Feat: add Upstash icon

src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json

@@ -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.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 Prompt.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.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"
               },
               "instructions": {
                 "advanced": false,
@@ -403,7 +403,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import StrInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\nimport re\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        StrInput(\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(content=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
               },
               "urls": {
                 "advanced": false,
@@ -508,7 +508,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import BoolInput, DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        BoolInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        StrInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
               },
               "input_value": {
                 "advanced": false,
@@ -697,7 +697,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput\nfrom langflow.template import Output\n\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
+                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.template import Output\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\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
@@ -975,7 +975,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import StrInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\nimport re\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        StrInput(\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(content=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
               },
               "urls": {
                 "advanced": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json

@@ -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.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 Prompt.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.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"
               },
               "template": {
                 "advanced": false,
@@ -349,7 +349,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\nfrom langflow.field_typing import Text\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        StrInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            input_types=[],\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        StrInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            input_types=[],\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        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
               },
               "input_value": {
                 "advanced": false,
@@ -405,7 +405,7 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Text"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -427,7 +427,7 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Text"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -528,7 +528,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import BoolInput, DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        BoolInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        StrInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
               },
               "input_value": {
                 "advanced": false,
@@ -837,7 +837,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput\nfrom langflow.template import Output\n\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
+                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.template import Output\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\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json

@@ -227,7 +227,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\nfrom langflow.field_typing import Text\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        StrInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            input_types=[],\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        StrInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            input_types=[],\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        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
               },
               "input_value": {
                 "advanced": false,
@@ -283,7 +283,7 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Text"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -305,7 +305,7 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Text"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -406,7 +406,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import BoolInput, DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        BoolInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        StrInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
               },
               "input_value": {
                 "advanced": false,
@@ -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.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 Prompt.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.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"
               },
               "context": {
                 "advanced": false,
@@ -967,7 +967,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput\nfrom langflow.template import Output\n\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
+                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.template import Output\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\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json

@@ -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.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 Prompt.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.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"
               },
               "document": {
                 "advanced": false,
@@ -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.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 Prompt.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.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"
               },
               "summary": {
                 "advanced": false,
@@ -596,7 +596,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import BoolInput, DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        BoolInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        StrInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
               },
               "input_value": {
                 "advanced": false,
@@ -774,7 +774,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import BoolInput, DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        BoolInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import DropdownInput, StrInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\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        StrInput(\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        StrInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        StrInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        StrInput(\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        Output(display_name=\"Text\", name=\"text\", method=\"text_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\n    def text_response(self) -> Text:\n        text = self.message_response().text\n        return text\n"
               },
               "input_value": {
                 "advanced": false,
@@ -935,7 +935,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import StrInput\nfrom langflow.template import Output\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        StrInput(\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) -> Text:\n        return self.build(input_value=self.input_value)\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\nfrom langflow.inputs import TextInput\nfrom langflow.template import Output\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        TextInput(\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) -> Text:\n        return self.build(input_value=self.input_value)\n"
               },
               "input_value": {
                 "advanced": false,
@@ -945,7 +945,7 @@
                 "file_path": "",
                 "info": "Text to be passed as input.",
                 "input_types": [
-                  "Text"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -1167,7 +1167,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput\nfrom langflow.template import Output\n\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
+                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.template import Output\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\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
@@ -1584,7 +1584,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput\nfrom langflow.template import Output\n\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        StrInput(name=\"input_value\", display_name=\"Input\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
+                "value": "from 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 BaseLanguageModel, Text\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.template import Output\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\", input_types=[\"Text\", \"Data\", \"Prompt\"]),\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        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        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            info=\"Enable JSON mode for the model output.\",\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    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Text:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> BaseLanguageModel:\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = self.json_mode\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        response_format = None\n        if json_mode:\n            response_format = {\"type\": \"json_object\"}\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name or None,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            response_format=response_format,\n            seed=seed,\n        )\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,

src/frontend/src/icons/Upstash/UpstashIcon.jsx

@@ -0,0 +1,45 @@
+import React from 'react';
+
+const UpstashIcon = (props) => (
+  <svg
+    width="256px"
+    height="341px"
+    viewBox="0 0 256 341"
+    version="1.1"
+    xmlns="http://www.w3.org/2000/svg"
+    preserveAspectRatio="xMidYMid"
+    {...props}
+  >
+    <title>upstash</title>
+    <g>
+      <path
+        d="M0,298.416784 C56.5542815,354.970323 148.246768,354.970323 204.801032,298.416784 C261.354571,241.86252 261.354571,150.170106 204.801032,93.6158424 L179.200462,119.215688 C221.61634,161.631567 221.61634,230.401059 179.200462,272.816213 C136.785307,315.232092 68.0157428,315.232092 25.5998642,272.816213 L0,298.416784 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M51.200362,247.216367 C79.4772765,275.493137 125.323122,275.493137 153.600615,247.216367 C181.877385,218.939598 181.877385,173.093028 153.600615,144.816259 L128.000769,170.416105 C142.139154,184.55449 142.139154,207.477412 128.000769,221.616521 C113.86166,235.754906 90.9387378,235.754906 76.800353,221.616521 L51.200362,247.216367 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z"
+        fillOpacity="0.4"
+        fill="#FFFFFF"
+      ></path>
+      <path
+        d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z"
+        fillOpacity="0.4"
+        fill="#FFFFFF"
+      ></path>
+    </g>
+  </svg>
+);
+
+export default UpstashIcon;

src/frontend/src/icons/Upstash/index.tsx

@@ -0,0 +1,8 @@
+import React, { forwardRef } from "react";
+import UpstashIcon from "./UpstashIcon";
+
+export const UpstashSvgIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
+  (props, ref) => {
+    return <UpstashIcon ref={ref} {...props} />;
+  },
+);

src/frontend/src/icons/Upstash/upstash-icon-seeklogo.svg

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="256px" height="341px" viewBox="0 0 256 341" version="1.1" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="xMidYMid">
+    <title>upstash</title>
+    <g>
+        <path d="M0,298.416784 C56.5542815,354.970323 148.246768,354.970323 204.801032,298.416784 C261.354571,241.86252 261.354571,150.170106 204.801032,93.6158424 L179.200462,119.215688 C221.61634,161.631567 221.61634,230.401059 179.200462,272.816213 C136.785307,315.232092 68.0157428,315.232092 25.5998642,272.816213 L0,298.416784 Z" fill="#00C98D"></path>
+        <path d="M51.200362,247.216367 C79.4772765,275.493137 125.323122,275.493137 153.600615,247.216367 C181.877385,218.939598 181.877385,173.093028 153.600615,144.816259 L128.000769,170.416105 C142.139154,184.55449 142.139154,207.477412 128.000769,221.616521 C113.86166,235.754906 90.9387378,235.754906 76.800353,221.616521 L51.200362,247.216367 Z" fill="#00C98D"></path>
+        <path d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z" fill="#00C98D"></path>
+        <path d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z" fill="#00C98D"></path>
+        <path d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z" fill-opacity="0.4" fill="#FFFFFF"></path>
+        <path d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z" fill-opacity="0.4" fill="#FFFFFF"></path>
+    </g>
+</svg>

src/frontend/src/utils/styleUtils.ts

@@ -205,6 +205,7 @@ import { HackerNewsIcon } from "../icons/hackerNews";
 import { SupabaseIcon } from "../icons/supabase";
 import { iconsType } from "../types/components";
 import { MistralIcon } from "../icons/mistral";
+import { UpstashSvgIcon } from "../icons/Upstash";
 
 export const gradients = [
   "bg-gradient-to-br from-gray-800 via-rose-700 to-violet-900",
@@ -553,5 +554,6 @@ export const nodeIconsLucide: iconsType = {
   PaperclipIcon,
   Settings,
   Streamlit,
-  MistralAI:MistralIcon
+  MistralAI:MistralIcon,
+  Upstash: UpstashSvgIcon,
 };