "password": true,
@@ -2008,7 +2182,9 @@
                 "display_name": "Input",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "input_value",
@@ -2174,7 +2350,9 @@
                 "display_name": "System Message",
                 "dynamic": false,
                 "info": "System message to pass to the model.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "system_message",
@@ -2255,7 +2433,9 @@
         "data": {
           "id": "ChatOutput-KIkbc",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -2289,7 +2469,9 @@
                 "name": "message",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -2302,7 +2484,9 @@
                 "display_name": "Background Color",
                 "dynamic": false,
                 "info": "The background color of the icon.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "background_color",
@@ -2322,7 +2506,9 @@
                 "display_name": "Icon",
                 "dynamic": false,
                 "info": "The icon of the message.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "chat_icon",
@@ -2370,7 +2556,7 @@
                 "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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -2378,7 +2564,9 @@
                 "display_name": "Data Template",
                 "dynamic": false,
                 "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "data_template",
@@ -2398,7 +2586,11 @@
                 "display_name": "Text",
                 "dynamic": false,
                 "info": "Message to be passed as output.",
-                "input_types": ["Data", "DataFrame", "Message"],
+                "input_types": [
+                  "Data",
+                  "DataFrame",
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "input_value",
@@ -2419,7 +2611,10 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": ["Machine", "User"],
+                "options": [
+                  "Machine",
+                  "User"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -2435,7 +2630,9 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "sender_name",
@@ -2455,7 +2652,9 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "session_id",
@@ -2491,7 +2690,9 @@
                 "display_name": "Text Color",
                 "dynamic": false,
                 "info": "The text color of the name",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "text_color",
@@ -2527,7 +2728,10 @@
         "data": {
           "id": "URL-qOh1r",
           "node": {
-            "base_classes": ["Data", "Message"],
+            "base_classes": [
+              "Data",
+              "Message"
+            ],
             "beta": false,
             "category": "data",
             "conditional_paths": [],
@@ -2536,7 +2740,10 @@
             "display_name": "URL",
             "documentation": "",
             "edited": false,
-            "field_order": ["urls", "format"],
+            "field_order": [
+              "urls",
+              "format"
+            ],
             "frozen": false,
             "icon": "layout-template",
             "key": "URL",
@@ -2554,7 +2761,9 @@
                 "name": "data",
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -2565,7 +2774,9 @@
                 "name": "text",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -2576,7 +2787,9 @@
                 "name": "dataframe",
                 "selected": "DataFrame",
                 "tool_mode": true,
-                "types": ["DataFrame"],
+                "types": [
+                  "DataFrame"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -2610,7 +2823,10 @@
                 "dynamic": false,
                 "info": "Output Format. Use 'Text' to extract the text from the HTML or 'Raw HTML' for the raw HTML content.",
                 "name": "format",
-                "options": ["Text", "Raw HTML"],
+                "options": [
+                  "Text",
+                  "Raw HTML"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -2626,7 +2842,9 @@
                 "display_name": "URLs",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": true,
                 "load_from_db": false,
                 "name": "urls",
@@ -2678,7 +2896,9 @@
         "data": {
           "id": "LanguageRecursiveTextSplitter-KDtC3",
           "node": {
-            "base_classes": ["Data"],
+            "base_classes": [
+              "Data"
+            ],
             "beta": false,
             "category": "vectorstores",
             "conditional_paths": [],
@@ -2726,10 +2946,16 @@
                 "display_name": "Search Results",
                 "method": "search_documents",
                 "name": "search_results",
-                "required_inputs": ["api_endpoint", "collection_name", "token"],
+                "required_inputs": [
+                  "api_endpoint",
+                  "collection_name",
+                  "token"
+                ],
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -2741,7 +2967,9 @@
                 "required_inputs": [],
                 "selected": "DataFrame",
                 "tool_mode": true,
-                "types": ["DataFrame"],
+                "types": [
+                  "DataFrame"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -2755,7 +2983,9 @@
                 "display_name": "API Endpoint",
                 "dynamic": false,
                 "info": "API endpoint URL for the Astra DB service.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": false,
                 "name": "api_endpoint",
                 "password": true,
@@ -2894,7 +3124,9 @@
                 "display_name": "Embedding Model",
                 "dynamic": false,
                 "info": "Allows an embedding model configuration.",
-                "input_types": ["Embeddings"],
+                "input_types": [
+                  "Embeddings"
+                ],
                 "list": false,
                 "name": "embedding_model",
                 "placeholder": "",
@@ -2911,7 +3143,9 @@
                 "display_name": "Ingest Data",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Data"],
+                "input_types": [
+                  "Data"
+                ],
                 "list": false,
                 "name": "ingest_data",
                 "placeholder": "",
@@ -3004,7 +3238,11 @@
                 "dynamic": false,
                 "info": "Optional distance metric for vector comparisons in the vector store.",
                 "name": "metric",
-                "options": ["cosine", "dot_product", "euclidean"],
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -3071,7 +3309,9 @@
                 "display_name": "Search Query",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -3135,7 +3375,10 @@
                 "dynamic": false,
                 "info": "Configuration mode for setting up the vector store, with options like 'Sync', or 'Off'.",
                 "name": "setup_mode",
-                "options": ["Sync", "Off"],
+                "options": [
+                  "Sync",
+                  "Off"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -3169,7 +3412,9 @@
                 "display_name": "Astra DB Application Token",
                 "dynamic": false,
                 "info": "Authentication token for accessing Astra DB.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": false,
                 "name": "token",
                 "password": true,
@@ -3203,7 +3448,9 @@
         "data": {
           "id": "LanguageRecursiveTextSplitter-jefpx",
           "node": {
-            "base_classes": ["Data"],
+            "base_classes": [
+              "Data"
+            ],
             "beta": false,
             "category": "langchain_utilities",
             "conditional_paths": [],
@@ -3236,7 +3483,9 @@
                 "required_inputs": [],
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -3347,7 +3596,10 @@
                 "display_name": "Input",
                 "dynamic": false,
                 "info": "The texts to split.",
-                "input_types": ["Document", "Data"],
+                "input_types": [
+                  "Document",
+                  "Data"
+                ],
                 "list": false,
                 "name": "data_input",
                 "placeholder": "",
@@ -3383,7 +3635,9 @@
         "data": {
           "id": "HtmlLinkExtractor-LWuvQ",
           "node": {
-            "base_classes": ["Data"],
+            "base_classes": [
+              "Data"
+            ],
             "beta": false,
             "category": "langchain_utilities",
             "conditional_paths": [],
@@ -3392,7 +3646,11 @@
             "display_name": "HTML Link Extractor",
             "documentation": "https://python.langchain.com/v0.2/api_reference/community/graph_vectorstores/langchain_community.graph_vectorstores.extractors.html_link_extractor.HtmlLinkExtractor.html",
             "edited": false,
-            "field_order": ["kind", "drop_fragments", "data_input"],
+            "field_order": [
+              "kind",
+              "drop_fragments",
+              "data_input"
+            ],
             "frozen": false,
             "icon": "LangChain",
             "key": "HtmlLinkExtractor",
@@ -3411,7 +3669,9 @@
                 "required_inputs": [],
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -3443,7 +3703,10 @@
                 "display_name": "Input",
                 "dynamic": false,
                 "info": "The texts from which to extract links.",
-                "input_types": ["Document", "Data"],
+                "input_types": [
+                  "Document",
+                  "Data"
+                ],
                 "list": false,
                 "name": "data_input",
                 "placeholder": "",
@@ -3514,7 +3777,9 @@
         "data": {
           "id": "OpenAIEmbeddings-XKhhV",
           "node": {
-            "base_classes": ["Embeddings"],
+            "base_classes": [
+              "Embeddings"
+            ],
             "beta": false,
             "category": "embeddings",
             "conditional_paths": [],
@@ -3561,10 +3826,14 @@
                 "display_name": "Embeddings",
                 "method": "build_embeddings",
                 "name": "embeddings",
-                "required_inputs": ["openai_api_key"],
+                "required_inputs": [
+                  "openai_api_key"
+                ],
                 "selected": "Embeddings",
                 "tool_mode": true,
-                "types": ["Embeddings"],
+                "types": [
+                  "Embeddings"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -3595,7 +3864,9 @@
                 "display_name": "Client",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "client",
@@ -3667,7 +3938,9 @@
                 "display_name": "Deployment",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "deployment",
@@ -3777,7 +4050,9 @@
                 "display_name": "OpenAI API Base",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_api_base",
@@ -3797,7 +4072,9 @@
                 "display_name": "OpenAI API Key",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": true,
                 "name": "openai_api_key",
                 "password": true,
@@ -3814,7 +4091,9 @@
                 "display_name": "OpenAI API Type",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_api_type",
@@ -3834,7 +4113,9 @@
                 "display_name": "OpenAI API Version",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_api_version",
@@ -3854,7 +4135,9 @@
                 "display_name": "OpenAI Organization",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_organization",
@@ -3874,7 +4157,9 @@
                 "display_name": "OpenAI Proxy",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "openai_proxy",
@@ -3962,7 +4247,9 @@
                 "display_name": "TikToken Model Name",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "tiktoken_model_name",
@@ -4077,7 +4364,10 @@
         "data": {
           "id": "AstraDBGraph-xJiDN",
           "node": {
-            "base_classes": ["Data", "DataFrame"],
+            "base_classes": [
+              "Data",
+              "DataFrame"
+            ],
             "beta": false,
             "category": "vectorstores",
             "conditional_paths": [],
@@ -4125,10 +4415,16 @@
                 "display_name": "Search Results",
                 "method": "search_documents",
                 "name": "search_results",
-                "required_inputs": ["api_endpoint", "collection_name", "token"],
+                "required_inputs": [
+                  "api_endpoint",
+                  "collection_name",
+                  "token"
+                ],
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -4140,7 +4436,9 @@
                 "required_inputs": [],
                 "selected": "DataFrame",
                 "tool_mode": true,
-                "types": ["DataFrame"],
+                "types": [
+                  "DataFrame"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -4154,7 +4452,9 @@
                 "display_name": "API Endpoint",
                 "dynamic": false,
                 "info": "API endpoint URL for the Astra DB service.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": true,
                 "name": "api_endpoint",
                 "password": true,
@@ -4299,7 +4599,9 @@
                 "display_name": "Embedding Model",
                 "dynamic": false,
                 "info": "Allows an embedding model configuration.",
-                "input_types": ["Embeddings"],
+                "input_types": [
+                  "Embeddings"
+                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "embedding_model",
@@ -4317,7 +4619,9 @@
                 "display_name": "Ingest Data",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Data"],
+                "input_types": [
+                  "Data"
+                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "ingest_data",
@@ -4416,7 +4720,11 @@
                 "dynamic": false,
                 "info": "Optional distance metric for vector comparisons in the vector store.",
                 "name": "metric",
-                "options": ["cosine", "dot_product", "euclidean"],
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
                 "options_metadata": [],
                 "placeholder": "",
                 "required": false,
@@ -4487,7 +4795,9 @@
                 "display_name": "Search Query",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -4556,7 +4866,10 @@
                 "dynamic": false,
                 "info": "Configuration mode for setting up the vector store, with options like 'Sync', or 'Off'.",
                 "name": "setup_mode",
-                "options": ["Sync", "Off"],
+                "options": [
+                  "Sync",
+                  "Off"
+                ],
                 "options_metadata": [],
                 "placeholder": "",
                 "required": false,
@@ -4591,7 +4904,9 @@
                 "display_name": "Astra DB Application Token",
                 "dynamic": false,
                 "info": "Authentication token for accessing Astra DB.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": true,
                 "name": "token",
                 "password": true,
@@ -4625,7 +4940,10 @@
         "data": {
           "id": "AstraDBGraph-uza6S",
           "node": {
-            "base_classes": ["Data", "DataFrame"],
+            "base_classes": [
+              "Data",
+              "DataFrame"
+            ],
             "beta": false,
             "category": "vectorstores",
             "conditional_paths": [],
@@ -4673,10 +4991,16 @@
                 "display_name": "Search Results",
                 "method": "search_documents",
                 "name": "search_results",
-                "required_inputs": ["api_endpoint", "collection_name", "token"],
+                "required_inputs": [
+                  "api_endpoint",
+                  "collection_name",
+                  "token"
+                ],
                 "selected": "Data",
                 "tool_mode": true,
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -4688,7 +5012,9 @@
                 "required_inputs": [],
                 "selected": "DataFrame",
                 "tool_mode": true,
-                "types": ["DataFrame"],
+                "types": [
+                  "DataFrame"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -4702,7 +5028,9 @@
                 "display_name": "API Endpoint",
                 "dynamic": false,
                 "info": "API endpoint URL for the Astra DB service.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": true,
                 "name": "api_endpoint",
                 "password": true,
@@ -4847,7 +5175,9 @@
                 "display_name": "Embedding Model",
                 "dynamic": false,
                 "info": "Allows an embedding model configuration.",
-                "input_types": ["Embeddings"],
+                "input_types": [
+                  "Embeddings"
+                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "embedding_model",
@@ -4865,7 +5195,9 @@
                 "display_name": "Ingest Data",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Data"],
+                "input_types": [
+                  "Data"
+                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "ingest_data",
@@ -4964,7 +5296,11 @@
                 "dynamic": false,
                 "info": "Optional distance metric for vector comparisons in the vector store.",
                 "name": "metric",
-                "options": ["cosine", "dot_product", "euclidean"],
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
                 "options_metadata": [],
                 "placeholder": "",
                 "required": false,
@@ -5035,7 +5371,9 @@
                 "display_name": "Search Query",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -5104,7 +5442,10 @@
                 "dynamic": false,
                 "info": "Configuration mode for setting up the vector store, with options like 'Sync', or 'Off'.",
                 "name": "setup_mode",
-                "options": ["Sync", "Off"],
+                "options": [
+                  "Sync",
+                  "Off"
+                ],
                 "options_metadata": [],
                 "placeholder": "",
                 "required": false,
@@ -5139,7 +5480,9 @@
                 "display_name": "Astra DB Application Token",
                 "dynamic": false,
                 "info": "Authentication token for accessing Astra DB.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "load_from_db": true,
                 "name": "token",
                 "password": true,
@@ -5183,5 +5526,8 @@
   "is_component": false,
   "last_tested_version": "1.1.1",
   "name": "Graph RAG",
-  "tags": ["rag", "q-a"]
-}
+  "tags": [
+    "rag",
+    "q-a"
+  ]
+}
\ No newline at end of file
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 0e550bb18316..beed810551b9 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 08a7aa575a31..cab05fd6e11e 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 b77b26686439..c524e54257d2 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 45e89d181a47..4f02099601e6 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 2580f55081ee..5e09dfa1e910 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 afb559474376..4fbd0e006b47 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 4fb4cdc8dfec..ef22c06dda33 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 c1e0a944f25e..4dcf5260309e 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 606d9799ebc4..812b0fe4c169 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 4239797440a4..9745938d419a 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 efd7e1f2cf67..001b904d5025 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 9875e31d33f5..a57d9d4a0bce 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 a1260c56eeb5..d43c9b399dbc 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 5da2cc715f48..507c54c60341 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 8efa2c164d67..2bfc48a7b0d6 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 b3a6077021f3..d6c14c22a33d 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "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 bb745e2e7caf..2c13afa335aa 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	
@@ -1283,7 +1283,7 @@
                 "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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -3405,41 +3405,36 @@
                         ],
                         "name": "create_collection",
                         "template": {
-                          "new_collection_name": {
-                            "_input_type": "StrInput",
+                          "dimension": {
+                            "_input_type": "IntInput",
                             "advanced": false,
-                            "display_name": "Name",
+                            "display_name": "Dimensions (Required only for `Bring your own`)",
                             "dynamic": false,
-                            "info": "Name of the new collection to create in Astra DB.",
+                            "info": "Dimensions of the embeddings to generate.",
                             "list": false,
                             "list_add_label": "Add More",
-                            "load_from_db": false,
-                            "name": "new_collection_name",
+                            "name": "dimension",
                             "placeholder": "",
-                            "required": true,
+                            "required": false,
                             "show": true,
                             "title_case": false,
                             "tool_mode": false,
                             "trace_as_metadata": true,
-                            "type": "str",
+                            "type": "int",
                             "value": ""
                           },
-                          "embedding_generation_provider": {
+                          "embedding_generation_model": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
                             "combobox": false,
                             "dialog_inputs": {},
-                            "display_name": "Embedding generation method",
+                            "display_name": "Embedding model",
                             "dynamic": false,
-                            "info": "Provider to use for generating embeddings.",
-                            "name": "embedding_generation_provider",
-                            "options": [
-                              "Bring your own",
-                              "Nvidia"
-                            ],
+                            "info": "Model to use for generating embeddings.",
+                            "name": "embedding_generation_model",
+                            "options": [],
                             "options_metadata": [],
                             "placeholder": "",
-                            "real_time_refresh": true,
                             "required": true,
                             "show": true,
                             "title_case": false,
@@ -3448,18 +3443,22 @@
                             "type": "str",
                             "value": ""
                           },
-                          "embedding_generation_model": {
+                          "embedding_generation_provider": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
                             "combobox": false,
                             "dialog_inputs": {},
-                            "display_name": "Embedding model",
+                            "display_name": "Embedding generation method",
                             "dynamic": false,
-                            "info": "Model to use for generating embeddings.",
-                            "name": "embedding_generation_model",
-                            "options": [],
+                            "info": "Provider to use for generating embeddings.",
+                            "name": "embedding_generation_provider",
+                            "options": [
+                              "Bring your own",
+                              "Nvidia"
+                            ],
                             "options_metadata": [],
                             "placeholder": "",
+                            "real_time_refresh": true,
                             "required": true,
                             "show": true,
                             "title_case": false,
@@ -3468,22 +3467,23 @@
                             "type": "str",
                             "value": ""
                           },
-                          "dimension": {
-                            "_input_type": "IntInput",
+                          "new_collection_name": {
+                            "_input_type": "StrInput",
                             "advanced": false,
-                            "display_name": "Dimensions (Required only for `Bring your own`)",
+                            "display_name": "Name",
                             "dynamic": false,
-                            "info": "Dimensions of the embeddings to generate.",
+                            "info": "Name of the new collection to create in Astra DB.",
                             "list": false,
                             "list_add_label": "Add More",
-                            "name": "dimension",
+                            "load_from_db": false,
+                            "name": "new_collection_name",
                             "placeholder": "",
-                            "required": false,
+                            "required": true,
                             "show": true,
                             "title_case": false,
                             "tool_mode": false,
                             "trace_as_metadata": true,
-                            "type": "int",
+                            "type": "str",
                             "value": ""
                           }
                         }
@@ -3545,25 +3545,6 @@
                         ],
                         "name": "create_database",
                         "template": {
-                          "new_database_name": {
-                            "_input_type": "StrInput",
-                            "advanced": false,
-                            "display_name": "Name",
-                            "dynamic": false,
-                            "info": "Name of the new database to create in Astra DB.",
-                            "list": false,
-                            "list_add_label": "Add More",
-                            "load_from_db": false,
-                            "name": "new_database_name",
-                            "placeholder": "",
-                            "required": true,
-                            "show": true,
-                            "title_case": false,
-                            "tool_mode": false,
-                            "trace_as_metadata": true,
-                            "type": "str",
-                            "value": ""
-                          },
                           "cloud_provider": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
@@ -3589,6 +3570,25 @@
                             "type": "str",
                             "value": ""
                           },
+                          "new_database_name": {
+                            "_input_type": "StrInput",
+                            "advanced": false,
+                            "display_name": "Name",
+                            "dynamic": false,
+                            "info": "Name of the new database to create in Astra DB.",
+                            "list": false,
+                            "list_add_label": "Add More",
+                            "load_from_db": false,
+                            "name": "new_database_name",
+                            "placeholder": "",
+                            "required": true,
+                            "show": true,
+                            "title_case": false,
+                            "tool_mode": false,
+                            "trace_as_metadata": true,
+                            "type": "str",
+                            "value": ""
+                          },
                           "region": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
@@ -4103,41 +4103,36 @@
                         ],
                         "name": "create_collection",
                         "template": {
-                          "new_collection_name": {
-                            "_input_type": "StrInput",
+                          "dimension": {
+                            "_input_type": "IntInput",
                             "advanced": false,
-                            "display_name": "Name",
+                            "display_name": "Dimensions (Required only for `Bring your own`)",
                             "dynamic": false,
-                            "info": "Name of the new collection to create in Astra DB.",
+                            "info": "Dimensions of the embeddings to generate.",
                             "list": false,
                             "list_add_label": "Add More",
-                            "load_from_db": false,
-                            "name": "new_collection_name",
+                            "name": "dimension",
                             "placeholder": "",
-                            "required": true,
+                            "required": false,
                             "show": true,
                             "title_case": false,
                             "tool_mode": false,
                             "trace_as_metadata": true,
-                            "type": "str",
+                            "type": "int",
                             "value": ""
                           },
-                          "embedding_generation_provider": {
+                          "embedding_generation_model": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
                             "combobox": false,
                             "dialog_inputs": {},
-                            "display_name": "Embedding generation method",
+                            "display_name": "Embedding model",
                             "dynamic": false,
-                            "info": "Provider to use for generating embeddings.",
-                            "name": "embedding_generation_provider",
-                            "options": [
-                              "Bring your own",
-                              "Nvidia"
-                            ],
+                            "info": "Model to use for generating embeddings.",
+                            "name": "embedding_generation_model",
+                            "options": [],
                             "options_metadata": [],
                             "placeholder": "",
-                            "real_time_refresh": true,
                             "required": true,
                             "show": true,
                             "title_case": false,
@@ -4146,18 +4141,22 @@
                             "type": "str",
                             "value": ""
                           },
-                          "embedding_generation_model": {
+                          "embedding_generation_provider": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
                             "combobox": false,
                             "dialog_inputs": {},
-                            "display_name": "Embedding model",
+                            "display_name": "Embedding generation method",
                             "dynamic": false,
-                            "info": "Model to use for generating embeddings.",
-                            "name": "embedding_generation_model",
-                            "options": [],
+                            "info": "Provider to use for generating embeddings.",
+                            "name": "embedding_generation_provider",
+                            "options": [
+                              "Bring your own",
+                              "Nvidia"
+                            ],
                             "options_metadata": [],
                             "placeholder": "",
+                            "real_time_refresh": true,
                             "required": true,
                             "show": true,
                             "title_case": false,
@@ -4166,22 +4165,23 @@
                             "type": "str",
                             "value": ""
                           },
-                          "dimension": {
-                            "_input_type": "IntInput",
+                          "new_collection_name": {
+                            "_input_type": "StrInput",
                             "advanced": false,
-                            "display_name": "Dimensions (Required only for `Bring your own`)",
+                            "display_name": "Name",
                             "dynamic": false,
-                            "info": "Dimensions of the embeddings to generate.",
+                            "info": "Name of the new collection to create in Astra DB.",
                             "list": false,
                             "list_add_label": "Add More",
-                            "name": "dimension",
+                            "load_from_db": false,
+                            "name": "new_collection_name",
                             "placeholder": "",
-                            "required": false,
+                            "required": true,
                             "show": true,
                             "title_case": false,
                             "tool_mode": false,
                             "trace_as_metadata": true,
-                            "type": "int",
+                            "type": "str",
                             "value": ""
                           }
                         }
@@ -4243,25 +4243,6 @@
                         ],
                         "name": "create_database",
                         "template": {
-                          "new_database_name": {
-                            "_input_type": "StrInput",
-                            "advanced": false,
-                            "display_name": "Name",
-                            "dynamic": false,
-                            "info": "Name of the new database to create in Astra DB.",
-                            "list": false,
-                            "list_add_label": "Add More",
-                            "load_from_db": false,
-                            "name": "new_database_name",
-                            "placeholder": "",
-                            "required": true,
-                            "show": true,
-                            "title_case": false,
-                            "tool_mode": false,
-                            "trace_as_metadata": true,
-                            "type": "str",
-                            "value": ""
-                          },
                           "cloud_provider": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
@@ -4287,6 +4268,25 @@
                             "type": "str",
                             "value": ""
                           },
+                          "new_database_name": {
+                            "_input_type": "StrInput",
+                            "advanced": false,
+                            "display_name": "Name",
+                            "dynamic": false,
+                            "info": "Name of the new database to create in Astra DB.",
+                            "list": false,
+                            "list_add_label": "Add More",
+                            "load_from_db": false,
+                            "name": "new_database_name",
+                            "placeholder": "",
+                            "required": true,
+                            "show": true,
+                            "title_case": false,
+                            "tool_mode": false,
+                            "trace_as_metadata": true,
+                            "type": "str",
+                            "value": ""
+                          },
                           "region": {
                             "_input_type": "DropdownInput",
                             "advanced": false,
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 a3107898e26b..5e6d7b18519f 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 (\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"
+                "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\n                # Replace pipe characters to avoid markdown table issues\n                processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n                processed_data = processed_data.map(\n                    lambda x: str(x).replace(\"\\n\", \"<br/>\") if isinstance(x, str) else x\n                )\n\n                return processed_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"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",