Financial Report Parser.json

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              "DataFrame"
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        "animated": false,
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            "dataType": "ParserComponent",
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            "fieldName": "input_value",
            "id": "ChatOutput-XvCBo",
            "inputTypes": [
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    "nodes": [
      {
        "data": {
          "id": "ChatOutput-XvCBo",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "category": "outputs",
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Display a chat message in the Playground.",
            "display_name": "Chat Output",
            "documentation": "",
            "edited": false,
            "field_order": [
              "input_value",
              "should_store_message",
              "sender",
              "sender_name",
              "session_id",
              "data_template",
              "background_color",
              "chat_icon",
              "text_color"
            ],
            "frozen": false,
            "icon": "MessagesSquare",
            "key": "ChatOutput",
            "legacy": false,
            "lf_version": "1.6.0",
            "metadata": {
              "code_hash": "4848ad3e35d5",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "orjson",
                    "version": "3.10.15"
                  },
                  {
                    "name": "fastapi",
                    "version": "0.120.0"
                  },
                  {
                    "name": "lfx",
                    "version": null
                  }
                ],
                "total_dependencies": 3
              },
              "module": "lfx.components.input_output.chat_output.ChatOutput"
            },
            "minimized": true,
            "output_types": [],
            "outputs": [
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Output Message",
                "group_outputs": false,
                "method": "message_response",
                "name": "message",
                "selected": "Message",
                "tool_mode": true,
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "score": 0.003169567463043492,
            "template": {
              "_type": "Component",
              "clean_data": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Basic Clean Data",
                "dynamic": false,
                "info": "Whether to clean data before converting to string.",
                "list": false,
                "list_add_label": "Add More",
                "name": "clean_data",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              },
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
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                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Inputs\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            advanced=True,\n            info=\"Whether to clean data before converting to string.\",\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Output Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n\n        # Get source properties\n        source, _, display_name, source_id = self.get_properties_from_source_component()\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.context_id = self.context_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            clean_data: bool = getattr(self, \"clean_data\", False)\n            return \"\\n\".join([safe_convert(item, clean_data=clean_data) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return safe_convert(self.input_value)\n"
              },
              "context_id": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Context ID",
                "dynamic": false,
                "info": "The context ID of the chat. Adds an extra layer to the local memory.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "context_id",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "data_template": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Data Template",
                "dynamic": false,
                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "data_template",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "{text}"
              },
              "input_value": {
                "_input_type": "MessageInput",
                "advanced": false,
                "display_name": "Inputs",
                "dynamic": false,
                "info": "Message to be passed as output.",
                "input_types": [
                  "Data",
                  "DataFrame",
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "input_value",
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "sender": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "dialog_inputs": {},
                "display_name": "Sender Type",
                "dynamic": false,
                "info": "Type of sender.",
                "name": "sender",
                "options": [
                  "Machine",
                  "User"
                ],
                "options_metadata": [],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "Machine"
              },
              "sender_name": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Sender Name",
                "dynamic": false,
                "info": "Name of the sender.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "sender_name",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "AI"
              },
              "session_id": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Session ID",
                "dynamic": false,
                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "session_id",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "should_store_message": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Store Messages",
                "dynamic": false,
                "info": "Store the message in the history.",
                "list": false,
                "list_add_label": "Add More",
                "name": "should_store_message",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              }
            },
            "tool_mode": false
          },
          "showNode": false,
          "type": "ChatOutput"
        },
        "id": "ChatOutput-XvCBo",
        "measured": {
          "height": 48,
          "width": 192
        },
        "position": {
          "x": 2235,
          "y": 435
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "ChatInput-AT9yi",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "category": "inputs",
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Get chat inputs from the Playground.",
            "display_name": "Chat Input",
            "documentation": "",
            "edited": false,
            "field_order": [
              "input_value",
              "should_store_message",
              "sender",
              "sender_name",
              "session_id",
              "files",
              "background_color",
              "chat_icon",
              "text_color"
            ],
            "frozen": false,
            "icon": "MessagesSquare",
            "key": "ChatInput",
            "legacy": false,
            "lf_version": "1.6.0",
            "metadata": {
              "code_hash": "0014a5b41817",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "lfx",
                    "version": null
                  }
                ],
                "total_dependencies": 1
              },
              "module": "lfx.components.input_output.chat.ChatInput"
            },
            "minimized": true,
            "output_types": [],
            "outputs": [
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Chat Message",
                "group_outputs": false,
                "method": "message_response",
                "name": "message",
                "selected": "Message",
                "tool_mode": true,
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "score": 0.0020353564437605998,
            "template": {
              "_type": "Component",
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from lfx.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import (\n    DropdownInput,\n    FileInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n)\nfrom lfx.schema.message import Message\nfrom lfx.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_USER,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n    minimized = True\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n            input_types=[],\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"context_id\",\n            display_name=\"Context ID\",\n            info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n            value=\"\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n            temp_file=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        # Ensure files is a list and filter out empty/None values\n        files = self.files if self.files else []\n        if files and not isinstance(files, list):\n            files = [files]\n        # Filter out None/empty values\n        files = [f for f in files if f is not None and f != \"\"]\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            context_id=self.context_id,\n            files=files,\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
              },
              "context_id": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Context ID",
                "dynamic": false,
                "info": "The context ID of the chat. Adds an extra layer to the local memory.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "context_id",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "files": {
                "_input_type": "FileInput",
                "advanced": true,
                "display_name": "Files",
                "dynamic": false,
                "fileTypes": [
                  "csv",
                  "json",
                  "pdf",
                  "txt",
                  "md",
                  "mdx",
                  "yaml",
                  "yml",
                  "xml",
                  "html",
                  "htm",
                  "docx",
                  "py",
                  "sh",
                  "sql",
                  "js",
                  "ts",
                  "tsx",
                  "jpg",
                  "jpeg",
                  "png",
                  "bmp",
                  "image"
                ],
                "file_path": "",
                "info": "Files to be sent with the message.",
                "list": true,
                "list_add_label": "Add More",
                "name": "files",
                "placeholder": "",
                "required": false,
                "show": true,
                "temp_file": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "file",
                "value": ""
              },
              "input_value": {
                "_input_type": "MultilineInput",
                "advanced": false,
                "display_name": "Input Text",
                "dynamic": false,
                "info": "Message to be passed as input.",
                "input_types": [],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "multiline": true,
                "name": "input_value",
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                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "In 2022, the company demonstrated strong financial performance, reporting a gross profit of $1.2 billion, reflecting stable revenue generation and effective cost management. The EBITDA stood at $900 million, highlighting the company’s solid operational efficiency and profitability before interest, taxes, depreciation, and amortization. Despite a slight increase in operating expenses compared to 2021, the company maintained a healthy bottom line, achieving a net income of $500 million. This growth underscores the company’s ability to navigate economic challenges while sustaining profitability, reinforcing its financial stability and competitive position in the market."
              },
              "sender": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "dialog_inputs": {},
                "display_name": "Sender Type",
                "dynamic": false,
                "info": "Type of sender.",
                "name": "sender",
                "options": [
                  "Machine",
                  "User"
                ],
                "options_metadata": [],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "User"
              },
              "sender_name": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Sender Name",
                "dynamic": false,
                "info": "Name of the sender.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "sender_name",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "User"
              },
              "session_id": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Session ID",
                "dynamic": false,
                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "session_id",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "should_store_message": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Store Messages",
                "dynamic": false,
                "info": "Store the message in the history.",
                "list": false,
                "list_add_label": "Add More",
                "name": "should_store_message",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              }
            },
            "tool_mode": false
          },
          "selected_output": "message",
          "showNode": false,
          "type": "ChatInput"
        },
        "dragging": false,
        "id": "ChatInput-AT9yi",
        "measured": {
          "height": 48,
          "width": 192
        },
        "position": {
          "x": 866.761331501802,
          "y": 581.619639019103
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "note-khpnk",
          "node": {
            "description": "### 💡 Add your OpenAI API key here",
            "display_name": "",
            "documentation": "",
            "template": {
              "backgroundColor": "transparent"
            }
          },
          "type": "note"
        },
        "dragging": false,
        "id": "note-khpnk",
        "measured": {
          "height": 324,
          "width": 324
        },
        "position": {
          "x": 905.0310846743869,
          "y": -389.43902326187924
        },
        "selected": false,
        "type": "noteNode"
      },
      {
        "data": {
          "id": "note-zgFqi",
          "node": {
            "description": "This template extracts key financial metrics from a given financial report text. The extracted data is structured and formatted for chat consumption.\n\n## Quickstart\n\n1. Add your OpenAI API key to the **Language Model** component, or select a different provider and model.\n2. Open the **Playground** to start the chat and run the flow.\nFor this example, the **Chat Input** component is pre-loaded with a sample financial report. The **Language Model** component identifies and retrieves the gross profit, EBITDA, net income, and operating expenses information from the financial report. Then, the **Structured Output** component formats extracted data into a structured format for better readability and further processing. Finally, the **Parser** component converts extracted data into a messages to be returned to the user.",
            "display_name": "",
            "documentation": "",
            "template": {}
          },
          "type": "note"
        },
        "dragging": false,
        "height": 688,
        "id": "note-zgFqi",
        "measured": {
          "height": 688,
          "width": 619
        },
        "position": {
          "x": 270.9912976390468,
          "y": -396.43811550696176
        },
        "resizing": false,
        "selected": false,
        "type": "noteNode",
        "width": 619
      },
      {
        "data": {
          "id": "LanguageModelComponent-iAML1",
          "node": {
            "base_classes": [
              "LanguageModel",
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Runs a language model given a specified provider.",
            "display_name": "Language Model",
            "documentation": "",
            "edited": false,
            "field_order": [
              "provider",
              "model_name",
              "api_key",
              "input_value",
              "system_message",
              "stream",
              "temperature"
            ],
            "frozen": false,
            "icon": "brain-circuit",
            "last_updated": "2025-09-29T15:43:28.263Z",
            "legacy": false,
            "lf_version": "1.6.0",
            "metadata": {
              "code_hash": "bb5f8714781b",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "langchain_anthropic",
                    "version": "0.3.14"
                  },
                  {
                    "name": "langchain_google_genai",
                    "version": "2.0.6"
                  },
                  {
                    "name": "langchain_openai",
                    "version": "0.3.23"
                  },
                  {
                    "name": "lfx",
                    "version": null
                  }
                ],
                "total_dependencies": 4
              },
              "keywords": [
                "model",
                "llm",
                "language model",
                "large language model"
              ],
              "module": "lfx.components.models.language_model.LanguageModelComponent"
            },
            "minimized": false,
            "output_types": [],
            "outputs": [
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Model Response",
                "group_outputs": false,
                "method": "text_response",
                "name": "text_output",
                "options": null,
                "required_inputs": null,
                "selected": "Message",
                "tool_mode": true,
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              },
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Language Model",
                "group_outputs": false,
                "method": "build_model",
                "name": "model_output",
                "options": null,
                "required_inputs": null,
                "selected": "LanguageModel",
                "tool_mode": true,
                "types": [
                  "LanguageModel"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "priority": 0,
            "template": {
              "_type": "Component",
              "api_key": {
                "_input_type": "SecretStrInput",
                "advanced": false,
                "display_name": "OpenAI API Key",
                "dynamic": false,
                "info": "Model Provider API key",
                "input_types": [],
                "load_from_db": true,
                "name": "api_key",
                "password": true,
                "placeholder": "",
                "real_time_refresh": true,
                "required": false,
                "show": true,
                "title_case": false,
                "type": "str",
                "value": "OPENAI_API_KEY"
              },
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from typing import Any\n\nimport requests\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_ibm import ChatWatsonx\nfrom langchain_ollama import ChatOllama\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom lfx.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom lfx.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom lfx.base.models.google_generative_ai_model import ChatGoogleGenerativeAIFixed\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, MessageTextInput, StrInput\nfrom lfx.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# IBM watsonx.ai constants\nIBM_WATSONX_DEFAULT_MODELS = [\"ibm/granite-3-2b-instruct\", \"ibm/granite-3-8b-instruct\", \"ibm/granite-13b-instruct-v2\"]\nIBM_WATSONX_URLS = [\n    \"https://us-south.ml.cloud.ibm.com\",\n    \"https://eu-de.ml.cloud.ibm.com\",\n    \"https://eu-gb.ml.cloud.ibm.com\",\n    \"https://au-syd.ml.cloud.ibm.com\",\n    \"https://jp-tok.ml.cloud.ibm.com\",\n    \"https://ca-tor.ml.cloud.ibm.com\",\n]\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"completion\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass LanguageModelComponent(LCModelComponent):\n    display_name = \"Language Model\"\n    description = \"Runs a language model given a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-models\"\n    icon = \"brain-circuit\"\n    category = \"models\"\n    priority = 0  # Set priority to 0 to make it appear first\n\n    @staticmethod\n    def fetch_ibm_models(base_url: str) -> list[str]:\n        \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n        try:\n            endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n            params = {\"version\": \"2024-09-16\", \"filters\": \"function_text_chat,!lifecycle_withdrawn\"}\n            response = requests.get(endpoint, params=params, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n            return sorted(models)\n        except Exception:  # noqa: BLE001\n            logger.exception(\"Error fetching IBM watsonx models. Using default models.\")\n            return IBM_WATSONX_DEFAULT_MODELS\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\", \"Anthropic\", \"Google\", \"IBM watsonx.ai\", \"Ollama\"],\n            value=\"OpenAI\",\n            info=\"Select the model provider\",\n            real_time_refresh=True,\n            options_metadata=[\n                {\"icon\": \"OpenAI\"},\n                {\"icon\": \"Anthropic\"},\n                {\"icon\": \"GoogleGenerativeAI\"},\n                {\"icon\": \"WatsonxAI\"},\n                {\"icon\": \"Ollama\"},\n            ],\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n            value=OPENAI_CHAT_MODEL_NAMES[0],\n            info=\"Select the model to use\",\n            real_time_refresh=True,\n            refresh_button=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=False,\n            show=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"base_url_ibm_watsonx\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API (IBM watsonx.ai only)\",\n            options=IBM_WATSONX_URLS,\n            value=IBM_WATSONX_URLS[0],\n            show=False,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n            show=False,\n            required=False,\n        ),\n        MessageTextInput(\n            name=\"ollama_base_url\",\n            display_name=\"Ollama API URL\",\n            info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n            value=DEFAULT_OLLAMA_URL,\n            show=False,\n            real_time_refresh=True,\n            load_from_db=True,\n        ),\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            info=\"The input text to send to the model\",\n        ),\n        MultilineInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"A system message that helps set the behavior of the assistant\",\n            advanced=False,\n        ),\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=\"Whether to stream the response\",\n            value=False,\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            info=\"Controls randomness in responses\",\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        provider = self.provider\n        model_name = self.model_name\n        temperature = self.temperature\n        stream = self.stream\n\n        if provider == \"OpenAI\":\n            if not self.api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n\n            if model_name in OPENAI_REASONING_MODEL_NAMES:\n                # reasoning models do not support temperature (yet)\n                temperature = None\n\n            return ChatOpenAI(\n                model_name=model_name,\n                temperature=temperature,\n                streaming=stream,\n                openai_api_key=self.api_key,\n            )\n        if provider == \"Anthropic\":\n            if not self.api_key:\n                msg = \"Anthropic API key is required when using Anthropic provider\"\n                raise ValueError(msg)\n            return ChatAnthropic(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                anthropic_api_key=self.api_key,\n            )\n        if provider == \"Google\":\n            if not self.api_key:\n                msg = \"Google API key is required when using Google provider\"\n                raise ValueError(msg)\n            return ChatGoogleGenerativeAIFixed(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                google_api_key=self.api_key,\n            )\n        if provider == \"IBM watsonx.ai\":\n            if not self.api_key:\n                msg = \"IBM API key is required when using IBM watsonx.ai provider\"\n                raise ValueError(msg)\n            if not self.base_url_ibm_watsonx:\n                msg = \"IBM watsonx API Endpoint is required when using IBM watsonx.ai provider\"\n                raise ValueError(msg)\n            if not self.project_id:\n                msg = \"IBM watsonx Project ID is required when using IBM watsonx.ai provider\"\n                raise ValueError(msg)\n            return ChatWatsonx(\n                apikey=SecretStr(self.api_key).get_secret_value(),\n                url=self.base_url_ibm_watsonx,\n                project_id=self.project_id,\n                model_id=model_name,\n                params={\n                    \"temperature\": temperature,\n                },\n                streaming=stream,\n            )\n        if provider == \"Ollama\":\n            if not self.ollama_base_url:\n                msg = \"Ollama API URL is required when using Ollama provider\"\n                raise ValueError(msg)\n            if not model_name:\n                msg = \"Model name is required when using Ollama provider\"\n                raise ValueError(msg)\n\n            transformed_base_url = transform_localhost_url(self.ollama_base_url)\n\n            # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n            if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n                # Strip /v1 suffix and log warning\n                transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n                logger.warning(\n                    \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n                    \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n                    \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n                    \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n                )\n\n            return ChatOllama(\n                base_url=transformed_base_url,\n                model=model_name,\n                temperature=temperature,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    async def update_build_config(\n        self, build_config: dotdict, field_value: Any, field_name: str | None = None\n    ) -> dotdict:\n        if field_name == \"provider\":\n            if field_value == \"OpenAI\":\n                build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n                build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n                build_config[\"project_id\"][\"show\"] = False\n                build_config[\"ollama_base_url\"][\"show\"] = False\n            elif field_value == \"Anthropic\":\n                build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n                build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n                build_config[\"project_id\"][\"show\"] = False\n                build_config[\"ollama_base_url\"][\"show\"] = False\n            elif field_value == \"Google\":\n                build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n                build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n                build_config[\"project_id\"][\"show\"] = False\n                build_config[\"ollama_base_url\"][\"show\"] = False\n            elif field_value == \"IBM watsonx.ai\":\n                build_config[\"model_name\"][\"options\"] = IBM_WATSONX_DEFAULT_MODELS\n                build_config[\"model_name\"][\"value\"] = IBM_WATSONX_DEFAULT_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"IBM API Key\"\n                build_config[\"api_key\"][\"show\"] = True\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n                build_config[\"project_id\"][\"show\"] = True\n                build_config[\"ollama_base_url\"][\"show\"] = False\n            elif field_value == \"Ollama\":\n                # Fetch Ollama models from the API\n                build_config[\"api_key\"][\"show\"] = False\n                build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n                build_config[\"project_id\"][\"show\"] = False\n                build_config[\"ollama_base_url\"][\"show\"] = True\n                build_config[\"ollama_base_url\"][\"load_from_db\"] = True\n\n                # Try multiple sources to get the URL (in order of preference):\n                # 1. Instance attribute (already resolved from global/db)\n                # 2. Build config value (may be a global variable reference)\n                # 3. Default value\n                ollama_url = getattr(self, \"ollama_base_url\", None)\n                if not ollama_url:\n                    config_value = build_config[\"ollama_base_url\"].get(\"value\", DEFAULT_OLLAMA_URL)\n                    # If config_value looks like a variable name (all caps with underscores), use default\n                    is_variable_ref = (\n                        config_value\n                        and isinstance(config_value, str)\n                        and config_value.isupper()\n                        and \"_\" in config_value\n                    )\n                    if is_variable_ref:\n                        await logger.adebug(\n                            f\"Config value appears to be a variable reference: {config_value}, using default\"\n                        )\n                        ollama_url = DEFAULT_OLLAMA_URL\n                    else:\n                        ollama_url = config_value\n\n                await logger.adebug(f\"Fetching Ollama models for provider switch. URL: {ollama_url}\")\n                if await is_valid_ollama_url(url=ollama_url):\n                    try:\n                        models = await get_ollama_models(\n                            base_url_value=ollama_url,\n                            desired_capability=DESIRED_CAPABILITY,\n                            json_models_key=JSON_MODELS_KEY,\n                            json_name_key=JSON_NAME_KEY,\n                            json_capabilities_key=JSON_CAPABILITIES_KEY,\n                        )\n                        build_config[\"model_name\"][\"options\"] = models\n                        build_config[\"model_name\"][\"value\"] = models[0] if models else \"\"\n                    except ValueError:\n                        await logger.awarning(\"Failed to fetch Ollama models. Setting empty options.\")\n                        build_config[\"model_name\"][\"options\"] = []\n                        build_config[\"model_name\"][\"value\"] = \"\"\n                else:\n                    await logger.awarning(f\"Invalid Ollama URL: {ollama_url}\")\n                    build_config[\"model_name\"][\"options\"] = []\n                    build_config[\"model_name\"][\"value\"] = \"\"\n        elif (\n            field_name == \"base_url_ibm_watsonx\"\n            and field_value\n            and hasattr(self, \"provider\")\n            and self.provider == \"IBM watsonx.ai\"\n        ):\n            # Fetch IBM models when base_url changes\n            try:\n                models = self.fetch_ibm_models(base_url=field_value)\n                build_config[\"model_name\"][\"options\"] = models\n                build_config[\"model_name\"][\"value\"] = models[0] if models else IBM_WATSONX_DEFAULT_MODELS[0]\n                info_message = f\"Updated model options: {len(models)} models found in {field_value}\"\n                logger.info(info_message)\n            except Exception:  # noqa: BLE001\n                logger.exception(\"Error updating IBM model options.\")\n        elif field_name == \"ollama_base_url\":\n            # Fetch Ollama models when ollama_base_url changes\n            # Use the field_value directly since this is triggered when the field changes\n            logger.debug(\n                f\"Fetching Ollama models from updated URL: {build_config['ollama_base_url']} and value {self.ollama_base_url}\"\n            )\n            await logger.adebug(f\"Fetching Ollama models from updated URL: {self.ollama_base_url}\")\n            if await is_valid_ollama_url(url=self.ollama_base_url):\n                try:\n                    models = await get_ollama_models(\n                        base_url_value=self.ollama_base_url,\n                        desired_capability=DESIRED_CAPABILITY,\n                        json_models_key=JSON_MODELS_KEY,\n                        json_name_key=JSON_NAME_KEY,\n                        json_capabilities_key=JSON_CAPABILITIES_KEY,\n                    )\n                    build_config[\"model_name\"][\"options\"] = models\n                    build_config[\"model_name\"][\"value\"] = models[0] if models else \"\"\n                    info_message = f\"Updated model options: {len(models)} models found in {self.ollama_base_url}\"\n                    await logger.ainfo(info_message)\n                except ValueError:\n                    await logger.awarning(\"Error updating Ollama model options.\")\n                    build_config[\"model_name\"][\"options\"] = []\n                    build_config[\"model_name\"][\"value\"] = \"\"\n            else:\n                await logger.awarning(f\"Invalid Ollama URL: {self.ollama_base_url}\")\n                build_config[\"model_name\"][\"options\"] = []\n                build_config[\"model_name\"][\"value\"] = \"\"\n        elif field_name == \"model_name\":\n            # Refresh Ollama models when model_name field is accessed\n            if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n                ollama_url = getattr(self, \"ollama_base_url\", DEFAULT_OLLAMA_URL)\n                if await is_valid_ollama_url(url=ollama_url):\n                    try:\n                        models = await get_ollama_models(\n                            base_url_value=ollama_url,\n                            desired_capability=DESIRED_CAPABILITY,\n                            json_models_key=JSON_MODELS_KEY,\n                            json_name_key=JSON_NAME_KEY,\n                            json_capabilities_key=JSON_CAPABILITIES_KEY,\n                        )\n                        build_config[\"model_name\"][\"options\"] = models\n                    except ValueError:\n                        await logger.awarning(\"Failed to refresh Ollama models.\")\n                        build_config[\"model_name\"][\"options\"] = []\n                else:\n                    build_config[\"model_name\"][\"options\"] = []\n\n            # Hide system_message for o1 models - currently unsupported\n            if field_value and field_value.startswith(\"o1\") and hasattr(self, \"provider\") and self.provider == \"OpenAI\":\n                if \"system_message\" in build_config:\n                    build_config[\"system_message\"][\"show\"] = False\n            elif \"system_message\" in build_config:\n                build_config[\"system_message\"][\"show\"] = True\n        return build_config\n"
              },
              "input_value": {
                "_input_type": "MessageTextInput",
                "advanced": false,
                "display_name": "Input",
                "dynamic": false,
                "info": "The input text to send to the model",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "input_value",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "model_name": {
                "_input_type": "DropdownInput",
                "advanced": false,
                "combobox": false,
                "dialog_inputs": {},
                "display_name": "Model Name",
                "dynamic": false,
                "info": "Select the model to use",
                "name": "model_name",
                "options": [
                  "gpt-4o-mini",
                  "gpt-4o",
                  "gpt-4.1",
                  "gpt-4.1-mini",
                  "gpt-4.1-nano",
                  "gpt-4-turbo",
                  "gpt-4-turbo-preview",
                  "gpt-4",
                  "gpt-3.5-turbo",
                  "gpt-5",
                  "gpt-5-mini",
                  "gpt-5-nano",
                  "gpt-5-chat-latest",
                  "o1",
                  "o3-mini",
                  "o3",
                  "o3-pro",
                  "o4-mini",
                  "o4-mini-high"
                ],
                "options_metadata": [],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "toggle": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "gpt-4o-mini"
              },
              "provider": {
                "_input_type": "DropdownInput",
                "advanced": false,
                "combobox": false,
                "dialog_inputs": {},
                "display_name": "Model Provider",
                "dynamic": false,
                "info": "Select the model provider",
                "name": "provider",
                "options": [
                  "OpenAI",
                  "Anthropic",
                  "Google"
                ],
                "options_metadata": [
                  {
                    "icon": "OpenAI"
                  },
                  {
                    "icon": "Anthropic"
                  },
                  {
                    "icon": "Google"
                  }
                ],
                "placeholder": "",
                "real_time_refresh": true,
                "required": false,
                "show": true,
                "title_case": false,
                "toggle": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "OpenAI"
              },
              "stream": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Stream",
                "dynamic": false,
                "info": "Whether to stream the response",
                "list": false,
                "list_add_label": "Add More",
                "name": "stream",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": false
              },
              "system_message": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "System Message",
                "dynamic": false,
                "info": "A system message that helps set the behavior of the assistant",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "system_message",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "temperature": {
                "_input_type": "SliderInput",
                "advanced": true,
                "display_name": "Temperature",
                "dynamic": false,
                "info": "Controls randomness in responses",
                "max_label": "",
                "max_label_icon": "",
                "min_label": "",
                "min_label_icon": "",
                "name": "temperature",
                "placeholder": "",
                "range_spec": {
                  "max": 1,
                  "min": 0,
                  "step": 0.01,
                  "step_type": "float"
                },
                "required": false,
                "show": true,
                "slider_buttons": false,
                "slider_buttons_options": [],
                "slider_input": false,
                "title_case": false,
                "tool_mode": false,
                "type": "slider",
                "value": 0.1
              }
            },
            "tool_mode": false
          },
          "selected_output": "model_output",
          "showNode": true,
          "type": "LanguageModelComponent"
        },
        "dragging": false,
        "id": "LanguageModelComponent-iAML1",
        "measured": {
          "height": 451,
          "width": 320
        },
        "position": {
          "x": 912.2321256553546,
          "y": -350.4215686841271
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "StructuredOutput-il3eU",
          "node": {
            "base_classes": [
              "Data",
              "DataFrame"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Uses an LLM to generate structured data. Ideal for extraction and consistency.",
            "display_name": "Structured Output",
            "documentation": "https://docs.langflow.org/components-processing#structured-output",
            "edited": false,
            "field_order": [
              "llm",
              "input_value",
              "system_prompt",
              "schema_name",
              "output_schema"
            ],
            "frozen": false,
            "icon": "braces",
            "legacy": false,
            "lf_version": "1.6.0",
            "metadata": {
              "code_hash": "9ad8d8d8fc16",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "pydantic",
                    "version": "2.10.6"
                  },
                  {
                    "name": "trustcall",
                    "version": "0.0.39"
                  },
                  {
                    "name": "lfx",
                    "version": null
                  }
                ],
                "total_dependencies": 3
              },
              "module": "lfx.components.processing.structured_output.StructuredOutputComponent"
            },
            "minimized": false,
            "output_types": [],
            "outputs": [
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Structured Output",
                "group_outputs": false,
                "method": "build_structured_output",
                "name": "structured_output",
                "selected": null,
                "tool_mode": true,
                "types": [
                  "Data"
                ],
                "value": "__UNDEFINED__"
              },
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Structured Output",
                "group_outputs": false,
                "method": "build_structured_dataframe",
                "name": "dataframe_output",
                "selected": "DataFrame",
                "tool_mode": true,
                "types": [
                  "DataFrame"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from pydantic import BaseModel, Field, create_model\nfrom trustcall import create_extractor\n\nfrom lfx.base.models.chat_result import get_chat_result\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.helpers.base_model import build_model_from_schema\nfrom lfx.io import (\n    HandleInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n    TableInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.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    documentation: str = \"https://docs.langflow.org/components-processing#structured-output\"\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 structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\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        Output(\n            name=\"dataframe_output\",\n            display_name=\"Structured Output\",\n            method=\"build_structured_dataframe\",\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        output_model = create_model(\n            schema_name,\n            __doc__=f\"A list of {schema_name}.\",\n            objects=(\n                list[output_model_],\n                Field(\n                    description=f\"A list of {schema_name}.\",  # type: ignore[valid-type]\n                    min_length=1,  # help ensure non-empty output\n                ),\n            ),\n        )\n        # Tracing config\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        # Generate structured output using Trustcall first, then fallback to Langchain if it fails\n        result = self._extract_output_with_trustcall(output_model, config_dict)\n        if result is None:\n            result = self._extract_output_with_langchain(output_model, config_dict)\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\n        if isinstance(first_response, BaseModel):\n            structured_data = first_response.model_dump()\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            return Data(data=output[0])\n        if len(output) > 1:\n            # Multiple outputs - wrap them in a results container\n            return Data(data={\"results\": output})\n        return Data()\n\n    def build_structured_dataframe(self) -> DataFrame:\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            # For single dictionary, wrap in a list to create DataFrame with one row\n            return DataFrame([output[0]])\n        if len(output) > 1:\n            # Multiple outputs - convert to DataFrame directly\n            return DataFrame(output)\n        return DataFrame()\n\n    def _extract_output_with_trustcall(self, schema: BaseModel, config_dict: dict) -> list[BaseModel] | None:\n        try:\n            llm_with_structured_output = create_extractor(self.llm, tools=[schema], tool_choice=schema.__name__)\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        except Exception as e:  # noqa: BLE001\n            logger.warning(\n                f\"Trustcall extraction failed, falling back to Langchain: {e} \"\n                \"(Note: This may not be an error—some models or configurations do not support tool calling. \"\n                \"Falling back is normal in such cases.)\"\n            )\n            return None\n        return result or None  # langchain fallback is used if error occurs or the result is empty\n\n    def _extract_output_with_langchain(self, schema: BaseModel, config_dict: dict) -> list[BaseModel] | None:\n        try:\n            llm_with_structured_output = self.llm.with_structured_output(schema)\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                result = result.get(\"objects\", result)\n        except Exception as fallback_error:\n            msg = (\n                f\"Model does not support tool calling (trustcall failed) \"\n                f\"and fallback with_structured_output also failed: {fallback_error}\"\n            )\n            raise ValueError(msg) from fallback_error\n\n        return result or None\n"
              },
              "input_value": {
                "_input_type": "MultilineInput",
                "advanced": false,
                "copy_field": false,
                "display_name": "Input Message",
                "dynamic": false,
                "info": "The input message to the language model.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "multiline": true,
                "name": "input_value",
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "tool_mode": true,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "llm": {
                "_input_type": "HandleInput",
                "advanced": false,
                "display_name": "Language Model",
                "dynamic": false,
                "info": "The language model to use to generate the structured output.",
                "input_types": [
                  "LanguageModel"
                ],
                "list": false,
                "list_add_label": "Add More",
                "name": "llm",
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              },
              "output_schema": {
                "_input_type": "TableInput",
                "advanced": false,
                "display_name": "Output Schema",
                "dynamic": false,
                "info": "Define the structure and data types for the model's output.",
                "is_list": true,
                "list_add_label": "Add More",
                "name": "output_schema",
                "placeholder": "",
                "required": true,
                "show": true,
                "table_icon": "Table",
                "table_schema": [
                  {
                    "default": "field",
                    "description": "Specify the name of the output field.",
                    "display_name": "Name",
                    "edit_mode": "inline",
                    "formatter": "text",
                    "name": "name",
                    "type": "str"
                  },
                  {
                    "default": "description of field",
                    "description": "Describe the purpose of the output field.",
                    "display_name": "Description",
                    "edit_mode": "popover",
                    "formatter": "text",
                    "name": "description",
                    "type": "str"
                  },
                  {
                    "default": "str",
                    "description": "Indicate the data type of the output field (e.g., str, int, float, bool, dict).",
                    "display_name": "Type",
                    "edit_mode": "inline",
                    "formatter": "text",
                    "name": "type",
                    "options": [
                      "str",
                      "int",
                      "float",
                      "bool",
                      "dict"
                    ],
                    "type": "str"
                  },
                  {
                    "default": "False",
                    "description": "Set to True if this output field should be a list of the specified type.",
                    "display_name": "As List",
                    "edit_mode": "inline",
                    "formatter": "text",
                    "name": "multiple",
                    "type": "boolean"
                  }
                ],
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "trigger_icon": "Table",
                "trigger_text": "Open table",
                "type": "table",
                "value": [
                  {
                    "description": "description of field",
                    "multiple": "False",
                    "name": "EBITDA",
                    "type": "str"
                  },
                  {
                    "description": "description of field",
                    "multiple": "False",
                    "name": "NET_INCOME",
                    "type": "str"
                  },
                  {
                    "description": "description of field",
                    "multiple": "False",
                    "name": "GROSS_PROFIT",
                    "type": "str"
                  }
                ]
              },
              "schema_name": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Schema Name",
                "dynamic": false,
                "info": "Provide a name for the output data schema.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "schema_name",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "system_prompt": {
                "_input_type": "MultilineInput",
                "advanced": true,
                "copy_field": false,
                "display_name": "Format Instructions",
                "dynamic": false,
                "info": "The instructions to the language model for formatting the output.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "multiline": true,
                "name": "system_prompt",
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "You are an AI that extracts structured JSON objects from unstructured text. Use a predefined schema with expected types (str, int, float, bool, dict). Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. Fill missing or ambiguous values with defaults: null for missing values. Remove exact duplicates but keep variations that have different field values. Always return valid JSON in the expected format, never throw errors. If multiple objects can be extracted, return them all in the structured format."
              }
            },
            "tool_mode": false
          },
          "selected_output": "dataframe_output",
          "showNode": true,
          "type": "StructuredOutput"
        },
        "dragging": false,
        "id": "StructuredOutput-il3eU",
        "measured": {
          "height": 349,
          "width": 320
        },
        "position": {
          "x": 1322.636615615224,
          "y": 179.73895831803156
        },
        "selected": true,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "ParserComponent-FWDxd",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Extracts text using a template.",
            "display_name": "Parser",
            "documentation": "https://docs.langflow.org/components-processing#parser",
            "edited": false,
            "field_order": [
              "input_data",
              "mode",
              "pattern",
              "sep"
            ],
            "frozen": false,
            "icon": "braces",
            "legacy": false,
            "lf_version": "1.6.0",
            "metadata": {
              "code_hash": "17514953c7e8",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "lfx",
                    "version": null
                  }
                ],
                "total_dependencies": 1
              },
              "module": "lfx.components.processing.parser.ParserComponent"
            },
            "minimized": false,
            "output_types": [],
            "outputs": [
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Parsed Text",
                "group_outputs": false,
                "method": "parse_combined_text",
                "name": "parsed_text",
                "selected": "Message",
                "tool_mode": true,
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, HandleInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\n\n\nclass ParserComponent(Component):\n    display_name = \"Parser\"\n    description = \"Extracts text using a template.\"\n    documentation: str = \"https://docs.langflow.org/components-processing#parser\"\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"input_data\",\n            display_name=\"Data or DataFrame\",\n            input_types=[\"DataFrame\", \"Data\"],\n            info=\"Accepts either a DataFrame or a Data object.\",\n            required=True,\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Parser\", \"Stringify\"],\n            value=\"Parser\",\n            info=\"Convert into raw string instead of using a template.\",\n            real_time_refresh=True,\n        ),\n        MultilineInput(\n            name=\"pattern\",\n            display_name=\"Template\",\n            info=(\n                \"Use variables within curly brackets to extract column values for DataFrames \"\n                \"or key values for Data.\"\n                \"For example: `Name: {Name}, Age: {Age}, Country: {Country}`\"\n            ),\n            value=\"Text: {text}\",  # Example default\n            dynamic=True,\n            show=True,\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"sep\",\n            display_name=\"Separator\",\n            advanced=True,\n            value=\"\\n\",\n            info=\"String used to separate rows/items.\",\n        ),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Parsed Text\",\n            name=\"parsed_text\",\n            info=\"Formatted text output.\",\n            method=\"parse_combined_text\",\n        ),\n    ]\n\n    def update_build_config(self, build_config, field_value, field_name=None):\n        \"\"\"Dynamically hide/show `template` and enforce requirement based on `stringify`.\"\"\"\n        if field_name == \"mode\":\n            build_config[\"pattern\"][\"show\"] = self.mode == \"Parser\"\n            build_config[\"pattern\"][\"required\"] = self.mode == \"Parser\"\n            if field_value:\n                clean_data = BoolInput(\n                    name=\"clean_data\",\n                    display_name=\"Clean Data\",\n                    info=(\n                        \"Enable to clean the data by removing empty rows and lines \"\n                        \"in each cell of the DataFrame/ Data object.\"\n                    ),\n                    value=True,\n                    advanced=True,\n                    required=False,\n                )\n                build_config[\"clean_data\"] = clean_data.to_dict()\n            else:\n                build_config.pop(\"clean_data\", None)\n\n        return build_config\n\n    def _clean_args(self):\n        \"\"\"Prepare arguments based on input type.\"\"\"\n        input_data = self.input_data\n\n        match input_data:\n            case list() if all(isinstance(item, Data) for item in input_data):\n                msg = \"List of Data objects is not supported.\"\n                raise ValueError(msg)\n            case DataFrame():\n                return input_data, None\n            case Data():\n                return None, input_data\n            case dict() if \"data\" in input_data:\n                try:\n                    if \"columns\" in input_data:  # Likely a DataFrame\n                        return DataFrame.from_dict(input_data), None\n                    # Likely a Data object\n                    return None, Data(**input_data)\n                except (TypeError, ValueError, KeyError) as e:\n                    msg = f\"Invalid structured input provided: {e!s}\"\n                    raise ValueError(msg) from e\n            case _:\n                msg = f\"Unsupported input type: {type(input_data)}. Expected DataFrame or Data.\"\n                raise ValueError(msg)\n\n    def parse_combined_text(self) -> Message:\n        \"\"\"Parse all rows/items into a single text or convert input to string if `stringify` is enabled.\"\"\"\n        # Early return for stringify option\n        if self.mode == \"Stringify\":\n            return self.convert_to_string()\n\n        df, data = self._clean_args()\n\n        lines = []\n        if df is not None:\n            for _, row in df.iterrows():\n                formatted_text = self.pattern.format(**row.to_dict())\n                lines.append(formatted_text)\n        elif data is not None:\n            # Use format_map with a dict that returns default_value for missing keys\n            class DefaultDict(dict):\n                def __missing__(self, key):\n                    return data.default_value or \"\"\n\n            formatted_text = self.pattern.format_map(DefaultDict(data.data))\n            lines.append(formatted_text)\n\n        combined_text = self.sep.join(lines)\n        self.status = combined_text\n        return Message(text=combined_text)\n\n    def convert_to_string(self) -> Message:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        result = \"\"\n        if isinstance(self.input_data, list):\n            result = \"\\n\".join([safe_convert(item, clean_data=self.clean_data or False) for item in self.input_data])\n        else:\n            result = safe_convert(self.input_data or False)\n        self.log(f\"Converted to string with length: {len(result)}\")\n\n        message = Message(text=result)\n        self.status = message\n        return message\n"
              },
              "input_data": {
                "_input_type": "HandleInput",
                "advanced": false,
                "display_name": "Data or DataFrame",
                "dynamic": false,
                "info": "Accepts either a DataFrame or a Data object.",
                "input_types": [
                  "DataFrame",
                  "Data"
                ],
                "list": false,
                "list_add_label": "Add More",
                "name": "input_data",
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              },
              "mode": {
                "_input_type": "TabInput",
                "advanced": false,
                "display_name": "Mode",
                "dynamic": false,
                "info": "Convert into raw string instead of using a template.",
                "name": "mode",
                "options": [
                  "Parser",
                  "Stringify"
                ],
                "placeholder": "",
                "real_time_refresh": true,
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "tab",
                "value": "Parser"
              },
              "pattern": {
                "_input_type": "MultilineInput",
                "advanced": false,
                "copy_field": false,
                "display_name": "Template",
                "dynamic": true,
                "info": "Use variables within curly brackets to extract column values for DataFrames or key values for Data.For example: `Name: {Name}, Age: {Age}, Country: {Country}`",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "multiline": true,
                "name": "pattern",
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "EBITDA: {EBITDA}  ,  Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT}"
              },
              "sep": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Separator",
                "dynamic": false,
                "info": "String used to separate rows/items.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "sep",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "\n"
              }
            },
            "tool_mode": false
          },
          "showNode": true,
          "type": "ParserComponent"
        },
        "dragging": false,
        "id": "ParserComponent-FWDxd",
        "measured": {
          "height": 329,
          "width": 320
        },
        "position": {
          "x": 1773.483175339725,
          "y": 173.28856846175663
        },
        "selected": false,
        "type": "genericNode"
      }
    ],
    "viewport": {
      "x": -129.99735024931374,
      "y": 274.8803546176353,
      "zoom": 0.5746305066300074
    }
  },
  "description": "Extracts key financial metrics like Gross Profit, EBITDA, and Net Income from financial reports and structures them for easy analysis, using Structured Output Component",
  "endpoint_name": null,
  "id": "f8c24175-895d-4255-8014-e3f79a09539f",
  "is_component": false,
  "last_tested_version": "1.6.0",
  "name": "Financial Report Parser",
  "tags": [
    "chatbots",
    "content-generation"
  ]
}