diff --git a/src/backend/base/langflow/components/helpers/structured_output.py b/src/backend/base/langflow/components/helpers/structured_output.py
index 61cca1260..2832898e5 100644
--- a/src/backend/base/langflow/components/helpers/structured_output.py
+++ b/src/backend/base/langflow/components/helpers/structured_output.py
@@ -37,18 +37,20 @@ class StructuredOutputComponent(Component):
             name="output_schema",
             display_name="Output Schema",
             info="Define the structure and data types for the model's output.",
-            value=[
+            table_schema=[
                 {
                     "name": "name",
                     "display_name": "Name",
                     "type": "str",
                     "description": "Specify the name of the output field.",
+                    "default": "field",
                 },
                 {
                     "name": "description",
                     "display_name": "Description",
                     "type": "str",
                     "description": "Describe the purpose of the output field.",
+                    "default": "description of field",
                 },
                 {
                     "name": "type",
@@ -67,6 +69,7 @@ class StructuredOutputComponent(Component):
                     "default": "False",
                 },
             ],
+            value=[{"name": "field", "description": "description of field", "type": "text", "multiple": "False"}],
         ),
         BoolInput(
             name="multiple",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json
index 5dcffd061..0ce000caa 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json	
@@ -831,7 +831,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import TYPE_CHECKING, cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\nif TYPE_CHECKING:\n    from langflow.field_typing.constants import LanguageModel\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            value=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field (e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        output_model_ = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[output_model_], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = output_model_\n        try:\n            llm_with_structured_output = cast(\"LanguageModel\", self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
+                "value": "from typing import TYPE_CHECKING, cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\nif TYPE_CHECKING:\n    from langflow.field_typing.constants import LanguageModel\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field (e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n            value=[{\"name\": \"field\", \"description\": \"description of field\", \"type\": \"text\", \"multiple\": \"False\"}],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        output_model_ = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[output_model_], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = output_model_\n        try:\n            llm_with_structured_output = cast(\"LanguageModel\", self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json
index 552f7d2e2..cd9c70954 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
@@ -1137,7 +1137,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import TYPE_CHECKING, cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\nif TYPE_CHECKING:\n    from langflow.field_typing.constants import LanguageModel\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            value=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field (e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        output_model_ = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[output_model_], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = output_model_\n        try:\n            llm_with_structured_output = cast(\"LanguageModel\", self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
+                "value": "from typing import TYPE_CHECKING, cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\nif TYPE_CHECKING:\n    from langflow.field_typing.constants import LanguageModel\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field (e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n            value=[{\"name\": \"field\", \"description\": \"description of field\", \"type\": \"text\", \"multiple\": \"False\"}],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        output_model_ = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[output_model_], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = output_model_\n        try:\n            llm_with_structured_output = cast(\"LanguageModel\", self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",