diff --git a/src/backend/base/langflow/components/helpers/structured_output.py b/src/backend/base/langflow/components/helpers/structured_output.py
index 701573009..36301d1fe 100644
--- a/src/backend/base/langflow/components/helpers/structured_output.py
+++ b/src/backend/base/langflow/components/helpers/structured_output.py
@@ -5,7 +5,14 @@ from pydantic import BaseModel, Field, create_model
from langflow.base.models.chat_result import get_chat_result
from langflow.custom import Component
from langflow.helpers.base_model import build_model_from_schema
-from langflow.io import BoolInput, HandleInput, MessageTextInput, MultilineInput, Output, TableInput
+from langflow.io import (
+ BoolInput,
+ HandleInput,
+ MessageTextInput,
+ MultilineInput,
+ Output,
+ TableInput,
+)
from langflow.schema.data import Data
from langflow.schema.dataframe import DataFrame
from langflow.schema.table import EditMode
@@ -92,7 +99,8 @@ class StructuredOutputComponent(Component):
"description": (
"Indicate the data type of the output field (e.g., str, int, float, bool, list, dict)."
),
- "default": "text",
+ "options": ["str", "int", "float", "bool", "list", "dict"],
+ "default": "str",
},
{
"name": "multiple",
@@ -103,7 +111,14 @@ class StructuredOutputComponent(Component):
"edit_mode": EditMode.INLINE,
},
],
- value=[{"name": "field", "description": "description of field", "type": "text", "multiple": "False"}],
+ value=[
+ {
+ "name": "field",
+ "description": "description of field",
+ "type": "str",
+ "multiple": "False",
+ }
+ ],
),
BoolInput(
name="multiple",
@@ -115,8 +130,16 @@ class StructuredOutputComponent(Component):
]
outputs = [
- Output(name="structured_output", display_name="Structured Output", method="build_structured_output"),
- Output(name="structured_output_dataframe", display_name="DataFrame", method="as_dataframe"),
+ Output(
+ name="structured_output",
+ display_name="Structured Output",
+ method="build_structured_output",
+ ),
+ Output(
+ name="structured_output_dataframe",
+ display_name="DataFrame",
+ method="as_dataframe",
+ ),
]
def build_structured_output_base(self) -> Data:
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 07481c7f6..8a127a5a2 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
@@ -1251,7 +1251,7 @@
"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, MultilineInput, Output, TableInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.table import EditMode\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 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 MessageTextInput(\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\": (\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 \"edit_mode\": EditMode.INLINE,\n },\n ],\n value=[{\"name\": \"field\", \"description\": \"description of field\", \"type\": \"text\", \"multiple\": \"False\"}],\n ),\n BoolInput(\n name=\"multiple\",\n advanced=True,\n display_name=\"Generate Multiple\",\n info=\"[Deplrecated] Always set to True\",\n value=True,\n ),\n ]\n\n outputs = [\n Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n Output(name=\"structured_output_dataframe\", display_name=\"DataFrame\", method=\"as_dataframe\"),\n ]\n\n def build_structured_output_base(self) -> Data:\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 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 = 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 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 if isinstance(result, BaseModel):\n result = result.model_dump()\n if \"objects\" in result:\n return result[\"objects\"]\n return result\n\n def build_structured_output(self) -> Data:\n output = self.build_structured_output_base()\n\n return Data(results=output)\n\n def as_dataframe(self) -> DataFrame:\n output = self.build_structured_output_base()\n if isinstance(output, list):\n return DataFrame(data=output)\n return DataFrame(data=[output])\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 (\n BoolInput,\n HandleInput,\n MessageTextInput,\n MultilineInput,\n Output,\n TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.table import EditMode\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 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 MessageTextInput(\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\": (\n \"Indicate the data type of the output field (e.g., str, int, float, bool, list, dict).\"\n ),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"list\", \"dict\"],\n \"default\": \"str\",\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 \"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 BoolInput(\n name=\"multiple\",\n advanced=True,\n display_name=\"Generate Multiple\",\n info=\"[Deplrecated] Always set to True\",\n value=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"structured_output\",\n display_name=\"Structured Output\",\n method=\"build_structured_output\",\n ),\n Output(\n name=\"structured_output_dataframe\",\n display_name=\"DataFrame\",\n method=\"as_dataframe\",\n ),\n ]\n\n def build_structured_output_base(self) -> Data:\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 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 = 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 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 if isinstance(result, BaseModel):\n result = result.model_dump()\n if \"objects\" in result:\n return result[\"objects\"]\n return result\n\n def build_structured_output(self) -> Data:\n output = self.build_structured_output_base()\n\n return Data(results=output)\n\n def as_dataframe(self) -> DataFrame:\n output = self.build_structured_output_base()\n if isinstance(output, list):\n return DataFrame(data=output)\n return DataFrame(data=[output])\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 670a5e4ee..f8c8226d8 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
@@ -1823,7 +1823,7 @@
"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, MultilineInput, Output, TableInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.table import EditMode\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 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 MessageTextInput(\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\": (\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 \"edit_mode\": EditMode.INLINE,\n },\n ],\n value=[{\"name\": \"field\", \"description\": \"description of field\", \"type\": \"text\", \"multiple\": \"False\"}],\n ),\n BoolInput(\n name=\"multiple\",\n advanced=True,\n display_name=\"Generate Multiple\",\n info=\"[Deplrecated] Always set to True\",\n value=True,\n ),\n ]\n\n outputs = [\n Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n Output(name=\"structured_output_dataframe\", display_name=\"DataFrame\", method=\"as_dataframe\"),\n ]\n\n def build_structured_output_base(self) -> Data:\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 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 = 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 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 if isinstance(result, BaseModel):\n result = result.model_dump()\n if \"objects\" in result:\n return result[\"objects\"]\n return result\n\n def build_structured_output(self) -> Data:\n output = self.build_structured_output_base()\n\n return Data(results=output)\n\n def as_dataframe(self) -> DataFrame:\n output = self.build_structured_output_base()\n if isinstance(output, list):\n return DataFrame(data=output)\n return DataFrame(data=[output])\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 (\n BoolInput,\n HandleInput,\n MessageTextInput,\n MultilineInput,\n Output,\n TableInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.table import EditMode\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 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 MessageTextInput(\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\": (\n \"Indicate the data type of the output field (e.g., str, int, float, bool, list, dict).\"\n ),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"list\", \"dict\"],\n \"default\": \"str\",\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 \"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 BoolInput(\n name=\"multiple\",\n advanced=True,\n display_name=\"Generate Multiple\",\n info=\"[Deplrecated] Always set to True\",\n value=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"structured_output\",\n display_name=\"Structured Output\",\n method=\"build_structured_output\",\n ),\n Output(\n name=\"structured_output_dataframe\",\n display_name=\"DataFrame\",\n method=\"as_dataframe\",\n ),\n ]\n\n def build_structured_output_base(self) -> Data:\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 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 = 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 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 if isinstance(result, BaseModel):\n result = result.model_dump()\n if \"objects\" in result:\n return result[\"objects\"]\n return result\n\n def build_structured_output(self) -> Data:\n output = self.build_structured_output_base()\n\n return Data(results=output)\n\n def as_dataframe(self) -> DataFrame:\n output = self.build_structured_output_base()\n if isinstance(output, list):\n return DataFrame(data=output)\n return DataFrame(data=[output])\n"
},
"input_value": {
"_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/schema/table.py b/src/backend/base/langflow/schema/table.py
index 541204551..7cb5dfe54 100644
--- a/src/backend/base/langflow/schema/table.py
+++ b/src/backend/base/langflow/schema/table.py
@@ -34,6 +34,7 @@ class Column(BaseModel):
model_config = ConfigDict(populate_by_name=True)
name: str
display_name: str = Field(default="")
+ options: list[str] | None = Field(default=None)
sortable: bool = Field(default=True)
filterable: bool = Field(default=True)
formatter: FormatterType | str | None = Field(default=None)
diff --git a/src/frontend/src/App.css b/src/frontend/src/App.css
index f6be7e508..bb5d372eb 100644
--- a/src/frontend/src/App.css
+++ b/src/frontend/src/App.css
@@ -124,11 +124,12 @@ body {
/* This CSS is to not apply the border for the column having 'no-border' class */
.no-border.ag-cell:focus {
border: none !important;
- outline: none;
+ outline: none !important;
+ box-shadow: none !important;
}
.no-border.ag-cell {
border: none !important;
- outline: none;
+ outline: none !important;
}
.react-flow__edge.selected .react-flow__edge-path {
diff --git a/src/frontend/src/components/core/parameterRenderComponent/components/tableComponent/components/tableDropdownCellEditor/index.tsx b/src/frontend/src/components/core/parameterRenderComponent/components/tableComponent/components/tableDropdownCellEditor/index.tsx
new file mode 100644
index 000000000..918617d86
--- /dev/null
+++ b/src/frontend/src/components/core/parameterRenderComponent/components/tableComponent/components/tableDropdownCellEditor/index.tsx
@@ -0,0 +1,22 @@
+import { CustomCellEditorProps } from "ag-grid-react";
+import InputComponent from "../../../inputComponent";
+
+export default function TableDropdownCellEditor({
+ value,
+ values,
+ onValueChange,
+ colDef,
+}: CustomCellEditorProps & { values: string[] }) {
+ return (
+
+ onValueChange(value)}
+ value={value}
+ options={values}
+ password={false}
+ placeholder={"Select an option"}
+ id="apply-to-fields"
+ />
+
+ );
+}
diff --git a/src/frontend/src/types/utils/functions.ts b/src/frontend/src/types/utils/functions.ts
index 17816ea74..947117a04 100644
--- a/src/frontend/src/types/utils/functions.ts
+++ b/src/frontend/src/types/utils/functions.ts
@@ -28,4 +28,5 @@ export interface ColumnField {
default?: any;
edit_mode?: "modal" | "inline" | "popover";
hidden?: boolean;
+ options?: string[];
}
diff --git a/src/frontend/src/utils/utils.ts b/src/frontend/src/utils/utils.ts
index a479bbe06..1d66fe2a7 100644
--- a/src/frontend/src/utils/utils.ts
+++ b/src/frontend/src/utils/utils.ts
@@ -1,4 +1,5 @@
import TableAutoCellRender from "@/components/core/parameterRenderComponent/components/tableComponent/components/tableAutoCellRender";
+import TableDropdownCellEditor from "@/components/core/parameterRenderComponent/components/tableComponent/components/tableDropdownCellEditor";
import TableToggleCellEditor from "@/components/core/parameterRenderComponent/components/tableComponent/components/tableToggleCellEditor";
import useAlertStore from "@/stores/alertStore";
import { ColumnField, FormatterType } from "@/types/utils/functions";
@@ -566,8 +567,21 @@ export function FormatColumns(columns: ColumnField[]): ColDef[] {
newCol.cellRendererParams = {
formatter: col.formatter,
};
- if (col.formatter !== FormatterType.text || col.edit_mode !== "inline") {
- if (
+
+ if (
+ col.formatter !== FormatterType.text ||
+ col.edit_mode !== "inline" ||
+ col.options
+ ) {
+ if (col.options && col.formatter === FormatterType.text) {
+ newCol.cellEditor = TableDropdownCellEditor;
+ newCol.cellEditorPopup = false;
+ newCol.cellEditorParams = {
+ values: col.options,
+ };
+ newCol.autoHeight = false;
+ newCol.cellClass = "no-border !py-2";
+ } else if (
col.edit_mode === "popover" &&
col.formatter === FormatterType.text
) {