diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json
index 400e1bb9c..39c50244c 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json	
@@ -6,72 +6,91 @@
         "data": {
           "sourceHandle": {
             "dataType": "ChatInput",
-            "id": "ChatInput-qzVJh",
+            "id": "ChatInput-GBI4i",
             "name": "message",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "user_input",
-            "id": "Prompt-HNvvn",
-            "inputTypes": ["Message", "Text"],
+            "id": "Prompt-vrft7",
+            "inputTypes": [
+              "Message",
+              "Text"
+            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ChatInput-qzVJh{œdataTypeœ:œChatInputœ,œidœ:œChatInput-qzVJhœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-HNvvn{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-HNvvnœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ChatInput-qzVJh",
-        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-qzVJhœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-HNvvn",
-        "targetHandle": "{œfieldNameœ: œuser_inputœ, œidœ: œPrompt-HNvvnœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ChatInput-GBI4i{œdataTypeœ:œChatInputœ,œidœ:œChatInput-GBI4iœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-vrft7{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-vrft7œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-GBI4i",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-GBI4iœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-vrft7",
+        "targetHandle": "{œfieldNameœ: œuser_inputœ, œidœ: œPrompt-vrft7œ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "Prompt",
-            "id": "Prompt-HNvvn",
+            "id": "Prompt-vrft7",
             "name": "prompt",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "OpenAIModel-vzfsP",
-            "inputTypes": ["Message"],
+            "id": "OpenAIModel-Y1Uvp",
+            "inputTypes": [
+              "Message"
+            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-Prompt-HNvvn{œdataTypeœ:œPromptœ,œidœ:œPrompt-HNvvnœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-vzfsP{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-vzfsPœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "Prompt-HNvvn",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-HNvvnœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "OpenAIModel-vzfsP",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-vzfsPœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Prompt-vrft7{œdataTypeœ:œPromptœ,œidœ:œPrompt-vrft7œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-Y1Uvp{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Y1Uvpœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "Prompt-vrft7",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-vrft7œ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "OpenAIModel-Y1Uvp",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-Y1Uvpœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-vzfsP",
+            "id": "OpenAIModel-Y1Uvp",
             "name": "text_output",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "ChatOutput-kTyIy",
-            "inputTypes": ["Message"],
+            "id": "ChatOutput-QRfK5",
+            "inputTypes": [
+              "Message"
+            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-OpenAIModel-vzfsP{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-vzfsPœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-kTyIy{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-kTyIyœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "OpenAIModel-vzfsP",
-        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-vzfsPœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "ChatOutput-kTyIy",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-kTyIyœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-OpenAIModel-Y1Uvp{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Y1Uvpœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-QRfK5{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-QRfK5œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "OpenAIModel-Y1Uvp",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-Y1Uvpœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "ChatOutput-QRfK5",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-QRfK5œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
       {
         "data": {
-          "id": "ChatInput-qzVJh",
+          "description": "Get chat inputs from the Playground.",
+          "display_name": "Chat Input",
+          "id": "ChatInput-GBI4i",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -93,11 +112,12 @@
               {
                 "cache": true,
                 "display_name": "Message",
-                "hidden": false,
                 "method": "message_response",
                 "name": "message",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -120,7 +140,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "advanced": true,
@@ -168,7 +188,9 @@
                 "display_name": "Text",
                 "dynamic": false,
                 "info": "Message to be passed as input.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -188,7 +210,10 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": ["Machine", "User"],
+                "options": [
+                  "Machine",
+                  "User"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -202,7 +227,9 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "sender_name",
@@ -220,7 +247,9 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "Session ID for the message.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "session_id",
@@ -239,7 +268,7 @@
         },
         "dragging": false,
         "height": 309,
-        "id": "ChatInput-qzVJh",
+        "id": "ChatInput-GBI4i",
         "position": {
           "x": -493.6459512396177,
           "y": 1083.200545525551
@@ -256,37 +285,38 @@
         "data": {
           "description": "Create a prompt template with dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-HNvvn",
+          "id": "Prompt-vrft7",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {
-              "template": ["user_input"]
+              "template": [
+                "user_input"
+              ]
             },
             "description": "Create a prompt template with dynamic variables.",
             "display_name": "Prompt",
             "documentation": "",
             "edited": false,
-            "error": null,
-            "field_order": ["template"],
+            "field_order": [
+              "template"
+            ],
             "frozen": false,
-            "full_path": null,
             "icon": "prompts",
-            "is_composition": null,
-            "is_input": null,
-            "is_output": null,
-            "name": "",
             "output_types": [],
             "outputs": [
               {
                 "cache": true,
                 "display_name": "Prompt Message",
-                "hidden": false,
                 "method": "build_prompt",
                 "name": "prompt",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -309,7 +339,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(frontend_template=frontend_node, raw_template=current_build_config[\"template\"])\n        return frontend_node\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n"
               },
               "template": {
                 "advanced": false,
@@ -317,6 +347,7 @@
                 "dynamic": false,
                 "info": "",
                 "list": false,
+                "load_from_db": false,
                 "name": "template",
                 "placeholder": "",
                 "required": false,
@@ -334,7 +365,10 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": ["Message", "Text"],
+                "input_types": [
+                  "Message",
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -353,7 +387,7 @@
         },
         "dragging": false,
         "height": 423,
-        "id": "Prompt-HNvvn",
+        "id": "Prompt-vrft7",
         "position": {
           "x": 56.354011530798516,
           "y": 1157.2005405164796
@@ -368,9 +402,13 @@
       },
       {
         "data": {
-          "id": "ChatOutput-kTyIy",
+          "description": "Display a chat message in the Playground.",
+          "display_name": "Chat Output",
+          "id": "ChatOutput-QRfK5",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -395,7 +433,9 @@
                 "method": "message_response",
                 "name": "message",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -418,14 +458,16 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "advanced": true,
                 "display_name": "Data Template",
                 "dynamic": false,
                 "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "data_template",
@@ -443,7 +485,9 @@
                 "display_name": "Text",
                 "dynamic": false,
                 "info": "Message to be passed as output.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "input_value",
@@ -462,7 +506,10 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": ["Machine", "User"],
+                "options": [
+                  "Machine",
+                  "User"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -476,7 +523,9 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "sender_name",
@@ -494,7 +543,9 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "Session ID for the message.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "session_id",
@@ -513,7 +564,7 @@
         },
         "dragging": false,
         "height": 309,
-        "id": "ChatOutput-kTyIy",
+        "id": "ChatOutput-QRfK5",
         "position": {
           "x": 1219.477374823274,
           "y": 1200.950216973985
@@ -528,9 +579,14 @@
       },
       {
         "data": {
-          "id": "OpenAIModel-vzfsP",
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "id": "OpenAIModel-Y1Uvp",
           "node": {
-            "base_classes": ["LanguageModel", "Message"],
+            "base_classes": [
+              "LanguageModel",
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -559,11 +615,12 @@
               {
                 "cache": true,
                 "display_name": "Text",
-                "hidden": false,
                 "method": "text_response",
                 "name": "text_output",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -572,7 +629,9 @@
                 "method": "build_model",
                 "name": "model_output",
                 "selected": "LanguageModel",
-                "types": ["LanguageModel"],
+                "types": [
+                  "LanguageModel"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -595,14 +654,16 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
               },
               "input_value": {
                 "advanced": false,
                 "display_name": "Input",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "input_value",
@@ -796,7 +857,7 @@
         },
         "dragging": false,
         "height": 623,
-        "id": "OpenAIModel-vzfsP",
+        "id": "OpenAIModel-Y1Uvp",
         "position": {
           "x": 664.0296638933031,
           "y": 1026.5966174731725
@@ -811,15 +872,15 @@
       }
     ],
     "viewport": {
-      "x": 340.4717463783424,
-      "y": -366.3254079423334,
-      "zoom": 0.5173846230261138
+      "x": 283.5782991180689,
+      "y": -288.9547032063149,
+      "zoom": 0.43809300423581504
     }
   },
   "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ",
   "endpoint_name": null,
-  "id": "a969cee0-eca5-4ad4-88d4-5aa484c0f23b",
+  "id": "f672da95-a7e3-41d2-942d-7ceabe1c0daa",
   "is_component": false,
-  "last_tested_version": "1.0.5",
+  "last_tested_version": "1.0.7",
   "name": "Basic Prompting (Hello, World)"
-}
+}
\ No newline at end of file
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 b8540644f..692423903 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	
@@ -6,7 +6,7 @@
         "data": {
           "sourceHandle": {
             "dataType": "URL",
-            "id": "URL-omiXy",
+            "id": "URL-9mY8k",
             "name": "data",
             "output_types": [
               "Data"
@@ -14,25 +14,25 @@
           },
           "targetHandle": {
             "fieldName": "data",
-            "id": "ParseData-8NNxu",
+            "id": "ParseData-qtEJg",
             "inputTypes": [
               "Data"
             ],
             "type": "other"
           }
         },
-        "id": "reactflow__edge-URL-omiXy{œdataTypeœ:œURLœ,œidœ:œURL-omiXyœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-ParseData-8NNxu{œfieldNameœ:œdataœ,œidœ:œParseData-8NNxuœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
-        "source": "URL-omiXy",
-        "sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-omiXyœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
-        "target": "ParseData-8NNxu",
-        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-8NNxuœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
+        "id": "reactflow__edge-URL-9mY8k{œdataTypeœ:œURLœ,œidœ:œURL-9mY8kœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-ParseData-qtEJg{œfieldNameœ:œdataœ,œidœ:œParseData-qtEJgœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+        "source": "URL-9mY8k",
+        "sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-9mY8kœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+        "target": "ParseData-qtEJg",
+        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-qtEJgœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
       },
       {
         "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "ParseData",
-            "id": "ParseData-8NNxu",
+            "id": "ParseData-qtEJg",
             "name": "text",
             "output_types": [
               "Message"
@@ -40,7 +40,7 @@
           },
           "targetHandle": {
             "fieldName": "references",
-            "id": "Prompt-QlUrR",
+            "id": "Prompt-LvBLt",
             "inputTypes": [
               "Message",
               "Text"
@@ -48,18 +48,18 @@
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ParseData-8NNxu{œdataTypeœ:œParseDataœ,œidœ:œParseData-8NNxuœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-QlUrR{œfieldNameœ:œreferencesœ,œidœ:œPrompt-QlUrRœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ParseData-8NNxu",
-        "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-8NNxuœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-QlUrR",
-        "targetHandle": "{œfieldNameœ: œreferencesœ, œidœ: œPrompt-QlUrRœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ParseData-qtEJg{œdataTypeœ:œParseDataœ,œidœ:œParseData-qtEJgœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-LvBLt{œfieldNameœ:œreferencesœ,œidœ:œPrompt-LvBLtœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ParseData-qtEJg",
+        "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-qtEJgœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-LvBLt",
+        "targetHandle": "{œfieldNameœ: œreferencesœ, œidœ: œPrompt-LvBLtœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
         "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "TextInput",
-            "id": "TextInput-hzkYc",
+            "id": "TextInput-uN09a",
             "name": "text",
             "output_types": [
               "Message"
@@ -67,7 +67,7 @@
           },
           "targetHandle": {
             "fieldName": "instructions",
-            "id": "Prompt-QlUrR",
+            "id": "Prompt-LvBLt",
             "inputTypes": [
               "Message",
               "Text"
@@ -75,17 +75,18 @@
             "type": "str"
           }
         },
-        "id": "reactflow__edge-TextInput-hzkYc{œdataTypeœ:œTextInputœ,œidœ:œTextInput-hzkYcœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-QlUrR{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-QlUrRœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "TextInput-hzkYc",
-        "sourceHandle": "{œdataTypeœ: œTextInputœ, œidœ: œTextInput-hzkYcœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-QlUrR",
-        "targetHandle": "{œfieldNameœ: œinstructionsœ, œidœ: œPrompt-QlUrRœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-TextInput-uN09a{œdataTypeœ:œTextInputœ,œidœ:œTextInput-uN09aœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-LvBLt{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-LvBLtœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "TextInput-uN09a",
+        "sourceHandle": "{œdataTypeœ: œTextInputœ, œidœ: œTextInput-uN09aœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-LvBLt",
+        "targetHandle": "{œfieldNameœ: œinstructionsœ, œidœ: œPrompt-LvBLtœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "Prompt",
-            "id": "Prompt-QlUrR",
+            "id": "Prompt-LvBLt",
             "name": "prompt",
             "output_types": [
               "Message"
@@ -93,24 +94,25 @@
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "OpenAIModel-ipTGJ",
+            "id": "OpenAIModel-5Kl3e",
             "inputTypes": [
               "Message"
             ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-Prompt-QlUrR{œdataTypeœ:œPromptœ,œidœ:œPrompt-QlUrRœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-ipTGJ{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-ipTGJœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "Prompt-QlUrR",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-QlUrRœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "OpenAIModel-ipTGJ",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-ipTGJœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Prompt-LvBLt{œdataTypeœ:œPromptœ,œidœ:œPrompt-LvBLtœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-5Kl3e{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-5Kl3eœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "Prompt-LvBLt",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-LvBLtœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "OpenAIModel-5Kl3e",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-5Kl3eœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-ipTGJ",
+            "id": "OpenAIModel-5Kl3e",
             "name": "text_output",
             "output_types": [
               "Message"
@@ -118,24 +120,26 @@
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "ChatOutput-SW0lN",
+            "id": "ChatOutput-HyN2e",
             "inputTypes": [
               "Message"
             ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-OpenAIModel-ipTGJ{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-ipTGJœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-SW0lN{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-SW0lNœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "OpenAIModel-ipTGJ",
-        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-ipTGJœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "ChatOutput-SW0lN",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-SW0lNœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-OpenAIModel-5Kl3e{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-5Kl3eœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-HyN2e{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-HyN2eœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "OpenAIModel-5Kl3e",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-5Kl3eœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "ChatOutput-HyN2e",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-HyN2eœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
       {
         "data": {
-          "id": "URL-omiXy",
+          "description": "Fetch content from one or more URLs.",
+          "display_name": "URL",
+          "id": "URL-9mY8k",
           "node": {
             "base_classes": [
               "Data"
@@ -157,7 +161,6 @@
               {
                 "cache": true,
                 "display_name": "Data",
-                "hidden": false,
                 "method": "fetch_content",
                 "name": "data",
                 "selected": "Data",
@@ -186,7 +189,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema import Data\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema import Data\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n    name = \"URL\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(https?:\\/\\/)?\"  # optional protocol\n            r\"(www\\.)?\"  # optional www\n            r\"([a-zA-Z0-9.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,})?\"  # top-level domain\n            r\"(:\\d+)?\"  # optional port\n            r\"(\\/[^\\s]*)?$\",  # optional path\n            re.IGNORECASE,\n        )\n\n        if not url_regex.match(string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
               },
               "urls": {
                 "advanced": false,
@@ -217,7 +220,7 @@
         },
         "dragging": false,
         "height": 359,
-        "id": "URL-omiXy",
+        "id": "URL-9mY8k",
         "position": {
           "x": 220.79156431407534,
           "y": 498.8186168722667
@@ -232,7 +235,9 @@
       },
       {
         "data": {
-          "id": "ParseData-8NNxu",
+          "description": "Convert Data into plain text following a specified template.",
+          "display_name": "Parse Data",
+          "id": "ParseData-qtEJg",
           "node": {
             "base_classes": [
               "Message"
@@ -256,7 +261,6 @@
               {
                 "cache": true,
                 "display_name": "Text",
-                "hidden": false,
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -285,7 +289,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
               },
               "data": {
                 "advanced": false,
@@ -349,7 +353,7 @@
         },
         "dragging": false,
         "height": 385,
-        "id": "ParseData-8NNxu",
+        "id": "ParseData-qtEJg",
         "position": {
           "x": 754.3607306709101,
           "y": 736.8516961537598
@@ -366,7 +370,7 @@
         "data": {
           "description": "Create a prompt template with dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-QlUrR",
+          "id": "Prompt-LvBLt",
           "node": {
             "base_classes": [
               "Message"
@@ -383,23 +387,16 @@
             "display_name": "Prompt",
             "documentation": "",
             "edited": false,
-            "error": null,
             "field_order": [
               "template"
             ],
             "frozen": false,
-            "full_path": null,
             "icon": "prompts",
-            "is_composition": null,
-            "is_input": null,
-            "is_output": null,
-            "name": "",
             "output_types": [],
             "outputs": [
               {
                 "cache": true,
                 "display_name": "Prompt Message",
-                "hidden": false,
                 "method": "build_prompt",
                 "name": "prompt",
                 "selected": "Message",
@@ -428,7 +425,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(frontend_template=frontend_node, raw_template=current_build_config[\"template\"])\n        return frontend_node\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n"
               },
               "instructions": {
                 "advanced": false,
@@ -484,6 +481,7 @@
                 "dynamic": false,
                 "info": "",
                 "list": false,
+                "load_from_db": false,
                 "name": "template",
                 "placeholder": "",
                 "required": false,
@@ -499,7 +497,7 @@
         },
         "dragging": false,
         "height": 517,
-        "id": "Prompt-QlUrR",
+        "id": "Prompt-LvBLt",
         "position": {
           "x": 1368.0633591447076,
           "y": 467.19448061224284
@@ -514,7 +512,9 @@
       },
       {
         "data": {
-          "id": "TextInput-hzkYc",
+          "description": "Get text inputs from the Playground.",
+          "display_name": "Instructions",
+          "id": "TextInput-uN09a",
           "node": {
             "base_classes": [
               "Message"
@@ -536,7 +536,6 @@
               {
                 "cache": true,
                 "display_name": "Text",
-                "hidden": false,
                 "method": "text_response",
                 "name": "text",
                 "selected": "Message",
@@ -565,7 +564,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -593,7 +592,7 @@
         },
         "dragging": false,
         "height": 309,
-        "id": "TextInput-hzkYc",
+        "id": "TextInput-uN09a",
         "position": {
           "x": 743.7338453293725,
           "y": 301.58775454952183
@@ -608,7 +607,9 @@
       },
       {
         "data": {
-          "id": "ChatOutput-SW0lN",
+          "description": "Display a chat message in the Playground.",
+          "display_name": "Chat Output",
+          "id": "ChatOutput-HyN2e",
           "node": {
             "base_classes": [
               "Message"
@@ -662,7 +663,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "advanced": true,
@@ -768,7 +769,7 @@
         },
         "dragging": false,
         "height": 309,
-        "id": "ChatOutput-SW0lN",
+        "id": "ChatOutput-HyN2e",
         "position": {
           "x": 2449.3489426461606,
           "y": 571.2449700910389
@@ -783,7 +784,9 @@
       },
       {
         "data": {
-          "id": "OpenAIModel-ipTGJ",
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "id": "OpenAIModel-5Kl3e",
           "node": {
             "base_classes": [
               "LanguageModel",
@@ -817,7 +820,6 @@
               {
                 "cache": true,
                 "display_name": "Text",
-                "hidden": false,
                 "method": "text_response",
                 "name": "text_output",
                 "selected": "Message",
@@ -857,7 +859,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
               },
               "input_value": {
                 "advanced": false,
@@ -1060,7 +1062,7 @@
         },
         "dragging": false,
         "height": 623,
-        "id": "OpenAIModel-ipTGJ",
+        "id": "OpenAIModel-5Kl3e",
         "position": {
           "x": 1950.3830456413473,
           "y": 380.8161704718418
@@ -1075,15 +1077,15 @@
       }
     ],
     "viewport": {
-      "x": -70.94787944982488,
-      "y": -18.004163894553585,
-      "zoom": 0.47423908935061165
+      "x": 13.218836373936256,
+      "y": 51.81084733415963,
+      "zoom": 0.3400188154942784
     }
   },
   "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.",
   "endpoint_name": null,
-  "id": "3ef43c67-bc61-49ff-8610-e63d46e26aec",
+  "id": "5572261c-afd4-41d1-971e-12b3b7577a24",
   "is_component": false,
-  "last_tested_version": "1.0.5",
+  "last_tested_version": "1.0.7",
   "name": "Blog Writer"
 }
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json
index 4c9c15a69..c5ea61093 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json	
@@ -6,7 +6,7 @@
         "data": {
           "sourceHandle": {
             "dataType": "ChatInput",
-            "id": "ChatInput-dIJoM",
+            "id": "ChatInput-0DUC7",
             "name": "message",
             "output_types": [
               "Message"
@@ -14,7 +14,7 @@
           },
           "targetHandle": {
             "fieldName": "Question",
-            "id": "Prompt-BSMkG",
+            "id": "Prompt-LciEH",
             "inputTypes": [
               "Message",
               "Text"
@@ -22,17 +22,18 @@
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ChatInput-dIJoM{œdataTypeœ:œChatInputœ,œidœ:œChatInput-dIJoMœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-BSMkG{œfieldNameœ:œQuestionœ,œidœ:œPrompt-BSMkGœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ChatInput-dIJoM",
-        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-dIJoMœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-BSMkG",
-        "targetHandle": "{œfieldNameœ: œQuestionœ, œidœ: œPrompt-BSMkGœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ChatInput-0DUC7{œdataTypeœ:œChatInputœ,œidœ:œChatInput-0DUC7œ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-LciEH{œfieldNameœ:œQuestionœ,œidœ:œPrompt-LciEHœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-0DUC7",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-0DUC7œ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-LciEH",
+        "targetHandle": "{œfieldNameœ: œQuestionœ, œidœ: œPrompt-LciEHœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "Prompt",
-            "id": "Prompt-BSMkG",
+            "id": "Prompt-LciEH",
             "name": "prompt",
             "output_types": [
               "Message"
@@ -40,24 +41,25 @@
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "OpenAIModel-pRl0H",
+            "id": "OpenAIModel-ip5dm",
             "inputTypes": [
               "Message"
             ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-Prompt-BSMkG{œdataTypeœ:œPromptœ,œidœ:œPrompt-BSMkGœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-pRl0H{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-pRl0Hœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "Prompt-BSMkG",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-BSMkGœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "OpenAIModel-pRl0H",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-pRl0Hœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Prompt-LciEH{œdataTypeœ:œPromptœ,œidœ:œPrompt-LciEHœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-ip5dm{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-ip5dmœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "Prompt-LciEH",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-LciEHœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "OpenAIModel-ip5dm",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-ip5dmœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-pRl0H",
+            "id": "OpenAIModel-ip5dm",
             "name": "text_output",
             "output_types": [
               "Message"
@@ -65,24 +67,25 @@
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "ChatOutput-quQaq",
+            "id": "ChatOutput-IP9p0",
             "inputTypes": [
               "Message"
             ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-OpenAIModel-pRl0H{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-pRl0Hœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-quQaq{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-quQaqœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "OpenAIModel-pRl0H",
-        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-pRl0Hœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "ChatOutput-quQaq",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-quQaqœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-OpenAIModel-ip5dm{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-ip5dmœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-IP9p0{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-IP9p0œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "OpenAIModel-ip5dm",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-ip5dmœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "ChatOutput-IP9p0",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-IP9p0œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "ParseData",
-            "id": "ParseData-Wmnfq",
+            "id": "ParseData-FchNf",
             "name": "text",
             "output_types": [
               "Message"
@@ -90,7 +93,7 @@
           },
           "targetHandle": {
             "fieldName": "Document",
-            "id": "Prompt-BSMkG",
+            "id": "Prompt-LciEH",
             "inputTypes": [
               "Message",
               "Text"
@@ -98,17 +101,18 @@
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ParseData-Wmnfq{œdataTypeœ:œParseDataœ,œidœ:œParseData-Wmnfqœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-BSMkG{œfieldNameœ:œDocumentœ,œidœ:œPrompt-BSMkGœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ParseData-Wmnfq",
-        "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-Wmnfqœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-BSMkG",
-        "targetHandle": "{œfieldNameœ: œDocumentœ, œidœ: œPrompt-BSMkGœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ParseData-FchNf{œdataTypeœ:œParseDataœ,œidœ:œParseData-FchNfœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-LciEH{œfieldNameœ:œDocumentœ,œidœ:œPrompt-LciEHœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ParseData-FchNf",
+        "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-FchNfœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-LciEH",
+        "targetHandle": "{œfieldNameœ: œDocumentœ, œidœ: œPrompt-LciEHœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "File",
-            "id": "File-lVr1c",
+            "id": "File-2kzl4",
             "name": "data",
             "output_types": [
               "Data"
@@ -116,18 +120,18 @@
           },
           "targetHandle": {
             "fieldName": "data",
-            "id": "ParseData-Wmnfq",
+            "id": "ParseData-FchNf",
             "inputTypes": [
               "Data"
             ],
             "type": "other"
           }
         },
-        "id": "reactflow__edge-File-lVr1c{œdataTypeœ:œFileœ,œidœ:œFile-lVr1cœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-ParseData-Wmnfq{œfieldNameœ:œdataœ,œidœ:œParseData-Wmnfqœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
-        "source": "File-lVr1c",
-        "sourceHandle": "{œdataTypeœ: œFileœ, œidœ: œFile-lVr1cœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
-        "target": "ParseData-Wmnfq",
-        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-Wmnfqœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
+        "id": "reactflow__edge-File-2kzl4{œdataTypeœ:œFileœ,œidœ:œFile-2kzl4œ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-ParseData-FchNf{œfieldNameœ:œdataœ,œidœ:œParseData-FchNfœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+        "source": "File-2kzl4",
+        "sourceHandle": "{œdataTypeœ: œFileœ, œidœ: œFile-2kzl4œ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+        "target": "ParseData-FchNf",
+        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-FchNfœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
       }
     ],
     "nodes": [
@@ -135,7 +139,7 @@
         "data": {
           "description": "Create a prompt template with dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-BSMkG",
+          "id": "Prompt-LciEH",
           "node": {
             "base_classes": [
               "Message"
@@ -152,23 +156,16 @@
             "display_name": "Prompt",
             "documentation": "",
             "edited": false,
-            "error": null,
             "field_order": [
               "template"
             ],
             "frozen": false,
-            "full_path": null,
             "icon": "prompts",
-            "is_composition": null,
-            "is_input": null,
-            "is_output": null,
-            "name": "",
             "output_types": [],
             "outputs": [
               {
                 "cache": true,
                 "display_name": "Prompt Message",
-                "hidden": false,
                 "method": "build_prompt",
                 "name": "prompt",
                 "selected": "Message",
@@ -245,7 +242,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(frontend_template=frontend_node, raw_template=current_build_config[\"template\"])\n        return frontend_node\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n"
               },
               "template": {
                 "advanced": false,
@@ -253,6 +250,7 @@
                 "dynamic": false,
                 "info": "",
                 "list": false,
+                "load_from_db": false,
                 "name": "template",
                 "placeholder": "",
                 "required": false,
@@ -268,7 +266,7 @@
         },
         "dragging": false,
         "height": 517,
-        "id": "Prompt-BSMkG",
+        "id": "Prompt-LciEH",
         "position": {
           "x": 637.3518652087848,
           "y": 47.191730368560215
@@ -283,7 +281,9 @@
       },
       {
         "data": {
-          "id": "ChatInput-dIJoM",
+          "description": "Get chat inputs from the Playground.",
+          "display_name": "Chat Input",
+          "id": "ChatInput-0DUC7",
           "node": {
             "base_classes": [
               "Message"
@@ -309,7 +309,6 @@
               {
                 "cache": true,
                 "display_name": "Message",
-                "hidden": false,
                 "method": "message_response",
                 "name": "message",
                 "selected": "Message",
@@ -338,7 +337,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "advanced": true,
@@ -466,7 +465,7 @@
         },
         "dragging": false,
         "height": 309,
-        "id": "ChatInput-dIJoM",
+        "id": "ChatInput-0DUC7",
         "position": {
           "x": 50.08709924122684,
           "y": 320.88186720121615
@@ -481,7 +480,9 @@
       },
       {
         "data": {
-          "id": "ChatOutput-quQaq",
+          "description": "Display a chat message in the Playground.",
+          "display_name": "Chat Output",
+          "id": "ChatOutput-IP9p0",
           "node": {
             "base_classes": [
               "Message"
@@ -535,7 +536,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "advanced": true,
@@ -641,7 +642,7 @@
         },
         "dragging": false,
         "height": 309,
-        "id": "ChatOutput-quQaq",
+        "id": "ChatOutput-IP9p0",
         "position": {
           "x": 1831.1359796346408,
           "y": 139.5174517327903
@@ -656,7 +657,9 @@
       },
       {
         "data": {
-          "id": "OpenAIModel-pRl0H",
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "id": "OpenAIModel-ip5dm",
           "node": {
             "base_classes": [
               "LanguageModel",
@@ -690,7 +693,6 @@
               {
                 "cache": true,
                 "display_name": "Text",
-                "hidden": false,
                 "method": "text_response",
                 "name": "text_output",
                 "selected": "Message",
@@ -730,7 +732,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
               },
               "input_value": {
                 "advanced": false,
@@ -933,7 +935,7 @@
         },
         "dragging": false,
         "height": 623,
-        "id": "OpenAIModel-pRl0H",
+        "id": "OpenAIModel-ip5dm",
         "position": {
           "x": 1264.0039093582332,
           "y": -67.93731748926709
@@ -948,7 +950,9 @@
       },
       {
         "data": {
-          "id": "ParseData-Wmnfq",
+          "description": "Convert Data into plain text following a specified template.",
+          "display_name": "Parse Data",
+          "id": "ParseData-FchNf",
           "node": {
             "base_classes": [
               "Message"
@@ -972,7 +976,6 @@
               {
                 "cache": true,
                 "display_name": "Text",
-                "hidden": false,
                 "method": "parse_data",
                 "name": "text",
                 "selected": "Message",
@@ -1001,7 +1004,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
               },
               "data": {
                 "advanced": false,
@@ -1065,7 +1068,7 @@
         },
         "dragging": false,
         "height": 385,
-        "id": "ParseData-Wmnfq",
+        "id": "ParseData-FchNf",
         "position": {
           "x": 87.26129917199853,
           "y": -181.46350622708565
@@ -1080,7 +1083,9 @@
       },
       {
         "data": {
-          "id": "File-lVr1c",
+          "description": "A generic file loader.",
+          "display_name": "File",
+          "id": "File-2kzl4",
           "node": {
             "base_classes": [
               "Data"
@@ -1103,7 +1108,6 @@
               {
                 "cache": true,
                 "display_name": "Data",
-                "hidden": false,
                 "method": "load_file",
                 "name": "data",
                 "selected": "Data",
@@ -1132,7 +1136,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    inputs = [\n        FileInput(\n            name=\"path\",\n            display_name=\"Path\",\n            file_types=TEXT_FILE_TYPES,\n            info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n        ),\n        BoolInput(\n            name=\"silent_errors\",\n            display_name=\"Silent Errors\",\n            advanced=True,\n            info=\"If true, errors will not raise an exception.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n    ]\n\n    def load_file(self) -> Data:\n        if not self.path:\n            raise ValueError(\"Please, upload a file to use this component.\")\n        resolved_path = self.resolve_path(self.path)\n        silent_errors = self.silent_errors\n\n        extension = Path(resolved_path).suffix[1:].lower()\n\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n\n        data = parse_text_file_to_data(resolved_path, silent_errors)\n        self.status = data if data else \"No data\"\n        return data or Data()\n"
+                "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n    name = \"File\"\n\n    inputs = [\n        FileInput(\n            name=\"path\",\n            display_name=\"Path\",\n            file_types=TEXT_FILE_TYPES,\n            info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n        ),\n        BoolInput(\n            name=\"silent_errors\",\n            display_name=\"Silent Errors\",\n            advanced=True,\n            info=\"If true, errors will not raise an exception.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n    ]\n\n    def load_file(self) -> Data:\n        if not self.path:\n            raise ValueError(\"Please, upload a file to use this component.\")\n        resolved_path = self.resolve_path(self.path)\n        silent_errors = self.silent_errors\n\n        extension = Path(resolved_path).suffix[1:].lower()\n\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n\n        data = parse_text_file_to_data(resolved_path, silent_errors)\n        self.status = data if data else \"No data\"\n        return data or Data()\n"
               },
               "path": {
                 "advanced": false,
@@ -1158,7 +1162,7 @@
                   "ts",
                   "tsx"
                 ],
-                "file_path": "72e89c7f-4e02-47ba-8d96-c3822600c0ca/BSCNCL97P18E730Y_F24 (1).pdf",
+                "file_path": "",
                 "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
                 "list": false,
                 "name": "path",
@@ -1191,7 +1195,7 @@
         },
         "dragging": false,
         "height": 301,
-        "id": "File-lVr1c",
+        "id": "File-2kzl4",
         "position": {
           "x": -462.90407701896845,
           "y": -316.82165433756165
@@ -1200,21 +1204,21 @@
           "x": -462.90407701896845,
           "y": -316.82165433756165
         },
-        "selected": true,
+        "selected": false,
         "type": "genericNode",
         "width": 384
       }
     ],
     "viewport": {
-      "x": 0,
-      "y": 0,
-      "zoom": 1
+      "x": 188.5057346133326,
+      "y": 210.35779755360858,
+      "zoom": 0.36856730432277524
     }
   },
   "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.",
   "endpoint_name": null,
-  "id": "72e89c7f-4e02-47ba-8d96-c3822600c0ca",
+  "id": "cb65817a-dfdf-4df2-9bb0-16654bf3f3b3",
   "is_component": false,
-  "last_tested_version": "1.0.5",
+  "last_tested_version": "1.0.7",
   "name": "Document QA"
 }
\ No newline at end of file
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 f88d8a4d0..9411190a0 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	
@@ -6,85 +6,106 @@
         "data": {
           "sourceHandle": {
             "dataType": "ChatInput",
-            "id": "ChatInput-mqkzx",
+            "id": "ChatInput-x6Vrd",
             "name": "message",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "user_message",
-            "id": "Prompt-YKCf2",
-            "inputTypes": ["Message", "Text"],
+            "id": "Prompt-FEEsr",
+            "inputTypes": [
+              "Message",
+              "Text"
+            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ChatInput-mqkzx{œdataTypeœ:œChatInputœ,œidœ:œChatInput-mqkzxœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-YKCf2{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-YKCf2œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ChatInput-mqkzx",
-        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-mqkzxœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-YKCf2",
-        "targetHandle": "{œfieldNameœ: œuser_messageœ, œidœ: œPrompt-YKCf2œ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ChatInput-x6Vrd{œdataTypeœ:œChatInputœ,œidœ:œChatInput-x6Vrdœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-FEEsr{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-FEEsrœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-x6Vrd",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-x6Vrdœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-FEEsr",
+        "targetHandle": "{œfieldNameœ: œuser_messageœ, œidœ: œPrompt-FEEsrœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "Prompt",
-            "id": "Prompt-YKCf2",
+            "id": "Prompt-FEEsr",
             "name": "prompt",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "OpenAIModel-yLbKf",
-            "inputTypes": ["Message"],
+            "id": "OpenAIModel-M4C3O",
+            "inputTypes": [
+              "Message"
+            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-Prompt-YKCf2{œdataTypeœ:œPromptœ,œidœ:œPrompt-YKCf2œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-yLbKf{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-yLbKfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "Prompt-YKCf2",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-YKCf2œ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "OpenAIModel-yLbKf",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-yLbKfœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Prompt-FEEsr{œdataTypeœ:œPromptœ,œidœ:œPrompt-FEEsrœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-M4C3O{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-M4C3Oœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "Prompt-FEEsr",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-FEEsrœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "OpenAIModel-M4C3O",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-M4C3Oœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-yLbKf",
+            "id": "OpenAIModel-M4C3O",
             "name": "text_output",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "ChatOutput-V3TLa",
-            "inputTypes": ["Message"],
+            "id": "ChatOutput-gpEZp",
+            "inputTypes": [
+              "Message"
+            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-OpenAIModel-yLbKf{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-yLbKfœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-V3TLa{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-V3TLaœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "OpenAIModel-yLbKf",
-        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-yLbKfœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "ChatOutput-V3TLa",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-V3TLaœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-OpenAIModel-M4C3O{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-M4C3Oœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-gpEZp{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-gpEZpœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "OpenAIModel-M4C3O",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-M4C3Oœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "ChatOutput-gpEZp",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-gpEZpœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "Memory",
-            "id": "Memory-bJZYV",
+            "id": "Memory-zJQHe",
             "name": "messages_text",
-            "output_types": ["Message"]
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "context",
-            "id": "Prompt-YKCf2",
-            "inputTypes": ["Message", "Text"],
+            "id": "Prompt-FEEsr",
+            "inputTypes": [
+              "Message",
+              "Text"
+            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-Memory-bJZYV{œdataTypeœ:œMemoryœ,œidœ:œMemory-bJZYVœ,œnameœ:œmessages_textœ,œoutput_typesœ:[œMessageœ]}-Prompt-YKCf2{œfieldNameœ:œcontextœ,œidœ:œPrompt-YKCf2œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "Memory-bJZYV",
-        "sourceHandle": "{œdataTypeœ: œMemoryœ, œidœ: œMemory-bJZYVœ, œnameœ: œmessages_textœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-YKCf2",
-        "targetHandle": "{œfieldNameœ: œcontextœ, œidœ: œPrompt-YKCf2œ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Memory-zJQHe{œdataTypeœ:œMemoryœ,œidœ:œMemory-zJQHeœ,œnameœ:œmessages_textœ,œoutput_typesœ:[œMessageœ]}-Prompt-FEEsr{œfieldNameœ:œcontextœ,œidœ:œPrompt-FEEsrœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "Memory-zJQHe",
+        "sourceHandle": "{œdataTypeœ: œMemoryœ, œidœ: œMemory-zJQHeœ, œnameœ: œmessages_textœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-FEEsr",
+        "targetHandle": "{œfieldNameœ: œcontextœ, œidœ: œPrompt-FEEsrœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
@@ -92,37 +113,39 @@
         "data": {
           "description": "Create a prompt template with dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-YKCf2",
+          "id": "Prompt-FEEsr",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {
-              "template": ["context", "user_message"]
+              "template": [
+                "context",
+                "user_message"
+              ]
             },
             "description": "Create a prompt template with dynamic variables.",
             "display_name": "Prompt",
             "documentation": "",
             "edited": false,
-            "error": null,
-            "field_order": ["template"],
+            "field_order": [
+              "template"
+            ],
             "frozen": false,
-            "full_path": null,
             "icon": "prompts",
-            "is_composition": null,
-            "is_input": null,
-            "is_output": null,
-            "name": "",
             "output_types": [],
             "outputs": [
               {
                 "cache": true,
                 "display_name": "Prompt Message",
-                "hidden": false,
                 "method": "build_prompt",
                 "name": "prompt",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -145,7 +168,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(frontend_template=frontend_node, raw_template=current_build_config[\"template\"])\n        return frontend_node\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n"
               },
               "context": {
                 "advanced": false,
@@ -155,7 +178,10 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": ["Message", "Text"],
+                "input_types": [
+                  "Message",
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -174,6 +200,7 @@
                 "dynamic": false,
                 "info": "",
                 "list": false,
+                "load_from_db": false,
                 "name": "template",
                 "placeholder": "",
                 "required": false,
@@ -191,7 +218,10 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": ["Message", "Text"],
+                "input_types": [
+                  "Message",
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -210,7 +240,7 @@
         },
         "dragging": false,
         "height": 517,
-        "id": "Prompt-YKCf2",
+        "id": "Prompt-FEEsr",
         "position": {
           "x": 1880.8227904110583,
           "y": 625.8049209882275
@@ -225,9 +255,13 @@
       },
       {
         "data": {
-          "id": "ChatInput-mqkzx",
+          "description": "Get chat inputs from the Playground.",
+          "display_name": "Chat Input",
+          "id": "ChatInput-x6Vrd",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -249,11 +283,12 @@
               {
                 "cache": true,
                 "display_name": "Message",
-                "hidden": false,
                 "method": "message_response",
                 "name": "message",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -276,7 +311,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "advanced": true,
@@ -324,7 +359,9 @@
                 "display_name": "Text",
                 "dynamic": false,
                 "info": "Message to be passed as input.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -344,7 +381,10 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": ["Machine", "User"],
+                "options": [
+                  "Machine",
+                  "User"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -358,7 +398,9 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "sender_name",
@@ -376,7 +418,9 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "Session ID for the message.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "session_id",
@@ -395,7 +439,7 @@
         },
         "dragging": false,
         "height": 309,
-        "id": "ChatInput-mqkzx",
+        "id": "ChatInput-x6Vrd",
         "position": {
           "x": 1275.9262193671882,
           "y": 836.1228056896347
@@ -410,9 +454,14 @@
       },
       {
         "data": {
-          "id": "OpenAIModel-yLbKf",
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "id": "OpenAIModel-M4C3O",
           "node": {
-            "base_classes": ["LanguageModel", "Message"],
+            "base_classes": [
+              "LanguageModel",
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -441,11 +490,12 @@
               {
                 "cache": true,
                 "display_name": "Text",
-                "hidden": false,
                 "method": "text_response",
                 "name": "text_output",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -454,7 +504,9 @@
                 "method": "build_model",
                 "name": "model_output",
                 "selected": "LanguageModel",
-                "types": ["LanguageModel"],
+                "types": [
+                  "LanguageModel"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -477,14 +529,16 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
               },
               "input_value": {
                 "advanced": false,
                 "display_name": "Input",
                 "dynamic": false,
                 "info": "",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "input_value",
@@ -678,7 +732,7 @@
         },
         "dragging": false,
         "height": 623,
-        "id": "OpenAIModel-yLbKf",
+        "id": "OpenAIModel-M4C3O",
         "position": {
           "x": 2468.968379487559,
           "y": 560.0689522326683
@@ -693,9 +747,13 @@
       },
       {
         "data": {
-          "id": "ChatOutput-V3TLa",
+          "description": "Display a chat message in the Playground.",
+          "display_name": "Chat Output",
+          "id": "ChatOutput-gpEZp",
           "node": {
-            "base_classes": ["Message"],
+            "base_classes": [
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -720,7 +778,9 @@
                 "method": "message_response",
                 "name": "message",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -743,14 +803,16 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "advanced": true,
                 "display_name": "Data Template",
                 "dynamic": false,
                 "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "data_template",
@@ -768,7 +830,9 @@
                 "display_name": "Text",
                 "dynamic": false,
                 "info": "Message to be passed as output.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "input_value",
@@ -787,7 +851,10 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": ["Machine", "User"],
+                "options": [
+                  "Machine",
+                  "User"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -801,7 +868,9 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "sender_name",
@@ -819,7 +888,9 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "Session ID for the message.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "session_id",
@@ -837,7 +908,7 @@
           "type": "ChatOutput"
         },
         "height": 309,
-        "id": "ChatOutput-V3TLa",
+        "id": "ChatOutput-gpEZp",
         "position": {
           "x": 3083.1710516244116,
           "y": 701.521688846004
@@ -848,9 +919,14 @@
       },
       {
         "data": {
-          "id": "Memory-bJZYV",
+          "description": "Retrieves stored chat messages.",
+          "display_name": "Chat Memory",
+          "id": "Memory-zJQHe",
           "node": {
-            "base_classes": ["Data", "Message"],
+            "base_classes": [
+              "Data",
+              "Message"
+            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -876,17 +952,20 @@
                 "method": "retrieve_messages",
                 "name": "messages",
                 "selected": "Data",
-                "types": ["Data"],
+                "types": [
+                  "Data"
+                ],
                 "value": "__UNDEFINED__"
               },
               {
                 "cache": true,
                 "display_name": "Messages (Text)",
-                "hidden": false,
                 "method": "retrieve_messages_as_text",
                 "name": "messages_text",
                 "selected": "Message",
-                "types": ["Message"],
+                "types": [
+                  "Message"
+                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -909,7 +988,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass MemoryComponent(Component):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages.\"\n    icon = \"message-square-more\"\n\n    inputs = [\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\", \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"Session ID of the chat history.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Chat History\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        messages = get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        self.status = messages\n        return messages\n\n    def retrieve_messages_as_text(self) -> Message:\n        messages_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = messages_text\n        return Message(text=messages_text)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass MemoryComponent(Component):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\", \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"Session ID of the chat history.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Chat History\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        messages = get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        self.status = messages\n        return messages\n\n    def retrieve_messages_as_text(self) -> Message:\n        messages_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = messages_text\n        return Message(text=messages_text)\n"
               },
               "n_messages": {
                 "advanced": true,
@@ -932,7 +1011,10 @@
                 "dynamic": false,
                 "info": "Order of the messages.",
                 "name": "order",
-                "options": ["Ascending", "Descending"],
+                "options": [
+                  "Ascending",
+                  "Descending"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -947,7 +1029,11 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": ["Machine", "User", "Machine and User"],
+                "options": [
+                  "Machine",
+                  "User",
+                  "Machine and User"
+                ],
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -961,7 +1047,9 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "sender_name",
@@ -979,7 +1067,9 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "Session ID of the chat history.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "name": "session_id",
@@ -997,7 +1087,9 @@
                 "display_name": "Template",
                 "dynamic": false,
                 "info": "The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
-                "input_types": ["Message"],
+                "input_types": [
+                  "Message"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -1017,7 +1109,7 @@
         },
         "dragging": false,
         "height": 267,
-        "id": "Memory-bJZYV",
+        "id": "Memory-zJQHe",
         "position": {
           "x": 1301.98330242754,
           "y": 422.33865605652574
@@ -1032,15 +1124,15 @@
       }
     ],
     "viewport": {
-      "x": -586.5321077101332,
-      "y": -103.32118023592653,
-      "zoom": 0.5372819347267049
+      "x": -556.4579979699035,
+      "y": -102.83844480905748,
+      "zoom": 0.45494095964690673
     }
   },
   "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.",
   "endpoint_name": null,
-  "id": "3dd23463-467b-4a89-b7d4-d227ef538cba",
+  "id": "ec014aa6-106a-45bf-a898-bceddd9041e0",
   "is_component": false,
-  "last_tested_version": "1.0.5",
+  "last_tested_version": "1.0.7",
   "name": "Memory Chatbot"
-}
+}
\ No newline at end of file