diff --git a/src/backend/base/langflow/components/processing/structured_output.py b/src/backend/base/langflow/components/processing/structured_output.py
index 27112cc52..9c3bc73fd 100644
--- a/src/backend/base/langflow/components/processing/structured_output.py
+++ b/src/backend/base/langflow/components/processing/structured_output.py
@@ -41,14 +41,13 @@ class StructuredOutputComponent(Component):
             display_name="Format Instructions",
             info="The instructions to the language model for formatting the output.",
             value=(
-                "You are an AI system designed to extract structured information from unstructured text."
-                "Given the input_text, return a JSON object with predefined keys based on the expected structure."
-                "Extract values accurately and format them according to the specified type "
-                "(e.g., string, integer, float, date)."
-                "If a value is missing or cannot be determined, return a default "
-                "(e.g., null, 0, or 'N/A')."
-                "If multiple instances of the expected structure exist within the input_text, "
-                "stream each as a separate JSON object."
+                "You are an AI that extracts one structured JSON object from unstructured text. "
+                "Use a predefined schema with expected types (str, int, float, bool, dict). "
+                "If multiple structures exist, extract only the first most complete one. "
+                "Fill missing or ambiguous values with defaults: null for missing values. "
+                "Ignore duplicates and partial repeats. "
+                "Always return one valid JSON, never throw errors or return multiple objects."
+                "Output: A single well-formed JSON object, and nothing else."
             ),
             required=True,
             advanced=True,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json
index 1db1b4d83..ac92da745 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json	
@@ -1254,7 +1254,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI system designed to extract structured information from unstructured text.\"\n                \"Given the input_text, return a JSON object with predefined keys based on the expected structure.\"\n                \"Extract values accurately and format them according to the specified type \"\n                \"(e.g., string, integer, float, date).\"\n                \"If a value is missing or cannot be determined, return a default \"\n                \"(e.g., null, 0, or 'N/A').\"\n                \"If multiple instances of the expected structure exist within the input_text, \"\n                \"stream each as a separate JSON object.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
+                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI that extracts one structured JSON object from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"If multiple structures exist, extract only the first most complete one. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Ignore duplicates and partial repeats. \"\n                \"Always return one valid JSON, never throw errors or return multiple objects.\"\n                \"Output: A single well-formed JSON object, and nothing else.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json
index c53da20a9..c43a639f8 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json	
@@ -609,7 +609,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI system designed to extract structured information from unstructured text.\"\n                \"Given the input_text, return a JSON object with predefined keys based on the expected structure.\"\n                \"Extract values accurately and format them according to the specified type \"\n                \"(e.g., string, integer, float, date).\"\n                \"If a value is missing or cannot be determined, return a default \"\n                \"(e.g., null, 0, or 'N/A').\"\n                \"If multiple instances of the expected structure exist within the input_text, \"\n                \"stream each as a separate JSON object.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
+                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI that extracts one structured JSON object from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"If multiple structures exist, extract only the first most complete one. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Ignore duplicates and partial repeats. \"\n                \"Always return one valid JSON, never throw errors or return multiple objects.\"\n                \"Output: A single well-formed JSON object, and nothing else.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",
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 97bd390a0..940d7efda 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	
@@ -1009,7 +1009,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI system designed to extract structured information from unstructured text.\"\n                \"Given the input_text, return a JSON object with predefined keys based on the expected structure.\"\n                \"Extract values accurately and format them according to the specified type \"\n                \"(e.g., string, integer, float, date).\"\n                \"If a value is missing or cannot be determined, return a default \"\n                \"(e.g., null, 0, or 'N/A').\"\n                \"If multiple instances of the expected structure exist within the input_text, \"\n                \"stream each as a separate JSON object.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
+                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI that extracts one structured JSON object from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"If multiple structures exist, extract only the first most complete one. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Ignore duplicates and partial repeats. \"\n                \"Always return one valid JSON, never throw errors or return multiple objects.\"\n                \"Output: A single well-formed JSON object, and nothing else.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\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 3626710f3..c60372e3e 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	
@@ -879,7 +879,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI system designed to extract structured information from unstructured text.\"\n                \"Given the input_text, return a JSON object with predefined keys based on the expected structure.\"\n                \"Extract values accurately and format them according to the specified type \"\n                \"(e.g., string, integer, float, date).\"\n                \"If a value is missing or cannot be determined, return a default \"\n                \"(e.g., null, 0, or 'N/A').\"\n                \"If multiple instances of the expected structure exist within the input_text, \"\n                \"stream each as a separate JSON object.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
+                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI that extracts one structured JSON object from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"If multiple structures exist, extract only the first most complete one. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Ignore duplicates and partial repeats. \"\n                \"Always return one valid JSON, never throw errors or return multiple objects.\"\n                \"Output: A single well-formed JSON object, and nothing else.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json
index f0bfc7e45..924256e94 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json	
@@ -1509,7 +1509,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI system designed to extract structured information from unstructured text.\"\n                \"Given the input_text, return a JSON object with predefined keys based on the expected structure.\"\n                \"Extract values accurately and format them according to the specified type \"\n                \"(e.g., string, integer, float, date).\"\n                \"If a value is missing or cannot be determined, return a default \"\n                \"(e.g., null, 0, or 'N/A').\"\n                \"If multiple instances of the expected structure exist within the input_text, \"\n                \"stream each as a separate JSON object.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
+                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.table import EditMode\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = \"Uses an LLM to generate structured data. Ideal for extraction and consistency.\"\n    name = \"StructuredOutput\"\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            required=True,\n        ),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Message\",\n            info=\"The input message to the language model.\",\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Format Instructions\",\n            info=\"The instructions to the language model for formatting the output.\",\n            value=(\n                \"You are an AI that extracts one structured JSON object from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"If multiple structures exist, extract only the first most complete one. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Ignore duplicates and partial repeats. \"\n                \"Always return one valid JSON, never throw errors or return multiple objects.\"\n                \"Output: A single well-formed JSON object, and nothing else.\"\n            ),\n            required=True,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n            advanced=True,\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            required=True,\n            # TODO: remove deault value\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                    \"edit_mode\": EditMode.INLINE,\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                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n            value=[\n                {\n                    \"name\": \"field\",\n                    \"description\": \"description of field\",\n                    \"type\": \"str\",\n                    \"multiple\": \"False\",\n                }\n            ],\n        ),\n    ]\n\n    outputs = [\n        Output(\n            name=\"structured_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_output\",\n        ),\n    ]\n\n    def build_structured_output_base(self):\n        schema_name = self.schema_name or \"OutputModel\"\n\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\n        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(list[output_model_], Field(description=f\"A list of {schema_name}.\")),  # type: ignore[valid-type]\n        )\n\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[output_model])\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        result = get_chat_result(\n            runnable=llm_with_structured_output,\n            system_message=self.system_prompt,\n            input_value=self.input_value,\n            config=config_dict,\n        )\n\n        # OPTIMIZATION NOTE: Simplified processing based on trustcall response structure\n        # Handle non-dict responses (shouldn't happen with trustcall, but defensive)\n        if not isinstance(result, dict):\n            return result\n\n        # Extract first response and convert BaseModel to dict\n        responses = result.get(\"responses\", [])\n        if not responses:\n            return result\n\n        # Convert BaseModel to dict (creates the \"objects\" key)\n        first_response = responses[0]\n        structured_data = first_response.model_dump() if isinstance(first_response, BaseModel) else first_response\n\n        # Extract the objects array (guaranteed to exist due to our Pydantic model structure)\n        return structured_data.get(\"objects\", structured_data)\n\n    def build_structured_output(self) -> Data:\n        output = self.build_structured_output_base()\n        if not isinstance(output, list) or not output:\n            # handle empty or unexpected type case\n            msg = \"No structured output returned\"\n            raise ValueError(msg)\n        if len(output) != 1:\n            msg = \"Multiple structured outputs returned\"\n            raise ValueError(msg)\n        return Data(data=output[0])\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",