diff --git a/src/backend/base/langflow/components/outputs/chat.py b/src/backend/base/langflow/components/outputs/chat.py
index e97342d1c..f1a5012dd 100644
--- a/src/backend/base/langflow/components/outputs/chat.py
+++ b/src/backend/base/langflow/components/outputs/chat.py
@@ -1,6 +1,9 @@
 from collections.abc import Generator
 from typing import Any
 
+import orjson
+from fastapi.encoders import jsonable_encoder
+
 from langflow.base.io.chat import ChatComponent
 from langflow.inputs import BoolInput
 from langflow.inputs.inputs import HandleInput
@@ -119,6 +122,7 @@ class ChatOutput(ChatComponent):
     async def message_response(self) -> Message:
         # First convert the input to string if needed
         text = self.convert_to_string()
+
         # Get source properties
         source, icon, display_name, source_id = self.get_properties_from_source_component()
         background_color = self.background_color
@@ -176,6 +180,15 @@ class ChatOutput(ChatComponent):
             msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}"
             raise TypeError(msg)
 
+    def _serialize_data(self, data: Data) -> str:
+        """Serialize Data object to JSON string."""
+        # Convert data.data to JSON-serializable format
+        serializable_data = jsonable_encoder(data.data)
+        # Serialize with orjson, enabling pretty printing with indentation
+        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)
+        # Convert bytes to string and wrap in Markdown code blocks
+        return "```json\n" + json_bytes.decode("utf-8") + "\n```"
+
     def _safe_convert(self, data: Any) -> str:
         """Safely convert input data to string."""
         try:
@@ -184,10 +197,7 @@ class ChatOutput(ChatComponent):
             if isinstance(data, Message):
                 return data.get_text()
             if isinstance(data, Data):
-                if data.get_text() is None:
-                    msg = "Empty Data object"
-                    raise ValueError(msg)
-                return data.get_text()
+                return self._serialize_data(data)
             if isinstance(data, DataFrame):
                 if self.clean_data:
                     # Remove empty rows
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 3ef624cde..5d0849b33 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	
@@ -746,7 +746,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 9b019202e..a46fc6dcb 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	
@@ -695,7 +695,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 222d64659..f4e097f82 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	
@@ -553,7 +553,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 544ca845b..67ad7a4c2 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	
@@ -2142,7 +2142,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Diet Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Diet Analysis.json
index 7d5c09591..6dcdf6c27 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Diet Analysis.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Diet Analysis.json	
@@ -1025,7 +1025,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 e52a006a9..17a9cbbc2 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	
@@ -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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Financial Agent.json
index cf921bdf9..00e564214 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Financial Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Financial Agent.json	
@@ -1466,7 +1466,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 91af98dd3..c251e58a1 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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 ff6cbe8ae..be1d2b150 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	
@@ -1203,7 +1203,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Hybrid Search RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json
index 76ef93f0c..dd5b5b7a1 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json	
@@ -2320,7 +2320,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Image Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json
index 5278c518a..deb420495 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	
@@ -607,7 +607,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 2ebed7269..911bb2cbb 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	
@@ -1144,7 +1144,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Invoice Summarizer.json b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json
index 7cea4cafa..5b2760f7e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json	
@@ -404,7 +404,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 63b360c35..4bc953b97 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/LoopTemplate.json
@@ -1237,7 +1237,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 627fa3844..8912874d5 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	
@@ -595,7 +595,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 bcb3f5db7..03a7c184f 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	
@@ -1094,7 +1094,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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",
@@ -1395,7 +1395,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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",
@@ -2079,7 +2079,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 d033a4244..7521d3446 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	
@@ -545,7 +545,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 068adca56..c97c81645 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	
@@ -1960,7 +1960,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Pokédex Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json
index a987e6270..417743bc5 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json	
@@ -531,7 +531,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 ed094c740..c5ef525b5 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	
@@ -1106,7 +1106,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 fcf71e3c0..f8ee56229 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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 c27fcc19c..89260cdc2 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	
@@ -3222,7 +3222,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 9f6baef9a..0ec03f7b0 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	
@@ -652,7 +652,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 a0d36a2c3..f48a604fd 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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Search agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json
index 3980fab0f..88c5ddcf9 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Search agent.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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 eeb206150..5ba6efabe 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	
@@ -4158,7 +4158,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 6f7b24d06..788484bbf 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	
@@ -1008,7 +1008,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Social Media Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json
index 7f9c021f2..213c09ac8 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json	
@@ -1045,7 +1045,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Text Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json
index 63283d4a6..9554635ab 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json	
@@ -2285,7 +2285,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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",
@@ -2592,7 +2592,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 67876a05b..71b9d507a 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	
@@ -619,7 +619,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 b230209cf..ff27e41a4 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	
@@ -787,7 +787,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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 1166d8eef..239efc4e6 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	
@@ -1173,7 +1173,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/Youtube Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json
index c651fce1b..8ad02d169 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	
@@ -2087,7 +2087,7 @@
                 "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\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"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\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\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 _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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                return self._serialize_data(data)\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/tests/unit/components/outputs/test_chat_output_component.py b/src/backend/tests/unit/components/outputs/test_chat_output_component.py
index 5d7add96f..5fe86a893 100644
--- a/src/backend/tests/unit/components/outputs/test_chat_output_component.py
+++ b/src/backend/tests/unit/components/outputs/test_chat_output_component.py
@@ -53,7 +53,7 @@ class TestChatOutput(ComponentTestBaseWithClient):
         data = Data(text="Test data message")
         component.input_value = data
         result = await component.message_response()
-        assert result.text == "Test data message"
+        assert result.text == '```json\n{\n  "text": "Test data message"\n}\n```'
         assert result.sender == MESSAGE_SENDER_AI
 
     async def test_process_dataframe_input(self, component_class, default_kwargs):