Research Translation Loop.json

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            "custom_fields": {},
            "description": "Search and retrieve papers from arXiv.org",
            "display_name": "arXiv",
            "documentation": "",
            "edited": false,
            "field_order": [
              "search_query",
              "search_type",
              "max_results"
            ],
            "frozen": false,
            "icon": "arXiv",
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            "lf_version": "1.4.3",
            "metadata": {
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                "dependencies": [
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                    "name": "defusedxml",
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                ],
                "total_dependencies": 2
              },
              "module": "lfx.components.arxiv.arxiv.ArXivComponent"
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              "code": {
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                "value": "import urllib.request\nfrom urllib.parse import urlparse\nfrom xml.etree.ElementTree import Element\n\nfrom defusedxml.ElementTree import fromstring\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.io import DropdownInput, IntInput, MessageTextInput, Output\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\n\n\nclass ArXivComponent(Component):\n    display_name = \"arXiv\"\n    description = \"Search and retrieve papers from arXiv.org\"\n    icon = \"arXiv\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"search_query\",\n            display_name=\"Search Query\",\n            info=\"The search query for arXiv papers (e.g., 'quantum computing')\",\n            tool_mode=True,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Field\",\n            info=\"The field to search in\",\n            options=[\"all\", \"title\", \"abstract\", \"author\", \"cat\"],  # cat is for category\n            value=\"all\",\n        ),\n        IntInput(\n            name=\"max_results\",\n            display_name=\"Max Results\",\n            info=\"Maximum number of results to return\",\n            value=10,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"search_papers_dataframe\"),\n    ]\n\n    def build_query_url(self) -> str:\n        \"\"\"Build the arXiv API query URL.\"\"\"\n        base_url = \"http://export.arxiv.org/api/query?\"\n\n        # Build the search query based on search type\n        if self.search_type == \"all\":\n            search_query = self.search_query  # No prefix for all fields\n        else:\n            # Map dropdown values to ArXiv API prefixes\n            prefix_map = {\"title\": \"ti\", \"abstract\": \"abs\", \"author\": \"au\", \"cat\": \"cat\"}\n            prefix = prefix_map.get(self.search_type, \"\")\n            search_query = f\"{prefix}:{self.search_query}\"\n\n        # URL parameters\n        params = {\n            \"search_query\": search_query,\n            \"max_results\": str(self.max_results),\n        }\n\n        # Convert params to URL query string\n        query_string = \"&\".join([f\"{k}={urllib.parse.quote(str(v))}\" for k, v in params.items()])\n\n        return base_url + query_string\n\n    def parse_atom_response(self, response_text: str) -> list[dict]:\n        \"\"\"Parse the Atom XML response from arXiv.\"\"\"\n        # Parse XML safely using defusedxml\n        root = fromstring(response_text)\n\n        # Define namespace dictionary for XML parsing\n        ns = {\"atom\": \"http://www.w3.org/2005/Atom\", \"arxiv\": \"http://arxiv.org/schemas/atom\"}\n\n        papers = []\n        # Process each entry (paper)\n        for entry in root.findall(\"atom:entry\", ns):\n            paper = {\n                \"id\": self._get_text(entry, \"atom:id\", ns),\n                \"title\": self._get_text(entry, \"atom:title\", ns),\n                \"summary\": self._get_text(entry, \"atom:summary\", ns),\n                \"published\": self._get_text(entry, \"atom:published\", ns),\n                \"updated\": self._get_text(entry, \"atom:updated\", ns),\n                \"authors\": [author.find(\"atom:name\", ns).text for author in entry.findall(\"atom:author\", ns)],\n                \"arxiv_url\": self._get_link(entry, \"alternate\", ns),\n                \"pdf_url\": self._get_link(entry, \"related\", ns),\n                \"comment\": self._get_text(entry, \"arxiv:comment\", ns),\n                \"journal_ref\": self._get_text(entry, \"arxiv:journal_ref\", ns),\n                \"primary_category\": self._get_category(entry, ns),\n                \"categories\": [cat.get(\"term\") for cat in entry.findall(\"atom:category\", ns)],\n            }\n            papers.append(paper)\n\n        return papers\n\n    def _get_text(self, element: Element, path: str, ns: dict) -> str | None:\n        \"\"\"Safely extract text from an XML element.\"\"\"\n        el = element.find(path, ns)\n        return el.text.strip() if el is not None and el.text else None\n\n    def _get_link(self, element: Element, rel: str, ns: dict) -> str | None:\n        \"\"\"Get link URL based on relation type.\"\"\"\n        for link in element.findall(\"atom:link\", ns):\n            if link.get(\"rel\") == rel:\n                return link.get(\"href\")\n        return None\n\n    def _get_category(self, element: Element, ns: dict) -> str | None:\n        \"\"\"Get primary category.\"\"\"\n        cat = element.find(\"arxiv:primary_category\", ns)\n        return cat.get(\"term\") if cat is not None else None\n\n    def run_model(self) -> DataFrame:\n        return self.search_papers_dataframe()\n\n    def search_papers(self) -> list[Data]:\n        \"\"\"Search arXiv and return results.\"\"\"\n        try:\n            # Build the query URL\n            url = self.build_query_url()\n\n            # Validate URL scheme and host\n            parsed_url = urlparse(url)\n            if parsed_url.scheme not in {\"http\", \"https\"}:\n                error_msg = f\"Invalid URL scheme: {parsed_url.scheme}\"\n                raise ValueError(error_msg)\n            if parsed_url.hostname != \"export.arxiv.org\":\n                error_msg = f\"Invalid host: {parsed_url.hostname}\"\n                raise ValueError(error_msg)\n\n            # Create a custom opener that only allows http/https schemes\n            class RestrictedHTTPHandler(urllib.request.HTTPHandler):\n                def http_open(self, req):\n                    return super().http_open(req)\n\n            class RestrictedHTTPSHandler(urllib.request.HTTPSHandler):\n                def https_open(self, req):\n                    return super().https_open(req)\n\n            # Build opener with restricted handlers\n            opener = urllib.request.build_opener(RestrictedHTTPHandler, RestrictedHTTPSHandler)\n            urllib.request.install_opener(opener)\n\n            # Make the request with validated URL using restricted opener\n            response = opener.open(url)\n            response_text = response.read().decode(\"utf-8\")\n\n            # Parse the response\n            papers = self.parse_atom_response(response_text)\n\n            # Convert to Data objects\n            results = [Data(data=paper) for paper in papers]\n            self.status = results\n        except (urllib.error.URLError, ValueError) as e:\n            error_data = Data(data={\"error\": f\"Request error: {e!s}\"})\n            self.status = error_data\n            return [error_data]\n        else:\n            return results\n\n    def search_papers_dataframe(self) -> DataFrame:\n        \"\"\"Convert the Arxiv search results to a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the search results.\n        \"\"\"\n        data = self.search_papers()\n        return DataFrame(data)\n"
              },
              "max_results": {
                "_input_type": "IntInput",
                "advanced": false,
                "display_name": "Max Results",
                "dynamic": false,
                "info": "Maximum number of results to return",
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "max_results",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": 3
              },
              "search_query": {
                "_input_type": "MessageTextInput",
                "advanced": false,
                "display_name": "Search Query",
                "dynamic": false,
                "info": "The search query for arXiv papers (e.g., 'quantum computing')",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "search_query",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": true,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "search_type": {
                "_input_type": "DropdownInput",
                "advanced": false,
                "combobox": false,
                "dialog_inputs": {},
                "display_name": "Search Field",
                "dynamic": false,
                "info": "The field to search in",
                "name": "search_type",
                "options": [
                  "all",
                  "title",
                  "abstract",
                  "author",
                  "cat"
                ],
                "options_metadata": [],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "toggle": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "all"
              }
            },
            "tool_mode": false
          },
          "showNode": true,
          "type": "ArXivComponent"
        },
        "dragging": false,
        "id": "ArXivComponent-wpsLb",
        "measured": {
          "height": 369,
          "width": 320
        },
        "position": {
          "x": 81.59312530546094,
          "y": 3.9397854556273906
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "ChatOutput-hmMTq",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "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",
              "clean_data"
            ],
            "frozen": false,
            "icon": "MessagesSquare",
            "legacy": false,
            "lf_version": "1.4.3",
            "metadata": {
              "code_hash": "4848ad3e35d5",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "orjson",
                    "version": "3.10.15"
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                  {
                    "name": "fastapi",
                    "version": "0.120.0"
                  },
                  {
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                  }
                ],
                "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"
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                "value": "__UNDEFINED__"
              }
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            "pinned": false,
            "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,
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                "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": "HandleInput",
                "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",
                "name": "input_value",
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "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,
                "toggle": 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"
        },
        "dragging": false,
        "id": "ChatOutput-hmMTq",
        "measured": {
          "height": 48,
          "width": 192
        },
        "position": {
          "x": 1214.3761564586034,
          "y": 511.8564949852382
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "ChatInput-8bLHN",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "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",
            "legacy": false,
            "lf_version": "1.4.3",
            "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,
            "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,
                "copy_field": 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",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "ai"
              },
              "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,
                "toggle": 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
          },
          "showNode": true,
          "type": "ChatInput"
        },
        "dragging": false,
        "id": "ChatInput-8bLHN",
        "measured": {
          "height": 204,
          "width": 320
        },
        "position": {
          "x": -333.65585758816223,
          "y": 107.75353484470551
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "note-1UowE",
          "node": {
            "description": "# **Langflow Loop Component Template - ArXiv search result Translator** \nThis template translates research paper summaries on ArXiv into Portuguese and summarizes them. \n Using **Langflow’s looping mechanism**, the template iterates through multiple research papers, translates them with the **OpenAI** model component, and outputs an aggregated version of all translated papers.  \n\n## Quickstart \n 1. Add your OpenAI API key to the **Language Model** component. \n2. In the **Playground**, enter a query related to a research topic (for example, “Quantum Computing Advancements”).  \n\n  The flow fetches a list of research papers from ArXiv matching the query. Each paper in the retrieved list is processed one-by-one using the Langflow **Loop component**. \n\n  The abstract of each paper is translated into Portuguese by the **OpenAI** model component. \n\n Once all papers are translated, the system aggregates them into a **single structured output**.",
            "display_name": "",
            "documentation": "",
            "template": {}
          },
          "type": "note"
        },
        "dragging": false,
        "height": 647,
        "id": "note-1UowE",
        "measured": {
          "height": 647,
          "width": 576
        },
        "position": {
          "x": -890.9006297459302,
          "y": -233.44894493951168
        },
        "resizing": false,
        "selected": false,
        "type": "noteNode",
        "width": 576
      },
      {
        "data": {
          "id": "ParserComponent-aKcLF",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Extracts text using a template.",
            "display_name": "Parser",
            "documentation": "",
            "edited": false,
            "field_order": [
              "input_data",
              "mode",
              "pattern",
              "sep"
            ],
            "frozen": false,
            "icon": "braces",
            "legacy": false,
            "lf_version": "1.4.3",
            "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.",
                "load_from_db": false,
                "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": "Stringify"
              },
              "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": "Text: {dt}"
              },
              "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-aKcLF",
        "measured": {
          "height": 329,
          "width": 320
        },
        "position": {
          "x": 971.3987248215344,
          "y": -186.6658506576822
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "LoopComponent-GV15E",
          "node": {
            "base_classes": [
              "Data",
              "DataFrame"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.",
            "display_name": "Loop",
            "documentation": "",
            "edited": false,
            "field_order": [
              "data"
            ],
            "frozen": false,
            "icon": "infinity",
            "legacy": false,
            "lf_version": "1.4.3",
            "metadata": {
              "code_hash": "394284181e18",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "lfx",
                    "version": null
                  }
                ],
                "total_dependencies": 1
              },
              "module": "lfx.components.flow_controls.loop.LoopComponent"
            },
            "minimized": false,
            "output_types": [],
            "outputs": [
              {
                "allows_loop": true,
                "cache": true,
                "display_name": "Item",
                "group_outputs": true,
                "method": "item_output",
                "name": "item",
                "selected": "Data",
                "tool_mode": true,
                "types": [
                  "Data"
                ],
                "value": "__UNDEFINED__"
              },
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Done",
                "group_outputs": true,
                "method": "done_output",
                "name": "done",
                "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 lfx.custom.custom_component.component import Component\nfrom lfx.inputs.inputs import HandleInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.template.field.base import Output\n\n\nclass LoopComponent(Component):\n    display_name = \"Loop\"\n    description = (\n        \"Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.\"\n    )\n    documentation: str = \"https://docs.langflow.org/components-logic#loop\"\n    icon = \"infinity\"\n\n    inputs = [\n        HandleInput(\n            name=\"data\",\n            display_name=\"Inputs\",\n            info=\"The initial list of Data objects or DataFrame to iterate over.\",\n            input_types=[\"DataFrame\"],\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Item\", name=\"item\", method=\"item_output\", allows_loop=True, group_outputs=True),\n        Output(display_name=\"Done\", name=\"done\", method=\"done_output\", group_outputs=True),\n    ]\n\n    def initialize_data(self) -> None:\n        \"\"\"Initialize the data list, context index, and aggregated list.\"\"\"\n        if self.ctx.get(f\"{self._id}_initialized\", False):\n            return\n\n        # Ensure data is a list of Data objects\n        data_list = self._validate_data(self.data)\n\n        # Store the initial data and context variables\n        self.update_ctx(\n            {\n                f\"{self._id}_data\": data_list,\n                f\"{self._id}_index\": 0,\n                f\"{self._id}_aggregated\": [],\n                f\"{self._id}_initialized\": True,\n            }\n        )\n\n    def _validate_data(self, data):\n        \"\"\"Validate and return a list of Data objects.\"\"\"\n        if isinstance(data, DataFrame):\n            return data.to_data_list()\n        if isinstance(data, Data):\n            return [data]\n        if isinstance(data, list) and all(isinstance(item, Data) for item in data):\n            return data\n        msg = \"The 'data' input must be a DataFrame, a list of Data objects, or a single Data object.\"\n        raise TypeError(msg)\n\n    def evaluate_stop_loop(self) -> bool:\n        \"\"\"Evaluate whether to stop item or done output.\"\"\"\n        current_index = self.ctx.get(f\"{self._id}_index\", 0)\n        data_length = len(self.ctx.get(f\"{self._id}_data\", []))\n        return current_index > data_length\n\n    def item_output(self) -> Data:\n        \"\"\"Output the next item in the list or stop if done.\"\"\"\n        self.initialize_data()\n        current_item = Data(text=\"\")\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n        else:\n            # Get data list and current index\n            data_list, current_index = self.loop_variables()\n            if current_index < len(data_list):\n                # Output current item and increment index\n                try:\n                    current_item = data_list[current_index]\n                except IndexError:\n                    current_item = Data(text=\"\")\n            self.aggregated_output()\n            self.update_ctx({f\"{self._id}_index\": current_index + 1})\n\n        # Now we need to update the dependencies for the next run\n        self.update_dependency()\n        return current_item\n\n    def update_dependency(self):\n        item_dependency_id = self.get_incoming_edge_by_target_param(\"item\")\n        if item_dependency_id not in self.graph.run_manager.run_predecessors[self._id]:\n            self.graph.run_manager.run_predecessors[self._id].append(item_dependency_id)\n            # CRITICAL: Also update run_map so remove_from_predecessors() works correctly\n            # run_map[predecessor] = list of vertices that depend on predecessor\n            if self._id not in self.graph.run_manager.run_map[item_dependency_id]:\n                self.graph.run_manager.run_map[item_dependency_id].append(self._id)\n\n    def done_output(self) -> DataFrame:\n        \"\"\"Trigger the done output when iteration is complete.\"\"\"\n        self.initialize_data()\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n            self.start(\"done\")\n\n            aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n\n            return DataFrame(aggregated)\n        self.stop(\"done\")\n        return DataFrame([])\n\n    def loop_variables(self):\n        \"\"\"Retrieve loop variables from context.\"\"\"\n        return (\n            self.ctx.get(f\"{self._id}_data\", []),\n            self.ctx.get(f\"{self._id}_index\", 0),\n        )\n\n    def aggregated_output(self) -> list[Data]:\n        \"\"\"Return the aggregated list once all items are processed.\"\"\"\n        self.initialize_data()\n\n        # Get data list and aggregated list\n        data_list = self.ctx.get(f\"{self._id}_data\", [])\n        aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n        loop_input = self.item\n        if loop_input is not None and not isinstance(loop_input, str) and len(aggregated) <= len(data_list):\n            aggregated.append(loop_input)\n            self.update_ctx({f\"{self._id}_aggregated\": aggregated})\n        return aggregated\n"
              },
              "data": {
                "_input_type": "HandleInput",
                "advanced": false,
                "display_name": "Inputs",
                "dynamic": false,
                "info": "The initial list of Data objects or DataFrame to iterate over.",
                "input_types": [
                  "DataFrame"
                ],
                "list": false,
                "list_add_label": "Add More",
                "name": "data",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              }
            },
            "tool_mode": false
          },
          "showNode": true,
          "type": "LoopComponent"
        },
        "dragging": false,
        "id": "LoopComponent-GV15E",
        "measured": {
          "height": 242,
          "width": 320
        },
        "position": {
          "x": 541.1188345961908,
          "y": 181.38181401206583
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "LanguageModelComponent-ze4nq",
          "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",
            "legacy": false,
            "lf_version": "1.4.3",
            "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",
                "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",
                "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.base.models.watsonx_constants import IBM_WATSONX_URLS\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\"]\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\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']} \\\n                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": "MessageInput",
                "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",
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                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
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                "type": "bool",
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                "_input_type": "MultilineInput",
                "advanced": false,
                "copy_field": false,
                "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,
                "multiline": true,
                "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": "Translate to Portuguese and output in structured formatReturn only the JSON and no additional text."
              },
              "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": "text_output",
          "showNode": true,
          "type": "LanguageModelComponent"
        },
        "dragging": false,
        "id": "LanguageModelComponent-ze4nq",
        "measured": {
          "height": 534,
          "width": 320
        },
        "position": {
          "x": 1472.0991866325971,
          "y": -182.4108205734875
        },
        "selected": true,
        "type": "genericNode"
      },
      {
        "data": {
          "id": "TypeConverterComponent-NClYY",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "category": "processing",
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Convert between different types (Message, Data, DataFrame)",
            "display_name": "Type Convert",
            "documentation": "",
            "edited": false,
            "field_order": [
              "input_data",
              "output_type"
            ],
            "frozen": false,
            "icon": "repeat",
            "key": "TypeConverterComponent",
            "legacy": false,
            "metadata": {
              "code_hash": "aac23b807182",
              "dependencies": {
                "dependencies": [
                  {
                    "name": "lfx",
                    "version": null
                  },
                  {
                    "name": "pandas",
                    "version": "2.2.3"
                  }
                ],
                "total_dependencies": 2
              },
              "module": "lfx.components.processing.converter.TypeConverterComponent"
            },
            "minimized": false,
            "output_types": [],
            "outputs": [
              {
                "allows_loop": false,
                "cache": true,
                "display_name": "Data Output",
                "group_outputs": false,
                "hidden": null,
                "method": "convert_to_data",
                "name": "data_output",
                "options": null,
                "required_inputs": null,
                "selected": "Data",
                "tool_mode": true,
                "types": [
                  "Data"
                ],
                "value": "__UNDEFINED__"
              }
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            "pinned": false,
            "score": 0.007568328950209746,
            "template": {
              "_type": "Component",
              "auto_parse": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Auto Parse",
                "dynamic": false,
                "info": "Detect and convert JSON/CSV strings automatically.",
                "list": false,
                "list_add_label": "Add More",
                "name": "auto_parse",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": false
              },
              "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": "import json\nfrom typing import Any\n\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, HandleInput, Output, TabInput\nfrom lfx.schema import Data, DataFrame, Message\n\nMIN_CSV_LINES = 2\n\n\ndef convert_to_message(v) -> Message:\n    \"\"\"Convert input to Message type.\n\n    Args:\n        v: Input to convert (Message, Data, DataFrame, or dict)\n\n    Returns:\n        Message: Converted Message object\n    \"\"\"\n    return v if isinstance(v, Message) else v.to_message()\n\n\ndef convert_to_data(v: DataFrame | Data | Message | dict, *, auto_parse: bool) -> Data:\n    \"\"\"Convert input to Data type.\n\n    Args:\n        v: Input to convert (Message, Data, DataFrame, or dict)\n        auto_parse: Enable automatic parsing of structured data (JSON/CSV)\n\n    Returns:\n        Data: Converted Data object\n    \"\"\"\n    if isinstance(v, dict):\n        return Data(v)\n    if isinstance(v, Message):\n        data = Data(data={\"text\": v.data[\"text\"]})\n        return parse_structured_data(data) if auto_parse else data\n\n    return v if isinstance(v, Data) else v.to_data()\n\n\ndef convert_to_dataframe(v: DataFrame | Data | Message | dict, *, auto_parse: bool) -> DataFrame:\n    \"\"\"Convert input to DataFrame type.\n\n    Args:\n        v: Input to convert (Message, Data, DataFrame, or dict)\n        auto_parse: Enable automatic parsing of structured data (JSON/CSV)\n\n    Returns:\n        DataFrame: Converted DataFrame object\n    \"\"\"\n    import pandas as pd\n\n    if isinstance(v, dict):\n        return DataFrame([v])\n    if isinstance(v, DataFrame):\n        return v\n    # Handle pandas DataFrame\n    if isinstance(v, pd.DataFrame):\n        # Convert pandas DataFrame to our DataFrame by creating Data objects\n        return DataFrame(data=v)\n\n    if isinstance(v, Message):\n        data = Data(data={\"text\": v.data[\"text\"]})\n        return parse_structured_data(data).to_dataframe() if auto_parse else data.to_dataframe()\n    # For other types, call to_dataframe method\n    return v.to_dataframe()\n\n\ndef parse_structured_data(data: Data) -> Data:\n    \"\"\"Parse structured data (JSON, CSV) from Data's text field.\n\n    Args:\n        data: Data object with text content to parse\n\n    Returns:\n        Data: Modified Data object with parsed content or original if parsing fails\n    \"\"\"\n    raw_text = data.get_text() or \"\"\n    text = raw_text.lstrip(\"\\ufeff\").strip()\n\n    # Try JSON parsing first\n    parsed_json = _try_parse_json(text)\n    if parsed_json is not None:\n        return parsed_json\n\n    # Try CSV parsing\n    if _looks_like_csv(text):\n        try:\n            return _parse_csv_to_data(text)\n        except Exception:  # noqa: BLE001\n            # Heuristic misfire or malformed CSV — keep original data\n            return data\n\n    # Return original data if no parsing succeeded\n    return data\n\n\ndef _try_parse_json(text: str) -> Data | None:\n    \"\"\"Try to parse text as JSON and return Data object.\"\"\"\n    try:\n        parsed = json.loads(text)\n\n        if isinstance(parsed, dict):\n            # Single JSON object\n            return Data(data=parsed)\n        if isinstance(parsed, list) and all(isinstance(item, dict) for item in parsed):\n            # Array of JSON objects - create Data with the list\n            return Data(data={\"records\": parsed})\n\n    except (json.JSONDecodeError, ValueError):\n        pass\n\n    return None\n\n\ndef _looks_like_csv(text: str) -> bool:\n    \"\"\"Simple heuristic to detect CSV content.\"\"\"\n    lines = text.strip().split(\"\\n\")\n    if len(lines) < MIN_CSV_LINES:\n        return False\n\n    header_line = lines[0]\n    return \",\" in header_line and len(lines) > 1\n\n\ndef _parse_csv_to_data(text: str) -> Data:\n    \"\"\"Parse CSV text and return Data object.\"\"\"\n    from io import StringIO\n\n    import pandas as pd\n\n    # Parse CSV to DataFrame, then convert to list of dicts\n    parsed_df = pd.read_csv(StringIO(text))\n    records = parsed_df.to_dict(orient=\"records\")\n\n    return Data(data={\"records\": records})\n\n\nclass TypeConverterComponent(Component):\n    display_name = \"Type Convert\"\n    description = \"Convert between different types (Message, Data, DataFrame)\"\n    documentation: str = \"https://docs.langflow.org/components-processing#type-convert\"\n    icon = \"repeat\"\n\n    inputs = [\n        HandleInput(\n            name=\"input_data\",\n            display_name=\"Input\",\n            input_types=[\"Message\", \"Data\", \"DataFrame\"],\n            info=\"Accept Message, Data or DataFrame as input\",\n            required=True,\n        ),\n        BoolInput(\n            name=\"auto_parse\",\n            display_name=\"Auto Parse\",\n            info=\"Detect and convert JSON/CSV strings automatically.\",\n            advanced=True,\n            value=False,\n            required=False,\n        ),\n        TabInput(\n            name=\"output_type\",\n            display_name=\"Output Type\",\n            options=[\"Message\", \"Data\", \"DataFrame\"],\n            info=\"Select the desired output data type\",\n            real_time_refresh=True,\n            value=\"Message\",\n        ),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Message Output\",\n            name=\"message_output\",\n            method=\"convert_to_message\",\n        )\n    ]\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n        if field_name == \"output_type\":\n            # Start with empty outputs\n            frontend_node[\"outputs\"] = []\n\n            # Add only the selected output type\n            if field_value == \"Message\":\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Message Output\",\n                        name=\"message_output\",\n                        method=\"convert_to_message\",\n                    ).to_dict()\n                )\n            elif field_value == \"Data\":\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"Data Output\",\n                        name=\"data_output\",\n                        method=\"convert_to_data\",\n                    ).to_dict()\n                )\n            elif field_value == \"DataFrame\":\n                frontend_node[\"outputs\"].append(\n                    Output(\n                        display_name=\"DataFrame Output\",\n                        name=\"dataframe_output\",\n                        method=\"convert_to_dataframe\",\n                    ).to_dict()\n                )\n\n        return frontend_node\n\n    def convert_to_message(self) -> Message:\n        \"\"\"Convert input to Message type.\"\"\"\n        input_value = self.input_data[0] if isinstance(self.input_data, list) else self.input_data\n\n        # Handle string input by converting to Message first\n        if isinstance(input_value, str):\n            input_value = Message(text=input_value)\n\n        result = convert_to_message(input_value)\n        self.status = result\n        return result\n\n    def convert_to_data(self) -> Data:\n        \"\"\"Convert input to Data type.\"\"\"\n        input_value = self.input_data[0] if isinstance(self.input_data, list) else self.input_data\n\n        # Handle string input by converting to Message first\n        if isinstance(input_value, str):\n            input_value = Message(text=input_value)\n\n        result = convert_to_data(input_value, auto_parse=self.auto_parse)\n        self.status = result\n        return result\n\n    def convert_to_dataframe(self) -> DataFrame:\n        \"\"\"Convert input to DataFrame type.\"\"\"\n        input_value = self.input_data[0] if isinstance(self.input_data, list) else self.input_data\n\n        # Handle string input by converting to Message first\n        if isinstance(input_value, str):\n            input_value = Message(text=input_value)\n\n        result = convert_to_dataframe(input_value, auto_parse=self.auto_parse)\n        self.status = result\n        return result\n"
              },
              "input_data": {
                "_input_type": "HandleInput",
                "advanced": false,
                "display_name": "Input",
                "dynamic": false,
                "info": "Accept Message, Data or DataFrame as input",
                "input_types": [
                  "Message",
                  "Data",
                  "DataFrame"
                ],
                "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": ""
              },
              "output_type": {
                "_input_type": "TabInput",
                "advanced": false,
                "display_name": "Output Type",
                "dynamic": false,
                "info": "Select the desired output data type",
                "name": "output_type",
                "options": [
                  "Message",
                  "Data",
                  "DataFrame"
                ],
                "placeholder": "",
                "real_time_refresh": true,
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "tab",
                "value": "Data"
              }
            },
            "tool_mode": false
          },
          "showNode": true,
          "type": "TypeConverterComponent"
        },
        "dragging": false,
        "id": "TypeConverterComponent-NClYY",
        "measured": {
          "height": 262,
          "width": 320
        },
        "position": {
          "x": 1862.6410760135172,
          "y": 352.5532847926838
        },
        "selected": false,
        "type": "genericNode"
      }
    ],
    "viewport": {
      "x": 238.7929189073834,
      "y": 429.665322362372,
      "zoom": 0.5549571588412672
    }
  },
  "description": "This template iterates over search results using LoopComponent and translates each result into Portuguese automatically. 🚀",
  "endpoint_name": null,
  "id": "73f89c62-43bb-4c5e-80f3-9bec6cb898d2",
  "is_component": false,
  "last_tested_version": "1.4.3",
  "name": "Research Translation Loop",
  "tags": [
    "chatbots",
    "content-generation"
  ]
}