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 881089edd..8b07511cb 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	
@@ -40,7 +40,7 @@
             "id": "Prompt-3QeXa",
             "name": "prompt",
             "output_types": [
-              "Prompt"
+              "Message"
             ]
           },
           "targetHandle": {
@@ -56,7 +56,7 @@
         },
         "id": "reactflow__edge-Prompt-3QeXa{œdataTypeœ:œPromptœ,œidœ:œPrompt-3QeXaœ,œnameœ:œpromptœ,œoutput_typesœ:[œPromptœ]}-OpenAIModel-SsPHS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-SsPHSœ,œinputTypesœ:[œTextœ,œDataœ,œPromptœ],œtypeœ:œstrœ}",
         "source": "Prompt-3QeXa",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-3QeXaœ, œnameœ: œpromptœ, œoutput_typesœ: [œPromptœ]}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-3QeXaœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
         "target": "OpenAIModel-SsPHS",
         "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-SsPHSœ, œinputTypesœ: [œTextœ, œDataœ, œPromptœ], œtypeœ: œstrœ}"
       },
@@ -124,9 +124,9 @@
                 "display_name": "Prompt",
                 "method": "build_prompt",
                 "name": "prompt",
-                "selected": "Prompt",
+                "selected": "Message",
                 "types": [
-                  "Prompt"
+                  "Message"
                 ],
                 "value": "__UNDEFINED__"
               },
@@ -160,7 +160,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.field_typing.prompt import Prompt\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Prompt:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
               },
               "template": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json
index 73fd53773..1f2e44284 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json	
@@ -131,7 +131,7 @@
             "id": "Prompt-Rse03",
             "name": "prompt",
             "output_types": [
-              "Prompt"
+              "Message"
             ]
           },
           "targetHandle": {
@@ -148,7 +148,7 @@
         "id": "reactflow__edge-Prompt-Rse03{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-Rse03œ}-OpenAIModel-gi29P{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-gi29Pœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "selected": false,
         "source": "Prompt-Rse03",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-Rse03œ, œoutput_typesœ: [œPromptœ], œnameœ: œpromptœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-Rse03œ, œoutput_typesœ: [œMessageœ], œnameœ: œpromptœ}",
         "style": {
           "stroke": "#555"
         },
@@ -196,9 +196,9 @@
                 "display_name": "Prompt",
                 "method": "build_prompt",
                 "name": "prompt",
-                "selected": "Prompt",
+                "selected": "Message",
                 "types": [
-                  "Prompt"
+                  "Message"
                 ],
                 "value": "__UNDEFINED__"
               },
@@ -232,7 +232,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.field_typing.prompt import Prompt\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Prompt:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
               },
               "instructions": {
                 "advanced": false,
@@ -403,7 +403,7 @@
                 "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.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\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        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            value=\"\",\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\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> 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(content=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.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\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        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            value=\"\",\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\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> 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_key=\"content\", content=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
               },
               "urls": {
                 "advanced": false,
@@ -975,7 +975,7 @@
                 "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.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\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        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            value=\"\",\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\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> 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(content=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.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\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        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            value=\"\",\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\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> 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_key=\"content\", content=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
               },
               "urls": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json
index 56f65319c..f1ae7d4c9 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json	
@@ -64,7 +64,7 @@
             "id": "Prompt-9DNZG",
             "name": "prompt",
             "output_types": [
-              "Prompt"
+              "Message"
             ]
           },
           "targetHandle": {
@@ -80,7 +80,7 @@
         },
         "id": "reactflow__edge-Prompt-9DNZG{œdataTypeœ:œPromptœ,œidœ:œPrompt-9DNZGœ,œnameœ:œpromptœ,œoutput_typesœ:[œPromptœ]}-OpenAIModel-8b6nG{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-8b6nGœ,œinputTypesœ:[œTextœ,œDataœ,œPromptœ],œtypeœ:œstrœ}",
         "source": "Prompt-9DNZG",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-9DNZGœ, œnameœ: œpromptœ, œoutput_typesœ: [œPromptœ]}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-9DNZGœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
         "target": "OpenAIModel-8b6nG",
         "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-8b6nGœ, œinputTypesœ: [œTextœ, œDataœ, œPromptœ], œtypeœ: œstrœ}"
       },
@@ -149,9 +149,9 @@
                 "display_name": "Prompt",
                 "method": "build_prompt",
                 "name": "prompt",
-                "selected": "Prompt",
+                "selected": "Message",
                 "types": [
-                  "Prompt"
+                  "Message"
                 ],
                 "value": "__UNDEFINED__"
               },
@@ -238,7 +238,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.field_typing.prompt import Prompt\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Prompt:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
               },
               "template": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json
index bcd51ce95..c4863d65d 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json	
@@ -75,7 +75,7 @@
             "id": "Prompt-ODkUx",
             "name": "prompt",
             "output_types": [
-              "Prompt"
+              "Message"
             ]
           },
           "targetHandle": {
@@ -91,7 +91,7 @@
         },
         "id": "reactflow__edge-Prompt-ODkUx{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}-OpenAIModel-9RykF{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "source": "Prompt-ODkUx",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-ODkUxœ, œoutput_typesœ: [œPromptœ], œnameœ: œpromptœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-ODkUxœ, œoutput_typesœ: [œMessageœ], œnameœ: œpromptœ}",
         "style": {
           "stroke": "#555"
         },
@@ -755,9 +755,9 @@
                 "display_name": "Prompt",
                 "method": "build_prompt",
                 "name": "prompt",
-                "selected": "Prompt",
+                "selected": "Message",
                 "types": [
-                  "Prompt"
+                  "Message"
                 ],
                 "value": "__UNDEFINED__"
               },
@@ -791,7 +791,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.field_typing.prompt import Prompt\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Prompt:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
               },
               "context": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json
index ca4cd52e3..55eb9c9a8 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json	
@@ -71,7 +71,7 @@
             "id": "Prompt-amqBu",
             "name": "prompt",
             "output_types": [
-              "Prompt"
+              "Message"
             ]
           },
           "targetHandle": {
@@ -87,7 +87,7 @@
         },
         "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-OpenAIModel-uYXZJ{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "source": "Prompt-amqBu",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ, œoutput_typesœ: [œPromptœ], œnameœ: œpromptœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ, œoutput_typesœ: [œMessageœ], œnameœ: œpromptœ}",
         "style": {
           "stroke": "#555"
         },
@@ -193,7 +193,7 @@
             "id": "Prompt-gTNiz",
             "name": "prompt",
             "output_types": [
-              "Prompt"
+              "Message"
             ]
           },
           "targetHandle": {
@@ -209,7 +209,7 @@
         },
         "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-OpenAIModel-XawYB{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "source": "Prompt-gTNiz",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ, œoutput_typesœ: [œPromptœ], œnameœ: œpromptœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ, œoutput_typesœ: [œMessageœ], œnameœ: œpromptœ}",
         "style": {
           "stroke": "#555"
         },
@@ -284,9 +284,9 @@
                 "display_name": "Prompt",
                 "method": "build_prompt",
                 "name": "prompt",
-                "selected": "Prompt",
+                "selected": "Message",
                 "types": [
-                  "Prompt"
+                  "Message"
                 ],
                 "value": "__UNDEFINED__"
               },
@@ -320,7 +320,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.field_typing.prompt import Prompt\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Prompt:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
               },
               "document": {
                 "advanced": false,
@@ -426,9 +426,9 @@
                 "display_name": "Prompt",
                 "method": "build_prompt",
                 "name": "prompt",
-                "selected": "Prompt",
+                "selected": "Message",
                 "types": [
-                  "Prompt"
+                  "Message"
                 ],
                 "value": "__UNDEFINED__"
               },
@@ -462,7 +462,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.field_typing.prompt import Prompt\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Prompt:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
               },
               "summary": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
index bc77b612a..c68792af1 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
@@ -75,7 +75,7 @@
             "id": "Prompt-xeI6K",
             "name": "prompt",
             "output_types": [
-              "Prompt"
+              "Message"
             ]
           },
           "targetHandle": {
@@ -92,7 +92,7 @@
         "id": "reactflow__edge-Prompt-xeI6K{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-xeI6Kœ}-OpenAIModel-EjXlN{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-EjXlNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "selected": false,
         "source": "Prompt-xeI6K",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-xeI6Kœ, œoutput_typesœ: [œPromptœ], œnameœ: œpromptœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-xeI6Kœ, œoutput_typesœ: [œMessageœ], œnameœ: œpromptœ}",
         "style": {
           "stroke": "#555"
         },
@@ -692,7 +692,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, TextInput\nfrom langflow.template import Output\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
+                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, TextInput\nfrom langflow.template import Output\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout or None,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
               },
               "default_headers": {
                 "advanced": true,
@@ -1344,9 +1344,9 @@
                 "display_name": "Prompt",
                 "method": "build_prompt",
                 "name": "prompt",
-                "selected": "Prompt",
+                "selected": "Message",
                 "types": [
-                  "Prompt"
+                  "Message"
                 ],
                 "value": "__UNDEFINED__"
               },
@@ -1380,7 +1380,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.field_typing.prompt import Prompt\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Prompt:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"format_prompt\"),\n    ]\n\n    async def format_prompt(self) -> str:\n        prompt = await self.build_prompt()\n        formatted_text = prompt.format_text()\n        self.status = formatted_text\n        return formatted_text\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        kwargs = {k: v for k, v in self._arguments.items() if k != \"template\"}\n        prompt = await Message.from_template_and_variables(self.template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
               },
               "context": {
                 "advanced": false,
@@ -2949,7 +2949,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, TextInput\nfrom langflow.template import Output\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
+                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, TextInput\nfrom langflow.template import Output\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout or None,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
               },
               "default_headers": {
                 "advanced": true,