{ "data": { "edges": [ { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "TextInput", "id": "TextInput-4pPdA", "name": "text", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_data", "id": "TypeConverterComponent-jpa14", "inputTypes": [ "Message", "Data", "DataFrame" ], "type": "other" } }, "id": "xy-edge__TextInput-4pPdA{œdataTypeœ:œTextInputœ,œidœ:œTextInput-4pPdAœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-TypeConverterComponent-jpa14{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-jpa14œ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}", "selected": false, "source": "TextInput-4pPdA", "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-4pPdAœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}", "target": "TypeConverterComponent-jpa14", "targetHandle": "{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-jpa14œ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "TextInput", "id": "TextInput-P6ocs", "name": "text", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_data", "id": "TypeConverterComponent-4YqMq", "inputTypes": [ "Message", "Data", "DataFrame" ], "type": "other" } }, "id": "xy-edge__TextInput-P6ocs{œdataTypeœ:œTextInputœ,œidœ:œTextInput-P6ocsœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-TypeConverterComponent-4YqMq{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-4YqMqœ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}", "selected": false, "source": "TextInput-P6ocs", "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-P6ocsœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}", "target": "TypeConverterComponent-4YqMq", "targetHandle": "{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-4YqMqœ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "TextInput", "id": "TextInput-DDHo8", "name": "text", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_data", "id": "TypeConverterComponent-ddPzr", "inputTypes": [ "Message", "Data", "DataFrame" ], "type": "other" } }, "id": "xy-edge__TextInput-DDHo8{œdataTypeœ:œTextInputœ,œidœ:œTextInput-DDHo8œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-TypeConverterComponent-ddPzr{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-ddPzrœ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}", "selected": false, "source": "TextInput-DDHo8", "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-DDHo8œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}", "target": "TypeConverterComponent-ddPzr", "targetHandle": "{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-ddPzrœ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}" }, { "animated": false, "className": "", "data": { "sourceHandle": { "dataType": "TextInput", "id": "TextInput-hf5h7", "name": "text", "output_types": [ "Message" ] }, "targetHandle": { "fieldName": "input_data", "id": "TypeConverterComponent-VGrNj", "inputTypes": [ "Message", "Data", "DataFrame" ], "type": "other" } }, "id": "xy-edge__TextInput-hf5h7{œdataTypeœ:œTextInputœ,œidœ:œTextInput-hf5h7œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-TypeConverterComponent-VGrNj{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-VGrNjœ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}", "selected": false, "source": "TextInput-hf5h7", "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-hf5h7œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}", "target": "TypeConverterComponent-VGrNj", "targetHandle": "{œfieldNameœ:œinput_dataœ,œidœ:œTypeConverterComponent-VGrNjœ,œinputTypesœ:[œMessageœ,œDataœ,œDataFrameœ],œtypeœ:œotherœ}" } ], "nodes": [ { "data": { "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "id": "TypeConverterComponent-jpa14", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "documentation": "https://docs.langflow.org/components-processing#type-convert", "edited": false, "field_order": [ "input_data", "auto_parse", "output_type" ], "frozen": false, "icon": "repeat", "last_updated": "2025-09-05T12:25:31.230Z", "legacy": false, "lf_version": "1.6.0", "metadata": { "code_hash": "a99682150534", "dependencies": { "dependencies": [ { "name": "lfx", "version": null }, { "name": "pandas", "version": "2.2.3" } ], "total_dependencies": 2 }, "module": "custom_components.type_convert" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Message Output", "group_outputs": false, "hidden": null, "method": "convert_to_message", "name": "message_output", "options": null, "required_inputs": null, "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" } ], "pinned": false, "template": { "_type": "Component", "auto_parse": { "_input_type": "BoolInput", "advanced": false, "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": true }, "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 text = data.get_text().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 return _parse_csv_to_data(text)\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", "load_from_db": false, "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": "Message" } }, "tool_mode": false }, "showNode": true, "type": "TypeConverterComponent" }, "dragging": false, "id": "TypeConverterComponent-jpa14", "measured": { "height": 303, "width": 320 }, "position": { "x": 1855.9605564127553, "y": -448.5440275102738 }, "selected": false, "type": "genericNode" }, { "data": { "id": "TextInput-4pPdA", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Get user text inputs.", "display_name": "Ex4: CSV text", "documentation": "https://docs.langflow.org/components-io#text-input", "edited": true, "field_order": [ "input_value" ], "frozen": false, "icon": "type", "legacy": false, "lf_version": "1.6.0", "metadata": { "code_hash": "bdc011996aff", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "custom_components.ex4_csv_text" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Output Text", "group_outputs": false, "hidden": null, "method": "text_response", "name": "text", "options": null, "required_inputs": null, "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.io.text import TextComponent\nfrom lfx.io import MultilineInput, Output\nfrom lfx.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Ex4: CSV text\"\n description = \"Get user text inputs.\"\n documentation: str = \"https://docs.langflow.org/components-io#text-input\"\n icon = \"type\"\n name = \"TextInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n return Message(\n text=self.input_value,\n )\n" }, "input_value": { "_input_type": "MultilineInput", "advanced": false, "copy_field": false, "display_name": "Text", "dynamic": false, "info": "Text to be passed as input.", "input_types": [ "Message" ], "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": "nome,idade,email,cidade\nAna,28,ana@email.com,São Paulo\nBruno,34,bruno@email.com,Rio de Janeiro\nCarla,22,carla@email.com,Belo Horizonte\nDiego,40,diego@email.com,Curitiba\nElisa,31,elisa@email.com,Porto Alegre" } }, "tool_mode": false }, "showNode": true, "type": "TextInput" }, "dragging": false, "id": "TextInput-4pPdA", "measured": { "height": 203, "width": 320 }, "position": { "x": 1429.9066133239817, "y": -374.30522378649306 }, "selected": false, "type": "genericNode" }, { "data": { "id": "TextInput-P6ocs", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Get user text inputs.", "display_name": "Ex3: JSON text array", "documentation": "https://docs.langflow.org/components-io#text-input", "edited": true, "field_order": [ "input_value" ], "frozen": false, "icon": "type", "legacy": false, "metadata": { "code_hash": "89a0976a9dfc", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "custom_components.ex3_json_text_array" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Output Text", "group_outputs": false, "hidden": null, "method": "text_response", "name": "text", "options": null, "required_inputs": null, "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.io.text import TextComponent\nfrom lfx.io import MultilineInput, Output\nfrom lfx.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Ex3: JSON text array\"\n description = \"Get user text inputs.\"\n documentation: str = \"https://docs.langflow.org/components-io#text-input\"\n icon = \"type\"\n name = \"TextInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n return Message(\n text=self.input_value,\n )\n" }, "input_value": { "_input_type": "MultilineInput", "advanced": false, "copy_field": false, "display_name": "Text", "dynamic": false, "info": "Text to be passed as input.", "input_types": [ "Message" ], "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": "[\n {\n \"categories\": [\n \"food\"\n ],\n \"created_at\": \"2020-01-05 13:42:19.576875\",\n \"icon_url\": \"https://api.chucknorris.io/img/avatar/chuck-norris.png\",\n \"id\": \"muujj6xvr16hazym0b5tjw\",\n \"updated_at\": \"2020-01-05 13:42:19.576875\",\n \"url\": \"https://api.chucknorris.io/jokes/muujj6xvr16hazym0b5tjw\",\n \"value\": \"When Chuck Norris was a baby, he didn't suck his mother's breast. His mother served him whiskey, straight out of the bottle.\"\n },\n {\n \"categories\": [\n \"food\"\n ],\n \"created_at\": \"2020-01-05 13:42:19.576875\",\n \"icon_url\": \"https://api.chucknorris.io/img/avatar/chuck-norris.png\",\n \"id\": \"muujj6xvr16hazym0b5tjw\",\n \"updated_at\": \"2020-01-05 13:42:19.576875\",\n \"url\": \"https://api.chucknorris.io/jokes/muujj6xvr16hazym0b5tjw\",\n \"value\": \"When Chuck Norris was a baby, he didn't suck his mother's breast. His mother served him whiskey, straight out of the bottle.\"\n }\n]" } }, "tool_mode": false }, "showNode": true, "type": "TextInput" }, "dragging": false, "id": "TextInput-P6ocs", "measured": { "height": 203, "width": 320 }, "position": { "x": 1452.9260385355624, "y": -25.697764133400824 }, "selected": false, "type": "genericNode" }, { "data": { "id": "TextInput-DDHo8", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Get user text inputs.", "display_name": "Ex2: JSON text simple", "documentation": "https://docs.langflow.org/components-io#text-input", "edited": true, "field_order": [ "input_value" ], "frozen": false, "icon": "type", "legacy": false, "lf_version": "1.6.0", "metadata": { "code_hash": "c03bec92b698", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "custom_components.ex2_json_text_simple" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Output Text", "group_outputs": false, "hidden": null, "method": "text_response", "name": "text", "options": null, "required_inputs": null, "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.io.text import TextComponent\nfrom lfx.io import MultilineInput, Output\nfrom lfx.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Ex2: JSON text simple\"\n description = \"Get user text inputs.\"\n documentation: str = \"https://docs.langflow.org/components-io#text-input\"\n icon = \"type\"\n name = \"TextInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n return Message(\n text=self.input_value,\n )\n" }, "input_value": { "_input_type": "MultilineInput", "advanced": false, "copy_field": false, "display_name": "Text", "dynamic": false, "info": "Text to be passed as input.", "input_types": [ "Message" ], "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": "{\"categories\":[\"food\"],\"created_at\":\"2020-01-05 13:42:19.576875\",\"icon_url\":\"https://api.chucknorris.io/img/avatar/chuck-norris.png\",\"id\":\"muujj6xvr16hazym0b5tjw\",\"updated_at\":\"2020-01-05 13:42:19.576875\",\"url\":\"https://api.chucknorris.io/jokes/muujj6xvr16hazym0b5tjw\",\"value\":\"When Chuck Norris was a baby, he didn't suck his mother's breast. His mother served him whiskey, straight out of the bottle.\"}" } }, "tool_mode": false }, "showNode": true, "type": "TextInput" }, "dragging": false, "id": "TextInput-DDHo8", "measured": { "height": 203, "width": 320 }, "position": { "x": 1462.3340364296353, "y": 294.4016370023227 }, "selected": false, "type": "genericNode" }, { "data": { "id": "TextInput-hf5h7", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Get user text inputs.", "display_name": "Ex1: Text simple", "documentation": "https://docs.langflow.org/components-io#text-input", "edited": true, "field_order": [ "input_value" ], "frozen": false, "icon": "type", "legacy": false, "lf_version": "1.6.0", "metadata": { "code_hash": "35219c401c46", "dependencies": { "dependencies": [ { "name": "lfx", "version": null } ], "total_dependencies": 1 }, "module": "custom_components.ex1_text_simple" }, "minimized": false, "output_types": [], "outputs": [ { "allows_loop": false, "cache": true, "display_name": "Output Text", "group_outputs": false, "hidden": null, "method": "text_response", "name": "text", "options": null, "required_inputs": null, "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.io.text import TextComponent\nfrom lfx.io import MultilineInput, Output\nfrom lfx.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Ex1: Text simple\"\n description = \"Get user text inputs.\"\n documentation: str = \"https://docs.langflow.org/components-io#text-input\"\n icon = \"type\"\n name = \"TextInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n return Message(\n text=self.input_value,\n )\n" }, "input_value": { "_input_type": "MultilineInput", "advanced": false, "copy_field": false, "display_name": "Text", "dynamic": false, "info": "Text to be passed as input.", "input_types": [ "Message" ], "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": "Hello world" } }, "tool_mode": false }, "showNode": true, "type": "TextInput" }, "dragging": false, "id": "TextInput-hf5h7", "measured": { "height": 203, "width": 320 }, "position": { "x": 1440.4055196202007, "y": 663.3231528861243 }, "selected": true, "type": "genericNode" }, { "data": { "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "id": "TypeConverterComponent-4YqMq", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "documentation": "https://docs.langflow.org/components-processing#type-convert", "edited": false, "field_order": [ "input_data", "auto_parse", "output_type" ], "frozen": false, "icon": "repeat", "last_updated": "2025-09-05T12:25:06.757Z", "legacy": false, "lf_version": "1.6.0", "metadata": { "code_hash": "a99682150534", "dependencies": { "dependencies": [ { "name": "lfx", "version": null }, { "name": "pandas", "version": "2.2.3" } ], "total_dependencies": 2 }, "module": "custom_components.type_convert" }, "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__" } ], "pinned": false, "template": { "_type": "Component", "auto_parse": { "_input_type": "BoolInput", "advanced": false, "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": true }, "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 text = data.get_text().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 return _parse_csv_to_data(text)\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", "load_from_db": false, "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-4YqMq", "measured": { "height": 303, "width": 320 }, "position": { "x": 1864.5865312201836, "y": -83.38576388365755 }, "selected": false, "type": "genericNode" }, { "data": { "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "id": "TypeConverterComponent-ddPzr", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "documentation": "https://docs.langflow.org/components-processing#type-convert", "edited": false, "field_order": [ "input_data", "auto_parse", "output_type" ], "frozen": false, "icon": "repeat", "last_updated": "2025-09-05T12:25:07.723Z", "legacy": false, "lf_version": "1.6.0", "metadata": { "code_hash": "a99682150534", "dependencies": { "dependencies": [ { "name": "lfx", "version": null }, { "name": "pandas", "version": "2.2.3" } ], "total_dependencies": 2 }, "module": "custom_components.type_convert" }, "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__" } ], "pinned": false, "template": { "_type": "Component", "auto_parse": { "_input_type": "BoolInput", "advanced": false, "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": true }, "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 text = data.get_text().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 return _parse_csv_to_data(text)\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-ddPzr", "measured": { "height": 303, "width": 320 }, "position": { "x": 1867.4805548903184, "y": 262.2971024072469 }, "selected": false, "type": "genericNode" }, { "data": { "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "id": "TypeConverterComponent-VGrNj", "node": { "base_classes": [ "Message" ], "beta": false, "conditional_paths": [], "custom_fields": {}, "description": "Convert between different types (Message, Data, DataFrame)", "display_name": "Type Convert", "documentation": "https://docs.langflow.org/components-processing#type-convert", "edited": false, "field_order": [ "input_data", "auto_parse", "output_type" ], "frozen": false, "icon": "repeat", "last_updated": "2025-09-05T12:23:58.790Z", "legacy": false, "lf_version": "1.6.0", "metadata": { "code_hash": "a99682150534", "dependencies": { "dependencies": [ { "name": "lfx", "version": null }, { "name": "pandas", "version": "2.2.3" } ], "total_dependencies": 2 }, "module": "custom_components.type_convert" }, "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__" } ], "pinned": false, "template": { "_type": "Component", "auto_parse": { "_input_type": "BoolInput", "advanced": false, "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": true }, "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 text = data.get_text().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 return _parse_csv_to_data(text)\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", "load_from_db": false, "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-VGrNj", "measured": { "height": 303, "width": 320 }, "position": { "x": 1876.8434449257245, "y": 622.8172193606352 }, "selected": false, "type": "genericNode" } ], "viewport": { "x": -943.2486572945991, "y": 430.688440843751, "zoom": 0.8331142940816254 } }, "description": "Test Type Convert", "endpoint_name": null, "id": "597159e6-bf1e-4107-a6d1-9236c0096df2", "is_component": false, "last_tested_version": "1.6.0", "name": "TestTypeConvert", "tags": [ "assistants", "agents" ] }