diff --git a/src/backend/base/langflow/components/models/OpenAIModel.py b/src/backend/base/langflow/components/models/OpenAIModel.py
index e913273b5..932655c16 100644
--- a/src/backend/base/langflow/components/models/OpenAIModel.py
+++ b/src/backend/base/langflow/components/models/OpenAIModel.py
@@ -92,10 +92,10 @@ class OpenAIModelComponent(LCModelComponent):
     def build_model(self) -> BaseLanguageModel:
         # self.output_schea is a list of dictionaries
         # let's convert it to a dictionary
-        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})
+        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})
         openai_api_key = self.openai_api_key
         temperature = self.temperature
-        model_name = self.model_name
+        model_name: str = self.model_name
         max_tokens = self.max_tokens
         model_kwargs = self.model_kwargs
         openai_api_base = self.openai_api_base or "https://api.openai.com/v1"
@@ -108,7 +108,7 @@ class OpenAIModelComponent(LCModelComponent):
         output = ChatOpenAI(
             max_tokens=max_tokens or None,
             model_kwargs=model_kwargs or {},
-            model=model_name or None,
+            model=model_name,
             base_url=openai_api_base,
             api_key=api_key,
             temperature=temperature or 0.1,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json
index df228c8dd..d95f8eea3 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json	
@@ -637,7 +637,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})\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 = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs 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            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json
index ea7ebf7aa..1067e735f 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json	
@@ -950,7 +950,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})\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 = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs 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            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json
index 54f900e48..cfab7017f 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json	
@@ -811,7 +811,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})\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 = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs 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            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json
index dea8d410b..5e8c59de1 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json	
@@ -713,7 +713,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})\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 = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs 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            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json
index f0bcb586f..b6b64802e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json	
@@ -1124,7 +1124,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})\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 = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs 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            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
@@ -1537,7 +1537,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})\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 = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs 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            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
index 686114c8c..e61c27829 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
@@ -1067,7 +1067,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict = reduce(operator.ior, self.output_schema or {}, {})\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 = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs 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            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import BaseLanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\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\"),\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        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    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) -> Message:\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        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,